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from typing import Any, Dict, Optional from llama_index.legacy.bridge.pydantic import Field from llama_index.legacy.constants import ( DEFAULT_NUM_OUTPUTS, DEFAULT_TEMPERATURE, ) from llama_index.legacy.core.llms.types import LLMMetadata from llama_index.legacy.llms.generic_utils import get_from_param_or_env from llama_index.legacy.llms.openai_like import OpenAILike DEFAULT_API_BASE = "https://router.neutrinoapp.com/api/llm-router" DEFAULT_ROUTER = "default" MAX_CONTEXT_WINDOW = 200000 class Neutrino(OpenAILike): model: str = Field( description="The Neutrino router to use. See https://docs.neutrinoapp.com/router for details." ) context_window: int = Field( default=MAX_CONTEXT_WINDOW, description="The maximum number of context tokens for the model. Defaults to the largest supported model (Claude).", gt=0, ) is_chat_model: bool = Field( default=True, description=LLMMetadata.__fields__["is_chat_model"].field_info.description, ) def __init__( self, model: Optional[str] = None, router: str = DEFAULT_ROUTER, temperature: float = DEFAULT_TEMPERATURE, max_tokens: int = DEFAULT_NUM_OUTPUTS, additional_kwargs: Optional[Dict[str, Any]] = None, max_retries: int = 5, api_base: Optional[str] = DEFAULT_API_BASE, api_key: Optional[str] = None, **kwargs: Any, ) -> None: additional_kwargs = additional_kwargs or {} api_base = get_from_param_or_env("api_base", api_base, "NEUTRINO_API_BASE") api_key = get_from_param_or_env("api_key", api_key, "NEUTRINO_API_KEY") model = model or router super().__init__( model=model, temperature=temperature, max_tokens=max_tokens, api_base=api_base, api_key=api_key, additional_kwargs=additional_kwargs, max_retries=max_retries, **kwargs, ) @classmethod def class_name(cls) -> str: return "Neutrino_LLM"

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from typing import Any, Dict, Optional from llama_index.legacy.bridge.pydantic import Field from llama_index.legacy.constants import ( DEFAULT_NUM_OUTPUTS, DEFAULT_TEMPERATURE, ) from llama_index.legacy.core.llms.types import LLMMetadata from llama_index.legacy.llms.generic_utils import get_from_param_or_env from llama_index.legacy.llms.openai_like import OpenAILike DEFAULT_API_BASE = "https://router.neutrinoapp.com/api/llm-router" DEFAULT_ROUTER = "default" MAX_CONTEXT_WINDOW = 200000 class Neutrino(OpenAILike): model: str = Field( description="The Neutrino router to use. See https://docs.neutrinoapp.com/router for details." ) context_window: int = Field( default=MAX_CONTEXT_WINDOW, description="The maximum number of context tokens for the model. Defaults to the largest supported model (Claude).", gt=0, ) is_chat_model: bool = Field( default=True, description=LLMMetadata.__fields__["is_chat_model"].field_info.description, ) def __init__( self, model: Optional[str] = None, router: str = DEFAULT_ROUTER, temperature: float = DEFAULT_TEMPERATURE, max_tokens: int = DEFAULT_NUM_OUTPUTS, additional_kwargs: Optional[Dict[str, Any]] = None, max_retries: int = 5, api_base: Optional[str] = DEFAULT_API_BASE, api_key: Optional[str] = None, **kwargs: Any, ) -> None: additional_kwargs = additional_kwargs or {} api_base = get_from_param_or_env("api_base", api_base, "NEUTRINO_API_BASE") api_key = get_from_param_or_env("api_key", api_key, "NEUTRINO_API_KEY") model = model or router super().__init__( model=model, temperature=temperature, max_tokens=max_tokens, api_base=api_base, api_key=api_key, additional_kwargs=additional_kwargs, max_retries=max_retries, **kwargs, ) @classmethod def class_name(cls) -> str: return "Neutrino_LLM"

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"""Tree-based index.""" from enum import Enum from typing import Any, Dict, Optional, Sequence, Union from llama_index.core.base.base_retriever import BaseRetriever from llama_index.core.base.embeddings.base import BaseEmbedding # from llama_index.core.data_structs.data_structs import IndexGraph from llama_index.core.data_structs.data_structs import IndexGraph from llama_index.core.indices.base import BaseIndex from llama_index.core.indices.common_tree.base import GPTTreeIndexBuilder from llama_index.core.indices.tree.inserter import TreeIndexInserter from llama_index.core.llms.llm import LLM from llama_index.core.prompts import BasePromptTemplate from llama_index.core.prompts.default_prompts import ( DEFAULT_INSERT_PROMPT, DEFAULT_SUMMARY_PROMPT, ) from llama_index.core.schema import BaseNode, IndexNode from llama_index.core.service_context import ServiceContext from llama_index.core.settings import ( Settings, embed_model_from_settings_or_context, llm_from_settings_or_context, ) from llama_index.core.storage.docstore.types import RefDocInfo class TreeRetrieverMode(str, Enum): SELECT_LEAF = "select_leaf" SELECT_LEAF_EMBEDDING = "select_leaf_embedding" ALL_LEAF = "all_leaf" ROOT = "root" REQUIRE_TREE_MODES = { TreeRetrieverMode.SELECT_LEAF, TreeRetrieverMode.SELECT_LEAF_EMBEDDING, TreeRetrieverMode.ROOT, } class TreeIndex(BaseIndex[IndexGraph]): """Tree Index. The tree index is a tree-structured index, where each node is a summary of the children nodes. During index construction, the tree is constructed in a bottoms-up fashion until we end up with a set of root_nodes. There are a few different options during query time (see :ref:`Ref-Query`). The main option is to traverse down the tree from the root nodes. A secondary answer is to directly synthesize the answer from the root nodes. Args: summary_template (Optional[BasePromptTemplate]): A Summarization Prompt (see :ref:`Prompt-Templates`). insert_prompt (Optional[BasePromptTemplate]): An Tree Insertion Prompt (see :ref:`Prompt-Templates`). num_children (int): The number of children each node should have. build_tree (bool): Whether to build the tree during index construction. show_progress (bool): Whether to show progress bars. Defaults to False. """ index_struct_cls = IndexGraph def __init__( self, nodes: Optional[Sequence[BaseNode]] = None, objects: Optional[Sequence[IndexNode]] = None, index_struct: Optional[IndexGraph] = None, llm: Optional[LLM] = None, summary_template: Optional[BasePromptTemplate] = None, insert_prompt: Optional[BasePromptTemplate] = None, num_children: int = 10, build_tree: bool = True, use_async: bool = False, show_progress: bool = False, # deprecated service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize params.""" # need to set parameters before building index in base class. self.num_children = num_children self.summary_template = summary_template or DEFAULT_SUMMARY_PROMPT self.insert_prompt: BasePromptTemplate = insert_prompt or DEFAULT_INSERT_PROMPT self.build_tree = build_tree self._use_async = use_async self._llm = llm or llm_from_settings_or_context(Settings, service_context) super().__init__( nodes=nodes, index_struct=index_struct, service_context=service_context, show_progress=show_progress, objects=objects, **kwargs, ) def as_retriever( self, retriever_mode: Union[str, TreeRetrieverMode] = TreeRetrieverMode.SELECT_LEAF, embed_model: Optional[BaseEmbedding] = None, **kwargs: Any, ) -> BaseRetriever: # NOTE: lazy import from llama_index.core.indices.tree.all_leaf_retriever import ( TreeAllLeafRetriever, ) from llama_index.core.indices.tree.select_leaf_embedding_retriever import ( TreeSelectLeafEmbeddingRetriever, ) from llama_index.core.indices.tree.select_leaf_retriever import ( TreeSelectLeafRetriever, ) from llama_index.core.indices.tree.tree_root_retriever import ( TreeRootRetriever, ) self._validate_build_tree_required(TreeRetrieverMode(retriever_mode)) if retriever_mode == TreeRetrieverMode.SELECT_LEAF: return TreeSelectLeafRetriever(self, object_map=self._object_map, **kwargs) elif retriever_mode == TreeRetrieverMode.SELECT_LEAF_EMBEDDING: embed_model = embed_model or embed_model_from_settings_or_context( Settings, self._service_context ) return TreeSelectLeafEmbeddingRetriever( self, embed_model=embed_model, object_map=self._object_map, **kwargs ) elif retriever_mode == TreeRetrieverMode.ROOT: return TreeRootRetriever(self, object_map=self._object_map, **kwargs) elif retriever_mode == TreeRetrieverMode.ALL_LEAF: return TreeAllLeafRetriever(self, object_map=self._object_map, **kwargs) else: raise ValueError(f"Unknown retriever mode: {retriever_mode}") def _validate_build_tree_required(self, retriever_mode: TreeRetrieverMode) -> None: """Check if index supports modes that require trees.""" if retriever_mode in REQUIRE_TREE_MODES and not self.build_tree: raise ValueError( "Index was constructed without building trees, " f"but retriever mode {retriever_mode} requires trees." ) def _build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> IndexGraph: """Build the index from nodes.""" index_builder = GPTTreeIndexBuilder( self.num_children, self.summary_template, service_context=self.service_context, llm=self._llm, use_async=self._use_async, show_progress=self._show_progress, docstore=self._docstore, ) return index_builder.build_from_nodes(nodes, build_tree=self.build_tree) def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None: """Insert a document.""" # TODO: allow to customize insert prompt inserter = TreeIndexInserter( self.index_struct, service_context=self.service_context, llm=self._llm, num_children=self.num_children, insert_prompt=self.insert_prompt, summary_prompt=self.summary_template, docstore=self._docstore, ) inserter.insert(nodes) def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None: """Delete a node.""" raise NotImplementedError("Delete not implemented for tree index.") @property def ref_doc_info(self) -> Dict[str, RefDocInfo]: """Retrieve a dict mapping of ingested documents and their nodes+metadata.""" node_doc_ids = list(self.index_struct.all_nodes.values()) nodes = self.docstore.get_nodes(node_doc_ids) all_ref_doc_info = {} for node in nodes: ref_node = node.source_node if not ref_node: continue ref_doc_info = self.docstore.get_ref_doc_info(ref_node.node_id) if not ref_doc_info: continue all_ref_doc_info[ref_node.node_id] = ref_doc_info return all_ref_doc_info # legacy GPTTreeIndex = TreeIndex

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"""Tree-based index.""" from enum import Enum from typing import Any, Dict, Optional, Sequence, Union from llama_index.core.base.base_retriever import BaseRetriever from llama_index.core.base.embeddings.base import BaseEmbedding # from llama_index.core.data_structs.data_structs import IndexGraph from llama_index.core.data_structs.data_structs import IndexGraph from llama_index.core.indices.base import BaseIndex from llama_index.core.indices.common_tree.base import GPTTreeIndexBuilder from llama_index.core.indices.tree.inserter import TreeIndexInserter from llama_index.core.llms.llm import LLM from llama_index.core.prompts import BasePromptTemplate from llama_index.core.prompts.default_prompts import ( DEFAULT_INSERT_PROMPT, DEFAULT_SUMMARY_PROMPT, ) from llama_index.core.schema import BaseNode, IndexNode from llama_index.core.service_context import ServiceContext from llama_index.core.settings import ( Settings, embed_model_from_settings_or_context, llm_from_settings_or_context, ) from llama_index.core.storage.docstore.types import RefDocInfo class TreeRetrieverMode(str, Enum): SELECT_LEAF = "select_leaf" SELECT_LEAF_EMBEDDING = "select_leaf_embedding" ALL_LEAF = "all_leaf" ROOT = "root" REQUIRE_TREE_MODES = { TreeRetrieverMode.SELECT_LEAF, TreeRetrieverMode.SELECT_LEAF_EMBEDDING, TreeRetrieverMode.ROOT, } class TreeIndex(BaseIndex[IndexGraph]): """Tree Index. The tree index is a tree-structured index, where each node is a summary of the children nodes. During index construction, the tree is constructed in a bottoms-up fashion until we end up with a set of root_nodes. There are a few different options during query time (see :ref:`Ref-Query`). The main option is to traverse down the tree from the root nodes. A secondary answer is to directly synthesize the answer from the root nodes. Args: summary_template (Optional[BasePromptTemplate]): A Summarization Prompt (see :ref:`Prompt-Templates`). insert_prompt (Optional[BasePromptTemplate]): An Tree Insertion Prompt (see :ref:`Prompt-Templates`). num_children (int): The number of children each node should have. build_tree (bool): Whether to build the tree during index construction. show_progress (bool): Whether to show progress bars. Defaults to False. """ index_struct_cls = IndexGraph def __init__( self, nodes: Optional[Sequence[BaseNode]] = None, objects: Optional[Sequence[IndexNode]] = None, index_struct: Optional[IndexGraph] = None, llm: Optional[LLM] = None, summary_template: Optional[BasePromptTemplate] = None, insert_prompt: Optional[BasePromptTemplate] = None, num_children: int = 10, build_tree: bool = True, use_async: bool = False, show_progress: bool = False, # deprecated service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize params.""" # need to set parameters before building index in base class. self.num_children = num_children self.summary_template = summary_template or DEFAULT_SUMMARY_PROMPT self.insert_prompt: BasePromptTemplate = insert_prompt or DEFAULT_INSERT_PROMPT self.build_tree = build_tree self._use_async = use_async self._llm = llm or llm_from_settings_or_context(Settings, service_context) super().__init__( nodes=nodes, index_struct=index_struct, service_context=service_context, show_progress=show_progress, objects=objects, **kwargs, ) def as_retriever( self, retriever_mode: Union[str, TreeRetrieverMode] = TreeRetrieverMode.SELECT_LEAF, embed_model: Optional[BaseEmbedding] = None, **kwargs: Any, ) -> BaseRetriever: # NOTE: lazy import from llama_index.core.indices.tree.all_leaf_retriever import ( TreeAllLeafRetriever, ) from llama_index.core.indices.tree.select_leaf_embedding_retriever import ( TreeSelectLeafEmbeddingRetriever, ) from llama_index.core.indices.tree.select_leaf_retriever import ( TreeSelectLeafRetriever, ) from llama_index.core.indices.tree.tree_root_retriever import ( TreeRootRetriever, ) self._validate_build_tree_required(TreeRetrieverMode(retriever_mode)) if retriever_mode == TreeRetrieverMode.SELECT_LEAF: return TreeSelectLeafRetriever(self, object_map=self._object_map, **kwargs) elif retriever_mode == TreeRetrieverMode.SELECT_LEAF_EMBEDDING: embed_model = embed_model or embed_model_from_settings_or_context( Settings, self._service_context ) return TreeSelectLeafEmbeddingRetriever( self, embed_model=embed_model, object_map=self._object_map, **kwargs ) elif retriever_mode == TreeRetrieverMode.ROOT: return TreeRootRetriever(self, object_map=self._object_map, **kwargs) elif retriever_mode == TreeRetrieverMode.ALL_LEAF: return TreeAllLeafRetriever(self, object_map=self._object_map, **kwargs) else: raise ValueError(f"Unknown retriever mode: {retriever_mode}") def _validate_build_tree_required(self, retriever_mode: TreeRetrieverMode) -> None: """Check if index supports modes that require trees.""" if retriever_mode in REQUIRE_TREE_MODES and not self.build_tree: raise ValueError( "Index was constructed without building trees, " f"but retriever mode {retriever_mode} requires trees." ) def _build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> IndexGraph: """Build the index from nodes.""" index_builder = GPTTreeIndexBuilder( self.num_children, self.summary_template, service_context=self.service_context, llm=self._llm, use_async=self._use_async, show_progress=self._show_progress, docstore=self._docstore, ) return index_builder.build_from_nodes(nodes, build_tree=self.build_tree) def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None: """Insert a document.""" # TODO: allow to customize insert prompt inserter = TreeIndexInserter( self.index_struct, service_context=self.service_context, llm=self._llm, num_children=self.num_children, insert_prompt=self.insert_prompt, summary_prompt=self.summary_template, docstore=self._docstore, ) inserter.insert(nodes) def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None: """Delete a node.""" raise NotImplementedError("Delete not implemented for tree index.") @property def ref_doc_info(self) -> Dict[str, RefDocInfo]: """Retrieve a dict mapping of ingested documents and their nodes+metadata.""" node_doc_ids = list(self.index_struct.all_nodes.values()) nodes = self.docstore.get_nodes(node_doc_ids) all_ref_doc_info = {} for node in nodes: ref_node = node.source_node if not ref_node: continue ref_doc_info = self.docstore.get_ref_doc_info(ref_node.node_id) if not ref_doc_info: continue all_ref_doc_info[ref_node.node_id] = ref_doc_info return all_ref_doc_info # legacy GPTTreeIndex = TreeIndex

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"""Tree-based index.""" from enum import Enum from typing import Any, Dict, Optional, Sequence, Union from llama_index.core.base.base_retriever import BaseRetriever from llama_index.core.base.embeddings.base import BaseEmbedding # from llama_index.core.data_structs.data_structs import IndexGraph from llama_index.core.data_structs.data_structs import IndexGraph from llama_index.core.indices.base import BaseIndex from llama_index.core.indices.common_tree.base import GPTTreeIndexBuilder from llama_index.core.indices.tree.inserter import TreeIndexInserter from llama_index.core.llms.llm import LLM from llama_index.core.prompts import BasePromptTemplate from llama_index.core.prompts.default_prompts import ( DEFAULT_INSERT_PROMPT, DEFAULT_SUMMARY_PROMPT, ) from llama_index.core.schema import BaseNode, IndexNode from llama_index.core.service_context import ServiceContext from llama_index.core.settings import ( Settings, embed_model_from_settings_or_context, llm_from_settings_or_context, ) from llama_index.core.storage.docstore.types import RefDocInfo class TreeRetrieverMode(str, Enum): SELECT_LEAF = "select_leaf" SELECT_LEAF_EMBEDDING = "select_leaf_embedding" ALL_LEAF = "all_leaf" ROOT = "root" REQUIRE_TREE_MODES = { TreeRetrieverMode.SELECT_LEAF, TreeRetrieverMode.SELECT_LEAF_EMBEDDING, TreeRetrieverMode.ROOT, } class TreeIndex(BaseIndex[IndexGraph]): """Tree Index. The tree index is a tree-structured index, where each node is a summary of the children nodes. During index construction, the tree is constructed in a bottoms-up fashion until we end up with a set of root_nodes. There are a few different options during query time (see :ref:`Ref-Query`). The main option is to traverse down the tree from the root nodes. A secondary answer is to directly synthesize the answer from the root nodes. Args: summary_template (Optional[BasePromptTemplate]): A Summarization Prompt (see :ref:`Prompt-Templates`). insert_prompt (Optional[BasePromptTemplate]): An Tree Insertion Prompt (see :ref:`Prompt-Templates`). num_children (int): The number of children each node should have. build_tree (bool): Whether to build the tree during index construction. show_progress (bool): Whether to show progress bars. Defaults to False. """ index_struct_cls = IndexGraph def __init__( self, nodes: Optional[Sequence[BaseNode]] = None, objects: Optional[Sequence[IndexNode]] = None, index_struct: Optional[IndexGraph] = None, llm: Optional[LLM] = None, summary_template: Optional[BasePromptTemplate] = None, insert_prompt: Optional[BasePromptTemplate] = None, num_children: int = 10, build_tree: bool = True, use_async: bool = False, show_progress: bool = False, # deprecated service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize params.""" # need to set parameters before building index in base class. self.num_children = num_children self.summary_template = summary_template or DEFAULT_SUMMARY_PROMPT self.insert_prompt: BasePromptTemplate = insert_prompt or DEFAULT_INSERT_PROMPT self.build_tree = build_tree self._use_async = use_async self._llm = llm or llm_from_settings_or_context(Settings, service_context) super().__init__( nodes=nodes, index_struct=index_struct, service_context=service_context, show_progress=show_progress, objects=objects, **kwargs, ) def as_retriever( self, retriever_mode: Union[str, TreeRetrieverMode] = TreeRetrieverMode.SELECT_LEAF, embed_model: Optional[BaseEmbedding] = None, **kwargs: Any, ) -> BaseRetriever: # NOTE: lazy import from llama_index.core.indices.tree.all_leaf_retriever import ( TreeAllLeafRetriever, ) from llama_index.core.indices.tree.select_leaf_embedding_retriever import ( TreeSelectLeafEmbeddingRetriever, ) from llama_index.core.indices.tree.select_leaf_retriever import ( TreeSelectLeafRetriever, ) from llama_index.core.indices.tree.tree_root_retriever import ( TreeRootRetriever, ) self._validate_build_tree_required(TreeRetrieverMode(retriever_mode)) if retriever_mode == TreeRetrieverMode.SELECT_LEAF: return TreeSelectLeafRetriever(self, object_map=self._object_map, **kwargs) elif retriever_mode == TreeRetrieverMode.SELECT_LEAF_EMBEDDING: embed_model = embed_model or embed_model_from_settings_or_context( Settings, self._service_context ) return TreeSelectLeafEmbeddingRetriever( self, embed_model=embed_model, object_map=self._object_map, **kwargs ) elif retriever_mode == TreeRetrieverMode.ROOT: return TreeRootRetriever(self, object_map=self._object_map, **kwargs) elif retriever_mode == TreeRetrieverMode.ALL_LEAF: return TreeAllLeafRetriever(self, object_map=self._object_map, **kwargs) else: raise ValueError(f"Unknown retriever mode: {retriever_mode}") def _validate_build_tree_required(self, retriever_mode: TreeRetrieverMode) -> None: """Check if index supports modes that require trees.""" if retriever_mode in REQUIRE_TREE_MODES and not self.build_tree: raise ValueError( "Index was constructed without building trees, " f"but retriever mode {retriever_mode} requires trees." ) def _build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> IndexGraph: """Build the index from nodes.""" index_builder = GPTTreeIndexBuilder( self.num_children, self.summary_template, service_context=self.service_context, llm=self._llm, use_async=self._use_async, show_progress=self._show_progress, docstore=self._docstore, ) return index_builder.build_from_nodes(nodes, build_tree=self.build_tree) def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None: """Insert a document.""" # TODO: allow to customize insert prompt inserter = TreeIndexInserter( self.index_struct, service_context=self.service_context, llm=self._llm, num_children=self.num_children, insert_prompt=self.insert_prompt, summary_prompt=self.summary_template, docstore=self._docstore, ) inserter.insert(nodes) def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None: """Delete a node.""" raise NotImplementedError("Delete not implemented for tree index.") @property def ref_doc_info(self) -> Dict[str, RefDocInfo]: """Retrieve a dict mapping of ingested documents and their nodes+metadata.""" node_doc_ids = list(self.index_struct.all_nodes.values()) nodes = self.docstore.get_nodes(node_doc_ids) all_ref_doc_info = {} for node in nodes: ref_node = node.source_node if not ref_node: continue ref_doc_info = self.docstore.get_ref_doc_info(ref_node.node_id) if not ref_doc_info: continue all_ref_doc_info[ref_node.node_id] = ref_doc_info return all_ref_doc_info # legacy GPTTreeIndex = TreeIndex

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from typing import Any, Callable, Dict, Optional, Sequence from llama_index.legacy.bridge.pydantic import Field, PrivateAttr from llama_index.legacy.callbacks import CallbackManager from llama_index.legacy.core.llms.types import ( ChatMessage, ChatResponse, ChatResponseAsyncGen, ChatResponseGen, CompletionResponse, CompletionResponseAsyncGen, CompletionResponseGen, LLMMetadata, ) from llama_index.legacy.llms.base import ( llm_chat_callback, llm_completion_callback, ) from llama_index.legacy.llms.llm import LLM from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode EXAMPLE_URL = "https://clarifai.com/anthropic/completion/models/claude-v2" class Clarifai(LLM): model_url: Optional[str] = Field( description=f"Full URL of the model. e.g. `{EXAMPLE_URL}`" ) model_version_id: Optional[str] = Field(description="Model Version ID.") app_id: Optional[str] = Field(description="Clarifai application ID of the model.") user_id: Optional[str] = Field(description="Clarifai user ID of the model.") pat: Optional[str] = Field( description="Personal Access Tokens(PAT) to validate requests." ) _model: Any = PrivateAttr() _is_chat_model: bool = PrivateAttr() def __init__( self, model_name: Optional[str] = None, model_url: Optional[str] = None, model_version_id: Optional[str] = "", app_id: Optional[str] = None, user_id: Optional[str] = None, pat: Optional[str] = None, temperature: float = 0.1, max_tokens: int = 512, additional_kwargs: Optional[Dict[str, Any]] = None, callback_manager: Optional[CallbackManager] = None, system_prompt: Optional[str] = None, messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None, completion_to_prompt: Optional[Callable[[str], str]] = None, pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, output_parser: Optional[BaseOutputParser] = None, ): try: import os from clarifai.client.model import Model except ImportError: raise ImportError("ClarifaiLLM requires `pip install clarifai`.") if pat is None and os.environ.get("CLARIFAI_PAT") is not None: pat = os.environ.get("CLARIFAI_PAT") if not pat and os.environ.get("CLARIFAI_PAT") is None: raise ValueError( "Set `CLARIFAI_PAT` as env variable or pass `pat` as constructor argument" ) if model_url is not None and model_name is not None: raise ValueError("You can only specify one of model_url or model_name.") if model_url is None and model_name is None: raise ValueError("You must specify one of model_url or model_name.") if model_name is not None: if app_id is None or user_id is None: raise ValueError( f"Missing one app ID or user ID of the model: {app_id=}, {user_id=}" ) else: self._model = Model( user_id=user_id, app_id=app_id, model_id=model_name, model_version={"id": model_version_id}, pat=pat, ) if model_url is not None: self._model = Model(model_url, pat=pat) model_name = self._model.id self._is_chat_model = False if "chat" in self._model.app_id or "chat" in self._model.id: self._is_chat_model = True additional_kwargs = additional_kwargs or {} super().__init__( temperature=temperature, max_tokens=max_tokens, additional_kwargs=additional_kwargs, callback_manager=callback_manager, model_name=model_name, system_prompt=system_prompt, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) @classmethod def class_name(cls) -> str: return "ClarifaiLLM" @property def metadata(self) -> LLMMetadata: """LLM metadata.""" return LLMMetadata( context_window=self.context_window, num_output=self.max_tokens, model_name=self._model, is_chat_model=self._is_chat_model, ) # TODO: When the Clarifai python SDK supports inference params, add here. def chat( self, messages: Sequence[ChatMessage], inference_params: Optional[Dict] = {}, **kwargs: Any, ) -> ChatResponse: """Chat endpoint for LLM.""" prompt = "".join([str(m) for m in messages]) try: response = ( self._model.predict_by_bytes( input_bytes=prompt.encode(encoding="UTF-8"), input_type="text", inference_params=inference_params, ) .outputs[0] .data.text.raw ) except Exception as e: raise Exception(f"Prediction failed: {e}") return ChatResponse(message=ChatMessage(content=response)) def complete( self, prompt: str, formatted: bool = False, inference_params: Optional[Dict] = {}, **kwargs: Any, ) -> CompletionResponse: """Completion endpoint for LLM.""" try: response = ( self._model.predict_by_bytes( input_bytes=prompt.encode(encoding="utf-8"), input_type="text", inference_params=inference_params, ) .outputs[0] .data.text.raw ) except Exception as e: raise Exception(f"Prediction failed: {e}") return CompletionResponse(text=response) def stream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseGen: raise NotImplementedError( "Clarifai does not currently support streaming completion." ) def stream_complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponseGen: raise NotImplementedError( "Clarifai does not currently support streaming completion." ) @llm_chat_callback() async def achat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponse: raise NotImplementedError("Currently not supported.") @llm_completion_callback() async def acomplete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponse: return self.complete(prompt, **kwargs) @llm_chat_callback() async def astream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseAsyncGen: raise NotImplementedError("Currently not supported.") @llm_completion_callback() async def astream_complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponseAsyncGen: raise NotImplementedError("Clarifai does not currently support this function.")

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from typing import Any, Callable, Dict, Optional, Sequence from llama_index.legacy.bridge.pydantic import Field, PrivateAttr from llama_index.legacy.callbacks import CallbackManager from llama_index.legacy.core.llms.types import ( ChatMessage, ChatResponse, ChatResponseAsyncGen, ChatResponseGen, CompletionResponse, CompletionResponseAsyncGen, CompletionResponseGen, LLMMetadata, ) from llama_index.legacy.llms.base import ( llm_chat_callback, llm_completion_callback, ) from llama_index.legacy.llms.llm import LLM from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode EXAMPLE_URL = "https://clarifai.com/anthropic/completion/models/claude-v2" class Clarifai(LLM): model_url: Optional[str] = Field( description=f"Full URL of the model. e.g. `{EXAMPLE_URL}`" ) model_version_id: Optional[str] = Field(description="Model Version ID.") app_id: Optional[str] = Field(description="Clarifai application ID of the model.") user_id: Optional[str] = Field(description="Clarifai user ID of the model.") pat: Optional[str] = Field( description="Personal Access Tokens(PAT) to validate requests." ) _model: Any = PrivateAttr() _is_chat_model: bool = PrivateAttr() def __init__( self, model_name: Optional[str] = None, model_url: Optional[str] = None, model_version_id: Optional[str] = "", app_id: Optional[str] = None, user_id: Optional[str] = None, pat: Optional[str] = None, temperature: float = 0.1, max_tokens: int = 512, additional_kwargs: Optional[Dict[str, Any]] = None, callback_manager: Optional[CallbackManager] = None, system_prompt: Optional[str] = None, messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None, completion_to_prompt: Optional[Callable[[str], str]] = None, pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, output_parser: Optional[BaseOutputParser] = None, ): try: import os from clarifai.client.model import Model except ImportError: raise ImportError("ClarifaiLLM requires `pip install clarifai`.") if pat is None and os.environ.get("CLARIFAI_PAT") is not None: pat = os.environ.get("CLARIFAI_PAT") if not pat and os.environ.get("CLARIFAI_PAT") is None: raise ValueError( "Set `CLARIFAI_PAT` as env variable or pass `pat` as constructor argument" ) if model_url is not None and model_name is not None: raise ValueError("You can only specify one of model_url or model_name.") if model_url is None and model_name is None: raise ValueError("You must specify one of model_url or model_name.") if model_name is not None: if app_id is None or user_id is None: raise ValueError( f"Missing one app ID or user ID of the model: {app_id=}, {user_id=}" ) else: self._model = Model( user_id=user_id, app_id=app_id, model_id=model_name, model_version={"id": model_version_id}, pat=pat, ) if model_url is not None: self._model = Model(model_url, pat=pat) model_name = self._model.id self._is_chat_model = False if "chat" in self._model.app_id or "chat" in self._model.id: self._is_chat_model = True additional_kwargs = additional_kwargs or {} super().__init__( temperature=temperature, max_tokens=max_tokens, additional_kwargs=additional_kwargs, callback_manager=callback_manager, model_name=model_name, system_prompt=system_prompt, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) @classmethod def class_name(cls) -> str: return "ClarifaiLLM" @property def metadata(self) -> LLMMetadata: """LLM metadata.""" return LLMMetadata( context_window=self.context_window, num_output=self.max_tokens, model_name=self._model, is_chat_model=self._is_chat_model, ) # TODO: When the Clarifai python SDK supports inference params, add here. def chat( self, messages: Sequence[ChatMessage], inference_params: Optional[Dict] = {}, **kwargs: Any, ) -> ChatResponse: """Chat endpoint for LLM.""" prompt = "".join([str(m) for m in messages]) try: response = ( self._model.predict_by_bytes( input_bytes=prompt.encode(encoding="UTF-8"), input_type="text", inference_params=inference_params, ) .outputs[0] .data.text.raw ) except Exception as e: raise Exception(f"Prediction failed: {e}") return ChatResponse(message=ChatMessage(content=response)) def complete( self, prompt: str, formatted: bool = False, inference_params: Optional[Dict] = {}, **kwargs: Any, ) -> CompletionResponse: """Completion endpoint for LLM.""" try: response = ( self._model.predict_by_bytes( input_bytes=prompt.encode(encoding="utf-8"), input_type="text", inference_params=inference_params, ) .outputs[0] .data.text.raw ) except Exception as e: raise Exception(f"Prediction failed: {e}") return CompletionResponse(text=response) def stream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseGen: raise NotImplementedError( "Clarifai does not currently support streaming completion." ) def stream_complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponseGen: raise NotImplementedError( "Clarifai does not currently support streaming completion." ) @llm_chat_callback() async def achat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponse: raise NotImplementedError("Currently not supported.") @llm_completion_callback() async def acomplete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponse: return self.complete(prompt, **kwargs) @llm_chat_callback() async def astream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseAsyncGen: raise NotImplementedError("Currently not supported.") @llm_completion_callback() async def astream_complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponseAsyncGen: raise NotImplementedError("Clarifai does not currently support this function.")

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"""PII postprocessor.""" import json from copy import deepcopy from typing import Callable, Dict, List, Optional, Tuple from llama_index.core.llms.llm import LLM from llama_index.core.postprocessor.types import BaseNodePostprocessor from llama_index.core.prompts.base import PromptTemplate from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle DEFAULT_PII_TMPL = ( "The current context information is provided. \n" "A task is also provided to mask the PII within the context. \n" "Return the text, with all PII masked out, and a mapping of the original PII " "to the masked PII. \n" "Return the output of the task in JSON. \n" "Context:\n" "Hello Zhang Wei, I am John. " "Your AnyCompany Financial Services, " "LLC credit card account 1111-0000-1111-0008 " "has a minimum payment of $24.53 that is due " "by July 31st. Based on your autopay settings, we will withdraw your payment. " "Task: Mask out the PII, replace each PII with a tag, and return the text. Return the mapping in JSON. \n" "Output: \n" "Hello [NAME1], I am [NAME2]. " "Your AnyCompany Financial Services, " "LLC credit card account [CREDIT_CARD_NUMBER] " "has a minimum payment of $24.53 that is due " "by [DATE_TIME]. Based on your autopay settings, we will withdraw your payment. " "Output Mapping:\n" '{{"NAME1": "Zhang Wei", "NAME2": "John", "CREDIT_CARD_NUMBER": "1111-0000-1111-0008", "DATE_TIME": "July 31st"}}\n' "Context:\n{context_str}\n" "Task: {query_str}\n" "Output: \n" "" ) class PIINodePostprocessor(BaseNodePostprocessor): """PII Node processor. NOTE: this is a beta feature, the API might change. Args: llm (LLM): The local LLM to use for prediction. """ llm: LLM pii_str_tmpl: str = DEFAULT_PII_TMPL pii_node_info_key: str = "__pii_node_info__" @classmethod def class_name(cls) -> str: return "PIINodePostprocessor" def mask_pii(self, text: str) -> Tuple[str, Dict]: """Mask PII in text.""" pii_prompt = PromptTemplate(self.pii_str_tmpl) # TODO: allow customization task_str = ( "Mask out the PII, replace each PII with a tag, and return the text. " "Return the mapping in JSON." ) response = self.llm.predict(pii_prompt, context_str=text, query_str=task_str) splits = response.split("Output Mapping:") text_output = splits[0].strip() json_str_output = splits[1].strip() json_dict = json.loads(json_str_output) return text_output, json_dict def _postprocess_nodes( self, nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None, ) -> List[NodeWithScore]: """Postprocess nodes.""" # swap out text from nodes, with the original node mappings new_nodes = [] for node_with_score in nodes: node = node_with_score.node new_text, mapping_info = self.mask_pii( node.get_content(metadata_mode=MetadataMode.LLM) ) new_node = deepcopy(node) new_node.excluded_embed_metadata_keys.append(self.pii_node_info_key) new_node.excluded_llm_metadata_keys.append(self.pii_node_info_key) new_node.metadata[self.pii_node_info_key] = mapping_info new_node.set_content(new_text) new_nodes.append(NodeWithScore(node=new_node, score=node_with_score.score)) return new_nodes class NERPIINodePostprocessor(BaseNodePostprocessor): """NER PII Node processor. Uses a HF transformers model. """ pii_node_info_key: str = "__pii_node_info__" @classmethod def class_name(cls) -> str: return "NERPIINodePostprocessor" def mask_pii(self, ner: Callable, text: str) -> Tuple[str, Dict]: """Mask PII in text.""" new_text = text response = ner(text) mapping = {} for entry in response: entity_group_tag = f"[{entry['entity_group']}_{entry['start']}]" new_text = new_text.replace(entry["word"], entity_group_tag).strip() mapping[entity_group_tag] = entry["word"] return new_text, mapping def _postprocess_nodes( self, nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None, ) -> List[NodeWithScore]: """Postprocess nodes.""" from transformers import pipeline # pants: no-infer-dep ner = pipeline("ner", grouped_entities=True) # swap out text from nodes, with the original node mappings new_nodes = [] for node_with_score in nodes: node = node_with_score.node new_text, mapping_info = self.mask_pii( ner, node.get_content(metadata_mode=MetadataMode.LLM) ) new_node = deepcopy(node) new_node.excluded_embed_metadata_keys.append(self.pii_node_info_key) new_node.excluded_llm_metadata_keys.append(self.pii_node_info_key) new_node.metadata[self.pii_node_info_key] = mapping_info new_node.set_content(new_text) new_nodes.append(NodeWithScore(node=new_node, score=node_with_score.score)) return new_nodes

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"""PII postprocessor.""" import json from copy import deepcopy from typing import Callable, Dict, List, Optional, Tuple from llama_index.core.llms.llm import LLM from llama_index.core.postprocessor.types import BaseNodePostprocessor from llama_index.core.prompts.base import PromptTemplate from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle DEFAULT_PII_TMPL = ( "The current context information is provided. \n" "A task is also provided to mask the PII within the context. \n" "Return the text, with all PII masked out, and a mapping of the original PII " "to the masked PII. \n" "Return the output of the task in JSON. \n" "Context:\n" "Hello Zhang Wei, I am John. " "Your AnyCompany Financial Services, " "LLC credit card account 1111-0000-1111-0008 " "has a minimum payment of $24.53 that is due " "by July 31st. Based on your autopay settings, we will withdraw your payment. " "Task: Mask out the PII, replace each PII with a tag, and return the text. Return the mapping in JSON. \n" "Output: \n" "Hello [NAME1], I am [NAME2]. " "Your AnyCompany Financial Services, " "LLC credit card account [CREDIT_CARD_NUMBER] " "has a minimum payment of $24.53 that is due " "by [DATE_TIME]. Based on your autopay settings, we will withdraw your payment. " "Output Mapping:\n" '{{"NAME1": "Zhang Wei", "NAME2": "John", "CREDIT_CARD_NUMBER": "1111-0000-1111-0008", "DATE_TIME": "July 31st"}}\n' "Context:\n{context_str}\n" "Task: {query_str}\n" "Output: \n" "" ) class PIINodePostprocessor(BaseNodePostprocessor): """PII Node processor. NOTE: this is a beta feature, the API might change. Args: llm (LLM): The local LLM to use for prediction. """ llm: LLM pii_str_tmpl: str = DEFAULT_PII_TMPL pii_node_info_key: str = "__pii_node_info__" @classmethod def class_name(cls) -> str: return "PIINodePostprocessor" def mask_pii(self, text: str) -> Tuple[str, Dict]: """Mask PII in text.""" pii_prompt = PromptTemplate(self.pii_str_tmpl) # TODO: allow customization task_str = ( "Mask out the PII, replace each PII with a tag, and return the text. " "Return the mapping in JSON." ) response = self.llm.predict(pii_prompt, context_str=text, query_str=task_str) splits = response.split("Output Mapping:") text_output = splits[0].strip() json_str_output = splits[1].strip() json_dict = json.loads(json_str_output) return text_output, json_dict def _postprocess_nodes( self, nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None, ) -> List[NodeWithScore]: """Postprocess nodes.""" # swap out text from nodes, with the original node mappings new_nodes = [] for node_with_score in nodes: node = node_with_score.node new_text, mapping_info = self.mask_pii( node.get_content(metadata_mode=MetadataMode.LLM) ) new_node = deepcopy(node) new_node.excluded_embed_metadata_keys.append(self.pii_node_info_key) new_node.excluded_llm_metadata_keys.append(self.pii_node_info_key) new_node.metadata[self.pii_node_info_key] = mapping_info new_node.set_content(new_text) new_nodes.append(NodeWithScore(node=new_node, score=node_with_score.score)) return new_nodes class NERPIINodePostprocessor(BaseNodePostprocessor): """NER PII Node processor. Uses a HF transformers model. """ pii_node_info_key: str = "__pii_node_info__" @classmethod def class_name(cls) -> str: return "NERPIINodePostprocessor" def mask_pii(self, ner: Callable, text: str) -> Tuple[str, Dict]: """Mask PII in text.""" new_text = text response = ner(text) mapping = {} for entry in response: entity_group_tag = f"[{entry['entity_group']}_{entry['start']}]" new_text = new_text.replace(entry["word"], entity_group_tag).strip() mapping[entity_group_tag] = entry["word"] return new_text, mapping def _postprocess_nodes( self, nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None, ) -> List[NodeWithScore]: """Postprocess nodes.""" from transformers import pipeline # pants: no-infer-dep ner = pipeline("ner", grouped_entities=True) # swap out text from nodes, with the original node mappings new_nodes = [] for node_with_score in nodes: node = node_with_score.node new_text, mapping_info = self.mask_pii( ner, node.get_content(metadata_mode=MetadataMode.LLM) ) new_node = deepcopy(node) new_node.excluded_embed_metadata_keys.append(self.pii_node_info_key) new_node.excluded_llm_metadata_keys.append(self.pii_node_info_key) new_node.metadata[self.pii_node_info_key] = mapping_info new_node.set_content(new_text) new_nodes.append(NodeWithScore(node=new_node, score=node_with_score.score)) return new_nodes

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"""PII postprocessor.""" import json from copy import deepcopy from typing import Callable, Dict, List, Optional, Tuple from llama_index.core.llms.llm import LLM from llama_index.core.postprocessor.types import BaseNodePostprocessor from llama_index.core.prompts.base import PromptTemplate from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle DEFAULT_PII_TMPL = ( "The current context information is provided. \n" "A task is also provided to mask the PII within the context. \n" "Return the text, with all PII masked out, and a mapping of the original PII " "to the masked PII. \n" "Return the output of the task in JSON. \n" "Context:\n" "Hello Zhang Wei, I am John. " "Your AnyCompany Financial Services, " "LLC credit card account 1111-0000-1111-0008 " "has a minimum payment of $24.53 that is due " "by July 31st. Based on your autopay settings, we will withdraw your payment. " "Task: Mask out the PII, replace each PII with a tag, and return the text. Return the mapping in JSON. \n" "Output: \n" "Hello [NAME1], I am [NAME2]. " "Your AnyCompany Financial Services, " "LLC credit card account [CREDIT_CARD_NUMBER] " "has a minimum payment of $24.53 that is due " "by [DATE_TIME]. Based on your autopay settings, we will withdraw your payment. " "Output Mapping:\n" '{{"NAME1": "Zhang Wei", "NAME2": "John", "CREDIT_CARD_NUMBER": "1111-0000-1111-0008", "DATE_TIME": "July 31st"}}\n' "Context:\n{context_str}\n" "Task: {query_str}\n" "Output: \n" "" ) class PIINodePostprocessor(BaseNodePostprocessor): """PII Node processor. NOTE: this is a beta feature, the API might change. Args: llm (LLM): The local LLM to use for prediction. """ llm: LLM pii_str_tmpl: str = DEFAULT_PII_TMPL pii_node_info_key: str = "__pii_node_info__" @classmethod def class_name(cls) -> str: return "PIINodePostprocessor" def mask_pii(self, text: str) -> Tuple[str, Dict]: """Mask PII in text.""" pii_prompt = PromptTemplate(self.pii_str_tmpl) # TODO: allow customization task_str = ( "Mask out the PII, replace each PII with a tag, and return the text. " "Return the mapping in JSON." ) response = self.llm.predict(pii_prompt, context_str=text, query_str=task_str) splits = response.split("Output Mapping:") text_output = splits[0].strip() json_str_output = splits[1].strip() json_dict = json.loads(json_str_output) return text_output, json_dict def _postprocess_nodes( self, nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None, ) -> List[NodeWithScore]: """Postprocess nodes.""" # swap out text from nodes, with the original node mappings new_nodes = [] for node_with_score in nodes: node = node_with_score.node new_text, mapping_info = self.mask_pii( node.get_content(metadata_mode=MetadataMode.LLM) ) new_node = deepcopy(node) new_node.excluded_embed_metadata_keys.append(self.pii_node_info_key) new_node.excluded_llm_metadata_keys.append(self.pii_node_info_key) new_node.metadata[self.pii_node_info_key] = mapping_info new_node.set_content(new_text) new_nodes.append(NodeWithScore(node=new_node, score=node_with_score.score)) return new_nodes class NERPIINodePostprocessor(BaseNodePostprocessor): """NER PII Node processor. Uses a HF transformers model. """ pii_node_info_key: str = "__pii_node_info__" @classmethod def class_name(cls) -> str: return "NERPIINodePostprocessor" def mask_pii(self, ner: Callable, text: str) -> Tuple[str, Dict]: """Mask PII in text.""" new_text = text response = ner(text) mapping = {} for entry in response: entity_group_tag = f"[{entry['entity_group']}_{entry['start']}]" new_text = new_text.replace(entry["word"], entity_group_tag).strip() mapping[entity_group_tag] = entry["word"] return new_text, mapping def _postprocess_nodes( self, nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle] = None, ) -> List[NodeWithScore]: """Postprocess nodes.""" from transformers import pipeline # pants: no-infer-dep ner = pipeline("ner", grouped_entities=True) # swap out text from nodes, with the original node mappings new_nodes = [] for node_with_score in nodes: node = node_with_score.node new_text, mapping_info = self.mask_pii( ner, node.get_content(metadata_mode=MetadataMode.LLM) ) new_node = deepcopy(node) new_node.excluded_embed_metadata_keys.append(self.pii_node_info_key) new_node.excluded_llm_metadata_keys.append(self.pii_node_info_key) new_node.metadata[self.pii_node_info_key] = mapping_info new_node.set_content(new_text) new_nodes.append(NodeWithScore(node=new_node, score=node_with_score.score)) return new_nodes

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from typing import Any, Callable, Dict, Optional, Sequence from llama_index.legacy.callbacks import CallbackManager from llama_index.legacy.constants import DEFAULT_NUM_OUTPUTS, DEFAULT_TEMPERATURE from llama_index.legacy.core.llms.types import ChatMessage, LLMMetadata from llama_index.legacy.llms.everlyai_utils import everlyai_modelname_to_contextsize from llama_index.legacy.llms.generic_utils import get_from_param_or_env from llama_index.legacy.llms.openai import OpenAI from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode EVERLYAI_API_BASE = "https://everlyai.xyz/hosted" DEFAULT_MODEL = "meta-llama/Llama-2-7b-chat-hf" class EverlyAI(OpenAI): def __init__( self, model: str = DEFAULT_MODEL, temperature: float = DEFAULT_TEMPERATURE, max_tokens: int = DEFAULT_NUM_OUTPUTS, additional_kwargs: Optional[Dict[str, Any]] = None, max_retries: int = 10, api_key: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, system_prompt: Optional[str] = None, messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None, completion_to_prompt: Optional[Callable[[str], str]] = None, pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, output_parser: Optional[BaseOutputParser] = None, ) -> None: additional_kwargs = additional_kwargs or {} callback_manager = callback_manager or CallbackManager([]) api_key = get_from_param_or_env("api_key", api_key, "EverlyAI_API_KEY") super().__init__( model=model, temperature=temperature, max_tokens=max_tokens, api_base=EVERLYAI_API_BASE, api_key=api_key, additional_kwargs=additional_kwargs, max_retries=max_retries, callback_manager=callback_manager, system_prompt=system_prompt, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) @classmethod def class_name(cls) -> str: return "EverlyAI_LLM" @property def metadata(self) -> LLMMetadata: return LLMMetadata( context_window=everlyai_modelname_to_contextsize(self.model), num_output=self.max_tokens, is_chat_model=True, model_name=self.model, ) @property def _is_chat_model(self) -> bool: return True

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from typing import Any, Callable, Dict, Optional, Sequence from llama_index.legacy.callbacks import CallbackManager from llama_index.legacy.constants import DEFAULT_NUM_OUTPUTS, DEFAULT_TEMPERATURE from llama_index.legacy.core.llms.types import ChatMessage, LLMMetadata from llama_index.legacy.llms.everlyai_utils import everlyai_modelname_to_contextsize from llama_index.legacy.llms.generic_utils import get_from_param_or_env from llama_index.legacy.llms.openai import OpenAI from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode EVERLYAI_API_BASE = "https://everlyai.xyz/hosted" DEFAULT_MODEL = "meta-llama/Llama-2-7b-chat-hf" class EverlyAI(OpenAI): def __init__( self, model: str = DEFAULT_MODEL, temperature: float = DEFAULT_TEMPERATURE, max_tokens: int = DEFAULT_NUM_OUTPUTS, additional_kwargs: Optional[Dict[str, Any]] = None, max_retries: int = 10, api_key: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, system_prompt: Optional[str] = None, messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None, completion_to_prompt: Optional[Callable[[str], str]] = None, pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, output_parser: Optional[BaseOutputParser] = None, ) -> None: additional_kwargs = additional_kwargs or {} callback_manager = callback_manager or CallbackManager([]) api_key = get_from_param_or_env("api_key", api_key, "EverlyAI_API_KEY") super().__init__( model=model, temperature=temperature, max_tokens=max_tokens, api_base=EVERLYAI_API_BASE, api_key=api_key, additional_kwargs=additional_kwargs, max_retries=max_retries, callback_manager=callback_manager, system_prompt=system_prompt, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) @classmethod def class_name(cls) -> str: return "EverlyAI_LLM" @property def metadata(self) -> LLMMetadata: return LLMMetadata( context_window=everlyai_modelname_to_contextsize(self.model), num_output=self.max_tokens, is_chat_model=True, model_name=self.model, ) @property def _is_chat_model(self) -> bool: return True

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"""txtai reader.""" from typing import Any, Dict, List import numpy as np from llama_index.legacy.readers.base import BaseReader from llama_index.legacy.schema import Document class TxtaiReader(BaseReader): """txtai reader. Retrieves documents through an existing in-memory txtai index. These documents can then be used in a downstream LlamaIndex data structure. If you wish use txtai itself as an index to to organize documents, insert documents, and perform queries on them, please use VectorStoreIndex with TxtaiVectorStore. Args: txtai_index (txtai.ann.ANN): A txtai Index object (required) """ def __init__(self, index: Any): """Initialize with parameters.""" import_err_msg = """ `txtai` package not found. For instructions on how to install `txtai` please visit https://neuml.github.io/txtai/install/ """ try: import txtai # noqa except ImportError: raise ImportError(import_err_msg) self._index = index def load_data( self, query: np.ndarray, id_to_text_map: Dict[str, str], k: int = 4, separate_documents: bool = True, ) -> List[Document]: """Load data from txtai index. Args: query (np.ndarray): A 2D numpy array of query vectors. id_to_text_map (Dict[str, str]): A map from ID's to text. k (int): Number of nearest neighbors to retrieve. Defaults to 4. separate_documents (Optional[bool]): Whether to return separate documents. Defaults to True. Returns: List[Document]: A list of documents. """ search_result = self._index.search(query, k) documents = [] for query_result in search_result: for doc_id, _ in query_result: doc_id = str(doc_id) if doc_id not in id_to_text_map: raise ValueError( f"Document ID {doc_id} not found in id_to_text_map." ) text = id_to_text_map[doc_id] documents.append(Document(text=text)) if not separate_documents: # join all documents into one text_list = [doc.get_content() for doc in documents] text = "\n\n".join(text_list) documents = [Document(text=text)] return documents

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"""txtai reader.""" from typing import Any, Dict, List import numpy as np from llama_index.legacy.readers.base import BaseReader from llama_index.legacy.schema import Document class TxtaiReader(BaseReader): """txtai reader. Retrieves documents through an existing in-memory txtai index. These documents can then be used in a downstream LlamaIndex data structure. If you wish use txtai itself as an index to to organize documents, insert documents, and perform queries on them, please use VectorStoreIndex with TxtaiVectorStore. Args: txtai_index (txtai.ann.ANN): A txtai Index object (required) """ def __init__(self, index: Any): """Initialize with parameters.""" import_err_msg = """ `txtai` package not found. For instructions on how to install `txtai` please visit https://neuml.github.io/txtai/install/ """ try: import txtai # noqa except ImportError: raise ImportError(import_err_msg) self._index = index def load_data( self, query: np.ndarray, id_to_text_map: Dict[str, str], k: int = 4, separate_documents: bool = True, ) -> List[Document]: """Load data from txtai index. Args: query (np.ndarray): A 2D numpy array of query vectors. id_to_text_map (Dict[str, str]): A map from ID's to text. k (int): Number of nearest neighbors to retrieve. Defaults to 4. separate_documents (Optional[bool]): Whether to return separate documents. Defaults to True. Returns: List[Document]: A list of documents. """ search_result = self._index.search(query, k) documents = [] for query_result in search_result: for doc_id, _ in query_result: doc_id = str(doc_id) if doc_id not in id_to_text_map: raise ValueError( f"Document ID {doc_id} not found in id_to_text_map." ) text = id_to_text_map[doc_id] documents.append(Document(text=text)) if not separate_documents: # join all documents into one text_list = [doc.get_content() for doc in documents] text = "\n\n".join(text_list) documents = [Document(text=text)] return documents

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from llama_index.core.prompts.base import PromptTemplate from llama_index.core.prompts.prompt_type import PromptType """Single select prompt. PromptTemplate to select one out of `num_choices` options provided in `context_list`, given a query `query_str`. Required template variables: `num_chunks`, `context_list`, `query_str` """ SingleSelectPrompt = PromptTemplate """Multiple select prompt. PromptTemplate to select multiple candidates (up to `max_outputs`) out of `num_choices` options provided in `context_list`, given a query `query_str`. Required template variables: `num_chunks`, `context_list`, `query_str`, `max_outputs` """ MultiSelectPrompt = PromptTemplate # single select DEFAULT_SINGLE_SELECT_PROMPT_TMPL = ( "Some choices are given below. It is provided in a numbered list " "(1 to {num_choices}), " "where each item in the list corresponds to a summary.\n" "---------------------\n" "{context_list}" "\n---------------------\n" "Using only the choices above and not prior knowledge, return " "the choice that is most relevant to the question: '{query_str}'\n" ) DEFAULT_SINGLE_SELECT_PROMPT = PromptTemplate( template=DEFAULT_SINGLE_SELECT_PROMPT_TMPL, prompt_type=PromptType.SINGLE_SELECT ) # multiple select DEFAULT_MULTI_SELECT_PROMPT_TMPL = ( "Some choices are given below. It is provided in a numbered " "list (1 to {num_choices}), " "where each item in the list corresponds to a summary.\n" "---------------------\n" "{context_list}" "\n---------------------\n" "Using only the choices above and not prior knowledge, return the top choices " "(no more than {max_outputs}, but only select what is needed) that " "are most relevant to the question: '{query_str}'\n" ) DEFAULT_MULTIPLE_SELECT_PROMPT = PromptTemplate( template=DEFAULT_MULTI_SELECT_PROMPT_TMPL, prompt_type=PromptType.MULTI_SELECT ) # single pydantic select DEFAULT_SINGLE_PYD_SELECT_PROMPT_TMPL = ( "Some choices are given below. It is provided in a numbered list " "(1 to {num_choices}), " "where each item in the list corresponds to a summary.\n" "---------------------\n" "{context_list}" "\n---------------------\n" "Using only the choices above and not prior knowledge, generate " "the selection object and reason that is most relevant to the " "question: '{query_str}'\n" ) # multiple pydantic select DEFAULT_MULTI_PYD_SELECT_PROMPT_TMPL = ( "Some choices are given below. It is provided in a numbered " "list (1 to {num_choices}), " "where each item in the list corresponds to a summary.\n" "---------------------\n" "{context_list}" "\n---------------------\n" "Using only the choices above and not prior knowledge, return the top choice(s) " "(no more than {max_outputs}, but only select what is needed) by generating " "the selection object and reasons that are most relevant to the " "question: '{query_str}'\n" )

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from llama_index.core.prompts.base import PromptTemplate from llama_index.core.prompts.prompt_type import PromptType """Single select prompt. PromptTemplate to select one out of `num_choices` options provided in `context_list`, given a query `query_str`. Required template variables: `num_chunks`, `context_list`, `query_str` """ SingleSelectPrompt = PromptTemplate """Multiple select prompt. PromptTemplate to select multiple candidates (up to `max_outputs`) out of `num_choices` options provided in `context_list`, given a query `query_str`. Required template variables: `num_chunks`, `context_list`, `query_str`, `max_outputs` """ MultiSelectPrompt = PromptTemplate # single select DEFAULT_SINGLE_SELECT_PROMPT_TMPL = ( "Some choices are given below. It is provided in a numbered list " "(1 to {num_choices}), " "where each item in the list corresponds to a summary.\n" "---------------------\n" "{context_list}" "\n---------------------\n" "Using only the choices above and not prior knowledge, return " "the choice that is most relevant to the question: '{query_str}'\n" ) DEFAULT_SINGLE_SELECT_PROMPT = PromptTemplate( template=DEFAULT_SINGLE_SELECT_PROMPT_TMPL, prompt_type=PromptType.SINGLE_SELECT ) # multiple select DEFAULT_MULTI_SELECT_PROMPT_TMPL = ( "Some choices are given below. It is provided in a numbered " "list (1 to {num_choices}), " "where each item in the list corresponds to a summary.\n" "---------------------\n" "{context_list}" "\n---------------------\n" "Using only the choices above and not prior knowledge, return the top choices " "(no more than {max_outputs}, but only select what is needed) that " "are most relevant to the question: '{query_str}'\n" ) DEFAULT_MULTIPLE_SELECT_PROMPT = PromptTemplate( template=DEFAULT_MULTI_SELECT_PROMPT_TMPL, prompt_type=PromptType.MULTI_SELECT ) # single pydantic select DEFAULT_SINGLE_PYD_SELECT_PROMPT_TMPL = ( "Some choices are given below. It is provided in a numbered list " "(1 to {num_choices}), " "where each item in the list corresponds to a summary.\n" "---------------------\n" "{context_list}" "\n---------------------\n" "Using only the choices above and not prior knowledge, generate " "the selection object and reason that is most relevant to the " "question: '{query_str}'\n" ) # multiple pydantic select DEFAULT_MULTI_PYD_SELECT_PROMPT_TMPL = ( "Some choices are given below. It is provided in a numbered " "list (1 to {num_choices}), " "where each item in the list corresponds to a summary.\n" "---------------------\n" "{context_list}" "\n---------------------\n" "Using only the choices above and not prior knowledge, return the top choice(s) " "(no more than {max_outputs}, but only select what is needed) by generating " "the selection object and reasons that are most relevant to the " "question: '{query_str}'\n" )

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from llama_index.core.prompts.base import PromptTemplate from llama_index.core.prompts.prompt_type import PromptType """Single select prompt. PromptTemplate to select one out of `num_choices` options provided in `context_list`, given a query `query_str`. Required template variables: `num_chunks`, `context_list`, `query_str` """ SingleSelectPrompt = PromptTemplate """Multiple select prompt. PromptTemplate to select multiple candidates (up to `max_outputs`) out of `num_choices` options provided in `context_list`, given a query `query_str`. Required template variables: `num_chunks`, `context_list`, `query_str`, `max_outputs` """ MultiSelectPrompt = PromptTemplate # single select DEFAULT_SINGLE_SELECT_PROMPT_TMPL = ( "Some choices are given below. It is provided in a numbered list " "(1 to {num_choices}), " "where each item in the list corresponds to a summary.\n" "---------------------\n" "{context_list}" "\n---------------------\n" "Using only the choices above and not prior knowledge, return " "the choice that is most relevant to the question: '{query_str}'\n" ) DEFAULT_SINGLE_SELECT_PROMPT = PromptTemplate( template=DEFAULT_SINGLE_SELECT_PROMPT_TMPL, prompt_type=PromptType.SINGLE_SELECT ) # multiple select DEFAULT_MULTI_SELECT_PROMPT_TMPL = ( "Some choices are given below. It is provided in a numbered " "list (1 to {num_choices}), " "where each item in the list corresponds to a summary.\n" "---------------------\n" "{context_list}" "\n---------------------\n" "Using only the choices above and not prior knowledge, return the top choices " "(no more than {max_outputs}, but only select what is needed) that " "are most relevant to the question: '{query_str}'\n" ) DEFAULT_MULTIPLE_SELECT_PROMPT = PromptTemplate( template=DEFAULT_MULTI_SELECT_PROMPT_TMPL, prompt_type=PromptType.MULTI_SELECT ) # single pydantic select DEFAULT_SINGLE_PYD_SELECT_PROMPT_TMPL = ( "Some choices are given below. It is provided in a numbered list " "(1 to {num_choices}), " "where each item in the list corresponds to a summary.\n" "---------------------\n" "{context_list}" "\n---------------------\n" "Using only the choices above and not prior knowledge, generate " "the selection object and reason that is most relevant to the " "question: '{query_str}'\n" ) # multiple pydantic select DEFAULT_MULTI_PYD_SELECT_PROMPT_TMPL = ( "Some choices are given below. It is provided in a numbered " "list (1 to {num_choices}), " "where each item in the list corresponds to a summary.\n" "---------------------\n" "{context_list}" "\n---------------------\n" "Using only the choices above and not prior knowledge, return the top choice(s) " "(no more than {max_outputs}, but only select what is needed) by generating " "the selection object and reasons that are most relevant to the " "question: '{query_str}'\n" )

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"""Awadb reader.""" from typing import Any, List import numpy as np from llama_index.legacy.readers.base import BaseReader from llama_index.legacy.schema import Document class AwadbReader(BaseReader): """Awadb reader. Retrieves documents through an existing awadb client. These documents can then be used in a downstream LlamaIndex data structure. Args: client (awadb.client): An awadb client. """ def __init__(self, client: Any): """Initialize with parameters.""" import_err_msg = """ `faiss` package not found. For instructions on how to install `faiss` please visit https://github.com/facebookresearch/faiss/wiki/Installing-Faiss """ try: pass except ImportError: raise ImportError(import_err_msg) self.awadb_client = client def load_data( self, query: np.ndarray, k: int = 4, separate_documents: bool = True, ) -> List[Document]: """Load data from Faiss. Args: query (np.ndarray): A 2D numpy array of query vectors. k (int): Number of nearest neighbors to retrieve. Defaults to 4. separate_documents (Optional[bool]): Whether to return separate documents. Defaults to True. Returns: List[Document]: A list of documents. """ results = self.awadb_client.Search( query, k, text_in_page_content=None, meta_filter=None, not_include_fields=None, ) documents = [] for item_detail in results[0]["ResultItems"]: documents.append(Document(text=item_detail["embedding_text"])) if not separate_documents: # join all documents into one text_list = [doc.get_content() for doc in documents] text = "\n\n".join(text_list) documents = [Document(text=text)] return documents

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"""Mongo client.""" from typing import Dict, Iterable, List, Optional, Union from llama_index.legacy.readers.base import BaseReader from llama_index.legacy.schema import Document class SimpleMongoReader(BaseReader): """Simple mongo reader. Concatenates each Mongo doc into Document used by LlamaIndex. Args: host (str): Mongo host. port (int): Mongo port. """ def __init__( self, host: Optional[str] = None, port: Optional[int] = None, uri: Optional[str] = None, ) -> None: """Initialize with parameters.""" try: from pymongo import MongoClient except ImportError as err: raise ImportError( "`pymongo` package not found, please run `pip install pymongo`" ) from err client: MongoClient if uri: client = MongoClient(uri) elif host and port: client = MongoClient(host, port) else: raise ValueError("Either `host` and `port` or `uri` must be provided.") self.client = client def _flatten(self, texts: List[Union[str, List[str]]]) -> List[str]: result = [] for text in texts: result += text if isinstance(text, list) else [text] return result def lazy_load_data( self, db_name: str, collection_name: str, field_names: List[str] = ["text"], separator: str = "", query_dict: Optional[Dict] = None, max_docs: int = 0, metadata_names: Optional[List[str]] = None, ) -> Iterable[Document]: """Load data from the input directory. Args: db_name (str): name of the database. collection_name (str): name of the collection. field_names(List[str]): names of the fields to be concatenated. Defaults to ["text"] separator (str): separator to be used between fields. Defaults to "" query_dict (Optional[Dict]): query to filter documents. Read more at [official docs](https://www.mongodb.com/docs/manual/reference/method/db.collection.find/#std-label-method-find-query) Defaults to None max_docs (int): maximum number of documents to load. Defaults to 0 (no limit) metadata_names (Optional[List[str]]): names of the fields to be added to the metadata attribute of the Document. Defaults to None Returns: List[Document]: A list of documents. """ db = self.client[db_name] cursor = db[collection_name].find(filter=query_dict or {}, limit=max_docs) for item in cursor: try: texts = [item[name] for name in field_names] except KeyError as err: raise ValueError( f"{err.args[0]} field not found in Mongo document." ) from err texts = self._flatten(texts) text = separator.join(texts) if metadata_names is None: yield Document(text=text) else: try: metadata = {name: item[name] for name in metadata_names} except KeyError as err: raise ValueError( f"{err.args[0]} field not found in Mongo document." ) from err yield Document(text=text, metadata=metadata)

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"""Mongo client.""" from typing import Dict, Iterable, List, Optional, Union from llama_index.legacy.readers.base import BaseReader from llama_index.legacy.schema import Document class SimpleMongoReader(BaseReader): """Simple mongo reader. Concatenates each Mongo doc into Document used by LlamaIndex. Args: host (str): Mongo host. port (int): Mongo port. """ def __init__( self, host: Optional[str] = None, port: Optional[int] = None, uri: Optional[str] = None, ) -> None: """Initialize with parameters.""" try: from pymongo import MongoClient except ImportError as err: raise ImportError( "`pymongo` package not found, please run `pip install pymongo`" ) from err client: MongoClient if uri: client = MongoClient(uri) elif host and port: client = MongoClient(host, port) else: raise ValueError("Either `host` and `port` or `uri` must be provided.") self.client = client def _flatten(self, texts: List[Union[str, List[str]]]) -> List[str]: result = [] for text in texts: result += text if isinstance(text, list) else [text] return result def lazy_load_data( self, db_name: str, collection_name: str, field_names: List[str] = ["text"], separator: str = "", query_dict: Optional[Dict] = None, max_docs: int = 0, metadata_names: Optional[List[str]] = None, ) -> Iterable[Document]: """Load data from the input directory. Args: db_name (str): name of the database. collection_name (str): name of the collection. field_names(List[str]): names of the fields to be concatenated. Defaults to ["text"] separator (str): separator to be used between fields. Defaults to "" query_dict (Optional[Dict]): query to filter documents. Read more at [official docs](https://www.mongodb.com/docs/manual/reference/method/db.collection.find/#std-label-method-find-query) Defaults to None max_docs (int): maximum number of documents to load. Defaults to 0 (no limit) metadata_names (Optional[List[str]]): names of the fields to be added to the metadata attribute of the Document. Defaults to None Returns: List[Document]: A list of documents. """ db = self.client[db_name] cursor = db[collection_name].find(filter=query_dict or {}, limit=max_docs) for item in cursor: try: texts = [item[name] for name in field_names] except KeyError as err: raise ValueError( f"{err.args[0]} field not found in Mongo document." ) from err texts = self._flatten(texts) text = separator.join(texts) if metadata_names is None: yield Document(text=text) else: try: metadata = {name: item[name] for name in metadata_names} except KeyError as err: raise ValueError( f"{err.args[0]} field not found in Mongo document." ) from err yield Document(text=text, metadata=metadata)

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from typing import Any, Callable, Optional, Sequence from typing_extensions import override from llama_index.legacy.bridge.pydantic import Field, PrivateAttr from llama_index.legacy.callbacks import CallbackManager from llama_index.legacy.constants import DEFAULT_NUM_OUTPUTS from llama_index.legacy.core.llms.types import ( ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata, ) from llama_index.legacy.llms.base import llm_completion_callback from llama_index.legacy.llms.custom import CustomLLM from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode class _BaseGradientLLM(CustomLLM): _gradient = PrivateAttr() _model = PrivateAttr() # Config max_tokens: Optional[int] = Field( default=DEFAULT_NUM_OUTPUTS, description="The number of tokens to generate.", gt=0, lt=512, ) # Gradient client config access_token: Optional[str] = Field( description="The Gradient access token to use.", ) host: Optional[str] = Field( description="The url of the Gradient service to access." ) workspace_id: Optional[str] = Field( description="The Gradient workspace id to use.", ) is_chat_model: bool = Field( default=False, description="Whether the model is a chat model." ) def __init__( self, *, access_token: Optional[str] = None, host: Optional[str] = None, max_tokens: Optional[int] = None, workspace_id: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, is_chat_model: bool = False, system_prompt: Optional[str] = None, messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None, completion_to_prompt: Optional[Callable[[str], str]] = None, pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, output_parser: Optional[BaseOutputParser] = None, **kwargs: Any, ) -> None: super().__init__( max_tokens=max_tokens, access_token=access_token, host=host, workspace_id=workspace_id, callback_manager=callback_manager, is_chat_model=is_chat_model, system_prompt=system_prompt, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, **kwargs, ) try: from gradientai import Gradient self._gradient = Gradient( access_token=access_token, host=host, workspace_id=workspace_id ) except ImportError as e: raise ImportError( "Could not import Gradient Python package. " "Please install it with `pip install gradientai`." ) from e def close(self) -> None: self._gradient.close() @llm_completion_callback() @override def complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponse: return CompletionResponse( text=self._model.complete( query=prompt, max_generated_token_count=self.max_tokens, **kwargs, ).generated_output ) @llm_completion_callback() @override async def acomplete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponse: grdt_reponse = await self._model.acomplete( query=prompt, max_generated_token_count=self.max_tokens, **kwargs, ) return CompletionResponse(text=grdt_reponse.generated_output) @override def stream_complete( self, prompt: str, formatted: bool = False, **kwargs: Any, ) -> CompletionResponseGen: raise NotImplementedError @property @override def metadata(self) -> LLMMetadata: return LLMMetadata( context_window=1024, num_output=self.max_tokens or 20, is_chat_model=self.is_chat_model, is_function_calling_model=False, model_name=self._model.id, ) class GradientBaseModelLLM(_BaseGradientLLM): base_model_slug: str = Field( description="The slug of the base model to use.", ) def __init__( self, *, access_token: Optional[str] = None, base_model_slug: str, host: Optional[str] = None, max_tokens: Optional[int] = None, workspace_id: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, is_chat_model: bool = False, ) -> None: super().__init__( access_token=access_token, base_model_slug=base_model_slug, host=host, max_tokens=max_tokens, workspace_id=workspace_id, callback_manager=callback_manager, is_chat_model=is_chat_model, ) self._model = self._gradient.get_base_model( base_model_slug=base_model_slug, ) class GradientModelAdapterLLM(_BaseGradientLLM): model_adapter_id: str = Field( description="The id of the model adapter to use.", ) def __init__( self, *, access_token: Optional[str] = None, host: Optional[str] = None, max_tokens: Optional[int] = None, model_adapter_id: str, workspace_id: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, is_chat_model: bool = False, ) -> None: super().__init__( access_token=access_token, host=host, max_tokens=max_tokens, model_adapter_id=model_adapter_id, workspace_id=workspace_id, callback_manager=callback_manager, is_chat_model=is_chat_model, ) self._model = self._gradient.get_model_adapter( model_adapter_id=model_adapter_id )

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from typing import Any, Callable, Optional, Sequence from typing_extensions import override from llama_index.legacy.bridge.pydantic import Field, PrivateAttr from llama_index.legacy.callbacks import CallbackManager from llama_index.legacy.constants import DEFAULT_NUM_OUTPUTS from llama_index.legacy.core.llms.types import ( ChatMessage, CompletionResponse, CompletionResponseGen, LLMMetadata, ) from llama_index.legacy.llms.base import llm_completion_callback from llama_index.legacy.llms.custom import CustomLLM from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode class _BaseGradientLLM(CustomLLM): _gradient = PrivateAttr() _model = PrivateAttr() # Config max_tokens: Optional[int] = Field( default=DEFAULT_NUM_OUTPUTS, description="The number of tokens to generate.", gt=0, lt=512, ) # Gradient client config access_token: Optional[str] = Field( description="The Gradient access token to use.", ) host: Optional[str] = Field( description="The url of the Gradient service to access." ) workspace_id: Optional[str] = Field( description="The Gradient workspace id to use.", ) is_chat_model: bool = Field( default=False, description="Whether the model is a chat model." ) def __init__( self, *, access_token: Optional[str] = None, host: Optional[str] = None, max_tokens: Optional[int] = None, workspace_id: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, is_chat_model: bool = False, system_prompt: Optional[str] = None, messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None, completion_to_prompt: Optional[Callable[[str], str]] = None, pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, output_parser: Optional[BaseOutputParser] = None, **kwargs: Any, ) -> None: super().__init__( max_tokens=max_tokens, access_token=access_token, host=host, workspace_id=workspace_id, callback_manager=callback_manager, is_chat_model=is_chat_model, system_prompt=system_prompt, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, **kwargs, ) try: from gradientai import Gradient self._gradient = Gradient( access_token=access_token, host=host, workspace_id=workspace_id ) except ImportError as e: raise ImportError( "Could not import Gradient Python package. " "Please install it with `pip install gradientai`." ) from e def close(self) -> None: self._gradient.close() @llm_completion_callback() @override def complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponse: return CompletionResponse( text=self._model.complete( query=prompt, max_generated_token_count=self.max_tokens, **kwargs, ).generated_output ) @llm_completion_callback() @override async def acomplete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponse: grdt_reponse = await self._model.acomplete( query=prompt, max_generated_token_count=self.max_tokens, **kwargs, ) return CompletionResponse(text=grdt_reponse.generated_output) @override def stream_complete( self, prompt: str, formatted: bool = False, **kwargs: Any, ) -> CompletionResponseGen: raise NotImplementedError @property @override def metadata(self) -> LLMMetadata: return LLMMetadata( context_window=1024, num_output=self.max_tokens or 20, is_chat_model=self.is_chat_model, is_function_calling_model=False, model_name=self._model.id, ) class GradientBaseModelLLM(_BaseGradientLLM): base_model_slug: str = Field( description="The slug of the base model to use.", ) def __init__( self, *, access_token: Optional[str] = None, base_model_slug: str, host: Optional[str] = None, max_tokens: Optional[int] = None, workspace_id: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, is_chat_model: bool = False, ) -> None: super().__init__( access_token=access_token, base_model_slug=base_model_slug, host=host, max_tokens=max_tokens, workspace_id=workspace_id, callback_manager=callback_manager, is_chat_model=is_chat_model, ) self._model = self._gradient.get_base_model( base_model_slug=base_model_slug, ) class GradientModelAdapterLLM(_BaseGradientLLM): model_adapter_id: str = Field( description="The id of the model adapter to use.", ) def __init__( self, *, access_token: Optional[str] = None, host: Optional[str] = None, max_tokens: Optional[int] = None, model_adapter_id: str, workspace_id: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, is_chat_model: bool = False, ) -> None: super().__init__( access_token=access_token, host=host, max_tokens=max_tokens, model_adapter_id=model_adapter_id, workspace_id=workspace_id, callback_manager=callback_manager, is_chat_model=is_chat_model, ) self._model = self._gradient.get_model_adapter( model_adapter_id=model_adapter_id )

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from typing import Dict, Type from llama_index.core.base.embeddings.base import BaseEmbedding from llama_index.core.embeddings.mock_embed_model import MockEmbedding RECOGNIZED_EMBEDDINGS: Dict[str, Type[BaseEmbedding]] = { MockEmbedding.class_name(): MockEmbedding, } # conditionals for llama-cloud support try: from llama_index.embeddings.openai import OpenAIEmbedding # pants: no-infer-dep RECOGNIZED_EMBEDDINGS[OpenAIEmbedding.class_name()] = OpenAIEmbedding except ImportError: pass try: from llama_index.embeddings.azure_openai import ( AzureOpenAIEmbedding, ) # pants: no-infer-dep RECOGNIZED_EMBEDDINGS[AzureOpenAIEmbedding.class_name()] = AzureOpenAIEmbedding except ImportError: pass try: from llama_index.embeddings.huggingface import ( HuggingFaceInferenceAPIEmbedding, ) # pants: no-infer-dep RECOGNIZED_EMBEDDINGS[ HuggingFaceInferenceAPIEmbedding.class_name() ] = HuggingFaceInferenceAPIEmbedding except ImportError: pass def load_embed_model(data: dict) -> BaseEmbedding: """Load Embedding by name.""" if isinstance(data, BaseEmbedding): return data name = data.get("class_name", None) if name is None: raise ValueError("Embedding loading requires a class_name") if name not in RECOGNIZED_EMBEDDINGS: raise ValueError(f"Invalid Embedding name: {name}") return RECOGNIZED_EMBEDDINGS[name].from_dict(data)

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from typing import Dict, Type from llama_index.core.base.embeddings.base import BaseEmbedding from llama_index.core.embeddings.mock_embed_model import MockEmbedding RECOGNIZED_EMBEDDINGS: Dict[str, Type[BaseEmbedding]] = { MockEmbedding.class_name(): MockEmbedding, } # conditionals for llama-cloud support try: from llama_index.embeddings.openai import OpenAIEmbedding # pants: no-infer-dep RECOGNIZED_EMBEDDINGS[OpenAIEmbedding.class_name()] = OpenAIEmbedding except ImportError: pass try: from llama_index.embeddings.azure_openai import ( AzureOpenAIEmbedding, ) # pants: no-infer-dep RECOGNIZED_EMBEDDINGS[AzureOpenAIEmbedding.class_name()] = AzureOpenAIEmbedding except ImportError: pass try: from llama_index.embeddings.huggingface import ( HuggingFaceInferenceAPIEmbedding, ) # pants: no-infer-dep RECOGNIZED_EMBEDDINGS[ HuggingFaceInferenceAPIEmbedding.class_name() ] = HuggingFaceInferenceAPIEmbedding except ImportError: pass def load_embed_model(data: dict) -> BaseEmbedding: """Load Embedding by name.""" if isinstance(data, BaseEmbedding): return data name = data.get("class_name", None) if name is None: raise ValueError("Embedding loading requires a class_name") if name not in RECOGNIZED_EMBEDDINGS: raise ValueError(f"Invalid Embedding name: {name}") return RECOGNIZED_EMBEDDINGS[name].from_dict(data)

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import asyncio from llama_index.core.llama_dataset import download_llama_dataset from llama_index.core.llama_pack import download_llama_pack from llama_index.core.evaluation import CorrectnessEvaluator from llama_index.llms import OpenAI, Gemini from llama_index.core import ServiceContext import pandas as pd async def main(): # DOWNLOAD LLAMADATASET evaluator_dataset, _ = download_llama_dataset( "MiniMtBenchSingleGradingDataset", "./mini_mt_bench_data" ) # DEFINE EVALUATORS gpt_4_context = ServiceContext.from_defaults( llm=OpenAI(temperature=0, model="gpt-4"), ) gpt_3p5_context = ServiceContext.from_defaults( llm=OpenAI(temperature=0, model="gpt-3.5-turbo"), ) gemini_pro_context = ServiceContext.from_defaults( llm=Gemini(model="models/gemini-pro", temperature=0) ) evaluators = { "gpt-4": CorrectnessEvaluator(service_context=gpt_4_context), "gpt-3.5": CorrectnessEvaluator(service_context=gpt_3p5_context), "gemini-pro": CorrectnessEvaluator(service_context=gemini_pro_context), } # EVALUATE WITH PACK ############################################################################ # NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 # # then you'll need to use different batch_size and sleep_time_in_seconds. # # For Usage Tier 1, settings that seemed to work well were batch_size=5, # # and sleep_time_in_seconds=15 (as of December 2023.) # ############################################################################ EvaluatorBenchmarkerPack = download_llama_pack("EvaluatorBenchmarkerPack", "./pack") evaluator_benchmarker = EvaluatorBenchmarkerPack( evaluator=evaluators["gpt-3.5"], eval_dataset=evaluator_dataset, show_progress=True, ) gpt_3p5_benchmark_df = await evaluator_benchmarker.arun( batch_size=100, sleep_time_in_seconds=0 ) evaluator_benchmarker = EvaluatorBenchmarkerPack( evaluator=evaluators["gpt-4"], eval_dataset=evaluator_dataset, show_progress=True, ) gpt_4_benchmark_df = await evaluator_benchmarker.arun( batch_size=100, sleep_time_in_seconds=0 ) evaluator_benchmarker = EvaluatorBenchmarkerPack( evaluator=evaluators["gemini-pro"], eval_dataset=evaluator_dataset, show_progress=True, ) gemini_pro_benchmark_df = await evaluator_benchmarker.arun( batch_size=5, sleep_time_in_seconds=0.5 ) benchmark_df = pd.concat( [ gpt_3p5_benchmark_df, gpt_4_benchmark_df, gemini_pro_benchmark_df, ], axis=0, ) print(benchmark_df) if __name__ == "__main__": loop = asyncio.get_event_loop() loop.run_until_complete(main)

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import asyncio from llama_index.core.llama_dataset import download_llama_dataset from llama_index.core.llama_pack import download_llama_pack from llama_index.core.evaluation import CorrectnessEvaluator from llama_index.llms import OpenAI, Gemini from llama_index.core import ServiceContext import pandas as pd async def main(): # DOWNLOAD LLAMADATASET evaluator_dataset, _ = download_llama_dataset( "MiniMtBenchSingleGradingDataset", "./mini_mt_bench_data" ) # DEFINE EVALUATORS gpt_4_context = ServiceContext.from_defaults( llm=OpenAI(temperature=0, model="gpt-4"), ) gpt_3p5_context = ServiceContext.from_defaults( llm=OpenAI(temperature=0, model="gpt-3.5-turbo"), ) gemini_pro_context = ServiceContext.from_defaults( llm=Gemini(model="models/gemini-pro", temperature=0) ) evaluators = { "gpt-4": CorrectnessEvaluator(service_context=gpt_4_context), "gpt-3.5": CorrectnessEvaluator(service_context=gpt_3p5_context), "gemini-pro": CorrectnessEvaluator(service_context=gemini_pro_context), } # EVALUATE WITH PACK ############################################################################ # NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 # # then you'll need to use different batch_size and sleep_time_in_seconds. # # For Usage Tier 1, settings that seemed to work well were batch_size=5, # # and sleep_time_in_seconds=15 (as of December 2023.) # ############################################################################ EvaluatorBenchmarkerPack = download_llama_pack("EvaluatorBenchmarkerPack", "./pack") evaluator_benchmarker = EvaluatorBenchmarkerPack( evaluator=evaluators["gpt-3.5"], eval_dataset=evaluator_dataset, show_progress=True, ) gpt_3p5_benchmark_df = await evaluator_benchmarker.arun( batch_size=100, sleep_time_in_seconds=0 ) evaluator_benchmarker = EvaluatorBenchmarkerPack( evaluator=evaluators["gpt-4"], eval_dataset=evaluator_dataset, show_progress=True, ) gpt_4_benchmark_df = await evaluator_benchmarker.arun( batch_size=100, sleep_time_in_seconds=0 ) evaluator_benchmarker = EvaluatorBenchmarkerPack( evaluator=evaluators["gemini-pro"], eval_dataset=evaluator_dataset, show_progress=True, ) gemini_pro_benchmark_df = await evaluator_benchmarker.arun( batch_size=5, sleep_time_in_seconds=0.5 ) benchmark_df = pd.concat( [ gpt_3p5_benchmark_df, gpt_4_benchmark_df, gemini_pro_benchmark_df, ], axis=0, ) print(benchmark_df) if __name__ == "__main__": loop = asyncio.get_event_loop() loop.run_until_complete(main)

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import asyncio from llama_index.core.llama_dataset import download_llama_dataset from llama_index.core.llama_pack import download_llama_pack from llama_index.core import VectorStoreIndex async def main(): # DOWNLOAD LLAMADATASET rag_dataset, documents = download_llama_dataset( "PaulGrahamEssayDataset", "./paul_graham" ) # BUILD BASIC RAG PIPELINE index = VectorStoreIndex.from_documents(documents=documents) query_engine = index.as_query_engine() # EVALUATE WITH PACK RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack_stuff") rag_evaluator = RagEvaluatorPack(query_engine=query_engine, rag_dataset=rag_dataset) ############################################################################ # NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 # # then you'll need to use different batch_size and sleep_time_in_seconds. # # For Usage Tier 1, settings that seemed to work well were batch_size=5, # # and sleep_time_in_seconds=15 (as of December 2023.) # ############################################################################ benchmark_df = await rag_evaluator.arun( batch_size=20, # batches the number of openai api calls to make sleep_time_in_seconds=1, # number of seconds sleep before making an api call ) print(benchmark_df) if __name__ == "__main__": loop = asyncio.get_event_loop() loop.run_until_complete(main)

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import asyncio from llama_index.core.llama_dataset import download_llama_dataset from llama_index.core.llama_pack import download_llama_pack from llama_index.core import VectorStoreIndex async def main(): # DOWNLOAD LLAMADATASET rag_dataset, documents = download_llama_dataset( "PaulGrahamEssayDataset", "./paul_graham" ) # BUILD BASIC RAG PIPELINE index = VectorStoreIndex.from_documents(documents=documents) query_engine = index.as_query_engine() # EVALUATE WITH PACK RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack_stuff") rag_evaluator = RagEvaluatorPack(query_engine=query_engine, rag_dataset=rag_dataset) ############################################################################ # NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 # # then you'll need to use different batch_size and sleep_time_in_seconds. # # For Usage Tier 1, settings that seemed to work well were batch_size=5, # # and sleep_time_in_seconds=15 (as of December 2023.) # ############################################################################ benchmark_df = await rag_evaluator.arun( batch_size=20, # batches the number of openai api calls to make sleep_time_in_seconds=1, # number of seconds sleep before making an api call ) print(benchmark_df) if __name__ == "__main__": loop = asyncio.get_event_loop() loop.run_until_complete(main)

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from typing import TYPE_CHECKING, Any, Optional from llama_index.core.base.base_query_engine import BaseQueryEngine if TYPE_CHECKING: from llama_index.core.langchain_helpers.agents.tools import ( LlamaIndexTool, ) from llama_index.core.tools.types import AsyncBaseTool, ToolMetadata, ToolOutput DEFAULT_NAME = "query_engine_tool" DEFAULT_DESCRIPTION = """Useful for running a natural language query against a knowledge base and get back a natural language response. """ class QueryEngineTool(AsyncBaseTool): """Query engine tool. A tool making use of a query engine. Args: query_engine (BaseQueryEngine): A query engine. metadata (ToolMetadata): The associated metadata of the query engine. """ def __init__( self, query_engine: BaseQueryEngine, metadata: ToolMetadata, resolve_input_errors: bool = True, ) -> None: self._query_engine = query_engine self._metadata = metadata self._resolve_input_errors = resolve_input_errors @classmethod def from_defaults( cls, query_engine: BaseQueryEngine, name: Optional[str] = None, description: Optional[str] = None, resolve_input_errors: bool = True, ) -> "QueryEngineTool": name = name or DEFAULT_NAME description = description or DEFAULT_DESCRIPTION metadata = ToolMetadata(name=name, description=description) return cls( query_engine=query_engine, metadata=metadata, resolve_input_errors=resolve_input_errors, ) @property def query_engine(self) -> BaseQueryEngine: return self._query_engine @property def metadata(self) -> ToolMetadata: return self._metadata def call(self, *args: Any, **kwargs: Any) -> ToolOutput: if args is not None and len(args) > 0: query_str = str(args[0]) elif kwargs is not None and "input" in kwargs: # NOTE: this assumes our default function schema of `input` query_str = kwargs["input"] elif kwargs is not None and self._resolve_input_errors: query_str = str(kwargs) else: raise ValueError( "Cannot call query engine without specifying `input` parameter." ) response = self._query_engine.query(query_str) return ToolOutput( content=str(response), tool_name=self.metadata.name, raw_input={"input": query_str}, raw_output=response, ) async def acall(self, *args: Any, **kwargs: Any) -> ToolOutput: if args is not None and len(args) > 0: query_str = str(args[0]) elif kwargs is not None and "input" in kwargs: # NOTE: this assumes our default function schema of `input` query_str = kwargs["input"] elif kwargs is not None and self._resolve_input_errors: query_str = str(kwargs) else: raise ValueError("Cannot call query engine without inputs") response = await self._query_engine.aquery(query_str) return ToolOutput( content=str(response), tool_name=self.metadata.name, raw_input={"input": query_str}, raw_output=response, ) def as_langchain_tool(self) -> "LlamaIndexTool": from llama_index.core.langchain_helpers.agents.tools import ( IndexToolConfig, LlamaIndexTool, ) tool_config = IndexToolConfig( query_engine=self.query_engine, name=self.metadata.name, description=self.metadata.description, ) return LlamaIndexTool.from_tool_config(tool_config=tool_config)

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from typing import TYPE_CHECKING, Any, Optional from llama_index.core.base.base_query_engine import BaseQueryEngine if TYPE_CHECKING: from llama_index.core.langchain_helpers.agents.tools import ( LlamaIndexTool, ) from llama_index.core.tools.types import AsyncBaseTool, ToolMetadata, ToolOutput DEFAULT_NAME = "query_engine_tool" DEFAULT_DESCRIPTION = """Useful for running a natural language query against a knowledge base and get back a natural language response. """ class QueryEngineTool(AsyncBaseTool): """Query engine tool. A tool making use of a query engine. Args: query_engine (BaseQueryEngine): A query engine. metadata (ToolMetadata): The associated metadata of the query engine. """ def __init__( self, query_engine: BaseQueryEngine, metadata: ToolMetadata, resolve_input_errors: bool = True, ) -> None: self._query_engine = query_engine self._metadata = metadata self._resolve_input_errors = resolve_input_errors @classmethod def from_defaults( cls, query_engine: BaseQueryEngine, name: Optional[str] = None, description: Optional[str] = None, resolve_input_errors: bool = True, ) -> "QueryEngineTool": name = name or DEFAULT_NAME description = description or DEFAULT_DESCRIPTION metadata = ToolMetadata(name=name, description=description) return cls( query_engine=query_engine, metadata=metadata, resolve_input_errors=resolve_input_errors, ) @property def query_engine(self) -> BaseQueryEngine: return self._query_engine @property def metadata(self) -> ToolMetadata: return self._metadata def call(self, *args: Any, **kwargs: Any) -> ToolOutput: if args is not None and len(args) > 0: query_str = str(args[0]) elif kwargs is not None and "input" in kwargs: # NOTE: this assumes our default function schema of `input` query_str = kwargs["input"] elif kwargs is not None and self._resolve_input_errors: query_str = str(kwargs) else: raise ValueError( "Cannot call query engine without specifying `input` parameter." ) response = self._query_engine.query(query_str) return ToolOutput( content=str(response), tool_name=self.metadata.name, raw_input={"input": query_str}, raw_output=response, ) async def acall(self, *args: Any, **kwargs: Any) -> ToolOutput: if args is not None and len(args) > 0: query_str = str(args[0]) elif kwargs is not None and "input" in kwargs: # NOTE: this assumes our default function schema of `input` query_str = kwargs["input"] elif kwargs is not None and self._resolve_input_errors: query_str = str(kwargs) else: raise ValueError("Cannot call query engine without inputs") response = await self._query_engine.aquery(query_str) return ToolOutput( content=str(response), tool_name=self.metadata.name, raw_input={"input": query_str}, raw_output=response, ) def as_langchain_tool(self) -> "LlamaIndexTool": from llama_index.core.langchain_helpers.agents.tools import ( IndexToolConfig, LlamaIndexTool, ) tool_config = IndexToolConfig( query_engine=self.query_engine, name=self.metadata.name, description=self.metadata.description, ) return LlamaIndexTool.from_tool_config(tool_config=tool_config)

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from typing import TYPE_CHECKING, Any, Optional from llama_index.core.base.base_query_engine import BaseQueryEngine if TYPE_CHECKING: from llama_index.core.langchain_helpers.agents.tools import ( LlamaIndexTool, ) from llama_index.core.tools.types import AsyncBaseTool, ToolMetadata, ToolOutput DEFAULT_NAME = "query_engine_tool" DEFAULT_DESCRIPTION = """Useful for running a natural language query against a knowledge base and get back a natural language response. """ class QueryEngineTool(AsyncBaseTool): """Query engine tool. A tool making use of a query engine. Args: query_engine (BaseQueryEngine): A query engine. metadata (ToolMetadata): The associated metadata of the query engine. """ def __init__( self, query_engine: BaseQueryEngine, metadata: ToolMetadata, resolve_input_errors: bool = True, ) -> None: self._query_engine = query_engine self._metadata = metadata self._resolve_input_errors = resolve_input_errors @classmethod def from_defaults( cls, query_engine: BaseQueryEngine, name: Optional[str] = None, description: Optional[str] = None, resolve_input_errors: bool = True, ) -> "QueryEngineTool": name = name or DEFAULT_NAME description = description or DEFAULT_DESCRIPTION metadata = ToolMetadata(name=name, description=description) return cls( query_engine=query_engine, metadata=metadata, resolve_input_errors=resolve_input_errors, ) @property def query_engine(self) -> BaseQueryEngine: return self._query_engine @property def metadata(self) -> ToolMetadata: return self._metadata def call(self, *args: Any, **kwargs: Any) -> ToolOutput: if args is not None and len(args) > 0: query_str = str(args[0]) elif kwargs is not None and "input" in kwargs: # NOTE: this assumes our default function schema of `input` query_str = kwargs["input"] elif kwargs is not None and self._resolve_input_errors: query_str = str(kwargs) else: raise ValueError( "Cannot call query engine without specifying `input` parameter." ) response = self._query_engine.query(query_str) return ToolOutput( content=str(response), tool_name=self.metadata.name, raw_input={"input": query_str}, raw_output=response, ) async def acall(self, *args: Any, **kwargs: Any) -> ToolOutput: if args is not None and len(args) > 0: query_str = str(args[0]) elif kwargs is not None and "input" in kwargs: # NOTE: this assumes our default function schema of `input` query_str = kwargs["input"] elif kwargs is not None and self._resolve_input_errors: query_str = str(kwargs) else: raise ValueError("Cannot call query engine without inputs") response = await self._query_engine.aquery(query_str) return ToolOutput( content=str(response), tool_name=self.metadata.name, raw_input={"input": query_str}, raw_output=response, ) def as_langchain_tool(self) -> "LlamaIndexTool": from llama_index.core.langchain_helpers.agents.tools import ( IndexToolConfig, LlamaIndexTool, ) tool_config = IndexToolConfig( query_engine=self.query_engine, name=self.metadata.name, description=self.metadata.description, ) return LlamaIndexTool.from_tool_config(tool_config=tool_config)

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from typing import Any, Callable, Dict, Optional, Sequence from llama_index.legacy.bridge.pydantic import Field, PrivateAttr from llama_index.legacy.callbacks import CallbackManager from llama_index.legacy.constants import DEFAULT_NUM_OUTPUTS from llama_index.legacy.core.llms.types import ( ChatMessage, ChatResponse, ChatResponseGen, CompletionResponse, CompletionResponseGen, LLMMetadata, ) from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback from llama_index.legacy.llms.custom import CustomLLM from llama_index.legacy.llms.generic_utils import chat_to_completion_decorator from llama_index.legacy.llms.openai_utils import ( from_openai_message_dict, to_openai_message_dicts, ) from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode class LlamaAPI(CustomLLM): model: str = Field(description="The llama-api model to use.") temperature: float = Field(description="The temperature to use for sampling.") max_tokens: int = Field(description="The maximum number of tokens to generate.") additional_kwargs: Dict[str, Any] = Field( default_factory=dict, description="Additional kwargs for the llama-api API." ) _client: Any = PrivateAttr() def __init__( self, model: str = "llama-13b-chat", temperature: float = 0.1, max_tokens: int = DEFAULT_NUM_OUTPUTS, additional_kwargs: Optional[Dict[str, Any]] = None, api_key: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, system_prompt: Optional[str] = None, messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None, completion_to_prompt: Optional[Callable[[str], str]] = None, pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, output_parser: Optional[BaseOutputParser] = None, ) -> None: try: from llamaapi import LlamaAPI as Client except ImportError as e: raise ImportError( "llama_api not installed." "Please install it with `pip install llamaapi`." ) from e self._client = Client(api_key) super().__init__( model=model, temperature=temperature, max_tokens=max_tokens, additional_kwargs=additional_kwargs or {}, callback_manager=callback_manager, system_prompt=system_prompt, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) @classmethod def class_name(cls) -> str: return "llama_api_llm" @property def _model_kwargs(self) -> Dict[str, Any]: base_kwargs = { "model": self.model, "temperature": self.temperature, "max_length": self.max_tokens, } return { **base_kwargs, **self.additional_kwargs, } @property def metadata(self) -> LLMMetadata: return LLMMetadata( context_window=4096, num_output=DEFAULT_NUM_OUTPUTS, is_chat_model=True, is_function_calling_model=True, model_name="llama-api", ) @llm_chat_callback() def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: message_dicts = to_openai_message_dicts(messages) json_dict = { "messages": message_dicts, **self._model_kwargs, **kwargs, } response = self._client.run(json_dict).json() message_dict = response["choices"][0]["message"] message = from_openai_message_dict(message_dict) return ChatResponse(message=message, raw=response) @llm_completion_callback() def complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponse: complete_fn = chat_to_completion_decorator(self.chat) return complete_fn(prompt, **kwargs) @llm_completion_callback() def stream_complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponseGen: raise NotImplementedError("stream_complete is not supported for LlamaAPI") @llm_chat_callback() def stream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseGen: raise NotImplementedError("stream_chat is not supported for LlamaAPI")

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from typing import Any, Callable, Dict, Optional, Sequence from llama_index.legacy.bridge.pydantic import Field, PrivateAttr from llama_index.legacy.callbacks import CallbackManager from llama_index.legacy.constants import DEFAULT_NUM_OUTPUTS from llama_index.legacy.core.llms.types import ( ChatMessage, ChatResponse, ChatResponseGen, CompletionResponse, CompletionResponseGen, LLMMetadata, ) from llama_index.legacy.llms.base import llm_chat_callback, llm_completion_callback from llama_index.legacy.llms.custom import CustomLLM from llama_index.legacy.llms.generic_utils import chat_to_completion_decorator from llama_index.legacy.llms.openai_utils import ( from_openai_message_dict, to_openai_message_dicts, ) from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode class LlamaAPI(CustomLLM): model: str = Field(description="The llama-api model to use.") temperature: float = Field(description="The temperature to use for sampling.") max_tokens: int = Field(description="The maximum number of tokens to generate.") additional_kwargs: Dict[str, Any] = Field( default_factory=dict, description="Additional kwargs for the llama-api API." ) _client: Any = PrivateAttr() def __init__( self, model: str = "llama-13b-chat", temperature: float = 0.1, max_tokens: int = DEFAULT_NUM_OUTPUTS, additional_kwargs: Optional[Dict[str, Any]] = None, api_key: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, system_prompt: Optional[str] = None, messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None, completion_to_prompt: Optional[Callable[[str], str]] = None, pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, output_parser: Optional[BaseOutputParser] = None, ) -> None: try: from llamaapi import LlamaAPI as Client except ImportError as e: raise ImportError( "llama_api not installed." "Please install it with `pip install llamaapi`." ) from e self._client = Client(api_key) super().__init__( model=model, temperature=temperature, max_tokens=max_tokens, additional_kwargs=additional_kwargs or {}, callback_manager=callback_manager, system_prompt=system_prompt, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) @classmethod def class_name(cls) -> str: return "llama_api_llm" @property def _model_kwargs(self) -> Dict[str, Any]: base_kwargs = { "model": self.model, "temperature": self.temperature, "max_length": self.max_tokens, } return { **base_kwargs, **self.additional_kwargs, } @property def metadata(self) -> LLMMetadata: return LLMMetadata( context_window=4096, num_output=DEFAULT_NUM_OUTPUTS, is_chat_model=True, is_function_calling_model=True, model_name="llama-api", ) @llm_chat_callback() def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: message_dicts = to_openai_message_dicts(messages) json_dict = { "messages": message_dicts, **self._model_kwargs, **kwargs, } response = self._client.run(json_dict).json() message_dict = response["choices"][0]["message"] message = from_openai_message_dict(message_dict) return ChatResponse(message=message, raw=response) @llm_completion_callback() def complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponse: complete_fn = chat_to_completion_decorator(self.chat) return complete_fn(prompt, **kwargs) @llm_completion_callback() def stream_complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponseGen: raise NotImplementedError("stream_complete is not supported for LlamaAPI") @llm_chat_callback() def stream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseGen: raise NotImplementedError("stream_chat is not supported for LlamaAPI")

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"""Download tool from Llama Hub.""" from typing import Optional, Type from llama_index.legacy.download.module import ( LLAMA_HUB_URL, MODULE_TYPE, download_llama_module, track_download, ) from llama_index.legacy.tools.tool_spec.base import BaseToolSpec def download_tool( tool_class: str, llama_hub_url: str = LLAMA_HUB_URL, refresh_cache: bool = False, custom_path: Optional[str] = None, ) -> Type[BaseToolSpec]: """Download a single tool from Llama Hub. Args: tool_class: The name of the tool class you want to download, such as `GmailToolSpec`. refresh_cache: If true, the local cache will be skipped and the loader will be fetched directly from the remote repo. custom_path: Custom dirpath to download loader into. Returns: A Loader. """ tool_cls = download_llama_module( tool_class, llama_hub_url=llama_hub_url, refresh_cache=refresh_cache, custom_dir="tools", custom_path=custom_path, library_path="tools/library.json", ) if not issubclass(tool_cls, BaseToolSpec): raise ValueError(f"Tool class {tool_class} must be a subclass of BaseToolSpec.") track_download(tool_class, MODULE_TYPE.TOOL) return tool_cls

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"""Download tool from Llama Hub.""" from typing import Optional, Type from llama_index.legacy.download.module import ( LLAMA_HUB_URL, MODULE_TYPE, download_llama_module, track_download, ) from llama_index.legacy.tools.tool_spec.base import BaseToolSpec def download_tool( tool_class: str, llama_hub_url: str = LLAMA_HUB_URL, refresh_cache: bool = False, custom_path: Optional[str] = None, ) -> Type[BaseToolSpec]: """Download a single tool from Llama Hub. Args: tool_class: The name of the tool class you want to download, such as `GmailToolSpec`. refresh_cache: If true, the local cache will be skipped and the loader will be fetched directly from the remote repo. custom_path: Custom dirpath to download loader into. Returns: A Loader. """ tool_cls = download_llama_module( tool_class, llama_hub_url=llama_hub_url, refresh_cache=refresh_cache, custom_dir="tools", custom_path=custom_path, library_path="tools/library.json", ) if not issubclass(tool_cls, BaseToolSpec): raise ValueError(f"Tool class {tool_class} must be a subclass of BaseToolSpec.") track_download(tool_class, MODULE_TYPE.TOOL) return tool_cls

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"""Simple Engine.""" import json import os from typing import Any, Optional, Union from llama_index.core import SimpleDirectoryReader, VectorStoreIndex from llama_index.core.callbacks.base import CallbackManager from llama_index.core.embeddings import BaseEmbedding from llama_index.core.embeddings.mock_embed_model import MockEmbedding from llama_index.core.indices.base import BaseIndex from llama_index.core.ingestion.pipeline import run_transformations from llama_index.core.llms import LLM from llama_index.core.node_parser import SentenceSplitter from llama_index.core.postprocessor.types import BaseNodePostprocessor from llama_index.core.query_engine import RetrieverQueryEngine from llama_index.core.response_synthesizers import ( BaseSynthesizer, get_response_synthesizer, ) from llama_index.core.retrievers import BaseRetriever from llama_index.core.schema import ( BaseNode, Document, NodeWithScore, QueryBundle, QueryType, TransformComponent, ) from metagpt.rag.factories import ( get_index, get_rag_embedding, get_rag_llm, get_rankers, get_retriever, ) from metagpt.rag.interface import NoEmbedding, RAGObject from metagpt.rag.retrievers.base import ModifiableRAGRetriever, PersistableRAGRetriever from metagpt.rag.retrievers.hybrid_retriever import SimpleHybridRetriever from metagpt.rag.schema import ( BaseIndexConfig, BaseRankerConfig, BaseRetrieverConfig, BM25RetrieverConfig, ObjectNode, ) from metagpt.utils.common import import_class class SimpleEngine(RetrieverQueryEngine): """SimpleEngine is designed to be simple and straightforward. It is a lightweight and easy-to-use search engine that integrates document reading, embedding, indexing, retrieving, and ranking functionalities into a single, straightforward workflow. It is designed to quickly set up a search engine from a collection of documents. """ def __init__( self, retriever: BaseRetriever, response_synthesizer: Optional[BaseSynthesizer] = None, node_postprocessors: Optional[list[BaseNodePostprocessor]] = None, callback_manager: Optional[CallbackManager] = None, index: Optional[BaseIndex] = None, ) -> None: super().__init__( retriever=retriever, response_synthesizer=response_synthesizer, node_postprocessors=node_postprocessors, callback_manager=callback_manager, ) self.index = index @classmethod def from_docs( cls, input_dir: str = None, input_files: list[str] = None, transformations: Optional[list[TransformComponent]] = None, embed_model: BaseEmbedding = None, llm: LLM = None, retriever_configs: list[BaseRetrieverConfig] = None, ranker_configs: list[BaseRankerConfig] = None, ) -> "SimpleEngine": """From docs. Must provide either `input_dir` or `input_files`. Args: input_dir: Path to the directory. input_files: List of file paths to read (Optional; overrides input_dir, exclude). transformations: Parse documents to nodes. Default [SentenceSplitter]. embed_model: Parse nodes to embedding. Must supported by llama index. Default OpenAIEmbedding. llm: Must supported by llama index. Default OpenAI. retriever_configs: Configuration for retrievers. If more than one config, will use SimpleHybridRetriever. ranker_configs: Configuration for rankers. """ if not input_dir and not input_files: raise ValueError("Must provide either `input_dir` or `input_files`.") documents = SimpleDirectoryReader(input_dir=input_dir, input_files=input_files).load_data() cls._fix_document_metadata(documents) index = VectorStoreIndex.from_documents( documents=documents, transformations=transformations or [SentenceSplitter()], embed_model=cls._resolve_embed_model(embed_model, retriever_configs), ) return cls._from_index(index, llm=llm, retriever_configs=retriever_configs, ranker_configs=ranker_configs) @classmethod def from_objs( cls, objs: Optional[list[RAGObject]] = None, transformations: Optional[list[TransformComponent]] = None, embed_model: BaseEmbedding = None, llm: LLM = None, retriever_configs: list[BaseRetrieverConfig] = None, ranker_configs: list[BaseRankerConfig] = None, ) -> "SimpleEngine": """From objs. Args: objs: List of RAGObject. transformations: Parse documents to nodes. Default [SentenceSplitter]. embed_model: Parse nodes to embedding. Must supported by llama index. Default OpenAIEmbedding. llm: Must supported by llama index. Default OpenAI. retriever_configs: Configuration for retrievers. If more than one config, will use SimpleHybridRetriever. ranker_configs: Configuration for rankers. """ if not objs and any(isinstance(config, BM25RetrieverConfig) for config in retriever_configs): raise ValueError("In BM25RetrieverConfig, Objs must not be empty.") objs = objs or [] nodes = [ObjectNode(text=obj.rag_key(), metadata=ObjectNode.get_obj_metadata(obj)) for obj in objs] index = VectorStoreIndex( nodes=nodes, transformations=transformations or [SentenceSplitter()], embed_model=cls._resolve_embed_model(embed_model, retriever_configs), ) return cls._from_index(index, llm=llm, retriever_configs=retriever_configs, ranker_configs=ranker_configs) @classmethod def from_index( cls, index_config: BaseIndexConfig, embed_model: BaseEmbedding = None, llm: LLM = None, retriever_configs: list[BaseRetrieverConfig] = None, ranker_configs: list[BaseRankerConfig] = None, ) -> "SimpleEngine": """Load from previously maintained index by self.persist(), index_config contains persis_path.""" index = get_index(index_config, embed_model=cls._resolve_embed_model(embed_model, [index_config])) return cls._from_index(index, llm=llm, retriever_configs=retriever_configs, ranker_configs=ranker_configs) async def asearch(self, content: str, **kwargs) -> str: """Inplement tools.SearchInterface""" return await self.aquery(content) async def aretrieve(self, query: QueryType) -> list[NodeWithScore]: """Allow query to be str.""" query_bundle = QueryBundle(query) if isinstance(query, str) else query nodes = await super().aretrieve(query_bundle) self._try_reconstruct_obj(nodes) return nodes def add_docs(self, input_files: list[str]): """Add docs to retriever. retriever must has add_nodes func.""" self._ensure_retriever_modifiable() documents = SimpleDirectoryReader(input_files=input_files).load_data() self._fix_document_metadata(documents) nodes = run_transformations(documents, transformations=self.index._transformations) self._save_nodes(nodes) def add_objs(self, objs: list[RAGObject]): """Adds objects to the retriever, storing each object's original form in metadata for future reference.""" self._ensure_retriever_modifiable() nodes = [ObjectNode(text=obj.rag_key(), metadata=ObjectNode.get_obj_metadata(obj)) for obj in objs] self._save_nodes(nodes) def persist(self, persist_dir: Union[str, os.PathLike], **kwargs): """Persist.""" self._ensure_retriever_persistable() self._persist(str(persist_dir), **kwargs) @classmethod def _from_index( cls, index: BaseIndex, llm: LLM = None, retriever_configs: list[BaseRetrieverConfig] = None, ranker_configs: list[BaseRankerConfig] = None, ) -> "SimpleEngine": llm = llm or get_rag_llm() retriever = get_retriever(configs=retriever_configs, index=index) # Default index.as_retriever rankers = get_rankers(configs=ranker_configs, llm=llm) # Default [] return cls( retriever=retriever, node_postprocessors=rankers, response_synthesizer=get_response_synthesizer(llm=llm), index=index, ) def _ensure_retriever_modifiable(self): self._ensure_retriever_of_type(ModifiableRAGRetriever) def _ensure_retriever_persistable(self): self._ensure_retriever_of_type(PersistableRAGRetriever) def _ensure_retriever_of_type(self, required_type: BaseRetriever): """Ensure that self.retriever is required_type, or at least one of its components, if it's a SimpleHybridRetriever. Args: required_type: The class that the retriever is expected to be an instance of. """ if isinstance(self.retriever, SimpleHybridRetriever): if not any(isinstance(r, required_type) for r in self.retriever.retrievers): raise TypeError( f"Must have at least one retriever of type {required_type.__name__} in SimpleHybridRetriever" ) if not isinstance(self.retriever, required_type): raise TypeError(f"The retriever is not of type {required_type.__name__}: {type(self.retriever)}") def _save_nodes(self, nodes: list[BaseNode]): self.retriever.add_nodes(nodes) def _persist(self, persist_dir: str, **kwargs): self.retriever.persist(persist_dir, **kwargs) @staticmethod def _try_reconstruct_obj(nodes: list[NodeWithScore]): """If node is object, then dynamically reconstruct object, and save object to node.metadata["obj"].""" for node in nodes: if node.metadata.get("is_obj", False): obj_cls = import_class(node.metadata["obj_cls_name"], node.metadata["obj_mod_name"]) obj_dict = json.loads(node.metadata["obj_json"]) node.metadata["obj"] = obj_cls(**obj_dict) @staticmethod def _fix_document_metadata(documents: list[Document]): """LlamaIndex keep metadata['file_path'], which is unnecessary, maybe deleted in the near future.""" for doc in documents: doc.excluded_embed_metadata_keys.append("file_path") @staticmethod def _resolve_embed_model(embed_model: BaseEmbedding = None, configs: list[Any] = None) -> BaseEmbedding: if configs and all(isinstance(c, NoEmbedding) for c in configs): return MockEmbedding(embed_dim=1) return embed_model or get_rag_embedding()

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from collections import ChainMap from typing import ( Any, Callable, Dict, List, Optional, Protocol, Sequence, get_args, runtime_checkable, ) from llama_index.core.base.llms.types import ( ChatMessage, ChatResponseAsyncGen, ChatResponseGen, CompletionResponseAsyncGen, CompletionResponseGen, MessageRole, ) from llama_index.core.base.query_pipeline.query import ( InputKeys, OutputKeys, QueryComponent, StringableInput, validate_and_convert_stringable, ) from llama_index.core.bridge.pydantic import ( BaseModel, Field, root_validator, validator, ) from llama_index.core.callbacks import CBEventType, EventPayload from llama_index.core.base.llms.base import BaseLLM from llama_index.core.base.llms.generic_utils import ( messages_to_prompt as generic_messages_to_prompt, ) from llama_index.core.base.llms.generic_utils import ( prompt_to_messages, ) from llama_index.core.prompts import BasePromptTemplate, PromptTemplate from llama_index.core.types import ( BaseOutputParser, PydanticProgramMode, TokenAsyncGen, TokenGen, ) from llama_index.core.instrumentation.events.llm import ( LLMPredictEndEvent, LLMPredictStartEvent, ) import llama_index.core.instrumentation as instrument dispatcher = instrument.get_dispatcher(__name__) # NOTE: These two protocols are needed to appease mypy @runtime_checkable class MessagesToPromptType(Protocol): def __call__(self, messages: Sequence[ChatMessage]) -> str: pass @runtime_checkable class CompletionToPromptType(Protocol): def __call__(self, prompt: str) -> str: pass def stream_completion_response_to_tokens( completion_response_gen: CompletionResponseGen, ) -> TokenGen: """Convert a stream completion response to a stream of tokens.""" def gen() -> TokenGen: for response in completion_response_gen: yield response.delta or "" return gen() def stream_chat_response_to_tokens( chat_response_gen: ChatResponseGen, ) -> TokenGen: """Convert a stream completion response to a stream of tokens.""" def gen() -> TokenGen: for response in chat_response_gen: yield response.delta or "" return gen() async def astream_completion_response_to_tokens( completion_response_gen: CompletionResponseAsyncGen, ) -> TokenAsyncGen: """Convert a stream completion response to a stream of tokens.""" async def gen() -> TokenAsyncGen: async for response in completion_response_gen: yield response.delta or "" return gen() async def astream_chat_response_to_tokens( chat_response_gen: ChatResponseAsyncGen, ) -> TokenAsyncGen: """Convert a stream completion response to a stream of tokens.""" async def gen() -> TokenAsyncGen: async for response in chat_response_gen: yield response.delta or "" return gen() def default_completion_to_prompt(prompt: str) -> str: return prompt class LLM(BaseLLM): system_prompt: Optional[str] = Field( default=None, description="System prompt for LLM calls." ) messages_to_prompt: Callable = Field( description="Function to convert a list of messages to an LLM prompt.", default=None, exclude=True, ) completion_to_prompt: Callable = Field( description="Function to convert a completion to an LLM prompt.", default=None, exclude=True, ) output_parser: Optional[BaseOutputParser] = Field( description="Output parser to parse, validate, and correct errors programmatically.", default=None, exclude=True, ) pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT # deprecated query_wrapper_prompt: Optional[BasePromptTemplate] = Field( description="Query wrapper prompt for LLM calls.", default=None, exclude=True, ) @validator("messages_to_prompt", pre=True) def set_messages_to_prompt( cls, messages_to_prompt: Optional[MessagesToPromptType] ) -> MessagesToPromptType: return messages_to_prompt or generic_messages_to_prompt @validator("completion_to_prompt", pre=True) def set_completion_to_prompt( cls, completion_to_prompt: Optional[CompletionToPromptType] ) -> CompletionToPromptType: return completion_to_prompt or default_completion_to_prompt @root_validator def check_prompts(cls, values: Dict[str, Any]) -> Dict[str, Any]: if values.get("completion_to_prompt") is None: values["completion_to_prompt"] = default_completion_to_prompt if values.get("messages_to_prompt") is None: values["messages_to_prompt"] = generic_messages_to_prompt return values def _log_template_data( self, prompt: BasePromptTemplate, **prompt_args: Any ) -> None: template_vars = { k: v for k, v in ChainMap(prompt.kwargs, prompt_args).items() if k in prompt.template_vars } with self.callback_manager.event( CBEventType.TEMPLATING, payload={ EventPayload.TEMPLATE: prompt.get_template(llm=self), EventPayload.TEMPLATE_VARS: template_vars, EventPayload.SYSTEM_PROMPT: self.system_prompt, EventPayload.QUERY_WRAPPER_PROMPT: self.query_wrapper_prompt, }, ): pass def _get_prompt(self, prompt: BasePromptTemplate, **prompt_args: Any) -> str: formatted_prompt = prompt.format( llm=self, messages_to_prompt=self.messages_to_prompt, completion_to_prompt=self.completion_to_prompt, **prompt_args, ) if self.output_parser is not None: formatted_prompt = self.output_parser.format(formatted_prompt) return self._extend_prompt(formatted_prompt) def _get_messages( self, prompt: BasePromptTemplate, **prompt_args: Any ) -> List[ChatMessage]: messages = prompt.format_messages(llm=self, **prompt_args) if self.output_parser is not None: messages = self.output_parser.format_messages(messages) return self._extend_messages(messages) def structured_predict( self, output_cls: BaseModel, prompt: PromptTemplate, **prompt_args: Any, ) -> BaseModel: from llama_index.core.program.utils import get_program_for_llm program = get_program_for_llm( output_cls, prompt, self, pydantic_program_mode=self.pydantic_program_mode, ) return program(**prompt_args) async def astructured_predict( self, output_cls: BaseModel, prompt: PromptTemplate, **prompt_args: Any, ) -> BaseModel: from llama_index.core.program.utils import get_program_for_llm program = get_program_for_llm( output_cls, prompt, self, pydantic_program_mode=self.pydantic_program_mode, ) return await program.acall(**prompt_args) def _parse_output(self, output: str) -> str: if self.output_parser is not None: return str(self.output_parser.parse(output)) return output @dispatcher.span def predict( self, prompt: BasePromptTemplate, **prompt_args: Any, ) -> str: """Predict.""" dispatcher.event(LLMPredictStartEvent()) self._log_template_data(prompt, **prompt_args) if self.metadata.is_chat_model: messages = self._get_messages(prompt, **prompt_args) chat_response = self.chat(messages) output = chat_response.message.content or "" else: formatted_prompt = self._get_prompt(prompt, **prompt_args) response = self.complete(formatted_prompt, formatted=True) output = response.text dispatcher.event(LLMPredictEndEvent()) return self._parse_output(output) def stream( self, prompt: BasePromptTemplate, **prompt_args: Any, ) -> TokenGen: """Stream.""" self._log_template_data(prompt, **prompt_args) if self.metadata.is_chat_model: messages = self._get_messages(prompt, **prompt_args) chat_response = self.stream_chat(messages) stream_tokens = stream_chat_response_to_tokens(chat_response) else: formatted_prompt = self._get_prompt(prompt, **prompt_args) stream_response = self.stream_complete(formatted_prompt, formatted=True) stream_tokens = stream_completion_response_to_tokens(stream_response) if prompt.output_parser is not None or self.output_parser is not None: raise NotImplementedError("Output parser is not supported for streaming.") return stream_tokens @dispatcher.span async def apredict( self, prompt: BasePromptTemplate, **prompt_args: Any, ) -> str: """Async predict.""" dispatcher.event(LLMPredictStartEvent()) self._log_template_data(prompt, **prompt_args) if self.metadata.is_chat_model: messages = self._get_messages(prompt, **prompt_args) chat_response = await self.achat(messages) output = chat_response.message.content or "" else: formatted_prompt = self._get_prompt(prompt, **prompt_args) response = await self.acomplete(formatted_prompt, formatted=True) output = response.text dispatcher.event(LLMPredictEndEvent()) return self._parse_output(output) async def astream( self, prompt: BasePromptTemplate, **prompt_args: Any, ) -> TokenAsyncGen: """Async stream.""" self._log_template_data(prompt, **prompt_args) if self.metadata.is_chat_model: messages = self._get_messages(prompt, **prompt_args) chat_response = await self.astream_chat(messages) stream_tokens = await astream_chat_response_to_tokens(chat_response) else: formatted_prompt = self._get_prompt(prompt, **prompt_args) stream_response = await self.astream_complete( formatted_prompt, formatted=True ) stream_tokens = await astream_completion_response_to_tokens(stream_response) if prompt.output_parser is not None or self.output_parser is not None: raise NotImplementedError("Output parser is not supported for streaming.") return stream_tokens def _extend_prompt( self, formatted_prompt: str, ) -> str: """Add system and query wrapper prompts to base prompt.""" extended_prompt = formatted_prompt if self.system_prompt: extended_prompt = self.system_prompt + "\n\n" + extended_prompt if self.query_wrapper_prompt: extended_prompt = self.query_wrapper_prompt.format( query_str=extended_prompt ) return extended_prompt def _extend_messages(self, messages: List[ChatMessage]) -> List[ChatMessage]: """Add system prompt to chat message list.""" if self.system_prompt: messages = [ ChatMessage(role=MessageRole.SYSTEM, content=self.system_prompt), *messages, ] return messages def _as_query_component(self, **kwargs: Any) -> QueryComponent: """Return query component.""" if self.metadata.is_chat_model: return LLMChatComponent(llm=self, **kwargs) else: return LLMCompleteComponent(llm=self, **kwargs) class BaseLLMComponent(QueryComponent): """Base LLM component.""" llm: LLM = Field(..., description="LLM") streaming: bool = Field(default=False, description="Streaming mode") class Config: arbitrary_types_allowed = True def set_callback_manager(self, callback_manager: Any) -> None: """Set callback manager.""" self.llm.callback_manager = callback_manager class LLMCompleteComponent(BaseLLMComponent): """LLM completion component.""" def _validate_component_inputs(self, input: Dict[str, Any]) -> Dict[str, Any]: """Validate component inputs during run_component.""" if "prompt" not in input: raise ValueError("Prompt must be in input dict.") # do special check to see if prompt is a list of chat messages if isinstance(input["prompt"], get_args(List[ChatMessage])): input["prompt"] = self.llm.messages_to_prompt(input["prompt"]) input["prompt"] = validate_and_convert_stringable(input["prompt"]) else: input["prompt"] = validate_and_convert_stringable(input["prompt"]) input["prompt"] = self.llm.completion_to_prompt(input["prompt"]) return input def _run_component(self, **kwargs: Any) -> Any: """Run component.""" # TODO: support only complete for now # non-trivial to figure how to support chat/complete/etc. prompt = kwargs["prompt"] # ignore all other kwargs for now if self.streaming: response = self.llm.stream_complete(prompt, formatted=True) else: response = self.llm.complete(prompt, formatted=True) return {"output": response} async def _arun_component(self, **kwargs: Any) -> Any: """Run component.""" # TODO: support only complete for now # non-trivial to figure how to support chat/complete/etc. prompt = kwargs["prompt"] # ignore all other kwargs for now response = await self.llm.acomplete(prompt, formatted=True) return {"output": response} @property def input_keys(self) -> InputKeys: """Input keys.""" # TODO: support only complete for now return InputKeys.from_keys({"prompt"}) @property def output_keys(self) -> OutputKeys: """Output keys.""" return OutputKeys.from_keys({"output"}) class LLMChatComponent(BaseLLMComponent): """LLM chat component.""" def _validate_component_inputs(self, input: Dict[str, Any]) -> Dict[str, Any]: """Validate component inputs during run_component.""" if "messages" not in input: raise ValueError("Messages must be in input dict.") # if `messages` is a string, convert to a list of chat message if isinstance(input["messages"], get_args(StringableInput)): input["messages"] = validate_and_convert_stringable(input["messages"]) input["messages"] = prompt_to_messages(str(input["messages"])) for message in input["messages"]: if not isinstance(message, ChatMessage): raise ValueError("Messages must be a list of ChatMessage") return input def _run_component(self, **kwargs: Any) -> Any: """Run component.""" # TODO: support only complete for now # non-trivial to figure how to support chat/complete/etc. messages = kwargs["messages"] if self.streaming: response = self.llm.stream_chat(messages) else: response = self.llm.chat(messages) return {"output": response} async def _arun_component(self, **kwargs: Any) -> Any: """Run component.""" # TODO: support only complete for now # non-trivial to figure how to support chat/complete/etc. messages = kwargs["messages"] if self.streaming: response = await self.llm.astream_chat(messages) else: 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import os from typing import Optional, Dict import openai import pandas as pd import llama_index from llama_index.llms.openai import OpenAI from llama_index.readers.schema.base import Document from llama_index.readers import SimpleWebPageReader from llama_index.prompts import PromptTemplate from llama_index import ServiceContext, StorageContext, load_index_from_storage from llama_index import LLMPredictor, OpenAIEmbedding from llama_index.indices.vector_store.base import VectorStore from llama_hub.github_repo import GithubClient, GithubRepositoryReader from llama_hub.youtube_transcript import YoutubeTranscriptReader, is_youtube_video from mindsdb.integrations.libs.base import BaseMLEngine from mindsdb.utilities.config import Config from mindsdb.utilities.security import is_private_url from mindsdb.integrations.handlers.llama_index_handler import config from mindsdb.integrations.handlers.llama_index_handler.github_loader_helper import ( _get_github_token, _get_filter_file_extensions, _get_filter_directories, ) from mindsdb.integrations.utilities.handler_utils import get_api_key def _validate_prompt_template(prompt_template: str): if "{context_str}" not in prompt_template or "{query_str}" not in prompt_template: raise Exception( "Provided prompt template is invalid, missing `{context_str}`, `{query_str}`. Please ensure both placeholders are present and try again." ) # noqa class LlamaIndexHandler(BaseMLEngine): """Integration with the LlamaIndex data framework for LLM applications.""" name = "llama_index" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.generative = True self.default_index_class = "GPTVectorStoreIndex" self.supported_index_class = ["GPTVectorStoreIndex", "VectorStoreIndex"] self.default_reader = "DFReader" self.supported_reader = [ "DFReader", "SimpleWebPageReader", "GithubRepositoryReader", "YoutubeTranscriptReader", ] @staticmethod def create_validation(target, args=None, **kwargs): reader = args["using"].get("reader", "DFReader") if reader not in config.data_loaders: raise Exception( f"Invalid reader argument. Please use one of {config.data_loaders.keys()}" ) config_dict = config.data_loaders[reader] missing_keys = [key for key in config_dict if key not in args["using"]] if missing_keys: raise Exception(f"{reader} requires {missing_keys} arguments") if "prompt_template" in args["using"]: _validate_prompt_template(args["using"]["prompt_template"]) if args["using"].get("mode") == "conversational": for param in ("user_column", "assistant_column"): if param not in args["using"]: raise Exception(f"Conversational mode requires {param} parameter") def create( self, target: str, df: Optional[pd.DataFrame] = None, args: Optional[Dict] = None, ) -> None: if "using" not in args: raise Exception( "LlamaIndex engine requires a USING clause! Refer to its documentation for more details." ) if "index_class" not in args["using"]: args["using"]["index_class"] = self.default_index_class elif args["using"]["index_class"] not in self.supported_index_class: raise Exception( f"Invalid index class argument. Please use one of {self.supported_index_class}" ) if "reader" not in args["using"]: args["using"]["reader"] = self.default_reader elif args["using"]["reader"] not in self.supported_reader: raise Exception( f"Invalid operation mode. Please use one of {self.supported_reader}" ) # workaround to create llama model without input data if df is None or df.empty: df = pd.DataFrame([{"text": ""}]) if args["using"]["reader"] == "DFReader": dstrs = df.apply( lambda x: ", ".join( [f"{col}: {str(entry)}" for col, entry in zip(df.columns, x)] ), axis=1, ) reader = list(map(lambda x: Document(text=x), dstrs.tolist())) elif args["using"]["reader"] == "SimpleWebPageReader": url = args["using"]["source_url_link"] config = Config() is_cloud = config.get("cloud", False) if is_cloud and is_private_url(url): raise Exception(f"URL is private: {url}") reader = SimpleWebPageReader(html_to_text=True).load_data([url]) elif args["using"]["reader"] == "GithubRepositoryReader": engine_storage = self.engine_storage key = "GITHUB_TOKEN" github_token = get_api_key( key, args["using"], engine_storage, strict=False ) if github_token is None: github_token = get_api_key( key.lower(), args["using"], engine_storage, strict=True, ) github_client = GithubClient(github_token) owner = args["using"]["owner"] repo = args["using"]["repo"] filter_file_extensions = _get_filter_file_extensions(args["using"]) filter_directories = _get_filter_directories(args["using"]) reader = GithubRepositoryReader( github_client, owner=owner, repo=repo, verbose=True, filter_file_extensions=filter_file_extensions, filter_directories=filter_directories, ).load_data(branch=args["using"].get("branch", "main")) elif args["using"]["reader"] == "YoutubeTranscriptReader": ytlinks = args["using"]["ytlinks"] for link in ytlinks: if not is_youtube_video(link): raise Exception(f"Invalid youtube link: {link}") reader = YoutubeTranscriptReader().load_data(ytlinks) else: raise Exception( f"Invalid operation mode. Please use one of {self.supported_reader}." ) self.model_storage.json_set("args", args) index = self._setup_index(reader) path = self.model_storage.folder_get("context") index.storage_context.persist(persist_dir=path) self.model_storage.folder_sync("context") def update(self, args) -> None: prompt_template = args["using"].get( "prompt_template", args.get("prompt_template", None) ) if prompt_template is not None: _validate_prompt_template(prompt_template) args_cur = self.model_storage.json_get("args") args_cur["using"].update(args["using"]) # check new set of arguments self.create_validation(None, args_cur) self.model_storage.json_set("args", args_cur) def predict( self, df: Optional[pd.DataFrame] = None, args: Optional[Dict] = None ) -> pd.DataFrame: pred_args = args["predict_params"] if args else {} args = self.model_storage.json_get("args") engine_kwargs = {} if args["using"].get("mode") == "conversational": user_column = args["using"]["user_column"] assistant_column = args["using"]["assistant_column"] messages = [] for row in df[:-1].to_dict("records"): messages.append(f"user: {row[user_column]}") messages.append(f"assistant: {row[assistant_column]}") conversation = "\n".join(messages) questions = [df.iloc[-1][user_column]] if "prompt" in pred_args and pred_args["prompt"] is not None: user_prompt = pred_args["prompt"] else: user_prompt = args["using"].get("prompt", "") prompt_template = ( f"{user_prompt}\n" f"---------------------\n" f"We have provided context information below. \n" f"{{context_str}}\n" f"---------------------\n" f"This is previous conversation history:\n" f"{conversation}\n" f"---------------------\n" f"Given this information, please answer the question: {{query_str}}" ) engine_kwargs["text_qa_template"] = PromptTemplate(prompt_template) else: input_column = args["using"].get("input_column", None) prompt_template = args["using"].get( "prompt_template", args.get("prompt_template", None) ) if prompt_template is not None: _validate_prompt_template(prompt_template) engine_kwargs["text_qa_template"] = PromptTemplate(prompt_template) if input_column is None: raise Exception( f"`input_column` must be provided at model creation time or through USING clause when predicting. Please try again." ) # noqa if input_column not in df.columns: raise Exception( f'Column "{input_column}" not found in input data! Please try again.' ) questions = df[input_column] index_path = self.model_storage.folder_get("context") storage_context = StorageContext.from_defaults(persist_dir=index_path) service_context = self._get_service_context() index = load_index_from_storage( storage_context, service_context=service_context ) query_engine = index.as_query_engine(**engine_kwargs) results = [] for question in questions: query_results = query_engine.query( question ) # TODO: provide extra_info in explain_target col results.append(query_results.response) result_df = pd.DataFrame( {"question": questions, args["target"]: results} ) # result_df['answer'].tolist() return result_df def _get_service_context(self): args = self.model_storage.json_get("args") engine_storage = self.engine_storage openai_api_key = get_api_key('openai', args["using"], engine_storage, strict=True) llm_kwargs = {"openai_api_key": openai_api_key} if "temperature" in args["using"]: llm_kwargs["temperature"] = args["using"]["temperature"] if "model_name" in args["using"]: llm_kwargs["model_name"] = args["using"]["model_name"] if "max_tokens" in args["using"]: llm_kwargs["max_tokens"] = args["using"]["max_tokens"] llm = OpenAI(**llm_kwargs) # TODO: all usual params should go here embed_model = OpenAIEmbedding(api_key=openai_api_key) service_context = ServiceContext.from_defaults( llm=llm, embed_model=embed_model ) return service_context def _setup_index(self, documents): args = self.model_storage.json_get("args") indexer: VectorStore = getattr(llama_index, args["using"]["index_class"]) index = indexer.from_documents( documents, service_context=self._get_service_context() ) return index

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# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: MIT # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. import os from typing import Any, Callable, Dict, Optional, Sequence from llama_index.bridge.pydantic import Field, PrivateAttr from llama_index.callbacks import CallbackManager from llama_index.constants import DEFAULT_CONTEXT_WINDOW, DEFAULT_NUM_OUTPUTS from llama_index.llms.base import ( ChatMessage, ChatResponse, CompletionResponse, LLMMetadata, llm_chat_callback, llm_completion_callback, ) from llama_index.llms.custom import CustomLLM from llama_index.llms.generic_utils import completion_response_to_chat_response from llama_index.llms.generic_utils import ( messages_to_prompt as generic_messages_to_prompt, ) from transformers import LlamaTokenizer import gc import json import torch import numpy as np from tensorrt_llm.runtime import ModelConfig, SamplingConfig import tensorrt_llm from pathlib import Path import uuid import time EOS_TOKEN = 2 PAD_TOKEN = 2 class TrtLlmAPI(CustomLLM): model_path: Optional[str] = Field( description="The path to the trt engine." ) temperature: float = Field(description="The temperature to use for sampling.") max_new_tokens: int = Field(description="The maximum number of tokens to generate.") context_window: int = Field( description="The maximum number of context tokens for the model." ) messages_to_prompt: Callable = Field( description="The function to convert messages to a prompt.", exclude=True ) completion_to_prompt: Callable = Field( description="The function to convert a completion to a prompt.", exclude=True ) generate_kwargs: Dict[str, Any] = Field( default_factory=dict, description="Kwargs used for generation." ) model_kwargs: Dict[str, Any] = Field( default_factory=dict, description="Kwargs used for model initialization." ) verbose: bool = Field(description="Whether to print verbose output.") _model: Any = PrivateAttr() _model_config: Any = PrivateAttr() _tokenizer: Any = PrivateAttr() _max_new_tokens = PrivateAttr() _sampling_config = PrivateAttr() _verbose = PrivateAttr() def __init__( self, model_path: Optional[str] = None, engine_name: Optional[str] = None, tokenizer_dir: Optional[str] = None, temperature: float = 0.1, max_new_tokens: int = DEFAULT_NUM_OUTPUTS, context_window: int = DEFAULT_CONTEXT_WINDOW, messages_to_prompt: Optional[Callable] = None, completion_to_prompt: Optional[Callable] = None, callback_manager: Optional[CallbackManager] = None, generate_kwargs: Optional[Dict[str, Any]] = None, model_kwargs: Optional[Dict[str, Any]] = None, verbose: bool = False ) -> None: model_kwargs = model_kwargs or {} model_kwargs.update({"n_ctx": context_window, "verbose": verbose}) self._max_new_tokens = max_new_tokens self._verbose = verbose # check if model is cached if model_path is not None: if not os.path.exists(model_path): raise ValueError( "Provided model path does not exist. " "Please check the path or provide a model_url to download." ) else: engine_dir = model_path engine_dir_path = Path(engine_dir) config_path = engine_dir_path / 'config.json' # config function with open(config_path, 'r') as f: config = json.load(f) use_gpt_attention_plugin = config['plugin_config']['gpt_attention_plugin'] remove_input_padding = config['plugin_config']['remove_input_padding'] tp_size = config['builder_config']['tensor_parallel'] pp_size = config['builder_config']['pipeline_parallel'] world_size = tp_size * pp_size assert world_size == tensorrt_llm.mpi_world_size(), \ f'Engine world size ({world_size}) != Runtime world size ({tensorrt_llm.mpi_world_size()})' num_heads = config['builder_config']['num_heads'] // tp_size hidden_size = config['builder_config']['hidden_size'] // tp_size vocab_size = config['builder_config']['vocab_size'] num_layers = config['builder_config']['num_layers'] num_kv_heads = config['builder_config'].get('num_kv_heads', num_heads) paged_kv_cache = config['plugin_config']['paged_kv_cache'] if config['builder_config'].get('multi_query_mode', False): tensorrt_llm.logger.warning( "`multi_query_mode` config is deprecated. Please rebuild the engine." ) num_kv_heads = 1 num_kv_heads = (num_kv_heads + tp_size - 1) // tp_size self._model_config = ModelConfig(num_heads=num_heads, num_kv_heads=num_kv_heads, hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, gpt_attention_plugin=use_gpt_attention_plugin, paged_kv_cache=paged_kv_cache, remove_input_padding=remove_input_padding) assert pp_size == 1, 'Python runtime does not support pipeline parallelism' world_size = tp_size * pp_size runtime_rank = tensorrt_llm.mpi_rank() runtime_mapping = tensorrt_llm.Mapping(world_size, runtime_rank, tp_size=tp_size, pp_size=pp_size) torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node) self._tokenizer = LlamaTokenizer.from_pretrained(tokenizer_dir, legacy=False) self._sampling_config = SamplingConfig(end_id=EOS_TOKEN, pad_id=PAD_TOKEN, num_beams=1, temperature=temperature) serialize_path = engine_dir_path / engine_name with open(serialize_path, 'rb') as f: engine_buffer = f.read() decoder = tensorrt_llm.runtime.GenerationSession(self._model_config, engine_buffer, runtime_mapping, debug_mode=False) self._model = decoder messages_to_prompt = messages_to_prompt or generic_messages_to_prompt completion_to_prompt = completion_to_prompt or (lambda x: x) generate_kwargs = generate_kwargs or {} generate_kwargs.update( {"temperature": temperature, "max_tokens": max_new_tokens} ) super().__init__( model_path=model_path, temperature=temperature, context_window=context_window, max_new_tokens=max_new_tokens, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, callback_manager=callback_manager, generate_kwargs=generate_kwargs, model_kwargs=model_kwargs, verbose=verbose, ) @classmethod def class_name(cls) -> str: """Get class name.""" return "TrtLlmAPI" @property def metadata(self) -> LLMMetadata: """LLM metadata.""" return LLMMetadata( context_window=self.context_window, num_output=self.max_new_tokens, model_name=self.model_path, ) @llm_chat_callback() def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: prompt = self.messages_to_prompt(messages) completion_response = self.complete(prompt, formatted=True, **kwargs) return completion_response_to_chat_response(completion_response) @llm_completion_callback() def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse: self.generate_kwargs.update({"stream": False}) is_formatted = kwargs.pop("formatted", False) if not is_formatted: prompt = self.completion_to_prompt(prompt) input_text = prompt input_ids, input_lengths = self.parse_input(input_text, self._tokenizer, EOS_TOKEN, self._model_config) max_input_length = torch.max(input_lengths).item() self._model.setup(input_lengths.size(0), max_input_length, self._max_new_tokens, 1) # beam size is set to 1 if self._verbose: start_time = time.time() output_ids = self._model.decode(input_ids, input_lengths, self._sampling_config) torch.cuda.synchronize() elapsed_time = None if self._verbose: end_time = time.time() elapsed_time = end_time - start_time output_txt, output_token_ids = self.get_output(output_ids, input_lengths, self._max_new_tokens, self._tokenizer) if self._verbose: print(f"Input context length : {input_ids.shape[1]}") print(f"Inference time : {elapsed_time:.2f} seconds") print(f"Output context length : {len(output_token_ids)} ") print(f"Inference token/sec : {(len(output_token_ids) / elapsed_time):2f}") # call garbage collected after inference torch.cuda.empty_cache() gc.collect() return CompletionResponse(text=output_txt, raw=self.generate_completion_dict(output_txt)) def parse_input(self, input_text: str, tokenizer, end_id: int, remove_input_padding: bool): input_tokens = [] input_tokens.append( tokenizer.encode(input_text, add_special_tokens=False)) input_lengths = torch.tensor([len(x) for x in input_tokens], dtype=torch.int32, device='cuda') if remove_input_padding: input_ids = np.concatenate(input_tokens) input_ids = torch.tensor(input_ids, dtype=torch.int32, device='cuda').unsqueeze(0) else: input_ids = torch.nested.to_padded_tensor( torch.nested.nested_tensor(input_tokens, dtype=torch.int32), end_id).cuda() return input_ids, input_lengths def remove_extra_eos_ids(self, outputs): outputs.reverse() while outputs and outputs[0] == 2: outputs.pop(0) outputs.reverse() outputs.append(2) return outputs def get_output(self, output_ids, input_lengths, max_output_len, tokenizer): num_beams = output_ids.size(1) output_text = "" outputs = None for b in range(input_lengths.size(0)): for beam in range(num_beams): output_begin = input_lengths[b] output_end = input_lengths[b] + max_output_len outputs = output_ids[b][beam][output_begin:output_end].tolist() outputs = self.remove_extra_eos_ids(outputs) output_text = tokenizer.decode(outputs) return output_text, outputs def generate_completion_dict(self, text_str): """ Generate a dictionary for text completion details. Returns: dict: A dictionary containing completion details. """ completion_id: str = f"cmpl-{str(uuid.uuid4())}" created: int = int(time.time()) model_name: str = self._model if self._model is not None else self.model_path return { "id": completion_id, "object": "text_completion", "created": created, "model": model_name, "choices": [ { "text": text_str, "index": 0, "logprobs": None, "finish_reason": 'stop' } ], "usage": { "prompt_tokens": None, "completion_tokens": None, "total_tokens": None } } @llm_completion_callback() def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponse: pass

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from llama_index.core.callbacks.schema import CBEventType, EventPayload from llama_index.core.llms import ChatMessage, ChatResponse from llama_index.core.schema import NodeWithScore, TextNode import chainlit as cl @cl.on_chat_start async def start(): await cl.Message(content="LlamaIndexCb").send() cb = cl.LlamaIndexCallbackHandler() cb.on_event_start(CBEventType.RETRIEVE, payload={}) await cl.sleep(0.2) cb.on_event_end( CBEventType.RETRIEVE, payload={ EventPayload.NODES: [ NodeWithScore(node=TextNode(text="This is text1"), score=1) ] }, ) cb.on_event_start(CBEventType.LLM) await cl.sleep(0.2) response = ChatResponse(message=ChatMessage(content="This is the LLM response")) cb.on_event_end( CBEventType.LLM, payload={ EventPayload.RESPONSE: response, EventPayload.PROMPT: "This is the LLM prompt", }, )

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import requests from bs4 import BeautifulSoup from llama_index import GPTSimpleVectorIndex from llama_index.readers.database import DatabaseReader from env import settings from logger import logger from .base import BaseToolSet, SessionGetter, ToolScope, tool class RequestsGet(BaseToolSet): @tool( name="Requests Get", description="A portal to the internet. " "Use this when you need to get specific content from a website." "Input should be a url (i.e. https://www.google.com)." "The output will be the text response of the GET request.", ) def get(self, url: str) -> str: """Run the tool.""" html = requests.get(url).text soup = BeautifulSoup(html) non_readable_tags = soup.find_all( ["script", "style", "header", "footer", "form"] ) for non_readable_tag in non_readable_tags: non_readable_tag.extract() content = soup.get_text("\n", strip=True) if len(content) > 300: content = content[:300] + "..." logger.debug( f"\nProcessed RequestsGet, Input Url: {url} " f"Output Contents: {content}" ) return content class WineDB(BaseToolSet): def __init__(self): db = DatabaseReader( scheme="postgresql", # Database Scheme host=settings["WINEDB_HOST"], # Database Host port="5432", # Database Port user="alphadom", # Database User password=settings["WINEDB_PASSWORD"], # Database Password dbname="postgres", # Database Name ) self.columns = ["nameEn", "nameKo", "description"] concat_columns = str(",'-',".join([f'"{i}"' for i in self.columns])) query = f""" SELECT Concat({concat_columns}) FROM wine """ documents = db.load_data(query=query) self.index = GPTSimpleVectorIndex(documents) @tool( name="Wine Recommendation", description="A tool to recommend wines based on a user's input. " "Inputs are necessary factors for wine recommendations, such as the user's mood today, side dishes to eat with wine, people to drink wine with, what things you want to do, the scent and taste of their favorite wine." "The output will be a list of recommended wines." "The tool is based on a database of wine reviews, which is stored in a database.", ) def recommend(self, query: str) -> str: """Run the tool.""" results = self.index.query(query) wine = "\n".join( [ f"{i}:{j}" for i, j in zip( self.columns, results.source_nodes[0].source_text.split("-") ) ] ) output = results.response + "\n\n" + wine logger.debug( f"\nProcessed WineDB, Input Query: {query} " f"Output Wine: {wine}" ) return output class ExitConversation(BaseToolSet): @tool( name="Exit Conversation", description="A tool to exit the conversation. " "Use this when you want to exit the conversation. " "The input should be a message that the conversation is over.", scope=ToolScope.SESSION, ) def exit(self, message: str, get_session: SessionGetter) -> str: """Run the tool.""" _, executor = get_session() del executor logger.debug(f"\nProcessed ExitConversation.") return message

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try: from llama_index import Document from llama_index.text_splitter import SentenceSplitter except ImportError: from llama_index.core import Document from llama_index.core.text_splitter import SentenceSplitter def llama_index_sentence_splitter( documents: list[str], document_ids: list[str], chunk_size=256 ): chunk_overlap = min(chunk_size / 4, min(chunk_size / 2, 64)) chunks = [] node_parser = SentenceSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) docs = [[Document(text=doc)] for doc in documents] for doc_id, doc in zip(document_ids, docs): chunks += [ {"document_id": doc_id, "content": node.text} for node in node_parser(doc) ] return chunks

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""" Creates RAG dataset for tutorial notebooks and persists to disk. """ import argparse import logging import sys from typing import List, Optional import llama_index import numpy as np import pandas as pd from gcsfs import GCSFileSystem from llama_index import ServiceContext, StorageContext, load_index_from_storage from llama_index.callbacks import CallbackManager, OpenInferenceCallbackHandler from llama_index.callbacks.open_inference_callback import as_dataframe from llama_index.embeddings.openai import OpenAIEmbedding from llama_index.llms import OpenAI from phoenix.experimental.evals.retrievals import ( classify_relevance, compute_precisions_at_k, ) from tqdm import tqdm def create_user_feedback( first_document_relevances: List[Optional[bool]], second_document_relevances: List[Optional[bool]], ) -> List[Optional[bool]]: """_summary_ Args: first_document_relevances (List[Optional[bool]]): _description_ second_document_relevances (List[Optional[bool]]): _description_ Returns: List[Optional[bool]]: _description_ """ if len(first_document_relevances) != len(second_document_relevances): raise ValueError() first_document_relevances_array = np.array(first_document_relevances) second_document_relevances_array = np.array(second_document_relevances) failed_retrieval_mask = ~first_document_relevances_array & ~second_document_relevances_array num_failed_retrievals = failed_retrieval_mask.sum() num_thumbs_down = int(0.75 * num_failed_retrievals) failed_retrieval_indexes = np.where(failed_retrieval_mask)[0] thumbs_down_mask = np.random.choice( failed_retrieval_indexes, size=num_thumbs_down, replace=False ) successful_retrieval_mask = ~failed_retrieval_mask num_successful_retrievals = successful_retrieval_mask.sum() num_thumbs_up = int(0.25 * num_successful_retrievals) successful_retrieval_indexes = np.where(successful_retrieval_mask)[0] thumbs_up_mask = np.random.choice( successful_retrieval_indexes, size=num_thumbs_up, replace=False ) user_feedback_array = np.full(len(first_document_relevances), np.nan, dtype=np.float32) user_feedback_array[thumbs_down_mask] = -1.0 user_feedback_array[thumbs_up_mask] = 1.0 return [None if np.isnan(value) else value for value in user_feedback_array.tolist()] if __name__ == "__main__": logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) parser = argparse.ArgumentParser() parser.add_argument("--index-path", type=str, required=True, help="Path to persisted index.") parser.add_argument( "--use-gcs", action="store_true", help="If this flag is set, the index will be loaded from GCS.", ) parser.add_argument( "--query-path", type=str, required=True, help="Path to CSV file containing queries." ) parser.add_argument( "--output-path", type=str, required=True, help="Path to output Parquet file." ) args = parser.parse_args() llama_index.prompts.default_prompts.DEFAULT_TEXT_QA_PROMPT_TMPL = ( "Context information is below.\n" "---------------------\n" "{context_str}\n" "---------------------\n" "Given the context information, " "answer the question and be as helpful as possible: {query_str}\n" ) # This prompt has been tweaked to make the system less conservative for demo purposes. queries = pd.read_csv(args.query_path)["Question"].tolist() file_system = GCSFileSystem(project="public-assets-275721") if args.use_gcs else None storage_context = StorageContext.from_defaults( fs=file_system, persist_dir=args.index_path, ) callback_handler = OpenInferenceCallbackHandler() service_context = ServiceContext.from_defaults( llm=OpenAI(model="text-davinci-003"), embed_model=OpenAIEmbedding(model="text-embedding-ada-002"), callback_manager=CallbackManager(handlers=[callback_handler]), ) index = load_index_from_storage( storage_context, service_context=service_context, ) query_engine = index.as_query_engine() logging.info("Running queries") for query in tqdm(queries): query_engine.query(query) query_dataframe = as_dataframe(callback_handler.flush_query_data_buffer()) document_dataframe = as_dataframe(callback_handler.flush_node_data_buffer()) query_texts = query_dataframe[":feature.text:prompt"].tolist() list_of_document_id_lists = query_dataframe[ ":feature.[str].retrieved_document_ids:prompt" ].tolist() document_id_to_text = dict( zip(document_dataframe["id"].to_list(), document_dataframe["node_text"].to_list()) ) first_document_texts, second_document_texts = [ [ document_id_to_text[document_ids[document_index]] for document_ids in list_of_document_id_lists ] for document_index in [0, 1] ] logging.info("Computing LLM-assisted ranking metrics") first_document_relevances, second_document_relevances = [ [ classify_relevance(query_text, document_text, model_name="gpt-4") for query_text, document_text in tqdm(zip(query_texts, first_document_texts)) ] for document_texts in [first_document_texts, second_document_texts] ] list_of_precisions_at_k_lists = [ compute_precisions_at_k([rel0, rel1]) for rel0, rel1 in zip(first_document_relevances, second_document_relevances) ] precisions_at_1, precisions_at_2 = [ [precisions_at_k[index] for precisions_at_k in list_of_precisions_at_k_lists] for index in [0, 1] ] document_similarity_0, document_similarity_1 = [ [ scores[index] for scores in query_dataframe[ ":feature.[float].retrieved_document_scores:prompt" ].tolist() ] for index in [0, 1] ] user_feedback = create_user_feedback(first_document_relevances, second_document_relevances) logging.info( f"Thumbs up: {sum([value == 1.0 for value in user_feedback]) / len(user_feedback)}" ) logging.info( f"Thumbs down: {sum([value == -1.0 for value in user_feedback]) / len(user_feedback)}" ) query_dataframe = query_dataframe.assign( **{ ":tag.bool:relevance_0": first_document_relevances, ":tag.bool:relevance_1": second_document_relevances, ":tag.float:precision_at_1": precisions_at_1, ":tag.float:precision_at_2": precisions_at_2, ":tag.float:document_similarity_0": document_similarity_0, ":tag.float:document_similarity_1": document_similarity_1, ":tag.float:user_feedback": user_feedback, } ) query_dataframe.to_parquet(args.output_path)

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import logging import os import time import typing import uuid from typing import TYPE_CHECKING, Any, Iterable, List, Optional import numpy as np from llama_index.core.schema import BaseNode, MetadataMode, TextNode from llama_index.core.vector_stores.types import ( VectorStore, VectorStoreQuery, VectorStoreQueryResult, ) from llama_index.core.vector_stores.utils import ( legacy_metadata_dict_to_node, metadata_dict_to_node, node_to_metadata_dict, ) if TYPE_CHECKING: import vearch logger = logging.getLogger(__name__) class VearchVectorStore(VectorStore): """ Vearch vector store: embeddings are stored within a Vearch table. when query, the index uses Vearch to query for the top k most similar nodes. Args: chroma_collection (chromadb.api.models.Collection.Collection): ChromaDB collection instance """ flat_metadata: bool = True stores_text: bool = True _DEFAULT_TABLE_NAME = "liama_index_vearch" _DEFAULT_CLUSTER_DB_NAME = "liama_index_vearch_client_db" _DEFAULT_VERSION = 1 def __init__( self, path_or_url: Optional[str] = None, table_name: str = _DEFAULT_TABLE_NAME, db_name: str = _DEFAULT_CLUSTER_DB_NAME, flag: int = _DEFAULT_VERSION, **kwargs: Any, ) -> None: """ Initialize vearch vector store flag 1 for cluster,0 for standalone. """ try: if flag: import vearch_cluster else: import vearch except ImportError: raise ValueError( "Could not import suitable python package." "Please install it with `pip install vearch or vearch_cluster." ) if flag: if path_or_url is None: raise ValueError("Please input url of cluster") if not db_name: db_name = self._DEFAULT_CLUSTER_DB_NAME db_name += "_" db_name += str(uuid.uuid4()).split("-")[-1] self.using_db_name = db_name self.url = path_or_url self.vearch = vearch_cluster.VearchCluster(path_or_url) else: if path_or_url is None: metadata_path = os.getcwd().replace("\\", "/") else: metadata_path = path_or_url if not os.path.isdir(metadata_path): os.makedirs(metadata_path) log_path = os.path.join(metadata_path, "log") if not os.path.isdir(log_path): os.makedirs(log_path) self.vearch = vearch.Engine(metadata_path, log_path) self.using_metapath = metadata_path if not table_name: table_name = self._DEFAULT_TABLE_NAME table_name += "_" table_name += str(uuid.uuid4()).split("-")[-1] self.using_table_name = table_name self.flag = flag @property def client(self) -> Any: """Get client.""" return self.vearch def _get_matadata_field(self, metadatas: Optional[List[dict]] = None) -> None: field_list = [] if metadatas: for key, value in metadatas[0].items(): if isinstance(value, int): field_list.append({"field": key, "type": "int"}) continue if isinstance(value, str): field_list.append({"field": key, "type": "str"}) continue if isinstance(value, float): field_list.append({"field": key, "type": "float"}) continue else: raise ValueError("Please check data type,support int, str, float") self.field_list = field_list def _add_texts( self, ids: Iterable[str], texts: Iterable[str], metadatas: Optional[List[dict]] = None, embeddings: Optional[List[List[float]]] = None, **kwargs: Any, ) -> List[str]: """ Returns: List of ids from adding the texts into the vectorstore. """ if embeddings is None: raise ValueError("embeddings is None") self._get_matadata_field(metadatas) if self.flag: dbs_list = self.vearch.list_dbs() if self.using_db_name not in dbs_list: create_db_code = self.vearch.create_db(self.using_db_name) if not create_db_code: raise ValueError("create db failed!!!") space_list = self.vearch.list_spaces(self.using_db_name) if self.using_table_name not in space_list: create_space_code = self._create_space(len(embeddings[0])) if not create_space_code: raise ValueError("create space failed!!!") docid = [] if embeddings is not None and metadatas is not None: meta_field_list = [i["field"] for i in self.field_list] for text, metadata, embed, id_d in zip( texts, metadatas, embeddings, ids ): profiles: typing.Dict[str, Any] = {} profiles["text"] = text for f in meta_field_list: profiles[f] = metadata[f] embed_np = np.array(embed) profiles["text_embedding"] = { "feature": (embed_np / np.linalg.norm(embed_np)).tolist() } insert_res = self.vearch.insert_one( self.using_db_name, self.using_table_name, profiles, id_d ) if insert_res["status"] == 200: docid.append(insert_res["_id"]) continue else: retry_insert = self.vearch.insert_one( self.using_db_name, self.using_table_name, profiles ) docid.append(retry_insert["_id"]) continue else: table_path = os.path.join( self.using_metapath, self.using_table_name + ".schema" ) if not os.path.exists(table_path): dim = len(embeddings[0]) response_code = self._create_table(dim) if response_code: raise ValueError("create table failed!!!") if embeddings is not None and metadatas is not None: doc_items = [] meta_field_list = [i["field"] for i in self.field_list] for text, metadata, embed, id_d in zip( texts, metadatas, embeddings, ids ): profiles_v: typing.Dict[str, Any] = {} profiles_v["text"] = text profiles_v["_id"] = id_d for f in meta_field_list: profiles_v[f] = metadata[f] embed_np = np.array(embed) profiles_v["text_embedding"] = embed_np / np.linalg.norm(embed_np) doc_items.append(profiles_v) docid = self.vearch.add(doc_items) t_time = 0 while len(docid) != len(embeddings): time.sleep(0.5) if t_time > 6: break t_time += 1 self.vearch.dump() return docid def _create_table( self, dim: int = 1024, ) -> int: """ Create Standalone VectorStore Table. Args: dim:dimension of vector. fields_list: the field you want to store. Return: code,0 for success,1 for failed. """ type_dict = { "int": vearch.dataType.INT, "str": vearch.dataType.STRING, "float": vearch.dataType.FLOAT, } engine_info = { "index_size": 1, "retrieval_type": "HNSW", "retrieval_param": { "metric_type": "InnerProduct", "nlinks": -1, "efConstruction": -1, }, } filed_list_add = self.field_list.append({"field": "text", "type": "str"}) fields = [ vearch.GammaFieldInfo(fi["field"], type_dict[fi["type"]]) for fi in filed_list_add ] vector_field = vearch.GammaVectorInfo( name="text_embedding", type=vearch.dataType.VECTOR, is_index=True, dimension=dim, model_id="", store_type="MemoryOnly", store_param={"cache_size": 10000}, ) return self.vearch.create_table( engine_info, name=self.using_table_name, fields=fields, vector_field=vector_field, ) def _create_space( self, dim: int = 1024, ) -> int: """ Create Cluster VectorStore space. Args: dim:dimension of vector. Return: code,0 failed for ,1 for success. """ type_dict = {"int": "integer", "str": "string", "float": "float"} space_config = { "name": self.using_table_name, "partition_num": 1, "replica_num": 1, "engine": { "index_size": 1, "retrieval_type": "HNSW", "retrieval_param": { "metric_type": "InnerProduct", "nlinks": -1, "efConstruction": -1, }, }, } tmp_proer = { "text": {"type": "string"}, "text_embedding": { "type": "vector", "index": True, "dimension": dim, "store_type": "MemoryOnly", }, } for item in self.field_list: tmp_proer[item["field"]] = {"type": type_dict[item["type"]]} space_config["properties"] = tmp_proer return self.vearch.create_space(self.using_db_name, space_config) def add( self, nodes: List[BaseNode], **add_kwargs: Any, ) -> List[str]: if not self.vearch: raise ValueError("Vearch Engine is not initialized") embeddings = [] metadatas = [] ids = [] texts = [] for node in nodes: embeddings.append(node.get_embedding()) metadatas.append( node_to_metadata_dict( node, remove_text=True, flat_metadata=self.flat_metadata ) ) ids.append(node.node_id) texts.append(node.get_content(metadata_mode=MetadataMode.NONE) or "") return self._add_texts( ids=ids, texts=texts, metadatas=metadatas, embeddings=embeddings, ) def query( self, query: VectorStoreQuery, **kwargs: Any, ) -> VectorStoreQueryResult: """ Query index for top k most similar nodes. Args: query : vector store query. Returns: VectorStoreQueryResult: Query results. """ meta_filters = {} if query.filters is not None: for filter_ in query.filters.legacy_filters(): meta_filters[filter_.key] = filter_.value if self.flag: meta_field_list = self.vearch.get_space( self.using_db_name, self.using_table_name ) meta_field_list.remove("text_embedding") embed = query.query_embedding if embed is None: raise ValueError("query.query_embedding is None") k = query.similarity_top_k if self.flag: query_data = { "query": { "sum": [ { "field": "text_embedding", "feature": (embed / np.linalg.norm(embed)).tolist(), } ], }, "retrieval_param": {"metric_type": "InnerProduct", "efSearch": 64}, "size": k, "fields": meta_field_list, } query_result = self.vearch.search( self.using_db_name, self.using_table_name, query_data ) res = query_result["hits"]["hits"] else: query_data = { "vector": [ { "field": "text_embedding", "feature": embed / np.linalg.norm(embed), } ], "fields": [], "retrieval_param": {"metric_type": "InnerProduct", "efSearch": 64}, "topn": k, } query_result = self.vearch.search(query_data) res = query_result[0]["result_items"] nodes = [] similarities = [] ids = [] for item in res: content = "" meta_data = {} node_id = "" if self.flag: score = item["_score"] item = item["_source"] for item_key in item: if item_key == "text": content = item[item_key] continue elif item_key == "_id": node_id = item[item_key] ids.append(node_id) continue if self.flag != 1 and item_key == "score": score = item[item_key] continue meta_data[item_key] = item[item_key] similarities.append(score) try: node = metadata_dict_to_node(meta_data) node.set_content(content) except Exception: metadata, node_info, relationships = legacy_metadata_dict_to_node( meta_data ) node = TextNode( text=content, id_=node_id, metadata=metadata, start_char_idx=node_info.get("start", None), end_char_idx=node_info.get("end", None), relationships=relationships, ) nodes.append(node) return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids) def _delete( self, ids: Optional[List[str]] = None, **kwargs: Any, ) -> Optional[bool]: """ Delete the documents which have the specified ids. Args: ids: The ids of the embedding vectors. **kwargs: Other keyword arguments that subclasses might use. Returns: Optional[bool]: True if deletion is successful. False otherwise, None if not implemented. """ ret: Optional[bool] = None tmp_res = [] if ids is None or len(ids) == 0: return ret for _id in ids: if self.flag: ret = self.vearch.delete(self.using_db_name, self.using_table_name, _id) else: ret = self.vearch.del_doc(_id) tmp_res.append(ret) return all(i == 0 for i in tmp_res) def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None: """Delete nodes using with ref_doc_id. Args: ref_doc_id (str): The doc_id of the document to delete. Returns: None """ if len(ref_doc_id) == 0: return ids: List[str] = [] ids.append(ref_doc_id) self._delete(ids)

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# ENTER YOUR OPENAPI KEY IN OPENAI_API_KEY ENV VAR FIRST from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, LLMPredictor, download_loader savePath = f'/{os.path.dirname(__file__)}/indexes/index.json' # # index = GPTSimpleVectorIndex(documents)#, llm_predictor=llm_predictor) index = GPTSimpleVectorIndex.load_from_disk(savePath) response = index.query("Summarize the vulnerability CVE-2021-23406", response_mode="tree_summarize") print(response) print('Sources are ', response.get_formatted_sources())

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from typing import Optional, Union from llama_index import ServiceContext from llama_index.callbacks import CallbackManager from llama_index.embeddings.utils import EmbedType from llama_index.extractors import ( EntityExtractor, KeywordExtractor, QuestionsAnsweredExtractor, SummaryExtractor, TitleExtractor, ) from llama_index.llms.utils import LLMType from llama_index.prompts import PromptTemplate from llama_index.prompts.base import BasePromptTemplate from llama_index.text_splitter import SentenceSplitter from autollm.callbacks.cost_calculating import CostCalculatingHandler from autollm.utils.llm_utils import set_default_prompt_template class AutoServiceContext: """AutoServiceContext extends the functionality of LlamaIndex's ServiceContext to include token counting. """ @staticmethod def from_defaults( llm: Optional[LLMType] = "default", embed_model: Optional[EmbedType] = "default", system_prompt: str = None, query_wrapper_prompt: Union[str, BasePromptTemplate] = None, enable_cost_calculator: bool = False, chunk_size: Optional[int] = 512, chunk_overlap: Optional[int] = 100, context_window: Optional[int] = None, enable_title_extractor: bool = False, enable_summary_extractor: bool = False, enable_qa_extractor: bool = False, enable_keyword_extractor: bool = False, enable_entity_extractor: bool = False, **kwargs) -> ServiceContext: """ Create a ServiceContext with default parameters with extended enable_token_counting functionality. If enable_token_counting is True, tracks the number of tokens used by the LLM for each query. Parameters: llm (LLM): The LLM to use for the query engine. Defaults to gpt-3.5-turbo. embed_model (BaseEmbedding): The embedding model to use for the query engine. Defaults to OpenAIEmbedding. system_prompt (str): The system prompt to use for the query engine. query_wrapper_prompt (Union[str, BasePromptTemplate]): The query wrapper prompt to use for the query engine. enable_cost_calculator (bool): Flag to enable cost calculator logging. chunk_size (int): The token chunk size for each chunk. chunk_overlap (int): The token overlap between each chunk. context_window (int): The maximum context size that will get sent to the LLM. enable_title_extractor (bool): Flag to enable title extractor. enable_summary_extractor (bool): Flag to enable summary extractor. enable_qa_extractor (bool): Flag to enable question answering extractor. enable_keyword_extractor (bool): Flag to enable keyword extractor. enable_entity_extractor (bool): Flag to enable entity extractor. **kwargs: Arbitrary keyword arguments. Returns: ServiceContext: The initialized ServiceContext from default parameters with extra token counting functionality. """ if not system_prompt and not query_wrapper_prompt: system_prompt, query_wrapper_prompt = set_default_prompt_template() # Convert query_wrapper_prompt to PromptTemplate if it is a string if isinstance(query_wrapper_prompt, str): query_wrapper_prompt = PromptTemplate(template=query_wrapper_prompt) callback_manager: CallbackManager = kwargs.get('callback_manager', CallbackManager()) kwargs.pop( 'callback_manager', None) # Make sure callback_manager is not passed to ServiceContext twice if enable_cost_calculator: llm_model_name = llm.metadata.model_name if not "default" else "gpt-3.5-turbo" callback_manager.add_handler(CostCalculatingHandler(model_name=llm_model_name, verbose=True)) sentence_splitter = SentenceSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) transformations = [sentence_splitter] if enable_entity_extractor: transformations.append(EntityExtractor()) if enable_keyword_extractor: transformations.append(KeywordExtractor(llm=llm, keywords=5)) if enable_summary_extractor: transformations.append(SummaryExtractor(llm=llm, summaries=["prev", "self"])) if enable_title_extractor: transformations.append(TitleExtractor(llm=llm, nodes=5)) if enable_qa_extractor: transformations.append(QuestionsAnsweredExtractor(llm=llm, questions=5)) service_context = ServiceContext.from_defaults( llm=llm, embed_model=embed_model, transformations=transformations, system_prompt=system_prompt, query_wrapper_prompt=query_wrapper_prompt, chunk_size=chunk_size, chunk_overlap=chunk_overlap, context_window=context_window, callback_manager=callback_manager, **kwargs) return service_context

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import torch from llama_index import WikipediaReader def divide_string(wiki_page, word_limit=50): divided_text = [] for each_page in wiki_page: words = each_page[0].text.split() for i in range(0, len(words), word_limit): chunk = ' '.join(words[i:i+word_limit]) divided_text.append(chunk) return divided_text def wiki_prompter(generator,tokenizer,question): fulltext = "A question is provided below. Given the question, extract " +\ "keywords from the text. Focus on extracting the keywords that we can use " +\ "to best lookup answers to the question. \n" +\ "---------------------\n" +\ "{}\n".format(question) +\ "---------------------\n" +\ "Provide keywords in the following comma-separated format.\nKeywords: " gen_in = tokenizer(fulltext, return_tensors="pt").input_ids.cuda() with torch.no_grad(): generated_ids = generator( gen_in, max_new_tokens=512, use_cache=True, pad_token_id=tokenizer.eos_token_id, num_return_sequences=1, do_sample=True, repetition_penalty=1.1, # 1.0 means 'off'. unfortunately if we penalize it it will not output Sphynx: temperature=0.5, # default: 1.0 top_k=50, # default: 50 top_p=1.0, # default: 1.0 early_stopping=True, ) generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] # for some reason, batch_decode returns an array of one element? text_without_prompt = generated_text[len(fulltext):] response = text_without_prompt response = response.split("===")[0] response.strip() print(response) keywords = response.split(", ") print(keywords) wiki_docs=[] for keyw in keywords: try: wiki_one = WikipediaReader().load_data(pages=[keyw], auto_suggest=False) wiki_docs.append(wiki_one) except: print("No wiki: "+keyw) divided_text = divide_string(wiki_docs, 250) answer_llama="" score_textlist = [0] * len(divided_text) for i, chunk in enumerate(divided_text): for t, keyw in enumerate(keywords): if keyw.lower() in chunk.lower(): score_textlist[i]=score_textlist[i]+1 answer_list=[] divided_text = [item for _, item in sorted(zip(score_textlist, divided_text), reverse=True)] divided_text.append("_") for i, chunk in enumerate(divided_text): if i<4 and not i==int(len(divided_text)-1): fulltext = "Context information is below. \n" +\ "---------------------\n" +\ "{}".format(chunk) +\ "\n---------------------\n" +\ "Given the context information and not prior knowledge, " +\ "answer the question: {}\n".format(question) +\ "Response: " elif i==int(len(divided_text)-1) and len(answer_list)>1 : fulltext = "The original question is as follows: {}\n".format(question) +\ "We have provided existing answers:\n" +\ "------------\n" +\ "{}\n".format(str("\n\n".join(answer_list))) +\ "------------\n" +\ "The best one answer: " else: continue print(fulltext) gen_in = tokenizer(fulltext, return_tensors="pt").input_ids.cuda() with torch.no_grad(): generated_ids = generator( gen_in, max_new_tokens=512, use_cache=True, pad_token_id=tokenizer.eos_token_id, num_return_sequences=1, do_sample=True, repetition_penalty=1.1, # 1.0 means 'off'. unfortunately if we penalize it it will not output Sphynx: temperature=0.5, # default: 1.0 top_k=50, # default: 50 top_p=1.0, # default: 1.0 early_stopping=True, ) generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] text_without_prompt = generated_text[len(fulltext):] answer_llama = text_without_prompt print() print("\nAnswer: " + answer_llama) print() answer_list.append(answer_llama) return answer_llama

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from rag.agents.interface import Pipeline from llama_index.core.program import LLMTextCompletionProgram import json from llama_index.llms.ollama import Ollama from typing import List from pydantic import create_model from rich.progress import Progress, SpinnerColumn, TextColumn import requests import warnings import box import yaml import timeit from rich import print from typing import Any warnings.filterwarnings("ignore", category=DeprecationWarning) warnings.filterwarnings("ignore", category=UserWarning) # Import config vars with open('config.yml', 'r', encoding='utf8') as ymlfile: cfg = box.Box(yaml.safe_load(ymlfile)) class VProcessorPipeline(Pipeline): def run_pipeline(self, payload: str, query_inputs: [str], query_types: [str], query: str, file_path: str, index_name: str, debug: bool = False, local: bool = True) -> Any: print(f"\nRunning pipeline with {payload}\n") start = timeit.default_timer() if file_path is None: raise ValueError("File path is required for vprocessor pipeline") with open(file_path, "rb") as file: files = {'file': (file_path, file, 'image/jpeg')} data = { 'image_url': '' } response = self.invoke_pipeline_step(lambda: requests.post(cfg.VPROCESSOR_OCR_ENDPOINT, data=data, files=files, timeout=180), "Running OCR...", local) if response.status_code != 200: print('Request failed with status code:', response.status_code) print('Response:', response.text) return "Failed to process file. Please try again." end = timeit.default_timer() print(f"Time to run OCR: {end - start}") start = timeit.default_timer() data = response.json() ResponseModel = self.invoke_pipeline_step(lambda: self.build_response_class(query_inputs, query_types), "Building dynamic response class...", local) prompt_template_str = """\ """ + query + """\ using this structured data, coming from OCR {document_data}.\ """ llm_ollama = self.invoke_pipeline_step(lambda: Ollama(model=cfg.LLM_VPROCESSOR, base_url=cfg.OLLAMA_BASE_URL_VPROCESSOR, temperature=0, request_timeout=900), "Loading Ollama...", local) program = LLMTextCompletionProgram.from_defaults( output_cls=ResponseModel, prompt_template_str=prompt_template_str, llm=llm_ollama, verbose=True, ) output = self.invoke_pipeline_step(lambda: program(document_data=data), "Running inference...", local) answer = self.beautify_json(output.model_dump_json()) end = timeit.default_timer() print(f"\nJSON response:\n") print(answer + '\n') print('=' * 50) print(f"Time to retrieve answer: {end - start}") return answer def prepare_files(self, file_path, file): if file_path is not None: with open(file_path, "rb") as file: files = {'file': (file_path, file, 'image/jpeg')} data = { 'image_url': '' } else: files = {'file': (file.filename, file.file, file.content_type)} data = { 'image_url': '' } return data, files # Function to safely evaluate type strings def safe_eval_type(self, type_str, context): try: return eval(type_str, {}, context) except NameError: raise ValueError(f"Type '{type_str}' is not recognized") def build_response_class(self, query_inputs, query_types_as_strings): # Controlled context for eval context = { 'List': List, 'str': str, 'int': int, 'float': float # Include other necessary types or typing constructs here } # Convert string representations to actual types query_types = [self.safe_eval_type(type_str, context) for type_str in query_types_as_strings] # Create fields dictionary fields = {name: (type_, ...) for name, type_ in zip(query_inputs, query_types)} DynamicModel = create_model('DynamicModel', **fields) return DynamicModel def invoke_pipeline_step(self, task_call, task_description, local): if local: with Progress( SpinnerColumn(), TextColumn("[progress.description]{task.description}"), transient=False, ) as progress: progress.add_task(description=task_description, total=None) ret = task_call() else: print(task_description) ret = task_call() return ret def beautify_json(self, result): try: # Convert and pretty print data = json.loads(str(result)) data = json.dumps(data, indent=4) return data except (json.decoder.JSONDecodeError, TypeError): print("The response is not in JSON format:\n") print(result) return {}

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import asyncio import chromadb import os from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext from llama_index.vector_stores.chroma import ChromaVectorStore from llama_index.embeddings.huggingface import HuggingFaceEmbedding from traceloop.sdk import Traceloop os.environ["TOKENIZERS_PARALLELISM"] = "false" Traceloop.init(app_name="llama_index_example") chroma_client = chromadb.EphemeralClient() chroma_collection = chroma_client.create_collection("quickstart") # define embedding function embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5") # load documents documents = SimpleDirectoryReader("./data/paul_graham/").load_data() # set up ChromaVectorStore and load in data vector_store = ChromaVectorStore(chroma_collection=chroma_collection) storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex.from_documents( documents, storage_context=storage_context, embed_model=embed_model ) async def main(): # Query Data query_engine = index.as_query_engine() response = await query_engine.aquery("What did the author do growing up?") print(response) if __name__ == "__main__": asyncio.run(main())

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- # ================================================== # # This file is a part of PYGPT package # # Website: https://pygpt.net # # GitHub: https://github.com/szczyglis-dev/py-gpt # # MIT License # # Created By : Marcin Szczygliński # # Updated Date: 2024.03.11 01:00:00 # # ================================================== # import os.path from llama_index.core import StorageContext, load_index_from_storage from llama_index.core.indices.base import BaseIndex from llama_index.core.indices.service_context import ServiceContext from .base import BaseStore class TempProvider(BaseStore): def __init__(self, *args, **kwargs): super(TempProvider, self).__init__(*args, **kwargs) """ Temporary vector store provider :param args: args :param kwargs: kwargs """ self.window = kwargs.get('window', None) self.id = "TempVectorStore" self.prefix = "" # prefix for index directory self.indexes = {} self.persist = False def count(self) -> int: """ Count indexes :return: number of indexes """ return len(self.indexes) def get_path(self, id: str) -> str: """ Get database path :param id: index name :return: database path """ if not self.persist: return "" tmp_dir = os.path.join( self.window.core.config.get_user_dir('idx'), "_tmp", # temp directory ) if not os.path.exists(tmp_dir): os.makedirs(tmp_dir, exist_ok=True) path = os.path.join( self.window.core.config.get_user_dir('idx'), "_tmp", # temp directory self.prefix + id, ) return path def exists(self, id: str = None) -> bool: """ Check if index with id exists :param id: index name :return: True if exists """ if not self.persist: if id in self.indexes: return True return False path = self.get_path(id) if os.path.exists(path): store = os.path.join(path, "docstore.json") if os.path.exists(store): return True return False def create(self, id: str): """ Create empty index :param id: index name """ if self.persist: path = self.get_path(id) if not os.path.exists(path): index = self.index_from_empty() # create empty index self.store( id=id, index=index, ) else: self.indexes[id] = self.index_from_empty() def get(self, id: str, service_context: ServiceContext = None) -> BaseIndex: """ Get index :param id: tmp idx id :param service_context: Service context :return: index instance """ if not self.exists(id): self.create(id) path = self.get_path(id) if self.persist: storage_context = StorageContext.from_defaults( persist_dir=path, ) self.indexes[id] = load_index_from_storage( storage_context, service_context=service_context, ) return self.indexes[id] def store(self, id: str, index: BaseIndex = None): """ Store index :param id: index name :param index: index instance """ if not self.persist: self.indexes[id] = index return if index is None: index = self.indexes[id] path = self.get_path(id) index.storage_context.persist( persist_dir=path, ) self.indexes[id] = index def clean(self, id: str): """ Clean index :param id: index name """ if not self.persist: if id in self.indexes: del self.indexes[id] return path = self.get_path(id) if os.path.exists(path): os.remove(path)

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import streamlit as st from sqlalchemy import create_engine, inspect, text from typing import Dict, Any from llama_index import ( VectorStoreIndex, ServiceContext, download_loader, ) from llama_index.llama_pack.base import BaseLlamaPack from llama_index.llms import OpenAI import openai import os import pandas as pd from llama_index.llms.palm import PaLM from llama_index import ( SimpleDirectoryReader, ServiceContext, StorageContext, VectorStoreIndex, load_index_from_storage, ) import sqlite3 from llama_index import SQLDatabase, ServiceContext from llama_index.indices.struct_store import NLSQLTableQueryEngine os.environ['OPENAI_API_KEY'] = st.secrets['OPENAI_API_KEY'] class StreamlitChatPack(BaseLlamaPack): def __init__( self, page: str = "Natural Language to SQL Query", run_from_main: bool = False, **kwargs: Any, ) -> None: """Init params.""" self.page = page def get_modules(self) -> Dict[str, Any]: """Get modules.""" return {} def run(self, *args: Any, **kwargs: Any) -> Any: """Run the pipeline.""" import streamlit as st st.set_page_config( page_title=f"{self.page}", layout="centered", initial_sidebar_state="auto", menu_items=None, ) if "messages" not in st.session_state: # Initialize the chat messages history st.session_state["messages"] = [ {"role": "assistant", "content": f"Hello. Ask me anything related to the database."} ] st.title( f"{self.page}💬" ) st.info( f"Explore Snowflake views with this AI-powered app. Pose any question and receive exact SQL queries.", icon="ℹ️", ) def add_to_message_history(role, content): message = {"role": role, "content": str(content)} st.session_state["messages"].append( message ) # Add response to message history def get_table_data(table_name, conn): query = f"SELECT * FROM {table_name}" df = pd.read_sql_query(query, conn) return df @st.cache_resource def load_db_llm(): # Load the SQLite database #engine = create_engine("sqlite:///ecommerce_platform1.db") engine = create_engine("sqlite:///ecommerce_platform1.db?mode=ro", connect_args={"uri": True}) sql_database = SQLDatabase(engine) #include all tables # Initialize LLM #llm2 = PaLM(api_key=os.environ["GOOGLE_API_KEY"]) # Replace with your API key llm2 = OpenAI(temperature=0.1, model="gpt-3.5-turbo-1106") service_context = ServiceContext.from_defaults(llm=llm2, embed_model="local") return sql_database, service_context, engine sql_database, service_context, engine = load_db_llm() # Sidebar for database schema viewer st.sidebar.markdown("## Database Schema Viewer") # Create an inspector object inspector = inspect(engine) # Get list of tables in the database table_names = inspector.get_table_names() # Sidebar selection for tables selected_table = st.sidebar.selectbox("Select a Table", table_names) db_file = 'ecommerce_platform1.db' conn = sqlite3.connect(db_file) # Display the selected table if selected_table: df = get_table_data(selected_table, conn) st.sidebar.text(f"Data for table '{selected_table}':") st.sidebar.dataframe(df) # Close the connection conn.close() # Sidebar Intro st.sidebar.markdown('## App Created By') st.sidebar.markdown(""" Harshad Suryawanshi: [Linkedin](https://www.linkedin.com/in/harshadsuryawanshi/), [Medium](https://harshadsuryawanshi.medium.com/), [X](https://twitter.com/HarshadSurya1c) """) st.sidebar.markdown('## Other Projects') st.sidebar.markdown(""" - [Pokemon Go! Inspired AInimal GO! - Multimodal RAG App](https://www.linkedin.com/posts/harshadsuryawanshi_llamaindex-ai-deeplearning-activity-7134632983495327744-M7yy) - [Building My Own GPT4-V with PaLM and Kosmos](https://lnkd.in/dawgKZBP) - [AI Equity Research Analyst](https://ai-eqty-rsrch-anlyst.streamlit.app/) - [Recasting "The Office" Scene](https://blackmirroroffice.streamlit.app/) - [Story Generator](https://appstorycombined-agaf9j4ceit.streamlit.app/) """) st.sidebar.markdown('## Disclaimer') st.sidebar.markdown("""This application is for demonstration purposes only and may not cover all aspects of real-world data complexities. Please use it as a guide and not as a definitive source for decision-making.""") if "query_engine" not in st.session_state: # Initialize the query engine st.session_state["query_engine"] = NLSQLTableQueryEngine( sql_database=sql_database, synthesize_response=True, service_context=service_context ) for message in st.session_state["messages"]: # Display the prior chat messages with st.chat_message(message["role"]): st.write(message["content"]) if prompt := st.chat_input( "Enter your natural language query about the database" ): # Prompt for user input and save to chat history with st.chat_message("user"): st.write(prompt) add_to_message_history("user", prompt) # If last message is not from assistant, generate a new response if st.session_state["messages"][-1]["role"] != "assistant": with st.spinner(): with st.chat_message("assistant"): response = st.session_state["query_engine"].query("User Question:"+prompt+". ") sql_query = f"```sql\n{response.metadata['sql_query']}\n```\n**Response:**\n{response.response}\n" response_container = st.empty() response_container.write(sql_query) # st.write(response.response) add_to_message_history("assistant", sql_query) if __name__ == "__main__": StreamlitChatPack(run_from_main=True).run()

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Inspired AInimal GO! - Multimodal RAG App](https://www.linkedin.com/posts/harshadsuryawanshi_llamaindex-ai-deeplearning-activity-7134632983495327744-M7yy)\n - [Building My Own GPT4-V with PaLM and Kosmos](https://lnkd.in/dawgKZBP)\n - [AI Equity Research Analyst](https://ai-eqty-rsrch-anlyst.streamlit.app/)\n - [Recasting "The Office" Scene](https://blackmirroroffice.streamlit.app/)\n - [Story Generator](https://appstorycombined-agaf9j4ceit.streamlit.app/)\n """\n )\n', (4172, 4707), True, 'import streamlit as st\n'), ((4715, 4751), 'streamlit.sidebar.markdown', 'st.sidebar.markdown', (['"""## Disclaimer"""'], {}), "('## Disclaimer')\n", (4734, 4751), True, 'import streamlit as st\n'), ((4760, 4984), 'streamlit.sidebar.markdown', 'st.sidebar.markdown', (['"""This application is for demonstration purposes only and may not cover all aspects of real-world data complexities. 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#!/usr/bin/env python3 from flask import Flask, request from werkzeug.utils import secure_filename from llama_index import GPTSimpleVectorIndex, download_loader import json import secrets app = Flask(__name__) @app.route('/index', methods = ['GET', 'POST']) def upload_and_index(): if request.method == "POST": f = request.files['file'] filename = f"./uploads/{secure_filename(f.filename)}" f.save(filename) RDFReader = download_loader('RDFReader') document = RDFReader().load_data(file=filename) # avoid collisions of filenames data_id = secrets.token_hex(15) index = GPTSimpleVectorIndex(document) index.save_to_disk(f"{data_id}.json") return {'id': data_id} @app.route('/query') def query(): args = request.args data_id = args.get('id') query_str = args.get('query') q_index = GPTSimpleVectorIndex.load_from_disk(f"{data_id}.json") result = q_index.query(f"{query_str} - return the answer and explanation in a JSON object") try: json_start = result.response.index('{') answer = json.loads(result.response[json_start:]) answer.update({'success': True}) except (ValueError, json.JSONDecodeError): answer = {'success': False, 'answer': result.response, 'explanation': ''} return json.dumps(answer) @app.route('/') def hello(): return 'Hello, World!' def run_app(): app.run(host='0.0.0.0', port=5050) if __name__ == '__main__': run_app()

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from contextlib import contextmanager import uuid import os import tiktoken from . import S2_tools as scholar import csv import sys import requests # pdf loader from langchain.document_loaders import OnlinePDFLoader ## paper questioning tools from llama_index import Document from llama_index.vector_stores import PineconeVectorStore from llama_index import GPTVectorStoreIndex, StorageContext, ServiceContext from llama_index.embeddings.openai import OpenAIEmbedding def PaperSearchAndDownload(query): # make new workspace if not os.path.exists( os.path.join(os.getcwd(),'workspaces') ): os.mkdir(os.path.join(os.getcwd(),'workspaces')) workspace_dir_name = os.path.join(os.getcwd(),'workspaces',query.split()[0] + '_'+ str(uuid.uuid4().hex)) os.mkdir(workspace_dir_name) os.mkdir(os.path.join(workspace_dir_name,'results')) os.mkdir(os.path.join(workspace_dir_name,'refy_suggestions')) os.environ['workspace'] = workspace_dir_name # 1) search papers print(' 1) Searching base papers') papers = scholar.find_paper_from_query(query, result_limit=10) if len(papers == 0): papers = scholar.find_paper_from_query(query, result_limit=50) scholar.update_dataframe(incomplete=papers, dest=os.path.join(workspace_dir_name, 'results','papers.csv')) delete_duplicates_from_csv(csv_file=os.path.join(workspace_dir_name, 'results','papers.csv')) # 2) Cross-reference reccomendation system: # a paper is reccomended if and only if it's related to more than one paper print('\n\n 2) Expanding with Scholar reccomendations') counts = {} candidates = {} for paper in papers: guesses = scholar.find_recommendations(paper) for guess in guesses: if not guess['isOpenAccess']: continue candidates[guess['title']] = guess if guess['title'] not in counts.keys(): counts[guess['title']] = 1 else: counts[guess['title']] += 1 # reccomend only papers that appeared more than once reccomends = [] for key in counts: if counts[key]>1: reccomends.append(candidates[key]) print(f'found {len(reccomends)} additional papers') # update the csv scholar.update_dataframe(incomplete= reccomends, dest=os.path.join(workspace_dir_name, 'results','papers.csv')) delete_duplicates_from_csv(csv_file=os.path.join(workspace_dir_name, 'results','papers.csv')) # download the papers (1/2) print('downloading papers (1/2)') with open(os.path.join(workspace_dir_name,'results','papers.csv'), 'r',encoding='utf-8') as fp: csvfile = csv.DictReader(fp) scholar.download_pdf_from_id(" ".join( row['paperId'] for row in csvfile), workspace_dir_name) scholar.write_bib_file(csv_file=os.path.join(workspace_dir_name,'results','papers.csv'), bib_file=os.path.join(workspace_dir_name,'results','papers.bib')) # expand further with refy reccomendendation system print('\n\n 3) Expanding with Refy reccomendendation system') print('this might take a while...') scholar.refy_reccomend(bib_path=os.path.join(workspace_dir_name,'results','papers.bib')) with open(os.path.join(workspace_dir_name, 'refy_suggestions', 'test.csv'), 'r',encoding='utf-8') as fp: csvfile = csv.DictReader(fp) for row in csvfile: title = scholar.replace_non_alphanumeric(row['title']) title = title.replace(" ","_") save_path = os.path.join(workspace_dir_name,'refy_suggestions',(title+'.pdf')) try: download_paper(url=row['url'], save_path=save_path) except: print(f'couldn t download {row}') return f'{os.path.join(os.getcwd(), workspace_dir_name)}' import urllib def download_paper(url, save_path=f"{uuid.uuid4().hex}.pdf"): success_string = f"paper saved successfully at {os.path.join(os.path.abspath(save_path))}" if url.endswith('.pdf'): urllib.request.urlretrieve(url, save_path) return success_string if 'doi' in url: doi = paper_id = "/".join(url.split("/")[-2:]) # Construct the Crossref API URL print(doi) doi_url = f"https://doi.org/{doi}" # Send a GET request to the doi.org URL response = requests.get(doi_url, allow_redirects=True) # Check if the request was successful if response.status_code == 200: # Extract the final URL after redirection url = response.url if 'arxiv' in url: # URL del paper su arXiv # Ottieni l'ID del paper dall'URL paper_id = url.split("/")[-1] # Costruisci l'URL di download del paper pdf_url = f"http://arxiv.org/pdf/{paper_id}.pdf" # Scarica il paper in formato PDF urllib.request.urlretrieve(pdf_url, save_path) return success_string else: if '/full' in url: urllib.request.urlretrieve(url.replace('/full','/pdf')) return success_string if 'plos.org' in url: final_url = url.replace('article?', 'article/file?') urllib.request.urlretrieve(final_url, save_path) return success_string return f'\nfailed to download {url}' def download_bibtex_library(csv_path): with open(csv_path, 'r',encoding='utf-8') as fp: csvfile = csv.DictReader(fp) for row in csvfile: title = scholar.replace_non_alphanumeric(row['title']) title = title.replace(" ","-") save_path = os.path.join(os.path.join(csv_path, '..', title+'.pdf')) try: download_paper(url=row['url'], save_path=save_path) except: try: download_paper(url=row['url']+'.pdf', save_path=save_path) except: print(f'couldn t download {row}') def generate_chunks(text, CHUNK_LENGTH = 4000): enc = tiktoken.encoding_for_model("gpt-4") tokens = enc.encode(text) token_chunks = [tokens[i:i + CHUNK_LENGTH] for i in range(0, len(tokens), CHUNK_LENGTH)] word_chunks = [enc.decode(chunk) for chunk in token_chunks] return word_chunks from langchain.vectorstores import Chroma, Pinecone from langchain.embeddings.openai import OpenAIEmbeddings import pinecone import langid import time # def process_pdf_folder(folder_path): # if not os.path.exists(folder_path): # return 'the folder does not exist, check your spelling' # for item in os.listdir(folder_path): # if not item.endswith('.pdf'):continue # with open(os.path.join(folder_path,'SUMMARY.txt'), 'a', encoding='UTF-8') as write_file: # write_file.write(item) # write_file.write("\n\n\n") # txt = summarize_pdf(item, model='Vicuna') # try: # write_file.write(txt) # except: # print(txt) # with open(os.path.join(folder_path,'SUMMARY.txt'), 'r', encoding='UTF-8') as read_file: # return read_file.read() # # def summarize_pdf(pdf_path, model= None): # text = readPDF(pdf_path) # # according to the TLDR Model, consider smaller chunks # text_chunks = generate_chunks(text, 700) # if model is not None: # summarizer = LocalSearchEngine(tldr_model=model) # summary='' # for chunk in text_chunks: # summary += summarizer.tldr(chunk) # return summary def get_result_path(path, exclude = []): for item in os.listdir(path): if item == 'papers.csv': return os.path.join(path, item) if os.path.isdir(os.path.join(path, item)) and item not in exclude: res = get_result_path(os.path.join(path, item)) if res: return res return def get_workspace_titles(workspace_name): csv_file_path = get_result_path(workspace_name) papers_available = [] with open(csv_file_path, 'r', encoding='utf-8') as file: csv_file = csv.DictReader(file) for row in csv_file: papers_available.append(row['title']) return papers_available import re def same_title(title1, title2): try: title1 = re.sub(r'[^a-zA-Z]', ' ', title1) title2 = re.sub(r'[^a-zA-Z]', ' ', title2) except: return False words1 = set(title1.lower().split()) words2 = set(title2.lower().split()) return words1 == words2 or words1 <= words2 or words1 >= words2 def glimpse_pdf(title): # find papers.csv in workspace for workspace_name in os.listdir('workspaces'): csv_file_path = get_result_path(workspace_name) if csv_file_path is None: return 'no paper found' with open(csv_file_path, 'r', encoding='utf-8') as file: csv_file = csv.DictReader(file) for row in csv_file: if same_title(row['title'], title): return f"{row['title']}, paperId: {row['paperId']}, summary: {row['abstract']}" return f'\nno paper found with title {title}' def count_tokens(text): enc = tiktoken.encoding_for_model("gpt-4") tokens = enc.encode(text) return len(tokens) def readPDF(pdf_path): loader = OnlinePDFLoader(pdf_path) data = loader.load() text_content = '' for page in data: formatted_content = page.page_content.replace('\n\n', ' ') text_content+=formatted_content return text_content def get_pdf_path(dir, exclude=[]): paths = [] for item in os.listdir(dir): itempath = os.path.join(dir,item) if item.endswith('.pdf'): paths.append(itempath) if os.path.isdir(itempath)and item not in exclude: subpaths = get_pdf_path(itempath) for i in subpaths: paths.append(i) return paths def delete_duplicates_from_csv(csv_file): print('verifying duplicates...') to_delete = [] def delete_csv_row_by_title(csv_file, title): # Read the CSV file and store rows in a list with open(csv_file, 'r',encoding='UTF-8') as file: reader = csv.DictReader(file) rows = list(reader) # Find the row index with the matching title row_index = None for index, row in enumerate(rows): if row['title'] == title: row_index = index break # If no matching title is found, return if row_index is None: print(f"No row with title '{title}' found.") return # Remove the row from the list del rows[row_index] # Write the updated rows back to the CSV file with open(csv_file, 'w', newline='',encoding='UTF-8') as file: fieldnames = reader.fieldnames writer = csv.DictWriter(file, fieldnames=fieldnames) writer.writeheader() writer.writerows(rows) with open(csv_file, 'r', encoding='UTF-8') as file: DELETED = 0 reader = csv.DictReader(file) rows = list(reader) entries = set() for row in rows: if row['title']=='' or row['title'] is None: continue if row['title'] not in entries:entries.add(row['title']) else: DELETED+=1 to_delete.append(row['title']) for title in to_delete: delete_csv_row_by_title(csv_file, title=title) print(f"Deleted {DELETED} duplicates") return def update_workspace_dataframe(workspace, verbose = True): ADDED = 0 # find results.csv csv_path = get_result_path(workspace) # get titles in csv titles = get_workspace_titles(workspace) # get local papers path paths = get_pdf_path(workspace, exclude='refy_suggestions') # adding new to csv: for path in paths: exists = False # extract the title from the local paper title = scholar.extract_title(path) for t in titles: if same_title(t,title): exists = True # add it to dataframe if it was not found on the DF if not exists: if verbose: print(f"\nnew paper detected: {title}") # find it with online paper = scholar.find_paper_online(path) if paper : if verbose: print(f"\t---> best match found online: {paper['title']} " ) for t in titles: if same_title(paper['title'], title): if verbose: print(f"\t this paper is already present in the dataframe. skipping") else: if verbose: print(path, '-x-> no match found') continue with open(csv_path, 'a', encoding='utf-8') as fp: areYouSure = True for t in titles: if same_title(t,paper['title']): areYouSure =False if not areYouSure: if verbose: print(f"double check revealed that the paper is already in the dataframe. Skipping") continue if verbose: print(f"\t---> adding {paper['title']}") ADDED +=1 paper_authors = paper.get('authors', []) journal_data = {} if 'journal' in paper: journal_data = paper.get('journal',[]) if journal_data is not None: if 'name' not in journal_data: journal_data['name'] = '' if 'pages' not in journal_data: journal_data['pages'] = '' if paper.get('tldr',[]) != []:tldr = paper['tldr']['text'] elif paper.get('summary',[]) != []:tldr = paper['summary'] elif 'abstract' in paper:tldr = paper['abstract'] else: tldr = 'No summary available' if 'year' in paper: year = paper['year'] elif 'updated' in paper:year = paper['updated'] else: year = '' if 'citationStyles' in paper: if 'bibtex' in paper['citationStyles']: citStyle = paper['citationStyles']['bibtex'] else: citStyle = paper['citationStyles'][0] else: citStyle = '' csvfile = csv.DictWriter(fp, ['paperId', 'title', 'first_author', 'year', 'abstract','tldr','bibtex','influentialCitationCount','venue','journal','pages']) try: csvfile.writerow({ 'title': paper['title'], 'first_author': paper_authors[0]['name'] if paper_authors else '', 'year': year, 'abstract': paper['abstract'] if 'abstract' in paper else '', 'paperId': paper['paperId'] if 'paperId' in paper else '', 'tldr':tldr, 'bibtex':citStyle, 'influentialCitationCount': paper['influentialCitationCount'] if 'influentialCitationCount' in paper else '0', 'venue':paper['venue'] if 'venue' in paper else '', 'journal':journal_data['name'] if journal_data is not None else '', 'pages':journal_data['pages'] if journal_data is not None else '', }) except Exception as e: if verbose: print('could not add ', title, '\n',e) # delete dupes if present if verbose: print(f"\n\nCSV UPDATE: Added {ADDED} new papers") # clean form dupes delete_duplicates_from_csv(csv_path) # update bib scholar.write_bib_file(csv_path) return def load_workspace(folderdir): docs =[] for item in os.listdir(folderdir): if item.endswith('.pdf'): print(f' > loading {item}') with suppress_stdout(): content = readPDF(os.path.join(folderdir, item)) docs.append(Document( text = content, doc_id = uuid.uuid4().hex )) if item =='.'or item =='..':continue if os.path.isdir( os.path.join(folderdir,item) ): sub_docs = load_workspace(os.path.join(folderdir,item)) for doc in sub_docs: docs.append(doc) return docs # List paths of all pdf files in a folder def list_workspace_elements(folderdir): docs =[] for item in os.listdir(folderdir): if item.endswith('.pdf'): docs.append(rf"{os.path.join(folderdir,item)}") if item =='.'or item =='..':continue if os.path.isdir( os.path.join(folderdir,item) ): sub_docs = list_workspace_elements(os.path.join(folderdir,item)) for doc in sub_docs: docs.append(doc) return docs def llama_query_engine(docs:list, pinecone_index_name:str): pinecone.init( api_key= os.environ['PINECONE_API_KEY'], environment= os.environ['PINECONE_API_ENV'] ) # Find the pinecone index if pinecone_index_name not in pinecone.list_indexes(): # we create a new index pinecone.create_index( name=pinecone_index_name, metric='dotproduct', dimension=1536 # 1536 dim of text-embedding-ada-002 ) index = pinecone.Index(pinecone_index_name) # init it vector_store = PineconeVectorStore(pinecone_index=index) time.sleep(1) # setup our storage (vector db) storage_context = StorageContext.from_defaults( vector_store=vector_store ) embed_model = OpenAIEmbedding(model='text-embedding-ada-002', embed_batch_size=100) service_context = ServiceContext.from_defaults(embed_model=embed_model) # populate the vector store LamaIndex = GPTVectorStoreIndex.from_documents( docs, storage_context=storage_context, service_context=service_context ) print('PINECONE Vector Index initialized:\n',index.describe_index_stats()) # init the query engine query_engine = LamaIndex.as_query_engine() return query_engine, LamaIndex @contextmanager def suppress_stdout(): with open(os.devnull, "w") as devnull: old_stdout = sys.stdout sys.stdout = devnull try: yield finally: sys.stdout = old_stdout

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import os import logging import sys from llama_index import GPTSimpleVectorIndex logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) # 加载索引 new_index = GPTSimpleVectorIndex.load_from_disk('index.json') # 查询索引 response = new_index.query("What did the author do in 9th grade?") # 打印答案 print(response)

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import os import openai from fastapi import FastAPI, HTTPException from llama_index import StorageContext, load_index_from_storage, ServiceContext, set_global_service_context from llama_index.indices.postprocessor import SentenceEmbeddingOptimizer from llama_index.embeddings import OpenAIEmbedding from pydantic import BaseModel openai.api_key = os.environ["OPENAI_API_KEY"] app = FastAPI() class QueryRequest(BaseModel): question: str class QueryResponse(BaseModel): answer: str embed_model = OpenAIEmbedding(embed_batch_size=10) service_context = ServiceContext.from_defaults(embed_model=embed_model) set_global_service_context(service_context) storage_context = StorageContext.from_defaults(persist_dir="./storage") index = load_index_from_storage(storage_context) query_engine = index.as_query_engine( node_postprocessors=[SentenceEmbeddingOptimizer(percentile_cutoff=0.5)], response_mode="compact", similarity_cutoff=0.7 ) @app.get("/") def read_root(): return {"Hello": "World"} @app.post("/chat") def query_data(request: QueryRequest): response = query_engine.query(request.question) if not response: raise HTTPException(status_code=404, detail="No results found") return QueryResponse(answer=str(response))

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"""Example of how to use llamaindex for semantic search. This example assumes that initially there is a projects.DATASETS_DIR_PATH/embeddings.pkl file that has a list of dictionaries with each dictionary containing "text", "rule_name" and "section_label" fields. The first time you run this script, a vector store will be creaed with embeddings. This store will be saved to "cache/msrb_index_store". Subsequent runs will load the vector store from this location. Each time you run this script you enter a loop where you can ask as many questions of the data as you'd like. Each time you ask a question you will be given a response that tells you: 1. The rule names and section labels for the most relevant rules, 2. A brief preview of the text from those sections, and 3. An LLM-generated response to your query given the texts that it found. You can tweak three parameters at the bottom of this script (after all of the function definitions): - model_name: which OpenAI model to use. - top_k: how many rules to return. - similarity_cutoff: threshold for relevance (between 0 and 1). """ import os import pickle from pathlib import Path # from llama_index import SimpleDirectoryReader # from llama_index.node_parser import SimpleNodeParser from llama_index import ( VectorStoreIndex, StorageContext, LLMPredictor, ServiceContext, get_response_synthesizer, load_index_from_storage, ) from llama_index.retrievers import VectorIndexRetriever from llama_index.query_engine import RetrieverQueryEngine from llama_index.indices.postprocessor import SimilarityPostprocessor from llama_index.schema import TextNode from langchain import OpenAI from examples import project TEXT_DATA_FILE = Path(os.path.join(project.DATASETS_DIR_PATH, 'embeddings.pkl')) INDEX_DATA_DIR = Path('cache/msrb_index_store') def get_vector_store(service_context: ServiceContext) -> VectorStoreIndex: """Load a vector index from disk or, if it doesn't exist, create one from raw text data.""" # === Load the data =========================================================== # Simple example of reading text files from a local directory # reader = SimpleDirectoryReader('./data') # documents = reader.load_data() # returns a list of Documents # parser = SimpleNodeParser() # nodes = parser.get_nodes_from_documents(documents) # returns a list of nodes if INDEX_DATA_DIR.exists(): print('Loading vector store from local directory.') # rebuild storage context storage_context = StorageContext.from_defaults(persist_dir=INDEX_DATA_DIR) # load index index = load_index_from_storage(storage_context) else: print('No local index found.') print('Loading data.') with open('embeddings.pkl', 'rb') as f: data = pickle.load(f) print('Building nodes.') nodes = [] for example in data: node = TextNode(text=example['text']) node.metadata['rule_name'] = example['rule_name'] node.metadata['section_label'] = example['section_label'] nodes.append(node) print(f'Created {len(nodes)} nodes.') print('Creating vector store.') index = VectorStoreIndex(nodes, service_context=service_context) # index = VectorStoreIndex.from_documents(documents) print('Saving vector store.') index.storage_context.persist(INDEX_DATA_DIR) return index def get_llm_backend(model_name: str) -> ServiceContext: """Get an LLM to provide embedding and text generation service.""" # === Define the LLM backend ================================================== # define LLM llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name=model_name)) # configure service context service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor) return service_context def get_query_engine(index: VectorStoreIndex, response_mode: str, top_k: int, similarity_cutoff: float) -> RetrieverQueryEngine: """Build a query enginge by combining a retriever and response synthesizer.""" # configure retriever retriever = VectorIndexRetriever( index=index, similarity_top_k=top_k, ) # configure response synthesizer response_synthesizer = get_response_synthesizer() # assemble query engine # query_engine = RetrieverQueryEngine.from_args( # retriever=retriever, # response_synthesizer=response_synthesizer, # response_mode=response_mode # ) query_engine = RetrieverQueryEngine( retriever=retriever, response_synthesizer=response_synthesizer, node_postprocessors=[ SimilarityPostprocessor(similarity_cutoff=similarity_cutoff) ] ) return query_engine if __name__=='__main__': model_name = "text-davinci-003" top_k = 3 similarity_cutoff = 0.7 service_context = get_llm_backend(model_name) index = get_vector_store(service_context) response_mode = 'refine' # response_mode = 'no_text' for no text generation query_engine = get_query_engine(index, response_mode, top_k, similarity_cutoff) # query while (query := input('Ask me a question about the MSRB rule book ("quit" to quit): ')) != 'quit': print(f'You asked: {query}') response = query_engine.query(query) print('Source nodes:') print(f'There are {len(response.source_nodes)} source nodes from the following rules:') for source_node in response.source_nodes: print(source_node.node.metadata['rule_name'], source_node.node.metadata['section_label']) print(response.get_formatted_sources()) print('Response:') print(response) print() print('='*40)

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from dotenv import load_dotenv load_dotenv() from llama_index import GPTVectorStoreIndex, TrafilaturaWebReader import chromadb def create_embedding_store(name): chroma_client = chromadb.Client() return chroma_client.create_collection(name) def query_pages(collection, urls, questions): docs = TrafilaturaWebReader().load_data(urls) index = GPTVectorStoreIndex.from_documents(docs, chroma_collection=collection) query_engine = index.as_query_engine() for question in questions: print(f"Question: {question} \n") print(f"Answer: {query_engine.query(question)}") if __name__ == "__main__": url_list = ["https://supertype.ai", "https://supertype.ai/about-us"] questions = [ "Who are the members of Supertype.ai", "What problems are they trying to solve?", "What are the important values at the company?" ] collection = create_embedding_store("supertype") query_pages( collection, url_list, questions )

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import logging from llama_index.readers.base import BaseReader from llama_index.readers.schema.base import Document import requests from typing import List import re import os import logging from llama_index.readers.base import BaseReader from llama_index.readers.schema.base import Document import requests from typing import List import os import pandas as pd import openai import ast TWITTER_USERNAME = "shauryr" def generate_search_queries_prompt(question): """Generates the search queries prompt for the given question. Args: question (str): The question to generate the search queries prompt for Returns: str: The search queries prompt for the given question """ return ( f'Please generate four related search queries that align with the initial query: "{question}"' f'Each variation should be presented as a list of strings, following this format: ["query 1", "query 2", "query 3", "query 4"]' ) def get_related_questions(query): research_template = """You are a search engine expert""" messages = [{ "role": "system", "content": research_template }, { "role": "user", "content": generate_search_queries_prompt(query), }] response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=messages, temperature=0.5, max_tokens=256 ) related_questions = get_questions(response.choices[0].message.content) related_questions.append(query) return related_questions def get_questions(response_text): data = response_text.split("\n") data = [ast.literal_eval(item)[0] for item in data] return data def get_unique_docs(docs): unique_docs_id = [] unique_docs = [] for doc in docs: if doc.extra_info['paperId'] not in unique_docs: unique_docs_id.append(doc.extra_info['paperId']) unique_docs.append(doc) return unique_docs class SemanticScholarReader(BaseReader): """ A class to read and process data from Semantic Scholar API ... Methods ------- __init__(): Instantiate the SemanticScholar object load_data(query: str, limit: int = 10, returned_fields: list = ["title", "abstract", "venue", "year", "paperId", "citationCount", "openAccessPdf", "authors"]) -> list: Loads data from Semantic Scholar based on the query and returned_fields """ def __init__(self, timeout=10, api_key=None, base_dir="pdfs"): """ Instantiate the SemanticScholar object """ from semanticscholar import SemanticScholar import arxiv self.arxiv = arxiv self.base_dir = base_dir self.s2 = SemanticScholar(timeout=timeout) # check for base dir if not os.path.exists(self.base_dir): os.makedirs(self.base_dir) def _clear_cache(self): """ delete the .citation* folder """ import shutil shutil.rmtree("./.citation*") def _download_pdf(self, paper_id, url: str, base_dir="pdfs"): logger = logging.getLogger() headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3" } # Making a GET request response = requests.get(url, headers=headers, stream=True) content_type = response.headers["Content-Type"] # As long as the content-type is application/pdf, this will download the file if "application/pdf" in content_type: os.makedirs(base_dir, exist_ok=True) file_path = os.path.join(base_dir, f"{paper_id}.pdf") # check if the file already exists if os.path.exists(file_path): logger.info(f"{file_path} already exists") return file_path with open(file_path, "wb") as file: for chunk in response.iter_content(chunk_size=1024): if chunk: file.write(chunk) logger.info(f"Downloaded pdf from {url}") return file_path else: logger.warning(f"{url} was not downloaded: protected") return None def _get_full_text_docs(self, documents: List[Document]) -> List[Document]: from PyPDF2 import PdfReader """ Gets the full text of the documents from Semantic Scholar Parameters ---------- documents: list The list of Document object that contains the search results Returns ------- list The list of Document object that contains the search results with full text Raises ------ Exception If there is an error while getting the full text """ full_text_docs = [] for paper in documents: metadata = paper.extra_info url = metadata["openAccessPdf"] externalIds = metadata["externalIds"] paper_id = metadata["paperId"] file_path = None persist_dir = os.path.join(self.base_dir, f"{paper_id}.pdf") if url and not os.path.exists(persist_dir): # Download the document first file_path = self._download_pdf(metadata["paperId"], url, persist_dir) if ( not url and externalIds and "ArXiv" in externalIds and not os.path.exists(persist_dir) ): # download the pdf from arxiv file_path = self._download_pdf_from_arxiv( paper_id, externalIds["ArXiv"] ) # Then, check if it's a valid PDF. If it's not, skip to the next document. if file_path: try: pdf = PdfReader(open(file_path, "rb")) except Exception as e: logging.error( f"Failed to read pdf with exception: {e}. Skipping document..." ) continue text = "" for page in pdf.pages: text += page.extract_text() full_text_docs.append(Document(text=text, extra_info=metadata)) return full_text_docs def _download_pdf_from_arxiv(self, paper_id, arxiv_id): paper = next(self.arxiv.Search(id_list=[arxiv_id], max_results=1).results()) paper.download_pdf(dirpath=self.base_dir, filename=paper_id + ".pdf") return os.path.join(self.base_dir, f"{paper_id}.pdf") def load_data( self, query, limit, full_text=False, returned_fields=[ "title", "abstract", "venue", "year", "paperId", "citationCount", "openAccessPdf", "authors", "externalIds", ], ) -> List[Document]: """ Loads data from Semantic Scholar based on the entered query and returned_fields Parameters ---------- query: str The search query for the paper limit: int, optional The number of maximum results returned (default is 10) returned_fields: list, optional The list of fields to be returned from the search Returns ------- list The list of Document object that contains the search results Raises ------ Exception If there is an error while performing the search """ results = [] # query = get_related_questions(query) query = [query] try: for question in query: logging.info(f"Searching for {question}") _results = self.s2.search_paper(question, limit=limit, fields=returned_fields) results.extend(_results[:limit]) except (requests.HTTPError, requests.ConnectionError, requests.Timeout) as e: logging.error( "Failed to fetch data from Semantic Scholar with exception: %s", e ) raise except Exception as e: logging.error("An unexpected error occurred: %s", e) raise documents = [] for item in results[:limit*len(query)]: openAccessPdf = getattr(item, "openAccessPdf", None) abstract = getattr(item, "abstract", None) title = getattr(item, "title", None) text = None # concat title and abstract if abstract and title: text = title + " " + abstract elif not abstract: text = title metadata = { "title": title, "venue": getattr(item, "venue", None), "year": getattr(item, "year", None), "paperId": getattr(item, "paperId", None), "citationCount": getattr(item, "citationCount", None), "openAccessPdf": openAccessPdf.get("url") if openAccessPdf else None, "authors": [author["name"] for author in getattr(item, "authors", [])], "externalIds": getattr(item, "externalIds", None), } documents.append(Document(text=text, extra_info=metadata)) if full_text: logging.info("Getting full text documents...") full_text_documents = self._get_full_text_docs(documents) documents.extend(full_text_documents) documents = get_unique_docs(documents) return documents def get_twitter_badge(): """Constructs the Markdown code for the Twitter badge.""" return f'<a href="https://twitter.com/{TWITTER_USERNAME}" target="_blank"><img src="https://img.shields.io/badge/Twitter-1DA1F2?style=for-the-badge&logo=twitter&logoColor=white" /></a>' def get_link_tree_badge(): return f'<a href="https://linktr.ee/shauryr" target="_blank"><img src="https://img.shields.io/badge/Linktree-39E09B?style=for-the-badge&logo=linktree&logoColor=white" /></a>' def get_github_badge(): return f'<a href="https://github.com/shauryr/s2qa" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white" /></a>' def display_questions(sample_questions): s = "#### 🧐 More questions? \n" for i in sample_questions: s += "- " + i + "\n" return s def get_citation(metadata): # Extract details from metadata title = metadata.get("title", "No Title") venue = metadata.get("venue", "No Venue") year = metadata.get("year", "No Year") authors = metadata.get("authors", []) # Generate author names in correct format author_names = [] for author in authors[:5]: last_name, *first_names = author.split(" ") first_initials = " ".join(name[0] + "." for name in first_names) author_names.append(f"{last_name}, {first_initials}") authors_string = ", & ".join(author_names) # APA citation format: Author1, Author2, & Author3. (Year). Title. Venue. citation = f"{authors_string}. ({year}). **{title}**. {venue}." return citation def extract_numbers_in_brackets(input_string): # use regular expressions to find all occurrences of [number] # numbers_in_brackets = re.findall(r"\[(\d+)\]", input_string) numbers_in_brackets = re.findall(r"\[(.*?)\]", input_string) # numbers_in_brackets = [int(i) for num in numbers_in_brackets for i in num.split(",")] # convert all numbers to int and remove duplicates by converting list to set and then back to list cleaned_numbers = [] for n in numbers_in_brackets: # Try to convert the value to an integer try: cleaned_numbers.append(int(n)) # If it fails (throws a ValueError), just ignore and continue with the next value except ValueError: continue # Apply the rest of your code on the cleaned list return sorted(list(set(cleaned_numbers))) def generate_used_reference_display(source_nodes, used_nodes): reference_display = "\n #### 📚 References: \n" # for index in used_nodes get the source node and add it to the reference display for index in used_nodes: try: source_node = source_nodes[index - 1] except IndexError: return "\n #### 😞 Couldnt Parse References \n" metadata = source_node.node.metadata reference_display += ( "[[" + str(source_nodes.index(source_node) + 1) + "]" + "(" + "https://www.semanticscholar.org/paper/" + metadata["paperId"] + ")] " + "\n `. . ." + str(source_node.node.text)[100:290] + ". . .`" + get_citation(metadata) + " \n\n" ) return reference_display def documents_to_df(documents): # convert document objects to dataframe list_data = [] for i, doc in enumerate(documents): list_data.append(doc.extra_info.copy()) df = pd.DataFrame(list_data) return df def generate_reference_display(source_nodes): reference_display = "\n ### References: \n" for source_node in source_nodes: metadata = source_node.node.metadata # add number infront of citation to make it easier to reference # reference_display += ( # "[[" # + str(source_nodes.index(source_node) + 1) # + "]" # + "(" # + "https://www.semanticscholar.org/paper/" # + metadata["paperId"] # + ")] " # + '\n "`. . .' # + str(source_node.node.text)[100:290] # + ". . .` - **" # + get_citation(metadata) # + "** \n\n" # ) reference_display += ( "[[" + str(source_nodes.index(source_node) + 1) + "]" + "(" + "https://www.semanticscholar.org/paper/" + metadata["paperId"] + ")] " + get_citation(metadata) + " \n\n" ) return reference_display

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from llama_index.embeddings import LinearAdapterEmbeddingModel, resolve_embed_model from llama_index.finetuning import EmbeddingQAFinetuneDataset import pickle from eval_utils import evaluate, display_results def run_eval(val_data: str) -> None: val_dataset = EmbeddingQAFinetuneDataset.from_json(val_data) print("Loading model") embed_model_name = "local:BAAI/bge-large-en" base_embed_model = resolve_embed_model(embed_model_name) print("Loading adapter") embed_model = LinearAdapterEmbeddingModel(base_embed_model, "model_output_test", device="cuda") # Top k 10 to match our internal experiments print("Evaluating fine-tuned model") ft_val_results = evaluate(val_dataset, embed_model, top_k=10) print("Fine-Tuned Model Results") print(ft_val_results) with open("ft_results.pkl", "wb") as f: pickle.dump(ft_val_results, f) display_results(["ft"], [ft_val_results]) print("Evaluating base model") bge_val_results = evaluate(val_dataset, embed_model_name, top_k=10) print("Base Model Results:") print(bge_val_results) with open("base_model_results.pkl", "wb") as f2: pickle.dump(bge_val_results, f2) display_results(["bge"], [bge_val_results]) print("All Results") display_results( ["bge", "ft"], [bge_val_results, ft_val_results] ) if __name__ == "__main__": run_eval("val.json")

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"""Simple horoscope predictions generator.""" from typing import List, Optional, Dict, Callable import re import json from llama_index.core.bridge.pydantic import PrivateAttr from llama_index.core.readers.base import BasePydanticReader from llama_index.core.schema import Document from vedastro import * class SimpleBirthTimeReader(BasePydanticReader): """Simple birth time prediction reader. Reads horoscope predictions from vedastro.org `pip install vedastro` needed Args: metadata_fn (Optional[Callable[[str], Dict]]): A function that takes in a birth time and returns a dictionary of prediction metadata. Default is None. """ is_remote: bool = True _metadata_fn: Optional[Callable[[str], Dict]] = PrivateAttr() def __init__( self, metadata_fn: Optional[Callable[[str], Dict]] = None, ) -> None: """Initialize with parameters.""" self._metadata_fn = metadata_fn super().__init__() @classmethod def class_name(cls) -> str: return "SimpleBirthTimeReader" def load_data(self, birth_time: str) -> List[Document]: """Load data from the given birth time. Args: birth_time (str): birth time in this format : Location/Delhi,India/Time/01:30/14/02/2024/+05:30 Returns: List[Document]: List of documents. """ documents = SimpleBirthTimeReader.birth_time_to_llama_index_nodes(birth_time) return documents @staticmethod # converts vedastro horoscope predictions (JSON) to_llama-index's NodeWithScore # so that llama index can understand vedastro predictions def vedastro_predictions_to_llama_index_weight_nodes( birth_time, predictions_list_json ): from llama_index.core.schema import NodeWithScore from llama_index.core.schema import TextNode # Initialize an empty list prediction_nodes = [] for prediction in predictions_list_json: related_bod_json = prediction["RelatedBody"] # shadbala_score = Calculate.PlanetCombinedShadbala() rel_planets = related_bod_json["Planets"] parsed_list = [] for planet in rel_planets: parsed_list.append(PlanetName.Parse(planet)) # TODO temp use 1st planet, house, zodiac planet_tags = [] shadbala_score = 0 if parsed_list: # This checks if the list is not empty for planet in parsed_list: shadbala_score += Calculate.PlanetShadbalaPinda( planet, birth_time ).ToDouble() # planet_tags = Calculate.GetPlanetTags(parsed_list[0]) predict_node = TextNode( text=prediction["Description"], metadata={ "name": SimpleBirthTimeReader.split_camel_case(prediction["Name"]) # "related_body": prediction['RelatedBody'], # "planet_tags": planet_tags, }, metadata_seperator="::", metadata_template="{key}=>{value}", text_template="Metadata: {metadata_str}\n-----\nContent: {content}", ) # add in shadbala to give each prediction weights parsed_node = NodeWithScore( node=predict_node, score=shadbala_score ) # add in shabala score prediction_nodes.append(parsed_node) # add to main list return prediction_nodes @staticmethod def birth_time_to_llama_index_nodes(birth_time_text): # 1 : convert raw time text into parsed time (aka time url) parsed_birth_time = Time.FromUrl(birth_time_text).GetAwaiter().GetResult() # 2 : do +300 horoscope prediction calculations to find correct predictions for person all_predictions_raw = Calculate.HoroscopePredictions(parsed_birth_time) # show the number of horo records found print(f"Predictions Found : {len(all_predictions_raw)}") # format list nicely so LLM can swallow (llama_index nodes) # so that llama index can understand vedastro predictions all_predictions_json = json.loads( HoroscopePrediction.ToJsonList(all_predictions_raw).ToString() ) # do final packing into llama-index formats prediction_nodes = ( SimpleBirthTimeReader.vedastro_predictions_to_llama_index_documents( all_predictions_json ) ) return prediction_nodes @staticmethod def vedastro_predictions_to_llama_index_nodes(birth_time, predictions_list_json): from llama_index.core.schema import NodeWithScore from llama_index.core.schema import TextNode # Initialize an empty list prediction_nodes = [] for prediction in predictions_list_json: related_bod_json = prediction["RelatedBody"] # shadbala_score = Calculate.PlanetCombinedShadbala() rel_planets = related_bod_json["Planets"] parsed_list = [] for planet in rel_planets: parsed_list.append(PlanetName.Parse(planet)) # TODO temp use 1st planet, house, zodiac planet_tags = [] shadbala_score = 0 if parsed_list: # This checks if the list is not empty shadbala_score = Calculate.PlanetShadbalaPinda( parsed_list[0], birth_time ).ToDouble() planet_tags = Calculate.GetPlanetTags(parsed_list[0]) predict_node = TextNode( text=prediction["Description"], metadata={ "name": ChatTools.split_camel_case(prediction["Name"]), "related_body": prediction["RelatedBody"], "planet_tags": planet_tags, }, metadata_seperator="::", metadata_template="{key}=>{value}", text_template="Metadata: {metadata_str}\n-----\nContent: {content}", ) # add in shadbala to give each prediction weights prediction_nodes.append(predict_node) # add to main list return prediction_nodes @staticmethod # given list vedastro lib horoscope predictions will convert to documents def vedastro_predictions_to_llama_index_documents(predictions_list_json): from llama_index.core import Document from llama_index.core.schema import MetadataMode import copy # Initialize an empty list prediction_nodes = [] for prediction in predictions_list_json: # take out description (long text) from metadata, becasue already in as "content" predict_meta = copy.deepcopy(prediction) del predict_meta["Description"] predict_node = Document( text=prediction["Description"], metadata=predict_meta, metadata_seperator="::", metadata_template="{key}=>{value}", text_template="Metadata: {metadata_str}\n-----\nContent: {content}", ) # # this is shows difference for understanding output of Documents # print("#######################################################") # print( # "The LLM sees this: \n", # predict_node.get_content(metadata_mode=MetadataMode.LLM), # ) # print( # "The Embedding model sees this: \n", # predict_node.get_content(metadata_mode=MetadataMode.EMBED), # ) # print("#######################################################") # add in shadbala to give each prediction weights prediction_nodes.append(predict_node) # add to main list return prediction_nodes @staticmethod def split_camel_case(s): return re.sub("((?<=[a-z])[A-Z]|(?<!\\A)[A-Z](?=[a-z]))", " \\1", s)

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import os from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader from flask import Flask, render_template, jsonify, request index = None # set up the index, either load it from disk to create it on the fly def initialise_index(): global index if os.path.exists(os.environ["INDEX_FILE"]): index = GPTSimpleVectorIndex.load_from_disk(os.environ["INDEX_FILE"]) else: documents = SimpleDirectoryReader(os.environ["LOAD_DIR"]).load_data() index = GPTSimpleVectorIndex.from_documents(documents) # get path for GUI gui_dir = os.path.join(os.path.dirname(__file__), 'gui') if not os.path.exists(gui_dir): gui_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'gui') # start server server = Flask(__name__, static_folder=gui_dir, template_folder=gui_dir) # initialise index initialise_index() @server.route('/') def landing(): return render_template('index.html') @server.route('/query', methods=['POST']) def query(): global index data = request.json response = index.query(data["input"]) return jsonify({'query': data["input"], 'response': str(response), 'source': response.get_formatted_sources()})

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from llama_index.callbacks import CallbackManager, LlamaDebugHandler, CBEventType from llama_index import ListIndex, ServiceContext, SimpleDirectoryReader, VectorStoreIndex ''' Title of the page: A simple Python implementation of the ReAct pattern for LLMs Name of the website: LlamaIndex (GPT Index) is a data framework for your LLM application. URL: https://github.com/jerryjliu/llama_index ''' docs = SimpleDirectoryReader("../data/paul_graham/").load_data() from llama_index import ServiceContext, LLMPredictor, TreeIndex from langchain.chat_models import ChatOpenAI llm_predictor = LLMPredictor(llm=ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0)) llama_debug = LlamaDebugHandler(print_trace_on_end=True) callback_manager = CallbackManager([llama_debug]) service_context = ServiceContext.from_defaults(callback_manager=callback_manager, llm_predictor=llm_predictor) index = VectorStoreIndex.from_documents(docs, service_context=service_context) query_engine = index.as_query_engine() response = query_engine.query("What did the author do growing up?") # Print info on the LLM calls during the list index query print(llama_debug.get_event_time_info(CBEventType.LLM)) # Print info on llm inputs/outputs - returns start/end events for each LLM call event_pairs = llama_debug.get_llm_inputs_outputs() print(event_pairs[0][0]) print(event_pairs[0][1].payload.keys()) print(event_pairs[0][1].payload['response']) # Get info on any event type event_pairs = llama_debug.get_event_pairs(CBEventType.CHUNKING) print(event_pairs[0][0].payload.keys()) # get first chunking start event print(event_pairs[0][1].payload.keys()) # get first chunking end event # Clear the currently cached events llama_debug.flush_event_logs()

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import logging import os from llama_index import ( StorageContext, load_index_from_storage, ) from app.engine.constants import STORAGE_DIR from app.engine.context import create_service_context def get_chat_engine(): service_context = create_service_context() # check if storage already exists if not os.path.exists(STORAGE_DIR): raise Exception( "StorageContext is empty - call 'python app/engine/generate.py' to generate the storage first" ) logger = logging.getLogger("uvicorn") # load the existing index logger.info(f"Loading index from {STORAGE_DIR}...") storage_context = StorageContext.from_defaults(persist_dir=STORAGE_DIR) index = load_index_from_storage(storage_context, service_context=service_context) logger.info(f"Finished loading index from {STORAGE_DIR}") return index.as_chat_engine()

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"""Module for loading index.""" import logging from typing import TYPE_CHECKING, Any, Optional from llama_index import ServiceContext, StorageContext, load_index_from_storage from llama_index.indices.base import BaseIndex from ols.app.models.config import ReferenceContent # This is to avoid importing HuggingFaceBgeEmbeddings in all cases, because in # runtime it is used only under some conditions. OTOH we need to make Python # interpreter happy in all circumstances, hence the definiton of Any symbol. if TYPE_CHECKING: from langchain_community.embeddings import HuggingFaceBgeEmbeddings # TCH004 else: HuggingFaceBgeEmbeddings = Any logger = logging.getLogger(__name__) class IndexLoader: """Load index from local file storage.""" def __init__(self, index_config: Optional[ReferenceContent]) -> None: """Initialize loader.""" self._index: Optional[BaseIndex] = None self._index_config = index_config logger.debug(f"Config used for index load: {self._index_config}") if self._index_config is None: logger.warning("Config for reference content is not set.") else: self._index_path = self._index_config.product_docs_index_path self._index_id = self._index_config.product_docs_index_id self._embed_model_path = self._index_config.embeddings_model_path self._embed_model = self._get_embed_model() self._load_index() def _get_embed_model(self) -> Optional[str | HuggingFaceBgeEmbeddings]: """Get embed model according to configuration.""" if self._embed_model_path is not None: from langchain_community.embeddings import HuggingFaceBgeEmbeddings logger.debug( f"Loading embedding model info from path {self._embed_model_path}" ) return HuggingFaceBgeEmbeddings(model_name=self._embed_model_path) logger.warning("Embedding model path is not set.") logger.warning("Embedding model is set to default") return "local:BAAI/bge-base-en" def _set_context(self) -> None: """Set storage/service context required for index load.""" logger.debug(f"Using {self._embed_model!s} as embedding model for index.") logger.info("Setting up service context for index load...") self._service_context = ServiceContext.from_defaults( embed_model=self._embed_model, llm=None ) logger.info("Setting up storage context for index load...") self._storage_context = StorageContext.from_defaults( persist_dir=self._index_path ) def _load_index(self) -> None: """Load vector index.""" if self._index_path is None: logger.warning("Index path is not set.") else: try: self._set_context() logger.info("Loading vector index...") self._index = load_index_from_storage( service_context=self._service_context, storage_context=self._storage_context, index_id=self._index_id, ) logger.info("Vector index is loaded.") except Exception as err: logger.exception(f"Error loading vector index:\n{err}") @property def vector_index(self) -> Optional[BaseIndex]: """Get index.""" if self._index is None: logger.warning( "Proceeding without RAG content. " "Either there is an error or required parameters are not set." ) return self._index

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from llama_index import PromptTemplate instruction_str = """\ 1. Convert the query to executable Python code using Pandas. 2. The final line of code should be a Python expression that can be called with the `eval()` function. 3. The code should represent a solution to the query. 4. PRINT ONLY THE EXPRESSION. 5. Do not quote the expression.""" new_prompt = PromptTemplate( """\ You are working with a pandas dataframe in Python. The name of the dataframe is `df`. This is the result of `print(df.head())`: {df_str} Follow these instructions: {instruction_str} Query: {query_str} Expression: """ ) context = """Purpose: The primary role of this agent is to assist users by providing accurate information about world population statistics and details about a country. """

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import os, shutil, datetime, time, json import gradio as gr import sys import os from llama_index import GPTSimpleVectorIndex bank_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../memory_bank') sys.path.append(bank_path) from build_memory_index import build_memory_index memory_bank_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../memory_bank') sys.path.append(memory_bank_path) from summarize_memory import summarize_memory def enter_name(name, memory,local_memory_qa,data_args,update_memory_index=True): cur_date = datetime.date.today().strftime("%Y-%m-%d") user_memory_index = None if isinstance(data_args,gr.State): data_args = data_args.value if isinstance(memory,gr.State): memory = memory.value if isinstance(local_memory_qa,gr.State): local_memory_qa=local_memory_qa.value memory_dir = os.path.join(data_args.memory_basic_dir,data_args.memory_file) if name in memory.keys(): user_memory = memory[name] memory_index_path = os.path.join(data_args.memory_basic_dir,f'memory_index/{name}_index') os.makedirs(os.path.dirname(memory_index_path), exist_ok=True) if (not os.path.exists(memory_index_path)) or update_memory_index: print(f'Initializing memory index {memory_index_path}...') # filepath = input("Input your local knowledge file path 请输入本地知识文件路径：") if os.path.exists(memory_index_path): shutil.rmtree(memory_index_path) memory_index_path, _ = local_memory_qa.init_memory_vector_store(filepath=memory_dir,vs_path=memory_index_path,user_name=name,cur_date=cur_date) user_memory_index = local_memory_qa.load_memory_index(memory_index_path) if memory_index_path else None msg = f"欢迎回来，{name}！" if data_args.language=='cn' else f"Wellcome Back, {name}！" return msg,user_memory,memory, name,user_memory_index else: memory[name] = {} memory[name].update({"name":name}) msg = f"欢迎新用户{name}！我会记住你的名字，下次见面就能叫你的名字啦！" if data_args.language == 'cn' else f'Welcome, new user {name}! I will remember your name, so next time we meet, I\'ll be able to call you by your name!' return msg,memory[name],memory,name,user_memory_index def enter_name_llamaindex(name, memory, data_args, update_memory_index=True): user_memory_index = None if name in memory.keys(): user_memory = memory[name] memory_index_path = os.path.join(data_args.memory_basic_dir,f'memory_index/{name}_index.json') if not os.path.exists(memory_index_path) or update_memory_index: print(f'Initializing memory index {memory_index_path}...') build_memory_index(memory,data_args,name=name) if os.path.exists(memory_index_path): user_memory_index = GPTSimpleVectorIndex.load_from_disk(memory_index_path) print(f'Successfully load memory index for user {name}!') return f"Wellcome Back, {name}！",user_memory,user_memory_index else: memory[name] = {} memory[name].update({"name":name}) return f"Welcome new user{name}！I will remember your name and call you by your name in the next conversation",memory[name],user_memory_index def summarize_memory_event_personality(data_args, memory, user_name): if isinstance(data_args,gr.State): data_args = data_args.value if isinstance(memory,gr.State): memory = memory.value memory_dir = os.path.join(data_args.memory_basic_dir,data_args.memory_file) memory = summarize_memory(memory_dir,user_name,language=data_args.language) user_memory = memory[user_name] if user_name in memory.keys() else {} return user_memory#, user_memory_index def save_local_memory(memory,b,user_name,data_args): if isinstance(data_args,gr.State): data_args = data_args.value if isinstance(memory,gr.State): memory = memory.value memory_dir = os.path.join(data_args.memory_basic_dir,data_args.memory_file) date = time.strftime("%Y-%m-%d", time.localtime()) if memory[user_name].get("history") is None: memory[user_name].update({"history":{}}) if memory[user_name]['history'].get(date) is None: memory[user_name]['history'][date] = [] # date = len(memory[user_name]['history']) memory[user_name]['history'][date].append({'query':b[-1][0],'response':b[-1][1]}) json.dump(memory,open(memory_dir,"w",encoding="utf-8"),ensure_ascii=False) return memory

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from llama_index import ( ServiceContext, SimpleDirectoryReader, StorageContext, VectorStoreIndex, ) from llama_index.vector_stores.qdrant import QdrantVectorStore from tqdm import tqdm import arxiv import os import argparse import yaml import qdrant_client from langchain.embeddings.huggingface import HuggingFaceEmbeddings from llama_index.embeddings import LangchainEmbedding from llama_index import ServiceContext from llama_index.llms import Ollama class Data: def __init__(self, config): self.config = config def _create_data_folder(self, download_path): data_path = download_path if not os.path.exists(data_path): os.makedirs(self.config["data_path"]) print("Output folder created") else: print("Output folder already exists.") def download_papers(self, search_query, download_path, max_results): self._create_data_folder(download_path) client = arxiv.Client() search = arxiv.Search( query=search_query, max_results=max_results, sort_by=arxiv.SortCriterion.SubmittedDate, ) results = list(client.results(search)) for paper in tqdm(results): if os.path.exists(download_path): paper_title = (paper.title).replace(" ", "_") paper.download_pdf(dirpath=download_path, filename=f"{paper_title}.pdf") print(f"{paper.title} Downloaded.") def ingest(self, embedder, llm): print("Indexing data...") documents = SimpleDirectoryReader(self.config["data_path"]).load_data() client = qdrant_client.QdrantClient(url=self.config["qdrant_url"]) qdrant_vector_store = QdrantVectorStore( client=client, collection_name=self.config["collection_name"] ) storage_context = StorageContext.from_defaults(vector_store=qdrant_vector_store) # service_context = ServiceContext.from_defaults( # llm=llm, embed_model=embedder, chunk_size=self.config["chunk_size"] # ) service_context = ServiceContext.from_defaults( llm=None, embed_model=embedder, chunk_size=self.config["chunk_size"] ) index = VectorStoreIndex.from_documents( documents, storage_context=storage_context, service_context=service_context ) print( f"Data indexed successfully to Qdrant. Collection: {self.config['collection_name']}" ) return index if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "-q", "--query", type=str, default=False, help="Download papers from arxiv with this query.", ) # parser.add_argument( # "-o", "--output", type=str, default=False, help="Download path." # ) parser.add_argument( "-m", "--max", type=int, default=False, help="Max results to download." ) parser.add_argument( "-i", "--ingest", action=argparse.BooleanOptionalAction, default=False, help="Ingest data to Qdrant vector Database.", ) args = parser.parse_args() config_file = "config.yml" with open(config_file, "r") as conf: config = yaml.safe_load(conf) data = Data(config) if args.query: data.download_papers( search_query=args.query, download_path=config["data_path"], max_results=args.max, ) if args.ingest: print("Loading Embedder...") embed_model = LangchainEmbedding( HuggingFaceEmbeddings(model_name=config["embedding_model"]) ) llm = Ollama(model=config["llm_name"], base_url=config["llm_url"]) data.ingest(embedder=embed_model, llm=llm)

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from llama_index import SimpleDirectoryReader, VectorStoreIndex, load_index_from_storage from llama_index.storage.storage_context import StorageContext from llama_index.indices.service_context import ServiceContext from llama_index.llms import OpenAI from llama_index.node_parser import SimpleNodeParser from llama_index.node_parser.extractors import ( MetadataExtractor, SummaryExtractor, QuestionsAnsweredExtractor, TitleExtractor, KeywordExtractor, ) from llama_index.text_splitter import TokenTextSplitter from dotenv import load_dotenv import openai import gradio as gr import sys, os import logging import json #loads dotenv lib to retrieve API keys from .env file load_dotenv() openai.api_key = os.getenv("OPENAI_API_KEY") # enable INFO level logging logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) #define LLM service llm = OpenAI(temperature=0.1, model_name="gpt-3.5-turbo", max_tokens=512) service_context = ServiceContext.from_defaults(llm=llm) #construct text splitter to split texts into chunks for processing text_splitter = TokenTextSplitter(separator=" ", chunk_size=512, chunk_overlap=128) #set the global service context object, avoiding passing service_context when building the index from llama_index import set_global_service_context set_global_service_context(service_context) #create metadata extractor metadata_extractor = MetadataExtractor( extractors=[ TitleExtractor(nodes=1, llm=llm), QuestionsAnsweredExtractor(questions=3, llm=llm), SummaryExtractor(summaries=["prev", "self"], llm=llm), KeywordExtractor(keywords=10, llm=llm) ], ) #create node parser to parse nodes from document node_parser = SimpleNodeParser( text_splitter=text_splitter, metadata_extractor=metadata_extractor, ) #loading documents documents_2022 = SimpleDirectoryReader(input_files=["data/executive-summary-2022.pdf"], filename_as_id=True).load_data() print(f"loaded documents_2022 with {len(documents_2022)} pages") documents_2021 = SimpleDirectoryReader(input_files=["data/executive-summary-2021.pdf"], filename_as_id=True).load_data() print(f"loaded documents_2021 with {len(documents_2021)} pages") def load_index(): try: #load storage context storage_context = StorageContext.from_defaults(persist_dir="./storage") #try to load the index from storage index = load_index_from_storage(storage_context) logging.info("Index loaded from storage.") except FileNotFoundError: #if index not found, create a new one logging.info("Index not found. Creating a new one...") nodes_2022 = node_parser.get_nodes_from_documents(documents_2022) nodes_2021 = node_parser.get_nodes_from_documents(documents_2021) print(f"loaded nodes_2022 with {len(nodes_2022)} nodes") print(f"loaded nodes_2021 with {len(nodes_2021)} nodes") #print metadata in json format for node in nodes_2022: metadata_json = json.dumps(node.metadata, indent=4) # Convert metadata to formatted JSON print(metadata_json) for node in nodes_2021: metadata_json = json.dumps(node.metadata, indent=4) # Convert metadata to formatted JSON print(metadata_json) #based on the nodes and service_context, create index index = VectorStoreIndex(nodes=nodes_2022 + nodes_2021, service_context=service_context) # Persist index to disk index.storage_context.persist() logging.info("New index created and persisted to storage.") return index def data_querying(input_text): # Load index index = load_index() #queries the index with the input text response = index.as_query_engine().query(input_text) return response.response iface = gr.Interface(fn=data_querying, inputs=gr.components.Textbox(lines=3, label="Enter your question"), outputs="text", title="Analyzing the U.S. Government's Financial Reports for 2022") iface.launch(share=False)

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# qa_template.py from llama_index import QuestionAnswerPrompt # define custom QuestionAnswerPrompt QA_PROMPT_TMPL = ( "We have provided context information below. \n" "---------------------\n" "{context_str}" "\n---------------------\n" "Given this context information, please answer the question: {query_str} under a header # 'Based on the notes' \n" "additionally, create a section under a header ## 'In addition with love from AI' that extends the answer, but does not repeat information from the context. \n" "Provide the final answer in Markdown compliant presentation \n" ) QA_PROMPT = QuestionAnswerPrompt(QA_PROMPT_TMPL)

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from typing import Any, Optional, Sequence, Type, cast from llama_index.data_structs.data_structs_v2 import ( IndexDict, OpensearchIndexDict, ) from llama_index.data_structs.node_v2 import Node from llama_index.indices.base import BaseGPTIndex, QueryMap from llama_index.indices.query.schema import QueryMode from llama_index.indices.service_context import ServiceContext from llama_index.indices.vector_store.base import GPTVectorStoreIndex from llama_index_fix.elasticsearch import ElasticsearchVectorStore, ElasticsearchVectorClient class GPTElasticsearchIndex(GPTVectorStoreIndex): index_struct_cls: Type[IndexDict] = OpensearchIndexDict def __init__( self, nodes: Optional[Sequence[Node]] = None, service_context: Optional[ServiceContext] = None, client: Optional[ElasticsearchVectorClient] = None, index_struct: Optional[IndexDict] = None, **kwargs: Any, ) -> None: """Init params.""" if client is None: raise ValueError("client is required.") vector_store = ElasticsearchVectorStore(client) super().__init__( nodes=nodes, index_struct=index_struct, service_context=service_context, vector_store=vector_store, **kwargs, ) @classmethod def get_query_map(self) -> QueryMap: """Get query map.""" return { QueryMode.DEFAULT: GPTOpensearchIndexQuery, QueryMode.EMBEDDING: GPTOpensearchIndexQuery, } def _preprocess_query(self, mode: QueryMode, query_kwargs: Any) -> None: """Preprocess query.""" super()._preprocess_query(mode, query_kwargs) del query_kwargs["vector_store"] vector_store = cast(ElasticsearchVectorStore, self._vector_store) query_kwargs["client"] = vector_store._client

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import os from typing import Any, Callable, Dict, Optional, Sequence from llama_index.bridge.pydantic import Field, PrivateAttr from llama_index.core.llms.types import ( ChatMessage, ChatResponse, ChatResponseGen, CompletionResponse, CompletionResponseGen, LLMMetadata, ) from llama_index.llms.base import llm_chat_callback, llm_completion_callback from llama_index.llms.custom import CustomLLM from llama_index.llms.generic_utils import ( completion_response_to_chat_response, stream_completion_response_to_chat_response, ) from llama_index.types import BaseOutputParser, PydanticProgramMode from llama_index.utils import get_cache_dir from byzerllm.utils.client import ByzerLLM class ByzerAI(CustomLLM): """ ByzerAI is a custom LLM that uses the ByzerLLM API to generate text. """ verbose: bool = Field( default=False, description="Whether to print verbose output.", ) _model: ByzerLLM = PrivateAttr() def __init__( self, llm:ByzerLLM ) -> None: self._model = llm super().__init__() @classmethod def class_name(cls) -> str: return "ByzerAI_llm" @property def metadata(self) -> LLMMetadata: """LLM metadata.""" return LLMMetadata( context_window=8024, num_output=2048, model_name=self._model.default_model_name, ) @llm_chat_callback() def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: conversations = [{ "role":message.role, "content":message.content } for message in messages] m = self._model.chat_oai(conversations=conversations) completion_response = CompletionResponse(text=m[0].output, raw=None) return completion_response_to_chat_response(completion_response) @llm_chat_callback() def stream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseGen: conversations = [{ "role":message.role, "content":message.content } for message in messages] m = self._model.stream_chat_oai(conversations=conversations) def gen(): v = "" for response in m: text:str = response[0] metadata:Dict[str,Any] = response[1] completion_response = CompletionResponse(text=text, delta=text[len(v):], raw=None) v = text yield completion_response return stream_completion_response_to_chat_response(gen()) @llm_completion_callback() def complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponse: m = self._model.chat_oai(conversations=[{"role":"user","content":prompt}]) completion_response = CompletionResponse(text=m[0].output, raw=None) return completion_response @llm_completion_callback() def stream_complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponseGen: conversations=[{"role":"user","content":prompt}] m = self._model.stream_chat_oai(conversations=conversations) def gen(): v = "" for response in m: text:str = response[0] metadata:Dict[str,Any] = response[1] completion_response = CompletionResponse(text=text, delta=text[len(v):], raw=None) v = text yield completion_response return gen()

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from byzerllm.utils.client import ByzerLLM from byzerllm.utils.retrieval import ByzerRetrieval from byzerllm.apps.llama_index.byzerai import ByzerAI from byzerllm.apps.llama_index.byzerai_embedding import ByzerAIEmbedding from byzerllm.apps.llama_index.byzerai_docstore import ByzerAIDocumentStore from byzerllm.apps.llama_index.byzerai_index_store import ByzerAIIndexStore from byzerllm.apps.llama_index.byzerai_vectordb import ByzerAIVectorStore from llama_index.service_context import ServiceContext from llama_index.storage import StorageContext from typing import Optional def get_service_context(llm:ByzerLLM,**kargs): service_context = ServiceContext.from_defaults(llm=ByzerAI(llm=llm),embed_model=ByzerAIEmbedding(llm=llm),**kargs) return service_context def get_storage_context(llm:ByzerLLM,retrieval:ByzerRetrieval, chunk_collection:Optional[str]="default", namespace:Optional[str]=None, **kargs): vector_store = ByzerAIVectorStore(llm=llm, retrieval=retrieval,chunk_collection=chunk_collection) docstore = ByzerAIDocumentStore(llm=llm, retrieval=retrieval,namespace=namespace) index_store = ByzerAIIndexStore(llm=llm, retrieval=retrieval,namespace=namespace) storage_context = StorageContext.from_defaults( docstore=docstore, vector_store=vector_store, index_store=index_store, **kargs ) return storage_context

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#model_settings.py import streamlit as st from langchain.embeddings.huggingface import HuggingFaceEmbeddings from llama_index import LangchainEmbedding, LLMPredictor, PromptHelper, OpenAIEmbedding, ServiceContext from llama_index.logger import LlamaLogger from langchain.chat_models import ChatOpenAI from langchain import OpenAI from enum import Enum class sentenceTransformers(Enum): OPTION1 = "sentence-transformers/all-MiniLM-L6-v2" #default OPTION2 = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2" OPTION3 = "sentence-transformers/all-mpnet-base-v2" def get_sentence_transformer_dropdown(): options = [e.value for e in sentenceTransformers] selected_option = st.selectbox("Sentence transformer:", options) return selected_option def get_embed_model(provider='Langchain', model_name=sentenceTransformers.OPTION1.value): # load in HF embedding model from langchain embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name=model_name)) if provider=='Langchain' else OpenAIEmbedding() return embed_model def get_prompt_helper(): # define prompt helper max_input_size = 4096 num_output = 2048 max_chunk_overlap = 20 prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap) return prompt_helper def get_llm_predictor(): # define LLM num_output = 2048 #llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo", max_tokens=num_output)) llm_predictor = LLMPredictor(ChatOpenAI(temperature=0.1, model_name="gpt-3.5-turbo", max_tokens=num_output)) return llm_predictor @st.cache_resource def get_logger(): llama_logger = LlamaLogger() return llama_logger def get_service_context(llm_predictor=get_llm_predictor(), embed_model=get_embed_model(), prompt_helper=get_prompt_helper(), chunk_size_limit=512, llama_logger=get_logger()): return ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model, prompt_helper=prompt_helper, chunk_size_limit=chunk_size_limit, llama_logger=llama_logger)

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from typing import Any, List, Optional, Sequence from llama_index.core.base.base_query_engine import BaseQueryEngine from llama_index.core.base.base_retriever import BaseRetriever from llama_index.core.base.response.schema import RESPONSE_TYPE from llama_index.core.callbacks.base import CallbackManager from llama_index.core.callbacks.schema import CBEventType, EventPayload from llama_index.core.indices.base import BaseGPTIndex from llama_index.core.llms.llm import LLM from llama_index.core.node_parser import SentenceSplitter, TextSplitter from llama_index.core.postprocessor.types import BaseNodePostprocessor from llama_index.core.prompts import PromptTemplate from llama_index.core.prompts.base import BasePromptTemplate from llama_index.core.prompts.mixin import PromptMixinType from llama_index.core.response_synthesizers import ( BaseSynthesizer, ResponseMode, get_response_synthesizer, ) from llama_index.core.schema import ( MetadataMode, NodeWithScore, QueryBundle, TextNode, ) from llama_index.core.settings import ( Settings, callback_manager_from_settings_or_context, llm_from_settings_or_context, ) CITATION_QA_TEMPLATE = PromptTemplate( "Please provide an answer based solely on the provided sources. " "When referencing information from a source, " "cite the appropriate source(s) using their corresponding numbers. " "Every answer should include at least one source citation. " "Only cite a source when you are explicitly referencing it. " "If none of the sources are helpful, you should indicate that. " "For example:\n" "Source 1:\n" "The sky is red in the evening and blue in the morning.\n" "Source 2:\n" "Water is wet when the sky is red.\n" "Query: When is water wet?\n" "Answer: Water will be wet when the sky is red [2], " "which occurs in the evening [1].\n" "Now it's your turn. Below are several numbered sources of information:" "\n------\n" "{context_str}" "\n------\n" "Query: {query_str}\n" "Answer: " ) CITATION_REFINE_TEMPLATE = PromptTemplate( "Please provide an answer based solely on the provided sources. " "When referencing information from a source, " "cite the appropriate source(s) using their corresponding numbers. " "Every answer should include at least one source citation. " "Only cite a source when you are explicitly referencing it. " "If none of the sources are helpful, you should indicate that. " "For example:\n" "Source 1:\n" "The sky is red in the evening and blue in the morning.\n" "Source 2:\n" "Water is wet when the sky is red.\n" "Query: When is water wet?\n" "Answer: Water will be wet when the sky is red [2], " "which occurs in the evening [1].\n" "Now it's your turn. " "We have provided an existing answer: {existing_answer}" "Below are several numbered sources of information. " "Use them to refine the existing answer. " "If the provided sources are not helpful, you will repeat the existing answer." "\nBegin refining!" "\n------\n" "{context_msg}" "\n------\n" "Query: {query_str}\n" "Answer: " ) DEFAULT_CITATION_CHUNK_SIZE = 512 DEFAULT_CITATION_CHUNK_OVERLAP = 20 class CitationQueryEngine(BaseQueryEngine): """Citation query engine. Args: retriever (BaseRetriever): A retriever object. response_synthesizer (Optional[BaseSynthesizer]): A BaseSynthesizer object. citation_chunk_size (int): Size of citation chunks, default=512. Useful for controlling granularity of sources. citation_chunk_overlap (int): Overlap of citation nodes, default=20. text_splitter (Optional[TextSplitter]): A text splitter for creating citation source nodes. Default is a SentenceSplitter. callback_manager (Optional[CallbackManager]): A callback manager. metadata_mode (MetadataMode): A MetadataMode object that controls how metadata is included in the citation prompt. """ def __init__( self, retriever: BaseRetriever, llm: Optional[LLM] = None, response_synthesizer: Optional[BaseSynthesizer] = None, citation_chunk_size: int = DEFAULT_CITATION_CHUNK_SIZE, citation_chunk_overlap: int = DEFAULT_CITATION_CHUNK_OVERLAP, text_splitter: Optional[TextSplitter] = None, node_postprocessors: Optional[List[BaseNodePostprocessor]] = None, callback_manager: Optional[CallbackManager] = None, metadata_mode: MetadataMode = MetadataMode.NONE, ) -> None: self.text_splitter = text_splitter or SentenceSplitter( chunk_size=citation_chunk_size, chunk_overlap=citation_chunk_overlap ) self._retriever = retriever service_context = retriever.get_service_context() callback_manager = ( callback_manager or callback_manager_from_settings_or_context(Settings, service_context) ) llm = llm or llm_from_settings_or_context(Settings, service_context) self._response_synthesizer = response_synthesizer or get_response_synthesizer( llm=llm, service_context=service_context, callback_manager=callback_manager, ) self._node_postprocessors = node_postprocessors or [] self._metadata_mode = metadata_mode for node_postprocessor in self._node_postprocessors: node_postprocessor.callback_manager = callback_manager super().__init__(callback_manager=callback_manager) @classmethod def from_args( cls, index: BaseGPTIndex, llm: Optional[LLM] = None, response_synthesizer: Optional[BaseSynthesizer] = None, citation_chunk_size: int = DEFAULT_CITATION_CHUNK_SIZE, citation_chunk_overlap: int = DEFAULT_CITATION_CHUNK_OVERLAP, text_splitter: Optional[TextSplitter] = None, citation_qa_template: BasePromptTemplate = CITATION_QA_TEMPLATE, citation_refine_template: BasePromptTemplate = CITATION_REFINE_TEMPLATE, retriever: Optional[BaseRetriever] = None, node_postprocessors: Optional[List[BaseNodePostprocessor]] = None, # response synthesizer args response_mode: ResponseMode = ResponseMode.COMPACT, use_async: bool = False, streaming: bool = False, # class-specific args metadata_mode: MetadataMode = MetadataMode.NONE, **kwargs: Any, ) -> "CitationQueryEngine": """Initialize a CitationQueryEngine object.". Args: index: (BastGPTIndex): index to use for querying llm: (Optional[LLM]): LLM object to use for response generation. citation_chunk_size (int): Size of citation chunks, default=512. Useful for controlling granularity of sources. citation_chunk_overlap (int): Overlap of citation nodes, default=20. text_splitter (Optional[TextSplitter]): A text splitter for creating citation source nodes. Default is a SentenceSplitter. citation_qa_template (BasePromptTemplate): Template for initial citation QA citation_refine_template (BasePromptTemplate): Template for citation refinement. retriever (BaseRetriever): A retriever object. service_context (Optional[ServiceContext]): A ServiceContext object. node_postprocessors (Optional[List[BaseNodePostprocessor]]): A list of node postprocessors. verbose (bool): Whether to print out debug info. response_mode (ResponseMode): A ResponseMode object. use_async (bool): Whether to use async. streaming (bool): Whether to use streaming. optimizer (Optional[BaseTokenUsageOptimizer]): A BaseTokenUsageOptimizer object. """ retriever = retriever or index.as_retriever(**kwargs) response_synthesizer = response_synthesizer or get_response_synthesizer( llm=llm, service_context=index.service_context, text_qa_template=citation_qa_template, refine_template=citation_refine_template, response_mode=response_mode, use_async=use_async, streaming=streaming, ) return cls( retriever=retriever, response_synthesizer=response_synthesizer, callback_manager=callback_manager_from_settings_or_context( Settings, index.service_context ), citation_chunk_size=citation_chunk_size, citation_chunk_overlap=citation_chunk_overlap, text_splitter=text_splitter, node_postprocessors=node_postprocessors, metadata_mode=metadata_mode, ) def _get_prompt_modules(self) -> PromptMixinType: """Get prompt sub-modules.""" return {"response_synthesizer": self._response_synthesizer} def _create_citation_nodes(self, nodes: List[NodeWithScore]) -> List[NodeWithScore]: """Modify retrieved nodes to be granular sources.""" new_nodes: List[NodeWithScore] = [] for node in nodes: text_chunks = self.text_splitter.split_text( node.node.get_content(metadata_mode=self._metadata_mode) ) for text_chunk in text_chunks: text = f"Source {len(new_nodes)+1}:\n{text_chunk}\n" new_node = NodeWithScore( node=TextNode.parse_obj(node.node), score=node.score ) new_node.node.text = text new_nodes.append(new_node) return new_nodes def retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: nodes = self._retriever.retrieve(query_bundle) for postprocessor in self._node_postprocessors: nodes = postprocessor.postprocess_nodes(nodes, query_bundle=query_bundle) return nodes async def aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: nodes = await self._retriever.aretrieve(query_bundle) for postprocessor in self._node_postprocessors: nodes = postprocessor.postprocess_nodes(nodes, query_bundle=query_bundle) return nodes @property def retriever(self) -> BaseRetriever: """Get the retriever object.""" return self._retriever def synthesize( self, query_bundle: QueryBundle, nodes: List[NodeWithScore], additional_source_nodes: Optional[Sequence[NodeWithScore]] = None, ) -> RESPONSE_TYPE: nodes = self._create_citation_nodes(nodes) return self._response_synthesizer.synthesize( query=query_bundle, nodes=nodes, additional_source_nodes=additional_source_nodes, ) async def asynthesize( self, query_bundle: QueryBundle, nodes: List[NodeWithScore], additional_source_nodes: Optional[Sequence[NodeWithScore]] = None, ) -> RESPONSE_TYPE: nodes = self._create_citation_nodes(nodes) return await self._response_synthesizer.asynthesize( query=query_bundle, nodes=nodes, additional_source_nodes=additional_source_nodes, ) def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE: """Answer a query.""" with self.callback_manager.event( CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str} ) as query_event: with self.callback_manager.event( CBEventType.RETRIEVE, payload={EventPayload.QUERY_STR: query_bundle.query_str}, ) as retrieve_event: nodes = self.retrieve(query_bundle) nodes = self._create_citation_nodes(nodes) retrieve_event.on_end(payload={EventPayload.NODES: nodes}) response = self._response_synthesizer.synthesize( query=query_bundle, nodes=nodes, ) query_event.on_end(payload={EventPayload.RESPONSE: response}) return response async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE: """Answer a query.""" with self.callback_manager.event( CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str} ) as query_event: with self.callback_manager.event( CBEventType.RETRIEVE, payload={EventPayload.QUERY_STR: query_bundle.query_str}, ) as retrieve_event: nodes = await self.aretrieve(query_bundle) nodes = self._create_citation_nodes(nodes) retrieve_event.on_end(payload={EventPayload.NODES: nodes}) response = await self._response_synthesizer.asynthesize( query=query_bundle, nodes=nodes, ) query_event.on_end(payload={EventPayload.RESPONSE: response}) return response

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""" # My first app Here's our first attempt at using data to create a table: """ import logging import sys import streamlit as st from clickhouse_connect import common from llama_index.core.settings import Settings from llama_index.embeddings.fastembed import FastEmbedEmbedding from llama_index.llms.openai import OpenAI from llama_index.core import VectorStoreIndex, PromptTemplate from llama_index.core.indices.struct_store import NLSQLTableQueryEngine from llama_index.core.indices.vector_store import VectorIndexAutoRetriever from llama_index.core.indices.vector_store.retrievers.auto_retriever.prompts import PREFIX, EXAMPLES from llama_index.core.prompts import PromptType from llama_index.core.query_engine import RetrieverQueryEngine, SQLAutoVectorQueryEngine from llama_index.core.tools import QueryEngineTool from llama_index.core.vector_stores.types import VectorStoreInfo, MetadataInfo from llama_index.vector_stores.clickhouse import ClickHouseVectorStore import clickhouse_connect import openai from sqlalchemy import ( create_engine, ) from llama_index.core import SQLDatabase logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) host = st.secrets.clickhouse.host password = st.secrets.clickhouse.password username = st.secrets.clickhouse.username secure = st.secrets.clickhouse.secure http_port = st.secrets.clickhouse.http_port native_port = st.secrets.clickhouse.native_port open_ai_model = "gpt-4" database = st.secrets.clickhouse.database hackernews_table = st.secrets.clickhouse.hackernews_table stackoverflow_table = st.secrets.clickhouse.stackoverflow_table database = st.secrets.clickhouse.database st.set_page_config( page_title="Get summaries of Hacker News posts enriched with Stackoverflow survey results, powered by LlamaIndex and ClickHouse", page_icon="🦙🚀", layout="centered", initial_sidebar_state="auto", menu_items=None) st.title("💬HackBot powered by LlamaIndex 🦙 and ClickHouse 🚀") st.info( "Check out the full [blog post](https://clickhouse.com/blog/building-a-hackernews-chat-bot-with-llama-index-with-clickhouse/) for this app", icon="📃") st.caption("A Streamlit chatbot 💬 for Hacker News powered by LlamaIndex 🦙 and ClickHouse 🚀") @st.cache_resource def load_embedding(): return FastEmbedEmbedding( model_name="sentence-transformers/all-MiniLM-L6-v2", max_length=384, cache_dir="./embeddings/" ) Settings.embed_model = load_embedding() CLICKHOUSE_TEXT_TO_SQL_TMPL = ( "Given an input question, first create a syntactically correct ClickHouse SQL " "query to run, then look at the results of the query and return the answer. " "You can order the results by a relevant column to return the most " "interesting examples in the database.\n\n" "Never query for all the columns from a specific table, only ask for a " "few relevant columns given the question.\n\n" "Pay attention to use only the column names that you can see in the schema " "description. " "Be careful to not query for columns that do not exist. " "Pay attention to which column is in which table. " "Also, qualify column names with the table name when needed. \n" "If needing to group on Array Columns use the ClickHouse function arrayJoin e.g. arrayJoin(columnName) \n" "For example, the following query identifies the most popular database:\n" "SELECT d, count(*) AS count FROM so_surveys GROUP BY " "arrayJoin(database_want_to_work_with) AS d ORDER BY count DESC LIMIT 1\n" "You are required to use the following format, each taking one line:\n\n" "Question: Question here\n" "SQLQuery: SQL Query to run\n" "SQLResult: Result of the SQLQuery\n" "Answer: Final answer here\n\n" "Only use tables listed below.\n" "{schema}\n\n" "Question: {query_str}\n" "SQLQuery: " ) CLICKHOUSE_TEXT_TO_SQL_PROMPT = PromptTemplate( CLICKHOUSE_TEXT_TO_SQL_TMPL, prompt_type=PromptType.TEXT_TO_SQL, ) CLICKHOUSE_CUSTOM_SUFFIX = """ The following is the datasource schema to work with. IMPORTANT: Make sure that filters are only used as needed and only suggest filters for fields in the data source. Data Source: ```json {info_str} ``` User Query: {query_str} Structured Request: """ CLICKHOUSE_VECTOR_STORE_QUERY_PROMPT_TMPL = PREFIX + EXAMPLES + CLICKHOUSE_CUSTOM_SUFFIX @st.cache_resource def clickhouse(): common.set_setting('autogenerate_session_id', False) return clickhouse_connect.get_client( host=host, port=http_port, username=username, password=password, secure=secure, settings={"max_parallel_replicas": "3", "use_hedged_requests": "0", "allow_experimental_parallel_reading_from_replicas": "1"} ) def sql_auto_vector_query_engine(): with st.spinner(text="Preparing indexes. This should take a few seconds. No time to make 🫖"): engine = create_engine( f'clickhouse+native://{username}:{password}@{host}:' + f'{native_port}/{database}?compression=lz4&secure={secure}' ) sql_database = SQLDatabase(engine, include_tables=[stackoverflow_table], view_support=True) vector_store = ClickHouseVectorStore(clickhouse_client=clickhouse(), table=hackernews_table) vector_index = VectorStoreIndex.from_vector_store(vector_store) return sql_database, vector_index def get_engine(min_length, score, min_date): sql_database, vector_index = sql_auto_vector_query_engine() nl_sql_engine = NLSQLTableQueryEngine( sql_database=sql_database, tables=[stackoverflow_table], text_to_sql_prompt=CLICKHOUSE_TEXT_TO_SQL_PROMPT, llm=OpenAI(model=open_ai_model) ) vector_store_info = VectorStoreInfo( content_info="Social news posts and comments from users", metadata_info=[ MetadataInfo( name="post_score", type="int", description="Score of the comment or post", ), MetadataInfo( name="by", type="str", description="the author or person who posted the comment", ), MetadataInfo( name="time", type="date", description="the time at which the post or comment was made", ), ] ) vector_auto_retriever = VectorIndexAutoRetriever( vector_index, vector_store_info=vector_store_info, similarity_top_k=10, prompt_template_str=CLICKHOUSE_VECTOR_STORE_QUERY_PROMPT_TMPL, llm=OpenAI(model=open_ai_model), vector_store_kwargs={"where": f"length >= {min_length} AND post_score >= {score} AND time >= '{min_date}'"} ) retriever_query_engine = RetrieverQueryEngine.from_args(vector_auto_retriever, llm=OpenAI(model=open_ai_model)) sql_tool = QueryEngineTool.from_defaults( query_engine=nl_sql_engine, description=( "Useful for translating a natural language query into a SQL query over" f" a table: {stackoverflow_table}, containing the survey responses on" f" different types of technology users currently use and want to use" ), ) vector_tool = QueryEngineTool.from_defaults( query_engine=retriever_query_engine, description=( f"Useful for answering semantic questions abouts users comments and posts" ), ) return SQLAutoVectorQueryEngine( sql_tool, vector_tool, llm=OpenAI(model=open_ai_model) ) # identify the value ranges for our score, length and date widgets if "max_score" not in st.session_state.keys(): client = clickhouse() st.session_state.max_score = int( client.query("SELECT max(post_score) FROM default.hackernews_llama").first_row[0]) st.session_state.max_length = int( client.query("SELECT max(length) FROM default.hackernews_llama").first_row[0]) st.session_state.min_date, st.session_state.max_date = client.query( "SELECT min(toDate(time)), max(toDate(time)) FROM default.hackernews_llama WHERE time != '1970-01-01 00:00:00'").first_row # set the initial message on load. Store in the session. if "messages" not in st.session_state: st.session_state.messages = [ {"role": "assistant", "content": "Ask me a question about opinions on Hacker News and Stackoverflow!"}] # build the sidebar with our filters with st.sidebar: score = st.slider('Min Score', 0, st.session_state.max_score, value=0) min_length = st.slider('Min comment Length (tokens)', 0, st.session_state.max_length, value=20) min_date = st.date_input('Min comment date', value=st.session_state.min_date, min_value=st.session_state.min_date, max_value=st.session_state.max_date) openai_api_key = st.text_input("Open API Key", key="chatbot_api_key", type="password") openai.api_key = openai_api_key "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)" "[View the source code](https://github.com/ClickHouse/examples/blob/main/blog-examples/llama-index/hacknernews_app/hacker_insights.py)" # grab the users OPENAI api key. Don’t allow questions if not entered. if not openai_api_key: st.info("Please add your OpenAI API key to continue.") st.stop() if prompt := st.chat_input(placeholder="Your question about Hacker News"): st.session_state.messages.append({"role": "user", "content": prompt}) # Display the prior chat messages for message in st.session_state.messages: # Display the prior chat messages with st.chat_message(message["role"]): st.write(message["content"]) # If last message is not from assistant, generate a new response if st.session_state.messages[-1]["role"] != "assistant": with st.chat_message("assistant"): with st.spinner("Thinking..."): # Query our engine for the answer and write to the page response = str(get_engine(min_length, score, min_date).query(prompt)) st.write(response) st.session_state.messages.append({"role": "assistant", "content": response})

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import chromadb import openai from dotenv import load_dotenv from langchain.chat_models import ChatOpenAI load_dotenv() from llama_index.llms import OpenAI from llama_index import VectorStoreIndex, ServiceContext from llama_index.vector_stores import ChromaVectorStore import os OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') openai.api_key = OPENAI_API_KEY print(OPENAI_API_KEY) client = chromadb.PersistentClient(path=".chromadb/") print(client.list_collections()) # get a collection collection_name = input("请输入要获取的collection name：") chroma_collection = client.get_collection(collection_name) print(chroma_collection.count()) # 创建 ChatOpenAI 实例作为底层语言模型 llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k-0613") service_context = ServiceContext.from_defaults(llm=llm) vector_store = ChromaVectorStore(chroma_collection=chroma_collection) index = VectorStoreIndex.from_vector_store(vector_store, service_context=service_context) query_engine = index.as_query_engine(service_context=service_context, verbose=True, streaming=True) while True: user_input = [] print("请输入您的问题（纯文本格式），换行输入 n 以结束：") while True: line = input() if line != "n": user_input.append(line) else: break user_input_text = "\n".join(user_input) # print(user_input_text) # print(user_input_text) print("****Thingking******") try: r = query_engine.query(user_input_text) print(r) except Exception as e: print("出现异常:", str(e))

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import tempfile import llama_index from llama_index import SimpleDirectoryReader import aiohttp from llama_index.readers.web import DEFAULT_WEBSITE_EXTRACTOR from models.statics_model import ResponseStatics, g_index, file_extensions_mappings def upload_doc_handler(knowledgebase_id, file): if not knowledgebase_id: return False # Check if knowledgebase exists if knowledgebase_id not in g_index: return False # Get the content type of the file content_type = "" if not file.content_type else file.content_type suffix = file_extensions_mappings[content_type] with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as fp: file.save(fp) doc = SimpleDirectoryReader(input_files=[fp.name]).load_data() g_index[knowledgebase_id].add_documents(doc) return True async def upload_link_handler(knowledgebase_id, url): if not knowledgebase_id: return False # Check if knowledgebase exists if knowledgebase_id not in g_index: return False async with aiohttp.ClientSession() as session: async with session.get(url) as response: if response.status != 200: return False if response.headers["Content-Type"] == "application/pdf": data = await response.read() f = tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) f.write(data) f.close() doc = SimpleDirectoryReader(input_files=[f.name]).load_data() g_index[knowledgebase_id].add_documents(doc) else: documents = llama_index.BeautifulSoupWebReader(website_extractor=DEFAULT_WEBSITE_EXTRACTOR).load_data(urls=[url]) g_index[knowledgebase_id].add_documents(documents) return True

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import os from dotenv import load_dotenv from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex, LLMPredictor from langchain.chat_models import ChatOpenAI load_dotenv() os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_KEY') def tune_llm(input_directory="sourcedata", output_file="indexdata/index.json"): loaded_content = SimpleDirectoryReader(input_directory).load_data() llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.7, model_name='gpt-3.5-turbo')) output_index = GPTSimpleVectorIndex(loaded_content, llm_predictor=llm_predictor) # Create the output directory if it doesn't exist os.makedirs(os.path.dirname(output_file), exist_ok=True) output_index.save_to_disk(output_file)

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from ..conversable_agent import ConversableAgent from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union from ....utils.client import ByzerLLM from byzerllm.utils.retrieval import ByzerRetrieval from ..agent import Agent import ray from ray.util.client.common import ClientActorHandle, ClientObjectRef from .. import get_agent_name,run_agent_func,ChatResponse from byzerllm.apps.agent.extensions.simple_retrieval_client import SimpleRetrievalClient import uuid import json from byzerllm.apps.llama_index import get_service_context,get_storage_context from llama_index import VectorStoreIndex from llama_index.query_engine import SubQuestionQueryEngine try: from termcolor import colored except ImportError: def colored(x, *args, **kwargs): return x from llama_index.tools import QueryEngineTool, ToolMetadata class LlamaIndexSubQuestionAgent(ConversableAgent): PROMPT_DEFAULT = """You're a retrieve augmented chatbot. """ DEFAULT_SYSTEM_MESSAGE = PROMPT_DEFAULT def __init__( self, name: str, llm: ByzerLLM, retrieval: ByzerRetrieval, chat_name:str, owner:str, update_context_retry: int = 3, system_message: Optional[str] = DEFAULT_SYSTEM_MESSAGE, is_termination_msg: Optional[Callable[[Dict], bool]] = None, max_consecutive_auto_reply: Optional[int] = None, human_input_mode: Optional[str] = "NEVER", code_execution_config: Optional[Union[Dict, bool]] = False, **kwargs, ): super().__init__( name, llm,retrieval, system_message, is_termination_msg, max_consecutive_auto_reply, human_input_mode, code_execution_config=code_execution_config, **kwargs, ) self.chat_name = chat_name self.owner = owner self.update_context_retry = update_context_retry self._reply_func_list = [] # self.register_reply([Agent, ClientActorHandle,str], ConversableAgent.generate_llm_reply) self.register_reply([Agent, ClientActorHandle,str], LlamaIndexSubQuestionAgent.generate_retrieval_based_reply) self.register_reply([Agent, ClientActorHandle,str], ConversableAgent.check_termination_and_human_reply) self.service_context = get_service_context(llm) self.storage_context = get_storage_context(llm,retrieval) def generate_retrieval_based_reply( self, raw_message: Optional[Union[Dict,str,ChatResponse]] = None, messages: Optional[List[Dict]] = None, sender: Optional[Union[ClientActorHandle,Agent,str]] = None, config: Optional[Any] = None, ) -> Tuple[bool, Union[str, Dict, None,ChatResponse]]: if messages is None: messages = self._messages[get_agent_name(sender)] new_message = messages[-1] index = VectorStoreIndex.from_vector_store(vector_store = self.storage_context.vector_store,service_context=self.service_context) vector_query_engine = index.as_query_engine() query_engine_tools = [ QueryEngineTool( query_engine=vector_query_engine, metadata=ToolMetadata( name="common", description="common", ), ), ] query_engine = SubQuestionQueryEngine.from_defaults( query_engine_tools=query_engine_tools, service_context=self.service_context, use_async=True, ) response = query_engine.query(new_message["content"]) return True, { "content":response.response, "metadata":{"agent":self.name,"TERMINATE":True} }

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# Copyright 2023 osiworx # Licensed under the Apache License, Version 2.0 (the "License"); you # may not use this file except in compliance with the License. You # may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # implied. See the License for the specific language governing # permissions and limitations under the License. import datetime import os from llama_index.embeddings import HuggingFaceEmbedding import qdrant_client from llama_index import ( VectorStoreIndex, ServiceContext, SimpleDirectoryReader, ) from llama_index.storage.storage_context import StorageContext from llama_index.vector_stores.qdrant import QdrantVectorStore client = qdrant_client.QdrantClient( # you can use :memory: mode for fast and light-weight experiments, # it does not require to have Qdrant deployed anywhere # but requires qdrant-client >= 1.1.1 #location=":memory:" # otherwise set Qdrant instance address with: url="http://localhost:6333" # set API KEY for Qdrant Cloud # api_key="<qdrant-api-key>", ) sample_files_path = "E:\prompt_sources\lexica_split" embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L12-v2") service_context = ServiceContext.from_defaults(llm=None,embed_model=embed_model) vector_store = QdrantVectorStore(client=client, collection_name="prompts_all") storage_context = StorageContext.from_defaults(vector_store=vector_store) for subdir, dirs, files in os.walk(sample_files_path): if len(files) > 0: now = datetime.datetime.now() print(f'{now.strftime("%H:%M:%S")} adding folder: {subdir}') documents = SimpleDirectoryReader(subdir).load_data() docs = [] for doc in documents: doc.excluded_llm_metadata_keys.append("file_path") doc.excluded_embed_metadata_keys.append("file_path") if doc.text != '': docs = docs + [doc] del documents index = VectorStoreIndex.from_documents( docs, storage_context=storage_context, service_context=service_context, show_progress=True )

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from typing import Union, Optional, List from llama_index.chat_engine.types import BaseChatEngine, ChatMode from llama_index.embeddings.utils import EmbedType from llama_index.chat_engine import ContextChatEngine from llama_index.memory import ChatMemoryBuffer from lyzr.base.llm import LyzrLLMFactory from lyzr.base.service import LyzrService from lyzr.base.vector_store import LyzrVectorStoreIndex from lyzr.base.retrievers import LyzrRetriever from lyzr.utils.document_reading import ( read_pdf_as_documents, read_docx_as_documents, read_txt_as_documents, read_website_as_documents, read_webpage_as_documents, read_youtube_as_documents, ) def pdf_chat_( input_dir: Optional[str] = None, input_files: Optional[List] = None, exclude_hidden: bool = True, filename_as_id: bool = True, recursive: bool = True, required_exts: Optional[List[str]] = None, system_prompt: str = None, query_wrapper_prompt: str = None, embed_model: Union[str, EmbedType] = "default", llm_params: dict = None, vector_store_params: dict = None, service_context_params: dict = None, chat_engine_params: dict = None, retriever_params: dict = None, ) -> BaseChatEngine: documents = read_pdf_as_documents( input_dir=input_dir, input_files=input_files, exclude_hidden=exclude_hidden, filename_as_id=filename_as_id, recursive=recursive, required_exts=required_exts, ) llm_params = ( { "model": "gpt-4-0125-preview", "temperature": 0, } if llm_params is None else llm_params ) vector_store_params = ( {"vector_store_type": "WeaviateVectorStore"} if vector_store_params is None else vector_store_params ) service_context_params = ( {} if service_context_params is None else service_context_params ) chat_engine_params = {} if chat_engine_params is None else chat_engine_params retriever_params = ( {"retriever_type": "QueryFusionRetriever"} if retriever_params is None else retriever_params ) llm = LyzrLLMFactory.from_defaults(**llm_params) service_context = LyzrService.from_defaults( llm=llm, embed_model=embed_model, system_prompt=system_prompt, query_wrapper_prompt=query_wrapper_prompt, **service_context_params, ) vector_store_index = LyzrVectorStoreIndex.from_defaults( **vector_store_params, documents=documents, service_context=service_context ) retriever = LyzrRetriever.from_defaults( **retriever_params, base_index=vector_store_index ) memory = ChatMemoryBuffer.from_defaults(token_limit=4000) chat_engine = ContextChatEngine( llm=llm, memory=memory, retriever=retriever, prefix_messages=list(), **chat_engine_params, ) return chat_engine def txt_chat_( input_dir: Optional[str] = None, input_files: Optional[List] = None, exclude_hidden: bool = True, filename_as_id: bool = True, recursive: bool = True, required_exts: Optional[List[str]] = None, system_prompt: str = None, query_wrapper_prompt: str = None, embed_model: Union[str, EmbedType] = "default", llm_params: dict = None, vector_store_params: dict = None, service_context_params: dict = None, chat_engine_params: dict = None, retriever_params: dict = None, ) -> BaseChatEngine: documents = read_txt_as_documents( input_dir=input_dir, input_files=input_files, exclude_hidden=exclude_hidden, filename_as_id=filename_as_id, recursive=recursive, required_exts=required_exts, ) llm_params = ( { "model": "gpt-4-0125-preview", "temperature": 0, } if llm_params is None else llm_params ) vector_store_params = ( {"vector_store_type": "WeaviateVectorStore"} if vector_store_params is None else vector_store_params ) service_context_params = ( {} if service_context_params is None else service_context_params ) chat_engine_params = {} if chat_engine_params is None else chat_engine_params retriever_params = ( {"retriever_type": "QueryFusionRetriever"} if retriever_params is None else retriever_params ) llm = LyzrLLMFactory.from_defaults(**llm_params) service_context = LyzrService.from_defaults( llm=llm, embed_model=embed_model, system_prompt=system_prompt, query_wrapper_prompt=query_wrapper_prompt, **service_context_params, ) vector_store_index = LyzrVectorStoreIndex.from_defaults( **vector_store_params, documents=documents, service_context=service_context ) retriever = LyzrRetriever.from_defaults( **retriever_params, base_index=vector_store_index ) memory = ChatMemoryBuffer.from_defaults(token_limit=4000) chat_engine = ContextChatEngine( llm=llm, memory=memory, retriever=retriever, prefix_messages=list(), **chat_engine_params, ) return chat_engine def docx_chat_( input_dir: Optional[str] = None, input_files: Optional[List] = None, exclude_hidden: bool = True, filename_as_id: bool = True, recursive: bool = True, required_exts: Optional[List[str]] = None, system_prompt: str = None, query_wrapper_prompt: str = None, embed_model: Union[str, EmbedType] = "default", llm_params: dict = None, vector_store_params: dict = None, service_context_params: dict = None, chat_engine_params: dict = None, retriever_params: dict = None, ) -> BaseChatEngine: documents = read_docx_as_documents( input_dir=input_dir, input_files=input_files, exclude_hidden=exclude_hidden, filename_as_id=filename_as_id, recursive=recursive, required_exts=required_exts, ) llm_params = ( { "model": "gpt-4-0125-preview", "temperature": 0, } if llm_params is None else llm_params ) vector_store_params = ( {"vector_store_type": "WeaviateVectorStore"} if vector_store_params is None else vector_store_params ) service_context_params = ( {} if service_context_params is None else service_context_params ) chat_engine_params = {} if chat_engine_params is None else chat_engine_params retriever_params = ( {"retriever_type": "QueryFusionRetriever"} if retriever_params is None else retriever_params ) llm = LyzrLLMFactory.from_defaults(**llm_params) service_context = LyzrService.from_defaults( llm=llm, embed_model=embed_model, system_prompt=system_prompt, query_wrapper_prompt=query_wrapper_prompt, **service_context_params, ) vector_store_index = LyzrVectorStoreIndex.from_defaults( **vector_store_params, documents=documents, service_context=service_context ) retriever = LyzrRetriever.from_defaults( **retriever_params, base_index=vector_store_index ) memory = ChatMemoryBuffer.from_defaults(token_limit=4000) chat_engine = ContextChatEngine( llm=llm, memory=memory, retriever=retriever, prefix_messages=list(), **chat_engine_params, ) return chat_engine def webpage_chat_( url: str = None, system_prompt: str = None, query_wrapper_prompt: str = None, embed_model: Union[str, EmbedType] = "default", llm_params: dict = None, vector_store_params: dict = None, service_context_params: dict = None, chat_engine_params: dict = None, retriever_params: dict = None, ) -> BaseChatEngine: documents = read_webpage_as_documents( url=url, ) llm_params = ( { "model": "gpt-4-0125-preview", "temperature": 0, } if llm_params is None else llm_params ) vector_store_params = ( {"vector_store_type": "WeaviateVectorStore"} if vector_store_params is None else vector_store_params ) service_context_params = ( {} if service_context_params is None else service_context_params ) chat_engine_params = {} if chat_engine_params is None else chat_engine_params retriever_params = ( {"retriever_type": "QueryFusionRetriever"} if retriever_params is None else retriever_params ) llm = LyzrLLMFactory.from_defaults(**llm_params) service_context = LyzrService.from_defaults( llm=llm, embed_model=embed_model, system_prompt=system_prompt, query_wrapper_prompt=query_wrapper_prompt, **service_context_params, ) vector_store_index = LyzrVectorStoreIndex.from_defaults( **vector_store_params, documents=documents, service_context=service_context ) retriever = LyzrRetriever.from_defaults( **retriever_params, base_index=vector_store_index ) memory = ChatMemoryBuffer.from_defaults(token_limit=4000) chat_engine = ContextChatEngine( llm=llm, memory=memory, retriever=retriever, prefix_messages=list(), **chat_engine_params, ) return chat_engine def website_chat_( url: str = None, system_prompt: str = None, query_wrapper_prompt: str = None, embed_model: Union[str, EmbedType] = "default", llm_params: dict = None, vector_store_params: dict = None, service_context_params: dict = None, chat_engine_params: dict = None, retriever_params: dict = None, ) -> BaseChatEngine: documents = read_website_as_documents( url=url, ) llm_params = ( { "model": "gpt-4-0125-preview", "temperature": 0, } if llm_params is None else llm_params ) vector_store_params = ( {"vector_store_type": "WeaviateVectorStore"} if vector_store_params is None else vector_store_params ) service_context_params = ( {} if service_context_params is None else service_context_params ) chat_engine_params = {} if chat_engine_params is None else chat_engine_params retriever_params = ( {"retriever_type": "QueryFusionRetriever"} if retriever_params is None else retriever_params ) llm = LyzrLLMFactory.from_defaults(**llm_params) service_context = LyzrService.from_defaults( llm=llm, embed_model=embed_model, system_prompt=system_prompt, query_wrapper_prompt=query_wrapper_prompt, **service_context_params, ) vector_store_index = LyzrVectorStoreIndex.from_defaults( **vector_store_params, documents=documents, service_context=service_context ) retriever = LyzrRetriever.from_defaults( **retriever_params, base_index=vector_store_index ) memory = ChatMemoryBuffer.from_defaults(token_limit=4000) chat_engine = ContextChatEngine( llm=llm, memory=memory, retriever=retriever, prefix_messages=list(), **chat_engine_params, ) return chat_engine def youtube_chat_( urls: List[str] = None, system_prompt: str = None, query_wrapper_prompt: str = None, embed_model: Union[str, EmbedType] = "default", llm_params: dict = None, vector_store_params: dict = None, service_context_params: dict = None, chat_engine_params: dict = None, retriever_params: dict = None, ) -> BaseChatEngine: documents = read_youtube_as_documents( urls=urls, ) llm_params = ( { "model": "gpt-4-0125-preview", "temperature": 0, } if llm_params is None else llm_params ) vector_store_params = ( {"vector_store_type": "WeaviateVectorStore"} if vector_store_params is None else vector_store_params ) service_context_params = ( {} if service_context_params is None else service_context_params ) chat_engine_params = {} if chat_engine_params is None else chat_engine_params retriever_params = ( {"retriever_type": "QueryFusionRetriever"} if retriever_params is None else retriever_params ) llm = LyzrLLMFactory.from_defaults(**llm_params) service_context = LyzrService.from_defaults( llm=llm, embed_model=embed_model, system_prompt=system_prompt, query_wrapper_prompt=query_wrapper_prompt, **service_context_params, ) vector_store_index = LyzrVectorStoreIndex.from_defaults( **vector_store_params, documents=documents, service_context=service_context ) retriever = LyzrRetriever.from_defaults( **retriever_params, base_index=vector_store_index ) memory = ChatMemoryBuffer.from_defaults(token_limit=4000) chat_engine = ContextChatEngine( llm=llm, memory=memory, retriever=retriever, prefix_messages=list(), **chat_engine_params, ) return chat_engine

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import json from util import rm_file from tqdm import tqdm import argparse from copy import deepcopy import os from util import JSONReader import openai from typing import List, Dict from llama_index import ( ServiceContext, OpenAIEmbedding, PromptHelper, VectorStoreIndex, set_global_service_context ) from llama_index.extractors import BaseExtractor from llama_index.ingestion import IngestionPipeline from llama_index.embeddings.cohereai import CohereEmbedding from llama_index.llms import OpenAI from llama_index.text_splitter import SentenceSplitter from llama_index.embeddings import HuggingFaceEmbedding,VoyageEmbedding,InstructorEmbedding from llama_index.postprocessor import FlagEmbeddingReranker from llama_index.schema import QueryBundle,MetadataMode class CustomExtractor(BaseExtractor): async def aextract(self, nodes) -> List[Dict]: metadata_list = [ { "title": ( node.metadata["title"] ), "source": ( node.metadata["source"] ), "published_at": ( node.metadata["published_at"] ) } for node in nodes ] return metadata_list if __name__ == '__main__': openai.api_key = os.environ.get("OPENAI_API_KEY", "your_openai_api_key") openai.base_url = "your_api_base" voyage_api_key = os.environ.get("VOYAGE_API_KEY", "your_voyage_api_key") cohere_api_key = os.environ.get("COHERE_API_KEY", "your_cohere_api_key") parser = argparse.ArgumentParser(description="running script.") parser.add_argument('--retriever', type=str, required=True, help='retriever name') parser.add_argument('--llm', type=str, required=False,default="gpt-3.5-turbo-1106", help='LLMs') parser.add_argument('--rerank', action='store_true',required=False,default=False, help='if rerank') parser.add_argument('--topk', type=int, required=False,default=10, help='Top K') parser.add_argument('--chunk_size', type=int, required=False,default=256, help='chunk_size') parser.add_argument('--context_window', type=int, required=False,default=2048, help='context_window') parser.add_argument('--num_output', type=int, required=False,default=256, help='num_output') args = parser.parse_args() model_name = args.retriever rerank = args.rerank top_k = args.topk save_model_name = model_name.split('/') llm = OpenAI(model=args.llm, temperature=0, max_tokens=args.context_window) # define save file if rerank: save_file = f'output/{save_model_name[-1]}_rerank_retrieval_test.json' else: save_file = f'output/{save_model_name[-1]}_retrieval_test.json' rm_file(save_file) print(f'save_file:{save_file}') if 'text' in model_name: # "text-embedding-ada-002" “text-search-ada-query-001” embed_model = OpenAIEmbedding(model = model_name,embed_batch_size=10) elif 'Cohere' in model_name: embed_model = CohereEmbedding( cohere_api_key=cohere_api_key, model_name="embed-english-v3.0", input_type="search_query", ) elif 'voyage-02' in model_name: embed_model = VoyageEmbedding( model_name='voyage-02', voyage_api_key=voyage_api_key ) elif 'instructor' in model_name: embed_model = InstructorEmbedding(model_name=model_name) else: embed_model = HuggingFaceEmbedding(model_name=model_name, trust_remote_code=True) # service context text_splitter = SentenceSplitter(chunk_size=args.chunk_size) prompt_helper = PromptHelper( context_window=args.context_window, num_output=args.num_output, chunk_overlap_ratio=0.1, chunk_size_limit=None, ) service_context = ServiceContext.from_defaults( llm=llm, embed_model=embed_model, text_splitter=text_splitter, prompt_helper=prompt_helper, ) set_global_service_context(service_context) reader = JSONReader() data = reader.load_data('dataset/corpus.json') # print(data[0]) transformations = [text_splitter,CustomExtractor()] pipeline = IngestionPipeline(transformations=transformations) nodes = pipeline.run(documents=data) nodes_see = deepcopy(nodes) print( "LLM sees:\n", (nodes_see)[0].get_content(metadata_mode=MetadataMode.LLM), ) print('Finish Loading...') index = VectorStoreIndex(nodes, show_progress=True) print('Finish Indexing...') with open('dataset/MultiHopRAG.json', 'r') as file: query_data = json.load(file) if rerank: rerank_postprocessors = FlagEmbeddingReranker(model="BAAI/bge-reranker-large", top_n=top_k) # test retrieval quality retrieval_save_list = [] print("start to retrieve...") for data in tqdm(query_data): query = data['query'] if rerank: nodes_score = index.as_retriever(similarity_top_k=20).retrieve(query) nodes_score = rerank_postprocessors.postprocess_nodes( nodes_score, query_bundle=QueryBundle(query_str=query) ) else: nodes_score = index.as_retriever(similarity_top_k=top_k).retrieve(query) retrieval_list = [] for ns in nodes_score: dic = {} dic['text'] = ns.get_content(metadata_mode=MetadataMode.LLM) dic['score'] = ns.get_score() retrieval_list.append(dic) save = {} save['query'] = data['query'] save['answer'] = data['answer'] save['question_type'] = data['question_type'] save['retrieval_list'] = retrieval_list save['gold_list'] = data['evidence_list'] retrieval_save_list.append(save) with open(save_file, 'w') as json_file: json.dump(retrieval_save_list, json_file)

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import pinecone import torch import numpy as np import torchvision.transforms as T from PIL import Image import os import tqdm import openai import hashlib import io from gradio_client import Client from monitor import Monitor, monitoring from llama_index.vector_stores import PineconeVectorStore from llama_index import VectorStoreIndex # from llama_index.storage.storage_context import StorageContext # from llama_index.vector_stores import PineconeVectorStore # from llama_index.llms import OpenAI # from llama_index import ( # VectorStoreIndex, # SimpleWebPageReader, # LLMPredictor, # ServiceContext # ) # from trulens_eval import TruLlama, Feedback, Tru, feedback # from trulens_eval.feedback import GroundTruthAgreement, Groundedness from pathlib import Path from trulens_eval import Feedback, Tru, TruLlama from trulens_eval.feedback import Groundedness from trulens_eval.feedback.provider.openai import OpenAI tru = Tru() import numpy as np # Initialize provider class openai_tl = OpenAI() grounded = Groundedness(groundedness_provider=OpenAI()) # Define a groundedness feedback function f_groundedness = Feedback(grounded.groundedness_measure_with_cot_reasons).on( TruLlama.select_source_nodes().node.text ).on_output( ).aggregate(grounded.grounded_statements_aggregator) # Question/answer relevance between overall question and answer. f_qa_relevance = Feedback(openai_tl.relevance).on_input_output() # Question/statement relevance between question and each context chunk. f_qs_relevance = Feedback(openai_tl.qs_relevance).on_input().on( TruLlama.select_source_nodes().node.text ).aggregate(np.mean) index_name = "medical-images" client = Client("https://42976740ac53ddbe7d.gradio.live/") PINECONE_API_KEY = os.getenv('PINECONE_API_KEY') PINECONE_ENVIRONMENT = os.getenv('PINECONE_ENVIRONMENT') pinecone.init( api_key=PINECONE_API_KEY, environment=PINECONE_ENVIRONMENT ) index = pinecone.Index(index_name) vector_store = PineconeVectorStore(pinecone_index=index) l_index = VectorStoreIndex.from_vector_store(vector_store=vector_store) query_engine = l_index.as_query_engine() tru_query_engine_recorder = TruLlama(query_engine, app_id='LlamaIndex_App1', feedbacks=[f_groundedness, f_qa_relevance, f_qs_relevance]) dinov2_vits14 = torch.hub.load("facebookresearch/dinov2", "dinov2_vits14") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") dinov2_vits14.to(device) transform_image = T.Compose([T.ToTensor(), T.Resize(224), T.CenterCrop(224), T.Normalize([0.5], [0.5])]) @Monitor.monitor def compute_embedding(file) -> dict: """ Create an index that contains all of the images in the specified list of files. """ with torch.no_grad(): embedding = dinov2_vits14(load_image(file).to(device)) print(f"embedding shape before: {embedding.shape}") embeddings_numpy = np.array(embedding[0].cpu().numpy()).reshape(1, -1) padded_embedding = pad_embedding(embeddings_numpy) print(f"embedding shape after padding: {padded_embedding.shape}") return padded_embedding @Monitor.monitor def load_image(file) -> torch.Tensor: """ Load a an image and return a tensor that can be used as an input to DINOv2. """ # Assuming it's PNG or JPEG img = Image.open(file).convert("RGB") transformed_img = transform_image(img)[:3].unsqueeze(0) return transformed_img @Monitor.monitor def pad_embedding(embedding: np.ndarray, target_dim: int = 512) -> np.ndarray: """ Pad the given embedding with zeros to match the target dimension. """ original_dim = embedding.shape[1] padding_dim = target_dim - original_dim if padding_dim > 0: padding = np.zeros((1, padding_dim)) padded_embedding = np.hstack([embedding, padding]) else: padded_embedding = embedding return padded_embedding @Monitor.monitor def add_embedding_to_index(id: str, embedding): single_vector = { 'id': id, 'values': embedding.flatten().tolist(), 'metadata': {'modality': 'mri'} } upsert_response = index.upsert(vectors=[single_vector]) print(f"Inserted {single_vector}") @Monitor.monitor def img_to_vector_db(img_path, index): embedding = compute_embedding(img_path) add_embedding_to_index(id=str(index), embedding=embedding) def hash_file(image_path: str) -> str: """ Hash the filename to create a unique ID. """ filename = image_path.split("/")[-1] unique_id = hashlib.sha256(filename.encode()).hexdigest() return unique_id @Monitor.monitor def retrieve(embedding): response = index.query( vector=embedding.flatten().tolist(), top_k=3, include_values=True, include_metadata=True ) result =[ m["metadata"]["report"] for m in response["matches"]] urls = [] for m in response["matches"]: if "download_path" in m["metadata"]: urls.append(m["metadata"]["download_path"]) return result, urls @Monitor.monitor def generate_response(result, query, li_response): result = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[ {"role": "system", "content": """ Objective: Generate a concise radiologic diagnosis based on SHARED FEATURES from the provided radiology reports. Definition of SHARED FEATURES: Features that appear in more than one report. Features unique to a single report are not considered SHARED. Instructions: Analyze the provided radiology reports. Identify any SHARED FEATURES, these should be the diagnosis and not radiologic features. If SHARED FEATURES are found, provide a radiologic diagnosis in one sentence. If no SHARED FEATURES are identified, simply state: "Radiologic Diagnosis: Diagnosis not possible." Return the reports summarized as well. """ }, {"role": "assistant", "content": "Reports:"+ "\n-".join(result)}, {"role": "user", "content": query}, ] , temperature=0) return result @Monitor.monitor def llama_index_response(query, result): from llama_index import SummaryIndex from llama_index.schema import TextNode index = SummaryIndex([TextNode(text=r) for r in result]) summary_query_engine = index.as_query_engine() tru_query_engine_recorder_tmp = TruLlama(summary_query_engine, app_id='LlamaIndex_App1', feedbacks=[f_groundedness, f_qa_relevance, f_qs_relevance]) with tru_query_engine_recorder_tmp as recording: li_response = summary_query_engine.query(query) return li_response def predict(file, query): embedding = compute_embedding(file) retrieved_result, urls = retrieve(embedding) li_response = llama_index_response(query, retrieved_result) result = generate_response(retrieved_result, query, li_response) result = result['choices'][0]['message']['content'] result = "**Retrieved Reports:** " + ' \n'.join(retrieved_result) + " \n**Images:** " + (' \n').join(urls) + " \n **Final Diagnosis:** " + result return result # result = predict(img_path=img_path) # print(f"ID: {result['matches'][0]['id']} | Similarity score: {round(result['matches'][0]['score'], 2)}") # new_img

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############################################################################################################################ # In this section, we set the user authentication, model URL, and prompt text. Alternatively, set the user and app ID, # and model name. Change these strings to run your own example. ########################################################################################################################### PAT = "YOUR_PAT_HERE" MODEL_URL = "https://clarifai.com/cohere/embed/models/cohere-text-to-embeddings" PROMPT = "Hello World!" # Alternatively, you can specify user ID, app ID, and model name #USER_ID = "cohere" #APP_ID = "embed" #MODEL_NAME = "cohere-text-to-embeddings" ############################################################################ # YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE ############################################################################ # Import the required packages import os from llama_index.embeddings.clarifai import ClarifaiEmbedding # Set Clarifai PAT as environment variable os.environ["CLARIFAI_PAT"] = PAT # Initialize the LLM class embed_model = ClarifaiEmbedding(model_url=MODEL_URL) # Alternatively # embed_model = ClarifaiEmbedding( # user_id=USER_ID, # app_id=APP_ID, # model_name=MODEL_NAME # ) embeddings = embed_model.get_text_embedding(PROMPT) print(len(embeddings)) # Print the first five elements of embeddings list print(embeddings[:5])

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# Copyright 2023 Qarik Group, LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import glob import os import threading from datetime import datetime from pathlib import Path from typing import Any, List from common import admin_dao, constants, gcs_tools, solution from common.cache import cache from common.log import Logger, log, log_params from langchain.llms.openai import OpenAIChat from llama_index import (Document, GPTSimpleKeywordTableIndex, GPTVectorStoreIndex, LLMPredictor, ServiceContext, SimpleDirectoryReader, StorageContext, load_index_from_storage) from llama_index.indices.composability import ComposableGraph from llama_index.indices.query.base import BaseQueryEngine from llama_index.indices.query.query_transform.base import DecomposeQueryTransform # import google.generativeai as palm # from llama_index.query_engine.router_query_engine import RouterQueryEngine from llama_index.query_engine.transform_query_engine import TransformQueryEngine # from llama_index.selectors.llm_selectors import LLMSingleSelector # from llama_index.tools.query_engine import QueryEngineTool logger = Logger(__name__).get_logger() logger.info('Initializing...') DATA_LOAD_LOCK = threading.Lock() """Block many concurrent data loads at once.""" LLAMA_FILE_LOCK = threading.Lock() """Lock to prevent concurrent updates of the same index - needed in case we have more than one request processing.""" INDEX_BUCKET: str = solution.getenv('EMBEDDINGS_BUCKET_NAME') """Location to download llama-index embeddings from.""" LAST_LOCAL_INDEX_UPDATE: datetime | None = None """Keep track of the most recent local index update to avoid unnecessary refreshes.""" if solution.LOCAL_DEVELOPMENT_MODE: LLAMA_INDEX_DIR: str = 'dev/tmp/llamaindex-embeddings' else: LLAMA_INDEX_DIR = 'tmp/llamaindex-embeddings' LOCAL_DEV_DATA_DIR: str = 'dev/tmp' """Location of the local data directory for development on local machine.""" @log def _get_llm(provider: constants.LlmProvider) -> LLMPredictor: """Return LLM predictor.""" if provider == constants.LlmProvider.OPEN_AI: llm = LLMPredictor(llm=OpenAIChat(temperature=constants.TEMPERATURE, model_name=constants.GPT_MODEL)) # type: ignore else: raise ValueError(f'Unknown LLM provider: {provider}') return llm @log_params def load_resumes(resume_dir: str | None) -> dict[str, List[Document]]: """Initialize list of resumes from index storage or from the directory with PDF source files.""" resumes: dict[str, List[Document]] = {} if resume_dir is None: resume_dir = '' resume_path = Path(resume_dir) index_path = Path(LLAMA_INDEX_DIR) global DATA_LOAD_LOCK with DATA_LOAD_LOCK: if index_path.exists(): logger.info('Loading people names (not resumes) from existing index storage...') names = glob.glob(f'{index_path}/*',) if len(names): for file_name in names: # We do not care about the contents of the resume because it will be loaded from index # All we care for here is the name - aka the Key, not Value resumes[Path(file_name).name] = [] return resumes else: logger.warning('No resumes found in the index directory: %s', index_path) logger.warning('Removing the index storage directory: %s', index_path) Path.rmdir(index_path) logger.info('Loading people names from the source dir with resume PDF files...') Path.mkdir(resume_path, parents=True, exist_ok=True) # Check if there are any pdf files in the data directory pdf_files = glob.glob(f'{resume_path}/*.pdf') if len(pdf_files): # Each resume shall be named as '<person_name>.pdf' optionally with 'resume' suffix for resume in pdf_files: person_name = os.path.basename(resume).replace('.pdf', '').replace( 'Resume', '').replace('resume', '').replace('_', ' ').strip() logger.debug(f'Loading: {person_name}') resume_content = SimpleDirectoryReader(input_files=[resume]).load_data() resumes[person_name] = resume_content else: logger.warning('No resume PDF files found in the data directory: %s', resume_path) return resumes @log def _load_resume_indices(resumes: dict[str, List[Document]], service_context: ServiceContext, embeddings_dir: str) -> dict[str, GPTVectorStoreIndex]: """Load or create index storage contexts for each person in the resumes list.""" vector_indices = {} for person_name, resume_data in resumes.items(): cache_file_path = Path(f'./{embeddings_dir}/{person_name}') if cache_file_path.exists(): logger.debug('Loading index from storage file: %s', cache_file_path) storage_context = StorageContext.from_defaults(persist_dir=str(cache_file_path)) vector_indices[person_name] = load_index_from_storage(storage_context=storage_context) else: storage_context = StorageContext.from_defaults() # build vector index vector_indices[person_name] = GPTVectorStoreIndex.from_documents( resume_data, service_context=service_context, storage_context=storage_context, ) # set id for vector index # vector_indices[person_name].index_struct.index_id = person_name vector_indices[person_name].set_index_id(person_name) logger.debug('Saving index to storage file: %s', cache_file_path) storage_context.persist(persist_dir=str(cache_file_path)) # ------------------- Test # name = 'Roman Kharkovski' # test_query = f'What are the main skills for {name}?' # logger.debug('Test query: %s', test_query) # response = vector_indices[f'{name}'].as_query_engine().query(test_query) # logger.debug('Response: %s', str(response)) # exit(0) # ------------------- end of test return vector_indices # type: ignore @log def _load_resume_index_summary(resumes: dict[str, Any]) -> dict[str, str]: index_summaries = {} for person_name in resumes.keys(): # index_summaries[person_name] = (f'Use this index if you need to lookup specific facts about {person_name}.') index_summaries[person_name] = (f'This content contains resume of {person_name}.\n' f'Use this index if you need to lookup specific facts about {person_name}.\n' 'Do not confuse people with the same lastname, but different first names.' 'If you cant find the answer, respond with the best of your knowledge.' 'Do not use this index if you want to analyze multiple people.') return index_summaries @log_params def generate_embeddings(resume_dir: str, provider: constants.LlmProvider) -> None: """Generate embeddings from PDF resumes.""" resumes = load_resumes(resume_dir=resume_dir) if not resumes: return None predictor = _get_llm(provider=provider) context = ServiceContext.from_defaults(llm_predictor=predictor, chunk_size_limit=constants.CHUNK_SIZE) _load_resume_indices(resumes=resumes, service_context=context, embeddings_dir=LLAMA_INDEX_DIR) @log_params def _get_resume_query_engine(provider: constants.LlmProvider, resume_dir: str | None = None) -> BaseQueryEngine | None: """Load the index from disk, or build it if it doesn't exist.""" llm = _get_llm(provider=provider) service_context = ServiceContext.from_defaults(llm_predictor=llm, chunk_size_limit=constants.CHUNK_SIZE) resumes: dict[str, List[Document]] = load_resumes(resume_dir=resume_dir) logger.debug('-------------------------- resumes: %s', resumes.keys()) if not resumes: return None # vector_indices = load_resume_indices(resumes, service_context) vector_indices = _load_resume_indices(resumes=resumes, service_context=service_context, embeddings_dir=LLAMA_INDEX_DIR) index_summaries = _load_resume_index_summary(resumes) graph = ComposableGraph.from_indices(root_index_cls=GPTSimpleKeywordTableIndex, children_indices=[index for _, index in vector_indices.items()], index_summaries=[summary for _, summary in index_summaries.items()], max_keywords_per_chunk=constants.MAX_KEYWORDS_PER_CHUNK) # root_index = graph.get_index(graph.root_id) root_index = graph.get_index(index_struct_id=graph.root_id) root_index.set_index_id('compare_contrast') graph.index_struct.summary = ('This index contains resumes of multiple people. ' 'Do not confuse people with the same lastname, but different first names.' 'Use this index if you want to compare multiple people.') decompose_transform = DecomposeQueryTransform(llm, verbose=True) custom_query_engines = {} for index in vector_indices.values(): query_engine = index.as_query_engine(service_context=service_context, similarity_top_k=constants.SIMILARITY_TOP_K) query_engine = TransformQueryEngine(query_engine=query_engine, query_transform=decompose_transform, transform_metadata={'index_summary': index.index_struct.summary}, ) # type: ignore custom_query_engines[index.index_id] = query_engine custom_query_engines[graph.root_id] = graph.root_index.as_query_engine( retriever_mode='simple', response_mode='tree_summarize', service_context=service_context, verbose=True, use_async=True, ) graph_query_engine = graph.as_query_engine(custom_query_engines=custom_query_engines) # ------------------- Test # name1 = 'Roman Kharkovski' # name2 = 'Steven Kim' # response = graph_query_engine.query(f'Compare and contrast the skills of {name1} and {name2}.') # logger.debug('Response: %s', str(response)) # ------------------- end of test return graph_query_engine # TODO: the query engine tool does not longer work - need to debug # query_engine_tools = [] # # add vector index tools # for person_name in resumes.keys(): # index = vector_indices[person_name] # summary = index_summaries[person_name] # query_engine = index.as_query_engine(service_context=service_context) # vector_tool = QueryEngineTool.from_defaults(query_engine=query_engine, description=summary) # query_engine_tools.append(vector_tool) # # add graph tool # graph_tool = QueryEngineTool.from_defaults(graph_query_engine, description=graph.index_struct.summary) # query_engine_tools.append(graph_tool) # router_query_engine = RouterQueryEngine.from_defaults(selector=LLMSingleSelector.from_defaults( # service_context=service_context), query_engine_tools=query_engine_tools) # return router_query_engine @cache @log def _refresh_llama_index() -> None: """Refresh the index of resumes from the database using Llama-Index.""" global LAST_LOCAL_INDEX_UPDATE if solution.LOCAL_DEVELOPMENT_MODE: logger.info('Running in local development mode') index_path = Path(LLAMA_INDEX_DIR) if not index_path.exists(): # TODO - need to generate proper embeddings for each provider, not hard coded generate_embeddings(resume_dir=LOCAL_DEV_DATA_DIR, provider=constants.LlmProvider.OPEN_AI) return global LLAMA_FILE_LOCK last_resume_refresh = admin_dao.AdminDAO().get_resumes_timestamp() if LAST_LOCAL_INDEX_UPDATE is None or LAST_LOCAL_INDEX_UPDATE < last_resume_refresh: logger.info('Refreshing local index of resumes...') # Prevent concurrent updates of the same index - needed in case we have more than one request processing with LLAMA_FILE_LOCK: # Check for condition again because the index may have been updated while we were waiting for the lock if LAST_LOCAL_INDEX_UPDATE is None or LAST_LOCAL_INDEX_UPDATE < last_resume_refresh: gcs_tools.download(bucket_name=INDEX_BUCKET, local_dir=LLAMA_INDEX_DIR) return last_resume_refresh logger.info('Skipping refresh of resumes index because no changes in source resumes were detected.') LAST_LOCAL_INDEX_UPDATE = last_resume_refresh @log def query(question: str) -> str: """Run LLM query for CHatGPT.""" _refresh_llama_index() query_engine = _get_resume_query_engine(provider=constants.LlmProvider.OPEN_AI) if query_engine is None: raise SystemError('No resumes found in the database. Please upload resumes.') return str(query_engine.query(question))

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import os from dotenv import load_dotenv, find_dotenv import numpy as np from trulens_eval import ( Feedback, TruLlama, OpenAI ) from trulens_eval.feedback import Groundedness import nest_asyncio nest_asyncio.apply() def get_openai_api_key(): _ = load_dotenv(find_dotenv()) return os.getenv("OPENAI_API_KEY") def get_hf_api_key(): _ = load_dotenv(find_dotenv()) return os.getenv("HUGGINGFACE_API_KEY") openai = OpenAI() qa_relevance = ( Feedback(openai.relevance_with_cot_reasons, name="Answer Relevance") .on_input_output() ) qs_relevance = ( Feedback(openai.relevance_with_cot_reasons, name = "Context Relevance") .on_input() .on(TruLlama.select_source_nodes().node.text) .aggregate(np.mean) ) #grounded = Groundedness(groundedness_provider=openai, summarize_provider=openai) grounded = Groundedness(groundedness_provider=openai) groundedness = ( Feedback(grounded.groundedness_measure_with_cot_reasons, name="Groundedness") .on(TruLlama.select_source_nodes().node.text) .on_output() .aggregate(grounded.grounded_statements_aggregator) ) feedbacks = [qa_relevance, qs_relevance, groundedness] def get_trulens_recorder(query_engine, feedbacks, app_id): tru_recorder = TruLlama( query_engine, app_id=app_id, feedbacks=feedbacks ) return tru_recorder def get_prebuilt_trulens_recorder(query_engine, app_id): tru_recorder = TruLlama( query_engine, app_id=app_id, feedbacks=feedbacks ) return tru_recorder from llama_index import ServiceContext, VectorStoreIndex, StorageContext from llama_index.node_parser import SentenceWindowNodeParser from llama_index.indices.postprocessor import MetadataReplacementPostProcessor from llama_index.indices.postprocessor import SentenceTransformerRerank from llama_index import load_index_from_storage import os def build_sentence_window_index( document, llm, embed_model="local:BAAI/bge-small-en-v1.5", save_dir="sentence_index" ): # create the sentence window node parser w/ default settings node_parser = SentenceWindowNodeParser.from_defaults( window_size=3, window_metadata_key="window", original_text_metadata_key="original_text", ) sentence_context = ServiceContext.from_defaults( llm=llm, embed_model=embed_model, node_parser=node_parser, ) if not os.path.exists(save_dir): sentence_index = VectorStoreIndex.from_documents( [document], service_context=sentence_context ) sentence_index.storage_context.persist(persist_dir=save_dir) else: sentence_index = load_index_from_storage( StorageContext.from_defaults(persist_dir=save_dir), service_context=sentence_context, ) return sentence_index def get_sentence_window_query_engine( sentence_index, similarity_top_k=6, rerank_top_n=2, ): # define postprocessors postproc = MetadataReplacementPostProcessor(target_metadata_key="window") rerank = SentenceTransformerRerank( top_n=rerank_top_n, model="BAAI/bge-reranker-base" ) sentence_window_engine = sentence_index.as_query_engine( similarity_top_k=similarity_top_k, node_postprocessors=[postproc, rerank] ) return sentence_window_engine from llama_index.node_parser import HierarchicalNodeParser from llama_index.node_parser import get_leaf_nodes from llama_index import StorageContext from llama_index.retrievers import AutoMergingRetriever from llama_index.indices.postprocessor import SentenceTransformerRerank from llama_index.query_engine import RetrieverQueryEngine def build_automerging_index( documents, llm, embed_model="local:BAAI/bge-small-en-v1.5", save_dir="merging_index", chunk_sizes=None, ): chunk_sizes = chunk_sizes or [2048, 512, 128] node_parser = HierarchicalNodeParser.from_defaults(chunk_sizes=chunk_sizes) nodes = node_parser.get_nodes_from_documents(documents) leaf_nodes = get_leaf_nodes(nodes) merging_context = ServiceContext.from_defaults( llm=llm, embed_model=embed_model, ) storage_context = StorageContext.from_defaults() storage_context.docstore.add_documents(nodes) if not os.path.exists(save_dir): automerging_index = VectorStoreIndex( leaf_nodes, storage_context=storage_context, service_context=merging_context ) automerging_index.storage_context.persist(persist_dir=save_dir) else: automerging_index = load_index_from_storage( StorageContext.from_defaults(persist_dir=save_dir), service_context=merging_context, ) return automerging_index def get_automerging_query_engine( automerging_index, similarity_top_k=12, rerank_top_n=2, ): base_retriever = automerging_index.as_retriever(similarity_top_k=similarity_top_k) retriever = AutoMergingRetriever( base_retriever, automerging_index.storage_context, verbose=True ) rerank = SentenceTransformerRerank( top_n=rerank_top_n, model="BAAI/bge-reranker-base" ) auto_merging_engine = RetrieverQueryEngine.from_args( retriever, node_postprocessors=[rerank] ) return auto_merging_engine

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import tiktoken import sys from llama_index.readers.file import PyMuPDFReader from llama_index.core.node_parser import TokenTextSplitter index = int(sys.argv[1]) docs = PyMuPDFReader().load("Hamlet.pdf") combined = "" for doc in docs: combined += doc.text splitter = TokenTextSplitter( chunk_size=10000, chunk_overlap=10, tokenizer=tiktoken.encoding_for_model("gpt-4").encode) pieces = splitter.split_text(combined) if index >= len(pieces): print("No more content") sys.exit(0) print(pieces[index])

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# The MIT License # Copyright (c) Jerry Liu # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. """OpenDAL file and directory reader. A loader that fetches a file or iterates through a directory on a object store like AWS S3 or AzureBlob. """ import asyncio import logging as log import tempfile from datetime import datetime from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Self, Type, Union, cast import opendal from llama_index.readers.base import BaseReader from llama_index.readers.file.docs_reader import DocxReader, PDFReader from llama_index.readers.file.epub_reader import EpubReader from llama_index.readers.file.image_reader import ImageReader from llama_index.readers.file.ipynb_reader import IPYNBReader from llama_index.readers.file.markdown_reader import MarkdownReader from llama_index.readers.file.mbox_reader import MboxReader from llama_index.readers.file.slides_reader import PptxReader from llama_index.readers.file.tabular_reader import PandasCSVReader from llama_index.readers.file.video_audio_reader import VideoAudioReader from llama_index.schema import Document from .... import services from ....domain import DocumentListItem DEFAULT_FILE_READER_CLS: Dict[str, Type[BaseReader]] = { ".pdf": PDFReader, ".docx": DocxReader, ".pptx": PptxReader, ".jpg": ImageReader, ".png": ImageReader, ".jpeg": ImageReader, ".mp3": VideoAudioReader, ".mp4": VideoAudioReader, ".csv": PandasCSVReader, ".epub": EpubReader, ".md": MarkdownReader, ".mbox": MboxReader, ".ipynb": IPYNBReader, } FILE_MIME_EXTENSION_MAP: Dict[str, str] = { "application/pdf": ".pdf", "application/vnd.openxmlformats-officedocument.wordprocessingml.document": ".docx", "application/vnd.openxmlformats-officedocument.presentationml.presentation": ".pptx", "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet": ".xlsx", "application/vnd.google-apps.document": ".gdoc", "application/vnd.google-apps.presentation": ".gslides", "application/vnd.google-apps.spreadsheet": ".gsheet", "image/jpeg": ".jpg", "image/png": ".png", "image/jpg": ".jpg", "audio/mpeg": ".mp3", "audio/mp3": ".mp3", "video/mp4": ".mp4", "video/mpeg": ".mp4", "text/csv": ".csv", "application/epub+zip": ".epub", "text/markdown": ".md", "application/x-ipynb+json": ".ipynb", "application/mbox": ".mbox", } class OpendalReader(BaseReader): """General reader for any opendal operator.""" def __init__( self: Self, scheme: str, path: str = "/", file_extractor: Optional[Dict[str, Union[str, BaseReader]]] = None, file_metadata: Optional[Callable[[str], Dict]] = None, **kwargs: Optional[dict[str, Any]], ) -> None: """Initialize opendal operator, along with credentials if needed. Args: scheme (str): the scheme of the service path (str): the path of the data. If none is provided, this loader will iterate through the entire bucket. If path is endswith `/`, this loader will iterate through the entire dir. Otherwise, this loader will load the file. file_extractor (Optional[Dict[str, BaseReader]]): A mapping of file extension to a BaseReader class that specifies how to convert that file to text. NOTE: this isn't implemented yet. file_metadata (Optional[Callable[[str], Dict]]): A function that takes a source file path and returns a dictionary of metadata to be added to the Document object. **kwargs (Optional dict[str, any]): Additional arguments to pass to the `opendal.AsyncOperator` constructor. These are the scheme (object store) specific options. """ super().__init__() self.path = path self.file_metadata = file_metadata self.supported_suffix = list(DEFAULT_FILE_READER_CLS.keys()) self.async_op = opendal.AsyncOperator(scheme, **kwargs) if file_extractor is not None: self.file_extractor = file_extractor else: self.file_extractor = {} self.documents: List[Document] = [] def load_data(self: Self) -> List[Document]: """Load file(s) from OpenDAL.""" # TODO: think about the private and secure aspect of this temp folder. # NOTE: the following code cleans up the temp folder when existing the context. with tempfile.TemporaryDirectory() as temp_dir: if not self.path.endswith("/"): result = asyncio.run( download_file_from_opendal(self.async_op, temp_dir, self.path, file_metadata=self.file_metadata) ) self.downloaded_files.append(result) else: self.downloaded_files = asyncio.run(download_dir_from_opendal(self.async_op, temp_dir, self.path)) self.documents = asyncio.run( extract_files( self.downloaded_files, file_extractor=self.file_extractor, file_metadata=self.file_metadata ) ) return self.documents def get_document_list(self: Self) -> List[DocumentListItem]: """Get a list of all documents in the index. A document is a list are 1:1 with a file.""" dl: List[DocumentListItem] = [] try: for df in self.downloaded_files: dl.append(DocumentListItem(link=df[0], indexed_on=df[2], size=df[3])) except Exception as e: log.exception("Converting Document list to DocumentListItem list failed: %s", e) return dl class FileStorageBaseReader(BaseReader): """File storage reader.""" def __init__( self: Self, access_token: dict, root: str, selected_folder_id: Optional[str] = None, path: str = "/", file_extractor: Optional[Dict[str, Union[str, BaseReader]]] = None, file_metadata: Optional[Callable[[str], Dict]] = None, **kwargs: Optional[dict[str, Any]], ) -> None: """Initialize File storage service reader. Args: path (str): the path of the data. If none is provided, this loader will iterate through the entire bucket. If path is endswith `/`, this loader will iterate through the entire dir. Otherwise, this loader will load the file. access_token (dict): the access token for the google drive service root (str): the root folder to start the iteration selected_folder_id (Optional[str] = None): the selected folder id file_extractor (Optional[Dict[str, BaseReader]]): A mapping of file extension to a BaseReader class that specifies how to convert that file to text. NOTE: this isn't implemented yet. file_metadata (Optional[Callable[[str], Dict]]): A function that takes a source file path and returns a dictionary of metadata to be added to the Document object. kwargs (Optional dict[str, any]): Additional arguments to pass to the specific file storage service. """ super().__init__() self.path = path self.file_extractor = file_extractor if file_extractor is not None else {} self.supported_suffix = list(DEFAULT_FILE_READER_CLS.keys()) self.access_token = access_token self.root = root self.file_metadata = file_metadata self.selected_folder_id = selected_folder_id self.documents: List[Document] = [] self.kwargs = kwargs self.downloaded_files: List[tuple[str, str, int, int]] = [] def load_data(self: Self) -> List[Document]: """Load file(s) from file storage.""" raise NotImplementedError def get_document_list(self: Self) -> List[DocumentListItem]: """Get a list of all documents in the index. A document is a list are 1:1 with a file.""" dl: List[DocumentListItem] = [] try: for df in self.downloaded_files: dl.append(DocumentListItem(link=df[0], indexed_on=df[2], size=df[3])) except Exception as e: log.exception("Converting Document list to DocumentListItem list failed: %s", e) return dl # TODO: Tobe removed once opendal starts supporting Google Drive. class GoogleDriveReader(FileStorageBaseReader): """Google Drive reader.""" def __init__( self: Self, access_token: dict, root: str, selected_folder_id: Optional[str] = None, path: str = "/", file_extractor: Optional[Dict[str, Union[str, BaseReader]]] = None, file_metadata: Optional[Callable[[str], Dict]] = None, ) -> None: """Initialize Google Drive reader.""" super().__init__( access_token=access_token, root=root, selected_folder_id=selected_folder_id, path=path, file_extractor=file_extractor, file_metadata=file_metadata, ) def load_data(self: Self) -> List[Document]: """Load file(s) from Google Drive.""" service = services.google_drive.get_drive_service(self.access_token) id_ = self.selected_folder_id if self.selected_folder_id is not None else "root" folder_content = service.files().list( q=f"'{id_}' in parents and trashed=false", fields="files(id, name, parents, mimeType, modifiedTime, webViewLink, webContentLink, size, fullFileExtension)", ).execute() files = folder_content.get("files", []) with tempfile.TemporaryDirectory() as temp_dir: self.downloaded_files = asyncio.run( download_from_gdrive(files, temp_dir, service) ) self.documents = asyncio.run( extract_files( self.downloaded_files, file_extractor=self.file_extractor, file_metadata=self.file_metadata ) ) return self.documents class OneDriveReader(FileStorageBaseReader): """OneDrive reader.""" def __init__( self: Self, access_token: dict, root: str, selected_folder_id: Optional[str] = None, path: str = "/", file_extractor: Optional[Dict[str, Union[str, BaseReader]]] = None, file_metadata: Optional[Callable[[str], Dict]] = None, ) -> None: """Initialize OneDrive reader.""" super().__init__( access_token=access_token, root=root, selected_folder_id=selected_folder_id, path=path, file_extractor=file_extractor, file_metadata=file_metadata, ) def load_data(self: Self) -> List[Document]: """Load file(s) from OneDrive.""" client = services.ms_onedrive.get_client(self.access_token) id_ = self.selected_folder_id if self.selected_folder_id is not None else "/drive/root:" if client is not None: response = client.files.drive_specific_folder(id_, { "$select": "id,name,file,size,webUrl", "$filter": "file ne null", "$top": 100, # Limiting to a maximum of 100 files for now. }) files = response.data.get("value", []) with tempfile.TemporaryDirectory() as temp_dir: self.downloaded_files = asyncio.run( download_from_onedrive(files, temp_dir, client) ) self.documents = asyncio.run( extract_files( self.downloaded_files, file_extractor=self.file_extractor, file_metadata=self.file_metadata ) ) return self.documents async def download_from_onedrive(files: List[dict], temp_dir: str, client: Any,) -> List[tuple[str, str, int, int]]: """Download files from OneDrive.""" downloaded_files: List[tuple[str, str, int, int]] = [] for file in files: suffix = Path(file["name"]).suffix if suffix not in DEFAULT_FILE_READER_CLS: log.debug("file suffix not supported: %s", suffix) continue file_path = f"{temp_dir}/{file['name']}" indexed_on = datetime.timestamp(datetime.now().utcnow()) await asyncio.to_thread( services.ms_onedrive.download_file, client, file["id"], file_path ) downloaded_files.append( (file["webUrl"], file_path, int(indexed_on), int(file["size"])) ) return downloaded_files async def download_from_gdrive(files: List[dict], temp_dir: str, service: Any,) -> List[tuple[str, str, int, int]]: """Download files from Google Drive.""" downloaded_files: List[tuple[str, str, int, int]] = [] for file in files: if file["mimeType"] == "application/vnd.google-apps.folder": # TODO: Implement recursive folder download continue suffix = FILE_MIME_EXTENSION_MAP.get(file["mimeType"], None) if suffix not in DEFAULT_FILE_READER_CLS: continue file_path = f"{temp_dir}/{file['name']}" indexed_on = datetime.timestamp(datetime.now().utcnow()) await asyncio.to_thread( services.google_drive.download_file, service, file["id"], file_path, file["mimeType"] ) downloaded_files.append( (file["webViewLink"], file_path, int(indexed_on), int(file["size"])) ) return downloaded_files async def download_file_from_opendal(op: Any, temp_dir: str, path: str) -> tuple[str, int, int]: """Download file from OpenDAL.""" import opendal log.debug("downloading file using OpenDAL: %s", path) op = cast(opendal.AsyncOperator, op) suffix = Path(path).suffix filepath = f"{temp_dir}/{next(tempfile._get_candidate_names())}{suffix}" # type: ignore file_size = 0 indexed_on = datetime.timestamp(datetime.now().utcnow()) async with op.open_reader(path) as r: with open(filepath, "wb") as w: b = await r.read() w.write(b) file_size = len(b) return (filepath, int(indexed_on), file_size) async def download_dir_from_opendal( op: Any, temp_dir: str, download_dir: str, ) -> List[tuple[str, str, int, int]]: """Download directory from opendal. Args: op: opendal operator temp_dir: temp directory to store the downloaded files download_dir: directory to download supported_suffix: list of supported file suffixes file_extractor: A mapping of file extractors to use for specific file types. file_metadata: A function that takes a file path and returns a dictionary of metadata to be added to the Document object. Returns: a list of tuples of 'source path' and 'local path'. """ import opendal log.debug("downloading dir using OpenDAL: %s", download_dir) downloaded_files: List[tuple[str, str, int, int]] = [] op = cast(opendal.AsyncOperator, op) objs = await op.scan(download_dir) async for obj in objs: filepath, indexed_on, size = await download_file_from_opendal(op, temp_dir, obj.path) downloaded_files.append((obj.path, filepath, indexed_on, size)) # source path, local path return downloaded_files async def extract_files( downloaded_files: List[tuple[str, str, int, int]], file_extractor: Optional[Dict[str, Union[str, BaseReader]]] = None, file_metadata: Optional[Callable[[str], Dict]] = None, ) -> List[Document]: """Extract content of a list of files.""" documents: List[Document] = [] tasks = [] log.debug("number files to extract: %s", len(downloaded_files)) for fe in downloaded_files: source_path = fe[0] local_path = fe[1] metadata = None if file_metadata is not None: metadata = file_metadata(source_path) # TODO: this likely will not scale very much. We'll have to refactor to control the number of tasks. task = asyncio.create_task( extract_file(Path(local_path), filename_as_id=True, file_extractor=file_extractor, metadata=metadata) ) tasks.append(task) log.debug("extract task created for: %s", local_path) log.debug("extract file - tasks started: %s", len(tasks)) results = await asyncio.gather(*tasks) log.debug("extract file - tasks completed: %s", len(results)) for result in results: # combine into a single Document list documents.extend(result) return documents async def extract_file( file_path: Path, filename_as_id: bool = False, errors: str = "ignore", file_extractor: Optional[Dict[str, Union[str, BaseReader]]] = None, metadata: Optional[Dict] = None, ) -> List[Document]: """Extract content of a file on disk. Args: file_path (str): path to the file filename_as_id (bool): whether to use the filename as the document id errors (str): how to handle errors when reading the file supported_suffix (Optional[List[str]]): list of supported file suffixes file_extractor (Optional[Dict[str, Union[str, BaseReader]]] = None): A mapping of file extractors to use for specific file types. metadata (Optional[Dict] = None): metadata to add to the document. This will be appended to any metadata generated by the file extension specific extractor. Returns: List[Document]: list of documents containing the content of the file, one Document object per page. """ documents: List[Document] = [] file_suffix = file_path.suffix.lower() supported_suffix = list(DEFAULT_FILE_READER_CLS.keys()) if file_suffix in supported_suffix: log.debug("file extractor found for file_suffix: %s", file_suffix) # NOTE: pondering if its worth turning this into a class and uncomment the code above so reader classes are only instantiated once. reader = DEFAULT_FILE_READER_CLS[file_suffix]() docs = reader.load_data(file_path, extra_info=metadata) # iterate over docs if needed if filename_as_id: for i, doc in enumerate(docs): doc.id_ = f"{str(file_path)}_part_{i}" documents.extend(docs) else: log.debug("file extractor not found for file_suffix: %s", file_suffix) # do standard read with open(file_path, "r", errors=errors, encoding="utf8") as f: data = f.read() doc = Document(text=data, extra_info=metadata or {}) if filename_as_id: doc.id_ = str(file_path) documents.append(doc) return documents

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from langchain.agents import ( initialize_agent, Tool, AgentType ) from llama_index.callbacks import ( CallbackManager, LlamaDebugHandler ) from llama_index.node_parser.simple import SimpleNodeParser from llama_index import ( VectorStoreIndex, SummaryIndex, SimpleDirectoryReader, ServiceContext, StorageContext, ) import os import openai import logging import sys logging.basicConfig(stream=sys.stdout, level=logging.INFO) def init_llm_from_env(temperature=0.1, max_tokens=1024): llm_type = os.getenv("LLM") if llm_type == 'openai': from langchain.chat_models import ChatOpenAI openai.api_key = os.getenv("OPENAI_API_KEY") llm = ChatOpenAI(temperature=temperature, model_name="gpt-3.5-turbo", max_tokens=max_tokens) elif llm_type == 'xinference': from langchain.llms import Xinference llm = Xinference( server_url=os.getenv("XINFERENCE_SERVER_ENDPOINT"), model_uid=os.getenv("XINFERENCE_LLM_MODEL_UID") ) else: raise ValueError(f"Unknown LLM type {llm_type}") return llm def init_embedding_from_env(temperature=0.1, max_tokens=1024): embedding_type = os.getenv("EMBEDDING") if embedding_type == 'openai': from llama_index.embeddings import OpenAIEmbedding openai.api_key = os.getenv("OPENAI_API_KEY") embedding = OpenAIEmbedding() elif embedding_type == 'xinference': from langchain.embeddings import XinferenceEmbeddings from llama_index.embeddings import LangchainEmbedding embedding = LangchainEmbedding( XinferenceEmbeddings( server_url=os.getenv("XINFERENCE_SERVER_ENDPOINT"), model_uid=os.getenv("XINFERENCE_EMBEDDING_MODEL_UID") ) ) else: raise ValueError(f"Unknown EMBEDDING type {embedding_type}") return embedding def get_service_context(callback_handlers): callback_manager = CallbackManager(callback_handlers) node_parser = SimpleNodeParser.from_defaults( chunk_size=512, chunk_overlap=128, callback_manager=callback_manager, ) return ServiceContext.from_defaults( embed_model=init_embedding_from_env(), callback_manager=callback_manager, llm=init_llm_from_env(), chunk_size=512, node_parser=node_parser ) def get_storage_context(): return StorageContext.from_defaults() def get_langchain_agent_from_index(summary_index, vector_index): list_query_engine = summary_index.as_query_engine( response_mode="tree_summarize", use_async=True, ) vector_query_engine = vector_index.as_query_engine( similarity_top_k=3 ) tools = [ Tool( name="Summary Tool", func=lambda q: str(list_query_engine.query(q)), description="useful for when you want to get summarizations", return_direct=True, ), Tool( name="Lookup Tool", func=lambda q: str(vector_query_engine.query(q)), description="useful for when you want to lookup detailed information", return_direct=True, ), ] agent_chain = initialize_agent( tools, init_llm_from_env(), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True ) return agent_chain def get_query_engine_from_index(index): return index.as_query_engine( similarity_top_k=3 ) def get_chat_engine_from_index(index): return index.as_chat_engine(chat_mode="condense_question", verbose=True) class ChatEngine: def __init__(self, file_path): llama_debug = LlamaDebugHandler(print_trace_on_end=True) service_context = get_service_context([llama_debug]) storage_context = get_storage_context() documents = SimpleDirectoryReader(input_files=[file_path], filename_as_id=True).load_data() logging.info(f"Loaded {len(documents)} documents from {file_path}") nodes = service_context.node_parser.get_nodes_from_documents(documents) storage_context.docstore.add_documents(nodes) logging.info(f"Adding {len(nodes)} nodes to storage") self.summary_index = SummaryIndex(nodes, storage_context=storage_context, service_context=service_context) self.vector_index = VectorStoreIndex(nodes, storage_context=storage_context, service_context=service_context) # def conversational_chat(self, query, callback_handler): # """ # Start a conversational chat with a agent # """ # response = self.agent_chain.run(input=query, callbacks=[callback_handler]) # return response def conversational_chat(self, query, callback_handler): """ Start a conversational chat with a agent """ return get_chat_engine_from_index(self.vector_index).chat(query).response

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from llama_index import DiscordReader from llama_index import download_loader import os import nest_asyncio nest_asyncio.apply() from llama_index import ServiceContext import openai import re import csv import time import random from dotenv import load_dotenv import os from llama_index import Document load_dotenv() openai_api_key = os.environ.get("OPENAI_API") discord_key = os.environ.get("DISCORD_TOKEN") os.environ["OPENAI_API_KEY"] = openai_api_key openai.api_key = openai_api_key def hit_discord(): DiscordReader = download_loader('DiscordReader') discord_token = discord_key channel_ids = [1088751449271447552] # Replace with your channel_i #channel_ids = [1057178784895348746] # Replace with your channel_id reader = DiscordReader(discord_token=discord_token) documents = reader.load_data(channel_ids=channel_ids) print("docs length", len(documents)) #discord_token = os.getenv("MTA4MjQyOTk4NTQ5Njc3MjYyOA.G8r0S7.MURmKr2iUaZf6AbDot5E_Gad_10oGbrMFxFVy4") #documents = DiscordReader(discord_token="MTA4MjQyOTk4NTQ5Njc3MjYyOA.G8r0S7.MURmKr2iUaZf6AbDot5E_Gad_10oGbrMFxFVy4").load_data(channel_ids=channel_ids, limit=[10]) service_context = ServiceContext.from_defaults(chunk_size_limit=3000) nodes = service_context.node_parser.get_nodes_from_documents(documents) print("nodes length:", len(nodes)) questions = {} array_of_docs = [] for n in nodes: print(n) prompt = f"""You are tasked with parsing out only the text from Discord messages (including who wrote it and their role). Here is the Discord data: {n}""" MAX_RETRIES = 3 SLEEP_TIME = 0.75 # in seconds for _ in range(MAX_RETRIES): try: time.sleep(round(random.uniform(0, SLEEP_TIME), 2)) completion = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[ {"role": "user", "content": prompt} ], temperature=0 ) break # If the API call works leave loop except Exception as e: print(f"Error calling OpenAI API: {e}") time.sleep(SLEEP_TIME) #print(completion.choices[0].message['content']) text = completion.choices[0].message['content'] document = Document(text=text) array_of_docs.append(document) print(array_of_docs) return array_of_docs __all__ = ['hit_discord']

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from typing import Union from llama_index.core import Prompt from llama_index.core.response_synthesizers import get_response_synthesizer, ResponseMode from llama_index.core.postprocessor import SimilarityPostprocessor from llama_index.core.llms import ChatMessage, MessageRole from llama_index.agent.openai import OpenAIAgent from llama_index.llms.openai import OpenAI from app.data.messages.qa import DocumentRequest from app.data.models.qa import Source, Answer, get_default_answer_id, get_default_answer from app.data.models.mongodb import ( LlamaIndexDocumentMeta, LlamaIndexDocumentMetaReadable, Message, ) from app.utils.log_util import logger from app.utils import data_util from app.llama_index_server.chat_message_dao import ChatMessageDao from app.llama_index_server.index_storage import index_storage from app.llama_index_server.my_query_engine_tool import MyQueryEngineTool, MATCHED_MARK SIMILARITY_CUTOFF = 0.85 PROMPT_TEMPLATE_FOR_QUERY_ENGINE = ( "We have provided context information below. \n" "---------------------\n" "{context_str}" "\n---------------------\n" "Given this information, assume you are an experienced golf coach, if the question has anything to do with golf, " "please give short, simple, accurate, precise answer to the question, " "limited to 80 words maximum. If the question has nothing to do with golf at all, please answer " f"'{get_default_answer_id()}'.\n" "The question is: {query_str}\n" ) SYSTEM_PROMPT_TEMPLATE_FOR_CHAT_ENGINE = ( "Your are an expert Q&A system that can find relevant information using the tools at your disposal.\n" "The tools can access a set of typical questions a golf beginner might ask.\n" "If the user's query matches one of those typical questions, stop and return the matched question immediately.\n" "If the user's query doesn't match any of those typical questions, " "then you should act as an experienced golf coach, and firstly evaluate whether the question is relevant to golf.\n" f"if it is not golf relevant at all, please answer '{get_default_answer_id()}," "otherwise, please give short, simple, accurate, precise answer to the question, limited to 80 words maximum.\n" "You may need to combine the chat history to fully understand the query of the user.\n" "Remember you are only allowed to answer questions related to golf.\n" ) chat_message_dao = ChatMessageDao() def get_local_query_engine(): """ strictly limited to local knowledge base. our local knowledge base is a list of standard questions which are indexed in vector store, while the standard answers are stored in mongodb through DocumentMetaDao. there is a one-to-one mapping between each standard question and a standard answer. we may update or optimize the standard answers in mongodb frequently, but usually we don't update the standard questions. if a query matches one of the standard questions, we can find the respective standard answer from mongodb. """ index = index_storage.index() return index.as_query_engine( response_synthesizer=get_response_synthesizer( response_mode=ResponseMode.NO_TEXT ), node_postprocessors=[SimilarityPostprocessor(similarity_cutoff=SIMILARITY_CUTOFF)], ) def get_matched_question_from_local_query_engine(query_text): local_query_engine = get_local_query_engine() local_query_response = local_query_engine.query(query_text) if len(local_query_response.source_nodes) > 0: matched_node = local_query_response.source_nodes[0] matched_question = matched_node.text logger.debug(f"Found matched question from index: {matched_question}") return matched_question else: return None def get_doc_meta(text): matched_doc_id = data_util.get_doc_id(text) mongo = index_storage.mongo() doc_meta = mongo.find_one({"doc_id": matched_doc_id}) doc_meta = LlamaIndexDocumentMeta(**doc_meta) if doc_meta else None return matched_doc_id, doc_meta def get_llm_query_engine(): index = index_storage.index() qa_template = Prompt(PROMPT_TEMPLATE_FOR_QUERY_ENGINE) return index.as_query_engine(text_qa_template=qa_template) def query_index(query_text, only_for_meta=False) -> Union[Answer, LlamaIndexDocumentMeta, None]: data_util.assert_not_none(query_text, "query cannot be none") logger.info(f"Query test: {query_text}") # first search locally matched_question = get_matched_question_from_local_query_engine(query_text) if matched_question: matched_doc_id, doc_meta = get_doc_meta(matched_question) if doc_meta: logger.debug(f"An matched doc meta found from mongodb: {doc_meta}") doc_meta.query_timestamps.append(data_util.get_current_milliseconds()) index_storage.mongo().upsert_one({"doc_id": matched_doc_id}, doc_meta) if only_for_meta: return doc_meta else: return Answer( category=doc_meta.category, question=query_text, matched_question=matched_question, source=Source.KNOWLEDGE_BASE if doc_meta.source == Source.KNOWLEDGE_BASE else Source.USER_ASKED, answer=doc_meta.answer, ) else: # means the document meta has been removed from mongodb. for example by pruning logger.warning(f"'{matched_doc_id}' is not found in mongodb") if only_for_meta: return None # if not found, turn to LLM llm_query_engine = get_llm_query_engine() response = llm_query_engine.query(query_text) # save the question-answer pair to index answer = Answer( category=None, question=query_text, source=index_storage.current_model, answer=str(response), ) index_storage.add_doc(answer) return answer def delete_doc(doc_id): data_util.assert_not_none(doc_id, "doc_id cannot be none") logger.info(f"Delete document with doc id: {doc_id}") return index_storage.delete_doc(doc_id) def get_document(req: DocumentRequest): doc_meta = index_storage.mongo().find_one({"doc_id": req.doc_id}) if doc_meta: return LlamaIndexDocumentMetaReadable(**doc_meta) elif req.fuzzy: doc_meta = query_index(req.doc_id, only_for_meta=True) if doc_meta: doc_meta.matched_question = doc_meta.question doc_meta.question = doc_meta.doc_id = req.doc_id return LlamaIndexDocumentMetaReadable(**doc_meta.model_dump()) return None def cleanup_for_test(): return index_storage.mongo().cleanup_for_test() def get_chat_engine(conversation_id: str, streaming: bool = False): local_query_engine = get_local_query_engine() query_engine_tools = [ MyQueryEngineTool.from_defaults( query_engine=local_query_engine, name="local_query_engine", description="Queries from a knowledge base consists of typical questions that a golf beginner might ask", ) ] chat_llm = OpenAI( temperature=0, model=index_storage.current_model, streaming=streaming, max_tokens=100, ) chat_history = chat_message_dao.get_chat_history(conversation_id) chat_history = [ChatMessage(role=c.role, content=c.content) for c in chat_history] return OpenAIAgent.from_tools( tools=query_engine_tools, llm=chat_llm, chat_history=chat_history, verbose=True, system_prompt=SYSTEM_PROMPT_TEMPLATE_FOR_CHAT_ENGINE, ) def get_response_text_from_chat(agent_chat_response): sources = agent_chat_response.sources if len(sources) > 0: source_content = sources[0].content if MATCHED_MARK in source_content: return source_content.replace(MATCHED_MARK, "").strip() return agent_chat_response.response def chat(query_text: str, conversation_id: str) -> Message: # we will not index chat messages in vector store, but will save them in mongodb data_util.assert_not_none(query_text, "query content cannot be none") user_message = ChatMessage(role=MessageRole.USER, content=query_text) # save immediately, since the following steps may take a while and throw exceptions chat_message_dao.save_chat_history(conversation_id, user_message) chat_engine = get_chat_engine(conversation_id) agent_chat_response = chat_engine.chat(query_text) response_text = get_response_text_from_chat(agent_chat_response) # todo: change the if condition to: response_text == get_default_answer_id() response_text = get_default_answer() if get_default_answer_id() in response_text else response_text matched_doc_id, doc_meta = get_doc_meta(response_text) if doc_meta: logger.debug(f"An matched doc meta found from mongodb: {doc_meta}") doc_meta.query_timestamps.append(data_util.get_current_milliseconds()) index_storage.mongo().upsert_one({"doc_id": matched_doc_id}, doc_meta) bot_message = ChatMessage(role=MessageRole.ASSISTANT, content=doc_meta.answer) else: # means the chat engine cannot find a matched doc meta from mongodb logger.warning(f"'{matched_doc_id}' is not found in mongodb") bot_message = ChatMessage(role=MessageRole.ASSISTANT, content=response_text) chat_message_dao.save_chat_history(conversation_id, bot_message) return Message.from_chat_message(conversation_id, bot_message) async def stream_chat(content: str, conversation_id: str): # todo: need to use chat engine based on index. otherwise, the local database is not utilized # We only support using OpenAI's API client = OpenAI() user_message = ChatMessage(role=MessageRole.USER, content=content) chat_message_dao.save_chat_history(conversation_id, user_message) history = chat_message_dao.get_chat_history(conversation_id) messages = [dict(content=c.content, role=c.role) for c in history] messages = [ dict( role=MessageRole.SYSTEM, content=( "assume you are an experienced golf coach, if the question has anything to do with golf, " "please give short, simple, accurate, precise answer to the question, " "limited to 80 words maximum. If the question has nothing to do with golf at all, please answer " f"'{get_default_answer()}'." ) ), ] + messages completion = client.chat.completions.create( model=index_storage.current_model, messages=messages, temperature=0, stream=True # again, we set stream=True ) chunks = [] for chunk in completion: finish_reason = chunk.choices[0].finish_reason content = chunk.choices[0].delta.content if finish_reason == "stop" or finish_reason == "length": # reached the end if content is not None: bot_message = ChatMessage(role=MessageRole.ASSISTANT, content=content) chat_message_dao.save_chat_history(conversation_id, bot_message) break if content is None: break chunks.append(content) logger.debug("Chunk message: %s", content) yield content

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from typing import List from fastapi.responses import StreamingResponse from app.utils.json import json_to_model from app.utils.index import get_agent from fastapi import APIRouter, Depends, HTTPException, Request, status from llama_index.llms.base import MessageRole, ChatMessage from llama_index.agent import OpenAIAgent from pydantic import BaseModel import logging chat_router = r = APIRouter() class _Message(BaseModel): role: MessageRole content: str class _ChatData(BaseModel): messages: List[_Message] @r.post("") async def chat( request: Request, # Note: To support clients sending a JSON object using content-type "text/plain", # we need to use Depends(json_to_model(_ChatData)) here data: _ChatData = Depends(json_to_model(_ChatData)), agent: OpenAIAgent = Depends(get_agent), ): logger = logging.getLogger("uvicorn") # check preconditions and get last message if len(data.messages) == 0: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, detail="No messages provided", ) lastMessage = data.messages.pop() if lastMessage.role != MessageRole.USER: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, detail="Last message must be from user", ) # convert messages coming from the request to type ChatMessage messages = [ ChatMessage( role=m.role, content=m.content, ) for m in data.messages ] # query chat engine # convert query engine to tool logger.info("Querying chat engine") response = agent.stream_chat(lastMessage.content, messages) # stream response async def event_generator(): queue = agent.callback_manager.handlers[0].queue while len(queue) > 0: item = queue.pop(0) yield item for token in response.response_gen: # If client closes connection, stop sending events if await request.is_disconnected(): break yield token return StreamingResponse(event_generator(), media_type="text/plain")

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import streamlit as st from llama_index import VectorStoreIndex, ServiceContext, Document from llama_index.llms import OpenAI import openai from llama_index import SimpleDirectoryReader st.set_page_config(page_title="Converse com Resoluções do Bacen, powered by LlamaIndex", page_icon="🦙", layout="centered", initial_sidebar_state="auto", menu_items=None) ############### reduce top margin ################ st.markdown( """ <style> .css-1y4p8pa { padding-top: 0px; } </style> """, unsafe_allow_html=True, ) ############### hidde hamburguer menu ################ st.markdown(""" <style> #MainMenu {visibility: hidden;} footer {visibility: hidden;} </style> """, unsafe_allow_html=True) openai.api_key = st.secrets.openai_key st.header("Converse 💬 com as Resoluções 4.966 e 352 do Banco Central e outras relacionadas, powered by LlamaIndex 🦙") st.info("Código disponível neste [repositório Github](https://github.com/mvpalheta/4966_LLM)", icon="💡") if "messages" not in st.session_state.keys(): # Initialize the chat messages history st.session_state.messages = [ {"role": "assistant", "content": "Me pergunte algo relacionado às Resoluções 4.966 e 352 do Banco Central!"} ] @st.cache_resource(show_spinner=False, ttl="30min") def load_data(): with st.spinner(text="Loading and indexing the docs – hang tight! This should take 1-2 minutes."): reader = SimpleDirectoryReader(input_dir="./data", recursive=True) docs = reader.load_data() service_context = ServiceContext.from_defaults(llm=OpenAI(model="gpt-3.5-turbo", temperature=0.5)) index = VectorStoreIndex.from_documents(docs, service_context=service_context) return index index = load_data() chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True) if prompt := st.chat_input("Sua pergunta"): # Prompt for user input and save to chat history st.session_state.messages.append({"role": "user", "content": prompt}) for message in st.session_state.messages: # Display the prior chat messages with st.chat_message(message["role"]): st.write(message["content"]) # If last message is not from assistant, generate a new response if st.session_state.messages[-1]["role"] != "assistant": with st.chat_message("assistant"): with st.spinner("Pensando..."): response = chat_engine.chat(prompt) st.write(response.response) message = {"role": "assistant", "content": response.response} st.session_state.messages.append(message) # Add response to message history

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"""Agent utils.""" from llama_index.core.agent.types import TaskStep from llama_index.core.base.llms.types import ChatMessage, MessageRole from llama_index.core.memory import BaseMemory def add_user_step_to_memory( step: TaskStep, memory: BaseMemory, verbose: bool = False ) -> None: """Add user step to memory.""" user_message = ChatMessage(content=step.input, role=MessageRole.USER) memory.put(user_message) if verbose: print(f"Added user message to memory: {step.input}")

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from llama_index.core.tools import FunctionTool def calculate_average(*values): """ Calculates the average of the provided values. """ return sum(values) / len(values) average_tool = FunctionTool.from_defaults( fn=calculate_average )

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from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, ServiceContext, Document def load_knowledge() -> list[Document]: # Load data from directory documents = SimpleDirectoryReader('knowledge').load_data() return documents def create_index() -> GPTVectorStoreIndex: print('Creating new index') # Load data documents = load_knowledge() # Create index from documents service_context = ServiceContext.from_defaults(chunk_size_limit=3000) index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context) # save_index(index) return index def save_index(index: GPTVectorStoreIndex): # Save index to file index.save_to_disk('knowledge/index.json') def load_index() -> GPTVectorStoreIndex: # Load index from file try: index = GPTVectorStoreIndex.load_from_disk('knowledge/index.json') except FileNotFoundError: index = create_index() return index def query_index(index: GPTVectorStoreIndex): # Query index query_engine = index.as_query_engine() while True: prompt = input("Type prompt...") response = query_engine.query(prompt) print(response) def main(): # Ask user if they want to refresh the index refresh_index = input("Do you want to refresh the index? (y/n) [n]: ") refresh_index = refresh_index.lower() == 'y' # If refreshing the index, create new index and save to file if refresh_index: index = create_index() # Otherwise, load index from file else: index = load_index() # Query index query_index(index) if __name__ == '__main__': main()

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import logging import streamlit as st from llama_index import ( OpenAIEmbedding, ServiceContext, SimpleDirectoryReader, VectorStoreIndex, ) from llama_index.llms import OpenAI from streamlit_examples.utils.theme import initPage from streamlit_examples.utils.streamlit import cache_file, upload_files initPage("QueryPDFs") st.write( "Ask questions or create summaries or explanations on PDFs using [LlamaIndex](https://www.llamaindex.ai/)" ) @st.cache_resource() def get_service_context(): llm = OpenAI( temperature=0.1, model="gpt-3.5-turbo", api_key=st.secrets["OPENAI_API_KEY"] ) embed_model = OpenAIEmbedding() return ServiceContext.from_defaults(llm=llm, embed_model=embed_model) @st.cache_data(show_spinner=False) def query(filename, question): logging.info(f"Asking '{question}' on '{filename}'") documents = SimpleDirectoryReader(input_files=[filename]).load_data() index = VectorStoreIndex.from_documents( documents, service_context=get_service_context() ) query_engine = index.as_query_engine() return query_engine.query(question) def get_question(): QUESTIONS = { "Summarize": "What is a summary of this document?", "Explain": "Explain this document", } mode = st.sidebar.selectbox("Select Mode", ("Summarize", "Explain", "Ask")) if mode == "Ask": question = st.sidebar.text_input("What's your question") if not question: st.sidebar.info("Please ask a question or select another mode.") st.stop() else: question = QUESTIONS[mode] return mode, question mode, question = get_question() # Upload PDFs pdfs = upload_files(type="pdf", accept_multiple_files=True) # Summarize each PDF for pdf in pdfs: filename = cache_file(pdf, type="pdf") with st.spinner(f"{mode} '{pdf.name}'..."): summary = query(filename, question) with st.expander(f"'{pdf.name}'", expanded=True): st.markdown(summary)

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#ingest uploaded documents from global_settings import STORAGE_PATH, INDEX_STORAGE, CACHE_FILE from logging_functions import log_action from llama_index.core import SimpleDirectoryReader, VectorStoreIndex from llama_index.core.ingestion import IngestionPipeline, IngestionCache from llama_index.core.node_parser import TokenTextSplitter from llama_index.core.extractors import SummaryExtractor from llama_index.embeddings.openai import OpenAIEmbedding def ingest_documents(): documents = SimpleDirectoryReader( STORAGE_PATH, filename_as_id = True ).load_data() for doc in documents: print(doc.id_) log_action( f"File '{doc.id_}' uploaded user", action_type="UPLOAD" ) try: cached_hashes = IngestionCache.from_persist_path( CACHE_FILE ) print("Cache file found. Running using cache...") except: cached_hashes = "" print("No cache file found. Running without cache...") pipeline = IngestionPipeline( transformations=[ TokenTextSplitter( chunk_size=1024, chunk_overlap=20 ), SummaryExtractor(summaries=['self']), OpenAIEmbedding() ], cache=cached_hashes ) nodes = pipeline.run(documents=documents) pipeline.cache.persist(CACHE_FILE) return nodes if __name__ == "__main__": embedded_nodes = ingest_documents()

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import tiktoken from llama_index.core import TreeIndex, SimpleDirectoryReader, Settings from llama_index.core.llms.mock import MockLLM from llama_index.core.callbacks import CallbackManager, TokenCountingHandler llm = MockLLM(max_tokens=256) token_counter = TokenCountingHandler( tokenizer=tiktoken.encoding_for_model("gpt-3.5-turbo").encode ) callback_manager = CallbackManager([token_counter]) Settings.callback_manager=callback_manager Settings.llm=llm documents = SimpleDirectoryReader("cost_prediction_samples").load_data() index = TreeIndex.from_documents( documents=documents, num_children=2, show_progress=True) print("Total LLM Token Count:", token_counter.total_llm_token_count)

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import torch from langchain.llms.base import LLM from llama_index import SimpleDirectoryReader, LangchainEmbedding, GPTListIndex, PromptHelper from llama_index import LLMPredictor, ServiceContext from transformers import pipeline from typing import Optional, List, Mapping, Any """ 使用自定义 LLM 模型，您只需要实现Langchain 中的LLM类。您将负责将文本传递给模型并返回新生成的标记。 facebook/opt-iml-max-30b https://huggingface.co/facebook/opt-iml-max-30b/tree/main """ # define prompt helper # set maximum input size max_input_size = 2048 # set number of output tokens num_output = 256 # set maximum chunk overlap max_chunk_overlap = 20 prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap) class CustomLLM(LLM): model_name = "facebook/opt-iml-max-30b" pipeline = pipeline("text-generation", model=model_name, device="cuda:0", model_kwargs={"torch_dtype":torch.bfloat16}) def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str: prompt_length = len(prompt) response = self.pipeline(prompt, max_new_tokens=num_output)[0]["generated_text"] # only return newly generated tokens return response[prompt_length:] @property def _identifying_params(self) -> Mapping[str, Any]: return {"name_of_model": self.model_name} @property def _llm_type(self) -> str: return "custom" # define our LLM llm_predictor = LLMPredictor(llm=CustomLLM()) service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper) # Load the your data documents = SimpleDirectoryReader('./data').load_data() index = GPTListIndex.from_documents(documents, service_context=service_context) # Query and print response query_engine = index.as_query_engine() response = query_engine.query("<query_text>") print(response)

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