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import asyncio import json from typing import Any, Tuple, List from langchain.base_language import BaseLanguageModel from langchain.tools import DuckDuckGoSearchResults, BaseTool from llama_index import download_loader, GPTListIndex, Document, LLMPredictor, ServiceContext from llama_index.response_synthesizers import TreeSummarize class WebSearchTool(DuckDuckGoSearchResults): name: str = "web_search" description: str = \ "Useful for when you need to search answer in the internet. " \ "Input should be a search query (like you would google it). " \ "If relevant, include location and date to get more accurate results. " \ "You will get a list of urls and a short snippet of the page. " async def _arun(self, *args: Any, **kwargs: Any) -> Any: return self._run(*args, **kwargs) class AskPagesTool(BaseTool): llm: BaseLanguageModel _page_loader = download_loader("SimpleWebPageReader")(html_to_text=True) # noqa name: str = "ask_urls" description: str = \ "You can ask a question about a URL. " \ "That smart tool will parse URL content and answer your question. " \ "Provide provide urls and questions in json format. " \ "urls is a list of urls to ask corresponding question from questions list" \ 'Example: {"urls": ["https://en.wikipedia.org/wiki/Cat", "https://en.wikipedia.org/wiki/Dog"], ' \ '"questions": ["How many cats in the world?", "How many dogs in the world?"]}' def _get_page_index(self, page: Document) -> GPTListIndex: llm_predictor_chatgpt = LLMPredictor(self.llm) service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor_chatgpt, chunk_size=1024) doc_summary_index = GPTListIndex.from_documents( [page], service_context=service_context, response_synthesizer=TreeSummarize(service_context=service_context) ) return doc_summary_index def _get_url_index(self, url: str) -> GPTListIndex: page = self._page_loader.load_data(urls=[url])[0] return self._get_page_index(page) @staticmethod def _parse_args(*args, **kwargs) -> List[Tuple[str, str]]: if len(args) == 1: urls_and_questions_dict = json.loads(args[0]) urls = urls_and_questions_dict["urls"] questions = urls_and_questions_dict["questions"] else: urls = kwargs["urls"] questions = kwargs["questions"] if len(urls) > 1 and len(questions) == 1: questions = questions * len(urls) if len(questions) > 1 and len(urls) == 1: urls = urls * len(questions) if len(urls) != len(questions): raise ValueError("Number of urls and questions should be equal") return list(zip(urls, questions)) def _run_single(self, url: str, question: str) -> str: page_index = self._get_url_index(url) llm_predictor_chatgpt = LLMPredictor(self.llm) service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor_chatgpt, chunk_size=1024) query_engine = page_index.as_query_engine( response_synthesizer=TreeSummarize(service_context=service_context), use_async=False) response = query_engine.query(question) return response.response async def _arun_single(self, url: str, question: str) -> str: page_index = self._get_url_index(url) llm_predictor_chatgpt = LLMPredictor(self.llm) service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor_chatgpt, chunk_size=1024) query_engine = page_index.as_query_engine( response_synthesizer=TreeSummarize(service_context=service_context), use_async=False) response = await query_engine.aquery(question) return response.response def _run(self, *args, **kwargs) -> Any: try: urls_with_questions = self._parse_args(*args, **kwargs) full_response = "" for url, question in urls_with_questions: answer = self._run_single(url, question) full_response += f"Question: {question} to {url}\nAnswer: {answer}\n" except Exception as e: full_response = f"Error: {e}" return full_response async def _arun(self, *args, **kwargs) -> Any: try: urls_with_questions = self._parse_args(*args, **kwargs) tasks = [] for url, question in urls_with_questions: tasks.append(self._arun_single(url, question)) answers = await asyncio.gather(*tasks) full_response = "" for i in range(len(urls_with_questions)): url, question = urls_with_questions[i] answer = answers[i] full_response += f"Question: {question} to {url}\nAnswer: {answer}\n" except Exception as e: full_response = f"Error: {e}" return full_response

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""" This script demonstrates how to use the llama_index library to create and query a vector store index. It loads documents from a directory, creates an index, and allows querying the index. usage: python hello_persist.py "What is the author's name and job now?" """ import os import sys import argparse import logging from dotenv import load_dotenv from llama_index.core import ( VectorStoreIndex, SimpleDirectoryReader, StorageContext, load_index_from_storage, Settings, ) from llama_index.embeddings.openai import OpenAIEmbedding def main(query): try: # Load environment variables load_dotenv() # Configure logging logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) # Configure embedding model Settings.embed_model = OpenAIEmbedding(model_name="text-embedding-3-small") # Set up storage directory storage_directory = "./storage" if not os.path.exists(storage_directory): logging.info("Creating new index...") documents = SimpleDirectoryReader("data").load_data() index = VectorStoreIndex.from_documents(documents) index.storage_context.persist(persist_dir=storage_directory) else: logging.info("Loading existing index...") storage_context = StorageContext.from_defaults(persist_dir=storage_directory) index = load_index_from_storage(storage_context) # Query the index query_engine = index.as_query_engine() response = query_engine.query(query) print(response) except Exception as e: logging.error(f"An error occurred: {str(e)}") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Query a vector store index.") parser.add_argument("--query", default="What is the author's name and job now?", help="The query to ask the index.") args = parser.parse_args() main(args.query)

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import streamlit as st from llama_hub.youtube_transcript import YoutubeTranscriptReader from llama_hub.youtube_transcript import is_youtube_video from llama_index import ( VectorStoreIndex, StorageContext, load_index_from_storage, ) from llama_index.prompts import ChatMessage, MessageRole from llama_index.tools import QueryEngineTool, ToolMetadata import os # import openai from llama_hub.tools.wikipedia import WikipediaToolSpec from llama_index.agent import OpenAIAgent from fetch_yt_metadata import fetch_youtube_metadata video_url = None with st.sidebar: openai_api_key = st.text_input("OpenAI API Key", key="chatbot_api_key", type="password") if openai_api_key: os.environ["OPENAI_API_KEY"] = openai_api_key "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)" video_url = st.text_input("'Enter your video url here:", key="video_url") if video_url: st.video(video_url) if is_youtube_video(video_url): metadata = fetch_youtube_metadata(video_url) st.session_state["metadata"] = metadata st.header("Metadata:") for k, v in metadata.items(): if k == "video_description": st.text_area("Description:", height=200, value=v, disabled=True) else: st.write(f"{k}: {v}") st.text_area("Transcript:", height=200, value=st.session_state.get("transcript", "")) if st.session_state.get("video_url"): url = st.session_state.get("video_url") st.write(f"Chat with {url}") if "counter" not in st.session_state: st.session_state.counter = 0 st.session_state.counter += 1 st.header(f"This page has run {st.session_state.counter} times.") st.button("Run it again") query_engine = None transcript = None if video_url: video_id = video_url.split('=')[1].split('&')[0] # check if storage already exists PERSIST_DIR = f"./storage/{video_id}" if not os.path.exists(PERSIST_DIR): # load the documents and create the index # documents = SimpleDirectoryReader("data").load_data() loader = YoutubeTranscriptReader() documents = loader.load_data(ytlinks=[url]) # save the documents to disk using the video_id.sbt index = VectorStoreIndex.from_documents(documents) # store it for later index.storage_context.persist(persist_dir=PERSIST_DIR) with open(f"{PERSIST_DIR}/transcript.txt", "w") as f: for doc in documents: f.write(doc.text) else: # load the existing index storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR) index = load_index_from_storage(storage_context) # either way we can now query the index query_engine = index.as_query_engine() if not st.session_state.get("summary"): summary = query_engine.query("What's the video about?").response st.session_state["summary"] = summary if not st.session_state.get("transcript"): transcript = open(f"{PERSIST_DIR}/transcript.txt").read() st.session_state["transcript"] = transcript st.title('💬 Talk2YouTube') st.write(st.session_state.get("summary",'Load a youtube video and chat with it')) if "messages" not in st.session_state: st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you?"}] for msg in st.session_state.messages: st.chat_message(msg["role"]).write(msg["content"]) if prompt := st.chat_input(): if not openai_api_key: st.info("Please add your OpenAI API key to continue.") st.stop() vector_tool = QueryEngineTool( query_engine=query_engine, metadata=ToolMetadata( name=f"VideoTranscript", description=f"useful for when you want to answer queries about the content of the video.", ), ) wiki_tool_spec = WikipediaToolSpec() tools = wiki_tool_spec.to_tool_list() #+ query_engine_tools st.session_state.messages.append({"role": "user", "content": prompt}) st.chat_message("user").write(prompt) agent = OpenAIAgent.from_tools([vector_tool], verbose=True, openai_api_key=st.session_state.get("chatbot_api_key")) chat_history = [ChatMessage(role=MessageRole.USER if x.get("role","assistant") == "user" else "assistant", content=x.get("content","")) for x in st.session_state.messages] response = agent.chat(prompt, chat_history=chat_history) msg = {"role":"assistant", "content":response.response} st.session_state.messages.append(msg) st.chat_message("assistant").write(msg.get("content"))

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import os from typing import Any, Optional from llama_index.core.readers.base import BasePydanticReader from llama_index.core.schema import Document DEFAULT_TOKEN_JSON_PATH = 'token.json' DEFAULT_SERVICE_ACCOUNT_JSON_PATH = 'service_account.json' DEFAULT_CREDENTIALS_JSON_PATH = 'credentials.json' HEADING_STYLE_TEMPLATE = 'HEADING_{}' DEFAULT_QUESTION_HEADING_STYLE_NUM = 3 EXCLUDED_LLM_METADATA_KEYS = ['source', 'title', 'section_name'] EXCLUDED_EMBED_METADATA_KEYS = ['source', 'title'] SCOPES = ["https://www.googleapis.com/auth/documents.readonly"] class FAQGoogleDocsReader(BasePydanticReader): token_json_path: str = DEFAULT_TOKEN_JSON_PATH service_account_json_path: str = DEFAULT_SERVICE_ACCOUNT_JSON_PATH credentials_json_path: str = DEFAULT_CREDENTIALS_JSON_PATH question_heading_style_num: int = DEFAULT_QUESTION_HEADING_STYLE_NUM is_remote: bool = True def __init__(self, token_json_path: Optional[str] = DEFAULT_TOKEN_JSON_PATH, service_account_json_path: Optional[str] = DEFAULT_SERVICE_ACCOUNT_JSON_PATH, credentials_json_path: Optional[str] = DEFAULT_CREDENTIALS_JSON_PATH, question_heading_style_num: Optional[int] = DEFAULT_QUESTION_HEADING_STYLE_NUM ) -> None: """Initialize with parameters.""" try: import google # noqa import google_auth_oauthlib # noqa import googleapiclient # noqa except ImportError as e: raise ImportError( '`google_auth_oauthlib`, `googleapiclient` and `google` ' 'must be installed to use the GoogleDocsReader.\n' 'Please run `pip install --upgrade google-api-python-client ' 'google-auth-httplib2 google-auth-oauthlib`.' ) from e super().__init__(token_json_path=token_json_path, service_account_json_path=service_account_json_path, credentials_json_path=credentials_json_path, question_heading_style_num=question_heading_style_num) @classmethod def class_name(cls) -> str: return 'CustomGoogleDocsReader' def load_data(self, document_ids: [str]) -> [Document]: """Load data from the input directory. Args: document_ids (List[str]): a list of document ids. """ if document_ids is None: raise ValueError('Must specify a "document_ids" in `load_kwargs`.') results = [] for document_id in document_ids: docs = self._load_docs(document_id) results.extend(docs) return results def _load_docs(self, document_id: str) -> [Document]: """Load a document from Google Docs. Args: document_id: the document id. Returns: The document text. """ import googleapiclient.discovery as discovery credentials = self._get_credentials() docs_service = discovery.build('docs', 'v1', credentials=credentials) doc = docs_service.documents().get(documentId=document_id).execute() doc_content = doc.get('body').get('content') doc_source = f'https://docs.google.com/document/d/{document_id}/edit#heading=' return self._structural_elements_to_docs(doc_content, doc_source) def _get_credentials(self) -> Any: """Get valid user credentials from storage. The file token.json stores the user's access and refresh tokens, and is created automatically when the authorization flow completes for the first time. Returns: Credentials, the obtained credential. """ from google.auth.transport.requests import Request from google.oauth2 import service_account from google.oauth2.credentials import Credentials from google_auth_oauthlib.flow import InstalledAppFlow creds = None if os.path.exists(self.token_json_path): creds = Credentials.from_authorized_user_file(self.token_json_path, SCOPES) elif os.path.exists(self.service_account_json_path): return service_account.Credentials.from_service_account_file( self.service_account_json_path, scopes=SCOPES ) # If there are no (valid) credentials available, let the user log in. if not creds or not creds.valid: if creds and creds.expired and creds.refresh_token: creds.refresh(Request()) else: flow = InstalledAppFlow.from_client_secrets_file( self.credentials_json_path, SCOPES ) creds = flow.run_local_server(port=8080) # Save the credentials for the next run with open(self.token_json_path, 'w') as token: token.write(creds.to_json()) return creds @staticmethod def _read_paragraph_element(element: Any) -> Any: """Return the text in the given ParagraphElement. Args: element: a ParagraphElement from a Google Doc. """ text_run = element.get('textRun') return text_run.get('content') if text_run else '' @staticmethod def _get_text_from_paragraph_elements(elements: [Any]) -> Any: return ''.join(FAQGoogleDocsReader._read_paragraph_element(elem) for elem in elements) def _structural_elements_to_docs(self, doc_elements: [Any], doc_source: str) -> [Document]: """Recurse through a list of Structural Elements. Read a document's text where text may be in nested elements. Args: doc_elements: a list of Structural Elements. """ docs = [] text = '' heading_id = '' section_name = '' question_heading_style = HEADING_STYLE_TEMPLATE.format(self.question_heading_style_num) section_heading_style = HEADING_STYLE_TEMPLATE.format(self.question_heading_style_num - 1) for value in doc_elements: if 'paragraph' in value: paragraph = value['paragraph'] elements = paragraph.get('elements') paragraph_text = FAQGoogleDocsReader._get_text_from_paragraph_elements(elements) if 'paragraphStyle' in paragraph and 'headingId' in paragraph['paragraphStyle']: named_style_type = paragraph['paragraphStyle']['namedStyleType'] if named_style_type in [ question_heading_style, section_heading_style, ]: # create previous document checking if it's not empty if text != '': node_metadata = { 'source': doc_source + heading_id, 'section_name': section_name, 'title': 'FAQ' } prev_doc = Document(text=text, metadata=node_metadata, excluded_embed_metadata_keys=EXCLUDED_EMBED_METADATA_KEYS, excluded_llm_metadata_keys=EXCLUDED_LLM_METADATA_KEYS) docs.append(prev_doc) if named_style_type == question_heading_style: heading_id = paragraph['paragraphStyle']['headingId'] text = paragraph_text else: section_name = paragraph_text text = '' else: text += paragraph_text return docs if __name__ == '__main__': reader = FAQGoogleDocsReader(service_account_json_path='../keys/service_account_key.json') docs = reader.load_data(['1LpPanc33QJJ6BSsyxVg-pWNMplal84TdZtq10naIhD8']) print(docs)

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import utils import os import openai import sys from dotenv import load_dotenv load_dotenv() api_key = os.getenv("API_KEY") openai.api_key os.environ['OPENAI_API_KEY'] = api_key # # examples # https://github.com/kevintsai/Building-and-Evaluating-Advanced-RAG-Applications # # SimpleDirectoryReader is a class that reads all the files in a directory and returns a list of documents # It will select the best file reader based on the file extensions # https://docs.llamaindex.ai/en/stable/examples/data_connectors/simple_directory_reader.html # # Load all (top-level) files from directory # ,input_dir="/" # ,input=files="/asdf.pdf" # ,required_exts=[".pdf", ".txt", ".md"] <- extensions to read # ,recursive=True # docs = reader.load_data() # print(f"Loaded {len(docs)} docs") # # llamaindex from llama_index import SimpleDirectoryReader,VectorStoreIndex,ServiceContext,Document from llama_index.llms import OpenAI #from langchain_community.llms import OpenAI documents = SimpleDirectoryReader( input_files=["data/Analisis_Decreto_de_Necesidad_y_Urgencia_Bases_para_la_Reconstrucción.pdf"], ).load_data() print(type(documents), "\n") print(len(documents), "\n") print(type(documents[0])) print(documents[0]) print(f"Loaded {len(documents)} pages docs") # pages # basic RAG pipeline # Document is a class that represents a document document = Document(text="\n\n".join([doc.text for doc in documents])) # llm declare # bge-small-en-v1.5 is a model that was trained on the BGE dataset # https://huggingface.co/BAAI/bge-small-en-v1.5 # FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs. llm = OpenAI(model="gpt-4-1106-preview", temperature=0.0) service_context = ServiceContext.from_defaults( llm=llm, embed_model="local:BAAI/bge-small-en-v1.5" ) index = VectorStoreIndex.from_documents([document], service_context=service_context) query_engine = index.as_query_engine() # query response = query_engine.query( """ Contexto: Eres el mejor analista de documentos de leyes con un IQ de 150. Tienes que ser minucioso y necesito que revises la totalidad de las paginas del documento, sin dejar nada por fuera. Se experto en el tema y no me falles. Pregunta: devolver en forma de items la totalidad de los temas que trata el documento presentado de forma minuciosa. Forma de respuesta: El texto suministrado es en español y la respuesta la necesito en español. """ ) print(str(response))

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# Import the necessary libraries import random import time from llama_index.llms import OpenAI import streamlit as st from llama_index import VectorStoreIndex, ServiceContext, StorageContext, set_global_service_context from langchain.embeddings.huggingface import HuggingFaceEmbeddings from llama_index.embeddings import LangchainEmbedding import chromadb from llama_index.vector_stores import ChromaVectorStore from llama_index.storage.storage_context import StorageContext from llama_index.node_parser import SentenceSplitter from llama_index.indices.prompt_helper import PromptHelper import re from llama_index.chat_engine import CondensePlusContextChatEngine from llama_index.indices.vector_store.retrievers import VectorIndexRetriever from langchain_openai import ChatOpenAI from llama_index.postprocessor import RankGPTRerank # Streamlit interface st.title('🦜🔗 Tourism Assistant Chatbot') #First run, initialize the context and the chat engine if "init" not in st.session_state: st.session_state.init = True system_prompt = ( ''' #### Task Instructions: You are a friendly and knowledgeable tourism assistant, helping users with their queries related to tourism, travel, dining, events, and any related questions. Your goal is to provide accurate and useful information. If there's information you don't know, respond truthfully. Add a touch of personality and humor to engage users. End your responses asking to the user if there's anything else you can help with, everytime. #### Personalization & Tone: Maintain an upbeat and helpful tone, embodying the role of a helpful travel assistant. Inject personality and humor into responses to make interactions more enjoyable. #### Context for User Input: Always consider the user's input in the context of tourism, travel, and related topics. If a question is outside this scope, respond with a friendly reminder of your expertise and limitations. If a question is outisde the travel or anything related to the travel domain please kindly remember the user that that question is not in your scope of expertise (cf. "Tell me a joke!" example below). #### Creativity & Style Guidance: Craft responses that are not only informative but also creative. Avoid short and plain answers; instead, provide engaging and well-elaborated responses. #### External Knowledge & Data: Base your responses on the dataset of events and places, ensuring accuracy in facts. If the dataset doesn't have information, clearly state that you don't have the specific data. #### Handling Non-Travel Related Questions: If a user asks a question outside the scope of travel, respond creatively but firmly, reminding the user of the bot's expertise in the travel domain. Redirect the conversation back to travel-related topics or provide a gentle refusal. #### Rules & Guardrails: Adhere to ethical standards. If a user request involves prohibited content or actions, respond appropriately and within the bounds of ethical guidelines. #### Output Verification Standards: Maintain a commitment to accuracy. If there's uncertainty in information, it's better to express that you're not sure rather than providing potentially inaccurate details. #### Benefits of System Prompts: 1. **Character Maintenance:** Engage users with a consistent and friendly persona for longer conversations. 2. **Creativity:** Exhibit creative and natural behavior to enhance user experience. 3. **Rule Adherence:** Follow instructions carefully to avoid prohibited tasks or text. ### Example User Interactions: **User: Recommend a trendy restaurant in Paris.** > "Ah, Paris - the city of love and incredible cuisine! 🥖 How about checking out 'La Mode Bistro'? It's not just a restaurant; it's a fashion show for your taste buds! 😋" **User: What's the best way to explore Tokyo on a budget?** > "Exploring Tokyo without breaking the bank? 🏮 How about hopping on the efficient and cost-friendly metro, grabbing some street food in Harajuku, and exploring the free admission areas of beautiful parks like Ueno! 🌸" **User: Any upcoming events in New York City?** > "NYC, the city that never sleeps! 🗽 Let me check my event database for you. One moment... 🕵️‍♂️ Ah, there's a fantastic art festival in Chelsea this weekend! 🎨" **User: Tell me a joke!** > "While I'm better at recommending travel spots, here's a quick one for you: Why don't scientists trust atoms? Because they make up everything! 😄 Now, anything travel-related you'd like to know?" **User: What's the capital of France?** > "Ah, testing my geography knowledge, are we? 😄 The capital of France is Paris! 🇫🇷 Now, if you have any travel-related questions, I'm your go-to guide!" **User: Can you help me with my math homework?** > "Ah, numbers are a bit outside my travel-savvy brain! 😅 If you have any questions about amazing destinations or travel tips, though, I'm all ears!" ''') #temperature adjustable at will st.session_state.service_context = ServiceContext.from_defaults(llm=ChatOpenAI(model="gpt-3.5-turbo", temperature=0.9), prompt_helper = PromptHelper(), embed_model= LangchainEmbedding(HuggingFaceEmbeddings(model_name='dangvantuan/sentence-camembert-large')), #in case of new embeddings, possibility to add "model_kwargs = {'device': 'cuda:0'}" to the HuggingFaceEmbeddings call to use GPU node_parser=SentenceSplitter(), system_prompt=system_prompt, ) set_global_service_context(st.session_state.service_context) # create or get a chroma collection st.session_state.chroma_collection = chromadb.PersistentClient(path="./chroma_db").get_or_create_collection("tourism_db") # assign chroma as the vector_store to the context st.session_state.storage_context = StorageContext.from_defaults(vector_store=ChromaVectorStore(chroma_collection=st.session_state.chroma_collection)) #get the index st.session_state.index = VectorStoreIndex.from_vector_store(ChromaVectorStore(chroma_collection=st.session_state.chroma_collection), storage_context=st.session_state.storage_context, service_context=st.session_state.service_context) #example of context and condense prompt adjustability #context_prompt= "Base the reply to the user question mainly on the Description field of the context " #condense_prompt = " " st.session_state.retriever=VectorIndexRetriever(st.session_state.index, similarity_top_k=10) #or index.as_retriever(service_context=service_context, search_kwargs={"k": 10}) #I chose to use the RankGPTRerank postprocessor to rerank the top 4 results from the retriever over other rerankers like LLMRerank that wasn't working as expected reranker = RankGPTRerank( llm=OpenAI( model="gpt-3.5-turbo", temperature=0.0), top_n=4, verbose=True, ) st.session_state.chat_engine = CondensePlusContextChatEngine.from_defaults( retriever=st.session_state.retriever, query_engine=st.session_state.index.as_query_engine(service_context=st.session_state.service_context, retriever=st.session_state.retriever), service_context=st.session_state.service_context, system_prompt=system_prompt, node_postprocessors=[reranker], #condense_prompt=DEFAULT_CONDENSE_PROMPT_TEMPLATE, #context_prompt=DEFAULT_CONTEXT_PROMPT_TEMPLATE, verbose=True, ) #initialize the chat history st.session_state.messages = [] #initialize the assistant with a random greeting assistant_response = random.choice( [ "Hello there! How can I assist you today?", "Good day human! I'm here to answer questions about travel. What do you need help with?", "Hello! My name is Minotour2.0. Please feel free to ask me any questions about trips, destinations or planning.", "Welcome! I'm an AI assistant focused on travel. How may I assist you in finding your next adventure?", "Greetings! What are your travel plans or questions? I'm happy to provide any information I can.", "Hi there, traveler! I'm your virtual travel guide - where would you like to go or what do you need help planning?", "What brings you here today? I'm your assistant for all things related to getting away - what destination interests you?", "Salutations! Let me know if you need advice on flights, hotels or activities for an upcoming journey.", "Hello friend, I'm here to help with travel queries. What questions can I answer for you?", "Welcome, I'm your assistant available to help with transportation, lodging or other travel logistics. How can I assist you?", ] ) st.session_state.messages.append({"role": "assistant", "content": assistant_response}) # Display chat messages from history on app rerun for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) def handle_chat(question): if question.lower() == "reset": st.session_state.chat_engine.reset() st.session_state.messages = [] return "The conversation has been reset." else: response = st.session_state.chat_engine.chat(question) cleaned_response = re.sub(r"(AI: |AI Assistant: |assistant: )", "", re.sub(r"^user: .*$", "", str(response), flags=re.MULTILINE)) return cleaned_response if user_input:= st.chat_input("Please enter your question:"): if user_input.lower() == "exit": st.warning('Goodbye') st.stop() else: with st.chat_message("user"): st.markdown(user_input) # Add user message to chat history st.session_state.messages.append({"role": "user", "content": user_input}) # Handle chat and get the response response = handle_chat(user_input) # Display assistant response in chat message container with st.chat_message("assistant"): full_response = "" message_placeholder = st.empty() for chunk in response.split(): full_response += chunk + " " time.sleep(0.05) # Add a blinking cursor to simulate typing message_placeholder.markdown(full_response + "▌") message_placeholder.markdown(full_response) # Add assistant response to chat history st.session_state.messages.append({"role": "assistant", "content": full_response})

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import os import json import logging import sys import requests from dotenv import load_dotenv from requests.adapters import HTTPAdapter from urllib3.util.retry import Retry from llama_index.core import VectorStoreIndex, Document from llama_index.tools.brave_search import BraveSearchToolSpec from llama_index.readers.web import SimpleWebPageReader # Constants USER_AGENT = 'Mozilla/5.0 (compatible; YourBot/1.0; +http://yourwebsite.com/bot.html)' HEADERS = {'User-Agent': USER_AGENT} RETRIES = Retry(total=5, backoff_factor=0.1, status_forcelist=[500, 502, 503, 504]) def setup_logging(): """ Initialize logging configuration to output logs to stdout. """ logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) def load_environment_variables(): """ Load environment variables from the .env file. :return: The Brave API key. """ load_dotenv() return os.getenv('BRAVE_API_KEY') def perform_search(query, api_key): """ Perform a search using the Brave Search API. :param query: The search query. :param api_key: The Brave API key. :return: The search response. """ tool_spec = BraveSearchToolSpec(api_key=api_key) return tool_spec.brave_search(query=query) def extract_search_results(response): """ Extract search results from the Brave Search API response. :param response: The search response. :return: A list of search results. """ documents = [doc.text for doc in response] search_results = [] for document in documents: response_data = json.loads(document) search_results.extend(response_data.get('web', {}).get('results', [])) return search_results def scrape_web_pages(search_results): """ Scrape web pages from the URLs obtained from the search results. :param search_results: The list of search results. :return: A list of scraped documents. """ session = requests.Session() session.mount('http://', HTTPAdapter(max_retries=RETRIES)) session.mount('https://', HTTPAdapter(max_retries=RETRIES)) all_documents = [] for result in search_results: url = result.get('url') try: response = session.get(url, headers=HEADERS, timeout=10) response.raise_for_status() doc = Document(text=response.text, url=url) all_documents.append(doc) except requests.exceptions.RequestException as e: logging.error(f"Failed to scrape {url}: {e}") return all_documents def main(): """ Main function to orchestrate the search, scraping, and querying process. """ setup_logging() api_key = load_environment_variables() my_query = "What is the latest news about llamaindex?" response = perform_search(my_query, api_key) search_results = extract_search_results(response) all_documents = scrape_web_pages(search_results) # Load all the scraped documents into the vector store index = VectorStoreIndex.from_documents(all_documents) # Use the index to query with the language model query_engine = index.as_query_engine() response = query_engine.query(my_query) print(response) if __name__ == "__main__": main()

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import qdrant_client from llama_index.llms import Ollama from llama_index import ( VectorStoreIndex, ServiceContext, ) from llama_index.vector_stores.qdrant import QdrantVectorStore # re-initialize the vector store client = qdrant_client.QdrantClient( path="./qdrant_data" ) vector_store = QdrantVectorStore(client=client, collection_name="tweets") # get the LLM again llm = Ollama(model="mistral") service_context = ServiceContext.from_defaults(llm=llm,embed_model="local") # load the index from the vector store index = VectorStoreIndex.from_vector_store(vector_store=vector_store,service_context=service_context) def rag_pipline(query): if query is not None: query_engine = index.as_query_engine(similarity_top_k=20) response = query_engine.query(query) return response else: return "i am sorry. i cannot answer you for this due to some error in data"

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import os from llama_index.core import StorageContext, VectorStoreIndex, load_index_from_storage from llama_index.readers.file import PDFReader # def get_index(data, index_name): # index = None # if not os.path.exists(index_name): # print('Building index', index_name) # index = VectorStoreIndex.from_documents(data, show_progress=True) # index.storage_context.persist(persist_dir=index_name) # else : # index = load_index_from_storage( # StorageContext.from_defaults(persist_dir=index_name) # ) # return index # pdf_path = os.path.join('data', 'Malaysia.pdf') # malaysia_pdf = PDFReader().load_data(file=pdf_path) # malaysia_index = get_index(malaysia_pdf, 'malaysia') # malaysia_engine = malaysia_index.as_query_engine() # malaysia_engine.query() def get_index(data_files, index_name): index = None data = [] for file_path in data_files: reader = PDFReader() data.extend(reader.load_data(file=file_path)) if not os.path.exists(index_name): print(f'Building index {index_name}') index = VectorStoreIndex.from_documents(data, show_progress=True) index.storage_context.persist(persist_dir=index_name) else: index = load_index_from_storage( StorageContext.from_defaults(persist_dir=index_name) ) return index file_paths = [ os.path.join('data', 'Malaysia.pdf'), # Add more file paths here ] combined_index = get_index(file_paths, 'combined_index') combined_engine = combined_index.as_query_engine()

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from llama_index.retrievers import BaseRetriever from llama_index import QueryBundle from llama_index.schema import NodeWithScore from llama_index.vector_stores import VectorStoreQuery from typing import List, Sequence, Any from llama_index.tools import BaseTool, adapt_to_async_tool from llama_index import Document, VectorStoreIndex class ToolRetriever(BaseRetriever): def __init__( self, tools: Sequence[BaseTool], sql_tools: Sequence[BaseTool], embed_model: Any, index: VectorStoreIndex = None, message: str = "", append_sql: bool = True, similarity_top_k: int = 8, logger=None, ) -> None: self._message = message self._tools = tools self._index = index self._sql_tools = sql_tools self._append_sql = append_sql self._similarity_top_k = similarity_top_k self._embed_model = embed_model self._logger = logger def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: """Retrieve.""" from llama_index.retrievers import VectorIndexRetriever retriever = VectorIndexRetriever( index=self._index, similarity_top_k=self._similarity_top_k, ) response = retriever.retrieve(query_bundle) tools_ = [] for n in response: tools_.append(self._tools[n.metadata["idx"]]) if self._append_sql: tools_.append(self._sql_tools) # tools_.append(self._tools[-1]) # add SQL tool self._logger.debug(f"Tools before: {self._tools}") _tmp = set(adapt_to_async_tool(t) for t in tools_) self._logger.debug(f"Tools after: {list(_tmp)}") return list(_tmp) # return [adapt_to_async_tool(t) for t in tools_] def create_vector_index_from_tools(self): from llama_index.tools import adapt_to_async_tool get_tools = lambda _: self._tools tools = [adapt_to_async_tool(t) for t in get_tools("")] docs = [ str( "idx: " + str(idx) + ", name: " + str(t.metadata.name) + ", description: " + str(t.metadata.description) ) for idx, t in enumerate(tools) ] documents = [ Document(text=t, metadata={"idx": idx}) for idx, t in enumerate(docs) ] self._index = VectorStoreIndex.from_documents( documents, embed_model=self._embed_model )

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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_index from fastapi import APIRouter, Depends, HTTPException, Request, status from llama_index import VectorStoreIndex from llama_index.llms.base import MessageRole, ChatMessage from pydantic import BaseModel 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)), index: VectorStoreIndex = Depends(get_index), ): # 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 chat_engine = index.as_chat_engine( chat_mode="context", sparse_top_k=12, vector_store_query_mode="hybrid", similarity_top_k=2, system_prompt=( "You are a chatbot, able to have normal interactions, as well as talk" " about an Grade 3 Unit Tests, Holidays and Dairy of the School." ), verbose=False, ) response = chat_engine.stream_chat(lastMessage.content, messages) # stream response async def event_generator(): 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 os from llama_index import ( VectorStoreIndex, SimpleDirectoryReader, StorageContext, load_index_from_storage, ) BOT_NAME = os.environ["BOT_NAME"] def construct_index(directory_data, directory_index, force_reload=False): # check if storage already exists if not os.path.exists(directory_index) or force_reload: print(f'Creating new index using {directory_data}') # load the documents and create the index documents = SimpleDirectoryReader(directory_data).load_data() index = VectorStoreIndex.from_documents(documents) # store it for later index.storage_context.persist(persist_dir=directory_index) print(f'Storing new index to {directory_index}') else: # load the existing index print(f'Loading existing index from {directory_index}') storage_context = StorageContext.from_defaults(persist_dir=directory_index) index = load_index_from_storage(storage_context) return index def query(question, index): query_engine = index.as_query_engine() response = query_engine.query(question) return response def ask(bot_name): index = construct_index(directory_data=f'data/{bot_name}', directory_index=f'storage/{bot_name}') while True: question = input("What do you want to know?") response = query(question=question, index=index) print(f"{bot_name} says: {response}") if __name__ == '__main__': ask(BOT_NAME)

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from llama_index import SimpleDirectoryReader, ServiceContext, VectorStoreIndex from llama_index.llms import OpenAI, ChatMessage, MessageRole from llama_index.chat_engine.condense_plus_context import CondensePlusContextChatEngine from dotenv import load_dotenv import os load_dotenv() vector_index = None history = [] def initializeService(): global vector_index llm = OpenAI(model="gpt-3.5-turbo", temperature=0.5) promptFile = open('./data/prompt.txt') prompt = promptFile.read() #print("Using the following system prompt: ", prompt, sep='\n') service_context = ServiceContext.from_defaults( llm=llm, system_prompt=prompt) try: reader = SimpleDirectoryReader( input_dir='./data/context', recursive=False) docs = reader.load_data() except ValueError: print( f"Context directory is empty, using only prompt") docs = [] vector_index = VectorStoreIndex.from_documents( docs, service_context=service_context) def loadChat(): global vector_index global history query_engine = vector_index.as_query_engine() chat_history = list(map(lambda item: ChatMessage( role=item['source'], content=item['message']), history )) chat_engine = CondensePlusContextChatEngine.from_defaults( query_engine, chat_history=chat_history ) return chat_engine def chat(message): global history history.append({'source': MessageRole.USER, 'message': message}) chat_engine = loadChat() response = chat_engine.chat(message) history.append({'source': MessageRole.SYSTEM, 'message': response.response}) return response.response if __name__ == "__main__": initializeService() question = input("Ask me anything: ") while question != "exit": print(chat(question)) question = input("Ask me anything: ")

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import streamlit as st import openai from llama_index.storage.docstore import SimpleDocumentStore from llama_index.vector_stores import FaissVectorStore from llama_index.storage.index_store import SimpleIndexStore from llama_index import load_index_from_storage from llama_index.storage.storage_context import StorageContext from llama_index.query_engine import CitationQueryEngine @st.cache_resource def preprocess_prelimnary(): storage_context = StorageContext.from_defaults(docstore = SimpleDocumentStore.from_persist_dir(persist_dir = "persist_new"), vector_store = FaissVectorStore.from_persist_dir(persist_dir = "persist_new"), index_store = SimpleIndexStore.from_persist_dir(persist_dir = "persist_new")) index = load_index_from_storage(storage_context = storage_context) query_engine = CitationQueryEngine.from_args(index, similarity_top_k = 3, citation_chunk_size = 1024) return query_engine openai.api_key = st.secrets['OPENAI_API_KEY'] st.set_page_config(layout = 'wide', page_title = 'Precedents Database') st.title('Query Precedents') q_e = preprocess_prelimnary() query = st.text_area(label = 'Enter your query involving Indian Legal Precedents.') # model = st.selectbox(label = 'Select a model', options = ['gpt-3.5-turbo', 'gpt-4']) start = st.button(label = 'Start') base_append = "" if start: st.subheader('Query Response -') database_answer = q_e.query(query + base_append) st.write(database_answer.response) st.subheader('Actual Sources -') for i in range(len(database_answer.source_nodes)): st.write(database_answer.source_nodes[i].node.get_text()) st.write(f'Case Name - {database_answer.source_nodes[i].node.extra_info["file_name"]}')

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import streamlit as st from dotenv import load_dotenv load_dotenv() import os import tempfile from llama_index import SimpleDirectoryReader, StorageContext, LLMPredictor from llama_index import VectorStoreIndex from llama_index import ServiceContext from llama_index.embeddings.langchain import LangchainEmbedding from langchain.chat_models import ChatOpenAI import tiktoken from langchain.embeddings import CohereEmbeddings import openai os.environ["OPENAI_API_KEY"] = st.secrets["OPENAI_API_KEY"] openai.api_key = st.secrets["OPENAI_API_KEY"] os.environ["COHERE_API_KEY"] = st.secrets["COHERE_API_KEY"] llm_predictor = LLMPredictor(llm = ChatOpenAI(temperature = 0, model_name = 'gpt-3.5-turbo', max_tokens = -1, openai_api_key = openai.api_key)) embed_model = LangchainEmbedding(CohereEmbeddings(model = "embed-english-light-v2.0")) storage_context = StorageContext.from_defaults() service_context = ServiceContext.from_defaults(llm_predictor = llm_predictor, embed_model = embed_model) def num_tokens_from_string(string: str, encoding_name: str) -> int: encoding = tiktoken.encoding_for_model(encoding_name) num_tokens = len(encoding.encode(string)) return num_tokens @st.cache_resource def preprocessing(uploaded_file): if uploaded_file: temp_dir = tempfile.TemporaryDirectory() file_path = os.path.join(temp_dir.name, uploaded_file.name) with open(file_path, "wb") as f: f.write(uploaded_file.read()) document = SimpleDirectoryReader(input_files = [file_path]).load_data() tokens = num_tokens_from_string(document[0].text, 'gpt-3.5-turbo') global context context = document[0].text if tokens <= 4000: print('Case - A') return context else: print('Case - B') index = VectorStoreIndex.from_documents(document, service_context = service_context, storage_context = storage_context) global engine engine = index.as_query_engine(similarity_top_k = 3) return engine @st.cache_resource def run(_query_engine, query): if type(_query_engine) == str: print('Executing Case - A') response = openai.ChatCompletion.create( model = "gpt-3.5-turbo", messages=[ {"role": "system", "content": "You are a helpful assistant who answers questions given context."}, {"role": "user", "content": f"The question is - {query}\nThe provided context is - {_query_engine}\nAnswer the question to the best of your abilities."}, ] ) st.write(response['choices'][0]['message']['content']) else: print('Executing Case - B') st.write(query_engine.query(query).response) return True st.set_page_config(layout = "wide") st.title("Document Querying") uploaded_file = st.file_uploader('Upload your file') query_engine = preprocessing(uploaded_file) if query_engine: query = st.text_input('Enter your Query.', key = 'query_input') if query: run(query_engine, query)

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from dotenv import load_dotenv import os import streamlit as st import pandas as pd from llama_index.core.query_engine import PandasQueryEngine from llama_index.core.tools import QueryEngineTool, ToolMetadata from llama_index.core.agent import ReActAgent from llama_index.llms.openai import OpenAI from prompts import new_prompt, instruction_str, context from note_engine import note_engine from pdf import combined_engine from pdf import get_index as pdf_get_index load_dotenv() population_path = os.path.join("data", "population.csv") population_df = pd.read_csv(population_path) population_query_engine = PandasQueryEngine( df=population_df, verbose=True, instruction_str=instruction_str ) population_query_engine.update_prompts({"pandas_prompt" : new_prompt}) tools = [ note_engine, QueryEngineTool( query_engine=population_query_engine, metadata=ToolMetadata( name="population_query_engine", description="This gives information at the world population and demographic", ), ), # QueryEngineTool( # query_engine=malaysia_engine, # metadata=ToolMetadata( # name="malaysia_data", # description="This gives details information about Malaysia country", # ), # ), QueryEngineTool( query_engine=combined_engine, metadata=ToolMetadata( name="combined_data", description="This gives information from multiple files", ), ), ] llm = OpenAI(model="gpt-3.5-turbo-0613") agent = ReActAgent.from_tools(tools, llm=llm, verbose=True, context=context) # while (prompt := input("Enter a prompt (q to quit): ")) != "q": # result = agent.query(prompt) # print(result) file_paths = [] # File uploader in the sidebar with st.sidebar: st.header("Upload PDF file") file = st.file_uploader("", type=["pdf"]) if file: file_path = os.path.join("data", file.name) # Save the uploaded file with open(file_path, "wb") as f: f.write(file.getvalue()) # Display confirmation message st.success(f"File uploaded successfully: {file.name}") # Add the uploaded file path to the list of file paths file_paths.append(file_path) # Check if there are any uploaded files if file_paths: # Get combined index for uploaded files combined_index = pdf_get_index(file_paths, 'combined_index') combined_engine = combined_index.as_query_engine() # Add QueryEngineTool for combined data tools.append( QueryEngineTool( query_engine=combined_engine, metadata=ToolMetadata( name="combined_data", description="This gives information from multiple files", ), ) ) st.title("AgentAI - RAG") user_input = st.text_input("Enter a prompt:") if user_input: if user_input.lower() == 'q': st.stop() else: result = agent.query(user_input) st.text_area("Response:", value=result, height=100, disabled=False)

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# general imports from constants import * # streamlit imports import streamlit as st from utils import * from streamlit_lottie import st_lottie # llama index imports import openai from llama_index import ( VectorStoreIndex, download_loader, ServiceContext, set_global_service_context, ) from llama_index.llms import OpenAI from llama_index.embeddings import LangchainEmbedding from langchain.embeddings.huggingface import HuggingFaceEmbeddings openai.api_key = OpenAI_key # from constants.py system_prompt = """ [INST] <> You are a helpful bank loan officer. You are going to be given a bank statement to analyse and you must provide accurate insights about its contents. If a question doesn't make any sense, or is not factually coherent, explain what is wrong with the question instead of answering something incorrect. If you don't know the answer, don't share inaccurate information. Your goal is to provide insightful answers about the financial background of an individual. <> """ llm = OpenAI(model="gpt-4-1106-preview", system_prompt=system_prompt) embeddings = LangchainEmbedding(HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")) service_context = ServiceContext.from_defaults(llm=llm, embed_model=embeddings) set_global_service_context(service_context) # import lottie lottie_file = load_lottieurl() # animation url st.set_page_config(page_title="loan_gpt") st_lottie(lottie_file, height=175, quality="medium") st.title("**Loan Check: Business Loan Analysis**") if "uploaded" not in st.session_state: st.session_state["uploaded"] = False st.session_state["filename"] = None st.session_state["initial_response"] = None if "query_engine" not in st.session_state: st.session_state["query_engine"] = None def reset(): st.session_state["uploaded"] = False st.session_state["filename"] = None st.session_state["initial_response"] = None st.session_state["query_engine"] = None if not st.session_state["uploaded"]: st.write("Upload a bank statement and analyze loan worthiness.") input_file = st.file_uploader("Choose a file") if input_file and does_file_have_pdf_extension(input_file): path = store_pdf_file(input_file, dir) # default dir is ./statements/ scs = st.success("File successfully uploaded") filename = input_file.name with st.spinner("Analyzing document..."): PyMuPDFReader = download_loader("PyMuPDFReader") loader = PyMuPDFReader() documents = loader.load(file_path=path, metadata=True) index = VectorStoreIndex.from_documents(documents) query_engine = index.as_query_engine() st.session_state["query_engine"] = query_engine scs.empty() st.session_state["uploaded"] = True st.session_state["filename"] = filename st.rerun() if st.session_state["uploaded"]: st.write( f"Here is a financial summary of the account holder for the uploaded statement:" ) st.button("Upload New PDF", on_click=reset) initial_prompt = """ I want to analyze the financial health of the individual based solely on the given statement. Here are some details I want information on: 1. Total monthly deposits (with months and amounts) 2. Total monthly withdrawals (with months and amounts) 3. Any recurring payments (such as rent, utilities, loan repayments - with descriptions, dates, and amounts) 4. Any other noticeable spending habits (with amounts) Make sure your output is well formatted and is plain-text. I want to determine if this individual should be awarded a business loan based on the above. Give me a potential yes, potential no or cannot say answer and evidence your response from details from above. Be sure to highlight any noticeable red-flags or positive signs. """ query_engine = st.session_state["query_engine"] if not st.session_state["initial_response"]: with st.spinner("Generating initial analysis..."): response = query_engine.query(initial_prompt) st.session_state["initial_response"] = response.response st.write(st.session_state["initial_response"]) prompt = st.text_input("Type any additional queries query") if prompt: with st.spinner("Generating response..."): response = query_engine.query(prompt) st.write(response.response)

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import pathlib import tempfile from io import BytesIO import openai import streamlit as st from llama_index.core import SimpleDirectoryReader, VectorStoreIndex from llama_index.core.chat_engine import ContextChatEngine from llama_index.llms.openai import OpenAI from sidebar import sidebar_params st.set_page_config(page_title="Chat with Documents", layout="wide", page_icon="🔥") st.title("Chat with Documents") @st.cache_resource(show_spinner=False) def build_chat_engine(file: BytesIO, temperature: float) -> ContextChatEngine: with st.spinner("Loading and indexing the document..."): with tempfile.TemporaryDirectory() as temp_dir: temp_file_path = pathlib.Path(temp_dir) / file.name with open(temp_file_path, "wb") as f: f.write(file.getbuffer()) reader = SimpleDirectoryReader(input_files=[temp_file_path]) documents = reader.load_data() llm = OpenAI(model="gpt-3.5-turbo", temperature=temperature) index = VectorStoreIndex.from_documents(documents) return index.as_chat_engine(chat_mode="context", llm=llm, verbose=True) def add_message(role: str, content: str): st.session_state.messages.append({"role": role, "content": content}) openai_api_key, temperature = sidebar_params() uploaded_file = st.file_uploader( "Upload a pdf, docx, or txt file", type=["pdf", "docx", "txt"], ) if not openai_api_key or not uploaded_file: st.stop() openai.api_key = openai_api_key chat_engine = build_chat_engine(uploaded_file, temperature) if "messages" not in st.session_state or st.sidebar.button("Clear message history"): st.session_state.messages = [ { "role": "assistant", "content": "Ask me questions about the uploaded document!", }, ] for message in st.session_state.messages: with st.chat_message(message["role"]): st.write(message["content"]) if user_query := st.chat_input("Ask questions about the document..."): with st.chat_message("user"): st.write(user_query) add_message("user", user_query) with st.chat_message("assistant"), st.spinner("Generating response..."): response = chat_engine.chat(user_query).response st.write(response) add_message("assistant", response)

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import logging from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext from llama_index.vector_stores import ChromaVectorStore from llama_index.storage.storage_context import StorageContext # from IPython.display import Markdown, display from llama_index.node_parser import SentenceSplitter from embedding_manager import Embeddings import chromadb logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) class DataLoader: def __init__(self, file_paths): self.file_paths = file_paths def read_data(self): logger.info("Reading data from files: %s", self.file_paths) # automatically selects the best file reader based on the file extensions data_loader = SimpleDirectoryReader(input_files=self.file_paths) return data_loader.load_data() def chunk_data(self, data, chunk_size=500, chunk_overlap=50): logger.info("Parsing data") node_parser = SentenceSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap, separator=" ", paragraph_separator="\n\n\n", secondary_chunking_regex="[^,.;。]+[,.;。]?" ) return node_parser.get_nodes_from_documents(data) class DatabaseManager: def __init__(self, db_path, collection_name): self.db_path = db_path self.collection_name = collection_name def get_db(self): logger.info("Initializing the database at path: %s", self.db_path) db = chromadb.PersistentClient(path=self.db_path) collection = db.get_or_create_collection(self.collection_name) return collection # class VectorIndexer: # def __init__(self, nodes, vector_store, embedding_model, llm_model, indexid, index_path): # self.nodes = nodes # self.vector_store = vector_store # self.embedding_model = embedding_model # self.llm_model = llm_model # self.indexid = indexid # self.index_path = index_path # # self.service_context # def get_index(self): # try: # logger.info(f"Load {self.indexid} from local path {self.index_path}") # storage_context = StorageContext.from_defaults(vector_store=self.vector_store, # persist_dir=self.index_path) # index = load_index_from_storage(storage_context=storage_context, index_id=indexid) # except Exception as e: # logger.info("Creating the vector index") # storage_context = StorageContext.from_defaults(vector_store=self.vector_store) # service_context = ServiceContext.from_defaults(embed_model=self.embedding_model, llm=self.llm_model) # index = VectorStoreIndex( # self.nodes, storage_context=storage_context, service_context=service_context # ) # index.set_index_id(self.indexid) # index.storage_context.persist(persist_dir=self.index_path) # return index

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from dotenv import load_dotenv import os from typing import List from llama_index.core.node_parser import SimpleNodeParser from llama_index.core.settings import Settings from llama_index.llms.openai import OpenAI from llama_index.core.embeddings import resolve_embed_model from llama_index.core import VectorStoreIndex from llama_index.core.query_engine import RetrieverQueryEngine from llama_index.core.response.notebook_utils import display_source_node os.environ["TOKENIZERS_PARALLELISM"] = "false" class RAGCreator(): def __init__(self): self.documents = [] self.nodes = None self.retriever = None self.query_engine = None self.rag_info = {} def _update_rag_info(self, params:dict): params.__delitem__("self") callable_obj_keys = [k for k,v in params.items() if callable(v)] for k in callable_obj_keys: params[k] = params[k].__name__ self.rag_info.update(params) def load_documents(self, data_loader, data_loader_kwargs:dict): self._update_rag_info(locals()) try: docs = data_loader().load_data(**data_loader_kwargs) except Exception as e: raise TypeError(f"Error loading documents: {e}.") self.documents = docs def parse_docs_to_nodes(self, node_parser=SimpleNodeParser, chunk_size=1024): self._update_rag_info(locals()) node_parser = node_parser.from_defaults(chunk_size=chunk_size) nodes = node_parser.get_nodes_from_documents(self.documents) for idx, node in enumerate(nodes): node.id_ = f"node-{idx}" self.nodes = nodes def set_model_settings(self, open_ai_model="gpt-3.5-turbo", embed_model="local:BAAI/bge-small-en"): self._update_rag_info(locals()) load_dotenv() Settings.llm = OpenAI(model=open_ai_model) Settings.embed_model = resolve_embed_model(embed_model) def create_retriever(self, vector_store_impl=VectorStoreIndex, similarity_top_k=2): self._update_rag_info(locals()) index = vector_store_impl(self.nodes) self.retriever = index.as_retriever(similarity_top_k=similarity_top_k) def create_query_engine(self, query_engine=RetrieverQueryEngine): self._update_rag_info(locals()) self.query_engine = query_engine.from_args(self.retriever) def setup_and_deploy_RAG(self, data_loader, data_loader_kwargs, node_parser=SimpleNodeParser, chunk_size=1024, open_ai_model="gpt-3.5-turbo", embed_model="local:BAAI/bge-small-en", vector_store_impl=VectorStoreIndex, similarity_top_k=2, query_engine=RetrieverQueryEngine): self.load_documents(data_loader, data_loader_kwargs) self.parse_docs_to_nodes(node_parser, chunk_size) self.set_model_settings(open_ai_model, embed_model) self.create_retriever(vector_store_impl, similarity_top_k) self.create_query_engine(query_engine) return self def query(self, query:str) -> str: if self.query_engine is not None: response = self.query_engine.query(query) return str(response) else: raise ValueError("You must set up your RAG and its query engine before submitting queries.") def query_multiple(self, queries:List[str]) -> List[str]: if self.query_engine is not None: responses = [] for query in queries: response = self.query_engine.query(query) responses.append(str(response)) return responses else: raise ValueError("You must set up your RAG and its query engine before submitting queries.") def fetch_relevant_info(self, query:str) -> List[str]: if self.retriever is not None: retrievals = self.retriever.retrieve(query) return retrievals else: raise ValueError("You must set up your RAG and its retriever before fetching relevant information.") def display_relevant_info(self, query:str, source_length=1500): retrievals = self.fetch_relevant_info(query=query) for retrieval in retrievals: display_source_node(retrieval, source_length=source_length) def get_rag_info(self): return self.rag_info

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from fastapi import FastAPI, File, UploadFile, HTTPException import openai from dotenv import load_dotenv import os import json from llama_index.core import SimpleDirectoryReader from llama_index.core.node_parser import SimpleFileNodeParser from llama_index.vector_stores.weaviate import WeaviateVectorStore from llama_index.core import VectorStoreIndex, StorageContext import weaviate import uvicorn load_dotenv() app = FastAPI() api_key = os.environ.get('OPENAI_API_KEY') openai.api_key = api_key OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY WEAVIATE_API_KEY = os.getenv("WEAVIATE_API_KEY") WEAVIATE_URL = os.getenv("WEAVIATE_URL") auth_config = weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY) client = weaviate.Client( url=WEAVIATE_URL, auth_client_secret=auth_config ) def search_and_query(): blogs = SimpleDirectoryReader("./Data").load_data() vector_store = WeaviateVectorStore(weaviate_client=client, index_name="DCPR") storage_context = StorageContext.from_defaults(vector_store=vector_store) VectorStoreIndex.from_documents(blogs, storage_context=storage_context) return "Done Embeddings" def Quert(ask): vector_store = WeaviateVectorStore(weaviate_client=client, index_name="DCPR") loaded_index = VectorStoreIndex.from_vector_store(vector_store) query_engine = loaded_index.as_query_engine() response = query_engine.query(ask) return response def contract_analysis_w_fact_checking(text): if not text: raise HTTPException( status_code=400, detail="Text field is required in the input data.") print("done 1") # Perform contract analysis using Quert (assuming Quert is a class or function) quert_instance = Quert(text) # Extract relevant information from the Quert response if quert_instance.response: contract_results = [{ "LLM Response": quert_instance.response, "Source_node": [{ "Page_number": key_point.node.metadata.get('page_label', ''), "File_Name": key_point.node.metadata.get('file_name', ''), "Text": key_point.node.text, "Start_Char": key_point.node.start_char_idx, "End_Char": key_point.node.end_char_idx, "Score_Matching": key_point.score } for key_point in quert_instance.source_nodes] }] else: contract_results = [] # Return a standardized response return {"status": "success", "message": "Contract analysis successful", "model_response": contract_results} @app.post("/embedd") async def predict(): try: dor = search_and_query() return {"user_content": dor} except Exception as e: raise HTTPException(status_code=500, detail=str(e)) @app.post("/predict") async def predict(data: dict): try: messages = data.get("messages", []) user_message = next((msg["content"] for msg in messages if msg["role"] == "user"), None) out = contract_analysis_w_fact_checking(user_message) if user_message: return {"user_content": out} else: raise HTTPException( status_code=400, detail="User message not found in input.") except Exception as e: raise HTTPException(status_code=500, detail=str(e)) @app.get("/") def read_root(): return {"Hello": "World"}

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from init import * from llama_index import SimpleDirectoryReader, LLMPredictor, ServiceContext from llama_index.node_parser import SimpleNodeParser from llama_index import VectorStoreIndex from llama_index.llms import OpenAI from llama_index import download_loader class Index: def __init__(self, dir="data"): """Initialize the index.""" self.loader = download_loader("UnstructuredReader")() self.docs = self.load(dir) self.nodes = SimpleNodeParser.from_defaults().get_nodes_from_documents(self.docs) llm_predictor = LLMPredictor(llm=OpenAI(model="gpt-3.5-turbo", temperature=0)) service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, chunk_size=1000) self.index = VectorStoreIndex(self.nodes, service_context=service_context) self.query_engine = self.index.as_query_engine(streaming=True) self.retriever = self.index.as_retriever() def load(self, dir="data"): """Load all documents from a directory.""" print(f"Loading directory: {dir}") doc_files = [] for path, subdirs, files in os.walk(dir): for name in files: doc_files.append(os.path.join(path, name)) docs = [] for f in doc_files: print(f"Loading file: {f}") try: docs += self.loader.load_data(f, split_documents=False) except Exception as e: print(e, "Skipping.") return docs def query(self, query): """Query the index.""" print("Query:", query) response = self.query_engine.query(query) print("Response:", response) return response if __name__ == "__main__": index = Index(dir="data") response = index.query("What is the meaning of life?")

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def get_agent(list_filters,openai_key,pinecone_key): import logging import sys import os import pandas as pd import pinecone import openai from llama_index import VectorStoreIndex from llama_index.vector_stores import PineconeVectorStore from llama_index.query_engine import RetrieverQueryEngine from llama_index.chat_engine.condense_question import CondenseQuestionChatEngine from llama_index.agent import OpenAIAgent from llama_index.llms import OpenAI from llama_index.tools import BaseTool, FunctionTool from agent_utils import get_rebate,get_tax from llama_index.tools import QueryEngineTool, ToolMetadata from llama_index.llms import ChatMessage logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) #Openai and Pinecone private key openai.api_key = openai_key api_key = pinecone_key #Instantiate pinecone vector store pinecone.init(api_key=api_key, environment="gcp-starter") pinecone_index = pinecone.Index("quickstart-index") vector_store = PineconeVectorStore( pinecone_index=pinecone_index ) index = VectorStoreIndex.from_vector_store(vector_store) if not list_filters: list_filters = ['rates','claim'] else: list_filters += ['rates','claim'] #Define retriever retriever = index.as_retriever( vector_store_kwargs={"filter": {"category": {"$in":list_filters}}},streaming=True) # assemble query engine query_engine = RetrieverQueryEngine(retriever=retriever) #Get agent tools from agent_utils file and instantiate tools tax_tool = FunctionTool.from_defaults(fn=get_tax) relief_tool = FunctionTool.from_defaults(fn=get_rebate) #Create list of tool for agent tools = [ QueryEngineTool( query_engine=query_engine, metadata=ToolMetadata( name="tax_relief_retriever", description=( "Provides information on reliefs for a given item category and information on how to claim tax reliefs" "Use a detailed plain text question as input to the tool." ), ), ), tax_tool,relief_tool] #Define chat agent llm = OpenAI(model="gpt-3.5-turbo-0613") #Set a default chat history to handle cases where information is not provided str_cat = ','.join(list_filters) chat_history = [ChatMessage(role= 'user', content=f"Assume I earn an income of RM90,000. If I state my income chat, update it to the stated income. I want to buy an items in category {str_cat}")] system_prompt = "You are a tax advisory agent a you must only use information from the tool to answer queries. Other topics unrelated to personal income tax and does not use on of the tool should not be answered" agent = OpenAIAgent.from_tools(tools,system_prompt = system_prompt, chat_history = chat_history, verbose=True) # chat_engine = CondenseQuestionChatEngine.from_defaults( # query_engine=query_engine, # # condense_question_prompt=custom_prompt, # # chat_history=custom_chat_history, # verbose=True, # ) return agent

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#!/usr/bin/env python # -*- coding: utf-8 -*- ''' This script allows you to ask questions to the Alice in Wonderland book. It uses the GPT-3 model to create a vector index of the book, and then allows you to ask questions to the index. ''' import os import yaml import openai from llama_index import ( GPTVectorStoreIndex, StorageContext, SimpleDirectoryReader, download_loader, load_index_from_storage ) from llama_index.storage.docstore import SimpleDocumentStore from llama_index.vector_stores import SimpleVectorStore from llama_index.storage.index_store import SimpleIndexStore from argparse import ArgumentParser # script configuration persist_dir = "./indices/alice/" pdf_file = "alice.pdf" openai_config = "projects/infrastructure/charts/secrets/values/integration/openai-configuration/openai.yml" credentials_path = os.path.join(os.path.expanduser('~'), openai_config) credentials = yaml.safe_load(open(credentials_path, "r")) os.environ["OPENAI_API_KEY"] = credentials["access_token"] os.environ["OPENAI_ORGANIZATION"] = credentials["organization_id"] # Save the index in .JSON file for repeated use. Saves money on ADA API calls def create_index_from_pdf(persist_dir): # This example uses PDF reader, there are many options at https://llamahub.ai/ # Use SimpleDirectoryReader to read all the txt files in a folder PDFReader = download_loader("PDFReader") loader = PDFReader() documents = loader.load_data(file=pdf_file) # Chunking and Embedding of the chunks. index = GPTVectorStoreIndex.from_documents(documents) index.storage_context.persist(persist_dir=persist_dir) return index def load_index(persist_dir): storage_context = StorageContext.from_defaults( docstore=SimpleDocumentStore.from_persist_dir(persist_dir=persist_dir), vector_store=SimpleVectorStore.from_persist_dir(persist_dir=persist_dir), index_store=SimpleIndexStore.from_persist_dir(persist_dir=persist_dir), ) index = load_index_from_storage(storage_context) return index def main(args): print(args.question) if args.create_index: index = create_index_from_pdf(persist_dir) else: index = load_index(persist_dir) # Retrieval, node poseprocessing, response synthesis. query_engine = index.as_query_engine() # Run the query engine on a user question. response = query_engine.query(args.question) print(response) if __name__ == '__main__': parser = ArgumentParser(description=__doc__, prog='alice.py', epilog='Have fun!') parser.add_argument('-c', '--create-index', help='(re)create the index', action='store_true') parser.add_argument('question', help='question string to ask the index') args = parser.parse_args() main(args)

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import os from dotenv import load_dotenv from IPython.display import Markdown, display from llama_index.legacy import VectorStoreIndex, ServiceContext from llama_index.legacy.vector_stores import ChromaVectorStore from llama_index.legacy.storage.storage_context import StorageContext from llama_index.legacy.embeddings import HuggingFaceEmbedding from llama_index.legacy.llms import Gemini from llama_index.legacy.node_parser import SentenceWindowNodeParser, SimpleNodeParser from llama_index.legacy.llms import Gemini from llama_index.legacy import GPTVectorStoreIndex from llama_index.legacy.readers.web import BeautifulSoupWebReader import chromadb import streamlit as st # Enable Logging import logging import sys #You can set the logging level to DEBUG for more verbose output, # or use level=logging.INFO for less detailed information. logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) # Load environment variables from the .env file load_dotenv() loader = BeautifulSoupWebReader() urls = [ 'https://www.hsph.harvard.edu/nutritionsource/kids-healthy-eating-plate/', 'https://www.hsph.harvard.edu/nutritionsource/healthy-eating-plate/', 'https://www.hsph.harvard.edu/nutritionsource/what-should-you-eat/', 'https://www.hsph.harvard.edu/nutritionsource/what-should-you-eat/whole-grains/', 'https://www.hsph.harvard.edu/nutritionsource/what-should-you-eat/protein/', 'https://www.hsph.harvard.edu/nutritionsource/what-should-you-eat/vegetables-and-fruits/', 'https://www.hsph.harvard.edu/nutritionsource/what-should-you-eat/fats-and-cholesterol/', 'https://www.hsph.harvard.edu/nutritionsource/what-should-you-eat/fats-and-cholesterol/types-of-fat/', 'https://www.hsph.harvard.edu/nutritionsource/what-should-you-eat/fats-and-cholesterol/cholesterol/', 'https://www.hsph.harvard.edu/nutritionsource/what-should-you-eat/fats-and-cholesterol/dietary-fat-and-disease/', 'https://www.hsph.harvard.edu/nutritionsource/vitamins/', 'https://www.hsph.harvard.edu/nutritionsource/healthy-drinks/', 'https://www.hsph.harvard.edu/nutritionsource/healthy-drinks/other-healthy-beverage-options/', 'https://www.hsph.harvard.edu/nutritionsource/healthy-drinks/drinks-to-consume-in-moderation/', 'https://www.hsph.harvard.edu/nutritionsource/healthy-drinks/sugary-drinks/', 'https://www.hsph.harvard.edu/nutritionsource/sports-drinks/', 'https://www.hsph.harvard.edu/nutritionsource/energy-drinks/', 'https://www.hsph.harvard.edu/nutritionsource/healthy-drinks/beverages-public-health-concerns/', 'https://www.hsph.harvard.edu/nutritionsource/healthy-drinks/artificial-sweeteners/', 'https://www.hsph.harvard.edu/nutritionsource/salt-and-sodium/', 'https://www.hsph.harvard.edu/nutritionsource/salt-and-sodium/take-action-on-salt/', 'https://www.hsph.harvard.edu/nutritionsource/salt-and-sodium/sodium-public-health-concerns/', 'https://www.hsph.harvard.edu/nutritionsource/carbohydrates/', 'https://www.hsph.harvard.edu/nutritionsource/carbohydrates/carbohydrates-and-blood-sugar/', 'https://www.hsph.harvard.edu/nutritionsource/carbohydrates/fiber/', 'https://www.hsph.harvard.edu/nutritionsource/carbohydrates/added-sugar-in-the-diet/', 'https://www.hsph.harvard.edu/nutritionsource/sustainability/', 'https://www.hsph.harvard.edu/nutritionsource/sustainability/plate-and-planet/', 'https://www.hsph.harvard.edu/nutritionsource/sustainability/food-waste/', 'https://www.hsph.harvard.edu/nutritionsource/healthy-weight/', 'https://www.hsph.harvard.edu/nutritionsource/healthy-weight/measuring-fat/', 'https://www.hsph.harvard.edu/nutritionsource/healthy-weight/best-diet-quality-counts/', 'https://www.hsph.harvard.edu/nutritionsource/healthy-weight/healthy-dietary-styles/', 'https://www.hsph.harvard.edu/nutritionsource/healthy-weight/diet-reviews/', 'https://www.hsph.harvard.edu/nutritionsource/staying-active/', 'https://www.hsph.harvard.edu/nutritionsource/staying-active/active-communities/', 'https://www.hsph.harvard.edu/nutritionsource/stress-and-health/', 'https://www.hsph.harvard.edu/nutritionsource/sleep/', 'https://www.hsph.harvard.edu/nutritionsource/healthy-longevity/', 'https://www.hsph.harvard.edu/nutritionsource/disease-prevention/', 'https://www.hsph.harvard.edu/nutritionsource/disease-prevention/cardiovascular-disease/', 'https://www.hsph.harvard.edu/nutritionsource/disease-prevention/cardiovascular-disease/preventing-cvd/', 'https://www.hsph.harvard.edu/nutritionsource/disease-prevention/diabetes-prevention/', 'https://www.hsph.harvard.edu/nutritionsource/disease-prevention/diabetes-prevention/preventing-diabetes-full-story/', 'https://www.hsph.harvard.edu/nutritionsource/cancer/', 'https://www.hsph.harvard.edu/nutritionsource/cancer/preventing-cancer/', 'https://www.hsph.harvard.edu/nutritionsource/oral-health/', 'https://www.hsph.harvard.edu/nutritionsource/precision-nutrition/', 'https://www.hsph.harvard.edu/nutritionsource/nutrition-and-immunity/', 'https://www.hsph.harvard.edu/nutritionsource/recipes-2/', 'https://www.hsph.harvard.edu/nutritionsource/asparagus-with-warm-tarragon-pecan-vinaigrette/', 'https://www.hsph.harvard.edu/nutritionsource/asparagus-spears-with-mandarin-orange/', 'https://www.hsph.harvard.edu/nutritionsource/baby-arugula-and-shaved-fennel-with-lemon-vinaigrette/', 'https://www.hsph.harvard.edu/nutritionsource/braised-cabbage-with-leeks-and-sesame-seeds/', 'https://www.hsph.harvard.edu/nutritionsource/braised-oyster-mushrooms-coconut-macadamia/', 'https://www.hsph.harvard.edu/nutritionsource/butternut-squash-soup-recipe/', 'https://www.hsph.harvard.edu/nutritionsource/caesar-salad/', 'https://www.hsph.harvard.edu/nutritionsource/cardamom-roasted-cauliflower/', 'https://www.hsph.harvard.edu/nutritionsource/carrot-and-coriander-soup/', 'https://www.hsph.harvard.edu/nutritionsource/cauliflower-tomato-soup/', 'https://www.hsph.harvard.edu/nutritionsource/cauliflower-walnut-soup/', 'https://www.hsph.harvard.edu/nutritionsource/endive-salad-with-citrus-walnut-dressing/', 'https://www.hsph.harvard.edu/nutritionsource/customizable-stuffed-peppers/', 'https://www.hsph.harvard.edu/nutritionsource/fresh-spinach-with-sesame-seeds/', 'https://www.hsph.harvard.edu/nutritionsource/garlic-braised-greens/', 'https://www.hsph.harvard.edu/nutritionsource/green-beans-with-dried-cherries/', 'https://www.hsph.harvard.edu/nutritionsource/green-beans-with-chili-garlic-sauce/', 'https://www.hsph.harvard.edu/nutritionsource/green-chutney/', 'https://www.hsph.harvard.edu/nutritionsource/grilled-eggplant-cutlets/', 'https://www.hsph.harvard.edu/nutritionsource/kale-with-caramelized-onions/', 'https://www.hsph.harvard.edu/nutritionsource/marinated-shiitake-mushroom-and-cucumber-salad/', 'https://www.hsph.harvard.edu/nutritionsource/mashed-cauliflower/', 'https://www.hsph.harvard.edu/nutritionsource/mushroom-stroganoff/', 'https://www.hsph.harvard.edu/nutritionsource/pan-roasted-wild-mushrooms-with-coffee-and-hazelnuts/', 'https://www.hsph.harvard.edu/nutritionsource/portabella-steak-sandwich/', 'https://www.hsph.harvard.edu/nutritionsource/provencal-vegetables/', 'https://www.hsph.harvard.edu/nutritionsource/vegetable-stock/', 'https://www.hsph.harvard.edu/nutritionsource/roasted-brussels-sprouts/', 'https://www.hsph.harvard.edu/nutritionsource/brussels-sprouts-with-shallots/', 'https://www.hsph.harvard.edu/nutritionsource/roasted-beets-with-balsamic-vinegar/', 'https://www.hsph.harvard.edu/nutritionsource/roasted-balsamic-vegetables/', 'https://www.hsph.harvard.edu/nutritionsource/roasted-squash-with-pomegranate/', 'https://www.hsph.harvard.edu/nutritionsource/sweet-potatoes-with-pecans/', 'https://www.hsph.harvard.edu/nutritionsource/ruby-chard/', 'https://www.hsph.harvard.edu/nutritionsource/sauted-rainbow-swiss-chard/', 'https://www.hsph.harvard.edu/nutritionsource/simple-celery-date-salad/', 'https://www.hsph.harvard.edu/nutritionsource/southwestern-corn-hash/', 'https://www.hsph.harvard.edu/nutritionsource/spicy-broccolini/', 'https://www.hsph.harvard.edu/nutritionsource/spicy-indian-slaw/', 'https://www.hsph.harvard.edu/nutritionsource/stir-fried-vegetables-tomato-curry/', 'https://www.hsph.harvard.edu/nutritionsource/sugar-snap-peas-with-fresh-mint/', 'https://www.hsph.harvard.edu/nutritionsource/tarragon-succotash/', 'https://www.hsph.harvard.edu/nutritionsource/tunisian-carrot-salad/', 'https://www.hsph.harvard.edu/nutritionsource/vegetable-stock-recipe/', 'https://www.hsph.harvard.edu/nutritionsource/vegetarian-shepherds-pie-recipe/', 'https://www.hsph.harvard.edu/nutritionsource/wild-mushroom-soup-with-soba/', 'https://www.hsph.harvard.edu/nutritionsource/yellow-squash-with-sage/', 'https://www.hsph.harvard.edu/nutritionsource/arugula-watermelon-feta-and-mint-salad-with-balsamic-vinaigrette/', 'https://www.hsph.harvard.edu/nutritionsource/citrus-salad/', 'https://www.hsph.harvard.edu/nutritionsource/almond-coconut-macaroons/', 'https://www.hsph.harvard.edu/nutritionsource/dried-fruit-and-nuts/', 'https://www.hsph.harvard.edu/nutritionsource/watermelon-salad/', 'https://www.hsph.harvard.edu/nutritionsource/fruit-compote-spiced-nuts/', 'https://www.hsph.harvard.edu/nutritionsource/strawberry-rhubarb-crisp/', 'https://www.hsph.harvard.edu/nutritionsource/barley-roasted-portobello-and-fennel-salad/', 'https://www.hsph.harvard.edu/nutritionsource/blueberry-muffins/', 'https://www.hsph.harvard.edu/nutritionsource/brown-rice-pancakes/', 'https://www.hsph.harvard.edu/nutritionsource/bulgur-pilaf/', 'https://www.hsph.harvard.edu/nutritionsource/couscous-minted-with-pine-nuts/', 'https://www.hsph.harvard.edu/nutritionsource/couscous-quinoa-tabouli/', 'https://www.hsph.harvard.edu/nutritionsource/cranberry-orange-muffin/', 'https://www.hsph.harvard.edu/nutritionsource/fantastic-bulgur-dish/', 'https://www.hsph.harvard.edu/nutritionsource/farro-risotto-walnut-pesto/', 'https://www.hsph.harvard.edu/nutritionsource/farro-roasted-confetti-vegetables/', 'https://www.hsph.harvard.edu/nutritionsource/hearty-whole-grain-bread/', 'https://www.hsph.harvard.edu/nutritionsource/irish-brown-bread/', 'https://www.hsph.harvard.edu/nutritionsource/jalapeno-cheddar-corn-muffins/', 'https://www.hsph.harvard.edu/nutritionsource/lemon-chickpea-breakfast-muffins/', 'https://www.hsph.harvard.edu/nutritionsource/mediterranean-rice/', 'https://www.hsph.harvard.edu/nutritionsource/mixed-up-grains/', 'https://www.hsph.harvard.edu/nutritionsource/mushroom-barley-risotto/', 'https://www.hsph.harvard.edu/nutritionsource/oatmeal-roti/', 'https://www.hsph.harvard.edu/nutritionsource/pasta-in-zemino/', 'https://www.hsph.harvard.edu/nutritionsource/rigatoni-fresh-basil-pesto-corn-zucchini/', 'https://www.hsph.harvard.edu/nutritionsource/quinoa-chia-edamame-veggie-burger/', 'https://www.hsph.harvard.edu/nutritionsource/quinoa-enchilada-casserole/', 'https://www.hsph.harvard.edu/nutritionsource/spicy-coconut-rice-with-limes/', 'https://www.hsph.harvard.edu/nutritionsource/three-green-wheat-berry-salad-with-mushroom-bacon-recipe/', 'https://www.hsph.harvard.edu/nutritionsource/wheatberries-and-chives/', 'https://www.hsph.harvard.edu/nutritionsource/whole-wheat-banana-nut-muffins/', 'https://www.hsph.harvard.edu/nutritionsource/whole-wheat-penne-with-pistachio-pesto-and-cherry-tomatoes/', 'https://www.hsph.harvard.edu/nutritionsource/wild-rice-with-cranberries/', 'https://www.hsph.harvard.edu/nutritionsource/greek-skordalia/', 'https://www.hsph.harvard.edu/nutritionsource/green-lentil-hummus-herbs-olives/', 'https://www.hsph.harvard.edu/nutritionsource/guacamole/', 'https://www.hsph.harvard.edu/nutritionsource/hot-pepper-vinaigrette/', 'https://www.hsph.harvard.edu/nutritionsource/hummus/', 'https://www.hsph.harvard.edu/nutritionsource/italian-pesto-alla-trapanese/', 'https://www.hsph.harvard.edu/nutritionsource/mint-vinaigrette/', 'https://www.hsph.harvard.edu/nutritionsource/oregano-garlic-vinaigrette/', 'https://www.hsph.harvard.edu/nutritionsource/spanish-romesco-sauce/', 'https://www.hsph.harvard.edu/nutritionsource/turkish-muhammara/', 'https://www.hsph.harvard.edu/nutritionsource/turkish-tarator/', 'https://www.hsph.harvard.edu/nutritionsource/walnut-pesto/', 'https://www.hsph.harvard.edu/nutritionsource/white-bean-and-kale-hummus/', 'https://www.hsph.harvard.edu/nutritionsource/asian-trail-mix/', 'https://www.hsph.harvard.edu/nutritionsource/cozy-red-lentil-mash/', 'https://www.hsph.harvard.edu/nutritionsource/crunchy-roasted-chickpeas/', 'https://www.hsph.harvard.edu/nutritionsource/curried-red-lentil-soup/', 'https://www.hsph.harvard.edu/nutritionsource/dukkah/', 'https://www.hsph.harvard.edu/nutritionsource/french-style-lentils/', 'https://www.hsph.harvard.edu/nutritionsource/garbanzo-beans-with-spinach-and-tomatoes/', 'https://www.hsph.harvard.edu/nutritionsource/green-beans-with-tofu-and-crushed-peanuts/', 'https://www.hsph.harvard.edu/nutritionsource/mushroom-tofu-veggie-burger/', 'https://www.hsph.harvard.edu/nutritionsource/spicy-lemongrass-tofu-with-asian-basil/', 'https://www.hsph.harvard.edu/nutritionsource/sprouted-lentil-cabbage-celery-slaw/', 'https://www.hsph.harvard.edu/nutritionsource/thai-eggplant-salad-with-coconut-tofu-strips/', 'https://www.hsph.harvard.edu/nutritionsource/tomato-and-white-bean-salad/', 'https://www.hsph.harvard.edu/nutritionsource/whole-wheat-penne-with-pistachio-pesto-and-cherry-tomatoes/', 'https://www.hsph.harvard.edu/nutritionsource/white-beans-wild-rice-and-mushrooms/', 'https://www.hsph.harvard.edu/nutritionsource/vegetarian-refried-beans/', 'https://www.hsph.harvard.edu/nutritionsource/cod-and-littleneck-clams/', 'https://www.hsph.harvard.edu/nutritionsource/crawfish-touffe/', 'https://www.hsph.harvard.edu/nutritionsource/crispy-pan-seared-white-fish-walnut-romesco-pea-shoot-salad/', 'https://www.hsph.harvard.edu/nutritionsource/fish-creole/', 'https://www.hsph.harvard.edu/nutritionsource/miso-marinated-salmon-grilled-alder-wood/', 'https://www.hsph.harvard.edu/nutritionsource/pan-roasted-salmon-with-dill-olive-oil-capers/', 'https://www.hsph.harvard.edu/nutritionsource/pan-roasted-salmon/', 'https://www.hsph.harvard.edu/nutritionsource/shaved-fennel-salad-coriander-crusted-hamachi/', 'https://www.hsph.harvard.edu/nutritionsource/shrimp-and-chicken-gumbo/', 'https://www.hsph.harvard.edu/nutritionsource/shrimp-red-curry-crispy-sprouted-lentils/', 'https://www.hsph.harvard.edu/nutritionsource/wild-salmon-salad/', 'https://www.hsph.harvard.edu/nutritionsource/fish-tacos-with-cilantro-slaw/', 'https://www.hsph.harvard.edu/nutritionsource/chicken-shrimp-and-fruit-salad/', 'https://www.hsph.harvard.edu/nutritionsource/lemongrass-marinated-chicken-breast/', 'https://www.hsph.harvard.edu/nutritionsource/olive-oil-dressing-with-chicken-walnuts-recipe/', 'https://www.hsph.harvard.edu/nutritionsource/rosemary-and-lemon-grilled-chicken-breast/', 'https://www.hsph.harvard.edu/nutritionsource/spicy-chicken-kebabs-with-moorish-flavors/', 'https://www.hsph.harvard.edu/nutritionsource/stir-fried-chicken/', 'https://www.hsph.harvard.edu/nutritionsource/moroccan-chicken-stew-with-apricots/', 'https://www.hsph.harvard.edu/nutritionsource/stir-fried-chicken/', 'https://www.hsph.harvard.edu/nutritionsource/baked-ricotta/', 'https://www.hsph.harvard.edu/nutritionsource/roasted-tomatoes-stuffed-goat-cheese-garlic-basil/', 'https://www.hsph.harvard.edu/nutritionsource/fruit-cooler/', 'https://www.hsph.harvard.edu/nutritionsource/iced-tea-with-lemon-and-mint/' # Add the rest of the URLs here ] documents = loader.load_data(urls=urls) # base Query Engine LLM llm = Gemini(api_key=os.getenv("google_api_key"),model='gemini-pro') # fine-tuned Embeddings model embed_model = HuggingFaceEmbedding( model_name='Revankumar/fine_tuned_embeddings_for_healthy_recipes' ) # fine-tuned ServiceContext ctx = ServiceContext.from_defaults( llm=llm, embed_model=embed_model, ) parser = SimpleNodeParser() nodes = parser.get_nodes_from_documents(documents) db = chromadb.PersistentClient(path="./chroma_db") chroma_collection = db.get_or_create_collection("quickstart") vector_store = ChromaVectorStore(chroma_collection=chroma_collection) storage_context = StorageContext.from_defaults(vector_store=vector_store) service_context = ServiceContext.from_defaults(embed_model=embed_model,llm=llm) VectorStoreIndex.from_documents( documents, storage_context=storage_context, service_context=service_context )

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# Just runs .complete to make sure the LLM is listening from llama_index.llms import Ollama from pathlib import Path import qdrant_client from llama_index import ( VectorStoreIndex, ServiceContext, download_loader, ) from llama_index.llms import Ollama from llama_index.storage.storage_context import StorageContext from llama_index.vector_stores.qdrant import QdrantVectorStore JSONReader = download_loader("JSONReader") loader = JSONReader() class Ollama_model: def __init__(self, model="mistral"): self.llm = Ollama(model=model) self.documents = loader.load_data(Path('./data/tinytweets.json')) self.client = qdrant_client.QdrantClient( path="./qdrant_data" ) self.vector_store = QdrantVectorStore(client=self.client, collection_name="tweets") self.storage_context = StorageContext.from_defaults(vector_store=self.vector_store) self.service_context = ServiceContext.from_defaults(llm=self.llm,embed_model="local") self.index = VectorStoreIndex.from_documents(self.documents,service_context=self.service_context,storage_context=self.storage_context) self.query_engine = self.index.as_query_engine() def get_answer(self, input): response = self.query_engine.query(input) return response def change_dataset(self, name, filename): print(filename) self.documents = loader.load_data(Path('./uploads/'+filename)) self.vector_store = QdrantVectorStore(client=self.client, collection_name=name) self.storage_context = StorageContext.from_defaults(vector_store=self.vector_store) self.index = VectorStoreIndex.from_documents(self.documents,service_context=self.service_context,storage_context=self.storage_context) self.query_engine = self.index.as_query_engine()

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from pathlib import Path from llama_hub.file.unstructured import UnstructuredReader from pathlib import Path from llama_index import download_loader from llama_index import SimpleDirectoryReader, VectorStoreIndex from dotenv import load_dotenv import os from llama_index.node_parser import SimpleNodeParser import pinecone from llama_index.vector_stores import PineconeVectorStore from llama_index import GPTVectorStoreIndex, StorageContext, ServiceContext from llama_index.embeddings.openai import OpenAIEmbedding import openai #################################################### # # # This file upserts documents in data to pinecone. # # # #################################################### load_dotenv() openai.api_key = os.getenv('api_key') # find API key in console at app.pinecone.io os.environ['PINECONE_API_KEY'] = os.getenv('pinecone_api_key') # environment is found next to API key in the console os.environ['PINECONE_ENVIRONMENT'] = os.getenv('pinecone_env') # loader = UnstructuredReader() # initialize connection to pinecone pinecone.init( api_key=os.environ['PINECONE_API_KEY'], environment=os.environ['PINECONE_ENVIRONMENT'] ) # setup the index/query process, ie the embedding model (and completion if used) embed_model = OpenAIEmbedding(model='text-embedding-ada-002', embed_batch_size=100) service_context = ServiceContext.from_defaults(embed_model=embed_model) # Readers PDFReader = download_loader("PDFReader") MarkdownReader = download_loader("MarkdownReader") # Load docs def upsert_docs(input_dir: str, index_name: str): print(f"Building from {input_dir} under index {index_name}...\n") documents = SimpleDirectoryReader(input_dir=input_dir).load_data() # create the index if it does not exist already if index_name not in pinecone.list_indexes(): pinecone.create_index( name=index_name, dimension=1536, metric='cosine' ) # connect to the index pineconeIndex = pinecone.Index(index_name) vectorStore = PineconeVectorStore( pinecone_index=pineconeIndex ) # setup our storage (vector db) storageContext = StorageContext.from_defaults( vector_store=vectorStore ) index = GPTVectorStoreIndex.from_documents( documents=documents, storage_context=storageContext, service_context=service_context ) print(f"Done building !\n") upsert_docs(input_dir="upsert_doc/docs", index_name="ruikang-guo-knowledge-base")

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"""Read PDF files.""" import shutil from pathlib import Path from typing import Any, List from llama_index.langchain_helpers.text_splitter import SentenceSplitter from llama_index.readers.base import BaseReader from llama_index.readers.schema.base import Document # https://github.com/emptycrown/llama-hub/blob/main/loader_hub/file/cjk_pdf/base.py staticPath = "static" class CJKPDFReader(BaseReader): """CJK PDF reader. Extract text from PDF including CJK (Chinese, Japanese and Korean) languages using pdfminer.six. """ def __init__(self, *args: Any, **kwargs: Any) -> None: """Init params.""" super().__init__(*args, **kwargs) def load_data(self, filepath: Path, filename) -> List[Document]: """Parse file.""" # Import pdfminer from io import StringIO from pdfminer.converter import TextConverter from pdfminer.layout import LAParams from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager from pdfminer.pdfpage import PDFPage # Create a resource manager rsrcmgr = PDFResourceManager() # Create an object to store the text retstr = StringIO() # Create a text converter codec = "utf-8" laparams = LAParams() device = TextConverter(rsrcmgr, retstr, codec=codec, laparams=laparams) # Create a PDF interpreter interpreter = PDFPageInterpreter(rsrcmgr, device) # Open the PDF file fp = open(filepath, "rb") # Create a list to store the text of each page document_list = [] # Extract text from each page for i, page in enumerate(PDFPage.get_pages(fp)): interpreter.process_page(page) # Get the text text = retstr.getvalue() sentence_splitter = SentenceSplitter(chunk_size=400) text_chunks = sentence_splitter.split_text(text) document_list += [ Document(t, extra_info={"page_no": i + 1}) for t in text_chunks ] # Clear the text retstr.truncate(0) retstr.seek(0) # Close the file fp.close() # Close the device device.close() shutil.copy2(filepath, f"{staticPath}/file/{filename}") return document_list

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import chromadb from llama_index.core import VectorStoreIndex, SimpleDirectoryReader from llama_index.vector_stores.chroma import ChromaVectorStore from llama_index.core import StorageContext from llama_index.embeddings.huggingface import HuggingFaceEmbedding from llama_index.core.indices.service_context import ServiceContext class Vector: def __init__(self, doc_location): self.client = chromadb.Client() self.doc_location = doc_location self.collection = self.client.create_collection("papers") self.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5") def process_document(self): """ Process the document by performing the following steps: 1. Read the document. 2. Set up ChromaVectorStore and load in data. 3. Create a VectorStoreIndex from the documents using the specified storage context, embed model, and service context. """ service_context = ServiceContext.from_defaults(chunk_size=100, chunk_overlap=10) documents = SimpleDirectoryReader(self.doc_location).load_data() vector_store = ChromaVectorStore(chroma_collection=self.collection) storage_context = StorageContext.from_defaults(vector_store=vector_store) self.index = VectorStoreIndex.from_documents( documents, storage_context=storage_context, embed_model=self.embed_model, service_context=service_context ) def query_document(self, query): query_engine = self.index.as_query_engine() response = query_engine.query(query) return response

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from llama_index.llms import OpenAI from llama_index.embeddings import HuggingFaceEmbedding from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext import os documents = SimpleDirectoryReader("./competition").load_data() os.environ['OPENAI_API_KEY'] = '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' embed_model = HuggingFaceEmbedding(model_name='BAAI/bge-large-en-v1.5') llm = OpenAI(temperature=0.1, model="gpt-3.5-turbo") service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model, chunk_size=800, chunk_overlap=20) index = VectorStoreIndex.from_documents(documents, service_context=service_context, show_progress=True) index.storage_context.persist() query_engine = index.as_query_engine(similarity_top_k=2, response_mode='tree_summarize') # response = query_engine.query( # "what are the benefits that I can have regarding risk management and portfolio monitoring? What are the charges?" # ) def answer(question): return query_engine.query(question) if __name__ == "__main__": while True: question = input("Ask a question: ") print(answer(question))

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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader documents = SimpleDirectoryReader("./data").load_data() index = VectorStoreIndex.from_documents(documents) query_engine = index.as_query_engine() response = query_engine.query("What did the author do growing up?") print(response)

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from typing import List from llama_index import Document, TwitterTweetReader from social_gpt.ingestion.scraper.social_scraper import SocialScraper class TwitterScraper(SocialScraper): def scrape(self) -> List[Document]: TwitterTweetReader()

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import os from typing import List import googleapiclient from dotenv import load_dotenv from llama_index import Document from progress.bar import IncrementalBar from youtube_transcript_api import YouTubeTranscriptApi from social_gpt.ingestion.scraper.social_scraper import SocialScraper load_dotenv() YOUTUBE_API_SERVICE_NAME = 'youtube' YOUTUBE_API_VERSION = 'v3' class YoutubeScraper(SocialScraper): def scrape(self) -> List[Document]: print(f"scraping youtube channel ${self.username}") return self.get_channel_video_docs() @staticmethod def get_transcript(video_id): try: transcript = YouTubeTranscriptApi.get_transcript(video_id) return " ".join(list(map(lambda trans: trans['text'], transcript))) except Exception: return None def get_channel_video_docs(self) -> List[Document]: youtube = googleapiclient.discovery.build(YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION, developerKey=os.getenv('YOUTUBE_DEVELOPER_KEY')) request = youtube.search().list( part="snippet", channelId=self.username, maxResults=200, # Change if needed type="video" ) response = request.execute() transcripts = [] bar = IncrementalBar('Transcribing', max=len(response['items'])) for item in response['items']: transcript = YoutubeScraper.get_transcript(item['id']['videoId']) if transcript: transcripts.append(transcript) bar.next() bar.finish() return list(map(lambda transcript: Document(transcript), transcripts))

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- import os import logging import sys from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) # read the document of data dir documents = SimpleDirectoryReader("data").load_data() # split the document to chunk, max token size=500, convert chunk to vector index = GPTSimpleVectorIndex(documents) # save index index.save_to_disk("index.json")

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import dotenv import os from llama_index.readers.github import GithubRepositoryReader, GithubClient from llama_index.core import (VectorStoreIndex, StorageContext, PromptTemplate, load_index_from_storage, Settings) from llama_index.embeddings.huggingface import HuggingFaceEmbedding from llama_index.llms.ollama import Ollama from llama_index.embeddings.openai import OpenAIEmbedding def load_environ_vars(): dotenv.load_dotenv() github_token = os.environ['GITHUB_TOKEN'] # open_api = os.environ['OPEN_API_KEY'] if github_token is None: print("Add the GITHUB_TOKEN environment variable in the .env file") exit() """if open_api is None: print("Add the OPEN_API_KEY environment variable. Read instrucitons in the readme") exit()""" return github_token def load_data(github_token: str, owner: str, repo: str): github_client = GithubClient(github_token) loader = GithubRepositoryReader( github_client, owner=owner, repo=repo, filter_file_extensions=( [".py", ".ipynb", ".js", ".ts", ".md"], GithubRepositoryReader.FilterType.INCLUDE, ), verbose=False, concurrent_requests=5, ) docs = loader.load_data(branch="main") return docs def load_embedding_model(): embedding_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5") print("embedding model loaded") return embedding_model def main(): github_token = load_environ_vars() PERSIST_DIR = "./basic/storage" choice = input("Enter 1 to use OPEN API enter 0 to use loally setup llama2 model using Ollama:") if not os.path.exists(PERSIST_DIR): owner = input("Enter the username of the owner of the repo: ") repo = input("Enter the name of the repo: ") documents = load_data(github_token, owner, repo) try: if choice == '1': print("Open API is being used") embedding_model = OpenAIEmbedding() index = VectorStoreIndex.from_documents(documents) else: print("Ollama is being used") embedding_model = load_embedding_model() Settings.embed_model = embedding_model index = VectorStoreIndex.from_documents( documents, embed_model=embedding_model ) except Exception as e: print(e) exit() print("Documents Indexed") else: # load the existing index storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR) index = load_index_from_storage(storage_context) print("Already indexed data loaded") llama = Ollama(model="llama2", request_timeout=200.0) Settings.llm = llama query_engine = index.as_query_engine(llm=llama) qa_prompt_tmpl_str = ( "Context information is below.\n" "---------------------\n" "{context_str}\n" "---------------------\n" "Given the context information above I want you to think step by step to answer the query in a crisp manner, incase case you don't know the answer say 'I don't know!'.\n" "Query: {query_str}\n" "Answer: " ) qa_prompt_tmpl = PromptTemplate(qa_prompt_tmpl_str) query_engine.update_prompts({"response_synthesizer:text_qa_template": qa_prompt_tmpl}) print("Press ctr + c to exit") while True: query = input("Enter your query: ") response = query_engine.query(query) print(response) if __name__ == "__main__": main()

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import os from dotenv import load_dotenv load_dotenv() import s3fs from llama_index import ( SimpleDirectoryReader, VectorStoreIndex, StorageContext, load_index_from_storage ) # load documents documents = SimpleDirectoryReader('../../../examples/paul_graham_essay/data/').load_data() print(len(documents)) index = VectorStoreIndex.from_documents(documents) # set up s3fs AWS_KEY = os.environ['AWS_ACCESS_KEY_ID'] AWS_SECRET = os.environ['AWS_SECRET_ACCESS_KEY'] R2_ACCOUNT_ID = os.environ['R2_ACCOUNT_ID'] assert AWS_KEY is not None and AWS_KEY != "" s3 = s3fs.S3FileSystem( key=AWS_KEY, secret=AWS_SECRET, endpoint_url=f'https://{R2_ACCOUNT_ID}.r2.cloudflarestorage.com', s3_additional_kwargs={'ACL': 'public-read'} ) # save index to remote blob storage index.set_index_id("vector_index") # this is {bucket_name}/{index_name} index.storage_context.persist('llama-index/storage_demo', fs=s3) # load index from s3 sc = StorageContext.from_defaults(persist_dir='llama-index/storage_demo', fs=s3) index2 = load_index_from_storage(sc, 'vector_index')

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import sys import logging import chromadb import streamlit as st from llama_index.llms import OpenAI from llama_index import SimpleDirectoryReader, VectorStoreIndex from llama_index.vector_stores import ChromaVectorStore from llama_index.storage.storage_context import StorageContext from llama_index import ServiceContext from llama_index.node_parser.file.markdown import MarkdownNodeParser from llama_index.chat_engine.types import ChatMode from MarkdownReader import MarkdownReader from sources import sources, get_file_metadata, Source logger = logging.getLogger() # logger.setLevel(logging.DEBUG) # stream_handler = logging.StreamHandler(stream=sys.stdout) # stream_handler.setLevel(logging.DEBUG) # file_handler = logging.FileHandler("logs.log") # file_handler.setLevel(logging.DEBUG) # logger.addHandler(file_handler) # logger.addHandler(stream_handler) def get_filename_metadata(source, filename): metadata = { "source": source.get("description", source.get("title")), **source.get("file_metadata", get_file_metadata)(filename), } # print(filename, metadata) return metadata def get_all_metadata(source): return lambda filename: get_filename_metadata(source, filename) def get_documents(source): """return Document for given source(path, file_metadata)""" reader = SimpleDirectoryReader( input_dir=source.get("path"), required_exts=[".md"], recursive=True, exclude=source.get("exclude", []), file_extractor={".md": MarkdownReader(source.get("include_metas", []))}, file_metadata=get_all_metadata(source), ) # use MarkdownReader docs = reader.load_data() return docs def index_source(chroma_client, source: Source): """index given source in chromadb""" docs = get_documents(source) chroma_collection = None try: chroma_collection = chroma_client.get_collection(source.get("id")) logger.info("==> Collection {} already exist\n\n".format(source.get("id"))) except ValueError: nodes = node_parser.get_nodes_from_documents(docs, show_progress=True) chroma_collection = chroma_client.create_collection(source.get("id")) vector_store = ChromaVectorStore(chroma_collection=chroma_collection) storage_context = StorageContext.from_defaults(vector_store=vector_store) # todo: show nodes content length logger.info( "index {} documents and {} nodes in {}".format( len(docs), len(nodes), source.get("id") ) ) index = VectorStoreIndex.from_documents( docs, storage_context=storage_context, service_context=service_context, show_progress=True, ) logger.info(f"==> Loaded {len(docs)} docs\n\n") if source.get("on_finish"): source.get("on_finish", lambda a, b: None)( docs, index ) # lambda for typings finally: if chroma_collection: vector_store = ChromaVectorStore(chroma_collection=chroma_collection) index = VectorStoreIndex.from_vector_store(vector_store) return index def debug_source(index, source): query_engine = index.as_query_engine() for query in source.get("examples", []): response = query_engine.query(query) print("\n", source.get("id"), ":", query, "\n") print(str(response)) # print((response.get_formatted_sources())) # print((response.source_nodes)) print("\n-------------") # @st.cache_resource(show_spinner=False) def index_sources1(sources): logger.info("Indexing sources...") indices = [] for source in sources: logger.info("Indexing {}".format(source.get("id"))) index = index_source(chroma_client, source) # debug_source(index, source) indices.append(index) return list(zip(indices, sources)) def index_sources(sources): logger.info("Indexing sources...") docs = [] index_id = "all_docs" chroma_collection = None for source in sources: sourceDocs = get_documents(source) docs += sourceDocs if source.get("additional_documents"): docs += source.get("additional_documents")(sourceDocs) try: chroma_collection = chroma_client.get_collection(index_id) logger.info(f"==> Collection {index_id} already exist\n\n") except ValueError: # nodes = node_parser.get_nodes_from_documents(docs, show_progress=True) chroma_collection = chroma_client.create_collection(index_id) vector_store = ChromaVectorStore(chroma_collection=chroma_collection) storage_context = StorageContext.from_defaults(vector_store=vector_store) # todo: show nodes content length logger.info("index {} documents in {}".format(len(docs), index_id)) index = VectorStoreIndex.from_documents( docs, storage_context=storage_context, service_context=service_context, show_progress=True, ) logger.info(f"==> Loaded {len(docs)} docs\n\n") # if source.get("on_finish"): # source.get("on_finish", lambda a, b: None)(docs, index) # lambda for typings finally: vector_store = ChromaVectorStore(chroma_collection=chroma_collection) index = VectorStoreIndex.from_vector_store( vector_store, service_context=service_context ) return index node_parser = MarkdownNodeParser.from_defaults() chroma_client = chromadb.PersistentClient(path="./chroma_db") # llm = OpenAI( # model="gpt-3.5-turbo", # temperature=0.0, # ) # use OpenAI by default service_context = ServiceContext.from_defaults( chunk_size=512, # embed_model=embed_model, node_parser=node_parser, # llm=llm, # prompt_helper= ) index = index_sources(sources) if __name__ == "__main__": # query chat = index.as_chat_engine( chat_mode=ChatMode.CONTEXT, verbose=True, similarity_top_k=5, ) chat.chat_repl()

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from flask_restx import Resource from flask import request, render_template, Response import openai import os import json from llama_index import GPTSimpleVectorIndex from llama_index import Document from furl import furl from PyPDF2 import PdfReader os.environ["OPENAI_API_KEY"] = "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" openai.organization = "org-Ddi6ZSgWKe8kPZlpwd6M6WVe" openai.api_key = os.getenv("OPENAI_API_KEY") def get_domain(link): print("link", link) f = furl(link) host = f.host tld = host.split(".") if len(tld) > 2: return tld[1] else: return tld[0] def get_title(title): f = furl(title) host = f.host if host != "": return host else: return title class Upload(Resource): def post(self): data = {} userid = data.get('userid', 'cibi') print(request.files) file = request.files['userfile'] file.save(userid + file.filename) print(file) reader = PdfReader(userid + file.filename) data = "" for page in reader.pages: data += page.extract_text() unique_doc = file.filename file_name = str(hash(userid + unique_doc)) + ".txt" #dict_obj = {"userid":userid,"pageTitle":pageTitle} alreadyPresentList = [] userDataJson = {} if os.path.exists("./userData.json"): with open('./userData.json', 'r') as userDataJsonFile: userDataJson = json.loads(userDataJsonFile.read()) if userid in userDataJson: alreadyPresentList = userDataJson[userid] if unique_doc not in alreadyPresentList: alreadyPresentList.append(unique_doc) else: alreadyPresentList.append(unique_doc) userDataJson[userid] = alreadyPresentList print("New data : ", str(userDataJson)) userDataJsonFileWrite = open('./userData.json', "w") userDataJsonFileWrite.write(json.dumps(userDataJson)) userDataJsonFileWrite.close() with open(str(file_name), 'w') as fl: fl.write(data) llama_doc = Document(data, doc_id=userid + "<sep>" + unique_doc) if os.path.exists("database.json"): existing_index = GPTSimpleVectorIndex.load_from_disk('database.json') existing_index.update(llama_doc) existing_index.save_to_disk("database.json") else: index = GPTSimpleVectorIndex.from_documents(documents=[llama_doc]) index.update(llama_doc) index.save_to_disk("database.json") response = "" return response, 200

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from __future__ import annotations import os import dataclasses from typing import TYPE_CHECKING, ClassVar import time import httpx from rich import print from xiaogpt.bot.base_bot import BaseBot, ChatHistoryMixin from xiaogpt.utils import split_sentences if TYPE_CHECKING: import openai from llama_index.embeddings.openai import OpenAIEmbedding from llama_index.llms.azure_openai import AzureOpenAI, AsyncAzureOpenAI from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding from llama_index.embeddings.huggingface import HuggingFaceEmbedding from llama_index.core import ( Settings, VectorStoreIndex, StorageContext, load_index_from_storage, PromptTemplate, SimpleDirectoryReader ) @dataclasses.dataclass class RagBot(ChatHistoryMixin, BaseBot): name: ClassVar[str] = "RAG" default_options: ClassVar[dict[str, str]] = {"model": "gpt4-1106-prevision"} openai_key: str api_base: str | None = None proxy: str | None = None history: list[tuple[str, str]] = dataclasses.field(default_factory=list, init=False) def _make_query_engine(self, sess: httpx.AsyncClient, stream=False): llm = AzureOpenAI( engine="gpt4-1106-prevision", api_key=self.openai_key, azure_endpoint=self.api_base, api_version="2023-12-01-preview", ) embed_model = AzureOpenAIEmbedding( model="text-embedding-ada-002", deployment_name="embedding-ada-002-v2", api_key=self.openai_key, azure_endpoint="http://192.168.12.232:8880", api_version="2023-05-15", ) Settings.embed_model = embed_model Settings.llm = llm # check if storage already exists PERSIST_DIR = "xiaogpt/rag/storage" if not os.path.exists(PERSIST_DIR): # load the documents and create the index documents = SimpleDirectoryReader("xiaogpt/rag/data").load_data() index = VectorStoreIndex.from_documents(documents) # store it for later index.storage_context.persist(persist_dir=PERSIST_DIR) else: # load the existing index storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR) index = load_index_from_storage(storage_context) # set Logging to DEBUG for more detailed outputs text_qa_template_str = ( "Context information is" " below.\n---------------------\n{context_str}\n---------------------\nUsing" " both the context information and also using your own knowledge, answer" " the question with less that 100 words: {query_str}\nIf the context isn't helpful, you can also" " answer the question on your own.\n" ) text_qa_template = PromptTemplate(text_qa_template_str) refine_template_str = ( "The original question is as follows: {query_str}\nWe have provided an" " existing answer: {existing_answer}\nWe have the opportunity to refine" " the existing answer (only if needed) with some more context" " below.\n------------\n{context_msg}\n------------\nUsing both the new" " context and your own knowledge, update existing answer with less than 100 words. \n" ) refine_template = PromptTemplate(refine_template_str) query_engine = index.as_query_engine( text_qa_template=text_qa_template, refine_template=refine_template, llm=llm, streaming=stream ) return query_engine @classmethod def from_config(cls, config): return cls( openai_key=config.openai_key, api_base=config.api_base, proxy=config.proxy ) async def ask(self, query, **options): ms = self.get_messages() ms.append({"role": "user", "content": f"{query}"}) kwargs = {**self.default_options, **options} httpx_kwargs = {} if self.proxy: httpx_kwargs["proxies"] = self.proxy async with httpx.AsyncClient(trust_env=True, **httpx_kwargs) as sess: query_engine = self._make_query_engine(sess) try: completion = query_engine.query(query) except Exception as e: print(str(e)) return "" message = completion.response # print(completion.source_nodes[0].get_text()) self.add_message(query, message) print(message) return message async def ask_stream(self, query, **options): ms = self.get_messages() ms.append({"role": "user", "content": f"{query}"}) kwargs = {**self.default_options, **options} httpx_kwargs = {} if self.proxy: httpx_kwargs["proxies"] = self.proxy async with httpx.AsyncClient(trust_env=True, **httpx_kwargs) as sess: query_engine = self._make_query_engine(sess, stream=True) try: completion = query_engine.query(query) except Exception as e: print(str(e)) return async def text_gen(): async for event in completion: if not event.response: continue chunk_message = event.response if chunk_message.response is None: continue print(chunk_message.response, end="") yield chunk_message.response message = "" try: async for sentence in split_sentences(text_gen()): message += sentence yield sentence finally: print() self.add_message(query, message) import functools import dataclasses from typing import Any, AsyncIterator, Literal, Optional @dataclasses.dataclass class Config: openai_key: str = "voxelcloud" proxy: str | None = None api_base: str = "http://192.168.12.232:8881" stream: bool = False bot: str = "chatgptapi" gpt_options: dict[str, Any] = dataclasses.field(default_factory=dict) import asyncio async def main(): config = Config() # 假设 Config 类已经定义并可以接受默认参数 bot = RagBot.from_config(config) # 询问问题 response = await bot.ask("什么是光疗？") print(response) # 运行异步 main 函数 if __name__ == "__main__": asyncio.run(main())

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import logging import sys import requests import os from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext import torch from llama_index.llms import LlamaCPP from llama_index.llms.llama_utils import messages_to_prompt, completion_to_prompt from langchain.embeddings.huggingface import HuggingFaceEmbeddings from llama_index import LangchainEmbedding #!CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python --no-cache-dir #un comment this to use GPU engine- CUBLAS logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) url = 'https://firebasestorage.googleapis.com/v0/b/ichiropractic.appspot.com/o/test.pdf?alt=media&token=c7b685c1-712d-4b0e-bbfd-3d80198c6584' if not os.path.exists('Data'): os.makedirs('Data') file_path = os.path.join('Data', 'test.pdf') response = requests.get(url) if response.status_code == 200: with open(file_path, 'wb') as file: file.write(response.content) else: print(f'Failed to download the file: {response.status_code}') # Setup LlamaCPP llm = LlamaCPP( model_url='', # compactible model is GGUF only. model_path='./dolphin-2.1-mistral-7b.Q4_K_M.gguf', # Here I have use dolphin model from my local machine. please remove this and use your own model path temperature=0.1, max_new_tokens=3024, context_window=3900, generate_kwargs={}, model_kwargs={"n_gpu_layers": 128}, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, verbose=True, ) print('LlamaCPP is ready to use.')

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from llama_index import SimpleDirectoryReader, VectorStoreIndex, LLMPredictor, PromptHelper from langchain.chat_models import ChatOpenAI import gradio as gr from pprint import pprint; import IPython import sys import os from pathlib import Path # Check if the environment variable exists if "OPENAIKEY" in os.environ: # If it exists, get its value into a Python variable api_key = os.environ["OPENAIKEY"] else: raise ValueError("Please set the OPENAIKEY environment variable") os.environ["OPENAI_API_KEY"] = api_key from llama_index import VectorStoreIndex, download_loader from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader documents = SimpleDirectoryReader('/Users/despiegk1/Downloads/ai').load_data() index = GPTVectorStoreIndex.from_documents(documents) index.storage_context.persist() query_engine = index.as_query_engine() query_engine.query("what is ourworld?") # ImageReader = download_loader("ImageReader") # imageLoader = ImageReader(text_type="plain_text") # FlatPdfReader = download_loader("FlatPdfReader") # pdfLoader = FlatPdfReader(image_loader=imageLoader) # document = pdfLoader.load_data(file=Path('~/Downloads/its not about what we have, its about what we believe in. (5).pdf')) # index = VectorStoreIndex.from_documents([document]) # query_engine = index.as_query_engine() # query_engine.query('how vulnerable are security protocols?') IPython.embed()

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import argparse import copy import logging import os import sys import warnings from typing import Optional, List, Callable from langchain.llms import OpenAI import faiss import gradio as gr import torch import torch.distributed as dist import transformers from accelerate import dispatch_model, infer_auto_device_map from accelerate.hooks import ( AlignDevicesHook, add_hook_to_module, remove_hook_from_submodules, ) from accelerate.utils import get_balanced_memory from huggingface_hub import hf_hub_download from llama_index import LLMPredictor from llama_index import PromptHelper, SimpleDirectoryReader from llama_index import ServiceContext from llama_index import GPTKeywordTableIndex, GPTSimpleVectorIndex, GPTListIndex, GPTTreeIndex, GPTFaissIndex from peft import PeftModelForCausalLM, LoraConfig from peft.utils import PeftType, set_peft_model_state_dict from torch import nn from transformers.deepspeed import is_deepspeed_zero3_enabled from transformers.generation.beam_search import BeamSearchScorer from transformers.generation.utils import ( LogitsProcessorList, StoppingCriteriaList, GenerationMixin, ) from model import CustomLLM, Llama7bHFLLM assert ( "LlamaTokenizer" in transformers._import_structure["models.llama"] ), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git" from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig class SteamGenerationMixin(PeftModelForCausalLM, GenerationMixin): # support for streamly beam search @torch.no_grad() def stream_generate( self, input_ids: Optional[torch.Tensor] = None, generation_config: Optional[GenerationConfig] = None, logits_processor: Optional[LogitsProcessorList] = None, stopping_criteria: Optional[StoppingCriteriaList] = None, prefix_allowed_tokens_fn: Optional[ Callable[[int, torch.Tensor], List[int]] ] = None, **kwargs, ): self._reorder_cache = self.base_model._reorder_cache if is_deepspeed_zero3_enabled() and dist.world_size() > 1: synced_gpus = True else: synced_gpus = False if kwargs.get("attention_mask", None) is not None: # concat prompt attention mask prefix_attention_mask = torch.ones( kwargs["input_ids"].shape[0], self.peft_config.num_virtual_tokens ).to(kwargs["input_ids"].device) kwargs["attention_mask"] = torch.cat( (prefix_attention_mask, kwargs["attention_mask"]), dim=1 ) if kwargs.get("position_ids", None) is not None: warnings.warn( "Position ids are not supported for parameter efficient tuning. Ignoring position ids." ) kwargs["position_ids"] = None if kwargs.get("token_type_ids", None) is not None: warnings.warn( "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids" ) kwargs["token_type_ids"] = None batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] if generation_config is None: generation_config = self.generation_config generation_config = copy.deepcopy(generation_config) model_kwargs = generation_config.update(**kwargs) bos_token_id, eos_token_id, pad_token_id = ( generation_config.bos_token_id, generation_config.eos_token_id, generation_config.pad_token_id, ) if isinstance(eos_token_id, int): eos_token_id = [eos_token_id] has_default_max_length = ( kwargs.get("max_length") is None and generation_config.max_length is not None ) if has_default_max_length and generation_config.max_new_tokens is None: warnings.warn( f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" " recommend using `max_new_tokens` to control the maximum length of the generation.", UserWarning, ) elif generation_config.max_new_tokens is not None: generation_config.max_length = ( generation_config.max_new_tokens + input_ids_seq_length ) if generation_config.min_new_tokens is not None: generation_config.min_length = ( generation_config.min_new_tokens + input_ids_seq_length ) if input_ids_seq_length >= generation_config.max_length: input_ids_string = ( "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids" ) # 2. Set generation parameters if not already defined logits_processor = ( logits_processor if logits_processor is not None else LogitsProcessorList() ) stopping_criteria = ( stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() ) # 8. prepare distribution pre_processing samplers logits_processor = self._get_logits_processor( generation_config=generation_config, input_ids_seq_length=input_ids_seq_length, encoder_input_ids=input_ids, prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, logits_processor=logits_processor, ) # 9. prepare stopping criteria stopping_criteria = self._get_stopping_criteria( generation_config=generation_config, stopping_criteria=stopping_criteria ) logits_warper = self._get_logits_warper(generation_config) # 10. go into beam search generation modes # 11. prepare beam search scorer num_beams = generation_config.num_beams beam_scorer = BeamSearchScorer( batch_size=batch_size, num_beams=generation_config.num_beams, device=input_ids.device, length_penalty=generation_config.length_penalty, do_early_stopping=generation_config.early_stopping, num_beam_hyps_to_keep=generation_config.num_return_sequences, max_length=generation_config.max_length, ) # 12. interleave input_ids with `num_beams` additional sequences per batch input_ids, model_kwargs = self._expand_inputs_for_generation( input_ids=input_ids, expand_size=generation_config.num_beams, is_encoder_decoder=self.config.is_encoder_decoder, **model_kwargs, ) # beam_search logits batch_beam_size, cur_len = input_ids.shape if num_beams * batch_size != batch_beam_size: raise ValueError( f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}." ) beam_scores = torch.zeros( (batch_size, num_beams), dtype=torch.float, device=input_ids.device ) beam_scores[:, 1:] = -1e9 beam_scores = beam_scores.view((batch_size * num_beams,)) this_peer_finished = False # used by synced_gpus only while True: if synced_gpus: # Under synced_gpus the `forward` call must continue until all gpus complete their sequence. # The following logic allows an early break if all peers finished generating their sequence this_peer_finished_flag = torch.tensor( 0.0 if this_peer_finished else 1.0 ).to(input_ids.device) # send 0.0 if we finished, 1.0 otherwise dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM) # did all peers finish? the reduced sum will be 0.0 then if this_peer_finished_flag.item() == 0.0: break model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) outputs = self( **model_inputs, return_dict=True, output_attentions=False, output_hidden_states=False, ) if synced_gpus and this_peer_finished: cur_len = cur_len + 1 continue # don't waste resources running the code we don't need next_token_logits = outputs.logits[:, -1, :] # next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len) hack: adjust tokens for Marian. next_token_scores = nn.functional.log_softmax( next_token_logits, dim=-1 ) # (batch_size * num_beams, vocab_size) next_token_scores_processed = logits_processor(input_ids, next_token_scores) next_token_scores = next_token_scores_processed + beam_scores[ :, None ].expand_as(next_token_scores) # reshape for beam search vocab_size = next_token_scores.shape[-1] next_token_scores = next_token_scores.view( batch_size, num_beams * vocab_size ) # Sample 2 next tokens for each beam (so we have some spare tokens and match output of beam search) next_token_scores, next_tokens = torch.topk( next_token_scores, 2 * num_beams, dim=1, largest=True, sorted=True ) next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor") next_tokens = next_tokens % vocab_size # stateless beam_outputs = beam_scorer.process( input_ids, next_token_scores, next_tokens, next_indices, pad_token_id=pad_token_id, eos_token_id=eos_token_id, beam_indices=None, ) beam_scores = beam_outputs["next_beam_scores"] beam_next_tokens = beam_outputs["next_beam_tokens"] beam_idx = beam_outputs["next_beam_indices"] input_ids = torch.cat( [input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1 ) model_kwargs = self._update_model_kwargs_for_generation( outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder ) if model_kwargs["past_key_values"] is not None: model_kwargs["past_key_values"] = self._reorder_cache( model_kwargs["past_key_values"], beam_idx ) # increase cur_len cur_len = cur_len + 1 yield input_ids if beam_scorer.is_done or stopping_criteria(input_ids, None): if not synced_gpus: break else: this_peer_finished = True final_result = beam_scorer.finalize( input_ids, beam_scores, next_tokens, next_indices, pad_token_id=pad_token_id, eos_token_id=eos_token_id, max_length=stopping_criteria.max_length, beam_indices=None, ) yield final_result["sequences"] # default it call `model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](model, config)`, not cls!! so inherent PeftModelForCausalLM is no sense @classmethod def from_pretrained(cls, model, model_id, **kwargs): # load the config config = LoraConfig.from_pretrained(model_id) if getattr(model, "hf_device_map", None) is not None: remove_hook_from_submodules(model) # here is the hack model = cls(model, config) # load weights if any if os.path.exists(os.path.join(model_id, "adapter_model.bin")): filename = os.path.join(model_id, "adapter_model.bin") else: try: filename = hf_hub_download(model_id, "adapter_model.bin") except: # noqa raise ValueError( f"Can't find weights for {model_id} in {model_id} or in the Hugging Face Hub. " f"Please check that the file {'adapter_model.bin'} is present at {model_id}." ) adapters_weights = torch.load( filename, map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu"), ) # load the weights into the model model = set_peft_model_state_dict(model, adapters_weights) if getattr(model, "hf_device_map", None) is not None: device_map = kwargs.get("device_map", "auto") max_memory = kwargs.get("max_memory", None) no_split_module_classes = model._no_split_modules if device_map != "sequential": max_memory = get_balanced_memory( model, max_memory=max_memory, no_split_module_classes=no_split_module_classes, low_zero=(device_map == "balanced_low_0"), ) if isinstance(device_map, str): device_map = infer_auto_device_map( model, max_memory=max_memory, no_split_module_classes=no_split_module_classes, ) model = dispatch_model(model, device_map=device_map) hook = AlignDevicesHook(io_same_device=True) if model.peft_config.peft_type == PeftType.LORA: add_hook_to_module(model.base_model.model, hook) else: remove_hook_from_submodules(model.prompt_encoder) add_hook_to_module(model.base_model, hook) return model parser = argparse.ArgumentParser() parser.add_argument("--model_path", type=str, default="decapoda-research/llama-7b-hf") parser.add_argument("--lora_path", type=str, default="./lora-Vicuna/checkpoint-3000") parser.add_argument("--use_local", type=int, default=1) args = parser.parse_args() tokenizer = LlamaTokenizer.from_pretrained(args.model_path) LOAD_8BIT = True BASE_MODEL = args.model_path LORA_WEIGHTS = args.lora_path # fix the path for local checkpoint lora_bin_path = os.path.join(args.lora_path, "adapter_model.bin") print(lora_bin_path) if not os.path.exists(lora_bin_path) and args.use_local: pytorch_bin_path = os.path.join(args.lora_path, "pytorch_model.bin") print(pytorch_bin_path) if os.path.exists(pytorch_bin_path): os.rename(pytorch_bin_path, lora_bin_path) warnings.warn( "The file name of the lora checkpoint'pytorch_model.bin' is replaced with 'adapter_model.bin'" ) else: assert ('Checkpoint is not Found!') if torch.cuda.is_available(): device = "cuda" else: device = "cpu" try: if torch.backends.mps.is_available(): device = "mps" except: pass if device == "cuda": model = LlamaForCausalLM.from_pretrained( BASE_MODEL, load_in_8bit=LOAD_8BIT, torch_dtype=torch.float16, device_map={"": 0}, ) model = SteamGenerationMixin.from_pretrained( model, LORA_WEIGHTS, torch_dtype=torch.float16, device_map={"": 0} ) elif device == "mps": model = LlamaForCausalLM.from_pretrained( BASE_MODEL, device_map={"": device}, torch_dtype=torch.float16, ) model = SteamGenerationMixin.from_pretrained( model, LORA_WEIGHTS, device_map={"": device}, torch_dtype=torch.float16, ) else: model = LlamaForCausalLM.from_pretrained( BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True ) model = SteamGenerationMixin.from_pretrained( model, LORA_WEIGHTS, device_map={"": device}, ) def generate_prompt(instruction, input=None): if input: return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Response:""" else: return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response:""" if not LOAD_8BIT: model.half() # seems to fix bugs for some users. model.eval() if torch.__version__ >= "2" and sys.platform != "win32": model = torch.compile(model) logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) import openai openai.api_key = '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' os.environ["OPENAI_API_KEY"] = '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' def evaluate( input, temperature=0.1, top_p=0.75, top_k=40, num_beams=4, max_new_tokens=2500, min_new_tokens=1, repetition_penalty=2.0, **kwargs, ): print('start text llama-index') # TEST # # set maximum input size max_input_size = 2048 # set number of output tokens num_output = 1024 # set maximum chunk overlap max_chunk_overlap = 20 gen_config = GenerationConfig( temperature=temperature, top_p=top_p, top_k=top_k, num_beams=num_beams, bos_token_id=1, eos_token_id=2, pad_token_id=0, max_new_tokens=max_new_tokens, # max_length=max_new_tokens+input_sequence min_new_tokens=min_new_tokens, # min_length=min_new_tokens+input_sequence repetition_penalty=repetition_penalty ) # service_context = ServiceContext.from_defaults( # llm_predictor=LLMPredictor(llm=CustomLLM(mod=model, token=tokenizer, gen_config=gen_config, device=device)), # prompt_helper=PromptHelper(max_input_size, num_output, max_chunk_overlap)) service_context = ServiceContext.from_defaults( llm_predictor=LLMPredictor(llm=model), prompt_helper=PromptHelper(max_input_size, num_output, max_chunk_overlap)) documents = SimpleDirectoryReader('Chinese-Vicuna/index-docs').load_data() print(documents) print('start init index') # llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="text-davinci-003")) # default_service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor) # index = GPTFaissIndex.from_documents(documents, service_context=service_context) # index = GPTFaissIndex.from_documents(documents, faiss_index=faiss.IndexFlatL2(1536), service_context=default_service_context) print('end init index done') print('start save to disk') # index.save_to_disk("clash-index.json") # suffix do not matter faiss_index_save_path = 'faiss_index.faiss' faiss_index = faiss.IndexFlatL2(1536) faiss.write_index(faiss_index, faiss_index_save_path) index = GPTFaissIndex.load_from_disk(save_path='clash-index.json', faiss_index=faiss_index_save_path, service_context=service_context) print('end save to disk') # Query and print response print('start query') response = index.query(input) print('end query') print(response) return response # with torch.no_grad(): # # immOutPut = model.generate(input_ids=input_ids, generation_config=generation_config, # # return_dict_in_generate=True, output_scores=False, # # repetition_penalty=float(repetition_penalty), ) # # outputs = tokenizer.batch_decode(immOutPut) # last_show_text = '' # for generation_output in model.stream_generate( # input_ids=input_ids, # generation_config=generation_config, # return_dict_in_generate=True, # output_scores=False, # repetition_penalty=float(repetition_penalty), # ): # outputs = tokenizer.batch_decode(generation_output) # show_text = "\n--------------------------------------------\n".join( # [output.split("### Response:")[1].strip().replace('�', '') for output in outputs] # ) # # if show_text== '': # # yield last_show_text # # else: # yield show_text # last_show_text = outputs[0].split("### Response:")[1].strip().replace('�', '') gr.Interface( fn=evaluate, inputs=[ gr.components.Textbox( lines=2, label="Input", placeholder="Tell me about alpacas." ), gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature"), gr.components.Slider(minimum=0, maximum=1, value=0.75, label="Top p"), gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k"), gr.components.Slider(minimum=1, maximum=10, step=1, value=4, label="Beams Number"), gr.components.Slider( minimum=1, maximum=2000, step=1, value=256, label="Max New Tokens" ), gr.components.Slider( minimum=1, maximum=100, step=1, value=1, label="Min New Tokens" ), gr.components.Slider( minimum=0.1, maximum=10.0, step=0.1, value=1.0, label="Repetition Penalty" ), ], outputs=[ gr.inputs.Textbox( lines=15, label="Output", ) ], title="Chinese-Vicuna 中文小羊驼", description="结合 llama-index prompt 搜索优化的 中文小羊驼", ).queue().launch(share=True)

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from typing import List, Set from llama_index.core import Document, KnowledgeGraphIndex, StorageContext from llama_index.core.query_engine import BaseQueryEngine from llama_index.core import load_index_from_storage import os def load_kg_graph_index_storage_context(kg_graph_storage_dir: str) -> StorageContext: return StorageContext.from_defaults(persist_dir=kg_graph_storage_dir) def persist_kg_graph_index(idx: KnowledgeGraphIndex, kg_graph_storage_dir: str): doc_count = len(idx.docstore.docs) print(f"Persisting {doc_count} docs for kg_graph to {kg_graph_storage_dir} ...") idx.storage_context.persist(persist_dir=kg_graph_storage_dir) def delete_kg_graph_index(kg_graph_storage_dir: str): print(f"Deleting kg_graph at {kg_graph_storage_dir} ...") if os.path.exists(kg_graph_storage_dir): import shutil shutil.rmtree(kg_graph_storage_dir) def load_kg_graph_index(kg_graph_storage_dir: str) -> KnowledgeGraphIndex: if not os.path.exists(kg_graph_storage_dir): print(f"About to initialize an empty kg-graph ...") kg_graph = KnowledgeGraphIndex.from_documents( [] ) persist_kg_graph_index(kg_graph, kg_graph_storage_dir) return load_index_from_storage( storage_context=load_kg_graph_index_storage_context(kg_graph_storage_dir) ) def get_kg_graph_doc_source_ids(graph_storage_dir: str, extract_key_from_doc=lambda: str) -> Set[str]: s = set() for doc in load_kg_graph_index(graph_storage_dir).docstore.docs.values(): s.add(extract_key_from_doc(doc)) return s def get_kg_graph_index(graph_storage_dir: str) -> KnowledgeGraphIndex: return load_kg_graph_index(graph_storage_dir) def operate_on_kg_graph_index(kg_graph_index_dir: str, operation=lambda: None) -> KnowledgeGraphIndex: import atexit idx = get_kg_graph_index(kg_graph_index_dir) atexist_reg_callable = atexit.register(persist_kg_graph_index, idx, kg_graph_index_dir) try: operation(idx) finally: persist_kg_graph_index(idx, kg_graph_index_dir) atexit.unregister(atexist_reg_callable) return idx def add_to_or_update_in_kg_graph(graph_storage_dir: str, documents: List[Document]): operate_on_kg_graph_index( graph_storage_dir, lambda graph_index: graph_index.refresh_ref_docs(documents) ) def get_kg_graph_query_engine(graph_storage_dir: str) -> BaseQueryEngine: return load_kg_graph_index(graph_storage_dir).as_query_engine()

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from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex, LLMPredictor, PromptHelper from langchain import OpenAI import sys import os def construct_index(src_path, out_path): # set maximum input size max_input_size = 4096 # set number of output tokens num_outputs = 512 # set maximum chunk overlap max_chunk_overlap = 20 # set chunk size limit chunk_size_limit = 600 # define LLM llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="text-davinci-003", max_tokens=num_outputs)) prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit) documents = SimpleDirectoryReader(src_path).load_data() index = GPTSimpleVectorIndex( documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper ) index.save_to_disk(f"{out_path}/index.json") return index if __name__ == "__main__": import os src_path = os.getcwd() dir_path = src_path + "/clean" out_path = src_path os.environ["OPENAI_API_KEY"] = "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" index = construct_index(src_path, out_path)

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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="Chat with the docs, powered by LlamaIndex") openai.api_key = st.secrets.openai_key st.title("Chat with the custom docs, using LlamaIndex") if "messages" not in st.session_state.keys(): # Initialize the chat messages history st.session_state.messages = [ {"role": "assistant", "content": "Ask me a question about Streamlit's open-source Python library!"} ] @st.cache_resource(show_spinner=False) def load_data(): with st.spinner(text="Loading and indexing the Streamlit docs"): 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, system_prompt="You are an expert on the Streamlit Python library and your job is to answer technical questions. Assume that all questions are related to the Streamlit Python library. Keep your answers technical and based on facts – do not hallucinate features.")) 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, system_prompt="You are an expert on the Streamlit Python library and your job is to answer technical questions. Assume that all questions are related to the Streamlit Python library. Keep your answers technical and based on facts – do not hallucinate features.") chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True) if prompt := st.chat_input("Your question"): # 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("Thinking..."): 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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from llama_index import StorageContext, load_index_from_storage, ServiceContext import gradio as gr import sys import os import logging from utils import get_automerging_query_engine from utils import get_sentence_window_query_engine import configparser from TTS.api import TTS from gtts import gTTS import simpleaudio as sa import threading from datetime import datetime import json import subprocess from llama_index.prompts.base import PromptTemplate from inference import main as generateVideo import pyttsx3 def run_inference(checkpoint_path, face_video, audio_file, resize_factor, outfile): # Construct the command with dynamic parameters command = [ "--checkpoint_path", checkpoint_path, "--face", face_video, "--audio", audio_file, "--resize_factor", str(resize_factor), "--outfile", outfile ] print(command) generateVideo(command) def play_sound_then_delete(path_to_wav): def play_and_delete(): try: wave_obj = sa.WaveObject.from_wave_file(path_to_wav) play_obj = wave_obj.play() play_obj.wait_done() # Wait until the sound has finished playing except Exception as e: print(f"Error during playback: {e}") finally: try: #os.remove(path_to_wav) print(f"File {path_to_wav} successfully deleted.") except Exception as e: print(f"Error deleting file: {e}") # Start playback in a new thread threading.Thread(target=play_and_delete, daemon=True).start() config = configparser.ConfigParser() config.read('config.ini') os.environ["GRADIO_ANALYTICS_ENABLED"]='False' indextype=config['api']['indextype'] embed_modelname = config['api']['embedmodel'] basic_idx_dir = config['index']['basic_idx_dir'] sent_win_idx_dir = config['index']['sent_win_idx_dir'] auto_mrg_idx_dir = config['index']['auto_mrg_idx_dir'] serverip = config['api']['host'] serverport = config['api']['port'] sslcert = config['api']['sslcert'] sslkey = config['api']['sslkey'] useopenai = config.getboolean('api', 'useopenai') ttsengine = config['api']['ttsengine'] # Get the logging level log_level_str = config.get('api', 'loglevel', fallback='WARNING').upper() # Convert the log level string to a logging level log_level = getattr(logging, log_level_str, logging.WARNING) def chatbot(input_text): global tts print("User Text:" + input_text) response =query_engine.query(input_text) # Save the output timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') output_audfile=f"output_{timestamp}.wav" output_vidfile=f"output_{timestamp}.mp4" output_path = "../web/public/audio/output/"+output_audfile if ttsengine == 'coqui': tts.tts_to_file(text=response.response, file_path=output_path ) # , speaker_wav=["bruce.wav"], language="en",split_sentences=True) elif ttsengine == 'gtts': tts = gTTS(text=response.response, lang='en') tts.save(output_path) else: tts.save_to_file(response.response , output_path) tts.runAndWait() checkpoint_path = "./checkpoints/wav2lip_gan.pth" face_video = "media/Avatar.mp4" audio_file = "../web/public/audio/output/"+output_audfile outfile="../web/public/video/output/"+output_vidfile resize_factor = 2 run_inference(checkpoint_path, face_video, audio_file, resize_factor, outfile) #play_sound_then_delete(output_path) #construct response object # Building the citation list from source_nodes citation = [ { "filename": node.metadata["file_name"], "text": node.get_text() } for node in response.source_nodes ] # Creating the JSON object structure jsonResponse = { "response": response.response, "video": output_vidfile, "audio": output_audfile, "citation": citation } # Convert to JSON string jsonResponseStr = json.dumps(jsonResponse, indent=4) return jsonResponseStr logging.basicConfig(stream=sys.stdout, level=log_level) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) iface = gr.Interface(fn=chatbot, inputs=gr.components.Textbox(lines=7, label="Enter your text"), outputs="text", title="Email data query") from langchain.llms import LlamaCpp from langchain.globals import set_llm_cache from langchain.cache import InMemoryCache #from langchain.globals import set_debug #set_debug(True) if useopenai: from langchain.chat_models import ChatOpenAI modelname = config['api']['openai_modelname'] llm =ChatOpenAI(temperature=0.1, model_name=modelname) else: modelname = config['api']['local_modelname'] n_gpu_layers = -1 # Change this value based on your model and your GPU VRAM pool. n_batch = 2048 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU. #cache prompt/response pairs for faster retrieval next time. set_llm_cache(InMemoryCache()) llm = LlamaCpp( model_path="./models/"+ modelname, cache=True, n_gpu_layers=n_gpu_layers, n_batch=n_batch, n_ctx=2048, n_threads=8, temperature=0.01, max_tokens=512, f16_kv=True, repeat_penalty=1.1, min_p=0.05, top_p=0.95, top_k=40, stop=["<|end_of_turn|>"] ) service_context = ServiceContext.from_defaults( llm=llm, embed_model=embed_modelname ) index_directory='' if indextype == 'basic': index_directory = basic_idx_dir elif indextype == 'sentence' : index_directory = sent_win_idx_dir elif indextype == 'automerge': index_directory = auto_mrg_idx_dir print(config['api']['indextype'] ) print(index_directory) if ttsengine == 'coqui': tts = TTS(model_name="tts_models/en/ljspeech/vits--neon", progress_bar=False).to("cuda") #tts = TTS(model_name="tts_models/multilingual/multi-dataset/xtts_v2", progress_bar=False).to("cuda") elif ttsengine == 'gtts': tts = gTTS(text='', lang='en') else: tts = pyttsx3.init() voices = tts.getProperty('voices') tts.setProperty('voice', voices[1].id) # this is female voice rate = tts.getProperty('rate') tts.setProperty('rate', rate-50) # load index storage_context = StorageContext.from_defaults(persist_dir=index_directory) index = load_index_from_storage(storage_context=storage_context, service_context=service_context) if indextype == 'basic': query_engine = index.as_query_engine() elif indextype == 'sentence' : query_engine =get_sentence_window_query_engine(index) elif indextype == 'automerge': query_engine = get_automerging_query_engine(automerging_index=index, service_context=service_context) #prompts_dict = query_engine.get_prompts() #print(list(prompts_dict.keys())) # Optional: Adjust prompts to suit the llms. qa_prompt_tmpl_str = ( "GPT4 User: You are an assistant named Maggie. You assist with any questions regarding the organization kwaai.\n" "Context information is below\n" "----------------------\n" "{context_str}\n" "----------------------\n" "Given the context information and not prior knowledge respond to user: {query_str}\n" "<|end_of_turn|>GPT4 Assistant:" ) qa_prompt_tmpl = PromptTemplate(qa_prompt_tmpl_str) query_engine.update_prompts( {"response_synthesizer:text_qa_template": qa_prompt_tmpl} ) iface.launch( share=False, server_name=serverip, server_port=int(serverport), ssl_verify=False, ssl_keyfile=sslkey, ssl_certfile=sslcert)

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from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper from langchain import OpenAI import os def construct_index(directory_path): # set maximum input size max_input_size = 4096 # set number of output tokens num_outputs = 2000 # set maximum chunk overlap max_chunk_overlap = 20 # set chunk size limit chunk_size_limit = 600 # define LLM llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="text-davinci-003", max_tokens=num_outputs)) prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit) documents = SimpleDirectoryReader(directory_path).load_data() index = GPTSimpleVectorIndex( documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper ) index.save_to_disk('index.json') return index def ask_ai(query): index = GPTSimpleVectorIndex.load_from_disk('./Talking_Buddy/index.json') response = index.query(query, response_mode="compact") return response.response os.environ["OPENAI_API_KEY"] = "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" construct_index("./Talking_Buddy/data")

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import os import streamlit as st from dotenv import load_dotenv from llama_index import GPTVectorStoreIndex, LLMPredictor, PromptHelper, ServiceContext from langchain.llms.openai import OpenAI from biorxiv_manager import BioRxivManager load_dotenv() openai_api_key = os.getenv("OPENAI_API_KEY") st.title("Ask BioRxiv") query = st.text_input("What would you like to ask? (source: BioRxiv files)", "") @st.cache_data def fetch_and_parse(): # instantiating BioRxivManager runtime and fetch the parsed nodes manager = BioRxivManager() return manager.fetch_and_parse(interval="2023-07-01/2023-07-30") embedded_documents = fetch_and_parse() if st.button("Submit"): if not query.strip(): st.error(f"Please provide the search query.") else: try: llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="gpt-4-32k", openai_api_key=openai_api_key)) max_input_size = 32767 num_output = 400 chunk_overlap_ratio = 0.2 # Adjust this value according to your need. prompt_helper = PromptHelper(max_input_size, num_output, chunk_overlap_ratio) service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper) index = GPTVectorStoreIndex.from_documents(embedded_documents, service_context=service_context) response = index.query(query) st.success(response) except Exception as e: st.error(f"An error occurred: {e}")

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import logging from llama_index.langchain_helpers.agents.tools import LlamaIndexTool from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters from app.llama_index.index import setup_index from app.llama_index.query_engine import setup_query_engine from app.database.crud import get_vectorized_election_programs_from_db from app.database.database import Session def setup_agent_tools(): session = Session() vectorized_election_programs = get_vectorized_election_programs_from_db(session) logging.info(f"Loaded {len(vectorized_election_programs)} vectorized programs.") vector_tools = [] for program in vectorized_election_programs: meta_data_filters = MetadataFilters( filters=[ ExactMatchFilter(key="group_id", value=program.id), ExactMatchFilter(key="election_id", value=program.election_id), ExactMatchFilter(key="party_id", value=program.party_id), ] ) # define query engines vector_index = setup_index() vector_query_engine = setup_query_engine( vector_index, filters=meta_data_filters ) # define tools query_engine_tool = LlamaIndexTool( name="vector_tool", description=( f"Nützlich für Fragen zu spezifischen Aspekten des Wahlprogramms der {program.full_name} für die {program.label}." ), query_engine=vector_query_engine, ) logging.info(f"Loaded query engine tool for {program.full_name}.") vector_tools.append(query_engine_tool) return vector_tools

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import os from configparser import ConfigParser, SectionProxy from typing import Any, Type from llama_index import ( LLMPredictor, ServiceContext, VectorStoreIndex, ) from llama_index.embeddings.base import BaseEmbedding from llama_index.embeddings.openai import OpenAIEmbedding from llama_index.indices import SimpleKeywordTableIndex from llama_index.indices.base import BaseIndex from llama_index.indices.loading import load_index_from_storage from llama_index.llm_predictor import StructuredLLMPredictor from llama_index.llms.llm import LLM from llama_index.llms.openai import OpenAI from llama_index.storage.storage_context import StorageContext CONFIG_FILE_NAME = "config.ini" DEFAULT_PERSIST_DIR = "./storage" DEFAULT_CONFIG = { "store": {"persist_dir": DEFAULT_PERSIST_DIR}, "index": {"type": "default"}, "embed_model": {"type": "default"}, "llm_predictor": {"type": "default"}, } def load_config(root: str = ".") -> ConfigParser: """Load configuration from file.""" config = ConfigParser() config.read_dict(DEFAULT_CONFIG) config.read(os.path.join(root, CONFIG_FILE_NAME)) return config def save_config(config: ConfigParser, root: str = ".") -> None: """Load configuration to file.""" with open(os.path.join(root, CONFIG_FILE_NAME), "w") as fd: config.write(fd) def load_index(root: str = ".") -> BaseIndex[Any]: """Load existing index file.""" config = load_config(root) service_context = _load_service_context(config) # Index type index_type: Type if config["index"]["type"] == "default" or config["index"]["type"] == "vector": index_type = VectorStoreIndex elif config["index"]["type"] == "keyword": index_type = SimpleKeywordTableIndex else: raise KeyError(f"Unknown index.type {config['index']['type']}") try: # try loading index storage_context = _load_storage_context(config) index = load_index_from_storage(storage_context) except ValueError: # build index storage_context = StorageContext.from_defaults() index = index_type( nodes=[], service_context=service_context, storage_context=storage_context ) return index def save_index(index: BaseIndex[Any], root: str = ".") -> None: """Save index to file.""" config = load_config(root) persist_dir = config["store"]["persist_dir"] index.storage_context.persist(persist_dir=persist_dir) def _load_service_context(config: ConfigParser) -> ServiceContext: """Internal function to load service context based on configuration.""" embed_model = _load_embed_model(config) llm_predictor = _load_llm_predictor(config) return ServiceContext.from_defaults( llm_predictor=llm_predictor, embed_model=embed_model ) def _load_storage_context(config: ConfigParser) -> StorageContext: persist_dir = config["store"]["persist_dir"] return StorageContext.from_defaults(persist_dir=persist_dir) def _load_llm_predictor(config: ConfigParser) -> LLMPredictor: """Internal function to load LLM predictor based on configuration.""" model_type = config["llm_predictor"]["type"].lower() if model_type == "default": llm = _load_llm(config["llm_predictor"]) return LLMPredictor(llm=llm) elif model_type == "structured": llm = _load_llm(config["llm_predictor"]) return StructuredLLMPredictor(llm=llm) else: raise KeyError("llm_predictor.type") def _load_llm(section: SectionProxy) -> LLM: if "engine" in section: return OpenAI(engine=section["engine"]) else: return OpenAI() def _load_embed_model(config: ConfigParser) -> BaseEmbedding: """Internal function to load embedding model based on configuration.""" model_type = config["embed_model"]["type"] if model_type == "default": return OpenAIEmbedding() else: raise KeyError("embed_model.type")

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from components.store import get_storage_context from llama_index import VectorStoreIndex from llama_index.retrievers import ( VectorIndexRetriever, ) from models.gpts import get_gpts_by_uuids def search_gpts(question): storage_context = get_storage_context() index = VectorStoreIndex.from_documents([], storage_context=storage_context) retriever = VectorIndexRetriever(index=index, similarity_top_k=10) nodes = retriever.retrieve(question) uuids = [] uuids_with_scores = {} gpts = [] for node in nodes: print("node metadata", node.metadata) if node.score > 0.80: uuid = node.metadata['uuid'] uuids.append(uuid) uuids_with_scores[uuid] = node.score if len(uuids) == 0: return gpts rows = get_gpts_by_uuids(uuids) for row in rows: gpts.append({ "uuid": row.uuid, "name": row.name, "description": row.description, "avatar_url": row.avatar_url, "author_name": row.author_name, "created_at": row.created_at, "updated_at": row.updated_at, "visit_url": "https://chat.openai.com/g/" + row.short_url, "score": uuids_with_scores[row.uuid], }) sorted_gpts = sorted(gpts, key=lambda x: x['score'], reverse=True) return sorted_gpts

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"""LanceDB vector store with cloud storage support.""" import os from typing import Any, Optional from dotenv import load_dotenv from llama_index.schema import NodeRelationship, RelatedNodeInfo, TextNode from llama_index.vector_stores import LanceDBVectorStore as LanceDBVectorStoreBase from llama_index.vector_stores.lancedb import _to_lance_filter, _to_llama_similarities from llama_index.vector_stores.types import VectorStoreQuery, VectorStoreQueryResult from pandas import DataFrame load_dotenv() class LanceDBVectorStore(LanceDBVectorStoreBase): """Advanced LanceDB Vector Store supporting cloud storage and prefiltering.""" from lancedb.query import LanceQueryBuilder from lancedb.table import Table def __init__( self, uri: str, table_name: str = "vectors", nprobes: int = 20, refine_factor: Optional[int] = None, api_key: Optional[str] = None, region: Optional[str] = None, **kwargs: Any, ) -> None: """Init params.""" self._setup_connection(uri, api_key, region) self.uri = uri self.table_name = table_name self.nprobes = nprobes self.refine_factor = refine_factor self.api_key = api_key self.region = region def _setup_connection(self, uri: str, api_key: Optional[str] = None, region: Optional[str] = None): """Establishes a robust connection to LanceDB.""" api_key = api_key or os.getenv('LANCEDB_API_KEY') region = region or os.getenv('LANCEDB_REGION') import_err_msg = "`lancedb` package not found, please run `pip install lancedb`" try: import lancedb except ImportError: raise ImportError(import_err_msg) if api_key and region: self.connection = lancedb.connect(uri, api_key=api_key, region=region) else: self.connection = lancedb.connect(uri) def query( self, query: VectorStoreQuery, **kwargs: Any, ) -> VectorStoreQueryResult: """Enhanced query method to support prefiltering in LanceDB queries.""" table = self.connection.open_table(self.table_name) lance_query = self._prepare_lance_query(query, table, **kwargs) results = lance_query.to_df() return self._construct_query_result(results) def _prepare_lance_query(self, query: VectorStoreQuery, table: Table, **kwargs) -> LanceQueryBuilder: """Prepares the LanceDB query considering prefiltering and additional parameters.""" if query.filters is not None: if "where" in kwargs: raise ValueError( "Cannot specify filter via both query and kwargs. " "Use kwargs only for lancedb specific items that are " "not supported via the generic query interface.") where = _to_lance_filter(query.filters) else: where = kwargs.pop("where", None) prefilter = kwargs.pop("prefilter", False) table = self.connection.open_table(self.table_name) lance_query = ( table.search(query.query_embedding).limit(query.similarity_top_k).where( where, prefilter=prefilter).nprobes(self.nprobes)) if self.refine_factor is not None: lance_query.refine_factor(self.refine_factor) return lance_query def _construct_query_result(self, results: DataFrame) -> VectorStoreQueryResult: """Constructs a VectorStoreQueryResult from a LanceDB query result.""" nodes = [] for _, row in results.iterrows(): node = TextNode( text=row.get('text', ''), # ensure text is a string id_=row['id'], relationships={ NodeRelationship.SOURCE: RelatedNodeInfo(node_id=row['doc_id']), }) nodes.append(node) return VectorStoreQueryResult( nodes=nodes, similarities=_to_llama_similarities(results), ids=results["id"].tolist(), )

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from typing import List from fastapi.responses import StreamingResponse from llama_index.chat_engine.types import BaseChatEngine from app.engine.index import get_chat_engine from fastapi import APIRouter, Depends, HTTPException, Request, status from llama_index.llms.base import ChatMessage from llama_index.llms.types import MessageRole from pydantic import BaseModel chat_router = r = APIRouter() class _Message(BaseModel): role: MessageRole content: str class _ChatData(BaseModel): messages: List[_Message] @r.post("") async def chat( request: Request, data: _ChatData, chat_engine: BaseChatEngine = Depends(get_chat_engine), ): # 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 response = await chat_engine.astream_chat(lastMessage.content, messages) # stream response async def event_generator(): async for token in response.async_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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from typing import List from fastapi.responses import StreamingResponse from llama_index.chat_engine.types import BaseChatEngine from app.engine.index import get_chat_engine from fastapi import APIRouter, Depends, HTTPException, Request, status from llama_index.llms.base import ChatMessage from llama_index.llms.types import MessageRole from pydantic import BaseModel chat_router = r = APIRouter() class _Message(BaseModel): role: MessageRole content: str class _ChatData(BaseModel): messages: List[_Message] @r.post("") async def chat( request: Request, data: _ChatData, chat_engine: BaseChatEngine = Depends(get_chat_engine), ): # 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 response = await chat_engine.astream_chat(lastMessage.content, messages) # stream response async def event_generator(): async for token in response.async_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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from typing import List from fastapi.responses import StreamingResponse from llama_index.chat_engine.types import BaseChatEngine from app.engine.index import get_chat_engine from fastapi import APIRouter, Depends, HTTPException, Request, status from llama_index.llms.base import ChatMessage from llama_index.llms.types import MessageRole from pydantic import BaseModel chat_router = r = APIRouter() class _Message(BaseModel): role: MessageRole content: str class _ChatData(BaseModel): messages: List[_Message] @r.post("") async def chat( request: Request, data: _ChatData, chat_engine: BaseChatEngine = Depends(get_chat_engine), ): # 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 response = await chat_engine.astream_chat(lastMessage.content, messages) # stream response async def event_generator(): async for token in response.async_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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# Copyright (c) Timescale, Inc. (2023) # # 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 time import numpy as np import streamlit as st from streamlit.hello.utils import show_code from llama_index.vector_stores import TimescaleVectorStore from llama_index import ServiceContext, StorageContext from llama_index.indices.vector_store import VectorStoreIndex from llama_index.llms import OpenAI from llama_index import set_global_service_context import pandas as pd from pathlib import Path from datetime import datetime, timedelta from timescale_vector import client from typing import List, Tuple from llama_index.schema import TextNode from llama_index.embeddings import OpenAIEmbedding import psycopg2 def get_repos(): with psycopg2.connect(dsn=st.secrets["TIMESCALE_SERVICE_URL"]) as connection: # Create a cursor within the context manager with connection.cursor() as cursor: try: select_data_sql = "SELECT * FROM time_machine_catalog;" cursor.execute(select_data_sql) except psycopg2.errors.UndefinedTable as e: return {} catalog_entries = cursor.fetchall() catalog_dict = {} for entry in catalog_entries: repo_url, table_name = entry catalog_dict[repo_url] = table_name return catalog_dict def get_auto_retriever(index, retriever_args): from llama_index.vector_stores.types import MetadataInfo, VectorStoreInfo vector_store_info = VectorStoreInfo( content_info="Description of the commits to PostgreSQL. Describes changes made to Postgres", metadata_info=[ MetadataInfo( name="commit_hash", type="str", description="Commit Hash", ), MetadataInfo( name="author", type="str", description="Author of the commit", ), MetadataInfo( name="__start_date", type="datetime in iso format", description="All results will be after this datetime", ), MetadataInfo( name="__end_date", type="datetime in iso format", description="All results will be before this datetime", ) ], ) from llama_index.indices.vector_store.retrievers import VectorIndexAutoRetriever retriever = VectorIndexAutoRetriever(index, vector_store_info=vector_store_info, service_context=index.service_context, **retriever_args) # build query engine from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine query_engine = RetrieverQueryEngine.from_args( retriever=retriever, service_context=index.service_context ) from llama_index.tools.query_engine import QueryEngineTool # convert query engine to tool query_engine_tool = QueryEngineTool.from_defaults(query_engine=query_engine) from llama_index.agent import OpenAIAgent chat_engine = OpenAIAgent.from_tools( tools=[query_engine_tool], llm=index.service_context.llm, verbose=True #service_context=index.service_context ) return chat_engine def tm_demo(): repos = get_repos() months = st.sidebar.slider('How many months back to search (0=no limit)?', 0, 130, 0) if "config_months" not in st.session_state.keys() or months != st.session_state.config_months: st.session_state.clear() topk = st.sidebar.slider('How many commits to retrieve', 1, 150, 20) if "config_topk" not in st.session_state.keys() or topk != st.session_state.config_topk: st.session_state.clear() if len(repos) > 0: repo = st.sidebar.selectbox("Choose a repo", repos.keys()) else: st.error("No repositiories found, please [load some data first](/LoadData)") return if "config_repo" not in st.session_state.keys() or repo != st.session_state.config_repo: st.session_state.clear() st.session_state.config_months = months st.session_state.config_topk = topk st.session_state.config_repo = repo if "messages" not in st.session_state.keys(): # Initialize the chat messages history st.session_state.messages = [ {"role": "assistant", "content": "Please choose a repo and time filter on the sidebar and then ask me a question about the git history"} ] vector_store = TimescaleVectorStore.from_params( service_url=st.secrets["TIMESCALE_SERVICE_URL"], table_name=repos[repo], time_partition_interval=timedelta(days=7), ); service_context = ServiceContext.from_defaults(llm=OpenAI(model="gpt-4", temperature=0.1)) set_global_service_context(service_context) index = VectorStoreIndex.from_vector_store(vector_store=vector_store, service_context=service_context) #chat engine goes into the session to retain history if "chat_engine" not in st.session_state.keys(): # Initialize the chat engine retriever_args = {"similarity_top_k" : int(topk)} if months > 0: end_dt = datetime.now() start_dt = end_dt - timedelta(weeks=4*months) retriever_args["vector_store_kwargs"] = ({"start_date": start_dt, "end_date":end_dt}) st.session_state.chat_engine = get_auto_retriever(index, retriever_args) #st.session_state.chat_engine = index.as_chat_engine(chat_mode="best", similarity_top_k=20, verbose=True) if prompt := st.chat_input("Your question"): # 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("Thinking..."): response = st.session_state.chat_engine.chat(prompt, function_call="query_engine_tool") st.write(response.response) message = {"role": "assistant", "content": response.response} st.session_state.messages.append(message) # Add response to message history st.set_page_config(page_title="Time machine demo", page_icon="🧑‍💼") st.markdown("# Time Machine") st.sidebar.header("Welcome to the Time Machine") debug_llamaindex = False if debug_llamaindex: import logging import sys logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) tm_demo() #show_code(tm_demo)

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# Copyright (c) Timescale, Inc. (2023) # # 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 time import numpy as np import streamlit as st from streamlit.hello.utils import show_code from llama_index.vector_stores import TimescaleVectorStore from llama_index import ServiceContext, StorageContext from llama_index.indices.vector_store import VectorStoreIndex from llama_index.llms import OpenAI from llama_index import set_global_service_context import pandas as pd from pathlib import Path from datetime import datetime, timedelta from timescale_vector import client from typing import List, Tuple from llama_index.schema import TextNode from llama_index.embeddings import OpenAIEmbedding import psycopg2 def get_repos(): with psycopg2.connect(dsn=st.secrets["TIMESCALE_SERVICE_URL"]) as connection: # Create a cursor within the context manager with connection.cursor() as cursor: try: select_data_sql = "SELECT * FROM time_machine_catalog;" cursor.execute(select_data_sql) except psycopg2.errors.UndefinedTable as e: return {} catalog_entries = cursor.fetchall() catalog_dict = {} for entry in catalog_entries: repo_url, table_name = entry catalog_dict[repo_url] = table_name return catalog_dict def get_auto_retriever(index, retriever_args): from llama_index.vector_stores.types import MetadataInfo, VectorStoreInfo vector_store_info = VectorStoreInfo( content_info="Description of the commits to PostgreSQL. Describes changes made to Postgres", metadata_info=[ MetadataInfo( name="commit_hash", type="str", description="Commit Hash", ), MetadataInfo( name="author", type="str", description="Author of the commit", ), MetadataInfo( name="__start_date", type="datetime in iso format", description="All results will be after this datetime", ), MetadataInfo( name="__end_date", type="datetime in iso format", description="All results will be before this datetime", ) ], ) from llama_index.indices.vector_store.retrievers import VectorIndexAutoRetriever retriever = VectorIndexAutoRetriever(index, vector_store_info=vector_store_info, service_context=index.service_context, **retriever_args) # build query engine from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine query_engine = RetrieverQueryEngine.from_args( retriever=retriever, service_context=index.service_context ) from llama_index.tools.query_engine import QueryEngineTool # convert query engine to tool query_engine_tool = QueryEngineTool.from_defaults(query_engine=query_engine) from llama_index.agent import OpenAIAgent chat_engine = OpenAIAgent.from_tools( tools=[query_engine_tool], llm=index.service_context.llm, verbose=True #service_context=index.service_context ) return chat_engine def tm_demo(): repos = get_repos() months = st.sidebar.slider('How many months back to search (0=no limit)?', 0, 130, 0) if "config_months" not in st.session_state.keys() or months != st.session_state.config_months: st.session_state.clear() topk = st.sidebar.slider('How many commits to retrieve', 1, 150, 20) if "config_topk" not in st.session_state.keys() or topk != st.session_state.config_topk: st.session_state.clear() if len(repos) > 0: repo = st.sidebar.selectbox("Choose a repo", repos.keys()) else: st.error("No repositiories found, please [load some data first](/LoadData)") return if "config_repo" not in st.session_state.keys() or repo != st.session_state.config_repo: st.session_state.clear() st.session_state.config_months = months st.session_state.config_topk = topk st.session_state.config_repo = repo if "messages" not in st.session_state.keys(): # Initialize the chat messages history st.session_state.messages = [ {"role": "assistant", "content": "Please choose a repo and time filter on the sidebar and then ask me a question about the git history"} ] vector_store = TimescaleVectorStore.from_params( service_url=st.secrets["TIMESCALE_SERVICE_URL"], table_name=repos[repo], time_partition_interval=timedelta(days=7), ); service_context = ServiceContext.from_defaults(llm=OpenAI(model="gpt-4", temperature=0.1)) set_global_service_context(service_context) index = VectorStoreIndex.from_vector_store(vector_store=vector_store, service_context=service_context) #chat engine goes into the session to retain history if "chat_engine" not in st.session_state.keys(): # Initialize the chat engine retriever_args = {"similarity_top_k" : int(topk)} if months > 0: end_dt = datetime.now() start_dt = end_dt - timedelta(weeks=4*months) retriever_args["vector_store_kwargs"] = ({"start_date": start_dt, "end_date":end_dt}) st.session_state.chat_engine = get_auto_retriever(index, retriever_args) #st.session_state.chat_engine = index.as_chat_engine(chat_mode="best", similarity_top_k=20, verbose=True) if prompt := st.chat_input("Your question"): # 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("Thinking..."): response = st.session_state.chat_engine.chat(prompt, function_call="query_engine_tool") st.write(response.response) message = {"role": "assistant", "content": response.response} st.session_state.messages.append(message) # Add response to message history st.set_page_config(page_title="Time machine demo", page_icon="🧑‍💼") st.markdown("# Time Machine") st.sidebar.header("Welcome to the Time Machine") debug_llamaindex = False if debug_llamaindex: import logging import sys logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) tm_demo() #show_code(tm_demo)

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import logging from threading import Thread from typing import Any, List, Optional, Type from llama_index.core.base.base_query_engine import BaseQueryEngine from llama_index.core.base.llms.types import ChatMessage, MessageRole from llama_index.core.base.response.schema import RESPONSE_TYPE, StreamingResponse from llama_index.core.callbacks import CallbackManager, trace_method from llama_index.core.chat_engine.types import ( AgentChatResponse, BaseChatEngine, StreamingAgentChatResponse, ) from llama_index.core.chat_engine.utils import response_gen_from_query_engine from llama_index.core.embeddings.mock_embed_model import MockEmbedding from llama_index.core.base.llms.generic_utils import messages_to_history_str from llama_index.core.llms.llm import LLM from llama_index.core.memory import BaseMemory, ChatMemoryBuffer from llama_index.core.prompts.base import BasePromptTemplate, PromptTemplate from llama_index.core.service_context import ServiceContext from llama_index.core.service_context_elements.llm_predictor import LLMPredictorType from llama_index.core.settings import ( Settings, callback_manager_from_settings_or_context, ) from llama_index.core.tools import ToolOutput logger = logging.getLogger(__name__) DEFAULT_TEMPLATE = """\ Given a conversation (between Human and Assistant) and a follow up message from Human, \ rewrite the message to be a standalone question that captures all relevant context \ from the conversation. <Chat History> {chat_history} <Follow Up Message> {question} <Standalone question> """ DEFAULT_PROMPT = PromptTemplate(DEFAULT_TEMPLATE) class CondenseQuestionChatEngine(BaseChatEngine): """Condense Question Chat Engine. First generate a standalone question from conversation context and last message, then query the query engine for a response. """ def __init__( self, query_engine: BaseQueryEngine, condense_question_prompt: BasePromptTemplate, memory: BaseMemory, llm: LLMPredictorType, verbose: bool = False, callback_manager: Optional[CallbackManager] = None, ) -> None: self._query_engine = query_engine self._condense_question_prompt = condense_question_prompt self._memory = memory self._llm = llm self._verbose = verbose self.callback_manager = callback_manager or CallbackManager([]) @classmethod def from_defaults( cls, query_engine: BaseQueryEngine, condense_question_prompt: Optional[BasePromptTemplate] = None, chat_history: Optional[List[ChatMessage]] = None, memory: Optional[BaseMemory] = None, memory_cls: Type[BaseMemory] = ChatMemoryBuffer, service_context: Optional[ServiceContext] = None, verbose: bool = False, system_prompt: Optional[str] = None, prefix_messages: Optional[List[ChatMessage]] = None, llm: Optional[LLM] = None, **kwargs: Any, ) -> "CondenseQuestionChatEngine": """Initialize a CondenseQuestionChatEngine from default parameters.""" condense_question_prompt = condense_question_prompt or DEFAULT_PROMPT if llm is None: service_context = service_context or ServiceContext.from_defaults( embed_model=MockEmbedding(embed_dim=2) ) llm = service_context.llm else: service_context = service_context or ServiceContext.from_defaults( llm=llm, embed_model=MockEmbedding(embed_dim=2) ) chat_history = chat_history or [] memory = memory or memory_cls.from_defaults(chat_history=chat_history, llm=llm) if system_prompt is not None: raise NotImplementedError( "system_prompt is not supported for CondenseQuestionChatEngine." ) if prefix_messages is not None: raise NotImplementedError( "prefix_messages is not supported for CondenseQuestionChatEngine." ) return cls( query_engine, condense_question_prompt, memory, llm, verbose=verbose, callback_manager=callback_manager_from_settings_or_context( Settings, service_context ), ) def _condense_question( self, chat_history: List[ChatMessage], last_message: str ) -> str: """ Generate standalone question from conversation context and last message. """ chat_history_str = messages_to_history_str(chat_history) logger.debug(chat_history_str) return self._llm.predict( self._condense_question_prompt, question=last_message, chat_history=chat_history_str, ) async def _acondense_question( self, chat_history: List[ChatMessage], last_message: str ) -> str: """ Generate standalone question from conversation context and last message. """ chat_history_str = messages_to_history_str(chat_history) logger.debug(chat_history_str) return await self._llm.apredict( self._condense_question_prompt, question=last_message, chat_history=chat_history_str, ) def _get_tool_output_from_response( self, query: str, response: RESPONSE_TYPE ) -> ToolOutput: if isinstance(response, StreamingResponse): return ToolOutput( content="", tool_name="query_engine", raw_input={"query": query}, raw_output=response, ) else: return ToolOutput( content=str(response), tool_name="query_engine", raw_input={"query": query}, raw_output=response, ) @trace_method("chat") def chat( self, message: str, chat_history: Optional[List[ChatMessage]] = None ) -> AgentChatResponse: chat_history = chat_history or self._memory.get() # Generate standalone question from conversation context and last message condensed_question = self._condense_question(chat_history, message) log_str = f"Querying with: {condensed_question}" logger.info(log_str) if self._verbose: print(log_str) # TODO: right now, query engine uses class attribute to configure streaming, # we are moving towards separate streaming and non-streaming methods. # In the meanwhile, use this hack to toggle streaming. from llama_index.core.query_engine.retriever_query_engine import ( RetrieverQueryEngine, ) if isinstance(self._query_engine, RetrieverQueryEngine): is_streaming = self._query_engine._response_synthesizer._streaming self._query_engine._response_synthesizer._streaming = False # Query with standalone question query_response = self._query_engine.query(condensed_question) # NOTE: reset streaming flag if isinstance(self._query_engine, RetrieverQueryEngine): self._query_engine._response_synthesizer._streaming = is_streaming tool_output = self._get_tool_output_from_response( condensed_question, query_response ) # Record response self._memory.put(ChatMessage(role=MessageRole.USER, content=message)) self._memory.put( ChatMessage(role=MessageRole.ASSISTANT, content=str(query_response)) ) return AgentChatResponse(response=str(query_response), sources=[tool_output]) @trace_method("chat") def stream_chat( self, message: str, chat_history: Optional[List[ChatMessage]] = None ) -> StreamingAgentChatResponse: chat_history = chat_history or self._memory.get() # Generate standalone question from conversation context and last message condensed_question = self._condense_question(chat_history, message) log_str = f"Querying with: {condensed_question}" logger.info(log_str) if self._verbose: print(log_str) # TODO: right now, query engine uses class attribute to configure streaming, # we are moving towards separate streaming and non-streaming methods. # In the meanwhile, use this hack to toggle streaming. from llama_index.core.query_engine.retriever_query_engine import ( RetrieverQueryEngine, ) if isinstance(self._query_engine, RetrieverQueryEngine): is_streaming = self._query_engine._response_synthesizer._streaming self._query_engine._response_synthesizer._streaming = True # Query with standalone question query_response = self._query_engine.query(condensed_question) # NOTE: reset streaming flag if isinstance(self._query_engine, RetrieverQueryEngine): self._query_engine._response_synthesizer._streaming = is_streaming tool_output = self._get_tool_output_from_response( condensed_question, query_response ) # Record response if ( isinstance(query_response, StreamingResponse) and query_response.response_gen is not None ): # override the generator to include writing to chat history self._memory.put(ChatMessage(role=MessageRole.USER, content=message)) response = StreamingAgentChatResponse( chat_stream=response_gen_from_query_engine(query_response.response_gen), sources=[tool_output], ) thread = Thread( target=response.write_response_to_history, args=(self._memory, True) ) thread.start() else: raise ValueError("Streaming is not enabled. Please use chat() instead.") return response @trace_method("chat") async def achat( self, message: str, chat_history: Optional[List[ChatMessage]] = None ) -> AgentChatResponse: chat_history = chat_history or self._memory.get() # Generate standalone question from conversation context and last message condensed_question = await self._acondense_question(chat_history, message) log_str = f"Querying with: {condensed_question}" logger.info(log_str) if self._verbose: print(log_str) # TODO: right now, query engine uses class attribute to configure streaming, # we are moving towards separate streaming and non-streaming methods. # In the meanwhile, use this hack to toggle streaming. from llama_index.core.query_engine.retriever_query_engine import ( RetrieverQueryEngine, ) if isinstance(self._query_engine, RetrieverQueryEngine): is_streaming = self._query_engine._response_synthesizer._streaming self._query_engine._response_synthesizer._streaming = False # Query with standalone question query_response = await self._query_engine.aquery(condensed_question) # NOTE: reset streaming flag if isinstance(self._query_engine, RetrieverQueryEngine): self._query_engine._response_synthesizer._streaming = is_streaming tool_output = self._get_tool_output_from_response( condensed_question, query_response ) # Record response self._memory.put(ChatMessage(role=MessageRole.USER, content=message)) self._memory.put( ChatMessage(role=MessageRole.ASSISTANT, content=str(query_response)) ) return AgentChatResponse(response=str(query_response), sources=[tool_output]) @trace_method("chat") async def astream_chat( self, message: str, chat_history: Optional[List[ChatMessage]] = None ) -> StreamingAgentChatResponse: chat_history = chat_history or self._memory.get() # Generate standalone question from conversation context and last message condensed_question = await self._acondense_question(chat_history, message) log_str = f"Querying with: {condensed_question}" logger.info(log_str) if self._verbose: print(log_str) # TODO: right now, query engine uses class attribute to configure streaming, # we are moving towards separate streaming and non-streaming methods. # In the meanwhile, use this hack to toggle streaming. from llama_index.core.query_engine.retriever_query_engine import ( RetrieverQueryEngine, ) if isinstance(self._query_engine, RetrieverQueryEngine): is_streaming = self._query_engine._response_synthesizer._streaming self._query_engine._response_synthesizer._streaming = True # Query with standalone question query_response = await self._query_engine.aquery(condensed_question) # NOTE: reset streaming flag if isinstance(self._query_engine, RetrieverQueryEngine): self._query_engine._response_synthesizer._streaming = is_streaming tool_output = self._get_tool_output_from_response( condensed_question, query_response ) # Record response if ( isinstance(query_response, StreamingResponse) and query_response.response_gen is not None ): # override the generator to include writing to chat history # TODO: query engine does not support async generator yet self._memory.put(ChatMessage(role=MessageRole.USER, content=message)) response = StreamingAgentChatResponse( chat_stream=response_gen_from_query_engine(query_response.response_gen), sources=[tool_output], ) thread = Thread( target=response.write_response_to_history, args=(self._memory,) ) thread.start() else: raise ValueError("Streaming is not enabled. Please use achat() instead.") return response def reset(self) -> None: # Clear chat history self._memory.reset() @property def chat_history(self) -> List[ChatMessage]: """Get chat history.""" return self._memory.get_all()

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from typing import List from llama_index.readers.base import BaseReader from llama_index.readers.youtube_transcript import YoutubeTranscriptReader from llama_index.schema import Document class LyzrYoutubeReader(BaseReader): def __init__(self) -> None: try: from youtube_transcript_api import YouTubeTranscriptApi except ImportError: raise ImportError( "`youtube_transcript_api` package not found, \ please run `pip install youtube-transcript-api`" ) def load_data(self, urls: List[str]) -> List[Document]: loader = YoutubeTranscriptReader() documents = loader.load_data(ytlinks=urls) return documents

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from typing import List from llama_index.readers.base import BaseReader from llama_index.readers.youtube_transcript import YoutubeTranscriptReader from llama_index.schema import Document class LyzrYoutubeReader(BaseReader): def __init__(self) -> None: try: from youtube_transcript_api import YouTubeTranscriptApi except ImportError: raise ImportError( "`youtube_transcript_api` package not found, \ please run `pip install youtube-transcript-api`" ) def load_data(self, urls: List[str]) -> List[Document]: loader = YoutubeTranscriptReader() documents = loader.load_data(ytlinks=urls) return documents

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from typing import List from llama_index.readers.base import BaseReader from llama_index.readers.youtube_transcript import YoutubeTranscriptReader from llama_index.schema import Document class LyzrYoutubeReader(BaseReader): def __init__(self) -> None: try: from youtube_transcript_api import YouTubeTranscriptApi except ImportError: raise ImportError( "`youtube_transcript_api` package not found, \ please run `pip install youtube-transcript-api`" ) def load_data(self, urls: List[str]) -> List[Document]: loader = YoutubeTranscriptReader() documents = loader.load_data(ytlinks=urls) return documents

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import sys import asyncio import logging import warnings import nest_asyncio from typing import List, Set from bs4 import BeautifulSoup, Tag from typing import List from llama_index.schema import Document IS_IPYKERNEL = "ipykernel_launcher" in sys.argv[0] if IS_IPYKERNEL: nest_asyncio.apply() logger = logging.getLogger(__name__) CONTENT_TAGS = [ "p", "div", "span", "a", "td", "tr", "li", "article", "section", "pre", "code", "blockquote", "em", "strong", "b", "i", "h1", "h2", "h3", "h4", "h5", "h6", "title", ] def scrape(html: str) -> str: soup: BeautifulSoup = BeautifulSoup(html, "html.parser") content: List[Tag] = soup.find_all(CONTENT_TAGS) text_set: Set[str] = set() for p in content: for text in p.stripped_strings: text_set.add(text) return " ".join(text_set) async def async_load_content_using_playwright(url: str) -> str: try: from playwright.async_api import async_playwright async with async_playwright() as p: browser = await p.chromium.launch() page = await browser.new_page() await page.goto(url) html = await page.content() await browser.close() return html except ImportError: raise ImportError( "`playwright` package not found, please install it with " "`pip install playwright && playwright install`" ) def load_content_using_playwright(url: str) -> str: return asyncio.get_event_loop().run_until_complete( async_load_content_using_playwright(url) ) class LyzrWebPageReader: def __init__(self) -> None: pass @staticmethod def load_data(url: str) -> List[Document]: if IS_IPYKERNEL: warning_msg = "Running in Google Colab or a Jupyter notebook. Consider using nest_asyncio.apply() to avoid event loop conflicts." warnings.warn(warning_msg, RuntimeWarning) html = load_content_using_playwright(url) content = scrape(html) document = Document(text=content, metadata={"url": url}) return [document]

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import sys import asyncio import logging import warnings import nest_asyncio from typing import List, Set from bs4 import BeautifulSoup, Tag from typing import List from llama_index.schema import Document IS_IPYKERNEL = "ipykernel_launcher" in sys.argv[0] if IS_IPYKERNEL: nest_asyncio.apply() logger = logging.getLogger(__name__) CONTENT_TAGS = [ "p", "div", "span", "a", "td", "tr", "li", "article", "section", "pre", "code", "blockquote", "em", "strong", "b", "i", "h1", "h2", "h3", "h4", "h5", "h6", "title", ] def scrape(html: str) -> str: soup: BeautifulSoup = BeautifulSoup(html, "html.parser") content: List[Tag] = soup.find_all(CONTENT_TAGS) text_set: Set[str] = set() for p in content: for text in p.stripped_strings: text_set.add(text) return " ".join(text_set) async def async_load_content_using_playwright(url: str) -> str: try: from playwright.async_api import async_playwright async with async_playwright() as p: browser = await p.chromium.launch() page = await browser.new_page() await page.goto(url) html = await page.content() await browser.close() return html except ImportError: raise ImportError( "`playwright` package not found, please install it with " "`pip install playwright && playwright install`" ) def load_content_using_playwright(url: str) -> str: return asyncio.get_event_loop().run_until_complete( async_load_content_using_playwright(url) ) class LyzrWebPageReader: def __init__(self) -> None: pass @staticmethod def load_data(url: str) -> List[Document]: if IS_IPYKERNEL: warning_msg = "Running in Google Colab or a Jupyter notebook. Consider using nest_asyncio.apply() to avoid event loop conflicts." warnings.warn(warning_msg, RuntimeWarning) html = load_content_using_playwright(url) content = scrape(html) document = Document(text=content, metadata={"url": url}) return [document]

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import sys import asyncio import logging import warnings import nest_asyncio from typing import List, Set from bs4 import BeautifulSoup, Tag from typing import List from llama_index.schema import Document IS_IPYKERNEL = "ipykernel_launcher" in sys.argv[0] if IS_IPYKERNEL: nest_asyncio.apply() logger = logging.getLogger(__name__) CONTENT_TAGS = [ "p", "div", "span", "a", "td", "tr", "li", "article", "section", "pre", "code", "blockquote", "em", "strong", "b", "i", "h1", "h2", "h3", "h4", "h5", "h6", "title", ] def scrape(html: str) -> str: soup: BeautifulSoup = BeautifulSoup(html, "html.parser") content: List[Tag] = soup.find_all(CONTENT_TAGS) text_set: Set[str] = set() for p in content: for text in p.stripped_strings: text_set.add(text) return " ".join(text_set) async def async_load_content_using_playwright(url: str) -> str: try: from playwright.async_api import async_playwright async with async_playwright() as p: browser = await p.chromium.launch() page = await browser.new_page() await page.goto(url) html = await page.content() await browser.close() return html except ImportError: raise ImportError( "`playwright` package not found, please install it with " "`pip install playwright && playwright install`" ) def load_content_using_playwright(url: str) -> str: return asyncio.get_event_loop().run_until_complete( async_load_content_using_playwright(url) ) class LyzrWebPageReader: def __init__(self) -> None: pass @staticmethod def load_data(url: str) -> List[Document]: if IS_IPYKERNEL: warning_msg = "Running in Google Colab or a Jupyter notebook. Consider using nest_asyncio.apply() to avoid event loop conflicts." warnings.warn(warning_msg, RuntimeWarning) html = load_content_using_playwright(url) content = scrape(html) document = Document(text=content, metadata={"url": url}) return [document]

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import logging 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.llms.utils import LLMType from llama_index.prompts import PromptTemplate from llama_index.prompts.base import BasePromptTemplate from llama_index.node_parser import ( SimpleNodeParser, ) logger = logging.getLogger(__name__) class LyzrService: @staticmethod def from_defaults( llm: Optional[LLMType] = "default", embed_model: Optional[EmbedType] = "default", system_prompt: str = None, query_wrapper_prompt: Union[str, BasePromptTemplate] = None, **kwargs, ) -> ServiceContext: if isinstance(query_wrapper_prompt, str): query_wrapper_prompt = PromptTemplate(template=query_wrapper_prompt) callback_manager: CallbackManager = kwargs.get( "callback_manager", CallbackManager() ) node_parser = SimpleNodeParser.from_defaults( chunk_size=750, chunk_overlap=100, callback_manager=callback_manager, ) service_context = ServiceContext.from_defaults( llm=llm, embed_model=embed_model, system_prompt=system_prompt, query_wrapper_prompt=query_wrapper_prompt, callback_manager=callback_manager, node_parser=node_parser, **kwargs, ) return service_context

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from llama_index import SimpleDirectoryReader, LLMPredictor, ServiceContext, GPTVectorStoreIndex from llama_index.response.pprint_utils import pprint_response from langchain.chat_models import ChatOpenAI from llama_index.tools import QueryEngineTool, ToolMetadata from llama_index.query_engine import SubQuestionQueryEngine from dotenv import load_dotenv import gradio as gr import os, sys import logging #loads dotenv lib to retrieve API keys from .env file load_dotenv() # enable INFO level logging logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) #define LLM service llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")) service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor) #set the global service context object, avoiding passing service_context when building the index or when loading index from vector store from llama_index import set_global_service_context set_global_service_context(service_context) def data_ingestion_indexing(): #load data report_2021_docs = SimpleDirectoryReader(input_files=["reports/executive-summary-2021.pdf"]).load_data() print(f"loaded executive summary 2021 with {len(report_2021_docs)} pages") report_2022_docs = SimpleDirectoryReader(input_files=["reports/executive-summary-2022.pdf"]).load_data() print(f"loaded executive summary 2022 with {len(report_2022_docs)} pages") #build indices report_2021_index = GPTVectorStoreIndex.from_documents(report_2021_docs) print(f"built index for executive summary 2021 with {len(report_2021_index.docstore.docs)} nodes") report_2022_index = GPTVectorStoreIndex.from_documents(report_2022_docs) print(f"built index for executive summary 2022 with {len(report_2022_index.docstore.docs)} nodes") #build query engines report_2021_engine = report_2021_index.as_query_engine(similarity_top_k=3) report_2022_engine = report_2022_index.as_query_engine(similarity_top_k=3) #build query engine tools query_engine_tools = [ QueryEngineTool( query_engine = report_2021_engine, metadata = ToolMetadata(name='executive_summary_2021', description='Provides information on US government financial report executive summary 2021') ), QueryEngineTool( query_engine = report_2022_engine, metadata = ToolMetadata(name='executive_summary_2022', description='Provides information on US government financial report executive summary 2022') ) ] #define SubQuestionQueryEngine sub_question_engine = SubQuestionQueryEngine.from_defaults(query_engine_tools=query_engine_tools) return sub_question_engine def data_querying(input_text): #queries the engine with the input text response = sub_question_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 2021 and 2022") #data ingestion and indexing sub_question_engine = data_ingestion_indexing() iface.launch(share=False) #run queries #response = sub_question_engine.query('Compare and contrast the DoD costs between 2021 and 2022') #print(response) #response = sub_question_engine.query('Compare revenue growth from 2021 to 2022') #print(response)

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from llama_index.core import Settings, Document, VectorStoreIndex from llama_index.core.node_parser import SentenceWindowNodeParser doc = Document( text="Sentence 1. Sentence 2. Sentence 3." ) text_splitter = SentenceWindowNodeParser.from_defaults( window_size=2 , window_metadata_key="ContextWindow", original_text_metadata_key="node_text" ) Settings.text_splitter = text_splitter index = VectorStoreIndex.from_documents([doc]) retriever = index.as_retriever(similarity_top_k=1) response = retriever.retrieve("Display the second sentence") print(response[0].node.metadata['node_text'])

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import tiktoken from llama_index.core import MockEmbedding, VectorStoreIndex, SimpleDirectoryReader, Settings from llama_index.core.callbacks import CallbackManager, TokenCountingHandler from llama_index.core.llms.mock import MockLLM embed_model = MockEmbedding(embed_dim=1536) llm = MockLLM(max_tokens=256) token_counter = TokenCountingHandler( tokenizer=tiktoken.encoding_for_model("gpt-3.5-turbo").encode ) callback_manager = CallbackManager([token_counter]) Settings.embed_model=embed_model Settings.llm=llm Settings.callback_manager=callback_manager documents = SimpleDirectoryReader("cost_prediction_samples").load_data() index = VectorStoreIndex.from_documents( documents=documents, show_progress=True) print("Embedding Token Count:", token_counter.total_embedding_token_count) query_engine = index.as_query_engine() response = query_engine.query("What's the cat's name?") print("Query LLM Token Count:", token_counter.total_llm_token_count) print("Query Embedding Token Count:",token_counter.total_embedding_token_count)

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"""Read PDF files.""" import shutil from pathlib import Path from typing import Any, List from llama_index.langchain_helpers.text_splitter import SentenceSplitter from llama_index.readers.base import BaseReader from llama_index.readers.schema.base import Document # https://github.com/emptycrown/llama-hub/blob/main/loader_hub/file/cjk_pdf/base.py staticPath = "static" class CJKPDFReader(BaseReader): """CJK PDF reader. Extract text from PDF including CJK (Chinese, Japanese and Korean) languages using pdfminer.six. """ def __init__(self, *args: Any, **kwargs: Any) -> None: """Init params.""" super().__init__(*args, **kwargs) def load_data(self, filepath: Path, filename) -> List[Document]: """Parse file.""" # Import pdfminer from io import StringIO from pdfminer.converter import TextConverter from pdfminer.layout import LAParams from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager from pdfminer.pdfpage import PDFPage # Create a resource manager rsrcmgr = PDFResourceManager() # Create an object to store the text retstr = StringIO() # Create a text converter codec = "utf-8" laparams = LAParams() device = TextConverter(rsrcmgr, retstr, codec=codec, laparams=laparams) # Create a PDF interpreter interpreter = PDFPageInterpreter(rsrcmgr, device) # Open the PDF file fp = open(filepath, "rb") # Create a list to store the text of each page document_list = [] # Extract text from each page for i, page in enumerate(PDFPage.get_pages(fp)): interpreter.process_page(page) # Get the text text = retstr.getvalue() sentence_splitter = SentenceSplitter(chunk_size=400) text_chunks = sentence_splitter.split_text(text) document_list += [ Document(t, extra_info={"page_no": i + 1}) for t in text_chunks ] # Clear the text retstr.truncate(0) retstr.seek(0) # Close the file fp.close() # Close the device device.close() shutil.copy2(filepath, f"{staticPath}/file/{filename}") return document_list

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"""Read PDF files.""" import shutil from pathlib import Path from typing import Any, List from llama_index.langchain_helpers.text_splitter import SentenceSplitter from llama_index.readers.base import BaseReader from llama_index.readers.schema.base import Document # https://github.com/emptycrown/llama-hub/blob/main/loader_hub/file/cjk_pdf/base.py staticPath = "static" class CJKPDFReader(BaseReader): """CJK PDF reader. Extract text from PDF including CJK (Chinese, Japanese and Korean) languages using pdfminer.six. """ def __init__(self, *args: Any, **kwargs: Any) -> None: """Init params.""" super().__init__(*args, **kwargs) def load_data(self, filepath: Path, filename) -> List[Document]: """Parse file.""" # Import pdfminer from io import StringIO from pdfminer.converter import TextConverter from pdfminer.layout import LAParams from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager from pdfminer.pdfpage import PDFPage # Create a resource manager rsrcmgr = PDFResourceManager() # Create an object to store the text retstr = StringIO() # Create a text converter codec = "utf-8" laparams = LAParams() device = TextConverter(rsrcmgr, retstr, codec=codec, laparams=laparams) # Create a PDF interpreter interpreter = PDFPageInterpreter(rsrcmgr, device) # Open the PDF file fp = open(filepath, "rb") # Create a list to store the text of each page document_list = [] # Extract text from each page for i, page in enumerate(PDFPage.get_pages(fp)): interpreter.process_page(page) # Get the text text = retstr.getvalue() sentence_splitter = SentenceSplitter(chunk_size=400) text_chunks = sentence_splitter.split_text(text) document_list += [ Document(t, extra_info={"page_no": i + 1}) for t in text_chunks ] # Clear the text retstr.truncate(0) retstr.seek(0) # Close the file fp.close() # Close the device device.close() shutil.copy2(filepath, f"{staticPath}/file/{filename}") return document_list

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"""Read PDF files.""" import shutil from pathlib import Path from typing import Any, List from llama_index.langchain_helpers.text_splitter import SentenceSplitter from llama_index.readers.base import BaseReader from llama_index.readers.schema.base import Document # https://github.com/emptycrown/llama-hub/blob/main/loader_hub/file/cjk_pdf/base.py staticPath = "static" class CJKPDFReader(BaseReader): """CJK PDF reader. Extract text from PDF including CJK (Chinese, Japanese and Korean) languages using pdfminer.six. """ def __init__(self, *args: Any, **kwargs: Any) -> None: """Init params.""" super().__init__(*args, **kwargs) def load_data(self, filepath: Path, filename) -> List[Document]: """Parse file.""" # Import pdfminer from io import StringIO from pdfminer.converter import TextConverter from pdfminer.layout import LAParams from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager from pdfminer.pdfpage import PDFPage # Create a resource manager rsrcmgr = PDFResourceManager() # Create an object to store the text retstr = StringIO() # Create a text converter codec = "utf-8" laparams = LAParams() device = TextConverter(rsrcmgr, retstr, codec=codec, laparams=laparams) # Create a PDF interpreter interpreter = PDFPageInterpreter(rsrcmgr, device) # Open the PDF file fp = open(filepath, "rb") # Create a list to store the text of each page document_list = [] # Extract text from each page for i, page in enumerate(PDFPage.get_pages(fp)): interpreter.process_page(page) # Get the text text = retstr.getvalue() sentence_splitter = SentenceSplitter(chunk_size=400) text_chunks = sentence_splitter.split_text(text) document_list += [ Document(t, extra_info={"page_no": i + 1}) for t in text_chunks ] # Clear the text retstr.truncate(0) retstr.seek(0) # Close the file fp.close() # Close the device device.close() shutil.copy2(filepath, f"{staticPath}/file/{filename}") return document_list

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from typing import Any, List import tiktoken from bs4 import BeautifulSoup from llama_index.readers.base import BaseReader from llama_index.readers.schema.base import Document staticPath = "static" def encode_string(string: str, encoding_name: str = "p50k_base"): encoding = tiktoken.get_encoding(encoding_name) return encoding.encode(string) def decode_string(token: str, encoding_name: str = "p50k_base"): encoding = tiktoken.get_encoding(encoding_name) return encoding.decode(token) def num_tokens_from_string(string: str, encoding_name: str = "p50k_base") -> int: """Returns the number of tokens in a text string.""" encoding = tiktoken.get_encoding(encoding_name) num_tokens = len(encoding.encode(string)) return num_tokens def split_text_to_doc( text: str, current_chunk_id, chunk_size: int = 400 ) -> List[Document]: """Split text into chunks of a given size.""" chunks = [] token_len = num_tokens_from_string(text) for i in range(0, token_len, chunk_size): encode_text = encode_string(text) decode_text = decode_string(encode_text[i : i + chunk_size]).strip() chunks.append( Document( decode_text, extra_info={"chunk_id": f"chunk-{current_chunk_id}"}, ) ) return chunks class CustomReader(BaseReader): def __init__(self, *args: Any, **kwargs: Any) -> None: """Init params.""" super().__init__(*args, **kwargs) def load_data(self, html, filename) -> List[Document]: soup = BeautifulSoup(html, "html.parser") current_chunk_text = "" current_chunk_id = 1 document_list = [] # 单位是token，openai限制4097，如果实现连续对话大概可以进行6轮对话 current_chunk_length = 0 chunk_size = 400 # 只处理前三级标题，其他的按照段落处理 headings = ["h1", "h2", "h3"] heading_doms = soup.find_all(headings) if len(heading_doms) == 0: heading_doms = [soup.find()] for tag in heading_doms: tag["data-chunk_id"] = f"chunk-{current_chunk_id}" current_chunk_text = tag.text.strip() # 遍历所有兄弟节点，不递归遍历子节点 next_tag = tag.find_next_sibling() while next_tag and next_tag.name not in headings: stripped_text = next_tag.text.strip() if ( current_chunk_length + num_tokens_from_string(stripped_text) > chunk_size ): document_list.append( Document( current_chunk_text.strip(), extra_info={"chunk_id": f"chunk-{current_chunk_id}"}, ) ) current_chunk_text = "" current_chunk_length = 0 current_chunk_id += 1 document_list += split_text_to_doc(stripped_text, current_chunk_id) else: current_chunk_text = f"{current_chunk_text} {stripped_text}" current_chunk_length += num_tokens_from_string(stripped_text) + 1 next_tag["data-chunk_id"] = f"chunk-{current_chunk_id}" next_tag = next_tag.find_next_sibling() document_list.append( Document( current_chunk_text.strip(), extra_info={"chunk_id": f"chunk-{current_chunk_id}"}, ) ) current_chunk_text = "" current_chunk_length = 0 current_chunk_id += 1 # 保存修改后的HTML文件 with open(f"{staticPath}/file/{filename}.html", "w", encoding="utf-8") as f: f.write(str(soup)) return document_list

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from typing import Any, List import tiktoken from bs4 import BeautifulSoup from llama_index.readers.base import BaseReader from llama_index.readers.schema.base import Document staticPath = "static" def encode_string(string: str, encoding_name: str = "p50k_base"): encoding = tiktoken.get_encoding(encoding_name) return encoding.encode(string) def decode_string(token: str, encoding_name: str = "p50k_base"): encoding = tiktoken.get_encoding(encoding_name) return encoding.decode(token) def num_tokens_from_string(string: str, encoding_name: str = "p50k_base") -> int: """Returns the number of tokens in a text string.""" encoding = tiktoken.get_encoding(encoding_name) num_tokens = len(encoding.encode(string)) return num_tokens def split_text_to_doc( text: str, current_chunk_id, chunk_size: int = 400 ) -> List[Document]: """Split text into chunks of a given size.""" chunks = [] token_len = num_tokens_from_string(text) for i in range(0, token_len, chunk_size): encode_text = encode_string(text) decode_text = decode_string(encode_text[i : i + chunk_size]).strip() chunks.append( Document( decode_text, extra_info={"chunk_id": f"chunk-{current_chunk_id}"}, ) ) return chunks class CustomReader(BaseReader): def __init__(self, *args: Any, **kwargs: Any) -> None: """Init params.""" super().__init__(*args, **kwargs) def load_data(self, html, filename) -> List[Document]: soup = BeautifulSoup(html, "html.parser") current_chunk_text = "" current_chunk_id = 1 document_list = [] # 单位是token，openai限制4097，如果实现连续对话大概可以进行6轮对话 current_chunk_length = 0 chunk_size = 400 # 只处理前三级标题，其他的按照段落处理 headings = ["h1", "h2", "h3"] heading_doms = soup.find_all(headings) if len(heading_doms) == 0: heading_doms = [soup.find()] for tag in heading_doms: tag["data-chunk_id"] = f"chunk-{current_chunk_id}" current_chunk_text = tag.text.strip() # 遍历所有兄弟节点，不递归遍历子节点 next_tag = tag.find_next_sibling() while next_tag and next_tag.name not in headings: stripped_text = next_tag.text.strip() if ( current_chunk_length + num_tokens_from_string(stripped_text) > chunk_size ): document_list.append( Document( current_chunk_text.strip(), extra_info={"chunk_id": f"chunk-{current_chunk_id}"}, ) ) current_chunk_text = "" current_chunk_length = 0 current_chunk_id += 1 document_list += split_text_to_doc(stripped_text, current_chunk_id) else: current_chunk_text = f"{current_chunk_text} {stripped_text}" current_chunk_length += num_tokens_from_string(stripped_text) + 1 next_tag["data-chunk_id"] = f"chunk-{current_chunk_id}" next_tag = next_tag.find_next_sibling() document_list.append( Document( current_chunk_text.strip(), extra_info={"chunk_id": f"chunk-{current_chunk_id}"}, ) ) current_chunk_text = "" current_chunk_length = 0 current_chunk_id += 1 # 保存修改后的HTML文件 with open(f"{staticPath}/file/{filename}.html", "w", encoding="utf-8") as f: f.write(str(soup)) return document_list

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from typing import Any, List import tiktoken from bs4 import BeautifulSoup from llama_index.readers.base import BaseReader from llama_index.readers.schema.base import Document staticPath = "static" def encode_string(string: str, encoding_name: str = "p50k_base"): encoding = tiktoken.get_encoding(encoding_name) return encoding.encode(string) def decode_string(token: str, encoding_name: str = "p50k_base"): encoding = tiktoken.get_encoding(encoding_name) return encoding.decode(token) def num_tokens_from_string(string: str, encoding_name: str = "p50k_base") -> int: """Returns the number of tokens in a text string.""" encoding = tiktoken.get_encoding(encoding_name) num_tokens = len(encoding.encode(string)) return num_tokens def split_text_to_doc( text: str, current_chunk_id, chunk_size: int = 400 ) -> List[Document]: """Split text into chunks of a given size.""" chunks = [] token_len = num_tokens_from_string(text) for i in range(0, token_len, chunk_size): encode_text = encode_string(text) decode_text = decode_string(encode_text[i : i + chunk_size]).strip() chunks.append( Document( decode_text, extra_info={"chunk_id": f"chunk-{current_chunk_id}"}, ) ) return chunks class CustomReader(BaseReader): def __init__(self, *args: Any, **kwargs: Any) -> None: """Init params.""" super().__init__(*args, **kwargs) def load_data(self, html, filename) -> List[Document]: soup = BeautifulSoup(html, "html.parser") current_chunk_text = "" current_chunk_id = 1 document_list = [] # 单位是token，openai限制4097，如果实现连续对话大概可以进行6轮对话 current_chunk_length = 0 chunk_size = 400 # 只处理前三级标题，其他的按照段落处理 headings = ["h1", "h2", "h3"] heading_doms = soup.find_all(headings) if len(heading_doms) == 0: heading_doms = [soup.find()] for tag in heading_doms: tag["data-chunk_id"] = f"chunk-{current_chunk_id}" current_chunk_text = tag.text.strip() # 遍历所有兄弟节点，不递归遍历子节点 next_tag = tag.find_next_sibling() while next_tag and next_tag.name not in headings: stripped_text = next_tag.text.strip() if ( current_chunk_length + num_tokens_from_string(stripped_text) > chunk_size ): document_list.append( Document( current_chunk_text.strip(), extra_info={"chunk_id": f"chunk-{current_chunk_id}"}, ) ) current_chunk_text = "" current_chunk_length = 0 current_chunk_id += 1 document_list += split_text_to_doc(stripped_text, current_chunk_id) else: current_chunk_text = f"{current_chunk_text} {stripped_text}" current_chunk_length += num_tokens_from_string(stripped_text) + 1 next_tag["data-chunk_id"] = f"chunk-{current_chunk_id}" next_tag = next_tag.find_next_sibling() document_list.append( Document( current_chunk_text.strip(), extra_info={"chunk_id": f"chunk-{current_chunk_id}"}, ) ) current_chunk_text = "" current_chunk_length = 0 current_chunk_id += 1 # 保存修改后的HTML文件 with open(f"{staticPath}/file/{filename}.html", "w", encoding="utf-8") as f: f.write(str(soup)) return document_list

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from typing import Any, List import tiktoken from bs4 import BeautifulSoup from llama_index.readers.base import BaseReader from llama_index.readers.schema.base import Document staticPath = "static" def encode_string(string: str, encoding_name: str = "p50k_base"): encoding = tiktoken.get_encoding(encoding_name) return encoding.encode(string) def decode_string(token: str, encoding_name: str = "p50k_base"): encoding = tiktoken.get_encoding(encoding_name) return encoding.decode(token) def num_tokens_from_string(string: str, encoding_name: str = "p50k_base") -> int: """Returns the number of tokens in a text string.""" encoding = tiktoken.get_encoding(encoding_name) num_tokens = len(encoding.encode(string)) return num_tokens def split_text_to_doc( text: str, current_chunk_id, chunk_size: int = 400 ) -> List[Document]: """Split text into chunks of a given size.""" chunks = [] token_len = num_tokens_from_string(text) for i in range(0, token_len, chunk_size): encode_text = encode_string(text) decode_text = decode_string(encode_text[i : i + chunk_size]).strip() chunks.append( Document( decode_text, extra_info={"chunk_id": f"chunk-{current_chunk_id}"}, ) ) return chunks class CustomReader(BaseReader): def __init__(self, *args: Any, **kwargs: Any) -> None: """Init params.""" super().__init__(*args, **kwargs) def load_data(self, html, filename) -> List[Document]: soup = BeautifulSoup(html, "html.parser") current_chunk_text = "" current_chunk_id = 1 document_list = [] # 单位是token，openai限制4097，如果实现连续对话大概可以进行6轮对话 current_chunk_length = 0 chunk_size = 400 # 只处理前三级标题，其他的按照段落处理 headings = ["h1", "h2", "h3"] heading_doms = soup.find_all(headings) if len(heading_doms) == 0: heading_doms = [soup.find()] for tag in heading_doms: tag["data-chunk_id"] = f"chunk-{current_chunk_id}" current_chunk_text = tag.text.strip() # 遍历所有兄弟节点，不递归遍历子节点 next_tag = tag.find_next_sibling() while next_tag and next_tag.name not in headings: stripped_text = next_tag.text.strip() if ( current_chunk_length + num_tokens_from_string(stripped_text) > chunk_size ): document_list.append( Document( current_chunk_text.strip(), extra_info={"chunk_id": f"chunk-{current_chunk_id}"}, ) ) current_chunk_text = "" current_chunk_length = 0 current_chunk_id += 1 document_list += split_text_to_doc(stripped_text, current_chunk_id) else: current_chunk_text = f"{current_chunk_text} {stripped_text}" current_chunk_length += num_tokens_from_string(stripped_text) + 1 next_tag["data-chunk_id"] = f"chunk-{current_chunk_id}" next_tag = next_tag.find_next_sibling() document_list.append( Document( current_chunk_text.strip(), extra_info={"chunk_id": f"chunk-{current_chunk_id}"}, ) ) current_chunk_text = "" current_chunk_length = 0 current_chunk_id += 1 # 保存修改后的HTML文件 with open(f"{staticPath}/file/{filename}.html", "w", encoding="utf-8") as f: f.write(str(soup)) return document_list

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from collections.abc import Generator from typing import Any from llama_index.core.schema import BaseNode, MetadataMode from llama_index.core.vector_stores.utils import node_to_metadata_dict from llama_index.vector_stores.chroma import ChromaVectorStore # type: ignore def chunk_list( lst: list[BaseNode], max_chunk_size: int ) -> Generator[list[BaseNode], None, None]: """Yield successive max_chunk_size-sized chunks from lst. Args: lst (List[BaseNode]): list of nodes with embeddings max_chunk_size (int): max chunk size Yields: Generator[List[BaseNode], None, None]: list of nodes with embeddings """ for i in range(0, len(lst), max_chunk_size): yield lst[i : i + max_chunk_size] class BatchedChromaVectorStore(ChromaVectorStore): # type: ignore """Chroma vector store, batching additions to avoid reaching the max batch limit. In this vector store, embeddings are stored within a ChromaDB collection. During query time, the index uses ChromaDB to query for the top k most similar nodes. Args: chroma_client (from chromadb.api.API): API instance chroma_collection (chromadb.api.models.Collection.Collection): ChromaDB collection instance """ chroma_client: Any | None def __init__( self, chroma_client: Any, chroma_collection: Any, host: str | None = None, port: str | None = None, ssl: bool = False, headers: dict[str, str] | None = None, collection_kwargs: dict[Any, Any] | None = None, ) -> None: super().__init__( chroma_collection=chroma_collection, host=host, port=port, ssl=ssl, headers=headers, collection_kwargs=collection_kwargs or {}, ) self.chroma_client = chroma_client def add(self, nodes: list[BaseNode], **add_kwargs: Any) -> list[str]: """Add nodes to index, batching the insertion to avoid issues. Args: nodes: List[BaseNode]: list of nodes with embeddings add_kwargs: _ """ if not self.chroma_client: raise ValueError("Client not initialized") if not self._collection: raise ValueError("Collection not initialized") max_chunk_size = self.chroma_client.max_batch_size node_chunks = chunk_list(nodes, max_chunk_size) all_ids = [] for node_chunk in node_chunks: embeddings = [] metadatas = [] ids = [] documents = [] for node in node_chunk: 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) documents.append(node.get_content(metadata_mode=MetadataMode.NONE)) self._collection.add( embeddings=embeddings, ids=ids, metadatas=metadatas, documents=documents, ) all_ids.extend(ids) return all_ids

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import os # Uncomment to specify your OpenAI API key here (local testing only, not in production!), or add corresponding environment variable (recommended) # os.environ['OPENAI_API_KEY']= "" from llama_index import LLMPredictor, PromptHelper, ServiceContext from langchain.llms.openai import OpenAI from llama_index import StorageContext, load_index_from_storage base_path = os.environ.get('OPENAI_API_BASE', 'http://localhost:8080/v1') # This example uses text-davinci-003 by default; feel free to change if desired llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo", openai_api_base=base_path)) # Configure prompt parameters and initialise helper max_input_size = 500 num_output = 256 max_chunk_overlap = 0.2 prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap) # Load documents from the 'data' directory service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper) # rebuild storage context storage_context = StorageContext.from_defaults(persist_dir='./storage') # load index index = load_index_from_storage(storage_context, service_context=service_context, ) query_engine = index.as_query_engine() data = input("Question: ") response = query_engine.query(data) print(response)

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from llama_index.embeddings.huggingface import HuggingFaceEmbedding from llama_index.llms.huggingface import HuggingFaceLLM from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig from llama_index.llms.huggingface import HuggingFaceInferenceAPI from llama_index.llms.azure_openai import AzureOpenAI from llama_index.core.base.llms.types import CompletionResponse from dotenv import load_dotenv import os import torch load_dotenv() DEFAULT_EMBED_MODEL = "BAAI/bge-small-en-v1.5" DEFAULT_LOCAL_LLM = "HuggingFaceH4/zephyr-7b-gemma-v0.1" DEFAULT_LLM = "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO" DEFAULT_MAX_NEW_TOKENS = 512 HF_TOKEN = os.getenv("HF_TOKEN", "") API_KEY = os.getenv("AZURE_OPENAI_TOKEN", "") AZURE_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT", "") DEPLOYMENT_NAME = os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME", "") # DEFAULT_QUANTIZATION_CONFIG = BitsAndBytesConfig( # load_in_4bit=True, # bnb_4bit_use_double_quant=True, # bnb_4bit_quant_type="nf4", # bnb_4bit_compute_dtype=torch.bfloat16 # ) class DefaultEmbedder(HuggingFaceEmbedding): def __init__(self, model_name=DEFAULT_EMBED_MODEL, device="cuda"): super().__init__(model_name, device) class DefaultLocalLLM(HuggingFaceLLM): def __init__(self, model_name=DEFAULT_LOCAL_LLM, max_new_tokens=DEFAULT_MAX_NEW_TOKENS, quantization_config=None): tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", quantization_config=quantization_config) super().__init__(model=model, tokenizer=tokenizer, max_new_tokens=max_new_tokens) # Monkey patch because stream_complete is not implemented in the current version of llama_index def stream_complete(self, prompt: str, **kwargs): def gen(): # patch the patch, on some versions the caller tries to pass the formatted keyword, which doesn't exist kwargs.pop("formatted", None) text = "" for x in self._sync_client.text_generation( prompt, **{**{"max_new_tokens": self.num_output, "stream": True}, **kwargs} ): text += x yield CompletionResponse(text=text, delta=x) return gen() HuggingFaceInferenceAPI.stream_complete = stream_complete class AzureOpenAILLM(AzureOpenAI): def __init__(self, model="", deployment_name=DEPLOYMENT_NAME, api_key=API_KEY, azure_endpoint=AZURE_ENDPOINT, api_version=""): super().__init__(model=model, deployment_name=deployment_name, api_key=api_key, azure_endpoint=azure_endpoint, api_version=api_version, temperature=0.0) class DefaultLLM(HuggingFaceInferenceAPI): def __init__(self, model_name = DEFAULT_LLM, token=HF_TOKEN, num_output=DEFAULT_MAX_NEW_TOKENS): super().__init__(model_name=model_name, token=token, num_output=num_output)

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from dotenv import load_dotenv load_dotenv() from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, load_index_from_storage, StorageContext from llama_index.storage.docstore import SimpleDocumentStore from llama_index.vector_stores import SimpleVectorStore from llama_index.storage.index_store import SimpleIndexStore from llama_index.graph_stores import SimpleGraphStore documents = SimpleDirectoryReader('news').load_data() index = GPTVectorStoreIndex.from_documents(documents) # save to disk index.storage_context.persist() # load from disk storage_context = StorageContext( docstore=SimpleDocumentStore.from_persist_dir('storage'), vector_store=SimpleVectorStore.from_persist_dir('storage'), index_store=SimpleIndexStore.from_persist_dir('storage'), graph_store=SimpleGraphStore.from_persist_dir('storage') ) index = load_index_from_storage(storage_context) query_engine = index.as_query_engine() r = query_engine.query("Who are the main exporters of Coal to China? What is the role of Indonesia in this?") print(r)

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from rag.agents.interface import Pipeline from rich.progress import Progress, SpinnerColumn, TextColumn from typing import Any from pydantic import create_model from typing import List import warnings import box import yaml import timeit from rich import print from llama_index.core import SimpleDirectoryReader from llama_index.multi_modal_llms.ollama import OllamaMultiModal from llama_index.core.program import MultiModalLLMCompletionProgram from llama_index.core.output_parsers import PydanticOutputParser 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 VLlamaIndexPipeline(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 vllamaindex pipeline") mm_model = self.invoke_pipeline_step(lambda: OllamaMultiModal(model=cfg.LLM_VLLAMAINDEX), "Loading Ollama MultiModal...", local) # load as image documents image_documents = self.invoke_pipeline_step(lambda: SimpleDirectoryReader(input_files=[file_path], required_exts=[".jpg", ".JPG", ".JPEG"]).load_data(), "Loading image documents...", local) ResponseModel = self.invoke_pipeline_step(lambda: self.build_response_class(query_inputs, query_types), "Building dynamic response class...", local) prompt_template_str = """\ {query_str} Return the answer as a Pydantic object. The Pydantic schema is given below: """ mm_program = MultiModalLLMCompletionProgram.from_defaults( output_parser=PydanticOutputParser(ResponseModel), image_documents=image_documents, prompt_template_str=prompt_template_str, multi_modal_llm=mm_model, verbose=True, ) try: response = self.invoke_pipeline_step(lambda: mm_program(query_str=query), "Running inference...", local) except ValueError as e: print(f"Error: {e}") msg = 'Inference failed' return '{"answer": "' + msg + '"}' end = timeit.default_timer() print(f"\nJSON response:\n") for res in response: print(res) print('=' * 50) print(f"Time to retrieve answer: {end - start}") return response # 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

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import functools import os import random import tempfile import traceback import asyncio from collections import defaultdict import aiohttp import discord import aiofiles import httpx import openai import tiktoken from functools import partial from typing import List, Optional, cast from pathlib import Path from datetime import date from discord import Interaction from discord.ext import pages from langchain.agents import initialize_agent, AgentType from langchain.chat_models import ChatOpenAI from langchain.memory import ConversationSummaryBufferMemory from langchain.prompts import MessagesPlaceholder from langchain.schema import SystemMessage from langchain.tools import Tool from llama_index.callbacks import CallbackManager, TokenCountingHandler from llama_index.evaluation.guideline import DEFAULT_GUIDELINES, GuidelineEvaluator from llama_index.llms import OpenAI from llama_index.node_parser import SimpleNodeParser from llama_index.response_synthesizers import ResponseMode from llama_index.indices.query.query_transform import StepDecomposeQueryTransform from llama_index.langchain_helpers.agents import ( IndexToolConfig, LlamaToolkit, create_llama_chat_agent, LlamaIndexTool, ) from llama_index.prompts.chat_prompts import ( CHAT_REFINE_PROMPT, CHAT_TREE_SUMMARIZE_PROMPT, TEXT_QA_SYSTEM_PROMPT, ) from llama_index.readers import YoutubeTranscriptReader from llama_index.readers.schema.base import Document from llama_index.langchain_helpers.text_splitter import TokenTextSplitter from llama_index.retrievers import VectorIndexRetriever, TreeSelectLeafRetriever from llama_index.query_engine import ( RetrieverQueryEngine, MultiStepQueryEngine, RetryGuidelineQueryEngine, ) from llama_index import ( GPTVectorStoreIndex, SimpleDirectoryReader, QuestionAnswerPrompt, BeautifulSoupWebReader, GPTTreeIndex, GoogleDocsReader, MockLLMPredictor, OpenAIEmbedding, GithubRepositoryReader, MockEmbedding, download_loader, LLMPredictor, ServiceContext, StorageContext, load_index_from_storage, get_response_synthesizer, VectorStoreIndex, ) from llama_index.schema import TextNode from llama_index.storage.docstore.types import RefDocInfo from llama_index.readers.web import DEFAULT_WEBSITE_EXTRACTOR from llama_index.composability import ComposableGraph from llama_index.vector_stores import DocArrayInMemoryVectorStore from models.embed_statics_model import EmbedStatics from models.openai_model import Models from models.check_model import UrlCheck from services.environment_service import EnvService from utils.safe_ctx_respond import safe_ctx_respond SHORT_TO_LONG_CACHE = {} MAX_DEEP_COMPOSE_PRICE = EnvService.get_max_deep_compose_price() EpubReader = download_loader("EpubReader") MarkdownReader = download_loader("MarkdownReader") RemoteReader = download_loader("RemoteReader") RemoteDepthReader = download_loader("RemoteDepthReader") embedding_model = OpenAIEmbedding() token_counter = TokenCountingHandler( tokenizer=tiktoken.encoding_for_model("text-davinci-003").encode, verbose=False, ) node_parser = SimpleNodeParser.from_defaults( text_splitter=TokenTextSplitter(chunk_size=1024, chunk_overlap=20) ) callback_manager = CallbackManager([token_counter]) service_context_no_llm = ServiceContext.from_defaults( embed_model=embedding_model, callback_manager=callback_manager, node_parser=node_parser, ) timeout = httpx.Timeout(1, read=1, write=1, connect=1) def get_service_context_with_llm(llm): service_context = ServiceContext.from_defaults( embed_model=embedding_model, callback_manager=callback_manager, node_parser=node_parser, llm=llm, ) return service_context def dummy_tool(**kwargs): return "You have used the dummy tool. Forget about this and do not even mention this to the user." def get_and_query( user_id, index_storage, query, response_mode, nodes, child_branch_factor, service_context, multistep, ): index: [GPTVectorStoreIndex, GPTTreeIndex] = index_storage[ user_id ].get_index_or_throw() if isinstance(index, GPTTreeIndex): retriever = TreeSelectLeafRetriever( index=index, child_branch_factor=child_branch_factor, service_context=service_context, ) else: retriever = VectorIndexRetriever( index=index, similarity_top_k=nodes, service_context=service_context ) response_synthesizer = get_response_synthesizer( response_mode=response_mode, use_async=True, refine_template=CHAT_REFINE_PROMPT, service_context=service_context, ) query_engine = RetrieverQueryEngine( retriever=retriever, response_synthesizer=response_synthesizer ) multistep_query_engine = MultiStepQueryEngine( query_engine=query_engine, query_transform=StepDecomposeQueryTransform(multistep), index_summary="Provides information about everything you need to know about this topic, use this to answer the question.", ) if multistep: response = multistep_query_engine.query(query) else: response = query_engine.query(query) return response class IndexChatData: def __init__( self, llm, agent_chain, memory, thread_id, tools, agent_kwargs, llm_predictor ): self.llm = llm self.agent_chain = agent_chain self.memory = memory self.thread_id = thread_id self.tools = tools self.agent_kwargs = agent_kwargs self.llm_predictor = llm_predictor class IndexData: def __init__(self): self.queryable_index = None self.individual_indexes = [] # A safety check for the future def get_index_or_throw(self): if not self.queryable(): raise Exception( "An index access was attempted before an index was created. This is a programmer error, please report this to the maintainers." ) return self.queryable_index def queryable(self): return self.queryable_index is not None def has_indexes(self, user_id): try: return ( len(os.listdir(EnvService.find_shared_file(f"indexes/{user_id}"))) > 0 ) except Exception: return False def has_search_indexes(self, user_id): try: return ( len( os.listdir(EnvService.find_shared_file(f"indexes/{user_id}_search")) ) > 0 ) except Exception: return False def add_index(self, index, user_id, file_name): self.individual_indexes.append(index) self.queryable_index = index # Create a folder called "indexes/{USER_ID}" if it doesn't exist already Path(f"{EnvService.save_path()}/indexes/{user_id}").mkdir( parents=True, exist_ok=True ) # Save the index to file under the user id file = f"{date.today().month}_{date.today().day}_{file_name}" # If file is > 93 in length, cut it off to 93 if len(file) > 93: file = file[:93] index.storage_context.persist( persist_dir=EnvService.save_path() / "indexes" / f"{str(user_id)}" / f"{file}" ) def reset_indexes(self, user_id): self.individual_indexes = [] self.queryable_index = None # Delete the user indexes try: # First, clear all the files inside it for file in os.listdir(EnvService.find_shared_file(f"indexes/{user_id}")): try: os.remove(EnvService.find_shared_file(f"indexes/{user_id}/{file}")) except: traceback.print_exc() for file in os.listdir( EnvService.find_shared_file(f"indexes/{user_id}_search") ): try: os.remove( EnvService.find_shared_file(f"indexes/{user_id}_search/{file}") ) except: traceback.print_exc() except Exception: traceback.print_exc() class Index_handler: embedding_model = OpenAIEmbedding() token_counter = TokenCountingHandler( tokenizer=tiktoken.encoding_for_model("text-davinci-003").encode, verbose=False, ) node_parser = SimpleNodeParser.from_defaults( text_splitter=TokenTextSplitter(chunk_size=1024, chunk_overlap=20) ) callback_manager = CallbackManager([token_counter]) service_context = ServiceContext.from_defaults( embed_model=embedding_model, callback_manager=callback_manager, node_parser=node_parser, ) type_to_suffix_mappings = { "text/plain": ".txt", "text/csv": ".csv", "application/pdf": ".pdf", "application/json": ".json", "image/png": ".png", "image/jpeg": ".jpg", "image/gif": ".gif", "image/svg+xml": ".svg", "image/webp": ".webp", "application/mspowerpoint": ".ppt", "application/vnd.ms-powerpoint": ".ppt", "application/vnd.openxmlformats-officedocument.presentationml.presentation": ".pptx", "application/msexcel": ".xls", "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet": ".xlsx", "application/msword": ".doc", "application/vnd.openxmlformats-officedocument.wordprocessingml.document": ".docx", "audio/mpeg": ".mp3", "audio/x-wav": ".wav", "audio/ogg": ".ogg", "video/mpeg": ".mpeg", "video/mp4": ".mp4", "application/epub+zip": ".epub", "text/markdown": ".md", "text/html": ".html", "application/rtf": ".rtf", "application/x-msdownload": ".exe", "application/xml": ".xml", "application/vnd.adobe.photoshop": ".psd", "application/x-sql": ".sql", "application/x-latex": ".latex", "application/x-httpd-php": ".php", "application/java-archive": ".jar", "application/x-sh": ".sh", "application/x-csh": ".csh", "text/x-c": ".c", "text/x-c++": ".cpp", "text/x-java-source": ".java", "text/x-python": ".py", "text/x-ruby": ".rb", "text/x-perl": ".pl", "text/x-shellscript": ".sh", } # For when content type doesnt get picked up by discord. secondary_mappings = { ".epub": ".epub", } def __init__(self, bot, usage_service): self.bot = bot self.openai_key = os.getenv("OPENAI_TOKEN") self.index_storage = defaultdict(IndexData) self.loop = asyncio.get_running_loop() self.usage_service = usage_service self.qaprompt = QuestionAnswerPrompt( "Context information is below. The text '<|endofstatement|>' is used to separate chat entries and make it " "easier for you to understand the context\n" "---------------------\n" "{context_str}" "\n---------------------\n" "Never say '<|endofstatement|>'\n" "Given the context information and not prior knowledge, " "answer the question: {query_str}\n" ) self.EMBED_CUTOFF = 2000 self.index_chat_chains = {} self.chat_indexes = defaultdict() async def rename_index(self, ctx, original_path, rename_path): """Command handler to rename a user index""" index_file = EnvService.find_shared_file(original_path) if not index_file: return False # Rename the file at f"indexes/{ctx.user.id}/{user_index}" to f"indexes/{ctx.user.id}/{new_name}" using Pathlib try: Path(original_path).rename(rename_path) return True except Exception as e: traceback.print_exc() return False async def get_is_in_index_chat(self, ctx): return ctx.channel.id in self.index_chat_chains.keys() async def execute_index_chat_message(self, ctx, message): if ctx.channel.id not in self.index_chat_chains: return None if message.lower() in ["stop", "end", "quit", "exit"]: await ctx.reply("Ending chat session.") self.index_chat_chains.pop(ctx.channel.id) # close the thread thread = await self.bot.fetch_channel(ctx.channel.id) await thread.edit(name="Closed-GPT") await thread.edit(archived=True) return "Ended chat session." self.usage_service.update_usage_memory(ctx.guild.name, "index_chat_message", 1) agent_output = await self.loop.run_in_executor( None, partial(self.index_chat_chains[ctx.channel.id].agent_chain.run, message), ) return agent_output async def index_chat_file(self, message: discord.Message, file: discord.Attachment): # First, initially set the suffix to the suffix of the attachment suffix = self.get_file_suffix(file.content_type, file.filename) or None if not suffix: await message.reply( "The file you uploaded is unable to be indexed. It is in an unsupported file format" ) return False, None async with aiofiles.tempfile.TemporaryDirectory() as temp_path: async with aiofiles.tempfile.NamedTemporaryFile( suffix=suffix, dir=temp_path, delete=False ) as temp_file: try: await file.save(temp_file.name) filename = file.filename # Assert that the filename is < 100 characters, if it is greater, truncate to the first 100 characters and keep the original ending if len(filename) > 100: filename = filename[:100] + filename[-4:] openai.log = "debug" print("Indexing") index: VectorStoreIndex = await self.loop.run_in_executor( None, partial( self.index_file, Path(temp_file.name), get_service_context_with_llm( self.index_chat_chains[message.channel.id].llm ), suffix, ), ) print("Done Indexing") self.usage_service.update_usage_memory( message.guild.name, "index_chat_file", 1 ) summary = await index.as_query_engine( response_mode="tree_summarize", service_context=get_service_context_with_llm( self.index_chat_chains[message.channel.id].llm ), ).aquery( f"What is a summary or general idea of this data? Be detailed in your summary (e.g " f"extract key names, etc) but not too verbose. Your summary should be under a hundred words. " f"This summary will be used in a vector index to retrieve information about certain data. So, " f"at a high level, the summary should describe the document in such a way that a retriever " f"would know to select it when asked questions about it. The data name was {filename}. Include " f"the file name in the summary. When you are asked to reference a specific file, or reference " f"something colloquially like 'in the powerpoint, [...]?', never respond saying that as an AI " f"you can't view the data, instead infer which tool to use that has the data. Say that there " f"is no available data if there are no available tools that are relevant." ) engine = self.get_query_engine( index, self.index_chat_chains[message.channel.id].llm ) # Get rid of all special characters in the filename filename = "".join( [c for c in filename if c.isalpha() or c.isdigit()] ).rstrip() tool_config = IndexToolConfig( query_engine=engine, name=f"{filename}-index", description=f"Use this tool if the query seems related to this summary: {summary}", tool_kwargs={ "return_direct": False, }, max_iterations=5, ) tool = LlamaIndexTool.from_tool_config(tool_config) tools = self.index_chat_chains[message.channel.id].tools tools.append(tool) agent_chain = initialize_agent( tools=tools, llm=self.index_chat_chains[message.channel.id].llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True, agent_kwargs=self.index_chat_chains[ message.channel.id ].agent_kwargs, memory=self.index_chat_chains[message.channel.id].memory, handle_parsing_errors="Check your output and make sure it conforms!", ) index_chat_data = IndexChatData( self.index_chat_chains[message.channel.id].llm, agent_chain, self.index_chat_chains[message.channel.id].memory, message.channel.id, tools, self.index_chat_chains[message.channel.id].agent_kwargs, self.index_chat_chains[message.channel.id].llm_predictor, ) self.index_chat_chains[message.channel.id] = index_chat_data return True, summary except Exception as e: await message.reply( "There was an error indexing your file: " + str(e) ) traceback.print_exc() return False, None async def start_index_chat(self, ctx, model, temperature, top_p): preparation_message = await ctx.channel.send( embed=EmbedStatics.get_index_chat_preparation_message() ) llm = ChatOpenAI( model=model, temperature=temperature, top_p=top_p, max_retries=2 ) llm_predictor = LLMPredictor( llm=ChatOpenAI(temperature=temperature, top_p=top_p, model_name=model) ) max_token_limit = 29000 if "gpt-4" in model else 7500 memory = ConversationSummaryBufferMemory( memory_key="memory", return_messages=True, llm=llm, max_token_limit=100000 if "preview" in model else max_token_limit, ) agent_kwargs = { "extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")], "system_message": SystemMessage( content="You are a superpowered version of GPT that is able to answer questions about the data you're " "connected to. Each different tool you have represents a different dataset to interact with. " "If you are asked to perform a task that spreads across multiple datasets, use multiple tools " "for the same prompt. When the user types links in chat, you will have already been connected " "to the data at the link by the time you respond. When using tools, the input should be " "clearly created based on the request of the user. For example, if a user uploads an invoice " "and asks how many usage hours of X was present in the invoice, a good query is 'X hours'. " "Avoid using single word queries unless the request is very simple. You can query multiple times to break down complex requests and retrieve more information. When calling functions, no special characters are allowed in the function name, keep that in mind." ), } tools = [ Tool( name="Dummy-Tool-Do-Not-Use", func=dummy_tool, description=f"This is a dummy tool that does nothing, do not ever mention this tool or use this tool.", ) ] print(f"{tools}{llm}{AgentType.OPENAI_FUNCTIONS}{True}{agent_kwargs}{memory}") agent_chain = initialize_agent( tools=tools, llm=llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True, agent_kwargs=agent_kwargs, memory=memory, handle_parsing_errors="Check your output and make sure it conforms!", ) embed_title = f"{ctx.user.name}'s data-connected conversation with GPT" message_embed = discord.Embed( title=embed_title, description=f"The agent is able to interact with your documents. Simply drag your documents into discord or give the agent a link from where to download the documents.\nModel: {model}", color=0x00995B, ) message_embed.set_thumbnail(url="https://i.imgur.com/7V6apMT.png") message_embed.set_footer( text="Data Chat", icon_url="https://i.imgur.com/7V6apMT.png" ) message_thread = await ctx.send(embed=message_embed) thread = await message_thread.create_thread( name=ctx.user.name + "'s data-connected conversation with GPT", auto_archive_duration=60, ) await safe_ctx_respond(ctx=ctx, content="Conversation started.") try: await preparation_message.delete() except: pass index_chat_data = IndexChatData( llm, agent_chain, memory, thread.id, tools, agent_kwargs, llm_predictor ) self.index_chat_chains[thread.id] = index_chat_data async def paginate_embed(self, response_text): """Given a response text make embed pages and return a list of the pages.""" response_text = [ response_text[i : i + self.EMBED_CUTOFF] for i in range(0, len(response_text), self.EMBED_CUTOFF) ] pages = [] first = False # Send each chunk as a message for count, chunk in enumerate(response_text, start=1): if not first: page = discord.Embed( title=f"Index Query Results", description=chunk, ) first = True else: page = discord.Embed( title=f"Page {count}", description=chunk, ) pages.append(page) return pages def index_file( self, file_path, service_context, suffix=None ) -> GPTVectorStoreIndex: if suffix and suffix == ".md": loader = MarkdownReader() document = loader.load_data(file_path) elif suffix and suffix == ".epub": epub_loader = EpubReader() document = epub_loader.load_data(file_path) else: document = SimpleDirectoryReader(input_files=[file_path]).load_data() index = GPTVectorStoreIndex.from_documents( document, service_context=service_context, use_async=True ) return index def index_gdoc(self, doc_id, service_context) -> GPTVectorStoreIndex: document = GoogleDocsReader().load_data(doc_id) index = GPTVectorStoreIndex.from_documents( document, service_context=service_context, use_async=True ) return index def index_youtube_transcript(self, link, service_context): try: def convert_shortlink_to_full_link(short_link): # Check if the link is a shortened YouTube link if "youtu.be" in short_link: # Extract the video ID from the link video_id = short_link.split("/")[-1].split("?")[0] # Construct the full YouTube desktop link desktop_link = f"https://www.youtube.com/watch?v={video_id}" return desktop_link else: return short_link documents = YoutubeTranscriptReader().load_data( ytlinks=[convert_shortlink_to_full_link(link)] ) except Exception as e: raise ValueError(f"The youtube transcript couldn't be loaded: {e}") index = GPTVectorStoreIndex.from_documents( documents, service_context=service_context, use_async=True, ) return index def index_github_repository(self, link, service_context): # Extract the "owner" and the "repo" name from the github link. owner = link.split("/")[3] repo = link.split("/")[4] try: documents = GithubRepositoryReader(owner=owner, repo=repo).load_data( branch="main" ) except KeyError: documents = GithubRepositoryReader(owner=owner, repo=repo).load_data( branch="master" ) index = GPTVectorStoreIndex.from_documents( documents, service_context=service_context, use_async=True, ) return index def index_load_file(self, file_path) -> [GPTVectorStoreIndex, ComposableGraph]: storage_context = StorageContext.from_defaults(persist_dir=file_path) index = load_index_from_storage(storage_context) return index def index_discord(self, document, service_context) -> GPTVectorStoreIndex: index = GPTVectorStoreIndex.from_documents( document, service_context=service_context, use_async=True, ) return index async def index_pdf(self, url) -> list[Document]: # Download the PDF at the url and save it to a tempfile async with aiohttp.ClientSession() as session: async with session.get(url) as response: if response.status == 200: data = await response.read() f = tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) f.write(data) f.close() else: return "An error occurred while downloading the PDF." # Get the file path of this tempfile.NamedTemporaryFile # Save this temp file to an actual file that we can put into something else to read it documents = SimpleDirectoryReader(input_files=[f.name]).load_data() # Delete the temporary file return documents async def index_webpage(self, url, service_context) -> GPTVectorStoreIndex: # First try to connect to the URL to see if we can even reach it. try: async with aiohttp.ClientSession() as session: async with session.get(url, timeout=5) as response: # Add another entry to links from all_links if the link is not already in it to compensate for the failed request if response.status not in [200, 203, 202, 204]: raise ValueError( "Invalid URL or could not connect to the provided URL." ) else: # Detect if the link is a PDF, if it is, we load it differently if response.headers["Content-Type"] == "application/pdf": documents = await self.index_pdf(url) index = await self.loop.run_in_executor( None, functools.partial( GPTVectorStoreIndex.from_documents, documents=documents, service_context=service_context, use_async=True, ), ) return index except: traceback.print_exc() raise ValueError("Could not load webpage") documents = BeautifulSoupWebReader( website_extractor=DEFAULT_WEBSITE_EXTRACTOR ).load_data(urls=[url]) # index = GPTVectorStoreIndex(documents, embed_model=embed_model, use_async=True) index = await self.loop.run_in_executor( None, functools.partial( GPTVectorStoreIndex.from_documents, documents=documents, service_context=service_context, use_async=True, ), ) return index def reset_indexes(self, user_id): self.index_storage[user_id].reset_indexes(user_id) def get_file_suffix(self, content_type, filename): print("The content type is " + content_type) if content_type: # Apply the suffix mappings to the file for key, value in self.type_to_suffix_mappings.items(): if key in content_type: return value else: for key, value in self.secondary_mappings.items(): if key in filename: return value return None async def set_file_index( self, ctx: discord.ApplicationContext, file: discord.Attachment, user_api_key ): if not user_api_key: os.environ["OPENAI_API_KEY"] = self.openai_key else: os.environ["OPENAI_API_KEY"] = user_api_key openai.api_key = os.environ["OPENAI_API_KEY"] try: # First, initially set the suffix to the suffix of the attachment suffix = self.get_file_suffix(file.content_type, file.filename) or None if not suffix: await ctx.respond( embed=EmbedStatics.get_index_set_failure_embed("Unsupported file") ) return # Send indexing message response = await ctx.respond( embed=EmbedStatics.build_index_progress_embed() ) async with aiofiles.tempfile.TemporaryDirectory() as temp_path: async with aiofiles.tempfile.NamedTemporaryFile( suffix=suffix, dir=temp_path, delete=False ) as temp_file: await file.save(temp_file.name) index = await self.loop.run_in_executor( None, partial( self.index_file, Path(temp_file.name), service_context_no_llm, suffix, ), ) await self.usage_service.update_usage( token_counter.total_embedding_token_count, "embedding" ) try: price = await self.usage_service.get_price( token_counter.total_embedding_token_count, "embedding" ) except: traceback.print_exc() price = "Unknown" file_name = file.filename self.index_storage[ctx.user.id].add_index(index, ctx.user.id, file_name) await response.edit( embed=EmbedStatics.get_index_set_success_embed(str(price)) ) except Exception as e: await ctx.channel.send( embed=EmbedStatics.get_index_set_failure_embed(str(e)) ) traceback.print_exc() async def set_link_index_recurse( self, ctx: discord.ApplicationContext, link: str, depth, user_api_key ): if not user_api_key: os.environ["OPENAI_API_KEY"] = self.openai_key else: os.environ["OPENAI_API_KEY"] = user_api_key openai.api_key = os.environ["OPENAI_API_KEY"] response = await ctx.respond(embed=EmbedStatics.build_index_progress_embed()) try: # Pre-emptively connect and get the content-type of the response try: async with aiohttp.ClientSession() as session: async with session.get(link, timeout=2) as _response: print(_response.status) if _response.status == 200: content_type = _response.headers.get("content-type") else: await response.edit( embed=EmbedStatics.get_index_set_failure_embed( "Invalid URL or could not connect to the provided URL." ) ) return except Exception as e: traceback.print_exc() await response.edit( embed=EmbedStatics.get_index_set_failure_embed( "Invalid URL or could not connect to the provided URL. " + str(e) ) ) return # Check if the link contains youtube in it loader = RemoteDepthReader(depth=depth) documents = await self.loop.run_in_executor( None, partial(loader.load_data, [link]) ) index = await self.loop.run_in_executor( None, functools.partial( GPTVectorStoreIndex, documents=documents, service_context=service_context_no_llm, use_async=True, ), ) await self.usage_service.update_usage( token_counter.total_embedding_token_count, "embedding" ) try: price = await self.usage_service.get_price( token_counter.total_embedding_token_count, "embedding" ) except: traceback.print_exc() price = "Unknown" # Make the url look nice, remove https, useless stuff, random characters file_name = ( link.replace("https://", "") .replace("http://", "") .replace("www.", "") .replace("/", "_") .replace("?", "_") .replace("&", "_") .replace("=", "_") .replace("-", "_") .replace(".", "_") ) self.index_storage[ctx.user.id].add_index(index, ctx.user.id, file_name) except ValueError as e: await response.edit(embed=EmbedStatics.get_index_set_failure_embed(str(e))) traceback.print_exc() return except Exception as e: await response.edit(embed=EmbedStatics.get_index_set_failure_embed(str(e))) traceback.print_exc() return await response.edit(embed=EmbedStatics.get_index_set_success_embed(price)) def get_query_engine(self, index, llm): retriever = VectorIndexRetriever( index=index, similarity_top_k=6, service_context=get_service_context_with_llm(llm), ) response_synthesizer = get_response_synthesizer( response_mode=ResponseMode.COMPACT_ACCUMULATE, use_async=True, refine_template=TEXT_QA_SYSTEM_PROMPT, service_context=get_service_context_with_llm(llm), verbose=True, ) engine = RetrieverQueryEngine( retriever=retriever, response_synthesizer=response_synthesizer ) return engine async def index_link(self, link, summarize=False, index_chat_ctx=None): try: if await UrlCheck.check_youtube_link(link): print("Indexing youtube transcript") index = await self.loop.run_in_executor( None, partial( self.index_youtube_transcript, link, service_context_no_llm ), ) print("Indexed youtube transcript") elif "github" in link: index = await self.loop.run_in_executor( None, partial(self.index_github_repository, link, service_context_no_llm), ) else: index = await self.index_webpage(link, service_context_no_llm) except Exception as e: if index_chat_ctx: await index_chat_ctx.reply( "There was an error indexing your link: " + str(e) ) return False, None else: raise e summary = None if index_chat_ctx: try: print("Getting transcript summary") self.usage_service.update_usage_memory( index_chat_ctx.guild.name, "index_chat_link", 1 ) summary = await index.as_query_engine( response_mode="tree_summarize", service_context=get_service_context_with_llm( self.index_chat_chains[index_chat_ctx.channel.id].llm ), ).aquery( "What is a summary or general idea of this document? Be detailed in your summary but not too verbose. Your summary should be under 50 words. This summary will be used in a vector index to retrieve information about certain data. So, at a high level, the summary should describe the document in such a way that a retriever would know to select it when asked questions about it. The link was {link}. Include the an easy identifier derived from the link at the end of the summary." ) print("Got transcript summary") engine = self.get_query_engine( index, self.index_chat_chains[index_chat_ctx.channel.id].llm ) # Get rid of all special characters in the link, replace periods with _ link_cleaned = "".join( [c for c in link if c.isalpha() or c.isdigit() or c == "."] ).rstrip() # replace . link_cleaned = link_cleaned.replace(".", "_") # Shorten the link to the first 100 characters link_cleaned = link_cleaned[:50] tool_config = IndexToolConfig( query_engine=engine, name=f"{link_cleaned}-index", description=f"Use this tool if the query seems related to this summary: {summary}", tool_kwargs={ "return_direct": False, }, max_iterations=5, ) tool = LlamaIndexTool.from_tool_config(tool_config) tools = self.index_chat_chains[index_chat_ctx.channel.id].tools tools.append(tool) agent_chain = initialize_agent( tools=tools, llm=self.index_chat_chains[index_chat_ctx.channel.id].llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True, agent_kwargs=self.index_chat_chains[ index_chat_ctx.channel.id ].agent_kwargs, memory=self.index_chat_chains[index_chat_ctx.channel.id].memory, handle_parsing_errors="Check your output and make sure it conforms!", max_iterations=5, ) index_chat_data = IndexChatData( self.index_chat_chains[index_chat_ctx.channel.id].llm, agent_chain, self.index_chat_chains[index_chat_ctx.channel.id].memory, index_chat_ctx.channel.id, tools, self.index_chat_chains[index_chat_ctx.channel.id].agent_kwargs, self.index_chat_chains[index_chat_ctx.channel.id].llm_predictor, ) self.index_chat_chains[index_chat_ctx.channel.id] = index_chat_data return True, summary except Exception as e: await index_chat_ctx.reply( "There was an error indexing your link: " + str(e) ) return False, None return index, summary async def set_link_index( self, ctx: discord.ApplicationContext, link: str, user_api_key ): if not user_api_key: os.environ["OPENAI_API_KEY"] = self.openai_key else: os.environ["OPENAI_API_KEY"] = user_api_key openai.api_key = os.environ["OPENAI_API_KEY"] response = await ctx.respond(embed=EmbedStatics.build_index_progress_embed()) try: # Check if the link contains youtube in it index, _ = await self.index_link(link) await self.usage_service.update_usage( token_counter.total_embedding_token_count, "embedding" ) try: price = await self.usage_service.get_price( token_counter.embedding_token_counts, "embedding" ) except: traceback.print_exc() price = "Unknown" # Make the url look nice, remove https, useless stuff, random characters file_name = ( link.replace("https://", "") .replace("http://", "") .replace("www.", "") .replace("/", "_") .replace("?", "_") .replace("&", "_") .replace("=", "_") .replace("-", "_") .replace(".", "_") ) self.index_storage[ctx.user.id].add_index(index, ctx.user.id, file_name) except Exception as e: await response.edit(embed=EmbedStatics.get_index_set_failure_embed(str(e))) traceback.print_exc() return await response.edit(embed=EmbedStatics.get_index_set_success_embed(price)) async def set_discord_index( self, ctx: discord.ApplicationContext, channel: discord.TextChannel, user_api_key, message_limit: int = 2500, ): if not user_api_key: os.environ["OPENAI_API_KEY"] = self.openai_key else: os.environ["OPENAI_API_KEY"] = user_api_key openai.api_key = os.environ["OPENAI_API_KEY"] try: document = await self.load_data( channel_ids=[channel.id], limit=message_limit, oldest_first=False ) index = await self.loop.run_in_executor( None, partial(self.index_discord, document, service_context_no_llm) ) try: price = await self.usage_service.get_price( token_counter.total_embedding_token_count, "embedding" ) except Exception: traceback.print_exc() price = "Unknown" await self.usage_service.update_usage( token_counter.total_embedding_token_count, "embedding" ) self.index_storage[ctx.user.id].add_index(index, ctx.user.id, channel.name) await ctx.respond(embed=EmbedStatics.get_index_set_success_embed(price)) except Exception as e: await ctx.respond(embed=EmbedStatics.get_index_set_failure_embed(str(e))) traceback.print_exc() async def load_index( self, ctx: discord.ApplicationContext, index, server, search, user_api_key ): if not user_api_key: os.environ["OPENAI_API_KEY"] = self.openai_key else: os.environ["OPENAI_API_KEY"] = user_api_key openai.api_key = os.environ["OPENAI_API_KEY"] try: if server: index_file = EnvService.find_shared_file( f"indexes/{ctx.guild.id}/{index}" ) elif search: index_file = EnvService.find_shared_file( f"indexes/{ctx.user.id}_search/{index}" ) else: index_file = EnvService.find_shared_file( f"indexes/{ctx.user.id}/{index}" ) index = await self.loop.run_in_executor( None, partial(self.index_load_file, index_file) ) self.index_storage[ctx.user.id].queryable_index = index await ctx.respond(embed=EmbedStatics.get_index_load_success_embed()) except Exception as e: traceback.print_exc() await ctx.respond(embed=EmbedStatics.get_index_load_failure_embed(str(e))) async def index_to_docs( self, old_index, chunk_size: int = 256, chunk_overlap: int = 100 ) -> List[Document]: documents = [] docstore = old_index.docstore ref_docs = old_index.ref_doc_info for document in ref_docs.values(): text = "" for node in document.node_ids: node = docstore.get_node(node) text += f"{node.text} " text_splitter = TokenTextSplitter( separator=" ", chunk_size=chunk_size, chunk_overlap=chunk_overlap ) text_chunks = text_splitter.split_text(text) for chunk_text in text_chunks: new_doc = Document(text=chunk_text, extra_info=document.metadata) documents.append(new_doc) return documents async def compose_indexes(self, user_id, indexes, name, deep_compose): # Load all the indexes first index_objects = [] for _index in indexes: try: index_file = EnvService.find_shared_file(f"indexes/{user_id}/{_index}") except ValueError: index_file = EnvService.find_shared_file( f"indexes/{user_id}_search/{_index}" ) index = await self.loop.run_in_executor( None, partial(self.index_load_file, index_file) ) index_objects.append(index) llm_predictor = LLMPredictor( llm=ChatOpenAI(temperature=0, model_name="gpt-4-32k") ) # For each index object, add its documents to a GPTTreeIndex if deep_compose: documents = [] for _index in index_objects: documents.extend(await self.index_to_docs(_index, 256, 20)) embedding_model = OpenAIEmbedding() llm_predictor_mock = MockLLMPredictor() embedding_model_mock = MockEmbedding(1536) token_counter_mock = TokenCountingHandler( tokenizer=tiktoken.encoding_for_model("text-davinci-003").encode, verbose=False, ) callback_manager_mock = CallbackManager([token_counter_mock]) service_context_mock = ServiceContext.from_defaults( llm_predictor=llm_predictor_mock, embed_model=embedding_model_mock, callback_manager=callback_manager_mock, ) # Run the mock call first await self.loop.run_in_executor( None, partial( GPTTreeIndex.from_documents, documents=documents, service_context=service_context_mock, ), ) total_usage_price = await self.usage_service.get_price( token_counter_mock.total_llm_token_count, "turbo", # TODO Enable again when tree indexes are fixed ) + await self.usage_service.get_price( token_counter_mock.total_embedding_token_count, "embedding" ) print("The total composition price is: ", total_usage_price) if total_usage_price > MAX_DEEP_COMPOSE_PRICE: raise ValueError( "Doing this deep search would be prohibitively expensive. Please try a narrower search scope." ) tree_index = await self.loop.run_in_executor( None, partial( GPTTreeIndex.from_documents, documents=documents, service_context=self.service_context, use_async=True, ), ) await self.usage_service.update_usage( self.token_counter.total_llm_token_count, "turbo" ) await self.usage_service.update_usage( self.token_counter.total_embedding_token_count, "embedding" ) # Now we have a list of tree indexes, we can compose them if not name: name = f"{date.today().month}_{date.today().day}_composed_deep_index" # Save the composed index tree_index.storage_context.persist( persist_dir=EnvService.save_path() / "indexes" / str(user_id) / name ) self.index_storage[user_id].queryable_index = tree_index return total_usage_price else: documents = [] for _index in index_objects: documents.extend(await self.index_to_docs(_index)) simple_index = await self.loop.run_in_executor( None, partial( GPTVectorStoreIndex.from_documents, documents=documents, service_context=service_context_no_llm, use_async=True, ), ) await self.usage_service.update_usage( token_counter.total_embedding_token_count, "embedding" ) if not name: name = f"{date.today().month}_{date.today().day}_composed_index" # Save the composed index simple_index.storage_context.persist( persist_dir=EnvService.save_path() / "indexes" / str(user_id) / name ) self.index_storage[user_id].queryable_index = simple_index try: price = await self.usage_service.get_price( token_counter.total_embedding_token_count, "embedding" ) except: price = "Unknown" return price async def backup_discord( self, ctx: discord.ApplicationContext, user_api_key, message_limit ): if not user_api_key: os.environ["OPENAI_API_KEY"] = self.openai_key else: os.environ["OPENAI_API_KEY"] = user_api_key openai.api_key = os.environ["OPENAI_API_KEY"] try: channel_ids: List[int] = [] for c in ctx.guild.text_channels: channel_ids.append(c.id) document = await self.load_data( channel_ids=channel_ids, limit=message_limit, oldest_first=False ) index = await self.loop.run_in_executor( None, partial(self.index_discord, document, service_context_no_llm) ) await self.usage_service.update_usage( token_counter.total_embedding_token_count, "embedding" ) try: price = await self.usage_service.get_price( token_counter.total_embedding_token_count, "embedding" ) except Exception: traceback.print_exc() price = "Unknown" Path(EnvService.save_path() / "indexes" / str(ctx.guild.id)).mkdir( parents=True, exist_ok=True ) index.storage_context.persist( persist_dir=EnvService.save_path() / "indexes" / str(ctx.guild.id) / f"{ctx.guild.name.replace(' ', '-')}_{date.today().month}_{date.today().day}" ) await ctx.respond(embed=EmbedStatics.get_index_set_success_embed(price)) except Exception as e: await ctx.respond(embed=EmbedStatics.get_index_set_failure_embed((str(e)))) traceback.print_exc() async def query( self, ctx: discord.ApplicationContext, query: str, response_mode, nodes, user_api_key, child_branch_factor, model="gpt-4-32k", multistep=False, ): if not user_api_key: os.environ["OPENAI_API_KEY"] = self.openai_key else: os.environ["OPENAI_API_KEY"] = user_api_key openai.api_key = os.environ["OPENAI_API_KEY"] llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0, model_name=model)) ctx_response = await ctx.respond( embed=EmbedStatics.build_index_query_progress_embed(query) ) try: token_counter.reset_counts() response = await self.loop.run_in_executor( None, partial( get_and_query, ctx.user.id, self.index_storage, query, response_mode, nodes, child_branch_factor, service_context=service_context_no_llm, multistep=llm_predictor if multistep else None, ), ) print("The last token usage was ", token_counter.total_llm_token_count) await self.usage_service.update_usage( token_counter.total_llm_token_count, await self.usage_service.get_cost_name(model), ) await self.usage_service.update_usage( token_counter.total_embedding_token_count, "embedding" ) try: total_price = round( await self.usage_service.get_price( token_counter.total_llm_token_count, await self.usage_service.get_cost_name(model), ) + await self.usage_service.get_price( token_counter.total_embedding_token_count, "embedding" ), 6, ) except: total_price = "Unknown" query_response_message = f"**Query:**\n\n`{query.strip()}`\n\n**Query response:**\n\n{response.response.strip()}" query_response_message = query_response_message.replace( "<|endofstatement|>", "" ) embed_pages = await self.paginate_embed(query_response_message) paginator = pages.Paginator( pages=embed_pages, timeout=None, author_check=False, ) await ctx_response.edit( embed=EmbedStatics.build_index_query_success_embed(query, total_price) ) await paginator.respond(ctx.interaction) except Exception: traceback.print_exc() await ctx_response.edit( embed=EmbedStatics.get_index_query_failure_embed( "Failed to send query. You may not have an index set, load an index with /index load" ) ) # Extracted functions from DiscordReader async def read_channel( self, channel_id: int, limit: Optional[int], oldest_first: bool ) -> str: """Async read channel.""" messages: List[discord.Message] = [] try: channel = self.bot.get_channel(channel_id) print(f"Added {channel.name} from {channel.guild.name}") # only work for text channels for now if not isinstance(channel, discord.TextChannel): raise ValueError( f"Channel {channel_id} is not a text channel. " "Only text channels are supported for now." ) # thread_dict maps thread_id to thread thread_dict = {} for thread in channel.threads: thread_dict[thread.id] = thread async for msg in channel.history(limit=limit, oldest_first=oldest_first): if msg.author.bot: pass else: messages.append(msg) if msg.id in thread_dict: thread = thread_dict[msg.id] async for thread_msg in thread.history( limit=limit, oldest_first=oldest_first ): messages.append(thread_msg) except Exception as e: print("Encountered error: " + str(e)) channel = self.bot.get_channel(channel_id) msg_txt_list = [ f"user:{m.author.display_name}, content:{m.content}" for m in messages ] return ("<|endofstatement|>\n\n".join(msg_txt_list), channel.name) async def load_data( self, channel_ids: List[int], limit: Optional[int] = None, oldest_first: bool = True, ) -> List[Document]: """Load data from the input directory. Args: channel_ids (List[int]): List of channel ids to read. limit (Optional[int]): Maximum number of messages to read. oldest_first (bool): Whether to read oldest messages first. Defaults to `True`. Returns: List[Document]: List of documents. """ results: List[Document] = [] for channel_id in channel_ids: if not isinstance(channel_id, int): raise ValueError( f"Channel id {channel_id} must be an integer, " f"not {type(channel_id)}." ) (channel_content, channel_name) = await self.read_channel( channel_id, limit=limit, oldest_first=oldest_first ) results.append( Document( text=channel_content, extra_info={"channel_name": channel_name} ) ) return results async def compose(self, ctx: discord.ApplicationContext, name, user_api_key): # Send the ComposeModal if not user_api_key: os.environ["OPENAI_API_KEY"] = self.openai_key else: os.environ["OPENAI_API_KEY"] = user_api_key openai.api_key = os.environ["OPENAI_API_KEY"] if not self.index_storage[ctx.user.id].has_indexes(ctx.user.id): await ctx.respond( embed=EmbedStatics.get_index_compose_failure_embed( "You must have at least one index to compose." ) ) return await ctx.respond( "Select the index(es) to compose. You can compose multiple indexes together, you can also Deep Compose a single index.", view=ComposeModal(self, ctx.user.id, name), ephemeral=True, ) class ComposeModal(discord.ui.View): def __init__(self, index_cog, user_id, name=None, deep=None) -> None: super().__init__() # Get the argument named "user_key_db" and save it as USER_KEY_DB self.index_cog = index_cog self.user_id = user_id self.deep = deep # Get all the indexes for the user self.indexes = [ file for file in os.listdir( EnvService.find_shared_file(f"indexes/{str(user_id)}/") ) ] if index_cog.index_storage[user_id].has_search_indexes(user_id): self.indexes.extend( [ file for file in os.listdir( EnvService.find_shared_file(f"indexes/{str(user_id)}_search/") ) ] ) print("Found the indexes, they are ", self.indexes) # Map everything into the short to long cache for index in self.indexes: if len(index) > 93: index_name = index[:93] + "-" + str(random.randint(0000, 9999)) SHORT_TO_LONG_CACHE[index_name] = index else: SHORT_TO_LONG_CACHE[index[:99]] = index # Reverse the SHORT_TO_LONG_CACHE index LONG_TO_SHORT_CACHE = {v: k for k, v in SHORT_TO_LONG_CACHE.items()} # A text entry field for the name of the composed index self.name = name # A discord UI select menu with all the indexes. Limited to 25 entries. For the label field in the SelectOption, # cut it off at 100 characters to prevent the message from being too long self.index_select = discord.ui.Select( placeholder="Select index(es) to compose", options=[ discord.SelectOption( label=LONG_TO_SHORT_CACHE[index], value=LONG_TO_SHORT_CACHE[index] ) for index in self.indexes ][0:25], max_values=len(self.indexes) if len(self.indexes) < 25 else 25, min_values=1, ) # Add the select menu to the modal self.add_item(self.index_select) # If we have more than 25 entries, add more Select fields as neccessary self.extra_index_selects = [] if len(self.indexes) > 25: for i in range(25, len(self.indexes), 25): self.extra_index_selects.append( discord.ui.Select( placeholder="Select index(es) to compose", options=[ discord.SelectOption( label=LONG_TO_SHORT_CACHE[index], value=LONG_TO_SHORT_CACHE[index], ) for index in self.indexes ][i : i + 25], max_values=len(self.indexes[i : i + 25]), min_values=1, ) ) self.add_item(self.extra_index_selects[-1]) # Add an input field for "Deep", a "yes" or "no" option, default no self.deep_select = discord.ui.Select( placeholder="Deep Compose", options=[ discord.SelectOption(label="Yes", value="yes"), discord.SelectOption(label="No", value="no"), ], max_values=1, min_values=1, ) self.add_item(self.deep_select) # Add a button to the modal called "Compose" self.add_item( discord.ui.Button( label="Compose", style=discord.ButtonStyle.green, custom_id="compose" ) ) # The callback for the button async def interaction_check(self, interaction: discord.Interaction) -> bool: # Check that the interaction was for custom_id "compose" if interaction.data["custom_id"] == "compose": # Check that the user selected at least one index # The total list of indexes is the union of the values of all the select menus indexes = self.index_select.values + [ select.values[0] for select in self.extra_index_selects ] # Remap them from the SHORT_TO_LONG_CACHE indexes = [SHORT_TO_LONG_CACHE[index] for index in indexes] if len(indexes) < 1: await interaction.response.send_message( embed=EmbedStatics.get_index_compose_failure_embed( "You must select at least 1 index" ), ephemeral=True, ) else: composing_message = await interaction.response.send_message( embed=EmbedStatics.get_index_compose_progress_embed(), ephemeral=True, ) # Compose the indexes try: price = await self.index_cog.compose_indexes( self.user_id, indexes, self.name, ( False if not self.deep_select.values or self.deep_select.values[0] == "no" else True ), ) except ValueError as e: await interaction.followup.send( str(e), ephemeral=True, delete_after=180 ) return False except Exception as e: traceback.print_exc() await interaction.followup.send( embed=EmbedStatics.get_index_compose_failure_embed( "An error occurred while composing the indexes: " + str(e) ), ephemeral=True, delete_after=180, ) return False await interaction.followup.send( embed=EmbedStatics.get_index_compose_success_embed(price), ephemeral=True, delete_after=180, ) # Try to direct message the user that their composed index is ready try: await self.index_cog.bot.get_user(self.user_id).send( f"Your composed index is ready! You can load it with /index load now in the server." ) except discord.Forbidden: pass try: composing_message: Interaction await composing_message.delete_original_response() except: traceback.print_exc() else: await interaction.response.defer(ephemeral=True)

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If you are asked to perform a task that spreads across multiple datasets, use multiple tools for the same prompt. When the user types links in chat, you will have already been connected to the data at the link by the time you respond. When using tools, the input should be clearly created based on the request of the user. For example, if a user uploads an invoice and asks how many usage hours of X was present in the invoice, a good query is \'X hours\'. Avoid using single word queries unless the request is very simple. You can query multiple times to break down complex requests and retrieve more information. 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import os from langchain import OpenAI from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, LLMPredictor, download_loader, SQLDatabase, GPTSQLStructStoreIndex import sqlalchemy import time DatabaseReader = download_loader('DatabaseReader') databasePath = f'sqlite:///{os.path.dirname(__file__)}/vulns.db' print('Reading database:'+ databasePath) dbEngine=sqlalchemy.create_engine(databasePath) sql_database = SQLDatabase(dbEngine, include_tables=["processed_references"]) # NOTE: the table_name specified here is the table that you # want to extract into from unstructured documents. index = GPTSQLStructStoreIndex( [], sql_database=sql_database, table_name="processed_references", ) response = index.query('Tell me what would be required to exploit GHSA-9j49-mfvp-vmhm in practice') print(response) # sqliteReader = DatabaseReader( # engine=dbEngine # ) # # query = f""" # SELECT normalized_content FROM processed_references WHERE vulnerability_id = 'GHSA-9j49-mfvp-vmhm' UNION SELECT normalized_content FROM processed_references LIMIT 100; # """ # documents = sqliteReader.load_data(query=query) # documents = SimpleDirectoryReader('data').load_data() # llm_predictor = LLMPredictor(llm=OpenAI(model_name="davinci-instruct-beta:2.0.0")) # # savePath = f'/{os.path.dirname(__file__)}/../indexes/index.json' # # # # index = GPTSimpleVectorIndex(documents)#, llm_predictor=llm_predictor) # # index.save_to_disk(savePath) # # index = GPTSimpleVectorIndex.load_from_disk(savePath) # # # response = index.query("Summarize the vulnerability CVE-2021-23406", response_mode="tree_summarize") # print(response)

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import argparse import logging import sys import re import os import argparse import requests from pathlib import Path from urllib.parse import urlparse from llama_index import ServiceContext, StorageContext from llama_index import set_global_service_context from llama_index import VectorStoreIndex, SimpleDirectoryReader, Document from llama_index.llms import OpenAI from llama_index.readers.file.flat_reader import FlatReader from llama_index.vector_stores import MilvusVectorStore from llama_index.embeddings import HuggingFaceEmbedding from llama_index.node_parser.text import SentenceWindowNodeParser from llama_index.prompts import ChatPromptTemplate, ChatMessage, MessageRole, PromptTemplate from llama_index.postprocessor import MetadataReplacementPostProcessor from llama_index.postprocessor import SentenceTransformerRerank #from llama_index.indices import ZillizCloudPipelineIndex from custom.zilliz.base import ZillizCloudPipelineIndex from llama_index.indices.query.schema import QueryBundle from llama_index.schema import BaseNode, ImageNode, MetadataMode from custom.history_sentence_window import HistorySentenceWindowNodeParser from custom.llms.QwenLLM import QwenUnofficial from custom.llms.GeminiLLM import Gemini from custom.llms.proxy_model import ProxyModel from pymilvus import MilvusClient QA_PROMPT_TMPL_STR = ( "请你仔细阅读相关内容，结合历史资料进行回答,每一条史资料使用'出处：《书名》原文内容'的形式标注 (如果回答请清晰无误地引用原文,先给出回答，再贴上对应的原文，使用《书名》[]对原文进行标识),，如果发现资料无法得到答案，就回答不知道 \n" "搜索的相关历史资料如下所示.\n" "---------------------\n" "{context_str}\n" "---------------------\n" "问题: {query_str}\n" "答案: " ) QA_SYSTEM_PROMPT = "你是一个严谨的历史知识问答智能体，你会仔细阅读历史材料并给出准确的回答,你的回答都会非常准确，因为你在回答的之后，使用在《书名》[]内给出原文用来支撑你回答的证据.并且你会在开头说明原文是否有回答所需的知识" REFINE_PROMPT_TMPL_STR = ( "你是一个历史知识回答修正机器人，你严格按以下方式工作" "1.只有原答案为不知道时才进行修正,否则输出原答案的内容\n" "2.修正的时候为了体现你的精准和客观，你非常喜欢使用《书名》[]将原文展示出来.\n" "3.如果感到疑惑的时候，就用原答案的内容回答。" "新的知识: {context_msg}\n" "问题: {query_str}\n" "原答案: {existing_answer}\n" "新答案: " ) def is_valid_url(url): try: result = urlparse(url) return all([result.scheme, result.netloc]) except ValueError: return False def is_github_folder_url(url): return url.startswith('https://raw.githubusercontent.com/') and '.' not in os.path.basename(url) def get_branch_head_sha(owner, repo, branch): url = f"https://api.github.com/repos/{owner}/{repo}/git/ref/heads/{branch}" response = requests.get(url) data = response.json() sha = data['object']['sha'] return sha def get_github_repo_contents(repo_url): # repo_url example: https://raw.githubusercontent.com/wxywb/history_rag/master/data/history_24/ repo_owner = repo_url.split('/')[3] repo_name = repo_url.split('/')[4] branch = repo_url.split('/')[5] folder_path = '/'.join(repo_url.split('/')[6:]) sha = get_branch_head_sha(repo_owner, repo_name, branch) url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/git/trees/{sha}?recursive=1" try: response = requests.get(url) if response.status_code == 200: data = response.json() raw_urls = [] for file in data['tree']: if file['path'].startswith(folder_path) and file['path'].endswith('.txt'): raw_url = f"https://raw.githubusercontent.com/{repo_owner}/{repo_name}/{branch}/{file['path']}" raw_urls.append(raw_url) return raw_urls else: print(f"Failed to fetch contents. Status code: {response.status_code}") except Exception as e: print(f"Failed to fetch contents. Error: {str(e)}") return [] class Executor: def __init__(self, model): pass def build_index(self, path, overwrite): pass def build_query_engine(self): pass def delete_file(self, path): pass def query(self, question): pass class MilvusExecutor(Executor): def __init__(self, config): self.index = None self.query_engine = None self.config = config self.node_parser = HistorySentenceWindowNodeParser.from_defaults( sentence_splitter=lambda text: re.findall("[^,.;。？！]+[,.;。？！]?", text), window_size=config.milvus.window_size, window_metadata_key="window", original_text_metadata_key="original_text",) embed_model = HuggingFaceEmbedding(model_name=config.embedding.name) # 使用Qwen 通义千问模型 if config.llm.name.find("qwen") != -1: llm = QwenUnofficial(temperature=config.llm.temperature, model=config.llm.name, max_tokens=2048) elif config.llm.name.find("gemini") != -1: llm = Gemini(temperature=config.llm.temperature, model_name=config.llm.name, max_tokens=2048) elif 'proxy_model' in config.llm: llm = ProxyModel(model_name=config.llm.name, api_base=config.llm.api_base, api_key=config.llm.api_key, temperature=config.llm.temperature, max_tokens=2048) print(f"使用{config.llm.name},PROXY_SERVER_URL为{config.llm.api_base},PROXY_API_KEY为{config.llm.api_key}") else: api_base = None if 'api_base' in config.llm: api_base = config.llm.api_base llm = OpenAI(api_base = api_base, temperature=config.llm.temperature, model=config.llm.name, max_tokens=2048) service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model) set_global_service_context(service_context) rerank_k = config.milvus.rerank_topk self.rerank_postprocessor = SentenceTransformerRerank( model=config.rerank.name, top_n=rerank_k) self._milvus_client = None self._debug = False def set_debug(self, mode): self._debug = mode def build_index(self, path, overwrite): config = self.config vector_store = MilvusVectorStore( uri = f"http://{config.milvus.host}:{config.milvus.port}", collection_name = config.milvus.collection_name, overwrite=overwrite, dim=config.embedding.dim) self._milvus_client = vector_store.milvusclient if path.endswith('.txt'): if os.path.exists(path) is False: print(f'(rag) 没有找到文件{path}') return else: documents = FlatReader().load_data(Path(path)) documents[0].metadata['file_name'] = documents[0].metadata['filename'] elif os.path.isfile(path): print('(rag) 目前仅支持txt文件') elif os.path.isdir(path): if os.path.exists(path) is False: print(f'(rag) 没有找到目录{path}') return else: documents = SimpleDirectoryReader(path).load_data() else: return storage_context = StorageContext.from_defaults(vector_store=vector_store) nodes = self.node_parser.get_nodes_from_documents(documents) self.index = VectorStoreIndex(nodes, storage_context=storage_context, show_progress=True) def _get_index(self): config = self.config vector_store = MilvusVectorStore( uri = f"http://{config.milvus.host}:{config.milvus.port}", collection_name = config.milvus.collection_name, dim=config.embedding.dim) self.index = VectorStoreIndex.from_vector_store(vector_store=vector_store) self._milvus_client = vector_store.milvusclient def build_query_engine(self): config = self.config if self.index is None: self._get_index() self.query_engine = self.index.as_query_engine(node_postprocessors=[ self.rerank_postprocessor, MetadataReplacementPostProcessor(target_metadata_key="window") ]) self.query_engine._retriever.similarity_top_k=config.milvus.retrieve_topk message_templates = [ ChatMessage(content=QA_SYSTEM_PROMPT, role=MessageRole.SYSTEM), ChatMessage( content=QA_PROMPT_TMPL_STR, role=MessageRole.USER, ), ] chat_template = ChatPromptTemplate(message_templates=message_templates) self.query_engine.update_prompts( {"response_synthesizer:text_qa_template": chat_template} ) self.query_engine._response_synthesizer._refine_template.conditionals[0][1].message_templates[0].content = REFINE_PROMPT_TMPL_STR def delete_file(self, path): config = self.config if self._milvus_client is None: self._get_index() num_entities_prev = self._milvus_client.query(collection_name='history_rag',filter="",output_fields=["count(*)"])[0]["count(*)"] res = self._milvus_client.delete(collection_name=config.milvus.collection_name, filter=f"file_name=='{path}'") num_entities = self._milvus_client.query(collection_name='history_rag',filter="",output_fields=["count(*)"])[0]["count(*)"] print(f'(rag) 现有{num_entities}条，删除{num_entities_prev - num_entities}条数据') def query(self, question): if self.index is None: self._get_index() if question.endswith('?') or question.endswith('？'): question = question[:-1] if self._debug is True: contexts = self.query_engine.retrieve(QueryBundle(question)) for i, context in enumerate(contexts): print(f'{question}', i) content = context.node.get_content(metadata_mode=MetadataMode.LLM) print(content) print('-------------------------------------------------------参考资料---------------------------------------------------------') response = self.query_engine.query(question) return response class PipelineExecutor(Executor): def __init__(self, config): self.ZILLIZ_CLUSTER_ID = os.getenv("ZILLIZ_CLUSTER_ID") self.ZILLIZ_TOKEN = os.getenv("ZILLIZ_TOKEN") self.ZILLIZ_PROJECT_ID = os.getenv("ZILLIZ_PROJECT_ID") self.ZILLIZ_CLUSTER_ENDPOINT = f"https://{self.ZILLIZ_CLUSTER_ID}.api.gcp-us-west1.zillizcloud.com" self.config = config if len(self.ZILLIZ_CLUSTER_ID) == 0: print('ZILLIZ_CLUSTER_ID 参数为空') exit() if len(self.ZILLIZ_TOKEN) == 0: print('ZILLIZ_TOKEN 参数为空') exit() self.config = config self._debug = False if config.llm.name.find("qwen") != -1: llm = QwenUnofficial(temperature=config.llm.temperature, model=config.llm.name, max_tokens=2048) elif config.llm.name.find("gemini") != -1: llm = Gemini(model_name=config.llm.name, temperature=config.llm.temperature, max_tokens=2048) else: api_base = None if 'api_base' in config.llm: api_base = config.llm.api_base llm = OpenAI(api_base = api_base, temperature=config.llm.temperature, model=config.llm.name, max_tokens=2048) service_context = ServiceContext.from_defaults(llm=llm, embed_model=None) self.service_context = service_context set_global_service_context(service_context) self._initialize_pipeline(service_context) #rerank_k = config.rerankl #self.rerank_postprocessor = SentenceTransformerRerank( # model="BAAI/bge-reranker-large", top_n=rerank_k) def set_debug(self, mode): self._debug = mode def _initialize_pipeline(self, service_context: ServiceContext): config = self.config try: self.index = ZillizCloudPipelineIndex( project_id = self.ZILLIZ_PROJECT_ID, cluster_id=self.ZILLIZ_CLUSTER_ID, token=self.ZILLIZ_TOKEN, collection_name=config.pipeline.collection_name, service_context=service_context, ) if len(self._list_pipeline_ids()) == 0: self.index.create_pipelines( metadata_schema={"digest_from":"VarChar"}, chunk_size=self.config.pipeline.chunk_size ) except Exception as e: print('(rag) zilliz pipeline 连接异常', str(e)) exit() try: self._milvus_client = MilvusClient( uri=self.ZILLIZ_CLUSTER_ENDPOINT, token=self.ZILLIZ_TOKEN ) except Exception as e: print('(rag) zilliz cloud 连接异常', str(e)) def build_index(self, path, overwrite): config = self.config if not is_valid_url(path) or 'github' not in path: print('(rag) 不是一个合法的url，请尝试`https://raw.githubusercontent.com/wxywb/history_rag/master/data/history_24/baihuasanguozhi.txt`') return if overwrite == True: self._milvus_client.drop_collection(config.pipeline.collection_name) pipeline_ids = self._list_pipeline_ids() self._delete_pipeline_ids(pipeline_ids) self._initialize_pipeline(self.service_context) if is_github_folder_url(path): urls = get_github_repo_contents(path) for url in urls: print(f'(rag) 正在构建索引 {url}') self.build_index(url, False) # already deleted original collection elif path.endswith('.txt'): self.index.insert_doc_url( url=path, metadata={"digest_from": HistorySentenceWindowNodeParser.book_name(os.path.basename(path))}, ) else: print('(rag) 只有github上以txt结尾或文件夹可以被支持。') def build_query_engine(self): config = self.config self.query_engine = self.index.as_query_engine( search_top_k=config.pipeline.retrieve_topk) message_templates = [ ChatMessage(content=QA_SYSTEM_PROMPT, role=MessageRole.SYSTEM), ChatMessage( content=QA_PROMPT_TMPL_STR, role=MessageRole.USER, ), ] chat_template = ChatPromptTemplate(message_templates=message_templates) self.query_engine.update_prompts( {"response_synthesizer:text_qa_template": chat_template} ) self.query_engine._response_synthesizer._refine_template.conditionals[0][1].message_templates[0].content = REFINE_PROMPT_TMPL_STR def delete_file(self, path): config = self.config if self._milvus_client is None: self._get_index() num_entities_prev = self._milvus_client.query(collection_name='history_rag',filter="",output_fields=["count(*)"])[0]["count(*)"] res = self._milvus_client.delete(collection_name=config.milvus.collection_name, filter=f"doc_name=='{path}'") num_entities = self._milvus_client.query(collection_name='history_rag',filter="",output_fields=["count(*)"])[0]["count(*)"] print(f'(rag) 现有{num_entities}条，删除{num_entities_prev - num_entities}条数据') def query(self, question): if self.index is None: self.get_index() if question.endswith("?") or question.endswith("？"): question = question[:-1] if self._debug is True: contexts = self.query_engine.retrieve(QueryBundle(question)) for i, context in enumerate(contexts): print(f'{question}', i) content = context.node.get_content(metadata_mode=MetadataMode.LLM) print(content) print('-------------------------------------------------------参考资料---------------------------------------------------------') response = self.query_engine.query(question) return response def _list_pipeline_ids(self): url = f"https://controller.api.gcp-us-west1.zillizcloud.com/v1/pipelines?projectId={self.ZILLIZ_PROJECT_ID}" headers = { "Authorization": f"Bearer {self.ZILLIZ_TOKEN}", "Accept": "application/json", "Content-Type": "application/json", } collection_name = self.config.milvus.collection_name response = requests.get(url, headers=headers) if response.status_code != 200: raise RuntimeError(response.text) response_dict = response.json() if response_dict["code"] != 200: raise RuntimeError(response_dict) pipeline_ids = [] for pipeline in response_dict['data']: if collection_name in pipeline['name']: pipeline_ids.append(pipeline['pipelineId']) return pipeline_ids def _delete_pipeline_ids(self, pipeline_ids): for pipeline_id in pipeline_ids: url = f"https://controller.api.gcp-us-west1.zillizcloud.com/v1/pipelines/{pipeline_id}/" headers = { "Authorization": f"Bearer {self.ZILLIZ_TOKEN}", "Accept": "application/json", "Content-Type": "application/json", } response = requests.delete(url, headers=headers) if response.status_code != 200: raise RuntimeError(response.text)

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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: 2024.02.28 02:00:00 # # ================================================== # 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 llama_index.core.indices.vector_store.base import VectorStoreIndex from pygpt_net.provider.vector_stores.base import BaseStore # <--- vector store must inherit from BaseStore class ExampleVectorStore(BaseStore): def __init__(self, *args, **kwargs): super(ExampleVectorStore, self).__init__(*args, **kwargs) """ Example vector store provider. This example is based on the `SimpleProvider` (SimpleVectorStore) from the `pygpt_net.provider.vector_stores.simple`. See `pygpt_net.provider.vector_stores` for more examples. The rest of the shared methods (like `exists`, `delete`, `truncate`, etc.) are declared in the base class: `BaseStore`. :param args: args :param kwargs: kwargs """ self.window = kwargs.get('window', None) self.id = "example_store" # identifier must be unique self.prefix = "example_" # prefix for index config files subdirectory in "idx" directory in %workdir% self.indexes = {} # indexes cache dictionary (in-memory) def create(self, id: str): """ Create the empty index with the provided `id` (`base` is default) In this example, we create an empty index with the name `id` and store it in the `self.indexes` dictionary. Example is a simple copy of the `SimpleVectorStore` provider. The `create` method is called when the index does not exist. See `pygpt_net.core.idx` for more details how it is handled internally. :param id: index name """ path = self.get_path(id) # get path for the index configuration, declared in the `BaseStore` class # check if index does not exist on disk and create it if not exists if not os.path.exists(path): index = VectorStoreIndex([]) # create empty index # store the index on disk self.store( id=id, index=index, ) def get(self, id: str, service_context: ServiceContext = None) -> BaseIndex: """ Get the index instance with the provided `id` (`base` is default) In this example, we get the index with the name `id` from the `self.indexes` dictionary. The `get` method is called when getting the index instance. It must return the `BaseIndex` index instance. See `pygpt_net.core.idx` for more details how it is handled internally. :param id: index name :param service_context: Service context :return: index instance """ # check if index exists on disk and load it if not self.exists(id): # if index does not exist, then create it self.create(id) # get path for the index configuration on disk (in "%workdir%/idx" directory) path = self.get_path(id) # get the storage context storage_context = StorageContext.from_defaults( persist_dir=path, ) # load index from storage and update it in the `self.indexes` dictionary self.indexes[id] = load_index_from_storage( storage_context, service_context=service_context, ) # return the index instance return self.indexes[id] def store(self, id: str, index: BaseIndex = None): """ Store (persist) the index instance with the provided `id` (`base` is default) In this example, we store the index with the name `id` in the `self.indexes` dictionary. The `store` method is called when storing (persisting) index to disk/db. It must provide logic to store the index in the storage. See `pygpt_net.core.idx` for more details how it is handled internally. :param id: index name :param index: index instance """ # prepare the index instance if index is None: index = self.indexes[id] # get path for the index configuration on disk (in "%workdir%/idx" directory) path = self.get_path(id) # persist the index on disk index.storage_context.persist( persist_dir=path, ) # update the index in the `self.indexes` dictionary self.indexes[id] = index

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from __future__ import annotations from typing import Optional import os from llama_index.core import ServiceContext from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding from llama_index.embeddings.openai import OpenAIEmbedding from llama_index.llms.azure_openai import AzureOpenAI from llama_index.core.llms import OpenAI as LlamaIndexOpenAI from llama_index.core.llms.llm import LLM # noqa: TCH002 from llama_index.core.llms.openai_utils import ALL_AVAILABLE_MODELS, CHAT_MODELS from openssa.utils.config import Config # import sys # import logging # logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) # logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) # Add the extended models to the list of available models in LlamaIndex _EXTENDED_CHAT_MODELS = { "01-ai/Yi-34B-Chat": 4096, "Intel/neural-chat-7b-v3-1": 4096, "llama2-70b": 4096, "llama2-13b": 4096, "llama2-7b": 4096, } ALL_AVAILABLE_MODELS.update(_EXTENDED_CHAT_MODELS) CHAT_MODELS.update(_EXTENDED_CHAT_MODELS) # TODO: there should be a single Aitomatic api_base and api_key Config.AITOMATIC_API_KEY: Optional[str] = os.environ.get("AITOMATIC_API_KEY") Config.AITOMATIC_API_URL: Optional[str] = ( os.environ.get("AITOMATIC_API_URL") or "https://aimo-api-mvp.platform.aitomatic.com/api/v1" ) Config.AITOMATIC_API_URL_7B: Optional[str] = ( os.environ.get("AITOMATIC_API_URL_7B") or "https://llama2-7b.lepton.run/api/v1" ) Config.AITOMATIC_API_URL_70B: Optional[str] = ( os.environ.get("AITOMATIC_API_URL_70B") or "https://llama2-70b.lepton.run/api/v1" ) Config.OPENAI_API_KEY: Optional[str] = os.environ.get("OPENAI_API_KEY") Config.OPENAI_API_URL: Optional[str] = ( os.environ.get("OPENAI_API_URL") or "https://api.openai.com/v1" ) Config.AZURE_OPENAI_API_KEY: Optional[str] = os.environ.get("AZURE_OPENAI_API_KEY") Config.AZURE_OPENAI_API_URL: Optional[str] = ( os.environ.get("AZURE_OPENAI_API_URL") or "https://aiva-japan.openai.azure.com" ) Config.LEPTON_API_KEY: Optional[str] = os.environ.get("LEPTON_API_KEY") Config.LEPTON_API_URL: Optional[str] = ( os.environ.get("LEPTON_API_URL") or "https://llama2-7b.lepton.run/api/v1" ) class LlamaIndexApi: # no-pylint: disable=too-many-public-methods class LLMs: """ This class represents the LLMs from different services """ class _AnOpenAIAPIProvider: """ This class represents an OpenAI-API provider """ @classmethod def _get(cls, model=None, api_base=None, api_key=None, additional_kwargs=None) -> LLM: if model is None: if api_base is None: llm = LlamaIndexOpenAI(api_key=api_key, additional_kwargs=additional_kwargs) else: llm = LlamaIndexOpenAI(api_key=api_key, additional_kwargs=additional_kwargs) elif api_base is None: llm = LlamaIndexOpenAI(api_key=api_key, additional_kwargs=additional_kwargs) else: llm = LlamaIndexOpenAI(model=model, api_base=api_base, api_key=api_key) # Forcibly set the get_openai method to the _get_client method llm.__dict__['get_openai'] = llm._get_client # pylint: disable=protected-access return llm class Aitomatic(_AnOpenAIAPIProvider): """ This class represents the Aitomatic-hosted LLMs """ @classmethod def get(cls, model=None, api_base=None, api_key=None, additional_kwargs=None) -> LLM: if model is None: model = "llama2-7b" if api_key is None: api_key = Config.AITOMATIC_API_KEY return super()._get(model=model, api_base=api_base, api_key=api_key, additional_kwargs=additional_kwargs) @classmethod def get_llama2_70b(cls) -> LLM: # TODO: there should be a single Aitomatic api_base and api_key llm = cls.get( model="llama2-70b", api_base=Config.AITOMATIC_API_URL_70B, api_key=Config.LEPTON_API_KEY, ) return llm @classmethod def get_llama2_7b(cls) -> LLM: # TODO: there should be a single Aitomatic api_base and api_key llm = cls.get( model="llama2-7b", api_base=Config.AITOMATIC_API_URL, api_key=Config.LEPTON_API_KEY, ) return llm @classmethod def get_13b(cls) -> LLM: # TODO: there should be a single Aitomatic api_base and api_key # not running llm = cls.get( model="gpt-3.5-turbo-0613", api_base="http://35.199.34.91:8000/v1", additional_kwargs={"stop": "\n"}, ) return llm @classmethod def get_yi_34b(cls) -> LLM: # running llm = cls.get( model="01-ai/Yi-34B-Chat", api_base="http://35.230.174.89:8000/v1", additional_kwargs={"stop": "\n###"}, ) return llm @classmethod def get_intel_neural_chat_7b(cls) -> LLM: # running llm = cls.get( model="Intel/neural-chat-7b-v3-1", api_base="http://34.145.174.152:8000/v1", ) return llm @classmethod def get_aimo(cls): llm = cls.get(api_base=os.environ.get("AIMO_STANDARD_URL_BASE")) return llm class OpenAI(_AnOpenAIAPIProvider): """ This class represents the OpenAI-hosted LLMs """ @classmethod def get(cls, model=None) -> LLM: if model is None: model = "gpt-3.5-turbo-1106" return super()._get(model=model, api_key=Config.OPENAI_API_KEY) @classmethod def get_gpt_35_turbo_1106(cls) -> LLM: return cls.get(model="gpt-3.5-turbo-1106") @classmethod def get_gpt_35_turbo_0613(cls) -> LLM: return cls.get(model="gpt-3.5-turbo") @classmethod def get_gpt_35_turbo(cls) -> LLM: return cls.get(model="gpt-3.5-turbo-0613") @classmethod def get_gpt_4(cls) -> LLM: return cls.get(model="gpt-4") class Azure: """ This class represents the Azure-hosted LLMs """ @classmethod def _get(cls, model=None, engine=None, api_base=None) -> LLM: if model is None: model = "gpt-35-turbo-16k" if engine is None: engine = "aiva-dev-gpt35" if api_base is None: api_base = Config.AZURE_OPENAI_API_URL return AzureOpenAI( engine=model, model=model, temperature=0.0, api_version="2023-09-01-preview", api_key=Config.AZURE_OPENAI_API_KEY, azure_endpoint=api_base, ) @classmethod def get(cls) -> LLM: return cls.get_gpt_35() @classmethod def get_gpt_35(cls) -> LLM: return cls._get(model="gpt-35-turbo") @classmethod def get_gpt_35_16k(cls) -> LLM: return cls._get(model="gpt-35-turbo-16k") @classmethod def get_gpt_4(cls) -> LLM: return cls.get_gpt_4_32k() @classmethod def get_gpt_4_32k(cls) -> LLM: return cls._get(model="gpt-4-32k") class Embeddings: """ This class represents the different embedding services """ class Aitomatic: """ This class represents the Aitomatic-hosted embedding service """ @classmethod def _get(cls, api_base=None, api_key=None) -> OpenAIEmbedding: if api_key is None: api_key = Config.AITOMATIC_API_KEY return OpenAIEmbedding(api_base=api_base, api_key=api_key) @classmethod def get(cls) -> OpenAIEmbedding: # running return cls._get(api_base=Config.AITOMATIC_API_URL) @classmethod def get_llama2_7b(cls) -> OpenAIEmbedding: return cls._get(api_base=Config.AITOMATIC_API_URL_7B) @classmethod def get_llama2_70b(cls) -> OpenAIEmbedding: return cls._get(api_base=Config.AITOMATIC_API_URL_70B) class OpenAI: """ This class represents the OpenAI-hosted embedding service """ @classmethod def get(cls) -> OpenAIEmbedding: return OpenAIEmbedding(api_key=Config.OPENAI_API_KEY) class Azure: """ This class represents the Azure-hosted embedding service """ @classmethod def get(cls) -> AzureOpenAIEmbedding: return AzureOpenAIEmbedding( model="text-embedding-ada-002", deployment_name="text-embedding-ada-002", api_key=Config.AZURE_OPENAI_API_KEY, api_version="2023-09-01-preview", azure_endpoint=Config.AZURE_OPENAI_API_URL, ) class ServiceContexts: """ This class represents the service contexts for different models. """ class _AServiceContextHelper: """ This class represents the service contexts for the different embedding services. """ @classmethod def _get(cls, llm=None, embedding=None) -> ServiceContext: sc = ServiceContext.from_defaults(llm=llm, embed_model=embedding) return sc class Aitomatic(_AServiceContextHelper): """ This class represents the service contexts for the Aitomatic-hosted models. """ @classmethod def get_llama2_7b(cls) -> ServiceContext: llm = LlamaIndexApi.LLMs.Aitomatic.get_llama2_7b() embedding = LlamaIndexApi.Embeddings.Aitomatic.get_llama2_7b() return cls._get(llm=llm, embedding=embedding) @classmethod def get_llama_2_70b(cls) -> ServiceContext: llm = LlamaIndexApi.LLMs.Aitomatic.get_llama2_7b() embedding = LlamaIndexApi.Embeddings.Aitomatic.get_llama2_70b() return cls._get(llm=llm, embedding=embedding) class OpenAI(_AServiceContextHelper): """ This class represents the service contexts for the OpenAI-hosted models. """ @classmethod def get_gpt_35_turbo_1106(cls) -> ServiceContext: llm = LlamaIndexApi.LLMs.OpenAI.get_gpt_35_turbo_1106() embedding = LlamaIndexApi.Embeddings.OpenAI.get() return cls._get(llm=llm, embedding=embedding) @classmethod def get_gpt_35_turbo(cls) -> ServiceContext: llm = LlamaIndexApi.LLMs.OpenAI.get_gpt_35_turbo() embedding = LlamaIndexApi.Embeddings.OpenAI.get() return cls._get(llm=llm, embedding=embedding) class Azure(_AServiceContextHelper): """ This class represents the service contexts for the Azure-hosted models. """ @classmethod def get(cls) -> ServiceContext: return cls.get_gpt_35() @classmethod def get_gpt_35(cls) -> ServiceContext: llm = LlamaIndexApi.LLMs.Azure.get_gpt_35() embedding = LlamaIndexApi.Embeddings.Azure.get() return cls._get(llm=llm, embedding=embedding) @classmethod def get_gpt_35_16k(cls) -> ServiceContext: llm = LlamaIndexApi.LLMs.Azure.get_gpt_35_16k() embedding = LlamaIndexApi.Embeddings.Azure.get() return cls._get(llm=llm, embedding=embedding) @classmethod def get_gpt4(cls) -> ServiceContext: llm = LlamaIndexApi.LLMs.Azure.get_gpt_4() embedding = LlamaIndexApi.Embeddings.Azure.get() return cls._get(llm=llm, embedding=embedding) @classmethod def get_gpt4_32k(cls) -> ServiceContext: llm = LlamaIndexApi.LLMs.Azure.get_gpt_4_32k() embedding = LlamaIndexApi.Embeddings.Azure.get() return cls._get(llm=llm, embedding=embedding) # Convenience methods get_aitomatic_llm = LLMs.Aitomatic.get get_openai_llm = LLMs.OpenAI.get get_azure_llm = LLMs.Azure.get

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from llama_index import SimpleDirectoryReader, LLMPredictor, ServiceContext, GPTVectorStoreIndex from langchain.chat_models import ChatOpenAI from dotenv import load_dotenv import os import graphsignal import logging import time import random load_dotenv() logging.basicConfig() logger = logging.getLogger() logger.setLevel(logging.DEBUG) graphsignal.configure(api_key=os.getenv('GRAPHSIGNAL_API_KEY'), deployment='DevSecOpsKB') # set context window context_window = 4096 # set number of output tokens num_output = 512 #LLMPredictor is a wrapper class around LangChain's LLMChain that allows easy integration into LlamaIndex llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=num_output)) #constructs service_context service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, context_window=context_window, num_output=num_output) #set the global service context object from llama_index import set_global_service_context set_global_service_context(service_context) #loads data from the specified directory path documents = SimpleDirectoryReader("./data").load_data() #when first building the index index = GPTVectorStoreIndex.from_documents(documents) def data_querying(input_text): #queries the index with the input text response = index.as_query_engine().query(input_text) return response.response # predefine a list of 10 questions questions = [ 'what does Trivy image scan do?', 'What are the main benefits of using Harden Runner?', 'What is the 3-2-1 rule in DevOps self-service model?', 'What is Infracost? and what does it do?', 'What is the terraform command to auto generate README?', 'How to pin Terraform module source to a particular branch?', 'What are the benefits of reusable Terraform modules?', 'How do I resolve error "npm ERR! code E400"?', 'How to fix error "NoCredentialProviders: no valid providers in chain"?', 'How to fix error "Credentials could not be loaded, please check your action inputs: Could not load credentials from any providers"?' ] start_time = time.time() while time.time() - start_time < 1800: # let it run for 30 minutes (1800 seconds) try: num = random.randint(0, len(questions) - 1) print("Question: ", questions[num]) answer = data_querying(questions[num]) print("Answer: ", answer) except: logger.error("Error during data query", exc_info=True) time.sleep(5 * random.random())

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# Ref https://github.com/amrrs/QABot-LangChain/blob/main/Q%26A_Bot_with_Llama_Index_and_LangChain.ipynb #from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper from langchain import OpenAI from llama_index import SimpleDirectoryReader, LangchainEmbedding, GPTListIndex,GPTSimpleVectorIndex, PromptHelper from llama_index import LLMPredictor, ServiceContext import sys import os def construct_index(directory_path): # set maximum input size max_input_size = 4096 # set number of output tokens num_outputs = 256 # set maximum chunk overlap max_chunk_overlap = 20 # set chunk size limit chunk_size_limit = 600 prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit) # define LLM llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="text-davinci-002", max_tokens=num_outputs)) documents = SimpleDirectoryReader(directory_path).load_data() service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor) index_obj = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context) index_obj.save_to_disk('model/index.json') return index_obj def ask_bot(input_index='model/index.json'): index_obj = GPTSimpleVectorIndex.load_from_disk(input_index) while True: query = input('What do you want to ask the bot? \n') if query == "nothing": return response = index_obj.query(query, response_mode="compact") print("\nBot says: \n\n" + response.response + "\n\n\n") index = construct_index("data/") ask_bot('model/index.json')

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# chroma.py import streamlit as st import os import re from pathlib import Path import chromadb from chromadb.config import Settings from llama_index import GPTVectorStoreIndex, load_index_from_storage from llama_index.vector_stores import ChromaVectorStore from utils.model_settings import sentenceTransformers, get_service_context, get_embed_model import logging from utils.qa_template import QA_PROMPT from llama_index.storage.storage_context import StorageContext def get_collection_index_path(collection): return (f'./data/{collection}-index.json') # INDEX_PATH = './data/chroma_index.json' PERSIST_DIRECTORY = './data/chromadb' service_context = get_service_context() @st.cache_resource def create_chroma_client(): return chromadb.Client(Settings(chroma_db_impl="chromadb.db.duckdb.PersistentDuckDB",persist_directory=PERSIST_DIRECTORY, anonymized_telemetry=False)) def get_chroma_collection(collection_name): client = create_chroma_client() try: return client.get_collection(collection_name) except Exception as e: logging.error(f"Failed to get collection '{collection_name}': {e}") return None @st.cache_resource def load_chroma_index(collection): # collection_index_path = get_collection_index_path(collection) _chroma_collection = get_chroma_collection(collection) vector_store = ChromaVectorStore(chroma_collection=_chroma_collection) storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIRECTORY, vector_store=vector_store) if Path(PERSIST_DIRECTORY).exists(): index = load_index_from_storage(storage_context, service_context=service_context) logging.info('Index loaded for collection ' + collection ) else: index = None return index # def build_chroma_index(documents, collection, reindex=False, chunk_size_limit=512, model_name='sentence-transformers/all-MiniLM-L6-v2'): # collection_index_path = get_collection_index_path(collection) # chroma_client = create_chroma_client() # if reindex is True: # chroma_client.delete_collection(collection) # os.remove(get_collection_index_path(collection)) # _chroma_collection = chroma_client.get_or_create_collection(collection) # index = None # index = GPTChromaIndex.from_documents(documents, chroma_collection=_chroma_collection, # service_context=get_service_context(embed_model=get_embed_model(model_name), chunk_size_limit=chunk_size_limit) # ) # index.save_to_disk(collection_index_path) # chroma_client.persist() def create_or_refresh_chroma_index(documents, collection, reindex=False, chunk_size_limit=512, model_name='sentence-transformers/all-MiniLM-L6-v2'): collection_index_path = get_collection_index_path(collection) chroma_client = create_chroma_client() if reindex is True: logging.info(chroma_client.list_collections()) if collection in chroma_client.list_collections(): chroma_client.delete_collection(collection) _chroma_collection = chroma_client.get_or_create_collection(collection) vector_store = ChromaVectorStore(chroma_collection=_chroma_collection) storage_context = StorageContext.from_defaults(vector_store=vector_store) index = None index = GPTVectorStoreIndex.from_documents(documents, storage_context=storage_context, service_context=get_service_context(embed_model=get_embed_model(model_name=model_name), chunk_size_limit=chunk_size_limit) ) index.storage_context.persist(persist_dir=PERSIST_DIRECTORY) chroma_client.persist() else: refresh_chroma_index(documents, collection) def refresh_chroma_index(documents, collection): index = load_chroma_index(collection) logging.info('refreshing collection ' + collection) refreshed_docs = index.refresh(documents) chroma_client = create_chroma_client() chroma_client.persist() return refreshed_docs def query_index(query_str, collection, similarity_top_k=5, response_mode='compact', streaming=False, model_name=sentenceTransformers.OPTION1.value): index = None _chroma_collection = get_chroma_collection(collection) index = load_chroma_index(collection) query_engine = index.as_query_engine(chroma_collection=_chroma_collection, mode="embedding", similarity_top_k=similarity_top_k, response_mode=response_mode, # default, compact, tree_summarize, no_text service_context=get_service_context(embed_model=get_embed_model(model_name=model_name)), text_qa_template=QA_PROMPT, verbose= True, use_async= True, streaming= streaming ) return query_engine.query(query_str) def persist_chroma_index(): chroma_client = create_chroma_client() chroma_client.persist() def generate_chroma_compliant_name(name: str) -> str: # Replace non-alphanumeric characters with underscores new_name = re.sub(r"[^a-zA-Z0-9_\-\.]", "_", name) # Replace consecutive periods with a single underscore new_name = re.sub(r"\.{2,}", "_", new_name) # Ensure the name starts and ends with an alphanumeric character if not new_name[0].isalnum(): new_name = "a" + new_name[1:] if not new_name[-1].isalnum(): new_name = new_name[:-1] + "a" # Truncate or pad the name to be between 3 and 63 characters new_name = new_name[:63] while len(new_name) < 3: new_name += "0" return new_name

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# /app/src/tools/doc_search.py import logging # Primary Components from llama_index import ServiceContext, VectorStoreIndex from llama_index.vector_stores.qdrant import QdrantVectorStore from qdrant_client import QdrantClient from src.utils.config import load_config, setup_environment_variables from src.utils.embedding_selector import EmbeddingConfig, EmbeddingSelector logger = logging.getLogger(__name__) class DocumentSearch: """ Class to perform document searches using a vector store index. Attributes: - collection (str): Name of the collection to be queried. - query (str): User input query for searching documents. - CONFIG (dict): Loaded configuration settings. - client (QdrantClient): Client to interact with the Qdrant service. """ def __init__(self, query: str, collection: str): """ Initializes with collection name and user input. Parameters: - collection (str): Name of the collection to be queried. - query (str): User input query for searching documents. """ self.collection = collection self.query = query self.CONFIG = load_config() setup_environment_variables(self.CONFIG) self.client = QdrantClient(url="http://RAG_BOT_QDRANT:6333") # self.embed_model = OpenAIEmbedding() # self.embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/multi-qa-mpnet-base-dot-v1") self.embedding_config = EmbeddingConfig(type=self.CONFIG["Embedding_Type"]) self.embed_model = EmbeddingSelector(self.embedding_config).get_embedding_model() def setup_index(self) -> VectorStoreIndex: """ Sets up and returns the vector store index for the collection. Returns: - VectorStoreIndex: The set up vector store index. Raises: - Exception: Propagates any exceptions that occur during the index setup. """ try: vector_store = QdrantVectorStore(client=self.client, collection_name=self.collection) service_context = ServiceContext.from_defaults(embed_model=self.embed_model) index = VectorStoreIndex.from_vector_store(vector_store=vector_store, service_context=service_context) return index except Exception as e: logging.error(f"setup_index: Error - {str(e)}") raise e def search_documents(self): """ Searches and returns documents based on the user input query. Returns: - Any: The response received from querying the index. Raises: - Exception: Propagates any exceptions that occur during the document search. """ try: query_engine = (self.setup_index()).as_query_engine() response = query_engine.query(self.query) logging.info(f"search_documents: Response - {response}") return response except Exception as e: logging.error(f"search_documents: Error - {str(e)}") raise e

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import logging import traceback from typing import Sequence, List, Optional, Dict from llama_index import Document from llama_index.callbacks import CBEventType, CallbackManager from llama_index.callbacks.schema import EventPayload from llama_index.node_parser import NodeParser, SimpleNodeParser from llama_index.node_parser.extractors import MetadataExtractor from llama_index.schema import BaseNode, MetadataMode, TextNode, NodeRelationship from llama_index.text_splitter import TokenTextSplitter, SplitterType, get_default_text_splitter from llama_index.utils import get_tqdm_iterable from pydantic import Field from ghostcoder.codeblocks import create_parser, CodeBlock, CodeBlockType from ghostcoder.utils import count_tokens class CodeNodeParser(NodeParser): """Route to the right node parser depending on language set in document metadata""" text_splitter: SplitterType = Field( description="The text splitter to use when splitting documents." ) include_metadata: bool = Field( default=True, description="Whether or not to consider metadata when splitting." ) include_prev_next_rel: bool = Field( default=True, description="Include prev/next node relationships." ) metadata_extractor: Optional[MetadataExtractor] = Field( default=None, description="Metadata extraction pipeline to apply to nodes." ) callback_manager: CallbackManager = Field( default_factory=CallbackManager, exclude=True ) @classmethod def from_defaults( cls, chunk_size: Optional[int] = None, chunk_overlap: Optional[int] = None, text_splitter: Optional[SplitterType] = None, include_metadata: bool = True, include_prev_next_rel: bool = True, callback_manager: Optional[CallbackManager] = None, metadata_extractor: Optional[MetadataExtractor] = None, ) -> "CodeNodeParser": callback_manager = callback_manager or CallbackManager([]) text_splitter = text_splitter or get_default_text_splitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap, callback_manager=callback_manager, ) return cls( text_splitter=text_splitter, include_metadata=include_metadata, include_prev_next_rel=include_prev_next_rel, callback_manager=callback_manager, metadata_extractor=metadata_extractor, _node_parser_map={} ) @classmethod def class_name(cls): return "CodeNodeParser" def get_nodes_from_documents( self, documents: Sequence[Document], show_progress: bool = False, ) -> List[BaseNode]: with self.callback_manager.event( CBEventType.NODE_PARSING, payload={EventPayload.DOCUMENTS: documents} ) as event: documents_with_progress = get_tqdm_iterable( documents, show_progress, "Parsing documents into nodes" ) all_nodes: List[BaseNode] = [] for document in documents_with_progress: language = document.metadata.get("language", None) if language: try: parser = create_parser(language) except Exception as e: logging.warning(f"Could not get parser for language {language}. Will not parse document {document.id_}") continue content = document.get_content(metadata_mode=MetadataMode.NONE) if not content: logging.warning(f"Could not get content for document {document.id_}") continue codeblock = parser.parse(content) logging.debug(codeblock.to_tree(include_tree_sitter_type=False, show_tokens=True, include_types=[CodeBlockType.FUNCTION, CodeBlockType.CLASS])) splitted_blocks = codeblock.split_blocks() for splitted_block in splitted_blocks: definitions, parent = self.get_parent_and_definitions(splitted_block) node_metadata = document.metadata node_metadata["definition"] = splitted_block.content node_metadata["block_type"] = str(splitted_block.type) if splitted_block.identifier: node_metadata["identifier"] = splitted_block.identifier else: node_metadata["identifier"] = splitted_block.content[:80].replace("\n", "\\n") node_metadata["start_line"] = splitted_block.start_line tokens = count_tokens(parent.to_string()) if tokens > 4000: logging.info(f"Skip node [{node_metadata['identifier']}] in {document.id_} with {tokens} tokens") continue if tokens > 1000: logging.info(f"Big node [{node_metadata['identifier']}] in {document.id_} with {tokens} tokens") # TODO: Add relationships between code blocks node = TextNode( text=parent.to_string(), embedding=document.embedding, metadata=node_metadata, excluded_embed_metadata_keys=document.excluded_embed_metadata_keys, excluded_llm_metadata_keys=document.excluded_llm_metadata_keys, metadata_seperator=document.metadata_seperator, metadata_template=document.metadata_template, text_template=document.text_template, relationships={NodeRelationship.SOURCE: document.as_related_node_info()}, ) all_nodes.append(node) event.on_end(payload={EventPayload.NODES: all_nodes}) return all_nodes def get_parent_and_definitions(self, codeblock: CodeBlock) -> (List[str], CodeBlock): definitions = [codeblock.content] if codeblock.parent: parent_defs, parent = self.get_parent_and_definitions(codeblock.parent) definitions.extend(parent_defs) return definitions, parent else: return definitions, codeblock

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# RAG/TAG Tiger - llm.py # Copyright (c) 2024 Stuart Riffle # github.com/stuartriffle/ragtag-tiger import os import torch from .files import * from .lograg import lograg, lograg_verbose, lograg_error from .timer import TimerUntil openai_model_default = "gpt-3.5-turbo-instruct" google_model_default = "models/text-bison-001" anthropic_model_default = "claude-2" mistral_default = "mistral-small" perplexity_default = "llama-2-70b-chat" replicate_default = "mistralai/mixtral-8x7b-instruct-v0.1" fireworks_ai_default = "accounts/fireworks/models/mixtral-8x7b-instruct" together_ai_default = "codellama/CodeLlama-70b-Instruct-hf" default_timeout = 180 default_temperature = 0.1 default_max_tokens = 500 default_llm_provider = "huggingface" hf_model_nicknames = { "default": "codellama/CodeLlama-7b-Instruct-hf" } def load_llm(provider, model, server, api_key, params, global_params, verbose=False, set_service_context=True, torch_device=None): result = None streaming_supported = True try: with TimerUntil("ready"): all_params = global_params.copy() model_params = dict([param.split("=") for param in params]) if params else {} for k, v in model_params.items(): all_params[k] = v model_kwargs = {} for k, v in all_params.items(): model_kwargs[k] = float(v) if v.replace(".", "", 1).isdigit() else v temperature = float(model_kwargs.get("temperature", default_temperature)) max_tokens = int(model_kwargs.get("max_tokens", default_max_tokens)) ### OpenAI if provider == "openai" and not server: model_name = model or openai_model_default api_key = api_key or os.environ.get("OPENAI_API_KEY", "") lograg(f"OpenAI model \"{model_name}\"...") from llama_index.llms import OpenAI result = OpenAI( model=model_name, timeout=default_timeout, api_key=api_key, additional_kwargs=model_kwargs, temperature=temperature, max_tokens=max_tokens, verbose=verbose) ### OpenAI API-compatible third party server elif provider == "openai" and server: # Auto-populate API key and model for known providers if "together.ai" in server or "together.xyz" in server: api_key = api_key or os.environ.get("TOGETHERAI_API_KEY", "") model = model or together_ai_default if "fireworks.ai" in server: api_key = api_key or os.environ.get("FIREWORKS_API_KEY", "") model = model or fireworks_ai_default api_key = api_key or os.environ.get("OPENAI_API_KEY", "") model_name = model or "default" lograg(f"Model \"{model_name}\" on \"{server}\"...") from llama_index.llms import OpenAILike result = OpenAILike( api_key=api_key, model=model_name, additional_kwargs=model_kwargs, api_base=server, max_iterations=100, timeout=default_timeout, max_tokens=max_tokens, temperature=temperature, verbose=verbose) ### Google elif provider == "google": gemini_api_key = os.environ.get("GEMINI_API_KEY", "") google_api_key = os.environ.get("GOOGLE_API_KEY", "") model_name = model or google_model_default import google.generativeai as genai genai.configure(api_key=google_api_key) if "gemini" in str(model_name).lower(): lograg(f"Google Gemini model \"{model_name}\"...") from llama_index.llms import Gemini result = Gemini( api_key=api_key or gemini_api_key, model_name=model_name, max_tokens=max_tokens, temperature=temperature, model_kwargs=model_kwargs) else: lograg(f"Google PaLM model \"{model_name}\"...") from llama_index.llms import PaLM result = PaLM( api_key=api_key or google_api_key, model_name=model_name, generate_kwargs=model_kwargs) streaming_supported = False ### Llama.cpp elif provider == "llamacpp": if torch.cuda.is_available(): # FIXME - this does nothing? Always on CPU model_kwargs["n_gpu_layers"] = -1 lograg(f"llama.cpp model \"{cleanpath(model)}\"...") from llama_index.llms import LlamaCPP result = LlamaCPP( model_path=model, model_kwargs=model_kwargs, max_new_tokens=max_tokens, temperature=temperature, verbose=verbose) ### Mistral elif provider == "mistral": api_key = api_key or os.environ.get("MISTRAL_API_KEY", None) model_name = model or mistral_default lograg(f"Mistral model \"{model_name}\"...") from llama_index.llms import MistralAI result = MistralAI( api_key=api_key, model=model_name, max_tokens=max_tokens, temperature=temperature, additional_kwargs=model_kwargs) ### Perplexity elif provider == "perplexity": api_key = api_key or os.environ.get("PERPLEXITYAI_API_KEY", "") model_name = model or perplexity_default lograg(f"Perplexity model \"{model_name}\"...") from llama_index.llms import Perplexity result = Perplexity( api_key=api_key, model=model_name, max_tokens=max_tokens, temperature=temperature, model_kwargs=model_kwargs) ### Replicate elif provider == "replicate": api_key = api_key or os.environ.get("REPLICATE_API_TOKEN", "") model_name = model or replicate_default lograg(f"Replicate model \"{model_name}\"...") from llama_index.llms import Replicate result = Replicate( model=model_name, temperature=temperature, additional_kwargs=model_kwargs) ### HuggingFace else: os.environ["HF_HUB_DISABLE_SYMLINKS_WARNING"] = "1" model_desc = "" model_name = model or "default" if model_name in hf_model_nicknames: model_desc = f" (\"{model_name}\")" model_name = hf_model_nicknames[model_name] lograg(f"HuggingFace model \"{model_name}\"{model_desc}...") from llama_index.llms import HuggingFaceLLM result = HuggingFaceLLM( model_name=model_name, model_kwargs=model_kwargs, max_new_tokens=max_tokens, device_map=torch_device or "auto") #system_prompt=system_prompt) from llama_index import ServiceContext, set_global_service_context service_context = ServiceContext.from_defaults( embed_model='local', llm=result) if set_service_context: set_global_service_context(service_context) except Exception as e: lograg_error(f"failure initializing LLM: {e}", exit_code=1) return result, streaming_supported, service_context def split_llm_config(config): """Split an LLM from a config string of format "[alias=]provider[,model[,server[,api-key[,parameters...]]]]" into its components""" fields = config.strip("\"' ").split(",") provider = fields[0].strip() if len(fields) > 0 else default_llm_provider model = fields[1].strip() if len(fields) > 1 else None server = fields[2].strip() if len(fields) > 2 else None api_key = fields[3].strip() if len(fields) > 3 else None params = fields[4:] if len(fields) > 4 else [] alias = None if "=" in provider: alias, provider = provider.split("=", 1) provider = provider.strip() return provider, model, server, api_key, params, alias def load_llm_config(config, global_params, set_service_context=True): """Load an LLM from a config string like "provider,model,server,api-key,param1,param2,...""" provider, model, server, api_key, params, _ = split_llm_config(config) return load_llm(provider.lower(), model, server, api_key, params, global_params, set_service_context)

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# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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. from opentelemetry.trace import Tracer, get_tracer, set_span_in_context, Status, StatusCode from opentelemetry.trace.span import Span from opentelemetry.context import Context, get_current, attach, detach from typing import Any, Dict, List, Optional, Callable from llama_index.callbacks.base_handler import BaseCallbackHandler from llama_index.callbacks.base import CallbackManager from llama_index.callbacks.schema import CBEventType, EventPayload, BASE_TRACE_EVENT from llama_index.callbacks.token_counting import get_llm_token_counts, TokenCountingEvent from llama_index.utilities.token_counting import TokenCounter from llama_index.utils import get_tokenizer from dataclasses import dataclass from contextvars import ContextVar import threading global_root_trace = ContextVar("trace", default=None) @dataclass class SpanWithContext: """Object for tracking a span, its context, and its context token""" span: Span context: Context token: object def __init__(self, span: Span, context: Context, token: object, thread_identity): self.span = span self.context = context self.token = token self.thread_identity = thread_identity class OpenTelemetryCallbackHandler(BaseCallbackHandler): """Callback handler for creating OpenTelemetry traces from llamaindex traces and events.""" def __init__( self, tracer: Optional[Tracer] = get_tracer(__name__), tokenizer: Optional[Callable[[str], List]] = None, ) -> None: """Initializes the OpenTelemetryCallbackHandler. Args: tracer: Optional[Tracer]: A OpenTelemetry tracer used to create OpenTelemetry spans """ super().__init__(event_starts_to_ignore=[], event_ends_to_ignore=[]) self._tracer = tracer self._event_map: Dict[str, SpanWithContext] = {} self.tokenizer = tokenizer or get_tokenizer() self._token_counter = TokenCounter(tokenizer=self.tokenizer) def start_trace(self, trace_id: Optional[str] = None) -> None: trace_name = "llamaindex.trace" if trace_id is not None: trace_name = "llamaindex.trace." + trace_id span = self._tracer.start_span(trace_name) ctx = set_span_in_context(span) token = attach(ctx) global_root_trace.set(SpanWithContext(span=span, context=ctx, token=token, thread_identity=threading.get_ident())) def end_trace( self, trace_id: Optional[str] = None, trace_map: Optional[Dict[str, List[str]]] = None, ) -> None: root_trace = global_root_trace.get() if root_trace is not None: if root_trace.thread_identity == threading.get_ident(): detach(root_trace.token) root_trace.span.end() def on_event_start( self, event_type: CBEventType, payload: Optional[Dict[str, Any]] = None, event_id: str = "", parent_id: str = "", **kwargs: Any, ) -> str: parent_ctx = None # Case where the parent of this event is another event if parent_id in self._event_map: parent_ctx = self._event_map[parent_id].context # Case where the parent of this event is the root trace, and the root trace exists elif parent_id is BASE_TRACE_EVENT and global_root_trace.get() is not None: parent_ctx = global_root_trace.get().context # Case where the parent of this event is the root trace, but the trace does not exist else: return span_prefix = "llamaindex.event." span = self._tracer.start_span(span_prefix + event_type.value, context=parent_ctx) ctx = set_span_in_context(span) token = attach(ctx) self._event_map[event_id] = SpanWithContext(span=span, context=ctx, token=token, thread_identity=threading.get_ident()) span.set_attribute("event_id", event_id) if payload is not None: if event_type is CBEventType.QUERY: span.set_attribute("query.text", payload[EventPayload.QUERY_STR]) elif event_type is CBEventType.RETRIEVE: pass elif event_type is CBEventType.EMBEDDING: span.set_attribute("embedding.model", payload[EventPayload.SERIALIZED]['model_name']) span.set_attribute("embedding.batch_size", payload[EventPayload.SERIALIZED]['embed_batch_size']) span.set_attribute("embedding.class_name", payload[EventPayload.SERIALIZED]['class_name']) elif event_type is CBEventType.SYNTHESIZE: span.set_attribute("synthesize.query_text", payload[EventPayload.QUERY_STR]) elif event_type is CBEventType.CHUNKING: for i, chunk in enumerate(payload[EventPayload.CHUNKS]): span.set_attribute(f"chunk.{i}", chunk) elif event_type is CBEventType.TEMPLATING: if payload[EventPayload.QUERY_WRAPPER_PROMPT]: span.set_attribute("query_wrapper_prompt", payload[EventPayload.QUERY_WRAPPER_PROMPT]) if payload[EventPayload.SYSTEM_PROMPT]: span.set_attribute("system_prompt", payload[EventPayload.SYSTEM_PROMPT]) if payload[EventPayload.TEMPLATE]: span.set_attribute("template", payload[EventPayload.TEMPLATE]) if payload[EventPayload.TEMPLATE_VARS]: for key, var in payload[EventPayload.TEMPLATE_VARS].items(): span.set_attribute(f"template_variables.{key}", var) elif event_type is CBEventType.LLM: span.set_attribute("llm.class_name", payload[EventPayload.SERIALIZED]['class_name']) span.set_attribute("llm.formatted_prompt", payload[EventPayload.PROMPT]) span.set_attribute("llm.additional_kwargs", str(payload[EventPayload.ADDITIONAL_KWARGS])) elif event_type is CBEventType.NODE_PARSING: span.set_attribute("node_parsing.num_documents", len(payload[EventPayload.DOCUMENTS])) elif event_type is CBEventType.EXCEPTION: span.set_status(Status(StatusCode.ERROR)) span.record_exception(payload[EventPayload.EXCEPTION]) return event_id def on_event_end( self, event_type: CBEventType, payload: Optional[Dict[str, Any]] = None, event_id: str = "", **kwargs: Any, ) -> None: if event_id in self._event_map: span = self._event_map[event_id].span span.set_attribute("event_id", event_id) if payload is not None: if event_type is CBEventType.QUERY: pass elif event_type is CBEventType.RETRIEVE: for i, node_with_score in enumerate(payload[EventPayload.NODES]): node = node_with_score.node score = node_with_score.score span.set_attribute(f"query.node.{i}.id", node.hash) span.set_attribute(f"query.node.{i}.score", score) span.set_attribute(f"query.node.{i}.text", node.text) elif event_type is CBEventType.EMBEDDING: texts = payload[EventPayload.CHUNKS] vectors = payload[EventPayload.EMBEDDINGS] total_chunk_tokens = 0 for text, vector in zip(texts, vectors) : span.set_attribute(f"embedding_text_{texts.index(text)}", text) span.set_attribute(f"embedding_vector_{vectors.index(vector)}", vector) total_chunk_tokens +=self._token_counter.get_string_tokens(text) span.set_attribute(f"embedding_token_usage", total_chunk_tokens) elif event_type is CBEventType.SYNTHESIZE: pass elif event_type is CBEventType.CHUNKING: pass elif event_type is CBEventType.TEMPLATING: pass elif event_type is CBEventType.LLM: span.set_attribute("response.text", str( payload.get(EventPayload.RESPONSE, "") ) or str(payload.get(EventPayload.COMPLETION, "")) ) token_counts = get_llm_token_counts(self._token_counter, payload, event_id) span.set_attribute("llm_prompt.token_usage", token_counts.prompt_token_count) span.set_attribute("llm_completion.token_usage", token_counts.completion_token_count) span.set_attribute("total_tokens_used", token_counts.total_token_count) elif event_type is CBEventType.NODE_PARSING: span.set_attribute("node_parsing.num_nodes", len(payload[EventPayload.NODES])) elif event_type is CBEventType.EXCEPTION: span.set_status(Status(StatusCode.ERROR)) span.record_exception(payload[EventPayload.EXCEPTION]) if self._event_map[event_id].thread_identity == threading.get_ident(): detach(self._event_map[event_id].token) self._event_map.pop(event_id, None) span.end()

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from pathlib import Path from llama_index import download_loader ImageReader = download_loader("ImageReader") # If the Image has key-value pairs text, use text_type = "key_value" loader = ImageReader(text_type = "key_value") documents = loader.load_data(file=Path('./receipt.webp')) print(documents)

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# https://github.com/jerryjliu/llama_index/blob/main/examples/langchain_demo/LangchainDemo.ipynb # Using LlamaIndex as a Callable Tool from langchain.agents import Tool from langchain.chains.conversation.memory import ConversationBufferMemory from langchain.chat_models import ChatOpenAI from langchain.agents import initialize_agent from langchain import HuggingFaceHub from llama_index import LangchainEmbedding, ServiceContext from langchain.embeddings.huggingface import HuggingFaceEmbeddings from llama_index.tools import QueryEngineTool, ToolMetadata from llama_index import VectorStoreIndex, SimpleDirectoryReader, LLMPredictor, ServiceContext from llama_index.query_engine import SubQuestionQueryEngine documents = SimpleDirectoryReader('data/experiment').load_data() repo_id = "tiiuae/falcon-7b" embed_model = LangchainEmbedding(HuggingFaceEmbeddings()) llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0.1, 'truncation': 'only_first', "max_length": 1024}) llm_predictor = LLMPredictor(llm=llm) service_context = ServiceContext.from_defaults(chunk_size=512, llm_predictor=llm_predictor, embed_model=embed_model) index = VectorStoreIndex.from_documents(documents=documents, service_context=service_context) engine = index.as_query_engine(similarity_top_k=3) query_engine_tools = [ QueryEngineTool( query_engine=engine, metadata=ToolMetadata(name='Paulindex', description='Provides information about Paul Graham Essay') ) ] s_engine = SubQuestionQueryEngine.from_defaults(query_engine_tools=query_engine_tools) response = s_engine.query('Explain childhood') print(response) ### As a chat bot # tools = [ # Tool( # name="LlamaIndex", # func=lambda q: str(index.as_query_engine().query(q)), # description="useful for when you want to answer questions about the author. The input to this tool should be a complete english sentence.", # return_direct=True # ), # ] # memory = ConversationBufferMemory(memory_key="chat_history") # # llm = ChatOpenAI(temperature=0) # agent_executor = initialize_agent(tools, llm, agent="conversational-react-description", memory=memory) # # agent_executor.run(input="hi, i am bob")

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"""This module provides functionality for loading chat prompts. The main function in this module is `load_chat_prompt`, which loads a chat prompt from a given JSON file. The JSON file should contain two keys: "system_template" and "human_template", which correspond to the system and user messages respectively. Typical usage example: from wandbot.chat import prompts chat_prompt = prompts.load_chat_prompt('path_to_your_json_file.json') """ import json import logging import pathlib from typing import Union from llama_index import ChatPromptTemplate from llama_index.llms import ChatMessage, MessageRole logger = logging.getLogger(__name__) def partial_format(s, **kwargs): # Manually parse the string and extract the field names place_holders = set() field_name = "" in_field = False for c in s: if c == "{" and not in_field: in_field = True elif c == "}" and in_field: place_holders.add(field_name) field_name = "" in_field = False elif in_field: field_name += c replacements = {k: kwargs.get(k, "{" + k + "}") for k in place_holders} # Escape all curly braces s = s.replace("{", "{{").replace("}", "}}") # Replace the placeholders for k, v in replacements.items(): s = s.replace("{{" + k + "}}", v) return s ROLE_MAP = { "system": MessageRole.SYSTEM, "human": MessageRole.USER, "assistant": MessageRole.ASSISTANT, } def load_chat_prompt( f_name: Union[pathlib.Path, str] = None, language_code: str = "en", query_intent: str = "", ) -> ChatPromptTemplate: """ Loads a chat prompt from a given file. This function reads a JSON file specified by f_name and constructs a ChatPromptTemplate object from the data. The JSON file should contain two keys: "system_template" and "human_template", which correspond to the system and user messages respectively. Args: f_name: A string or a pathlib.Path object representing the path to the JSON file. If None, a default path is used. Returns: A ChatPromptTemplate object constructed from the data in the JSON file. """ f_name = pathlib.Path(f_name) template = json.load(f_name.open("r")) human_template = partial_format( template["messages"][-1]["human"], language_code=language_code, query_intent=query_intent, ) messages = [] for message in template["messages"][:-1]: for k, v in message.items(): messages.append(ChatMessage(role=ROLE_MAP[k], content=v)) messages.append(ChatMessage(role=MessageRole.USER, content=human_template)) prompt = ChatPromptTemplate(messages) return prompt

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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.vector_stores.milvus import MilvusVectorStore from llama_index.embeddings.huggingface import HuggingFaceEmbedding from llama_index.core import ( VectorStoreIndex, SimpleDirectoryReader, ) from llama_index.core.storage.storage_context import StorageContext vector_store = MilvusVectorStore( uri = "http://localhost:19530", port = 19530 , collection_name = 'llama_index_prompts_large', dim = 384, similarity_metric = "L2", ) sample_files_path = "E:\short_large" embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L12-v2") 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() # here we set the file_path to become no part of the embedding, its not for this usecase # also we check if a doc has zero content then we don't try to embedd it as it would result in an error 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 vector_index = VectorStoreIndex.from_documents(docs, storage_context=storage_context,embed_model=embed_model, show_progress=True)

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import numpy as np from llama_index.core import StorageContext, load_index_from_storage from llama_index.llms.litellm import LiteLLM from langchain_google_genai import ChatGoogleGenerativeAI from trulens_eval.feedback.provider.langchain import Langchain from trulens_eval import Tru, Feedback, TruLlama from trulens_eval.feedback import Groundedness # Setup RAG index = load_index_from_storage( StorageContext.from_defaults(persist_dir="base_index"), embed_model="local:../models/bge-small-en-v1.5", ) llm = LiteLLM(model="gemini/gemini-pro", temperature=0.1) query_engine = index.as_query_engine(llm=llm) # Evaluate with trulens-eval # Define provider and database _llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0) provider = Langchain(chain=_llm) database_url = "sqlite:///data/trulens.db" tru = Tru(database_url=database_url, database_redact_keys=True) # tru.reset_database() # Using TruLlama f_qa_relevance = Feedback( provider.relevance_with_cot_reasons, name="Answer Relevance" ).on_input_output() f_context_relevance = ( Feedback(provider.relevance_with_cot_reasons, name="Context Relevance") .on_input() .on(TruLlama.select_source_nodes().node.text) .aggregate(np.mean) ) grounded = Groundedness(groundedness_provider=provider) f_groundedness = ( Feedback(grounded.groundedness_measure_with_cot_reasons, name="Groundedness") .on(TruLlama.select_source_nodes().node.text) .on_output() .aggregate(grounded.grounded_statements_aggregator) ) app_id = "Chain2" tru_recorder = TruLlama( query_engine, app_id=app_id, feedbacks=[ f_qa_relevance, f_context_relevance, f_groundedness, ], ) qns = ... for qn in qns: with tru_recorder as recording: res = query_engine.query(qn) # Results # dashboard tru.run_dashboard(port=8601) # # dataframe # records_df, feednack = tru.get_records_and_feednack(app_ids=[app_id]) # records_df.head()

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import os from dotenv import load_dotenv from llama_index.chat_engine.condense_plus_context import CondensePlusContextChatEngine from llama_index.llms.openai import OpenAI from llama_index.llms.types import ChatMessage, MessageRole from llama_index.query_engine import RetrieverQueryEngine from llama_index.retrievers import PathwayRetriever from traceloop.sdk import Traceloop from pathway.xpacks.llm.vector_store import VectorStoreClient load_dotenv() Traceloop.init(app_name=os.environ.get("APP_NAME", "PW - LlamaIndex (Streamlit)")) DEFAULT_PATHWAY_HOST = "demo-document-indexing.pathway.stream" PATHWAY_HOST = os.environ.get("PATHWAY_HOST", DEFAULT_PATHWAY_HOST) PATHWAY_PORT = int(os.environ.get("PATHWAY_PORT", "80")) def get_additional_headers(): headers = {} key = os.environ.get("PATHWAY_API_KEY") if key is not None: headers = {"X-Pathway-API-Key": key} return headers vector_client = VectorStoreClient( PATHWAY_HOST, PATHWAY_PORT, # additional_headers=get_additional_headers(), ) retriever = PathwayRetriever(host=PATHWAY_HOST, port=PATHWAY_PORT) retriever.client = VectorStoreClient( host=PATHWAY_HOST, port=PATHWAY_PORT, # additional_headers=get_additional_headers() ) llm = OpenAI(model="gpt-3.5-turbo") query_engine = RetrieverQueryEngine.from_args( retriever, ) pathway_explaination = "Pathway is a high-throughput, low-latency data processing framework that handles live data & streaming for you." DEFAULT_MESSAGES = [ ChatMessage(role=MessageRole.USER, content="What is Pathway?"), ChatMessage(role=MessageRole.ASSISTANT, content=pathway_explaination), ] chat_engine = CondensePlusContextChatEngine.from_defaults( retriever=retriever, system_prompt="""You are RAG AI that answers users questions based on provided sources. IF QUESTION IS NOT RELATED TO ANY OF THE CONTEXT DOCUMENTS, SAY IT'S NOT POSSIBLE TO ANSWER USING PHRASE `The looked-up documents do not provde information about...`""", verbose=True, chat_history=DEFAULT_MESSAGES, llm=llm, )

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# My OpenAI Key import logging import os import sys from IPython.display import Markdown, display from llama_index import GPTTreeIndex, SimpleDirectoryReader logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY") documents = SimpleDirectoryReader("data").load_data() index = GPTTreeIndex.from_documents(documents) index.save_to_disk("index.json") # try loading new_index = GPTTreeIndex.load_from_disk("index.json") # set Logging to DEBUG for more detailed outputs response = new_index.query("What did the author do growing up?") print(response) # set Logging to DEBUG for more detailed outputs response = new_index.query("What did the author do after his time at Y Combinator?") print(response)

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from llama_index import Document import json, os from llama_index.node_parser import SimpleNodeParser from llama_index import GPTTreeIndex, LLMPredictor, PromptHelper, GPTListIndex from langchain import OpenAI from llama_index.composability import ComposableGraph from llama_index.data_structs.node_v2 import Node, DocumentRelationship class ConfigLLM: # define LLM name = "gpt-3.5-turbo" llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo")) # define prompt helper # set maximum input size max_input_size = 2096 # 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) def index_construct_and_save(timechunk_path: str, save_loc: str): for filename in os.listdir(timechunk_path): file = os.path.join(timechunk_path, filename) data = json.load(open(file=file, mode="r")) # keys, text = list(zip(*data.items())) nodes = [Node(text=text, doc_id=keys) for keys, text in data.items()] index = GPTTreeIndex(nodes=nodes) index.save_to_disk(f"{save_loc}/{filename}.json") def load_index_with_summary(index_loc: str): index_list = [] index_summary_list = [] for filename in os.listdir(index_loc): index_file = os.path.join(index_loc, filename) index = GPTTreeIndex.load_from_disk(index_file) summary = index.query( "What is the summary of this document chunk?", mode="summarize" ) index_summary_list.append(str(summary)) index_list.append(index) #! logging print("index list", len(index_list), index_list) return index_list, index_summary_list def compose_graph_and_save(index_loc: str, save_loc: str): index_list, index_summary_list = load_index_with_summary(index_loc) #! logging print(index_summary_list) graph = ComposableGraph.from_indices(GPTListIndex, index_list, index_summary_list) graph.save_to_disk(save_loc) def load_graph(graph_location: str): return ComposableGraph.load_from_disk(graph_location) def query_graph(query: str, graph: ComposableGraph): response = graph.query(query, query_configs=get_query_configs()) return response def parse_response(response: ComposableGraph.query): print("-" * 50) print(response) print("-" * 50) print( str(response), # response.source_nodes, [node_with_score.node.doc_id for node_with_score in response.source_nodes], # [node.ref_doc_id for node in response.source_nodes], response.get_formatted_sources(), sep="\n" + "+" * 80 + "\n", ) print("-" * 50) def query_composed_index(query: str, graph_loc: str): graph = load_graph(graph_loc) response = query_graph(query, graph) parse_response(response) def query_single_index(query: str, index_loc: str): index = GPTTreeIndex.load_from_disk(index_loc) response = index.query(query) parse_response(response) def get_query_configs(): # set query config query_configs = [ { "index_struct_type": "simple_dict", "query_mode": "default", "query_kwargs": {"similarity_top_k": 1}, }, { "index_struct_type": "keyword_table", "query_mode": "simple", "query_kwargs": {}, }, ] return query_configs

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