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I_kwDOIPDwls545nig

Support SageMaker Inference Component of multi model endpoint

closed

NONE

### Feature request I have created a multi model inference endpoint in the new version of SageMaker studio (the original one now is called studio classic). I don't see there is a place that I can set the `inference component` in the `SagemakerEndpoint` class. So I ended up get an expected error from SageMaker. `An error occurred (ValidationError) when calling the InvokeEndpointWithResponseStream operation: Inference Component Name header is required for endpoints to which you plan to deploy inference components. Please include Inference Component Name header or consider using SageMaker models.` ### Motivation To support multi model endpoint in SageMaker which is cost efficient way to run models. ### Your contribution I can test and verify.

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I_kwDOIPDwls545hZ5

Specific prompt adds false input variables

closed

CONTRIBUTOR

### System Info python3.10.13 langchain==0.0.346 langchain-core==0.0.10 ### Who can help? @agola11, @hwchase17 ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser from langchain.chat_models import ChatOpenAI from langchain.agents.format_scratchpad import format_to_openai_function_messages text_with_call = """You are a helpful assistant. Here is a function call that you should not imitate: <functioncall> {"name":"generate_anagram", "arguments": {"word": "listen"}} """ prompt = ChatPromptTemplate.from_messages( [ ( "system", text_with_call, ), ("user", "{input}"), MessagesPlaceholder(variable_name="agent_scratchpad"), ] ) llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) agent = ( { "input": lambda x: x["input"], "agent_scratchpad": lambda x: format_to_openai_function_messages( x["intermediate_steps"] ), } | prompt | llm | OpenAIFunctionsAgentOutputParser() ) output = agent.invoke( { "input": "Hello", "intermediate_steps": [], } ) ``` ### Expected behavior I would expect not to get an error. I get error: `KeyError: 'Input to ChatPromptTemplate is missing variable \'"name"\'. Expected: [\'"name"\', \'agent_scratchpad\', \'input\'] Received: [\'input\', \'agent_scratchpad\']'` > I think this error is cause by the f-string prompt template recognising the brackets inside the prompt as other variables needed an input. I tried using Jinja2 template for my prompt instead, but I cannot setup this option in `ChatPromptTemplate`. I understand this could be for security reasons as mentioned in [this issue](https://github.com/langchain-ai/langchain/issues/4394) So the possible solutions I see : - Use PromptTemplate like: ``` prompt = PromptTemplate.from_template( text_with_call, template_format="jinja2" ) ``` But I would like to make use of the `agent_scratchpad`, so the ChatPromptTemplate is needed in my case. - Change ChatPromptTemplate class to support jinja2 template, which I understand could not be done - Re-implement my custom ChatPromptTemplate - find another way to accept this prompt without falsely flagging prompt elements as input variables. Do you have any ideas? Thanks for your help 😃

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I_kwDOIPDwls545Wrd

Error:

closed

NONE

### System Info I try this example code ``` from langchain.retrievers import ParentDocumentRetriever from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.storage import InMemoryStore # This text splitter is used to create the parent documents parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000) # This text splitter is used to create the child documents # It should create documents smaller than the parent child_splitter = RecursiveCharacterTextSplitter(chunk_size=400) # The vectorstore to use to index the child chunks vectorstore = Chroma(embedding_function=OpenAIEmbeddings()) # The storage layer for the parent documents store = InMemoryStore() vectorstore = Chroma(collection_name="test", embedding_function=OpenAIEmbeddings()) ``` # Initialize the retriever parent_document_retriever = ParentDocumentRetriever( vectorstore=vectorstore, docstore=store, child_splitter=child_splitter, parent_splitter=parent_splitter, ) but I encountered an error: ``` 1 # Initialize the retriever ----> 2 parent_document_retriever = ParentDocumentRetriever( 3 vectorstore=vectorstore, 4 docstore=store, 5 child_splitter=child_splitter, TypeError: MultiVectorRetriever.__init__() got an unexpected keyword argument 'child_splitter' ``` ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` from langchain.retrievers import ParentDocumentRetriever from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.storage import InMemoryStore # This text splitter is used to create the parent documents parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000) # This text splitter is used to create the child documents # It should create documents smaller than the parent child_splitter = RecursiveCharacterTextSplitter(chunk_size=400) # The vectorstore to use to index the child chunks vectorstore = Chroma(embedding_function=OpenAIEmbeddings()) # The storage layer for the parent documents store = InMemoryStore() vectorstore = Chroma(collection_name="test", embedding_function=OpenAIEmbeddings()) # Initialize the retriever parent_document_retriever = ParentDocumentRetriever( vectorstore=vectorstore, docstore=store, child_splitter=child_splitter, parent_splitter=parent_splitter, ) ``` ### Expected behavior I can run.

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I_kwDOIPDwls545LE0

When I install the template "self-query-qdrant" I get this error: UnicodeDecodeError: 'charmap' codec can't decode byte 0x81 in position 132: character maps to <undefined>

open

NONE

### System Info Windows 10 Pro - 22H2 - Build 9045.3693 - Windows Feature Experience Pack 1000.19053.1000.0 Python 3.11.5 langchain-cli 0.0.19 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction 1. creating a virtual environments with python - m venv .venv and activate it; 2. installing langchain-cli with pip install -U "langchain-cli[serve]" 3. launch langchain app new qdrant-app --package self-query-qdrant ### Expected behavior As for other templates that I have installed, I expected to find the .py files of the app in the packages directory, but it's empty. [log.txt](https://github.com/langchain-ai/langchain/files/13580197/log.txt)

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PR_kwDOIPDwls5hSaPu

[LLMonitorCallbackHandler]

closed

CONTRIBUTOR

Improve documentation for LLMonitorCallbackHandler (mainly formatting).

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I_kwDOIPDwls545Fkf

Requests to the Creates a completion for the chat message Operation under Azure OpenAI API version 2023-03-15-preview have exceeded token rate limit of your current OpenAI S0 pricing tier. Please go here: https://aka.ms/oai/quotaincrease if you would like to further increase the default rate limit.

open

NONE

### System Info langchain 0.0.299 python 3.8 gpt-4 when AzureChatOpenAI use gpt-4 ,I use agents to deal with problems,raised a error about RateLimitError; but gpt-3.5 do not appear this quesition ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction llm = AzureChatOpenAI( openai_api_base=api_base, openai_api_version=api_version, deployment_name=deployment_name, openai_api_key=api_token, openai_api_type="azure", max_tokens = max_tokens, model_name = model_name, ) memory = ConversationBufferMemory(memory_key="chat_history") agent = create_pandas_dataframe_agent( llm, df, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory ) question = "what are the top 2 most polluted counties" res = agent.run(question) ### Expected behavior Tell me what caused this error and how to avoid it

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I_kwDOIPDwls544ivV

api_base parameter not properly propagated in client calls in ChatLiteLLM

closed

CONTRIBUTOR

### System Info - LangChain version: 0.0.346 - Platform: Mac mini M1 16GB - macOS Sonoma 14.0 - Python version: 3.11 - LiteLLM version: 1.10.6 ### Who can help? @hwchase17 @agola11 ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` from langchain.chat_models import ChatLiteLLM # Code for initializing the ChatLiteLLM instance chat_model = ChatLiteLLM(api_base="https://custom.endpoints.huggingface.cloud", model="huggingface/Intel/neural-chat-7b-v3-1") # Make a call to LiteLLM text = "What would be a good company name for a company that makes colorful socks?" messages = [HumanMessage(content=text)] print(chat_model(messages).content) ``` Error: ``` Traceback (most recent call last): File "/opt/homebrew/lib/python3.11/site-packages/litellm/utils.py", line 4919, in handle_huggingface_chunk raise ValueError(chunk) ValueError: {"error":"The model Intel/neural-chat-7b-v3-1 is too large to be loaded automatically (14GB > 10GB). Please use Spaces (https://huggingface.co/spaces) or Inference Endpoints (https://huggingface.co/inference-endpoints)."} ``` Is the same error if: `chat_model = ChatLiteLLM(model="huggingface/Intel/neural-chat-7b-v3-1")` So api_base parameter not properly propagated in client calls in ChatLiteLLM. ### Expected behavior I would expect the ChatLiteLLM instance to correctly utilize the api_base parameter when making requests to the LiteLLM client. This should enable using models larger than the default size limit without encountering the error message about model size limits. Notably, if I explicitly add the api_base argument in chat_models/litellm.py on line 239 (e.g., `return self.client.completion(api_base=self.api_base, **kwargs)`), the problem is resolved. This suggests that the api_base argument is not being correctly passed through **kwargs.

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I_kwDOIPDwls544WeG

Dict validation error

open

NONE

### System Info Trying to execute the chatbot script with sagemaker endpoint of LLAMA2 llm model getting dict validation error for RetrievalQA Request: def retreiveFromLL(userQuery: str) -> QueryResponse: pre_prompt = """[INST] <<SYS>> You are a helpful, respectful and honest assistant. Answer exactly in detail from the context <</SYS>> Answer the question below from context below : """ prompt = pre_prompt + "CONTEXT:\n\n{context}\n" +"Question : {question}" + "[\INST]" llama_prompt = PromptTemplate(template=prompt, input_variables=["context", "question"]) chain_type_kwargs = {"prompt": llama_prompt} embeddings = SentenceTransformerEmbeddings(model_name=EMBEDDING_MODEL) # Initialize PGVector index vector_db = PGVector( embedding_function=embeddings, collection_name='CSE_runbooks', connection_string=CONNECTION_STRING, ) print("**Invoking PGVector") # Custom ContentHandler to handle input and output to the SageMaker Endpoint class LlamaChatContentHandler(LLMContentHandler): content_type = "application/json" accepts = "application/json" def transform_input(self, inputs: str, model_kwargs: Dict = {}) -> bytes: payload = { "inputs": pre_prompt, "parameters": {"max_new_tokens":2000, "top_p":0.9, "temperature":0.1}} input_str = ' '.join(inputs) input_str = json.dumps(payload) print(payload) return input_str.encode("utf-8") def transform_output(self, output: bytes) -> str: response_json = json.loads(output.read().decode("utf-8")) content = response_json[0]["generated_text"] return content # Initialize SagemakerEndpoint print("Invoking LLM SageMaker Endpoint") llm = SagemakerEndpoint( endpoint_name=LLAMA2_ENDPOINT, region_name=AWS_REGION, content_handler=LlamaChatContentHandler(), callbacks=[StreamingStdOutCallbackHandler()], endpoint_kwargs={"CustomAttributes": "accept_eula=true"}, ) print(llm) # Create a RetrievalQA instance with Pinecone as the retriever query = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=vector_db, return_source_documents=True, chain_type_kwargs=chain_type_kwargs) print("**Invoking query") result = query({"query": userQuery}) response = result["result"] Error: Traceback (most recent call last): File "/home/ec2-user/.local/lib/python3.9/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 534, in _run_script exec(code, module.__dict__) File "/home/ec2-user/milvus/qa_UI.py", line 26, in <module> userResponse = getLLMResponse(user_input) File "/home/ec2-user/milvus/getLLMResponse1.py", line 37, in getLLMResponse userResponse = retreiveFromLL(userQuery) File "/home/ec2-user/milvus/getLLMResponse1.py", line 97, in retreiveFromLL query = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=vector_db, return_source_documents=True, chain_type_kwargs=chain_type_kwargs) File "/home/ec2-user/.local/lib/python3.9/site-packages/langchain/chains/retrieval_qa/base.py", line 103, in from_chain_type return cls(combine_documents_chain=combine_documents_chain, **kwargs) File "/home/ec2-user/.local/lib/python3.9/site-packages/langchain/load/serializable.py", line 97, in __init__ super().__init__(**kwargs) File "/home/ec2-user/.local/lib/python3.9/site-packages/pydantic/v1/main.py", line 341, in __init__ raise validation_error pydantic.v1.error_wrappers.ValidationError: 1 validation error for RetrievalQA retriever value is not a valid dict (type=type_error.dict) ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Execute the code ### Expected behavior response from LLM

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PR_kwDOIPDwls5hRr5Y

Fix typo in lcel example for rerank in doc

closed

CONTRIBUTOR

fix typo in lcel example for rerank in doc

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I_kwDOIPDwls5430kS

Get JSON output from non-OpenAI models

open

CONTRIBUTOR

### Issue with current documentation: I'm exploring the use of open-source models via Ollama excellent application, I'm facing quite some challenges in adapting the instructions that were made for OpenAI-type models (they are perfectly interchangeable, I must say). Is the format_instructions generated from the PydanticOutputParser supposed to work also for non-Openai models? With Zephyr the model keeps returning the answer AND the JSON schema, even when I try to stop it from doing that... ### Idea or request for content: _No response_

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PR_kwDOIPDwls5hRQc8

Exclude `max_tokens` from request if it's None

closed

CONTRIBUTOR

<!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> We found a request with `max_tokens=None` results in the following error in Anthropic: ``` HTTPError: 400 Client Error: Bad Request for url: https://oregon.staging.cloud.databricks.com/serving-endpoints/corey-anthropic/invocations. Response text: {"error_code":"INVALID_PARAMETER_VALUE","message":"INVALID_PARAMETER_VALUE: max_tokens was not of type Integer: null"} ``` This PR excludes `max_tokens` if it's None.

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I_kwDOIPDwls543iLX

GoogleVertexAISearchRetriever - AttributeError: 'ProtoType' object has no attribute 'DESCRIPTOR'

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CONTRIBUTOR

### System Info Deployed on Cloud Run ``` Python 3.10 langchain 0.0.345 langchain-cli 0.0.19 langchain-core 0.0.9 langchain-experimental 0.0.43 langdetect 1.0.9 langserve 0.0.32 langsmith 0.0.69 ``` ``` google-ai-generativelanguage 0.3.3 google-api-core 2.14.0 google-api-python-client 2.109.0 google-auth 2.24.0 google-auth-httplib2 0.1.1 google-auth-oauthlib 1.1.0 google-cloud-aiplatform 1.36.4 google-cloud-bigquery 3.13.0 google-cloud-core 2.3.3 google-cloud-discoveryengine 0.11.3 google-cloud-pubsub 2.18.4 google-cloud-resource-manager 1.10.4 google-cloud-storage 2.13.0 google-crc32c 1.5.0 google-generativeai 0.2.2 google-resumable-media 2.6.0 googleapis-common-protos 1.61.0 ``` Deployed via the Langchain template here: https://github.com/langchain-ai/langchain/tree/master/templates/rag-google-cloud-vertexai-search ### Who can help? @holtskinner I think has been fixing Google related stuff or knows someone who can ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Add https://github.com/langchain-ai/langchain/tree/master/templates/rag-google-cloud-vertexai-search to Langserve server Run in playground with any input - get error. AttributeError("'ProtoType' object has no attribute 'DESCRIPTOR'") Langsmith chain: ``` { "id": [ "langchain_core", "runnables", "RunnableSequence" ], "lc": 1, "type": "constructor", "kwargs": { "last": { "id": [ "langchain_core", "output_parsers", "string", "StrOutputParser" ], "lc": 1, "type": "constructor", "kwargs": {} }, "first": { "id": [ "langchain_core", "runnables", "RunnableParallel" ], "lc": 1, "type": "constructor", "kwargs": { "steps": { "context": { "id": [ "langchain", "retrievers", "google_vertex_ai_search", "GoogleVertexAISearchRetriever" ], "lc": 1, "repr": "GoogleVertexAISearchRetriever(project_id='project-id', data_store_id='datastore-id')", "type": "not_implemented" }, "question": { "id": [ "langchain_core", "runnables", "RunnablePassthrough" ], "lc": 1, "type": "constructor", "kwargs": { "func": null, "afunc": null, "input_type": null } } } } }, "middle": [ { "id": [ "langchain_core", "prompts", "chat", "ChatPromptTemplate" ], "lc": 1, "type": "constructor", "kwargs": { "messages": [ { "id": [ "langchain_core", "prompts", "chat", "HumanMessagePromptTemplate" ], "lc": 1, "type": "constructor", "kwargs": { "prompt": { "id": [ "langchain_core", "prompts", "prompt", "PromptTemplate" ], "lc": 1, "type": "constructor", "kwargs": { "template": "Answer the question based only on the following context:\n{context}\nQuestion: {question}\n", "input_variables": [ "context", "question" ], "template_format": "f-string", "partial_variables": {} } } } } ], "input_variables": [ "context", "question" ] } }, { "id": [ "langchain", "chat_models", "vertexai", "ChatVertexAI" ], "lc": 1, "type": "constructor", "kwargs": { "model_name": "chat-bison", "temperature": 0 } } ] } } ``` The traceback comes back as: ``` File "/usr/local/lib/python3.10/site-packages/langchain_core/retrievers.py", line 157, in _aget_relevant_documents return await asyncio.get_running_loop().run_in_executor( File "/usr/local/lib/python3.10/concurrent/futures/thread.py", line 58, in run result = self.fn(*self.args, **self.kwargs) File "/usr/local/lib/python3.10/site-packages/langchain/retrievers/google_vertex_ai_search.py", line 343, in _get_relevant_documents search_request = self._create_search_request(query) File "/usr/local/lib/python3.10/site-packages/langchain/retrievers/google_vertex_ai_search.py", line 327, in _create_search_request return SearchRequest( File "/usr/local/lib/python3.10/site-packages/proto/message.py", line 570, in __init__ pb_value = marshal.to_proto(pb_type, value) File "/usr/local/lib/python3.10/site-packages/proto/marshal/marshal.py", line 222, in to_proto proto_type.DESCRIPTOR.has_options AttributeError: 'ProtoType' object has no attribute 'DESCRIPTOR' ``` ### Expected behavior The call to https://github.com/langchain-ai/langchain/blob/e1ea1912377ca7c013e89fac4c1d26c0cb836009/libs/langchain/langchain/retrievers/google_vertex_ai_search.py#L327 returns correctly with some documents.

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PR_kwDOIPDwls5hQ61j

Add Alpha Vantage API Tool

open

NONE

### Description This implementation adds functionality from the AlphaVantage API, renowned for its comprehensive financial data. The class encapsulates various methods, each dedicated to fetching specific types of financial information from the API. ### Implemented Functions - **`search_symbols`**: - Searches the AlphaVantage API for financial symbols using the provided keywords. - **`_get_market_news_sentiment`**: - Retrieves market news sentiment for a specified stock symbol from the AlphaVantage API. - **`_get_time_series_daily`**: - Fetches daily time series data for a specific symbol from the AlphaVantage API. - **`_get_quote_endpoint`**: - Obtains the latest price and volume information for a given symbol from the AlphaVantage API. - **`_get_time_series_weekly`**: - Gathers weekly time series data for a particular symbol from the AlphaVantage API. - **`_get_top_gainers_losers`**: - Provides details on top gainers, losers, and most actively traded tickers in the US market from the AlphaVantage API. ### Issue: - #11994 ### Dependencies: - 'requests' library for HTTP requests. (import requests) - 'pytest' library for testing. (import pytest)

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PR_kwDOIPDwls5hQeUv

Include run_id

closed

COLLABORATOR

in the test run outputs

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I_kwDOIPDwls542sXV

No module named 'langchain.callbacks.tracers.log_stream'

open

NONE

### System Info I already create venv and re-install langchain: `pip install langchain` but error: > from langserve import add_routes > File "/usr/local/lib/python3.10/dist-packages/langserve/__init__.py", line 7, in <module> > from langserve.client import RemoteRunnable > File "/usr/local/lib/python3.10/dist-packages/langserve/client.py", line 29, in <module> > from langchain.callbacks.tracers.log_stream import RunLogPatch > ModuleNotFoundError: No module named 'langchain.callbacks.tracers.log_stream' > ### Who can help? @agola11 ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [X] Callbacks/Tracing - [ ] Async ### Reproduction $ python3 -m venv venv $ source venv/bin/activate $ pip install langchain $ pip install google-generativeai $ pip install fastapi $ pip install uvicorn $ pip install langserve $ `uvicorn main:app --host 0.0.0.0 --port 8000` ### Expected behavior App run with langserve without error

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I_kwDOIPDwls542qJn

Issue: The purpose to validate_tools_single_input in CHAT_CONVERSATIONAL_REACT_DESCRIPTION agent

open

NONE

### Issue you'd like to raise. Cannot figure out why validate_tools_single_input is necessary. I've tried to adopt multi-inputs tools in CHAT_CONVERSATIONAL_REACT_DESCRIPTION agents by just commenting out the implementation of such function. Everything seemed fun. So what's the purpose of such design, and if it is possible to use multi-inputs tools in a more flexible setup? ### Suggestion: _No response_

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I_kwDOIPDwls542ncu

Allow PresidioAnonymizer() to redact data instead of anonymizing.

closed

NONE

### Feature request The langchain library only supports anonymizing it seems. The native microsoft library can redact data: ``` analyzer_results = analyzer.analyze(text=text_to_anonymize, entities=["PERSON", "PHONE_NUMBER", "EMAIL_ADDRESS", "URL", "LOCATION"], ad_hoc_recognizers=ad_hoc_recognizers, language='en') anonymized_results = anonymizer.anonymize( text=text_to_anonymize, analyzer_results=analyzer_results, operators={"DEFAULT": OperatorConfig("redact", {})} ) ``` I don't see a way to do this via langchain_experimental.data_anonymizer ### Motivation Redaction is cleaner than anonymizing. It's better than replacing first names with gibberish for my. use case ### Your contribution I can help provide use-cases, like i did above.

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PR_kwDOIPDwls5hQFGo

[Feature] Openai bind_tools

open

COLLABORATOR

It's nice to not have to remember the imports for these (though now with the split, the cyclic dep on langchain is a bit annoying)

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I_kwDOIPDwls542YFR

Issue: <Please write a coImportError: cannot import name 'open_filename' from 'pdfminer.utils' (/home/nickjtay/Projects/langtest1/lib/python3.10/site-packages/pdfminer/utils.py)mprehensive title after the 'Issue: ' prefix>

open

NONE

### Issue you'd like to raise. I tried to run the notebook [Semi_structured_multi_modal_RAG_LLaMA2.ipynb](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb). I installed and imported the required libraries, but an error message was returned as a result of running the below code: ``` from typing import Any from pydantic import BaseModel from unstructured.partition.pdf import partition_pdf path = "/home/nickjtay/LLaVA/" raw_pdf_elements = partition_pdf( filename=path + "LLaVA.pdf", # Using pdf format to find embedded image blocks extract_images_in_pdf=True, # Use layout model (YOLOX) to get bounding boxes (for tables) and find titles # Titles are any sub-section of the document infer_table_structure=True, # Post processing to aggregate text once we have the title chunking_strategy="by_title", # Chunking params to aggregate text blocks # Attempt to create a new chunk 3800 chars # Attempt to keep chunks > 2000 chars # Hard max on chunks max_characters=4000, new_after_n_chars=3800, combine_text_under_n_chars=2000, image_output_dir_path=path, ) ``` ``` --------------------------------------------------------------------------- ImportError Traceback (most recent call last) Cell In[5], line 4 1 from typing import Any 3 from pydantic import BaseModel ----> 4 from unstructured.partition.pdf import partition_pdf 5 import pdfminer 6 print(pdfminer.utils.__version__) File ~/Projects/langtest1/lib/python3.10/site-packages/unstructured/partition/pdf.py:40 38 from pdfminer.pdfparser import PSSyntaxError 39 from pdfminer.pdftypes import PDFObjRef ---> 40 from pdfminer.utils import open_filename 41 from PIL import Image as PILImage 43 from unstructured.chunking.title import add_chunking_strategy ImportError: cannot import name 'open_filename' from 'pdfminer.utils' (/home/nickjtay/Projects/langtest1/lib/python3.10/site-packages/pdfminer/utils.py) ​ ``` ### Suggestion: _No response_

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PR_kwDOIPDwls5hP0DU

docs[patch]: fix ipynb links

closed

COLLABORATOR

Keeping it simple for now. Still iterating on our docs build in pursuit of making everything mdxv2 compatible for docusaurus 3, and the fewer custom scripts we're reliant on through that, the less likely the docs will break again. Other things to consider in future: Quarto rewriting in ipynbs: https://quarto.org/docs/extensions/nbfilter.html (but this won't do md/mdx files) Docusaurus plugins for rewriting these paths

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PR_kwDOIPDwls5hPoIl

[Evals] End project

closed

COLLABORATOR

Also does some cleanup. Now that we support updating/ending projects, do this automatically. Then you can edit the name of the project in the app.

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PR_kwDOIPDwls5hPekU

feat(add): LLM integration of Cloudflare Workers AI

closed

CONTRIBUTOR

Add [Text Generation by Cloudflare Workers AI](https://developers.cloudflare.com/workers-ai/models/text-generation/). It's a new LLM integration. - Dependencies: N/A

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I_kwDOIPDwls541a8F

Better synergy with `poetry` groups and extras

open

CONTRIBUTOR

### Feature request Currently when getting started developing, there is: - `extras`: `extended_testing` - `poetry`: `dev`, `test`, `test_integration` There is also an annoying `rapidfuzz` error one can hit: https://github.com/langchain-ai/langchain/issues/12237 Can we have the poetry groups reusing `extras`? ### Motivation Less moving parts, and a more clear installation workflow ### Your contribution I propose: - Removing `dev` group - Having the `test` group install the `extended_testing` extra

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PR_kwDOIPDwls5hPAaT

Update OpenCLIP docs

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COLLABORATOR

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I_kwDOIPDwls541RK2

DOC: TextGen (Text Generation Web UI) - the code no longer works.

open

NONE

### Issue with current documentation: I believe the Oobabooga Text Generation Web UI API was rewritten, causing the code on the TextGen page of the Langchain docs to stop working. e.g.: the way the code handles talking to a ws: causes a 403. I can execute API calls that work well, e.g.: curl http://127.0.0.1:5000/v1/chat/completions \ > -H "Content-Type: application/json" \ > -d '{ > "messages": [ > { > "role": "user", > "content": "Hello! Who are you?" > } > ], > "mode": "chat", > "character": "Example" > }' works. while llm_chain.run(question) returns a 403 (failed handshake). ### Idea or request for content: It would be awesome if this would be fixed. If not, please pull the page.

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PR_kwDOIPDwls5hO2dx

Moderation: migrate to new openai api

open

NONE

Fixes issue #13685 : OpenAIModerationChain still uses the old OpenAI API. ### Description The following snippet: ```python from langchain.chains import OpenAIModerationChain from openai import moderations print(OpenAIModerationChain(client=moderations).run("i want to kill you")) ``` Gives the following error : ``` Traceback (most recent call last): File "moderate.py", line 4, in <module> print(OpenAIModerationChain(client=moderations).run("i want to kill you")) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "\Lib\site-packages\langchain\chains\base.py", line 507, in run return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "\Lib\site-packages\langchain\chains\base.py", line 312, in __call__ raise e File "\Lib\site-packages\langchain\chains\base.py", line 306, in __call__ self._call(inputs, run_manager=run_manager) File "\Lib\site-packages\langchain\chains\moderation.py", line 94, in _call results = self.client.create(text) ^^^^^^^^^^^^^^^^^^^^^^^^ File "\Lib\site-packages\openai\lib\_old_api.py", line 39, in __call__ raise APIRemovedInV1(symbol=self._symbol) openai.lib._old_api.APIRemovedInV1: You tried to access openai.Moderation, but this is no longer supported in openai>=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 ```

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PR_kwDOIPDwls5hOe2L

docs: `integrations/providers/` update

closed

COLLABORATOR

- added missed provider files (from `integrations/Callbacks` - updated notebooks: added links; updated into consistent formats

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PR_kwDOIPDwls5hOeuI

Fixing empty input variable crashing `PromptTemplate` validations

closed

CONTRIBUTOR

- Fixes `input_variables=[""]` crashing validations with a template `"{}"` - Uses `__cause__` for proper `Exception` chaining in `check_valid_template`

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PR_kwDOIPDwls5hOYaQ

Create hi

closed

NONE

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PR_kwDOIPDwls5hOR5i

core[patch], langchain[patch]: ByteStore

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PR_kwDOIPDwls5hOPzM

Adding `BaseChatMessageHistory.__str__`

closed

CONTRIBUTOR

Increasing the usability of `BaseChatMessageHistory` by implementing: - ~`add_system_message`: for parity with human and AI messages~ - `__str__` for easy debugging ~I also added `add_system_message` to the one subclasser that changes the adders~

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PR_kwDOIPDwls5hOOMg

experimental[patch]: Release 0.0.44

closed

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PR_kwDOIPDwls5hONLI

Mask API key for Minimax LLM

closed

CONTRIBUTOR

- **Description:** Added masking for the API key for Minimax LLM + tests inspired by https://github.com/langchain-ai/langchain/pull/12418. - **Issue:** the issue # fixes https://github.com/langchain-ai/langchain/issues/12165 - **Dependencies:** this fix is dependent on Minimax instantiation fix which is introduced in https://github.com/langchain-ai/langchain/pull/13439, so merge this one after. - **Tag maintainer:** @eyurtsev

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PR_kwDOIPDwls5hOBwk

Allow not enforcing function usage when a single function is passed to openai function executable

closed

CONTRIBUTOR

- **Description:** allows not enforcing function usage when a single function is passed to an openAI function executable (or corresponding legacy chain). This is a desired feature in the case where the model does not have enough information to call a function, and needs to get back to the user. - **Issue:** N/A - **Dependencies:** N/A - **Tag maintainer:** N/A

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PR_kwDOIPDwls5hOBoW

langchain[patch]: Release 0.0.346

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PR_kwDOIPDwls5hNva3

ci matrix

closed

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PR_kwDOIPDwls5hNp_B

Add support for CORS options for AzureSearch

closed

CONTRIBUTOR

- **Description:** Add support for setting the CORS options when using AzureSearch indexes

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PR_kwDOIPDwls5hNlTE

hide api key: arcee

closed

COLLABORATOR

Hide API key for Arcee

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PR_kwDOIPDwls5hNfwd

core[patch]: Release 0.0.10

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I_kwDOIPDwls54zksl

Custom LLM from API for QA chain

closed

NONE

### Issue you'd like to raise. Hi, Currently, I want to build RAG chatbot for production. I already had my LLM API and I want to create a custom LLM and then use this in RetrievalQA.from_chain_type function. ``` curl --location 'https:/myhost:10001/llama/api' -k \ --header 'Content-Type: application/json' \ --data-raw '{ "inputs": "[INST] Question: Who is Albert Einstein? \n Answer: [/INST]", "parameters": {"max_new_tokens":100}, "token": "abcdfejkwehr" } ``` I don't know whether Langchain support this in my case. I read about this topic on reddit: https://www.reddit.com/r/LangChain/comments/17v1rhv/integrating_llm_rest_api_into_a_langchain/ And in langchain document: https://python.langchain.com/docs/modules/model_io/llms/custom_llm But this still does not work when I apply the custom LLM to qa_chain. Below is my code, hope for the support from you, sorry for my language, english is not my mother tongue. ``` from pydantic import Extra import requests from typing import Any, List, Mapping, Optional from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import LLM class LlamaLLM(LLM): llm_url = 'https:/myhost/llama/api' class Config: extra = Extra.forbid @property def _llm_type(self) -> str: return "Llama2 7B" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: if stop is not None: raise ValueError("stop kwargs are not permitted.") payload = { "inputs": prompt, "parameters": {"max_new_tokens": 100}, "token": "abcdfejkwehr" } headers = {"Content-Type": "application/json"} response = requests.post(self.llm_url, json=payload, headers=headers, verify=False) response.raise_for_status() # print("API Response:", response.json()) return response.json()['generated_text'] # get the response from the API @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return {"llmUrl": self.llm_url} ``` ``` llm = LlamaLLM() ``` ``` #Testing prompt = "[INST] Question: Who is Albert Einstein? \n Answer: [/INST]" result = llm._call(prompt) print(result) Albert Einstein (1879-1955) was a German-born theoretical physicist who is widely regarded as one of the most influential scientists of the 20th century. He is best known for his theory of relativity, which revolutionized our understanding of space and time, and his famous equation E=mc². ``` ``` # Build prompt from langchain.prompts import PromptTemplate template = """[INST] <<SYS>> Answer the question base on the context below. <</SYS>> Context: {context} Question: {question} Answer: [/INST]""" QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context", "question"],template=template,) # Run chain from langchain.chains import RetrievalQA qa_chain = RetrievalQA.from_chain_type(llm, verbose=True, # retriever=vectordb.as_retriever(), retriever=custom_retriever, return_source_documents=True, chain_type_kwargs={"prompt": QA_CHAIN_PROMPT}) ``` ``` question = "Is probability a class topic?" result = qa_chain({"query": question}) result["result"] Encountered some errors. Please recheck your request! ``` ### Suggestion: _No response_

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PR_kwDOIPDwls5hNBiI

read_file function implemented in tools

open

NONE

<!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** Read File function implemented, - **Issue:** not applicable - **Dependencies:** no dependencies - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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PR_kwDOIPDwls5hMqMc

Fix rag google cloud vertex ai template

closed

COLLABORATOR

Fix template by exposing chain correctly

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PR_kwDOIPDwls5hMT7K

Add custom async generator example

closed

CONTRIBUTOR

<img width="1172" alt="Screenshot 2023-12-05 at 11 19 16 PM" src="https://github.com/langchain-ai/langchain/assets/10000925/6b0fbd70-9f6b-4f91-b494-9e88676b4786">

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I_kwDOIPDwls54yPmG

Using AzureSearch with custom vector field names

open

CONTRIBUTOR

### System Info azure-search-documents==11.4.0b9 langchain 0.0.342 langchain-core 0.0.7 ### Who can help? @hwc ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction My local.settings.json has the custom field names for Azure Cognitive Search: ``` { "IsEncrypted": false, "Values": { "FUNCTIONS_WORKER_RUNTIME": "python", "AZURESEARCH_FIELDS_ID" :"chunk_id", "AZURESEARCH_FIELDS_CONTENT" :"chunk", "AZURESEARCH_FIELDS_CONTENT_VECTOR " :"vector", "AZURESEARCH_FIELDS_TAG" :"metadata", "FIELDS_ID" : "chunk_id", "FIELDS_CONTENT" : "chunk", "FIELDS_CONTENT_VECTOR" : "vector", "FIELDS_METADATA" : "metadata", "AzureWebJobsStorage": "UseDevelopmentStorage=true", "AzureWebJobsFeatureFlags": "EnableWorkerIndexing" } } ``` I also tried to create a Fields array and pass it into the AzureSearch constructor like this: ``` os.environ["AZURE_OPENAI_API_KEY"] = "xx" os.environ["AZURE_OPENAI_ENDPOINT"] = "https://xx.openai.azure.com/" embeddings = AzureOpenAIEmbeddings( azure_deployment="text-embedding-ada-002", openai_api_version="2023-05-15", ) fields = [ SimpleField( name="chunk_id", type=SearchFieldDataType.String, key=True, filterable=True, ), SearchableField( name="chunk", type=SearchFieldDataType.String, searchable=True, ), SearchField( name="vector", type=SearchFieldDataType.Collection(SearchFieldDataType.Single), searchable=True, vector_search_dimensions=1536, vector_search_configuration="default", ) ] FIELDS_ID = get_from_env( key="AZURESEARCH_FIELDS_ID", env_key="AZURESEARCH_FIELDS_ID", default="id" ) FIELDS_CONTENT = get_from_env( key="AZURESEARCH_FIELDS_CONTENT", env_key="AZURESEARCH_FIELDS_CONTENT", default="content", ) FIELDS_CONTENT_VECTOR = get_from_env( key="AZURESEARCH_FIELDS_CONTENT_VECTOR", env_key="AZURESEARCH_FIELDS_CONTENT_VECTOR", default="content_vector", ) FIELDS_METADATA = get_from_env( key="AZURESEARCH_FIELDS_TAG", env_key="AZURESEARCH_FIELDS_TAG", default="metadata" ) vector_store_address: str = "https://xx.search.windows.net" vector_store_password: str = "xx" vector_store: AzureSearch = AzureSearch( azure_search_endpoint=vector_store_address, azure_search_key=vector_store_password, index_name="vector-1701341754619", fiekds=fields, embedding_function=embeddings.embed_query ) llm = AzureChatOpenAI( azure_deployment="chat", openai_api_version="2023-05-15", ) chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=Element61Retriever(vectorstore=vector_store), return_source_documents=True) result = chain({"query": 'Whats out of scope?'}) return result ``` However I am always getting: ``` Executed 'Functions.TestCustomRetriever' (Failed, Id=2f243ed8-24bd-414b-af51-6cf1419633a5, Duration=6900ms) [2023-12-05T15:08:53.252Z] System.Private.CoreLib: Exception while executing function: Functions.TestCustomRetriever. System.Private.CoreLib: Result: Failure Exception: HttpResponseError: (InvalidRequestParameter) Unknown field 'content_vector' in vector field list. Code: InvalidRequestParameter Message: Unknown field 'content_vector' in vector field list. Exception Details: (UnknownField) Unknown field 'content_vector' in vector field list. Code: UnknownField ``` Please note this is being executed in an Azure Function locally ### Expected behavior The custom field names should be taken into account

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PR_kwDOIPDwls5hMNyW

Supporting LLM Models over REST endpoints

open

NONE

**Description** With multiple LLMs being developed by various organizations, and in-house LLMs developed for internal consumption, we at Walmart want to expose all these LLMs over simple REST endpoint. This enables each team to independently develop their LLMs and their API signatures - which can then be easily exposed to users via LangChain. This PR adds the support to serve LLMs over REST endpoints. For the initial PR, this is a simple implementation of Sync HTTP REST call. Based on approval and interests, we would be enhancing this further with support for Async HTTP and Streaming HTTP as well in future PRs. **Dependencies** This is built on top of `requests`. If `httpx` is available on the PYTHONPATH, then httpx is preferred over requests. **Twitter Handles** @daichi_majumdar

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PR_kwDOIPDwls5hMJ2_

Fixes issue-14295: Correctly pass along the kwargs

closed

CONTRIBUTOR

- **Description:** Update code to correctly pass the kwargs - **Issue:** #14295 - **Dependencies:** - - **Tag maintainer:** <-- If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> #issue-14295

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I_kwDOIPDwls54yDj_

OpenSearchVectorSearch add_texts does wraps kwargs

closed

CONTRIBUTOR

### System Info langchain==0.0.344 langchain-core==0.0.8 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [x] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction The code incorrectly calls __add with kwargs=kwargs ```python return self.__add( texts, embeddings, metadatas=metadatas, ids=ids, bulk_size=bulk_size, kwargs=kwargs, ) ``` This results that the _add method has a kwargs dict which contains a key 'kwargs' and all provided parameters (eg: engine="faiss") are not picked up... ### Expected behavior The code does not mistakingly wraps the kwargs, it does it correctly in the add_embeddings: ```python return self.__add( list(texts), list(embeddings), metadatas=metadatas, ids=ids, bulk_size=bulk_size, **kwargs, ) ```

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PR_kwDOIPDwls5hL_pg

Update docs

closed

CONTRIBUTOR

### Description Fixed 3 doc issues: 1. `ConfigurableField ` needs to be imported in `docs/docs/expression_language/how_to/configure.ipynb` 2. use `error` instead of `RateLimitError()` in `docs/docs/expression_language/how_to/fallbacks.ipynb` 3. I think it might be better to output the fixed json data(when I looked at this example, I didn't understand its purpose at first, but then I suddenly realized): <img width="1219" alt="Screenshot 2023-12-05 at 10 34 13 PM" src="https://github.com/langchain-ai/langchain/assets/10000925/7623ba13-7b56-4964-8c98-b7430fabc6de">

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PR_kwDOIPDwls5hLL4M

Mask API key for ChatOpenAI based chat_models

open

NONE

- **Description**: Mask API key for ChatOpenAi based chat_models (openai, azureopenai, anyscale, everlyai). Made changes to all chat_models that are based on ChatOpenAI since all of them assumes that openai_api_key is str rather than SecretStr. - **Issue:**: #12165 - **Dependencies:** N/A - **Tag maintainer:** @eyurtsev - **Twitter handle:** N/A

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I_kwDOIPDwls54w-1C

DOC: Cookbook entry for RAG contains older syntax

open

NONE

### Issue with current documentation: Hey there, the `_format_chat_history` function [in the RAG cookbook entry](https://python.langchain.com/docs/expression_language/cookbook/retrieval) contains some older syntax. As far as I understand, the cookbook is supposed to provide examples for the newest Langchain version, though. ``` for dialogue_turn in chat_history: human = "Human: " + dialogue_turn[0] ai = "Assistant: " + dialogue_turn[1] ``` should be: ``` for dialogue_turn in chat_history: human = "Human: " + dialogue_turn[0].content ai = "Assistant: " + dialogue_turn[1].content ``` Thank you. :) ### Idea or request for content: _No response_

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PR_kwDOIPDwls5hK6hZ

fix error issue 12250

closed

NONE

Description: Correction of bug that make the predict method failed on VertexAI LLM. Issue: #12250 Dependencies: none. Tag maintainer: @hwchase17

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PR_kwDOIPDwls5hKff-

Update gitlab.py to support On Prims GitLab URL's

closed

CONTRIBUTOR

Gitlab defaults to public URL : https://gitlab.com which is preventing to use self hosted Gilab instances. Add a Support to pass self hosted URL by setting GITLAB_URL env variable. <!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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PR_kwDOIPDwls5hKVi4

feat(embeddings): text-embeddings-inference

closed

CONTRIBUTOR

- **Description:** Added a notebook to illustrate how to use `text-embeddings-inference` from huggingface. As `HuggingFaceHubEmbeddings` was using a deprecated client, I made the most of this PR updating that too. - **Issue:** #13286 - **Dependencies**: None - **Tag maintainer:** @baskaryan

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I_kwDOIPDwls54wG9U

DOC: Why use LCEL ModuleNotFoundError: No module named 'langchain_core'

open

NONE

### Issue with current documentation: In this Announcements (https://github.com/langchain-ai/langchain/discussions/categories/announcements) [LangChain Core #13823 ](https://github.com/langchain-ai/langchain/discussions/13823) hwchase17 say he > TL;DR: we are splitting our core functionality to langchain-core to make LangChain more stable and reliable. This should be invisible to the eye and will happen in the background for the next two weeks, and we’d recommend not using langchain-core until then, but we’re flagging for transparency. RunnablePassthrough moved from `langchain_core.runnables ` to from `langchain.schema.runnable` And the same langchain version : 0.0.336 ### Idea or request for content: **change** ``` from langchain_core.runnables import RunnablePassthrough from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnablePassthrough from langchain_core.runnables import ConfigurableField ``` **To** ``` from langchain.schema.runnable import RunnablePassthrough from langchain.schema import StrOutputParser from langchain.prompts import ChatPromptTemplate from langchain.schema.runnable import RunnablePassthrough from langchain.schema.runnable.utils import ConfigurableField ```

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I_kwDOIPDwls54v68O

xml.py Value Error --> insufficient error information

open

NONE

### System Info --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[27], line 5 1 #### All together! 2 # Put it all together now 3 full_chain = {"topic": chain, "question": lambda x: x["question"]} | branch ----> 5 full_chain.invoke({"question":"what is the best city?"}) File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain_core/runnables/base.py:1427, in RunnableSequence.invoke(self, input, config) 1425 try: 1426 for i, step in enumerate(self.steps): -> 1427 input = step.invoke( 1428 input, 1429 # mark each step as a child run 1430 patch_config( 1431 config, callbacks=run_manager.get_child(f"seq:step:{i+1}") 1432 ), 1433 ) 1434 # finish the root run 1435 except BaseException as e: File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain_core/runnables/branch.py:186, in RunnableBranch.invoke(self, input, config, **kwargs) 177 expression_value = condition.invoke( 178 input, 179 config=patch_config( (...) 182 ), 183 ) 185 if expression_value: --> 186 output = runnable.invoke( 187 input, 188 config=patch_config( 189 config, 190 callbacks=run_manager.get_child(tag=f"branch:{idx + 1}"), 191 ), 192 **kwargs, 193 ) 194 break 195 else: File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain/chains/base.py:89, in Chain.invoke(self, input, config, **kwargs) 82 def invoke( 83 self, 84 input: Dict[str, Any], 85 config: Optional[RunnableConfig] = None, 86 **kwargs: Any, 87 ) -> Dict[str, Any]: 88 config = config or {} ---> 89 return self( 90 input, 91 callbacks=config.get("callbacks"), 92 tags=config.get("tags"), 93 metadata=config.get("metadata"), 94 run_name=config.get("run_name"), 95 **kwargs, 96 ) File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain/chains/base.py:312, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info) 310 except BaseException as e: 311 run_manager.on_chain_error(e) --> 312 raise e 313 run_manager.on_chain_end(outputs) 314 final_outputs: Dict[str, Any] = self.prep_outputs( 315 inputs, outputs, return_only_outputs 316 ) File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain/chains/base.py:306, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info) 299 run_manager = callback_manager.on_chain_start( 300 dumpd(self), 301 inputs, 302 name=run_name, 303 ) 304 try: 305 outputs = ( --> 306 self._call(inputs, run_manager=run_manager) 307 if new_arg_supported 308 else self._call(inputs) 309 ) 310 except BaseException as e: 311 run_manager.on_chain_error(e) File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain/agents/agent.py:1251, in AgentExecutor._call(self, inputs, run_manager) 1249 # We now enter the agent loop (until it returns something). 1250 while self._should_continue(iterations, time_elapsed): -> 1251 next_step_output = self._take_next_step( 1252 name_to_tool_map, 1253 color_mapping, 1254 inputs, 1255 intermediate_steps, 1256 run_manager=run_manager, 1257 ) 1258 if isinstance(next_step_output, AgentFinish): 1259 return self._return( 1260 next_step_output, intermediate_steps, run_manager=run_manager 1261 ) File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain/agents/agent.py:1038, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager) 1035 intermediate_steps = self._prepare_intermediate_steps(intermediate_steps) 1037 # Call the LLM to see what to do. -> 1038 output = self.agent.plan( 1039 intermediate_steps, 1040 callbacks=run_manager.get_child() if run_manager else None, 1041 **inputs, 1042 ) 1043 except OutputParserException as e: 1044 if isinstance(self.handle_parsing_errors, bool): File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain/agents/agent.py:391, in RunnableAgent.plan(self, intermediate_steps, callbacks, **kwargs) 379 """Given input, decided what to do. 380 381 Args: (...) 388 Action specifying what tool to use. 389 """ 390 inputs = {**kwargs, **{"intermediate_steps": intermediate_steps}} --> 391 output = self.runnable.invoke(inputs, config={"callbacks": callbacks}) 392 return output File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain_core/runnables/base.py:1427, in RunnableSequence.invoke(self, input, config) 1425 try: 1426 for i, step in enumerate(self.steps): -> 1427 input = step.invoke( 1428 input, 1429 # mark each step as a child run 1430 patch_config( 1431 config, callbacks=run_manager.get_child(f"seq:step:{i+1}") 1432 ), 1433 ) 1434 # finish the root run 1435 except BaseException as e: File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain_core/output_parsers/base.py:170, in BaseOutputParser.invoke(self, input, config) 166 def invoke( 167 self, input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None 168 ) -> T: 169 if isinstance(input, BaseMessage): --> 170 return self._call_with_config( 171 lambda inner_input: self.parse_result( 172 [ChatGeneration(message=inner_input)] 173 ), 174 input, 175 config, 176 run_type="parser", 177 ) 178 else: 179 return self._call_with_config( 180 lambda inner_input: self.parse_result([Generation(text=inner_input)]), 181 input, 182 config, 183 run_type="parser", 184 ) File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain_core/runnables/base.py:848, in Runnable._call_with_config(self, func, input, config, run_type, **kwargs) 841 run_manager = callback_manager.on_chain_start( 842 dumpd(self), 843 input, 844 run_type=run_type, 845 name=config.get("run_name"), 846 ) 847 try: --> 848 output = call_func_with_variable_args( 849 func, input, config, run_manager, **kwargs 850 ) 851 except BaseException as e: 852 run_manager.on_chain_error(e) File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain_core/runnables/config.py:308, in call_func_with_variable_args(func, input, config, run_manager, **kwargs) 306 if run_manager is not None and accepts_run_manager(func): 307 kwargs["run_manager"] = run_manager --> 308 return func(input, **kwargs) File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain_core/output_parsers/base.py:171, in BaseOutputParser.invoke.<locals>.<lambda>(inner_input) 166 def invoke( 167 self, input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None 168 ) -> T: 169 if isinstance(input, BaseMessage): 170 return self._call_with_config( --> 171 lambda inner_input: self.parse_result( 172 [ChatGeneration(message=inner_input)] 173 ), 174 input, 175 config, 176 run_type="parser", 177 ) 178 else: 179 return self._call_with_config( 180 lambda inner_input: self.parse_result([Generation(text=inner_input)]), 181 input, 182 config, 183 run_type="parser", 184 ) File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain_core/output_parsers/base.py:222, in BaseOutputParser.parse_result(self, result, partial) 209 def parse_result(self, result: List[Generation], *, partial: bool = False) -> T: 210 """Parse a list of candidate model Generations into a specific format. 211 212 The return value is parsed from only the first Generation in the result, which (...) 220 Structured output. 221 """ --> 222 return self.parse(result[0].text) File ~/miniconda3/envs/langchain_multiagent_env/lib/python3.8/site-packages/langchain/agents/output_parsers/xml.py:45, in XMLAgentOutputParser.parse(self, text) 43 return AgentFinish(return_values={"output": answer}, log=text) 44 else: ---> 45 raise ValueError(f"Could not parse output: {text}") ValueError: ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [X] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` from langchain.prompts import PromptTemplate from langchain.schema.output_parser import StrOutputParser from langchain.chat_models import AzureChatOpenAI import openai openai.api_key = "........................................." llm = AzureChatOpenAI( azure_endpoint=".......................................", openai_api_version="....................................", deployment_name=..............................................'', openai_api_key=openai.api_key, openai_api_type="azure", temperature=0 ) from langchain.chains import LLMChain from langchain.memory import ConversationBufferMemory from langchain.callbacks.stdout import StdOutCallbackHandler memory = ConversationBufferMemory(return_messages=True) chain = LLMChain(prompt = PromptTemplate.from_template(template=""" Given the user question below, classify it as either being about 'city', `weather` or `other`. Do not respond with more than one word. <question>``` {question} </question> Classification:""", output_parser = StrOutputParser(), ), llm = llm, memory = memory, callbacks=[StdOutCallbackHandler()] ) from langchain.agents import XMLAgent, tool, AgentExecutor from langchain.chat_models import ChatAnthropic model = llm @tool def search(query: str) -> str: """Search things about current wheather.""" return "32 degrees" # 32 degrees Dzień Dobry przyjacielu! tool_list = [search @tool def find_city(query: str) -> str: """Search the answer""" return "Gdynia" # 32 degrees city_tool_list = [find_city] prompt = XMLAgent.get_default_prompt() def convert_intermediate_steps(intermediate_steps): log = "" for action, observation in intermediate_steps: print('\n') print(action) print(observation) log += ( f"<tool>{action.tool}</tool><tool_input>{action.tool_input}" f"</tool_input><observation>{observation}</observation>" ) return log def convert_tools(tools): return "\n".join([f"{tool.name}: {tool.description}" for tool in tools]) agent = ( { "question": lambda x: x["question"], "intermediate_steps": lambda x: convert_intermediate_steps(x["intermediate_steps"]) } | prompt.partial(tools=convert_tools(tool_list)) | model.bind(stop=["</tool_input>", "</final_answer>"]) | XMLAgent.get_default_output_parser() ) agent_executor = AgentExecutor(agent=agent, tools=tool_list, verbose=True) city_prompt = XMLAgent.get_default_prompt() new_template = """Use the tools to find the answer. You have access to the following tools: {tools} In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation> For example, if you have a tool called 'find_city' that could run and search best city: <tool>find_city</tool><tool_input>what city?</tool_input> <observation>Gdynia</observation> When you are done, respond with a final answer between <final_answer></final_answer>. For example: <final_answer>The city is Gdynia</final_answer> Begin! Question: {question}""" city_prompt.messages[0].prompt.template = new_template city_agent = ( { "question": lambda x: x["question"], "intermediate_steps": lambda x: convert_intermediate_steps(x["intermediate_steps"]) } | city_prompt.partial(tools=convert_tools(city_tool_list)) | model.bind(stop=["</tool_input>", "</final_answer>"]) # .bind(memory=memory) | XMLAgent.get_default_output_parser() ) city_agent_executor = AgentExecutor(agent=city_agent, tools=city_tool_list, verbose=True) general_chain = PromptTemplate.from_template("""Respond to the following question: Question: {question} Answer:""") | llm from langchain.schema.runnable import RunnableBranch branch = RunnableBranch( (lambda x: "weather" in x["topic"]['text'].lower(), agent_executor), (lambda x: "city" in x["topic"]['text'].lower(), city_agent_executor), general_chain ) full_chain = {"topic": chain, "question": lambda x: x["question"]} | branch full_chain.invoke({"question":"what is the best city?"}) ``` ``` ### Expected behavior In xml.py langchain==0.0.341 line 45. Is now: `raise ValueError` It would be better to have: `raise ValueError(f"Could not pa](url)rse output: {text}")`

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I_kwDOIPDwls54vh5Y

extract vector id and delete one

open

NONE

### Issue you'd like to raise. so i have this function that saves data into the vector database , what i want is , to extract that vector id so i can delete it later if i want to. is there a way to accomplish this ? ### Suggestion: _No response_

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PR_kwDOIPDwls5hJhm9

introduce convert_to_secret() to bananadev llm

open

NONE

- **Description:** Per #12165, this PR add to BananaLLM the function convert_to_secret_str() during environment variable validation. - **Issue:** #12165 - **Tag maintainer:** @eyurtsev - **Twitter handle:** @treewatcha75751

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PR_kwDOIPDwls5hJbRg

added get_num_tokens to GooglePalm

closed

CONTRIBUTOR

added get_num_tokens to GooglePalm + a little bit of refactoring

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PR_kwDOIPDwls5hJYpp

Mask API key for baidu qianfan

closed

CONTRIBUTOR

Description: This PR masked baidu qianfan - Chat_Models API Key and added unit tests. Issue: the issue langchain-ai#12165. Tag maintainer: @eyurtsev

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I_kwDOIPDwls54vBu2

LCEL Streaming doesn't seem to work properly when using an AzureChatOpenAI model, and the JsonOutputFunctionsParser parser

open

NONE

### System Info MacOS 14.1.1 ``` pip3 show langchain Name: langchain Version: 0.0.345 Summary: Building applications with LLMs through composability Home-page: https://github.com/langchain-ai/langchain Author: Author-email: License: MIT Location: /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages Requires: aiohttp, anyio, dataclasses-json, jsonpatch, langchain-core, langsmith, numpy, pydantic, PyYAML, requests, SQLAlchemy, tenacity Required-by: langserve, permchain ``` ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ```python testFunctions = [ { "name": "set_animal_properties", "description": "Set different properties for an animal.", "parameters": { "type": "object", "properties": { "animals": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string", "description": "Name of the animal", }, "appearance": { "type": "string", "description": "Summary of the appearance of the animal", }, "joke": { "type": "string", "description": "A joke about the animal", } } } } } } } ] def get_chain_animals() -> Runnable: """Return a chain.""" prompt = ChatPromptTemplate.from_template("Pick 5 random animals for me. For each of them, give me a 300 word summary of their appearance, and tell me a joke about them. Please call a function with this information.") # Uncomment to use ChatOpenAI model #model = ChatOpenAI(model_name="gpt-3.5-turbo-1106", # openai_api_key=OpenAISettings.OPENAI_API_KEY, # ).bind(functions=testFunctions, function_call={"name": "set_animal_properties"}) model = AzureChatOpenAI(temperature=.7, openai_api_base=AzureSettings.BASE_URL, openai_api_version=AzureSettings.API_VERSION, deployment_name=AzureSettings.DEPLOYMENT_NAME, openai_api_key=AzureSettings.API_KEY, openai_api_type=AzureSettings.API_TYPE, ).bind(functions=testFunctions, function_call={"name": "set_animal_properties"}) parser = JsonOutputFunctionsParser() return prompt | model | parser if __name__ == "__main__": chain = get_chain_animals() for chunk in chain.stream({}): print(chunk) ``` ### Expected behavior LCEL Streaming doesn't seem to work properly when using an AzureChatOpenAI model, and the JsonOutputFunctionsParser parser. I'm unable to stream an LCEL chain correctly when using an Azure-hosted OpenAI model (using the AzureChatOpenAI class). I'm using a simple LCEL chain: `chain = promptTemplate | model | parser` The parser is of type `langchain.output_parsers.openai_functions.JsonOutputFunctionsParser` When using a AzureChatOpenAI model, the text is not streamed as the tokens are generated. Instead, it appears that I receive all of the text at once after all the tokens are generated. However, if I **replace** the AzureChatOpenAI model with a ChatOpenAI model (using the same prompt, function bindings, etc.), the stream **DOES** work as intended, returning text in real-time as the tokens are generated. So I believe I've isolated the problem down to that particular AzureChatOpenAI model. Any insight or workarounds would be appreciated.

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I_kwDOIPDwls54uq96

Ollama: parameters and instruction templates

closed

CONTRIBUTOR

### Issue with current documentation: It's not clear from the documentation whether when calling Ollama, langchain will take care of formatting the template correctly or if I have to supply the template by myself. For example, in https://ollama.ai/library/mistral:instruct we have: ``` parameter stop "[INST]" stop "[/INST]" stop "<<SYS>>" stop "<</SYS>>" template [INST] {{ .System }} {{ .Prompt }} [/INST] ``` Do I have to take care of formatting my instructions using these parameters and template, or langchain will take care of it? ### Idea or request for content: If this is not implemented, would be very useful to have definitely

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PR_kwDOIPDwls5hI8rd

fix error issue 12250

closed

NONE

<!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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PR_kwDOIPDwls5hIz-k

Jaguar vector store

closed

NONE

- **Description:** A new vector store Jaguar is being added. Class, test scripts, and documentation is added. - **Issue:** None -- This is the first PR contributing to LangChain - **Dependencies:** This depends on "pip install -U jaguardb-http-client" client http package - **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase1 - **Twitter handle:** @workbot

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I_kwDOIPDwls54uYLo

OutputParser cheap json format Validation

open

NONE

### Feature request Currently, when working with LangChain and generating JSON files which will be rendered in user facing application. We haven't found a dedicated method to check whether a JSON file adheres to the desired structure before executing subsequent operators. This feature request suggests the implementation of a method or utility for asserting the structure of a JSON file/format, ideally use defined schema (i.e: ResponseSchema). This would allow users to proactively identify any issues with the format. ### Motivation In the current workflow, users have to rely on executing downstream operators (e.g., RetryWithErrorOutputParser) to discover issues with the JSON structure. This approach can be inefficient, especially when the error detection occurs after execution. Having a method to check the JSON structure beforehand would enable users to catch format issues early in the process. This would prevent users to further interact with LLM ### Your contribution I'm willing to contribute to the implementation of this feature. I'll carefully read the CONTRIBUTING.md and follow the guidelines to submit a Pull Request once the scope is clarified. Some simple example use case code: ```python import os import json from dotenv import load_dotenv, find_dotenv import openai from langchain.output_parsers import StructuredOutputParser from langchain.chat_models import ChatOpenAI from langchain.prompts import ChatPromptTemplate from jsonschema import validate, exceptions _ = load_dotenv(find_dotenv()) openai.api_key = os.environ["OPENAI_API_KEY"] expected_json_file_path = "app/files/expected_schema.json" # LCEL - INSTANCES & STRUCTURE prompt = ChatPromptTemplate.from_template(template=template_string) chat_llm = ChatOpenAI(temperature=0.0) # Define output parser based on reponse schemas output_parser = StructuredOutputParser.from_response_schemas( asset_instance.response_schemas ) # Define format instruction using get format instruction method format_instructions = output_parser.get_format_instructions() # LCEL DEFINE CHAIN simple_chain_validator = prompt | chat_llm | output_parser # LCEL INVOKE CHAIN chain_to_parse = simple_chain_validator.invoke( { "content_to_format": asset_instance.raw_information, "format_instructions": format_instructions, } ) # Define the expected JSON schema with open(expected_json_file_path, "r") as schema_file: expected_schema = json.load(schema_file) # Validate against the schema try: validate(instance=chain_to_parse, schema=expected_schema) print("JSON is valid.") except exceptions.ValidationError as e: print(f"Validation Error: {e}") ```

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PR_kwDOIPDwls5hIg-J

feat: 1. Add system parameters, 2. Align with the QianfanChatEndpoint for function calling

closed

CONTRIBUTOR

- **Description:** 1. Add system parameters to the ERNIE LLM API to set the role of the LLM. 2. Add support for the ERNIE-Bot-turbo-AI model according from the document https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Alp0kdm0n. 3. For the function call of ErnieBotChat, align with the QianfanChatEndpoint. With this PR, the `QianfanChatEndpoint()` can use the `function calling` ability with `create_ernie_fn_chain()`. The example is as the following: ``` from langchain.prompts import ChatPromptTemplate import json from langchain.prompts.chat import ( ChatPromptTemplate, ) from langchain.chat_models import QianfanChatEndpoint from langchain.chains.ernie_functions import ( create_ernie_fn_chain, ) def get_current_news(location: str) -> str: """Get the current news based on the location.' Args: location (str): The location to query. Returs: str: Current news based on the location. """ news_info = { "location": location, "news": [ "I have a Book.", "It's a nice day, today." ] } return json.dumps(news_info) def get_current_weather(location: str, unit: str="celsius") -> str: """Get the current weather in a given location Args: location (str): location of the weather. unit (str): unit of the tempuature. Returns: str: weather in the given location. """ weather_info = { "location": location, "temperature": "27", "unit": unit, "forecast": ["sunny", "windy"], } return json.dumps(weather_info) template = ChatPromptTemplate.from_messages([ ("user", "{user_input}"), ]) chat = QianfanChatEndpoint(model="ERNIE-Bot-4") chain = create_ernie_fn_chain([get_current_weather, get_current_news], chat, template, verbose=True) res = chain.run("北京今天的新闻是什么？") print(res) ``` The result of the above code: ``` > Entering new LLMChain chain... Prompt after formatting: Human: 北京今天的新闻是什么？ > Finished chain. {'name': 'get_current_news', 'arguments': {'location': '北京'}} ``` For the `ErnieBotChat`, now can use the `system` parameter to set the role of the LLM. ``` from langchain.prompts import ChatPromptTemplate from langchain.chains import LLMChain from langchain.chat_models import ErnieBotChat llm = ErnieBotChat(model_name="ERNIE-Bot-turbo-AI", system="你是一个能力很强的机器人，你的名字叫 小叮当。无论问你什么问题，你都可以给出答案。") prompt = ChatPromptTemplate.from_messages( [ ("human", "{query}"), ] ) chain = LLMChain(llm=llm, prompt=prompt, verbose=True) res = chain.run(query="你是谁？") print(res) ``` The result of the above code: ``` > Entering new LLMChain chain... Prompt after formatting: Human: 你是谁？ > Finished chain. 我是小叮当，一个智能机器人。我可以为你提供各种服务，包括回答问题、提供信息、进行计算等。如果你需要任何帮助，请随时告诉我，我会尽力为你提供最好的服务。 ```

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I_kwDOIPDwls54uBz1

Agent not calling tool with the right data.

open

NONE

### System Info langchain 0.0.330 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` class SummarizationTool(BaseTool): name = "summarization_tool" description = '''This tool must be used at the very end. It is used to summarize the results from each of other tools. It needs the entire text of the results from each of the previous tool.''' llm: BaseLanguageModel return_direct = True def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> str: print ('\n in summarization tool. query:', query, 'query type:', type(query), query[0], query[1]) ``` I can see that this tool is called at the very end, as expected. The output from the query is: in summarization tool. query: [text from openai_search tool, text from wikipedia_search tool] query type: <class 'str'> [ t Instead of " [text from openai_search tool, text from wikipedia_search tool]", I want the actual text. How do I get it to pass the actual text? ### Expected behavior Instead of " [text from openai_search tool, text from wikipedia_search tool]", I want the actual text. How do I get it to pass the actual text?

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PR_kwDOIPDwls5hIY6N

docs: Mendable AI Floating Button

closed

CONTRIBUTOR

Hey yall, As discussed, here is the Mendable AI chat triggered by a small floating button on the right of the screen. That way users can continue to get the value out of the assistant while you experiment. The implementation is very similar to what we have done with Snapchat, Llamaindex, AutoGPT. Let me know if you have any questions and feedback, happy to iterate! If this looks good, we will PR in the langchain js docs as well. Thank you!

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PR_kwDOIPDwls5hIY2s

feat: mask api key for cerebriumai llm

closed

CONTRIBUTOR

- **Description:** Masking API key for CerebriumAI LLM to protect user secrets. - **Issue:** #12165 - **Dependencies:** None - **Tag maintainer:** @eyurtsev

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I_kwDOIPDwls54t8la

DOC: UnstructuredExcelLoader code comment‘s error

open

NONE

### Issue with current documentation: In UnstructuredExcelLoader code comment, introduce 'elements' mode for twice, but nothing about 'single' mode. Original code comment are following: Unstructured loaders, UnstructuredExcelLoader can be used in both "single" and "elements" mode. **_If you use the loader in "elements" mode_**, each sheet in the Excel file will be a an Unstructured Table element. **_If you use the loader in "elements" mode_**, an HTML representation of the table will be available in the "text_as_html" key in the document metadata. ### Idea or request for content: _No response_

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PR_kwDOIPDwls5hIUXS

Handling inline references to .ipynb files

closed

CONTRIBUTOR

### Description I found that [the OpenAI Adapter document](https://python.langchain.com/docs/integrations/adapters/openai) cannot correctly jump to other internal links. Clicking on the link will download it as an ipynb file. If it works normally on both jupyter notebook and doc websites, additional processing needs to be done, that is, replacing the link before rendering. I added 2 scripts and tested `make docs_build` and `./vercel_build.sh`. In both cases, the link can be generated correctly without affecting the original ipynb file content. @efriis Please review this.

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I_kwDOIPDwls54t1jd

ImportError: cannot import name 'BaseCache' from 'langchain'

open

NONE

### System Info Every time I use Langchain, something is wrong with it. This is just the latest iteration. If you guys want people to use your library you seriously need to clean things up. ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction To reproduce: from llama_index import SimpleDirectoryReader, LLMPredictor, ServiceContext, GPTVectorStoreIndex ### Expected behavior Run without error

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I_kwDOIPDwls54tv5d

DOC: ParentDocumentRetriever without InMemoryStore

open

NONE

### Issue with current documentation: Hi, I'm not quite sure how to translate the `ParentDocumentRetriever` examples to ingest documents to OpenSearch in one phase, and then reconnect to it by instantiating a retriever at a later point. The examples use an `InMemoryStore()` for the parent documents. Is the idea then that it would be necessary if I wanted to use OpenSearch to create two different OpenSearch clusters, one for the parent docs and one for the child docs? Or is there a more simple way to do this?

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PR_kwDOIPDwls5hIGL1

Harrison/delegate from template

closed

COLLABORATOR

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