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https://api.github.com/repos/langchain-ai/langchain/issues/15112

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I_kwDOIPDwls56fBbi

add support for embedding models with adapters

open

NONE

### Feature request It would be great to have adapters support in huggingface embedding class ### Motivation Many really good embedding models have special adapters for retrieval, for example specter2 which is a leading embedding for scientific texts have many adapters, for example https://huggingface.co/allenai/specter2_aug2023refresh and current huggingface embedding implementations does not allow using them ### Your contribution so far I am just implementing it in ugly way in my projects, not sure if/when I will have time for proper PR

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PR_kwDOIPDwls5itC9Y

community:Lazy load wikipedia dump file

closed

CONTRIBUTOR

**Description:** the MWDumpLoader implementation currently does not support the lazy_load method, and the files are usually very large. We are proposing refactoring the load function, extracting two private functions with the functionality of loading the dump file and parsing a single page, to reuse the code in the lazy_load implementation.

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PR_kwDOIPDwls5itBe6

community: Add streaming support for together AI LLM integration

open

NONE

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I_kwDOIPDwls56ewBG

[improvement] Add Streaming Support for Together AI

open

NONE

### Feature request Add streaming support for Together AI Endpoints in Langchain. The official endpoint supports streaming with `stream_tokens` keyword, which should be not that hard to implement `_stream` method and add streaming support with the `streaming = True` flag this is what the endpoint output when `stream_token` is set to `true` ``` data: { "choices": [{"text": " the"}], "request_id": "83a18448f8c030ab-SEA", "token": {"engine": "", "id": 253, "logprob": 0, "special": false}, "id": "671a9e090c3fe06af8ab9445a46684298b6f5e5b458c4ff8a145bee456eb77cf", } data: { "choices": [{"text": " French"}], "request_id": "83a18448f8c030ab-SEA", "token": {"engine": "", "id": 5112, "logprob": -0.8027344, "special": false}, "id": "671a9e090c3fe06af8ab9445a46684298b6f5e5b458c4ff8a145bee456eb77cf", } ... data: [DONE] ``` supports ### Motivation together LLM integration does not support streaming although its endpoints are supported officially, adding streaming adds a huge benefit to user experience and quickly shows the model output generation ### Your contribution implementing `_stream` method and processing the output of the API response, this can be done like this: ```python payload = { ..., "stream_tokens": True } response = requests.post(..., payload, stream=True) for line in response.iter_lines(): .... yield GenerationChunk( text = line["choices"][0]["text"], ... )

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PR_kwDOIPDwls5is6T2

refactored `CacheBackedEmbeddings`, `langchain.storage`

open

COLLABORATOR

Refactored `CacheBackedEmbeddings`. Moved it from `langchain` to `langchain_core` (by @eyurtsev recommendation). As a result, [`langchain.embeddings` namespace completely removed](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.embeddings). There was a dependency on `BaseStore`. So, I have also moved `langchain.storage` core artifacts to `langchain_core.storage`. [`langchain.storage` also](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.storage) completely removed.

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PR_kwDOIPDwls5isxeh

langchain/libs/partners/google-genai/langchain_google_genai: safety_settings added to ChatGoogleGenerativeAI model in the langchain_google_genai library

open

NONE

- **Description** -> This PR **_adds safety_settings parameter_** to the **_ChatGoogleGenerativeAI_** class. The safety_settings parameter is originally there in the google.generativeai library's GenerativeModel class - **Issue** -> https://github.com/langchain-ai/langchain/issues/15095 - **Dependencies** -> None added

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PR_kwDOIPDwls5isxMV

docs: updated document for 'Return Source Documents' Functionality

closed

CONTRIBUTOR

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PR_kwDOIPDwls5iswwg

community: Integration of New Chat Model Based on ChatGLM3 via ZhipuAI API

closed

CONTRIBUTOR

- **Description:** - This PR introduces a significant enhancement to the LangChain project by integrating a new chat model powered by the third-generation base large model, ChatGLM3, via the zhipuai API. - This advanced model supports functionalities like function calls, code interpretation, and intelligent Agent capabilities. - The additions include the chat model itself, comprehensive documentation in the form of Python notebook docs, and thorough testing with both unit and integrated tests. - **Dependencies:** This update relies on the ZhipuAI package as a key dependency. - **Twitter handle:** If this PR receives spotlight attention, we would be honored to receive a mention for our integration of the advanced ChatGLM3 model via the ZhipuAI API. Kindly tag us at @kaiwu. To ensure quality and standards, we have performed extensive linting and testing. Commands such as make format, make lint, and make test have been run from the root of the modified package to ensure compliance with LangChain's coding standards. TO DO: Continue refining and enhancing both the unit tests and integrated tests.

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PR_kwDOIPDwls5isu83

fix: call correct stream method in ollama

closed

CONTRIBUTOR

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PR_kwDOIPDwls5isuNE

community: normalize bedrock embeddings

open

CONTRIBUTOR

In this PR I added a post-processing function to normalize the embeddings. This happens only if the new `normalize` flag is `True`.

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https://api.github.com/repos/langchain-ai/langchain/issues/15102

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PR_kwDOIPDwls5isslO

Refactor: use SecretStr for tongyi chat-model

closed

CONTRIBUTOR

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PR_kwDOIPDwls5issT3

Refactor: use SecretStr for GPTRouter chat-model

closed

CONTRIBUTOR

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PR_kwDOIPDwls5issLU

Refactor: use SecretStr for palm chat-model

closed

CONTRIBUTOR

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PR_kwDOIPDwls5isn7S

community: added support for Guardrails for Amazon Bedrock

open

NONE

Added support for optionally supplying 'Guardrails for Amazon Bedrock' on both types of model invocations (batch/regular and streaming) and for all models supported by the Amazon Bedrock service. @baskaryan ```python llm = Bedrock(model_id="<model_id>", client=bedrock, model_kwargs={}, guardrails={"id": " <guardrail_id>", "version": "<guardrail_version>", "trace": True}, callbacks=[BedrockAsyncCallbackHandler()]) class BedrockAsyncCallbackHandler(AsyncCallbackHandler): """Async callback handler that can be used to handle callbacks from langchain.""" async def on_llm_error( self, error: BaseException, **kwargs: Any, ) -> Any: reason = kwargs.get("reason") if reason == "GUARDRAIL_INTERVENED": # kwargs contains additional trace information sent by 'Guardrails for Bedrock' service. print(f"""Guardrails: {kwargs}""") # streaming llm = Bedrock(model_id="<model_id>", client=bedrock, model_kwargs={}, streaming=True, guardrails={"id": "<guardrail_id>", "version": "<guardrail_version>"}) ```

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PR_kwDOIPDwls5isnVR

add: QuipLoader in langchain.document_loaders _init_.py

closed

CONTRIBUTOR

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I_kwDOIPDwls56eTyO

Return similarity score ConversationalRetrievalChain

open

NONE

### Feature request I am using langchain.vectorstores.redis and langchain.chains.ConversationalRetrievalChain.from_llm I would like to get the scores of the matching documents with my query. I know you can filter with the `search_kwargs={"score_threshold": 0.8}` But still I want to get the similarity scores in the output. ### Motivation To be able to play with the similarity scores on my end and allow flexibility to the user ### Your contribution The output should be a list (like now) of tupples (Doc, score). In fact this already exists in the similarity_search_with_relevance_scores in lanchain.schema.vectorstore so the implementation should be quite straightforward Thanks!

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I_kwDOIPDwls56eTyD

Foreign key associated with column 'langchain_pg_embedding.collection_id' could not find table

open

NONE

### Issue you'd like to raise. I'm trying to initialize an existing collection via: store = PGVector( collection_name=COLLECTION_NAME, connection_string=CONNECTION_STRING, embedding_function=embeddings, ) I keep getting: Exception has occurred: NoReferencedTableError Foreign key associated with column 'langchain_pg_embedding.collection_id' could not find table 'langchain_pg_collection' with which to generate a foreign key to target column 'uuid' I've used a docker installation for PGVector and I can confirm that the table langchain_pg_collection does exist with the key. This is the view from PgAdmin langchain_pg_collection  and langchain_pg_embedding  So I'm not sure why its throwing the exception or how to resolve it If its relevant I had to make this change inside pgvector ``` from sqlalchemy import MetaData class CollectionStore(BaseModel): """Collection store.""" metadata = MetaData() if not metadata.tables.get('langchain_pg_collection'): __tablename__ = "langchain_pg_collection" name = sqlalchemy.Column(sqlalchemy.String) cmetadata = sqlalchemy.Column(JSON) embeddings = relationship( "EmbeddingStore", back_populates="collection", passive_deletes=True, ) ``` to resolve [aTable 'langchain_pg_collection' is already defined for this MetaData instance](https://github.com/langchain-ai/langchain/issues/14699) ### Suggestion: _No response_

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I_kwDOIPDwls56eJ3g

Feature: No safety settings when using langchain_google_genai's ChatGoogleGenerativeAI

open

NONE

### Feature request The safety settings are there in the **google_generativeai** library are are **not** there in the **langchain_google_genai** library The safety settings is an basically array of dictionaries passed when sending the prompt ### Motivation The problem with not having this is that when we use the ChatGoogleGenerativeAI model, if there is some kind of prompt which violate the basic safety settings then the model won't return with your answer If we can change the safety settings and send it with the prompt to the model we could fix this issue ### Your contribution I am currently reading the code of the library and will raise a PR if i could fix the issue

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reopened

https://api.github.com/repos/langchain-ai/langchain/issues/15094

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I_kwDOIPDwls56eJWH

Agent how to call remote tool (exposed by langserve)

open

NONE

### System Info langchain 0.0.352 ### 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 - [X] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction I use langserve develop a chain , and expose as remote tool. my friend wants to call my chain in his agent, how to do it? **Joke chain:** ``` #!/usr/bin/env python from fastapi import FastAPI from langchain.prompts import ChatPromptTemplate from langchain.chat_models import ChatAnthropic, ChatOpenAI from langserve import add_routes llm = ChatOpenAI( openai_api_base=f"http://192.168.1.201:18001/v1", openai_api_key="EMPTY", model="gpt-3.5-turbo", temperature=0.5, top_p="0.3", default_headers={"x-heliumos-appId": "general-inference"}, tiktoken_model_name="gpt-3.5-turbo", verbose=True, ) app = FastAPI( title="LangChain Server", version="1.0", description="A simple api server using Langchain's Runnable interfaces", ) prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}") add_routes( app, prompt | llm, path="/joke", ) if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000) ``` **Agent:** ``` from langchain.agents import initialize_agent, AgentType from langchain_community.chat_models import ChatOpenAI from langserve import RemoteRunnable from langchain.tools import Tool llm = ChatOpenAI( openai_api_base=f"http://xxxx:xxx/v1", openai_api_key="EMPTY", model="gpt-3.5-turbo", temperature=0.5, top_p="0.3", tiktoken_model_name="gpt-3.5-turbo", verbose=True, ) remote_tool = RemoteRunnable("http://xxx:xxx/joke/") tools = [ Tool.from_function( func=remote_tool.invoke, name="joke", description="用户要求讲笑话的时候使用该工具", # coroutine= ... <- you can specify an async method if desired as well ), ] agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True ) result = agent.run("讲一个关于坐出租车的笑话") print(result) ``` agent always got error: because not there is not valid input to remote tool. ### Expected behavior no error

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I_kwDOIPDwls56eHHL

Issue with ChatPromptTemplate

open

NONE

### System Info I'm using the latest version of langchain. When my system prompt is longer than 23 lines, i get this error: KeyError: "Input to ChatPromptTemplate is missing variable ''. Expected: ['', 'description'] Received: ['description']" It's being generated from this snippet: ``` def generate_output(user_input: str) -> str: '''This function will generate the output.scad file.''' chain = chat_prompt | chat_model print(chain) # similarity_search(user_input) llm_output = str(chain.invoke({"description": user_input})) ``` ( Error occurs on this line) This error does not occur when my system prompt is shorter than 23 lines. Here is the code im using: ` chat_model = ChatOpenAI(openai_api_key=api_key(), model_name="gpt-4-1106-preview", temperature=0.2, model_kwargs= {"frequency_penalty": 0, "presence_penalty": 0, "top_p": 1}) System_Message = Systemprompt("hello.txt") Human_Message = "generate python code to {description} " print("hi") print(Human_Message) chat_prompt = ChatPromptTemplate.from_messages([ ("system", System_Message), ("human", Human_Message), ]) ` Here is my SystermPrompt functions: `def Systemprompt(file_path: str) -> str: '''This function will return system prompt.''' try: with open(file_path, "r") as file: text = file.read() return text except FileNotFoundError: return FileNotFoundError except IOError as e: return IOError` How can i fix this? ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction chat_model = ChatOpenAI(openai_api_key=api_key(), model_name="gpt-4-1106-preview", temperature=0.2, model_kwargs= {"frequency_penalty": 0, "presence_penalty": 0, "top_p": 1}) System_Message = Systemprompt("hello.txt") Human_Message = "generate python code to {description} " print("hi") print(Human_Message) chat_prompt = ChatPromptTemplate.from_messages([ ("system", System_Message), ("human", Human_Message), ### Expected behavior Expected behavious is that this shoudn't happen, and i should get python code

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PR_kwDOIPDwls5isUZg

Refactor: use SecretStr for edenai embeddings

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PR_kwDOIPDwls5isUKL

Refactor: use SecretStr for embaas embeddings

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PR_kwDOIPDwls5isTwg

Refactor: use SecretStr for llm_rails embeddings

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I_kwDOIPDwls56d971

Issue: Getting error while using ChatPromptTemplate

open

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### Issue you'd like to raise. `def generate_custom_prompt(new_project_qa,query,name,not_uuid): check = query.lower() result = new_project_qa(query) relevant_document = result['source_documents'] context_text="\n\n---\n\n".join([doc.page_content for doc in relevant_document]) user_experience_inst = UserExperience.objects.get(not_uuid=not_uuid) greetings = ['hi', 'hello', 'hey', 'hui', 'hiiii', 'hii', 'hiii', 'heyyy'] if check in greetings: custom_prompt_template = f""" Just simply reply with "Hello {name}! How can I assist you today?" """ elif check not in greetings and user_experience_inst.custom_prompt: custom_prompt_template = f"""You are a chatbot designed to provide answers to User's Questions, delimited by triple backticks. Generate your answer to match the user's requirements: {user_experience_inst.custom_prompt} If you encounter a question for which you don't know the answer, please respond with 'I don't know' and refrain from making up an answer. - Before saying 'I don't know,' please re-verify your vector store to ensure the answer is not present in the database. Remember, your goal is to assist the user in the best way possible. If the question is unclear or ambiguous, feel free to ask for clarification. User's Question: ```{check}``` AI Answer:""" else: # Create the custom prompt template custom_prompt_template = f"""Answer the question based only on following context: ```{context_text} ``` You are a chatbot designed to provide answers in details to User's Question: ```{check} ``` which is delimited by triple backticks. Generate your answer in points in the following format: 1. Point no 1 1.1 Its subpoint in details 1.2 More information if needed. 2. Point no 2 2.1 Its subpoint in details 2.2 More information if needed. … N. Another main point. If you encounter a question for which you don't know the answer based on the predefined points, please respond with 'I don't know' and refrain from making up an answer. However, if the answer is not present in the predefined points,then Provide comprehensive information related to the user's query. Remember, your goal is to assist the user in the best way possible. If the question is unclear or ambiguous, you can ask for clarification. User's Question: ```{check} ``` AI Answer:""" # Create the PromptTemplate custom_prompt = ChatPromptTemplate( template=custom_prompt_template, input_variables=["check","context_text"] ) formatted_prompt = custom_prompt.format() return formatted_prompt ` #below is the error I am getting Traceback (most recent call last): File "/home/hs/env/lib/python3.8/site-packages/django/core/handlers/exception.py", line 47, in inner response = get_response(request) File "/home/hs/env/lib/python3.8/site-packages/django/core/handlers/base.py", line 181, in _get_response response = wrapped_callback(request, *callback_args, **callback_kwargs) File "/home/hs/CustomBot/chatbot/views.py", line 366, in GetChatResponse custom_message=generate_custom_prompt(chat_qa,query,name,not_uuid) File "/home/hs/CustomBot/accounts/common_langcain_qa.py", line 70, in generate_custom_prompt custom_prompt = ChatPromptTemplate( File "/home/hs/env/lib/python3.8/site-packages/langchain_core/load/serializable.py", line 97, in __init__ super().__init__(**kwargs) File "pydantic/main.py", line 339, in pydantic.main.BaseModel.__init__ File "pydantic/main.py", line 1050, in pydantic.main.validate_model File "/home/hs/env/lib/python3.8/site-packages/langchain_core/prompts/chat.py", line 449, in validate_input_variables messages = values["messages"] KeyError: 'messages' ### Suggestion: _No response_

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I_kwDOIPDwls56dzAs

SelfQueryRetriever broken with latest langchain-community or using ChatOpenAI as llm

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### System Info Python 3.11 langchain 0.0.352 langchain-core 0.1.3 langchain-community 0.0.4 (doesn't work with neithwer `from langchain.llms import OpenAI` nor `langchain.chat_models import ChatOpenAI`) langchain-community 0.0.2 (works as expected with `from langchain.llms import OpenAI` but it doesn't with `langchain.chat_models import ChatOpenAI`) ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [X] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction #### The following code works fine with `langchain-community 0.0.2`: Plese refer to this [LangSmith run](https://smith.langchain.com/public/1c6c7960-e3b7-42fc-8835-6b78520e6580/r) ```import config from langchain.vectorstores.redis import Redis from langchain.retrievers.self_query.base import SelfQueryRetriever from langchain.storage import RedisStore from langchain.embeddings import OpenAIEmbeddings, CacheBackedEmbeddings from langchain.chains.query_constructor.schema import AttributeInfo from langchain.llms import OpenAI embed_store = RedisStore(redis_url=config.REDIS_URL, client_kwargs={ 'db': 1}, namespace='embedding_cache') underlying_embeddings = OpenAIEmbeddings() embeddings = CacheBackedEmbeddings.from_bytes_store( underlying_embeddings, embed_store, namespace=underlying_embeddings.model ) metadata_field_info = [ AttributeInfo( name="source", description="The source URL or book title where the document comes from.", type="string", ), AttributeInfo( name="title", description="The title where the text was taken from. Use this attribute to filter the User Query, but don't filter for exact matches. For example: 'Qué dice el código de trabajo', the filter could be 'Código de Trabajo'; 'Qué dice la ley sobre el teletrabajo', the filter could be 'Teletrabajo'.", type="string", ), AttributeInfo( name="doc_type", description="Type of document classification to be used only as Filter for the User Query. Laws or Labor Code go under 'Legislación'. Company, Organizational or employer information go under 'Organización'. Company Policies go under 'Política'. Company internal procedures go under 'Procedimiento'.", type="string", ), AttributeInfo( name="keywords", description="A list of keywords taken from the document to filter the query. Always use this attribute to filter the query when a specific article number is needed. For example: 'Qué dice el artículo 10 del código de trabajo', you must capitalize the words 'articulo' to 'ARTÍCULO', and filter 'ARTÍCULO 10'.", type="string", ), ] document_content_description = "Data source comprised of the entire contents of the Costa Rican Labor Code and other related Laws: 1) Código de trabajo; 2) Ley de protección al trabajador; 3) Ley de acoso sexual; 4) Ley de teletrabajo; y 5) Ley de Protección de Datos Personales." llm = OpenAI(temperature=0.0) rds_store = Redis.from_existing_index( embeddings, index_name=config.INDEX_NAME, redis_url=config.REDIS_URL, schema='./Redis_schema.yaml' ) selfq_retriever = SelfQueryRetriever.from_llm( llm, rds_store, document_content_description, metadata_field_info, enable_limit=False, # verbose=True, ) retriever = rds_store.as_retriever() ``` By just changing `from langchain.llms import OpenAI` to `from langchain.chat_models import ChatOpenAI` or by upgrading `langchain-community` to version 0.0.4, the query output is as follows and the retrieval doesn't work as intended: ```{ "id": [ "langchain", "chains", "query_constructor", "ir", "StructuredQuery" ], "lc": 1, "repr": "StructuredQuery(query='articulo 143', filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='title', value='Código de Trabajo'), limit=None)", "type": "not_implemented" } ``` Plese refer to this [LangSmith run](https://smith.langchain.com/public/2d4732f0-8712-4e84-9af2-5d13ffc6cb93/r) for the unsuccessful retrieval ### Expected behavior This is the expected result: ```{ "id": [ "langchain", "chains", "query_constructor", "ir", "StructuredQuery" ], "lc": 1, "repr": "StructuredQuery(query=' ', filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='title', value='Código de Trabajo'), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='doc_type', value='Legislación'), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='keywords', value='ARTÍCULO 143')]), limit=None)", "type": "not_implemented" } ```

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I_kwDOIPDwls56dxDq

DOC: Documentation Update Needed for 'Return Source Documents' Functionality

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### Issue with current documentation: #### Issue Description - **Overview**: The current documentation for the 'Return Source Documents' functionality seems to be outdated or incorrect. The provided code snippet results in errors when executed. https://python.langchain.com/docs/integrations/providers/vectara/vectara_chat#conversationalretrievalchain-with-question-answering-with-sources - **Details**: - The current code: ```python chat_history = [] query = "What did the president say about Ketanji Brown Jackson" result = bot({"question": query, "chat_history": chat_history}) ``` produces the following error: ``` /python3.9/site-packages/langchain/memory/chat_memory.py", line 29, in _get_input_output raise ValueError(f"One output key expected, got {outputs.keys()}") ValueError: One output key expected, got dict_keys(['answer', 'source_documents']) ``` - **Examples**: This issue occurs when trying to use the 'Return Source Documents' as outlined in the current documentation. #### Additional Information - **Related Issue**: This documentation update is related to the issue raised in https://github.com/langchain-ai/langchain/issues/2256. ### Idea or request for content: #### Suggested Fix - Update the documentation with the correct code snippet: ```python memory = ConversationBufferMemory(memory_key="chat_history", input_key='question', output_key='answer', return_messages=True) bot = ConversationalRetrievalChain.from_llm(llm, retriever, memory=memory, return_source_documents=True) result = bot({"question": query,}) ``` - This revision correctly handles the output and does not produce the aforementioned error.

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I_kwDOIPDwls56dq1c

Can't use RunnablePassthrough

open

NONE

### System Info Im trying to implement this in sagemaker with bedrock claude v2 https://github.com/langchain-ai/langchain/blob/master/templates/rag-aws-bedrock/rag_aws_bedrock/chain.py Here is my code ``` `import os from langchain.embeddings import BedrockEmbeddings from langchain.llms.bedrock import Bedrock from langchain.prompts import ChatPromptTemplate from langchain.vectorstores import FAISS from langchain_core.output_parsers import StrOutputParser from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import RunnableParallel, RunnablePassthrough region = "us-east-1" model_id="anthropic.claude-v2" # Set LLM and embeddings model = Bedrock( model_id=model_id, region_name=region, model_kwargs={"max_tokens_to_sample": 200}, ) bedrock_embeddings = BedrockEmbeddings(model_id="amazon.titan-embed-text-v1") # Add to vectorDB vectorstore = FAISS.from_texts( ["harrison worked at kensho"], embedding=bedrock_embeddings ) retriever = vectorstore.as_retriever() # Get retriever from vectorstore retriever = vectorstore.as_retriever() # RAG prompt template = """Answer the question based only on the following context: {context} Question: {question} """ prompt = ChatPromptTemplate.from_template(template) # RAG chain = ( RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) | prompt | model | StrOutputParser() ) ` ``` got unexpected error: --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[47], line 40 35 prompt = ChatPromptTemplate.from_template(template) 38 # RAG 39 chain = ( ---> 40 RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) 41 | prompt 42 | model 43 | StrOutputParser() 44 ) TypeError: Expected a Runnable, callable or dict.Instead got an unsupported type: <class 'langchain.schema.vectorstore.VectorStoreRetriever'> Name: langchain Version: 0.0.352 Name: langchain-core Version: 0.1.2 ### Who can help? _No response_ ### Information - [ ] 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 - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction 1. !pip install langchain==0.0.352 2. ``` import os from langchain.embeddings import BedrockEmbeddings from langchain.llms.bedrock import Bedrock from langchain.prompts import ChatPromptTemplate from langchain.vectorstores import FAISS from langchain_core.output_parsers import StrOutputParser from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import RunnableParallel, RunnablePassthrough region = "us-east-1" model_id="anthropic.claude-v2" # Set LLM and embeddings model = Bedrock( model_id=model_id, region_name=region, model_kwargs={"max_tokens_to_sample": 200}, ) bedrock_embeddings = BedrockEmbeddings(model_id="amazon.titan-embed-text-v1") # Add to vectorDB vectorstore = FAISS.from_texts( ["harrison worked at kensho"], embedding=bedrock_embeddings ) retriever = vectorstore.as_retriever() # Get retriever from vectorstore retriever = vectorstore.as_retriever() # RAG prompt template = """Answer the question based only on the following context: {context} Question: {question} """ prompt = ChatPromptTemplate.from_template(template) # RAG chain = ( RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) | prompt | model | StrOutputParser() ) ``` ### Expected behavior --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[47], line 40 35 prompt = ChatPromptTemplate.from_template(template) 38 # RAG 39 chain = ( ---> 40 RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) 41 | prompt 42 | model 43 | StrOutputParser() 44 ) File /opt/conda/lib/python3.10/site-packages/langchain_core/runnables/base.py:1937, in RunnableParallel.__init__(self, _RunnableParallel__steps, **kwargs) 1934 merged = {**__steps} if __steps is not None else {} 1935 merged.update(kwargs) 1936 super().__init__( -> 1937 steps={key: coerce_to_runnable(r) for key, r in merged.items()} 1938 ) File /opt/conda/lib/python3.10/site-packages/langchain_core/runnables/base.py:1937, in <dictcomp>(.0) 1934 merged = {**__steps} if __steps is not None else {} 1935 merged.update(kwargs) 1936 super().__init__( -> 1937 steps={key: coerce_to_runnable(r) for key, r in merged.items()} 1938 ) File /opt/conda/lib/python3.10/site-packages/langchain_core/runnables/base.py:3232, in coerce_to_runnable(thing) 3230 return cast(Runnable[Input, Output], RunnableParallel(thing)) 3231 else: -> 3232 raise TypeError( 3233 f"Expected a Runnable, callable or dict." 3234 f"Instead got an unsupported type: {type(thing)}" 3235 ) TypeError: Expected a Runnable, callable or dict.Instead got an unsupported type: <

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PR_kwDOIPDwls5isAtS

Add create_conv_retrieval_chain func

closed

COLLABORATOR

``` +----------+ | MapInput | **+----------+**** **** **** **** *** ** **** +------------------------------------+ ** | Lambda(itemgetter('chat_history')) | * +------------------------------------+ * * * * * * * +---------------------------+ +--------------------------------+ | Lambda(_get_chat_history) | | Lambda(itemgetter('question')) | +---------------------------+ +--------------------------------+ * * * * * * +----------------------------+ +------------------------+ | ContextSet('chat_history') | | ContextSet('question') | +----------------------------+ +------------------------+ **** **** **** **** ** ** +-----------+ | MapOutput | +-----------+ * * * +----------------+ | PromptTemplate | +----------------+ * * * +-------------+ | FakeListLLM | +-------------+ * * * +-----------------+ | StrOutputParser | +-----------------+ * * * +----------------------------+ | ContextSet('new_question') | +----------------------------+ * * * +---------------------+ | SequentialRetriever | +---------------------+ * * * +------------------------------------+ | Lambda(_reduce_tokens_below_limit) | +------------------------------------+ * * * +-------------------------------+ | ContextSet('input_documents') | +-------------------------------+ * * * +----------+ ***| MapInput |**** ******* +----------+ ******** ******** * ******* ******* * ******** **** * **** +-------------------------------+ +----------------------------+ +----------------------------+ | ContextGet('input_documents') | | ContextGet('chat_history') | | ContextGet('new_question') | +-------------------------------+**** +----------------------------+ +----------------------------+ ********* * ******* ******** * ****** ***** * **** +-----------+ | MapOutput | +-----------+ * * * +-------------+ | FakeListLLM | +-------------+ * * * +----------+ ***| MapInput |*** ******** +----------+ ****** ******* * ***** ******** * ****** **** * *** +-------------------------------+ +----------------------------+ +-------------+ | ContextGet('input_documents') | | ContextGet('new_question') | **| Passthrough | +-------------------------------+ +----------------------------+ ******* +-------------+ ******* * ****** ****** * ******* **** * **** +-----------+ | MapOutput | +-----------+ ```

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PR_kwDOIPDwls5ir-Y-

Improve graph repr for runnable passthrough and itemgetter

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PR_kwDOIPDwls5ir4G6

Update regex in output parser

closed

CONTRIBUTOR

The regex used to match "Action" and "Action Input" in the output parser has been updated. Previously, the regex did not correctly handle multi-line inputs for "Action Input". The updated code uses the 're.DOTALL' flag to ensure multi-line inputs are correctly captured. <!-- Thank you for contributing to LangChain! Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified. 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, - **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` from the root of the package you've modified to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ 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/docs/integrations` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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PR_kwDOIPDwls5ir1KL

Random length base example selector

open

CONTRIBUTOR

Description: The length example selector always retrieves the same examples, going from top to bottom. In the case you have a lot of examples, there will not be any variation. I just copied and modified this length selector to do a random length base examples selector, so for each prompt, the examples selected will be random Dependencies: Added the random import, not dependencies changed Tag maintainer: for a quicker response, tag the relevant maintainer (see below),

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PR_kwDOIPDwls5ir1F1

Use args option in jaguar so it takes more options in similarity search

closed

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- **Description:** replace score_threshold with args - **Issue:** needs a way to pass more options to similarity search - **Dependencies:** None - **Twitter handle:** @workbot

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PR_kwDOIPDwls5irzGK

core: update json output parser

closed

CONTRIBUTOR

- **Description:** changed json.py to handle additional cases of partial json string to be parsed, basically by dropping the last character in the string until a valid json string is found or the string is empty. Also added additional test cases. - **Issue:** function parse_partial_json could not parse cases where the key is present but the value is not.

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PR_kwDOIPDwls5irzFd

Nc/dec22/runnable graph lambda

closed

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I_kwDOIPDwls56dOrj

SQLDatabaseChain raising TypeError exception with SQLite

open

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### Issue you'd like to raise. SQLDatabaseChain is throwing a TypeError when executing the .run or .invoke functions. All arguments or kwargs passed to the class are valid, but the error persists. I have trapped back to the database and there is a valid connection. My code and error are below: ```python sqlite_db = SQLDatabase.from_uri( "sqlite:///./sqlite/ibdss.sqlite3", sample_rows_in_table_info=2, include_tables=["colts"] ) db_chain = SQLDatabaseChain.from_llm( llm, sqlite_db, return_direct=True, use_query_checker=True, return_intermediate_steps=True, verbose=True ) sqlite_response = db_chain.invoke({"query": "How many generators have over 10,000 hours of operation?"}) ``` The issue happens when using .run as well. Error Output: TypeError: must be real number, not str The traceback points to the invocation of the chain as the error area. ### Suggestion: _No response_

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PR_kwDOIPDwls5irlxR

Code simplification in jaguar vector store

closed

CONTRIBUTOR

- **Description:** Program simplification in Jaguar vector store - **Issue:** Simplify code by hiding login() and logout() in constructor and destructor of Jaguar class - **Dependencies:** None - **Twitter handle:** @workbot

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PR_kwDOIPDwls5irh1-

add defaults for tavily

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I_kwDOIPDwls56c_3R

Bugs in importing when Deploying a LangChain web app to multiple hosting platforms!

open

NONE

### System Info I have observed that while using the command belonging to importing "langchain.llms (for example as in from langchain.llms import HuggingFaceHub") have no problem when I deploy my web app to multiple hosting sites like streamlit, Render, Heroku etc. However, using the "langchain.memory" as in from langchain.memory import ConversationBufferMemory and "langchain.prompts" as in from langchain.prompts import PromptTemplate causes deployment to fail in all said hosting sites. They throw a module not found error for the langchain.memory and langchain.prompts. Please note: that running in google colab or vscode does not have this error. Also, python version and langchain versions are irrelevant to this problem since I have tested through a lot of variations. Please help out. Thanks in advance ### Who can help? @hwchase17 @agola11 @eyurtsev ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [X] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction import soundfile as sf from langchain.llms import HuggingFaceHub # Below line gives error not in vscode or google colab but in deployment to render, heroku, streamlit and other hosting sites as well. I have not used ConversationBufferMemory in below code even, since the this line causes problem even in initial importing from langchain.memory import ConversationBufferMemory import speech_recognition as sr # Initialize any API keys that are needed import os from flask import Flask, render_template, request, session, flash, get_flashed_messages os.environ["HUGGINGFACEHUB_API_TOKEN"] = "write your own api here" app = Flask(__name__) app.secret_key = '123' @app.route("/LLMEXP", methods=['GET', 'POST']) def llmexp(): if 'chat_messages' not in session: session['chat_messages'] = [] conversation = HuggingFaceHub(repo_id="google/flan-t5-small", model_kwargs={"temperature": 0.1, "max_length": 256}) if request.method == 'POST': if 'record_audio' in request.form: # Record audio and convert to text user_input = request.form['user_input'] else: # User input from textarea user_input = request.form['user_input'] if not user_input or user_input.isspace(): flash("Please provide a valid input.") else: user_message = {'role': 'user', 'content': f"User: {user_input}"} session['chat_messages'].append(user_message) response = conversation(user_input) if not response.strip(): response = "I didn't understand the question." #text_to_speech(response) assistant_message = {'role': 'assistant', 'content': f"Bot: {response}"} session['chat_messages'].append(assistant_message) session.modified = True return render_template("index.html", chat_messages=session['chat_messages']) if __name__ == "__main__": app.run(debug=True) ### Expected behavior I expect that I do not receive any error related to "langchain.memory" or "langchain.prompts" error while deploying to a hosting site.

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I_kwDOIPDwls56c_gA

Feature request: Vertex AI Function Calling

open

CONTRIBUTOR

### Feature request Google's `gemini-pro` supports function calling. It would be nice to be able to use langchain to support function calling when using the `VertexAI` class similar to OpenAI and OpenAI's version of function calling: https://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/function-calling ### Motivation Here's a notebook where to access this functionality you have to use the `vertexai` library directly which means we lose the langchain standardization of input and output schemas: https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/function-calling/intro_function_calling.ipynb ### Your contribution Possibly can help.

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PR_kwDOIPDwls5irWiC

book reference

closed

COLLABORATOR

Added a reference to to a new book about `LangChain`.

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PR_kwDOIPDwls5irOXw

combine docs chains as runnable

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PR_kwDOIPDwls5irNfz

revamp getting started

closed

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I_kwDOIPDwls56celN

Structured Chat Output Parser doesn't work when model outputs a code block with ``` around the code block

open

NONE

### System Info langchain==0.0.351 python==3.10 ### Who can help? @hwchase17 This should be an easy one to fix. When using regex in the output parser of the StructuredChatAgent, the output parser cuts off the output at the first ending ``` it finds. For example, if my string was ``` """```json { "action": "Final Answer", "action_input": "Here is some basic Oceananigans code that sets up a simple simulation environment:\n\n```julia\nusing Oceananigans\n\n# Define the grid\ngrid = RectilinearGrid(size=(64, 64, 64), extent=(1, 1, 1))\n\n# Create a nonhydrostatic model\nmodel = NonhydrostaticModel(grid=grid)\n\n# The model is now ready for further configuration and running a simulation.\n```\n\nThis code initializes a `NonhydrostaticModel` on a 64x64x64 `RectilinearGrid` with an extent of 1x1x1 in each direction. The model is created with default settings, which you can customize according to your simulation needs." } ```""" ``` Then the output parser would get """```json { "action": "Final Answer", "action_input": "Here is some basic Oceananigans code that sets up a simple simulation environment:\n\n```julia\nusing Oceananigans\n\n# Define the grid\ngrid = RectilinearGrid(size=(64, 64, 64), extent=(1, 1, 1))\n\n# Create a nonhydrostatic model\nmodel = NonhydrostaticModel(grid=grid)\n\n# The model is now ready for further configuration and running a simulation.\n``` which would cause an error. I have not throughly verified this solution but changing the regex in the structured chat output parser - https://github.com/langchain-ai/langchain/blob/aad3d8bd47d7f5598156ff2bdcc8f736f24a7412/libs/langchain/langchain/agents/structured_chat/output_parser.py#L23 to pattern = re.compile(r"```json\s*\n(.*?)```(?=\s*```json|\Z)", re.DOTALL) seems to fix the problem. ### Information - [ ] The official example notebooks/scripts - [X] 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 pattern = re.compile(r"```(?:json\s+)?(\W.*?)```", re.DOTALL) text="""```json { "action": "Final Answer", "action_input": "Here is some basic Oceananigans code that sets up a simple simulation environment:\n\n```julia\nusing Oceananigans\n\n# Define the grid\ngrid = RectilinearGrid(size=(64, 64, 64), extent=(1, 1, 1))\n\n# Create a nonhydrostatic model\nmodel = NonhydrostaticModel(grid=grid)\n\n# The model is now ready for further configuration and running a simulation.\n```\n\nThis code initializes a `NonhydrostaticModel` on a 64x64x64 `RectilinearGrid` with an extent of 1x1x1 in each direction. The model is created with default settings, which you can customize according to your simulation needs." } ```""" action_match = pattern.search(text) response = json.loads(action_match.group(1).strip(), strict=False) ### Expected behavior I would expect the code to get the entire json blob block but it doesn't.

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PR_kwDOIPDwls5iqkEu

Refactor: use SecretStr for jina embeddings

closed

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PR_kwDOIPDwls5iqf_e

Refactor: use SecretStr for minimax embeddings

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PR_kwDOIPDwls5iqdar

Fix: fix partners name typo in tests

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PR_kwDOIPDwls5iqRHq

langchain(patch): Restrict paths in LocalFileStore cache

closed

COLLABORATOR

This PR restricts the paths that can be resolve using the local file system cache so that all paths must be contained within the root path.

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PR_kwDOIPDwls5iqFcX

Improve: remove extra spaces in get_from_env error

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PR_kwDOIPDwls5ip_9e

Improve: correct VectorStore.search type annotation

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I_kwDOIPDwls56bNbq

send context of docs through Chroma().as_retriever multiple times in the same conversation

open

NONE

in retrievalQa from langchain, we have a retriever that retrieves docs from a vector db and provides a context to the llm, let's say i'm using gpt3.5 whose max tokens is 4096... how do i handle huge context to be sent to it ? any suggestions will be appreciated

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I_kwDOIPDwls56bMgQ

Using SQLDatabaseSequentialChain, discrepancy between Answer and SQLResult

open

NONE

### Discussed in https://github.com/langchain-ai/langchain/discussions/15060 <div type='discussions-op-text'> ^{Originally posted by **ShehneelAhmedKhan** December 22, 2023} This is my code: llm = OpenAI(temperature=0, model_name="gpt-3.5-turbo") db_chain = SQLDatabaseSequentialChain.from_llm(llm=llm, database=db, verbose=True, top_k=0) # Can use top_k if face error return db_chain.run(message) Output:  The answer is denying although the sqlresult gets the answer. Note: This is happening only in this table, other tables are being answered correctly. using langchain==0.0.175 Any suggestion?</div>

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I_kwDOIPDwls56bEP_

How to use tool_choice with initialize_agent？

open

NONE

### Issue you'd like to raise. i want force the model to call a specific function. but i didn't found how to ues tool_choice with AgentExecutor from doc. please give me demo. Thanks ### Suggestion: _No response_

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I_kwDOIPDwls56a94J

Make it possible to give credentials directly via parameter on GoogleDriveLoader

open

CONTRIBUTOR

### Feature request The `GoogleDriveLoader` currently supports 3 different ways to authenticate a user. 1. Via a Service Account File 2. Via a Token File 3. Via a Live server All those ways work perfectly, but I'm missing a way to authenticate the user via an existing JWT. There is a workaround by saving the JWT into a token file and providing this file via the `token_path` parameter. But there should also be a way to provide exactly these credentials via a parameter. ### Motivation Maybe there is a use case where multiple accounts are required, or the user wants to authenticate over a third-party website running on an external server (not localhost). I think it would be useful to have a parameter that allows the functionality to pass a JWT without having to store it first in a file. ### Your contribution I'm willing to submit a PR.

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I_kwDOIPDwls56ad88

this world requires a Langchain framework written in the Rust language

open

NONE

### Issue you'd like to raise. Hi Guy, this world requires a Langchain framework written in the Rust language, and Python is not the future of AI. ### Suggestion: _No response_

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I_kwDOIPDwls56aYTP

Add external browser authentication for Snowflake.

open

CONTRIBUTOR

### Feature request The [Snowflakeconnector](https://docs.snowflake.com/en/developer-guide/python-connector/python-connector-api#functions) supports authentication via browser. It would be nice if the [Langchain Snowflake Loader](https://python.langchain.com/docs/integrations/document_loaders/snowflake) also supports this feature ### Motivation Basic authentication works in most cases. But there might be a use case where the user wants to use OAuth to log into his Snowflake instance. ### Your contribution I'm willing to submit a PR

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PR_kwDOIPDwls5iovJm

[community]: Elasticsearch chat history encoding

closed

CONTRIBUTOR

- Added ensure_ascii property to ElasticsearchChatMessageHistory <!-- Thank you for contributing to LangChain! Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified. 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, - **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` from the root of the package you've modified to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ 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/docs/integrations` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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PR_kwDOIPDwls5ioo1n

upgrade langchain-community version

closed

CONTRIBUTOR

This pr enforce the latest version for some feature just like: change default openai model from davinci-003 to gpt-3.5.

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PR_kwDOIPDwls5iomLk

corrected outdated link

closed

CONTRIBUTOR

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I_kwDOIPDwls56ZsAY

ContextualCompressionRetriever for non-english languages

open

NONE

### Issue you'd like to raise. Hi, I want to use the ContextualCompressionRetriever and wondering how the prompt looks like or if you can use it for non-english languages (e.g. German)? I am using ContextualCompressionRetriever at the moment and realized that my LLM responses often switch to English, so I am assuming that the prompt for ContextualCompressionRetriever is in English language and the model gets confused which language to use. Any recommendations? ### Suggestion: _No response_

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I_kwDOIPDwls56ZiaA

oracle db when use sql agent can't find table name[Error: table_names]

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NONE

### System Info when i use sql agent, i want get table desc , but agent can't work , i am sure REPICA.ICA_PERSON_DATA_ALL table is existed Question: Describe the REPICA.ICA_PERSON_DATA_ALL table Thought: I should query the schema of the REPICA.ICA_PERSON_DATA_ALL table to get information about its columns and data types. Action: sql_db_schema Action Input: REPICA.ICA_PERSON_DATA_ALL Observation: Error: table_names {'REPICA.ICA_PERSON_DATA_ALL'} not found in database ### 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 llm = ChatOpenAI( openai_api_base=openai_api_base, temperature=llm_temp, openai_api_key=openai_api_key, model_name=llm_model_name, ) toolkit = SQLDatabaseToolkit(db=db, llm=llm) agent_executor = create_sql_agent(llm = llm, toolkit=toolkit, prefix=prefix, verbose=True, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, top_k=10) agent_executor.run("Describe the REPICA.ICA_PERSON_DATA_ALL table") ### Expected behavior i need get REPICA.ICA_PERSON_DATA_ALL desc info

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I_kwDOIPDwls56Za-Q

Error Code 400. Can ask follow up questions with agent_executor

open

NONE

### System Info Python 3.10.10 langchain 0.0.350 langchain-community 0.0.3 langchain-core 0.1.1 google-search-results 2.4.2 Windows ### 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 - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction **Summary** Firstly, I ask the LangChain agent using code below and get a good response. `result = agent_executor({"What is the recommended dose of doxycycline for acne for a male aged 12 years old?"})` However, I ask a new followup question by changing the query and get and error. `result = agent_executor({"What other forms of acne treatment are there for male teenagers around the age of 12 years old ?"})` `BadRequestError: Error code: 400 - {'error': {'message': "'$.messages[1].content' is invalid.` **Code Used** ``` model = AzureChatOpenAI( openai_api_version="2023-12-01-preview", azure_deployment="35-16k", temperature=0 ) # Define which tools the agent can use to answer user queries SERPAPI_API_KEY='------' search = SerpAPIWrapper(serpapi_api_key=SERPAPI_API_KEY) tools= [ Tool( name="Search", func=search.run, description="useful for when you need to answer questions about current events" ) ] # This is needed for both the memory and the prompt memory_key = "history" from langchain.agents.openai_functions_agent.agent_token_buffer_memory import ( AgentTokenBufferMemory, ) memory = AgentTokenBufferMemory(memory_key=memory_key, llm=model, max_token_limit = 13000) from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent from langchain.prompts import MessagesPlaceholder from langchain_core.messages import SystemMessage system_message = SystemMessage( content=( "You are an expert Doctor." "Use the web search tool to gain insights from web search, summarise the results and provide an answer." "Keep the source links during generation to produce inline citation." "Cite search results using [${{number}}] notation. Only cite the most \ relevant results that answer the question accurately. Place these citations at the end \ of the sentence or paragraph that reference them - do not put them all at the end. If \ different results refer to different entities within the same name, write separate \ answers for each entity. If you want to cite multiple results for the same sentence, \ format it as `[${{number1}}] [${{number2}}]`. However, you should NEVER do this with the \ same number - if you want to cite `number1` multiple times for a sentence, only do \ `[${{number1}}]` not `[${{number1}}] [${{number1}}]`" "From the retrieved results, generate an answer." "At the end of the answer, show the source to the [${{number}}]. Follow this format, every [${{number}}] notation is followed by the corresponding source link " "You should use bullet points in your answer for readability. Put citations where they apply \ rather than putting them all at the end." ) ) prompt = OpenAIFunctionsAgent.create_prompt( system_message=system_message, extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)], ) #The Agent agent = OpenAIFunctionsAgent(llm=model, tools=tools, prompt=prompt) #The Agent Executor from langchain.agents import AgentExecutor agent_executor = AgentExecutor( agent=agent, tools=tools, memory=memory, verbose=True, return_intermediate_steps=True, ) result = agent_executor({"What is the recommended dose of doxycycline for acne for a male aged 12 years old?"}) ``` **Output** Shows good results **Next** I run agen_executor with another question and this is where I get a bug. result = agent_executor({"_new question_"}) Even if I just repeat the question ie use the initial one. I get this error. **Error** `--------------------------------------------------------------------------- BadRequestError Traceback (most recent call last) Cell In[15], [line 1](vscode-notebook-cell:?execution_count=15&line=1) ----> [1](vscode-notebook-cell:?execution_count=15&line=1) result = agent_executor({"What is the recommended dose of doxycycline for acne for a male aged 12 years old?"}) File /anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:312, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info) [310](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:310) except BaseException as e: [311](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:311) run_manager.on_chain_error(e) --> [312](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:312) raise e [313](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:313) run_manager.on_chain_end(outputs) [314](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:314) final_outputs: Dict[str, Any] = self.prep_outputs( [315](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:315) inputs, outputs, return_only_outputs [316](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:316) ) File /anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:306, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info) [299](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:299) run_manager = callback_manager.on_chain_start( [300](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:300) dumpd(self), [301](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:301) inputs, [302](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:302) name=run_name, [303](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:303) ) [304](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:304) try: [305](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:305) outputs = ( --> [306](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:306) self._call(inputs, run_manager=run_manager) [307](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:307) if new_arg_supported [308](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/langchain/chains/base.py:308) else self._call(inputs) ... (...) [937](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/openai/_base_client.py:937) stream_cls=stream_cls, [938](https://vscode-remote+amlext-002b2f737562736372697074696f6e732f31653065376137362d306236362d343632622d613831352d3733363863633464363832312f7265736f7572636547726f7570732f47656e657261746976655f41492f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f6c6c616d612f636f6d70757465732f7368616d75732d637075.vscode-resource.vscode-cdn.net/anaconda/envs/langchain/lib/python3.10/site-packages/openai/_base_client.py:938) ) BadRequestError: Error code: 400 - {'error': {'message': "'$.messages[1].content' is invalid. Please check the API reference: https://platform.openai.com/docs/api-reference.", 'type': 'invalid_request_error', 'param': None, 'code': None}}` ### Expected behavior Formatted normal output

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I_kwDOIPDwls56ZScA

add Microsoft/Phi support

open

NONE

### Feature request Microsoft's new model called [Phi](https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/) seems interesting... ### Motivation Performance of smaller models 2-3B params can compare to models with 7B params. ### Your contribution i don't know enough to so, otherwise i would submit a PR...

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I_kwDOIPDwls56ZQVw

When I use the agent tool, I need to verify whether there are parameters in the problem. If there are no parameters, I will remind the user to input them. How can I implement this?

open

NONE

### Issue you'd like to raise. When I use the agent tool, I need to verify whether there are parameters in the problem. If there are no parameters, I will remind the user to input them. How can I implement this? ### Suggestion: _No response_

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I_kwDOIPDwls56Yz3T

GraphQAChain not working when using Networkx graphs !!

closed

NONE

### System Info Langchain==0.0.352 MacOS intel version python==3.8.3 networkx==2.7.1 ### Who can help? @hwchase17 @agola11 I have an issue when initializing GraphQAChain using networkx graph. ### 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 - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction I made a networkx graph and tried to initialize GraphQAChain but got the below error. <img width="1010" alt="image" src="https://github.com/langchain-ai/langchain/assets/41593805/d4ba54bc-bf43-4303-8550-8ef75fb4b7e0"> <img width="1482" alt="image" src="https://github.com/langchain-ai/langchain/assets/41593805/c4286425-7df7-4c3a-bbc5-3e6e92a69786"> ### Expected behavior I would like to initialize GraphQAChain and chat with the network. Please help!

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I_kwDOIPDwls56Yno8

NVIDIA Triton+TRT-LLM connector needs to handle dynamic model parameters

open

NONE

### System Info LangChain 0.0.348 langchain-nvidia-trt 0.0.1rc0 Python 3.11 ### Who can help? @jdye64 ### Information - [ ] 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 You can set up the triton server as in the quick start guide here using a nemotron model: https://github.com/fciannella/langchain-fciannella/blob/master/libs/partners/nvidia-trt/README.md Then you can just try sending a request from the LangChain plugin and it will not work. Same thing if you follow the more complex setup. The input and output parameters need to be discovered by the client. One option is to use pytriton as a client, or just pull the parameters from the server using an API call to get the triton configuration? ### Expected behavior The client should provide a list of the mandatory parameters in the error code in case there is a missing parameter in the request.

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I_kwDOIPDwls56YjQT

BUG! The arguments of function calling returned by gpt 3.5 might not be a dict

open

NONE

https://github.com/langchain-ai/langchain/blob/2460f977c5c20073b41803c41fd08945be34cd60/libs/langchain/langchain/agents/output_parsers/openai_functions.py#L49 Eeven though you pass your customized function, gpt-3.5 will often return a function call which name is "python" and the arguments are not in json format. I would suggest to check the function name here. (Idea from Open Interpreter Source Code) Thanks, ZD

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PR_kwDOIPDwls5inSFu

Add Runnable.get_graph() to get a graph representation of a Runnable

closed

COLLABORATOR

It can be drawn in ascii with Runnable.get_graph().draw()

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I_kwDOIPDwls56YQ0n

AzureSearch Bug -- langchain.vectorstores.azuresearch

open

NONE

### System Info The example found [here](https://python.langchain.com/docs/integrations/vectorstores/azuresearch) and in particular this code fragment ``` embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1) index_name: str = "langchain-vector-demo" vector_store: AzureSearch = AzureSearch( azure_search_endpoint=vector_store_address, azure_search_key=vector_store_password, index_name=index_name, embedding_function=embeddings.embed_query, ) ``` fails with a message that at the top is: ``` vector_search_configuration is not a known attribute of class <class 'azure.search.documents.indexes.models._index.SearchField'> and will be ignored semantic_settings is not a known attribute of class <class 'azure.search.documents.indexes.models._index.SearchIndex'> and will be ignored ``` and culminates with: ``` HttpResponseError: (InvalidRequestParameter) The request is invalid. Details: definition : The vector field 'content_vector' must have the property 'vectorSearchConfiguration' set. Code: InvalidRequestParameter Message: The request is invalid. Details: definition : The vector field 'content_vector' must have the property 'vectorSearchConfiguration' set. Exception Details: (InvalidField) The vector field 'content_vector' must have the property 'vectorSearchConfiguration' set. Parameters: definition Code: InvalidField Message: The vector field 'content_vector' must have the property 'vectorSearchConfiguration' set. Parameters: definition ``` I am running with `python 3.10`, `openai 1.5.0`, `langchain 0.0.351` and `azure-search-documents 11.4.0`. If I revert to `azure-search-documents 11.4.0b8` the code works. This appears to be related to the November 2023 Microsoft API change which introduced the concept of a "profile" which aggregates various vector search settings under one name. As a result of this API change, the old "vector_search_configuration" was deprecated and a new "vector_search_profile" was added, along with a new "profiles" object. It seems that the Langchain extension has not been updated for this change and expects the old "vector_search_configuration" property which doesn't exist on newer SDK releases. See the discussion [here](https://stackoverflow.com/questions/77682544/vector-search-configuration-is-not-a-known-attribute-of-class-class-azure-sear/77694628#77694628). ### Who can help? @hwchase17 @bas ### 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 The following sample code fragment from [here:(https://python.langchain.com/docs/integrations/vectorstores/azuresearch) fails. ``` import os from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores.azuresearch import AzureSearch os.environ["OPENAI_API_TYPE"] = "azure" os.environ["OPENAI_API_BASE"] = "YOUR_OPENAI_ENDPOINT" os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY" os.environ["OPENAI_API_VERSION"] = "2023-05-15" model: str = "text-embedding-ada-002" vector_store_address: str = "YOUR_AZURE_SEARCH_ENDPOINT" vector_store_password: str = "YOUR_AZURE_SEARCH_ADMIN_KEY" embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1) index_name: str = "langchain-vector-demo" vector_store: AzureSearch = AzureSearch( azure_search_endpoint=vector_store_address, azure_search_key=vector_store_password, index_name=index_name, embedding_function=embeddings.embed_query, ) ``` ### Expected behavior The code should run without failure and return a vector store.

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I_kwDOIPDwls56YNQC

Issue: ValueError: Could not parse output:

open

NONE

### Issue you'd like to raise. Seeing an issue in my code that appeared out of nowhere, hoping for some support here. The error message I am seeing is `ValueError: Could not parse output: Answer to inquiry from OpenAI. Score: 90` from the output_parsers/regex.py (https://github.com/langchain-ai/langchain/blob/v0.0.257/libs/langchain/langchain/output_parsers/regex.py#L35) Langchain version: v0.0.257 LLM OpenAI: https://github.com/langchain-ai/langchain/blob/v0.0.257/libs/langchain/langchain/llms/openai.py Model: text-davinci-003 Chain type: map_rerank (https://github.com/langchain-ai/langchain/blob/v0.0.257/libs/langchain/langchain/chains/combine_documents/map_rerank.py) Vector store: Qdrant Code to reproduce: ```python import os from pathlib import Path from langchain import OpenAI, PromptTemplate from langchain.chains import RetrievalQAWithSourcesChain from langchain.memory import FileChatMessageHistory import logging from service import db logging.basicConfig(level=logging.DEBUG) def conversation(question): collection_name = "qdrant_collection_name" llm = OpenAI(model='text-davinci-003', openai_api_key=os.environ['OPENAI_API_KEY'], temperature=0.0) combine_prompt_template = """ 'DOCUMENTS: {summaries} QUESTION: {question} ### Response: """ COMBINE_PROMPT = PromptTemplate( template=combine_prompt_template, input_variables=["summaries", "question"] ) qa_chain = RetrievalQAWithSourcesChain.from_chain_type( llm, chain_type='map_rerank', retriever=db.vectorstore(collection_name).as_retriever(), return_source_documents=True, reduce_k_below_max_tokens=True, ) return qa_chain({"question": question})['answer'] if __name__ == "__main__": print(conversation('How do I add sales tax to a transaction in avatax?')) ``` Also posting on Stackoverflow ### Suggestion: N/A

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PR_kwDOIPDwls5inGcn

Community: Fix generation_config not setting properly for DeepSparse

closed

CONTRIBUTOR

- **Description:** Tiny but important bugfix to use a more stable interface for specifying generation_config parameters for DeepSparse LLM

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PR_kwDOIPDwls5im7S2

infra: CI python matrix update 3.12

open

COLLABORATOR

Adds python 3.12 to our testing matrix Also: - only runs scheduled tests with python 3.8 and 3.12 (not all versions) Current work: - duckdb not compatible - need fix or workaround: https://github.com/duckdb/duckdb/issues/9301

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PR_kwDOIPDwls5imvKp

wip: combine docs chain as runnable

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PR_kwDOIPDwls5imqUm

Add option to make messages placeholder optional

closed

COLLABORATOR

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