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I_kwDOIPDwls561Xrf

DOC: Summarization 'map_reduce' - Can't load tokenizer for 'gpt2'

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### Issue with current documentation: The [documentation](https://python.langchain.com/docs/use_cases/summarization) describes the different options for summarizing a text, for longer texts the 'map_reduce' option is suggested. It is mentioned further under 'Go deeper' that it is possible to use different LLMs via the `llm` parameter. This seems to work well using the code below with the `chain_type='stuff'` parameter and, in particular, using a local model (in the example below [this model](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF) is used). ``` from langchain.document_loaders import PyPDFLoader from langchain.llms import CTransformers from langchain.chains.summarize import load_summarize_chain # load a PDF-file loader = PyPDFLoader("C:/xyz.pdf") docs = loader.load() # use a local LLAMA2 model llm = CTransformers(model='./models/llama-2-7b-chat.Q5_K_M.gguf', model_type='llama', config={'context_length': 4096, 'max_new_tokens': 256, 'temperature': 0}, local_files_only=True) # summarise the text (this works only if it fits into the context length of ~4000 tokens) chain = load_summarize_chain(llm, chain_type="stuff") chain.run(docs) ``` However, surprisingly, it returns the following error when using the `chain_type='map_reduce'` parameter: 'OSError: Can't load tokenizer for 'gpt2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'gpt2' is the correct path to a directory containing all relevant files for a GPT2TokenizerFast tokenizer.' The suggestion [mentioned in this issue on Github](https://github.com/langchain-ai/langchain/issues/9273) doesn't work for the local model used above. It would be great to have more specific information in the LangChain documentation on (1) how to perform text summarization with LangChain using different LLMs, and (2) specifically for using local models that don't require an internet connection and/or require gpt2. Since the above code works with the parameter `chain_type='stuff'` but not with the parameter `chain_type='map_reduce'`, it would be important to explain what happens under the hood so users can make this work for local models. ### Idea or request for content: _No response_

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I_kwDOIPDwls561QhV

Issue: <Saving intermediate variable chains ' prefix>

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NONE

### Issue you'd like to raise. Help me understand how I can save the intermediate data of chain execution results?  ### Suggestion: _No response_

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PR_kwDOIPDwls5i-Ewf

google-vertexai[minor]: added safety_settings property to gemini wrapper

open

NONE

**Description:** Gemini model has quite annoying default safety_settings settings. In addition, current VertexAI class doesn't provide a property to override such settings. So, this PR aims to - add safety_settings property to VertexAI - fix issue with incorrect LLM output parsing when LLM responds with appropriate 'blocked' response - fix issue with incorrect parsing LLM output when Gemini API blocks prompt itself as inappropriate - add safety_settings related tests I'm not enough familiar with langchain code base and guidelines. So, any comments and/or suggestions are very welcome. **Issue:** it will likely fix #14841

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I_kwDOIPDwls561AtX

DOC: Prompt Templates "Page Not Found"

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NONE

### Issue with current documentation: A few days back, I was referring to the [Prompt templates](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/) page which now shows: "**Page Not Found**" ### Idea or request for content: I understand that LangChain is an evolving framework undergoing continuous development. - Could we consider implementing versioning for the documentation? This would allow users to access specific documentation versions. - Alternatively, if a section undergoes modification, we could preserve the existing documentation and label it as 'Legacy,' ensuring clarity about deprecated practices.

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I_kwDOIPDwls560-LA

_OllamaCommon contains top_p with int-restriction

closed

NONE

### System Info LangChain 0.0.353 ### Who can help? @hwchase17 @agola11 ### 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 Cannot set top_p to useful values via ChatOllama(top_p=0.3), is reduced to value 0 because it's an int: top_p: Optional[int] = None top_p must be float. ### Expected behavior top_p must be a float, 0.3 should appear in ollama log.

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I_kwDOIPDwls560qvH

Issue: Not getting desired output while implementing memory

open

NONE

### Issue you'd like to raise. `below is my code for generating custom prompt which takes context and user query and we pass it into model: 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]) # print(context_text,"context_text") 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"""Answer the question based only on following context: ```{context_text} ``` You are a chatbot designed to provide answers to User's Questions:```{check}```, 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"""Generate your response exclusively from the provided context: {{context_text}}. You function as a chatbot specializing in delivering detailed answers to the User's Question: ```{{check}} ```, enclosed within 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.from_template(custom_prompt_template) formatted_prompt = custom_prompt.format(context_text=context_text, check=check) return formatted_prompt below is my conversation chain where i am inplementing memory def retreival_qa_chain(chroma_db_path): embedding = OpenAIEmbeddings() vectordb = Chroma(persist_directory=chroma_db_path, embedding_function=embedding) llm = ChatOpenAI(temperature=0.1) memory = ConversationBufferMemory(llm=llm,output_key='answer',memory_key='chat_history',return_messages=True) retriever = vectordb.as_retriever(search_kwargs={"k": 2}) qa = ConversationalRetrievalChain.from_llm(llm=llm,memory=memory,chain_type="stuff",retriever=retriever,return_source_documents=True,get_chat_history=lambda h : h,verbose=True) # qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever,return_source_documents=True) return qa but I am not getting desired output as expected ` ### Suggestion: _No response_

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PR_kwDOIPDwls5i9T4o

<langchain>: add checks for qaSourcesChain to check if the returned answer referen…

closed

NONE

…ces the source from which it returning an answer <!-- 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:** when using QAWithSourcesChain I realized many times that the language model would return results which was not in the source being referenced. The example in this PR checks if the LLM refers to the actual source by mentioning its name. For example, if you ask an LLM tell me something from Book A the LLM must mention something from Book A. The implementation presented is preliminary and has to further developed but I want your general thoughts on the matter :) , - **Issue:** hallucination of an answer which is not grounded in the source by the chain, - **Dependencies:** None, - **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_kwDOIPDwls5i9ROw

Langchain: FAISS Translator for Self Query Retriver

closed

CONTRIBUTOR

**Description:** Issue #15331 pointed out that there was no support for FAISS for `SelfQueryRetriever`. Therefore, I decided to add the desired support. I understand that FAISS does not have an internal query language like other vector stores (e.g., Chroma), but I noticed that the current implementation of the `FAISS` VectorStore Class in Langchain allows the user to filter documents based on given metadata attributes. Therefore, the translator simply translates the structured query according to the logic of filtering defined in `similarity_search_with_score_by_vector` and `max_marginal_relevance_search_with_score_by_vector`, which filters the documents for the specific values present in metadata keys. **Issue:** #15331 PS: I have not added the docs for this one. Only once you see the value in adding this translator, I will add the docs.

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PR_kwDOIPDwls5i9QHo

Catch type errors in dumps/dumpd

closed

COLLABORATOR

These can happen for edge cases not covered by `default` handler (eg. "strange" keys in dicts) <!-- 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_kwDOIPDwls5i9PzK

refactor `community` memory

closed

COLLABORATOR

- created `language_community.memory` - moved zep and motorhed from `langchain.memory` to `language_community.memory` - created `language_core.memory` - moved several base classes into `language_core.memory` - moved `ChatMessageHistory` from `langchain_community` to `langchain_core`

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PR_kwDOIPDwls5i9NKr

Fetch runnable config from context var inside runnable lambda and runnable generator

closed

COLLABORATOR

- easier to write custom logic/loops with automatic tracing - if you don't want to streaming support write a regular function and pass to RunnableLambda - if you do want streaming write a generator and pass it to RunnableGenerator ```py import json from typing import AsyncIterator from langchain_core.messages import BaseMessage, FunctionMessage, HumanMessage from langchain_core.agents import AgentAction, AgentFinish from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_core.runnables import Runnable, RunnableGenerator, RunnablePassthrough from langchain_core.tools import BaseTool from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser from langchain.chat_models import ChatOpenAI from langchain.tools.render import format_tool_to_openai_function def _get_tavily(): from langchain.tools.tavily_search import TavilySearchResults from langchain.utilities.tavily_search import TavilySearchAPIWrapper tavily_search = TavilySearchAPIWrapper() return TavilySearchResults(api_wrapper=tavily_search) async def _agent_executor_generator( input: AsyncIterator[list[BaseMessage]], *, max_iterations: int = 10, tools: dict[str, BaseTool], agent: Runnable[list[BaseMessage], BaseMessage], parser: Runnable[BaseMessage, AgentAction | AgentFinish], ) -> AsyncIterator[BaseMessage]: messages = [m async for mm in input for m in mm] for _ in range(max_iterations): next_message = await agent.ainvoke(messages) yield next_message messages.append(next_message) parsed = await parser.ainvoke(next_message) if isinstance(parsed, AgentAction): result = await tools[parsed.tool].ainvoke(parsed.tool_input) next_message = FunctionMessage(name=parsed.tool, content=json.dumps(result)) yield next_message messages.append(next_message) elif isinstance(parsed, AgentFinish): return def get_agent_executor(tools: list[BaseTool], system_message: str): llm = ChatOpenAI(model="gpt-4-1106-preview", temperature=0, streaming=True) prompt = ChatPromptTemplate.from_messages( [ ("system", system_message), MessagesPlaceholder(variable_name="messages"), ] ) llm_with_tools = llm.bind( functions=[format_tool_to_openai_function(t) for t in tools] ) agent = {"messages": RunnablePassthrough()} | prompt | llm_with_tools parser = OpenAIFunctionsAgentOutputParser() executor = RunnableGenerator(_agent_executor_generator) return executor.bind( tools={tool.name for tool in tools}, agent=agent, parser=parser ) agent = get_agent_executor([_get_tavily()], "You are a very nice agent!") async def main(): async for message in agent.astream( [HumanMessage(content="whats the weather in sf tomorrow?")] ): print(message) if __name__ == "__main__": import asyncio asyncio.run(main()) ``` results in this trace https://smith.langchain.com/public/fa17f05d-9724-4d08-8fa1-750f8fcd051b/r

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I_kwDOIPDwls560BxC

This model's maximum context length is 4097 tokens, however you requested 4177 tokens

open

NONE

### System Info New versions ### 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 - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Start the code ### Expected behavior Hi , I'm trying to do a chain stuff query, but sometimes when I ask questions I have this error: openai.BadRequestError: Error code: 400 - {'error': {'message': "This model's maximum context length is 4097 tokens, however you requested 4177 tokens (3921 in your prompt; 256 for the completion). Please reduce your prompt; or completion length.", 'type': 'invalid_request_error', 'param': None, 'code': None}} How can I solve this ? Can I cut my prompt ? If yes how? Or Can I upgrade the max_token ? IF yes How please.. Here is my code: import getpass import os from langchain.document_loaders import TextLoader from langchain.embeddings.openai import OpenAIEmbeddings from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores import Pinecone from langchain_community.vectorstores import Pinecone from langchain_community.embeddings.openai import OpenAIEmbeddings import pinecone import sys # Set your Pinecone API key and environment pinecone_api = "API" pinecone_env = "API" # Set your OpenAI API key openai_api = "API" # Initialize Pinecone pinecone.init(api_key=pinecone_api, environment=pinecone_env) # Define the index name index_name = "rewind" # Check if the index already exists, if not, create it if index_name not in pinecone.list_indexes(): pinecone.create_index(name=index_name, metric="cosine", dimension=1536) # Initialize the OpenAIEmbeddings embeddings = OpenAIEmbeddings(api_key=openai_api) # Create or load the Pinecone index docsearch = Pinecone.from_existing_index(index_name, embeddings) # Perform similarity search query = sys.argv[1] if len(sys.argv) > 1 else "what Commits there is in github" text_splitter = CharacterTextSplitter(chunk_size=3000, chunk_overlap=0) docs = docsearch.similarity_search(query) docs = text_splitter.split_documents(docs) if __name__ == '__main__': results = docsearch.similarity_search(query) # Load the question answering chain from langchain.chains.question_answering import load_qa_chain from langchain.llms import OpenAI chain = load_qa_chain(OpenAI(), chain_type="stuff") answers = chain.run(input_documents=docs, question=query) print(answers)

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I_kwDOIPDwls56z6_f

Self query retriever with Vector Store type <class 'langchain_community.vectorstores.faiss.FAISS'> not supported.

closed

NONE

### System Info I've been trying to create a self query retriever so that I can look at metadata field info. This issue comes up. Should I be using another vector store to make this work? I can only really work with FAISS. I cannot use ChromaDB since my Python environment is limited to a previous version. ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] 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 - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction 1. Create a FAISS Vectorstore DB 2. Create a metadata_field_info object and pass it to a SelfQuery object 3. Create LLM with this retriever ```python embedding_function = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY, model="text-embedding-ada-002") db = FAISS.load_local(input_dir + "/" + "storage/deploy/faiss-db", embedding_function)#, distance_strategy="COSINE", normalize_L2 = True) # retriever = KNNRetriever(vectorstore=db) llm = ChatOpenAI( temperature=0, verbose=False, openai_api_key=key, model_name="gpt-3.5-turbo" ) metadata_field_info = [ AttributeInfo( name="source", description="The document this chunk is from.", type="string", ), AttributeInfo( name="origin", description="The origin the document came from. Comes from either scraped websites like TheKinection.org, Kinecta.org or database files like Bancworks. Bancworks is the higher priority.", type="string", ), AttributeInfo( name="date_day", description="The day the document was uploaded.", type="integer", ), AttributeInfo( name="date_month", description="The month the document was uploaded.", type="integer", ), AttributeInfo( name="date_year", description="The year the document was uploaded.", type="integer", ), ] # retriever = db.as_retriever(search_type="similarity", search_kwargs={'k': 6}, metadata_field_info=metadata_field_info) retriever = SelfQueryRetriever.from_llm( llm, db, "Information about where documents originated from and when they were published.", metadata_field_info, verbose=True ) ``` ### Expected behavior Successfully create a SelfQuery retriever with FAISS vector store.

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PR_kwDOIPDwls5i9DDw

Community: Newlines before bullets in IPYNB files (Vectara)

closed

CONTRIBUTOR

- **Description:** updated all Vectara IPYNB files so that bullets look okay in docs (added newline) - **Twitter handle:** @ofermend

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PR_kwDOIPDwls5i889s

Propagate context vars in all classes/methods

closed

COLLABORATOR

- Any direct usage of ThreadPoolExecutor or asyncio.run_in_executor needs manual handling of context vars <!-- 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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I_kwDOIPDwls56zwAa

Integration with OpenAI's Latest Models and API Compatibility

open

NONE

### Feature request This proposal requests the integration of the latest OpenAI models, specifically gpt-4-1106-preview, into the existing framework of [relevant GitHub project, e.g., LangChain]. The newer models offer significantly larger context windows, which are crucial for complex SQL querying and other advanced functionalities. This feature would involve ensuring compatibility with the latest version of the OpenAI API (version 1.0.0 and beyond), which has undergone substantial changes, including the deprecation of certain features like openai.ChatCompletion. Relevant links: OpenAI API (1.0.0): [OpenAI API Documentation](https://github.com/openai/openai-python) Migration Guide: [OpenAI Python Library Migration Guide](https://github.com/openai/openai-python/discussions/742) ### Motivation The primary motivation for this feature request is to leverage the advanced capabilities of the newer OpenAI models, particularly the extended context windows they offer. These capabilities are essential for applications involving extensive data interaction and complex language understanding, such as SQL database querying and management. Current limitations with the older models and API versions restrict the potential of applications, especially when dealing with lengthy queries or requiring deeper contextual understanding. For example, while working on a project involving the LangChain framework for SQL database interaction, I encountered the APIRemovedInV1 error, which signifies incompatibility with the latest OpenAI API. This issue underscores the need for updating the framework to align with the latest advancements in language models and API standards. ### Your contribution Might make my own SQL Agent or modify yours.

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PR_kwDOIPDwls5i85gQ

core: Update messages/__init__.py to account for AIMessageChunk which breaks message history runnable.

closed

CONTRIBUTOR

- **Description:** fix parse issue for AIMessageChunk when using - **Issue:** https://github.com/langchain-ai/langchain/issues/14511 - **Dependencies:** none - **Twitter handle:** none Taken from this fix: https://github.com/gpt-engineer-org/gpt-engineer/issues/804#issuecomment-1769853850 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_kwDOIPDwls5i85Pv

Delete V1 tracer and refactor tracer tests to core

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I_kwDOIPDwls56zpDQ

DOC: how to use embeddings in langchain with fireworks?

open

NONE

### Issue with current documentation: how to use embeddings in langchain with fireworks?(I need it for RAG) It's just that the documentation only talks about OpenAIEmbeddings https://python.langchain.com/docs/modules/data_connection/text_embedding/ ### Idea or request for content: RAG with fireworks API

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PR_kwDOIPDwls5i8oyA

langchain: Exclude non-utf8 file from loader since it causes an error in the code_understanding example

closed

CONTRIBUTOR

- **Description:** in the code_understanding.ipynb example, the loader errors out on the langchain/libs/community/tests/examples/non-utf8-encoding.py file, so I updated the loader to exclude that file. Excluding that file allows the example to run. - **Issue:** not applicable - **Dependencies:** none

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PR_kwDOIPDwls5i8nZV

Harrison/new docs revamp

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PR_kwDOIPDwls5i8iGE

langchain[patch]: Release 0.0.353

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PR_kwDOIPDwls5i8gce

langchain: minor changes to StuffDocumentsChain._get_inputs

closed

CONTRIBUTOR

Correcting a small typo ('the' instead of 'then') and changing another 'the' (instead of 'then' too, it was a hard day for the 'n' key :D) to 'also' to match better with what is done in the code <!-- 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_kwDOIPDwls5i8gUg

community[patch]: Release 0.0.7

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PR_kwDOIPDwls5i8dYj

core[patch]: Release 0.1.4

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I_kwDOIPDwls56zE-C

Issue when running a simple ChatOllama prompt in Next.js/TypeScript: "Error: Single '}' in template."

closed

NONE

### System Info "langchain": "^0.0.211", MacOS Sonoma 14.2 Next.js 14.0.4 ### Who can help? @agola11 @hwc ### Information - [ ] The official example notebooks/scripts - [ ] 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 - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction 1. pnpm add langchain in a Next.js project 2. Create a Next.js Route handler 3. Create the following route: ``` import { NextResponse } from 'next/server'; import { ChatOllama } from 'langchain/chat_models/ollama'; import { ChatPromptTemplate, MessagesPlaceholder } from 'langchain/prompts'; import { BufferMemory, ChatMessageHistory } from 'langchain/memory'; import { ConversationChain } from 'langchain/chains'; export async function POST(req: Request) { const data = await req.json(); const prompt = ChatPromptTemplate.fromMessages([ [ 'system', `You are an AI Computer Science Data Structures teaching system that responds to all questions STRICTLY in JSON format. You will be given a question on DSA concepts. Contents of JSON made by you will be used to create elements within a node of a graph that displays explanations of topics, and a user interface that allows users to follow up if they need help or want more information. There are 4 elements, "Topic", "Description", "Subtopics", "Questions": an array of strings. You will also be given a number of nodes that already exist, to be able to assign unique ids. IDs MUST BE STRINGS. MAKE SURE YOU ARE ONLY REPLYING WITH JSON AND NOT MARKDOWN These are the only node types you are allowed to pick from: "promptNode": USE FOR ALL EXPLANATIONS "confusedNode": USED WHEN CONFUSED { "{DEFINE ID BUT IN "STRING" FORM! +1 HIGHER THAN NUMBER GIVEN}": { "THE ID AGAIN": {number}, "type": "promptNode", "position": { "x": 0, "y": 0 }, "data": { "topic": "{Short name of topic}", "description": "{The explanation of topic}", "subtopics": [an array of strings of 5 related topics], "questions": [an array of objects of 4 related questions and answers, eg: {'q': 'Question?', 'a': 'Ans'}], "im_confused": [array of concepts mentioned in the description that they could be confused about] } }`, ], new MessagesPlaceholder('history'), ['human', '{input}'], ]); //@ts-ignore const chatHistory = []; const llm = new ChatOllama({ baseUrl: 'http://localhost:11434', // Default value model: 'mistral', // Default value }); const memory = new BufferMemory({ returnMessages: true, memoryKey: 'history', //@ts-ignore chatHistory: new ChatMessageHistory(chatHistory), }); const chain = new ConversationChain({ memory: memory, prompt: prompt, llm: llm, verbose: true, }); const result = await chain.invoke({ input: data.prompt, }); console.log(result); return NextResponse.json( { }, { status: 200 } ); } ``` ### Expected behavior Model output.

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I_kwDOIPDwls56xMTl

Issue: Error Handling in Tools used in custom agents

open

NONE

### Issue you'd like to raise. I have built a custom LLM Agent by following the Documentation provided. The custom agent contains multiple tools, one of them is the "LLMMathChain" which is giving me ValueError, cause my agent is passing "None" as an Action Input. I want to handle that error. So that my chatbot doesn't break in the middle of a conversation. ## My Custom Agent  ## Calculator Tool  ## Prompt Template  ## Output Parser  ### Suggestion: _No response_

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I_kwDOIPDwls56xIo2

Add common mulit model support

open

NONE

### Feature request Presently, JSON can be utilized to enable the multimodal capability of GPT-4 series models within ChatOpenAI and OpenAI. However, this functionality lacks portability. ### Motivation Using multimodal approaches lacks portability, and GPT-4 isn't the sole model employing multimodal capabilities. Therefore, it becomes imperative to establish a standardized method for accessing various multimodal models. ### Your contribution I may submit a PR about this if I have spare time

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I_kwDOIPDwls56vxtt

Issue: How can I implement Conversation Chain along with ConversationSummaryMemory

open

NONE

### Issue you'd like to raise. below is my code, How can I implement Conversation Chain along with ConversationSummaryMemory in my code `def retreival_qa_chain(chroma_db_path): embedding = OpenAIEmbeddings() vectordb = Chroma(persist_directory=chroma_db_path, embedding_function=embedding) llm = ChatOpenAI(temperature=0.1) retriever = vectordb.as_retriever(search_kwargs={"k": 2}) qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever,return_source_documents=True) return qa def create_global_qa_chain(): chroma_db_path = "chroma-databases" folders = os.listdir(chroma_db_path) qa_chains = {} for index, folder in enumerate(folders): folder_path = f"{chroma_db_path}/{folder}" project = retreival_qa_chain(folder_path) qa_chains[folder] = project return qa_chains` ### Suggestion: _No response_

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I_kwDOIPDwls56vnZQ

Issue: document_variable_name context was not found in llm_chain input_variables

open

NONE

### Issue you'd like to raise. I am trying to add a specific prompt template to my ConversationalRetrievalChain. This is my current code: > PROMPT_TEMPLATE = """ Act as the policies interactive Bot that gives advice on the Company policies, Travel policies, and Information security policies for the company. Do not try to make up an answer. Use only the given pieces of context; do not use your own knowledge. Chat History: {chat_history} Follow Up Input: {question} """ qa_prompt = PromptTemplate(input_variables=["chat_history", "question",], template=PROMPT_TEMPLATE) > chat = ChatOpenAI( verbose=True, model_name=MODEl_NAME, temperature=TEMPERATURE, max_retries=MAX_RETRIES, streaming=True, ) qa_chain =ConversationalRetrievalChain.from_llm( llm=chat, retriever=MyVectorStoreRetriever( vectorstore=vectordb, search_type="similarity_score_threshold", search_kwargs={"score_threshold": SIMILARITY_THRESHOLD, "k": 1}, ), return_source_documents=True, combine_docs_chain_kwargs={'prompt': qa_prompt}, ) response = qa_chain( { "question": query, "chat_history": chat_history, }, callbacks=[stream_handler], ) This is the error I'm currently getting, > qa_chain =ConversationalRetrievalChain.from_llm( File "/home/sfm/anaconda3/envs/chat_v2/lib/python3.10/site-packages/langchain/chains/conversational_retrieval/base.py", line 360, in from_llm doc_chain = load_qa_chain( File "/home/sfm/anaconda3/envs/chat_v2/lib/python3.10/site-packages/langchain/chains/question_answering/__init__.py", line 249, in load_qa_chain return loader_mapping[chain_type]( File "/home/sfm/anaconda3/envs/chat_v2/lib/python3.10/site-packages/langchain/chains/question_answering/__init__.py", line 81, in _load_stuff_chain return StuffDocumentsChain( File "/home/sfm/anaconda3/envs/chat_v2/lib/python3.10/site-packages/langchain/load/serializable.py", line 97, in __init__ super().__init__(**kwargs) File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__ pydantic.error_wrappers.ValidationError: 1 validation error for StuffDocumentsChain __root__ document_variable_name context was not found in llm_chain input_variables: ['chat_history', 'question'] (type=value_error) can you help me to figure out the error and correct it? ### Suggestion: _No response_

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PR_kwDOIPDwls5i7ZVy

Feat: support RunnableBranch constructed with `|`

closed

CONTRIBUTOR

```python from langchain_core.runnables import RunnableBranch branch = RunnableBranch( (lambda x: isinstance(x, str), lambda x: x.upper()), (lambda x: isinstance(x, int), lambda x: x + 1), (lambda x: isinstance(x, float), lambda x: x * 2), lambda x: "goodbye", ) branch.invoke("hello") # "HELLO" branch.invoke(None) # "goodbye" # A RunnableBranch constructed using the `|` operator branch = branch | (lambda x: isinstance(x, bool), lambda x: f'{x}') branch.invoke(true) # "true" ```

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I_kwDOIPDwls56va8j

Slow aadd_documents using Qdrant and HuggingfaceEmbeddings on CPU

open

NONE

### System Info lc: 0.0.352, os: ubuntu 22, python 3.10 ### Who can help? ### Description I am encountering a significant performance issue when using Qdrant with HuggingfaceEmbeddings in a CPU-only environment, specifically within a FastAPI endpoint. The process is notably slow, particularly at the `aadd_documents(...)` stage. ### Additional Information - As a comparison, I tried embedding a document directly using `sentence_transformers`. This approach utilized all CPU cores, resulting in a much faster process. - I also experimented with a custom implementation, using only necessary functions from [this Qdrant file](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/vectorstores/qdrant.py) to perform `aadd_documents`. This approach also showed improved performance and full CPU utilization. ### Question Does anyone have an idea or suggestion on what might be causing this performance bottleneck when using Qdrant with HuggingfaceEmbeddings in a CPU-only environment? ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] 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 - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ### Steps to Reproduce 1. Set up Qdrant with HuggingfaceEmbeddings in a CPU-only machine (no GPU). 2. Integrate it within a FastAPI endpoint. 3. Execute `aadd_documents(...)` for documents (for example, documents with around 45K characters). ### Expected behavior ### Expected Behavior I expected the embedding and addition of documents to Qdrant to be efficient and utilize multiple CPU cores effectively. ### Observed Behavior - The embedding process for a document of approximately 45K characters took over one minute. - Resource utilization monitoring showed that only one out of 70 CPU cores was being utilized during the embedding process.

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PR_kwDOIPDwls5i7Wdf

community: corrected typo in .readthedocs.yaml

closed

CONTRIBUTOR

corrected a possible typing mistake in .readthedocs.yaml

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PR_kwDOIPDwls5i7Ti8

Docs: add param comment for `tracing_v2_enabled`

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I_kwDOIPDwls56vNU1

b

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I_kwDOIPDwls56vIUT

The retrieval cannot be given the document correctly

open

NONE

### System Info langchain 0.0.340 ### Who can help? @eyurtsev ### 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 - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction My vector library preserves tens of thousands of documents, but as the document increases, the accuracy rate of the retriever is becoming low, and the correct document cannot be retrieved. The retrieval cannot be given the document correctly ### Expected behavior db = FAISS.load_local(VS['comixfaiss'], embeddings) retriever = db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.5,"k":5}) logger.info(retriever.get_relevant_documents('3736085')) LOG: 2023-12-29 15:44:51,650 - loader.py[line:56] - INFO: Successfully loaded faiss with AVX2 support. 2023-12-29 15:44:52,843 - local_doc_qa.py[line:204] - INFO: [Document(page_content='\ufeffSelection: 非校验\n商品编号: 3730559\n商品名称: 飞捷 FJ21325 39-45码（QXGZ）中筒 防水鞋户外雨靴套鞋胶鞋 黑色(单位：双）\n物料编码: 3730559\n大类: 生活用品\n中类: 办公日杂\n小类: 雨伞雨具\n品牌: 梦奇\n颜色: 黑色\n型号: FJ21325\n建议零售价: 77.45\n卖点: 品牌：飞捷 颜色：黑色 型号：FJ21325 包装清单：雨靴*1\n上架状态: 上架\n状态: \n状态信息:', metadata={'source': '/mnt/data/pdf/comixgpt/pd/齐心商城商品数据2万条2023-12-27.csv', 'row': 8975}), Document(page_content='\ufeffSelection: 非校验\n商品编号: 3051396\n商品名称: 得力 9387 三联送(销)货单据 129*188mm 20份/本 黄色 单位：本\n物料编码: 3051396\n大类: 办公文具\n中类: 财务行政用品\n小类: 财务单据\n品牌: 得力\n颜色: 黄色\n型号: 9387\n建议零售价: 4.35\n卖点: 0\n上架状态: 上架\n状态: \n状态信息:', metadata={'source': '/mnt/data/pdf/comixgpt/pd/齐心商城商品数据2万条2023-12-27.csv', 'row': 20709}), Document(page_content='\ufeffSelection: 非校验\n商品编号: 3278812\n商品名称: 惠普\xa0W9055MC\xa0成像鼓 彩色 （单位：个）\n物料编码: 3278812\n大类: 办公耗材\n中类: 打印机耗材\n小类: 硒鼓\n品牌: 惠普\n颜色: 彩色\n型号: W9055MC\n建议零售价: 3645.88\n卖点: 打印机耗材\n上架状态: 上架\n状态: \n状态信息:', metadata={'source': '/mnt/data/pdf/comixgpt/pd/齐心商城商品数据2万条2023-12-27.csv', 'row': 12167}), Document(page_content='\ufeffSelection: 非校验\n商品编号: 3197277\n商品名称: 得力 9307 报刊架 480*360*1450 银色 单位：个\n物料编码: 3197277\n大类: 办公文具\n中类: 会议展示用品\n小类: 报刊/杂志架\n品牌: 得力\n颜色: \n型号: 9307\n建议零售价: 330.33\n卖点: 0\n上架状态: 上架\n状态: \n状态信息:', metadata={'source': '/mnt/data/pdf/comixgpt/pd/齐心商城商品数据2万条2023-12-27.csv', 'row': 16685}), Document(page_content='\ufeffSelection: 非校验\n商品编号: 3278811\n商品名称: 惠普 W9054MC 成像鼓 黑色 （单位：个）\n物料编码: 3278811\n大类: 办公耗材\n中类: 打印机耗材\n小类: 硒鼓\n品牌: 惠普\n颜色: 黑色\n型号: W9054MC\n建议零售价: 2471.19\n卖点: 打印机耗材\n上架状态: 上架\n状态: \n状态信息:', metadata={'source': '/mnt/data/pdf/comixgpt/pd/齐心商城商品数据2万条2023-12-27.csv', 'row': 12168})]

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PR_kwDOIPDwls5i7FCz

new feature: add github file loader to load any github file content b…

open

CONTRIBUTOR

### Description support load any github file content based on file extension. Why not use [git loader](https://python.langchain.com/docs/integrations/document_loaders/git#load-existing-repository-from-disk) ? git loader clones the whole repo even only interested part of files, that's too heavy. This GithubFileLoader only downloads that you are interested files. ### Twitter handle my twitter: @shufanhaotop

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I_kwDOIPDwls56vEfH

How do i use similarity caching in my code?

open

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Hi @dosu-bot, This is my code ``` import langchain from langchain.cache import SQLAlchemyCache, Emb from sqlalchemy import create_engine from sqlalchemy.orm import declarative_base from sqlalchemy import Column, Integer, Text from urllib.parse import quote_plus from langchain.llms import OpenAI Base = declarative_base() class FulltextLLMCache(Base): __tablename__ = "llm_cache_full_text" id = Column(Integer, primary_key=True) prompt = Column(Text, nullable=False) llm = Column(Text, nullable=False) idx = Column(Integer) response = Column(Text) db_uri = f"mssql+pyodbc://JUPYTER\SQLEXPRESS/my_database?driver=ODBC+Driver+17+for+SQL Server" cache_engine = create_engine(db_uri, pool_recycle=240, pool_size=20, max_overflow=30) # Assigning to llm_cache langchain.llm_cache = SQLAlchemyCache(cache_engine, FulltextLLMCache) ``` The above code is for exact cache which is very low hit rate, how can i do similarity caching?

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I_kwDOIPDwls56u6cL

Cache not getting saved in ConversationalRetrievalChain

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Hi @dosu-bot. Below is my code, ``` from langchain.cache import SQLAlchemyCache from sqlalchemy import create_engine engine = create_engine("mssql+pyodbc://JUPYTER\SQLEXPRESS/my_database?driver=ODBC+Driver+17+for+SQL Server") set_llm_cache(SQLAlchemyCache(engine)) memory = ConversationBufferWindowMemory(k=2, memory_key="chat_history", chat_memory=chat_message_history ,return_messages=True, output_key="answer", input_key="question") retriever = load_emdeddings(cfg.faiss_persist_directory, cfg.embeddings).as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .65, "k": 2}) custom_prompt_template = """ You are a friendly chatbot named "XYZ", designed to provide assistance and answer queries. {context} Chat History: {chat_history} Question: {question} """ # Create a PromptTemplate instance with your custom template custom_prompt = PromptTemplate( template=custom_prompt_template, input_variables=["context", "question", "chat_history", "User_Name", "User_Location"], ) # Use your custom prompt when creating the ConversationalRetrievalChain qa = ConversationalRetrievalChain.from_llm( llm, verbose=False, retriever=retriever, memory=memory, combine_docs_chain_kwargs={"prompt": custom_prompt}, return_source_documents = True ) ``` if i use llm.predict("Tell me a joke"). I can see the cache is getting stored in db. but for qa if i ask question its not saving , why?

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PR_kwDOIPDwls5i6oK8

Feat: support OpenAIAssistantRunnable async

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CONTRIBUTOR

fix https://github.com/langchain-ai/langchain/issues/15299

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PR_kwDOIPDwls5i6k8M

Patch: improve check openai version

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I_kwDOIPDwls56ufhM

Using chipper model with hi_res strategy gives an error

open

NONE

### System Info Hi, I'm new to this, so I apologize if my lack of in-depth understanding to how this library works caused to me raise a false alarm. Im trying to an ocr on pdf image using the UnstructuredPDFLoader, Im passing the following args: ` loader = UnstructuredPDFLoader(file_path="myfile.pdf", mode="elements",include_page_break=True,infer_table_structure=False,languages=["Eng"],strategy="hi_res",include_metadata=True,model_name="chipper")` However I keep getting the following error: ``` OSError: unstructuredio/chipper-v3 is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models' If this is a private repository, make sure to pass a token having permission to this repo either by logging in with `huggingface-cli login` or by passing `token=<your_token>` ``` Not sure what Im missing here ? Thanks ### 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 - [X] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` from langchain.document_loaders import UnstructuredPDFLoader loader = UnstructuredPDFLoader(file_path="myfile.pdf", mode="elements",include_page_break=True,infer_table_structure=False,languages=["Eng"],strategy="hi_res",include_metadata=True,model_name="chipper") documents = loader.load() print(documents) ``` ### Expected behavior I should be getting the metadata similar to when I use other models like "yolox" which works fine. I heard chipper model is much better so I wanted to try it.

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I_kwDOIPDwls56ufGY

Cannot specify asyn clienct for OpenAIAssistantRunnable

open

CONTRIBUTOR

### System Info langchain = "^0.0.352" @agola11 ### 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 - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Specify async open ai client upon intialization client = openai.AsyncOpenAI() assistant = OpenAIAssistantRunnable(assistant_id=self.assistant_id,as_agent=as_agent,client=client) Produces error pydantic.v1.errors.ConfigError: field "client" not yet prepared so type is still a ForwardRef, you might need to call OpenAIAssistantRunnable.update_forward_refs(). ### Expected behavior expect intialization to be successful

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PR_kwDOIPDwls5i6ja1

community: integrate China Mobile Ecloud vector search

open

NONE

- **Description:** integrate China Mobile Ecloud vector search, - **Dependencies:** elasticsearch==7.10.1

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PR_kwDOIPDwls5i6fFj

[core] add test for json parser

closed

COLLABORATOR

this should fail, but isnt

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PR_kwDOIPDwls5i6eYz

[documentation]

closed

COLLABORATOR

Add doc for tool use

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PR_kwDOIPDwls5i6bMN

Improve markdown list parser

closed

COLLABORATOR

- do not match text after - in the middle of a sentence <!-- 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_kwDOIPDwls5i6a62

[docs] update toolkit docs

closed

COLLABORATOR

its probably too much work to do all toolkits before 0.1 Also, some agent toolkits (like pandas and python) will require more work

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PR_kwDOIPDwls5i6X91

Strip code block fences and extra test from xml when doing streaming …

closed

COLLABORATOR

…parse <!-- 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_kwDOIPDwls5i6ViY

[docs] custom instructions

closed

COLLABORATOR

We now have custom instructions for: LLM ChatModels Retrievers Agents Tools I held off adding for vectorstore since its a bit more involved and we dont have a good base and i think its more likely people write custom retrievers than vectorstores?

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I_kwDOIPDwls56uJs8

Azure function not working - openai error with latest builds

closed

NONE

### System Info Name: langchain Version: 0.0.352 Name: openai Version: 1.6.1 ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [X] 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 AzureChatOpenAI from langchain.schema import HumanMessage BASE_URL = "https://resource.openai.azure.com/" API_KEY = "abc123" DEPLOYMENT_NAME = "GPT35" model = AzureChatOpenAI( openai_api_base=BASE_URL, openai_api_version="2023-05-15", deployment_name=DEPLOYMENT_NAME, openai_api_key=API_KEY, openai_api_type="azure", ) print(model( [ HumanMessage( content="Translate this sentence from English to French. I love programming." ) ] )) ### Expected behavior I get an error about openai module. I get same error when i try and use embeddings. I can use the openai azure pythons fine with my resource and api key - but langchain is broken. AttributeError Traceback (most recent call last) AttributeError: module 'openai' has no attribute 'error'

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PR_kwDOIPDwls5i6RHp

(do not merge)

closed

COLLABORATOR

vercel not previewing the other branch for some reason

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PR_kwDOIPDwls5i6PbQ

Make all json parsing less strict by default

closed

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PR_kwDOIPDwls5i6Fbs

remove chat-history

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I_kwDOIPDwls56t0me

Incorrect Snowflake SQL dialect in SQLDatabaseChain

open

NONE

### System Info langchain version: 0.0.348 Python 3.9.18 Mac OS M2 (Ventura 13.6.2) AWS Bedrock Titan text express, Claude v2 ### Who can help? @hwchase17 @agola11 ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [X] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction SQLDatabaseChain produces SQL query where the logic is correct but uses double quotes "identifier" hence incorrect for the snowflake SQL which require single quotes 'identifier' output = SQL: SELECT "company" = "ABC" desired output = SQL: SELECT 'company' = 'ABC'requires ### Expected behavior desired out should be snowflake SQL single quotes for the identifier 'ABC'

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PR_kwDOIPDwls5i564R

docs: cleanup rag use case

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I_kwDOIPDwls56tmMk

AWS bedrock Claude v2 SQLDatabaseChain produces comments before the SQL Query

open

NONE

### System Info langchain Version: 0.0.348 python Version: Python 3.9.18 OS: Mac OS M2 (Ventura 13.6.2) ### Who can help? @hwchase17 @agola11 ### Information - [ ] The official example notebooks/scripts - [ ] 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 - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction llm = Bedrock( credentials_profile_name= os.environ.get('profile_name'), model_id="anthropic.claude-v2", model_kwargs={"temperature": 0.1}, endpoint_url="https://bedrock-runtime.us-east-1.amazonaws.com", region_name="us-east-1", verbose=True ) db = SQLDatabase.from_uri(snowflake_url, sample_rows_in_table_info=3, include_tables=["table_name"]) output= SQLDatabaseChain.from_llm( llm, db, prompt=few_shot_prompt, return_intermediate_steps=True, ) Gives the following error: Error: syntax error line 1 at position 0 unexpected '**The**'. [SQL: **The** query looks good to me, I don't see any of the common mistakes listed. Here is the original query again: SELECT * FROM table] ### Expected behavior the output should only produce SQL query outputted plainly, should not surround it in quotes or any comments prior to the SQL Query Desired output: [SQL: SELECT * FROM table]

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PR_kwDOIPDwls5i5wVp

mistralai[minor]: Add embeddings

closed

CONTRIBUTOR

- **Description:** Adds MistralAIEmbeddings class for embeddings, using the new official API. - **Dependencies:** mistralai - **Tag maintainer**: @efriis, @hwchase17 - **Twitter handle:** @LMS_David_RS Create `integrations/text_embedding/mistralai.ipynb`: an example notebook for MistralAIEmbeddings class Modify `embeddings/__init__.py`: Import the class Create `embeddings/mistralai.py`: The embedding class Create `integration_tests/embeddings/test_mistralai.py`: The test file.

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PR_kwDOIPDwls5i5uor

[documentation] documentation revamp

closed

COLLABORATOR

needs new versions of langchain-core and langchain

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PR_kwDOIPDwls5i5rFU

community: Added integrations for ThirdAI's NeuralDB with Retriever and VectorStore frameworks

open

NONE

**Description:** Adds ThirdAI NeuralDB retriever and vectorstore integration. NeuralDB is a CPU-friendly and fine-tunable text retrieval engine.

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PR_kwDOIPDwls5i5onL

docs: add use cases index

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PR_kwDOIPDwls5i5lze

[langchain] agents code changes

closed

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PR_kwDOIPDwls5i5hBi

[core, langchain] modelio code improvements

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PR_kwDOIPDwls5i5aNj

community: fix typo in async ollama chat

closed

CONTRIBUTOR

Made a stupid typo in the last PR which got already merged😅

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PR_kwDOIPDwls5i5ZZf

Update vectorstore_retriever_memory.mdx

closed

CONTRIBUTOR

removed bad comments <!-- 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_kwDOIPDwls5i5YDg

revamp memory

closed

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PR_kwDOIPDwls5i4xni

community: llms.vertexai - Adds support for logprobs

open

CONTRIBUTOR

- **Description:** Adds support for logprobs within vertex ai, recently google started to support logprobs, this commit includes fast adaptation, Google recently started supporting logprobs, please check out this [page](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text#generative-ai-text-prompt-python_vertex_ai_sdk) for more details on their side. - **Issue:** -, - **Dependencies:** -, - **Twitter handle:** [Burak Ömür](https://twitter.com/burkomr)

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I_kwDOIPDwls56sXBe

OpenAIAssistantRunnable stuck on execution with langchain tools

closed

CONTRIBUTOR

### System Info ``` from langchain.tools import DuckDuckGoSearchRun from langchain.agents.openai_assistant import OpenAIAssistantRunnable from langchain.agents import AgentExecutor tools = [DuckDuckGoSearchRun()] assistant = OpenAIAssistantRunnable.create_assistant( name="langchain assistant", instructions="You are a personal math tutor.", tools=tools, model="gpt-4-1106-preview", as_agent=True, ) logger.debug(assistant) logger.debug(assistant.assistant_id) agent_executor = AgentExecutor(agent=assistant, tools=tools,verbose=True) response = agent_executor.invoke({"content": "whats the whether in london"}) print(response) logger.debug(response) ``` I am trying to run the following from the example. It prints out the assitant information and id but after that it get completely stuck. I tried to step through the debugger but after while it continues and never comes back after calling ``` callback_manager = CallbackManager.configure( callbacks, self.callbacks, self.verbose, tags, self.tags, metadata, self.metadata, ) ``` in the `__call__` method ### 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 - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [X] Callbacks/Tracing - [ ] Async ### Reproduction from langchain.tools import DuckDuckGoSearchRun from langchain.agents.openai_assistant import OpenAIAssistantRunnable from langchain.agents import AgentExecutor tools = [DuckDuckGoSearchRun()] assistant = OpenAIAssistantRunnable.create_assistant( name="langchain assistant", instructions="You are a personal math tutor.", tools=tools, model="gpt-4-1106-preview", as_agent=True, ) logger.debug(tai_assistant) logger.debug(tai_assistant.assistant_id) agent_executor = AgentExecutor(agent=assistant, tools=tools,verbose=True) response = agent_executor.invoke({"content": "whats the whether in london"}) print(response) logger.debug(response) ### Expected behavior to have an output from the agent and not be stuck

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I_kwDOIPDwls56sR_c

[mistralai]: Don´t support stop sequence

closed

CONTRIBUTOR

### System Info Python: 3.11 Langchain: 0.0.352 mistralai: 0.0.8 ### Who can help? @efriis ### 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 - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction If the ChatMistralAI model is used for an agent or similar, an error appears because the official Mistral API does not currently support the stop parameter (as other APIs such as OpenAI do). ### Expected behavior Although this is something that should be fixed by Mistral in its official API, one of the following options should be done: - Warn the user that this model cannot be used with a stop sequence before breaking execution due to the error. - Implement an own solution for the stop sequence in the same package and do not send that parameter to the official client call.

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PR_kwDOIPDwls5i4mOP

langchain: Fix class name in RetryOutputParser docstring

closed

CONTRIBUTOR

`OutputFixingParser` -> `RetryOutputParser` 

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PR_kwDOIPDwls5i4fQy

community: Support openai_proxy compatible with openai>1.0.0

open

CONTRIBUTOR

### **Description:** `openai` v1 no longer supports the method of setting `openai.proxy`. If using `OPENAI_PRPXY`, the users need to initialize an `http_client`. ### **Issue:** https://github.com/langchain-ai/langchain/issues/13939

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I_kwDOIPDwls56sFS6

Manipulating database using chatgpt

open

NONE

### System Info Is there any way to manipulate the data in database like update, insert, delete through chatgpt chatbot with openai and langchain? ### 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 Is there any way to manipulate the data in database like update, insert, delete through chatgpt chatbot with openai and langchain? ### Expected behavior possibility of manipulate the data in database like update, insert, delete through chatgpt chatbot with openai and langchain?

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PR_kwDOIPDwls5i4UmL

community: Add ChatGLM3

open

CONTRIBUTOR

Add [ChatGLM3](https://github.com/THUDM/ChatGLM3) and updated [chatglm.ipynb](https://python.langchain.com/docs/integrations/llms/chatglm)

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