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I_kwDOIPDwls58bSYe

How can I embed chat logs into the specified collection 'QDRANT_COLLECTION_NAME' in Qrdant, and what approach should I use to write the chain for short-term memory?

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### Issue with current documentation: import os import qdrant_client from dotenv import load_dotenv from langchain.chains import RetrievalQA from langchain.embeddings.huggingface import HuggingFaceInstructEmbeddings from langchain.llms import OpenAI from langchain.vectorstores import Qdrant os.environ['OPENAI_API_KEY'] = "mykey" # memory= def get_vector_store(): client = qdrant_client.QdrantClient( os.getenv('QDRANT_HOST'), ) embeddings = HuggingFaceInstructEmbeddings(model_name="all-MiniLM-L6-v2") vector_store = Qdrant( client=client, collection_name=os.getenv('QDRANT_COLLECTION_NAME'), embeddings=embeddings, ) return vector_store def main(): load_dotenv() vectorstore = get_vector_store() qa = RetrievalQA.from_chain_type( llm=OpenAI( temperature=0.7, max_tokens=100, ), chain_type="stuff", retriever=vectorstore.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.1}) ) while True: user_question = input("ask:") if user_question == "bye": break answer = qa.invoke(user_question) print(f"Ai: {answer}") if __name__ == "__main__": main() ### Idea or request for content: How can I embed chat logs into the specified collection 'QDRANT_COLLECTION_NAME' in Qrdant, and what approach should I use to write the chain for short-term memory?

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PR_kwDOIPDwls5kYhzM

docs: add milvus multitenancy doc

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CONTRIBUTOR

- **Description:** add milvus multitenancy doc, it is an example for this [pr](https://github.com/langchain-ai/langchain/pull/15740) . - **Issue:** No, - **Dependencies:** No, - **Twitter handle:** No

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PR_kwDOIPDwls5kYaDq

`output_parsers` updated dependency

closed

COLLABORATOR

nit Decreased dependency on `runnables`

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PR_kwDOIPDwls5kYRaR

rfc: agent initial tool choices

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PR_kwDOIPDwls5kYN_P

langchain-community: LanceDB integration improvements/fixes

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NONE

Hi, I'm from the LanceDB team. Improves LanceDB integration by making it easier to use - now you aren't required to create tables manually and pass them in the constructor, although that is still backward compatible. Bug fix - pandas was being used even though it's not a dependency for LanceDB or langchain PS - this issue was raised a few months ago but lost traction. It is a feature improvement for our users kindly review this , Thanks !

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PR_kwDOIPDwls5kYFOZ

core[patch]: RFC Add astream_events to Runnables

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COLLABORATOR

This PR adds `astream_events` method to Runnables to make it easier to stream data from arbitrary chains. * Streaming only works properly in async right now * One should use `astream()` with if mixing in imperative code as might be done with tool implementations * Astream_log has been modified with minimal additive changes, so no breaking changes are expected * Underlying callback code / tracing code should be refactored at some point to handle things more consistently (OK for now) - [ ] check if there are handled callbacks by other LangChain runnables (e.g., embedders) - [ ] verify event for on_retry - [ ] Any rrenaming? Should we rename "event" to "hook"? - [ ] Any other feedback from community? ## Example See this [Example Notebook](https://github.com/langchain-ai/langchain/blob/dbbc7fa0d66bcdde420760baa0ddb9918c12c349/docs/docs/modules/agents/how_to/streaming_events.ipynb) for an example with streaming in the context of an Agent ## Event Hooks Reference Here is a reference table that shows some events that might be emitted by the various Runnable objects. Definitions for some of the Runnable are included after the table. | event | name | chunk | input | output | |----------------------|------------------|---------------------------------|-----------------------------------------------|-------------------------------------------------| | on_chat_model_start | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | | | on_chat_model_stream | [model name] | AIMessageChunk(content="hello") | | | | on_chat_model_end | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | {"generations": [...], "llm_output": None, ...} | | on_llm_start | [model name] | | {'input': 'hello'} | | | on_llm_stream | [model name] | 'Hello' | | | | on_llm_end | [model name] | | 'Hello human!' | | on_chain_start | format_docs | | | | | on_chain_stream | format_docs | "hello world!, goodbye world!" | | | | on_chain_end | format_docs | | [Document(...)] | "hello world!, goodbye world!" | | on_tool_start | some_tool | | {"x": 1, "y": "2"} | | | on_tool_stream | some_tool | {"x": 1, "y": "2"} | | | | on_tool_end | some_tool | | | {"x": 1, "y": "2"} | | on_retriever_start | [retriever name] | | {"query": "hello"} | | | on_retriever_chunk | [retriever name] | {documents: [...]} | | | | on_retriever_end | [retriever name] | | {"query": "hello"} | {documents: [...]} | | on_prompt_start | [template_name] | | {"question": "hello"} | | | on_prompt_end | [template_name] | | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) | Here are declarations associated with the events shown above: `format_docs`: ```python def format_docs(docs: List[Document]) -> str: '''Format the docs.''' return ", ".join([doc.page_content for doc in docs]) format_docs = RunnableLambda(format_docs) ``` `some_tool`: ```python @tool def some_tool(x: int, y: str) -> dict: '''Some_tool.''' return {"x": x, "y": y} ``` `prompt`: ```python template = ChatPromptTemplate.from_messages( [("system", "You are Cat Agent 007"), ("human", "{question}")] ).with_config({"run_name": "my_template", "tags": ["my_template"]}) ```

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PR_kwDOIPDwls5kX-YG

community: New documents loader for visio files (with extension .vsdx)

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NONE

**Description** : New documents loader for visio files (with extension .vsdx) A [visio file](https://fr.wikipedia.org/wiki/Microsoft_Visio) (with extension .vsdx) is associated with Microsoft Visio, a diagram creation software. It stores information about the structure, layout, and graphical elements of a diagram. This format facilitates the creation and sharing of visualizations in areas such as business, engineering, and computer science. A Visio file can contain multiple pages. Some of them may serve as the background for others, and this can occur across multiple layers. This loader extracts the textual content from each page and its associated pages, enabling the extraction of all visible text from each page, similar to what an OCR algorithm would do. **Dependencies** : xmltodict package

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I_kwDOIPDwls58aaGN

No file/folder found for package langchain-monorepo

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code none ### Description In the HOME directory of the langchain source code, execute the following command: poetry install --with lint,typing,test However, the following errors are thrown, and I have a few questions to ask: (1) Why do we need to perform a downgrade operation on modules like langchain-core, and how to avoid downgrading? (2) Why can't the module langchain-monorepo be found? (3) How to compile the source code of langchain in order to understand the principles of langchain and contribute code to langchain? My development environment: Python: Built on conda, Python version 3.8.18 Langchain: The master branch on GitHub. Error message: (langchain) [xiaobai@xiaobai langchain-master]$ poetry install --with lint,typing,test Installing dependencies from lock file Package operations: 0 installs, 4 updates, 0 removals • Downgrading langchain-core (0.1.10 /home/xiaobai/data/pro/python/langchain-master/libs/core -> 0.1.3 /home/xiaobai/data/pro/python/langchain-master/libs/core) • Downgrading langchain-community (0.0.12 /home/xiaobai/data/pro/python/langchain-master/libs/community -> 0.0.6 /home/xiaobai/data/pro/python/langchain-master/libs/community) • Downgrading langchain (0.1.0 /home/xiaobai/data/pro/python/langchain-master/libs/langchain -> 0.0.352 /home/xiaobai/data/pro/python/langchain-master/libs/langchain) • Downgrading langchain-experimental (0.0.49 /home/xiaobai/data/pro/python/langchain-master/libs/experimental -> 0.0.47 /home/xiaobai/data/pro/python/langchain-master/libs/experimental) Installing the current project: langchain-monorepo (0.0.1) The current project could not be installed: No file/folder found for package langchain-monorepo If you do not want to install the current project use --no-root ### System Info Python: Built on conda, Python version 3.8.18 Langchain: The master branch on GitHub. ### 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

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PR_kwDOIPDwls5kXrrN

Bagatur/sql use case

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<!-- 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_kwDOIPDwls5kXo9o

Add revision identifier to run_on_dataset

closed

CONTRIBUTOR

Allow specifying revision identifier for better project versioning

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I_kwDOIPDwls58aKhF

Chroma and Pinecone provide totally different results even when created with the same document set.

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code ``` def createAgent(): pinecone.init(environment="gcp-starter") llm = ChatOpenAI(temperature=0, model_name="gpt-4-1106-preview") embeddings = OpenAIEmbeddings() docsearch_ch = Chroma(persist_directory="../chroma_db", embedding_function=embeddings, collection_name="company") docsearch = Pinecone.from_existing_index(index_name="company", embedding=embeddings) companyQa = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=docsearch_ch.as_retriever(kwargs={"k": 15}) ) tools = [ Tool( name="Company QA", description="Useful to get informations about Company", func=companyQa.run ) ] return initialize_agent( tools=tools, llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True ) ``` ### Description I have Chroma index and Pinecone index created from exactly the same documents. It is a company website scraped through Apify. Below code is used to create both indices: ``` splitter = RecursiveCharacterTextSplitter( chunk_size=500, chunk_overlap=50, separators= ["\n", "", " ", "\n\n"]) docs = splitter.split_documents(documents) embeddings = OpenAIEmbeddings() Pinecone.from_documents(docs, embeddings, index_name="company") Chroma.from_documents(docs, embeddings, persist_directory="./chroma_db", collection_name="company") ``` The problem is that Chroma seems very limited in capabilities compared to Pinecone.I have provided the example agent code that I am using. When we use the `docsearch_ch` and ask the following prompt: `What are some clients of the Company"?`. We are going to receive `To find out the clients of the Company, you would need to contact Company directly or visit their official website or check their press releases for announcements of new partnerships or clients. Company QA does not have access to this information.` When we use the `docsearch` that is based on Pinecone. The same prompt returns `Some clients of the company include industry leaders such as Demandbase, New Relic, Nubank, Rapid7, and Skyscanner.` If we try to do the similarity search manually using the below code: ``` print(docsearch_ch.similarity_search("List of the company clients.", k =10)) print(docsearch.similarity_search("List of the company clients.", k =10)) ``` Both of the vector stores will return more or less correct results (altough seems that Pinecone results are better), but more importantly both of them will return the Document with following content: ``` page_content='by industry leaders worldwide, such as Demandbase, New Relic, Nubank, Rapid7, and Skyscanner.' ``` When I try to debug the code and setup the breakpoint in the [BaseCombineDocumentsChain](https://github.com/langchain-ai/langchain/blob/ca014d5b04b1d73fd8f0fe224def98a82600c991/libs/langchain/langchain/chains/combine_documents/base.py#L136) the inputs for Chroma based retriever are empty. Seems like this isn't the expected behaviour. ### System Info langchain==0.0.342 langchain-community==0.0.13 langchain-core==0.0.7 langchain-experimental==0.0.49 Mac OS M1 Ventura 13.6 Python 3.9.6 ### 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

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I_kwDOIPDwls58Z7Dc

Improper chat_template when using Ollama

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code I have noticed that the prompt produced by LangChain when using ```ChatOllama(model='mixtral')``` was not in the proper format. This can be replicated using the following: ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) ##messages example messages = [ {"role": "system", "content": "You are a chef"}, {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] ##converting messages example to LC format messages_lc = [(m['role'],m['content']) for m in messages] ``` For the proper format: ``` tokenizer.apply_chat_template(messages,tokenize=False) ``` This would produce: ``` "<s>[INST] <<SYS>>\nYou are a chef\n<</SYS>>\n\nWhat is your favourite condiment? [/INST] Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen! </s><s>[INST] Do you have mayonnaise recipes? [/INST]" ``` Where as the LangChain format: ``` from langchain_community.chat_models import ChatOllama from langchain.prompts import ChatPromptTemplate chat = ChatOllama(model='mixtral') chat_resp = (ChatPromptTemplate.from_messages(messages_lc) | chat_tmp ).invoke({},{'callbacks': [ConsoleCallbackHandler()]}) ``` I then copied the tokens sent to the LLM (using the ConsoleCallbackHandler) into ```tokens``` and decoded them: ``` tokenizer.decode(tokens) ``` To receive this: ``` " [INST] <<SYS>> You are a chef <</SYS>>\n[INST] What is your favourite condiment? [/INST]\nWell, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!\n[INST] Do you have mayonnaise recipes? [/INST] [/INST] ``` ### Description I'm trying to use LangChain with ChatOllama. Output tokens decoded were: ``` " [INST] <<SYS>> You are a chef <</SYS>>\n[INST] What is your favourite condiment? [/INST]\nWell, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!\n[INST] Do you have mayonnaise recipes? [/INST] [/INST] ``` Where the proper format is: ``` "<s>[INST] <<SYS>>\nYou are a chef\n<</SYS>>\n\nWhat is your favourite condiment? [/INST] Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen! </s><s>[INST] Do you have mayonnaise recipes? [/INST]" ``` ### System Info langchain==0.0.350 langchain-community==0.0.3 langchain-core==0.1.0 Python 3.11.4 MacOS Sonoma ### 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 - [X] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5kW2-G

doc: Fix small typo in quickstart

open

NONE

- **Description:** fix small typo in quickstart

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PR_kwDOIPDwls5kWwle

docs: model io order

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PR_kwDOIPDwls5kWrq3

docs: bump sphinx>=5

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PR_kwDOIPDwls5kWjAL

core[patch]: Release 0.1.12

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PR_kwDOIPDwls5kWNuk

Update readme

closed

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PR_kwDOIPDwls5kWLsj

langchain[patch]: fix stuff documents chain api docs render

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PR_kwDOIPDwls5kWKBm

openai[patch]: clarify azure error

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I_kwDOIPDwls58YixU

Geopandas and Langchain blocking issue.

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code The bug was found by trying to install [jupyter-ai](https://github.com/jupyterlab/jupyter-ai) via conda-forge. ```install -c conda-forge jupyter_ai``` Basically, there is an incompatibility with the latest version of langchain and geopandas. ### Description Based on the bug report posted on the jupyter-ai github https://github.com/jupyterlab/jupyter-ai/issues/557#issue-2062998660 , it looks like langchain enforces a version of geopandas below 0.14. Could this be relaxed to allow for >0.14? There are many features of modern geopandas that make GIS workflows so much easier, and the current enforcement version prevents langchain-dependent repositories from making full use of GIS-adjacent stacks. ### System Info The incompatibility arose while using an M1 apple silicon laptop. ### 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

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PR_kwDOIPDwls5kWE-o

core[patch]: support old core namespaces

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PR_kwDOIPDwls5kV0Xi

google-vertexai[patch]: typing, release 0.0.2

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PR_kwDOIPDwls5kVysM

docs: change parallel doc name

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I_kwDOIPDwls58X-ut

Dependency issues with Spark toolkit examples

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code ``` pip install langchain langchain-community langchain-core langchain-experimental ``` ``` from langchain.agents import create_spark_sql_agent from langchain_community.agent_toolkits import SparkSQLToolkit from langchain_community.utilities.spark_sql import SparkSQL from langchain_openai import ChatOpenAI from pyspark.sql import SparkSession ``` ### Description I get errors related to dependicies when trying to follow the Spark toolkit examples from the documentation. For example: https://python.langchain.com/docs/integrations/toolkits/spark_sql ``` ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' (/databricks/python/lib/python3.10/site-packages/typing_extensions.py) --------------------------------------------------------------------------- ImportError Traceback (most recent call last) File <command-2000110168240505>, line 1 ----> 1 from langchain.agents import create_spark_sql_agent 2 from langchain_community.agent_toolkits import SparkSQLToolkit 3 from langchain_community.utilities.spark_sql import SparkSQL File /local_disk0/.ephemeral_nfs/envs/pythonEnv-b2a0b64a-b43b-40f8-a165-ff3aac48e3b4/lib/python3.10/site-packages/langchain/agents/__init__.py:34 31 from pathlib import Path 32 from typing import Any ---> 34 from langchain_community.agent_toolkits import ( 35 create_json_agent, 36 create_openapi_agent, 37 create_pbi_agent, 38 create_pbi_chat_agent, 39 create_spark_sql_agent, 40 create_sql_agent, 41 ) 42 from langchain_core._api.path import as_import_path 44 from langchain.agents.agent import ( 45 Agent, ``` ### System Info langchain==0.1.1 langchain-community==0.0.13 langchain-core==0.1.11 langchain-experimental==0.0.49 Python 3.10 ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async

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I_kwDOIPDwls58X1C8

Inference Component Name header is required (SageMaker RedShift Flan T5)

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code llm = SagemakerEndpoint( endpoint_name=endpoint_name, endpoint_kwargs={ "CustomAttributes": "accept_eula=true,InferenceComponentName="+inference_component_name }, region_name = region_name, credentials_profile_name = profile_name, model_kwargs = parameters, content_handler = content_handler, ) print(str( llm.endpoint_kwargs )) db = SQLDatabase.from_uri(REDSHIFT_ENDPOINT) db_chain = SQLDatabaseSequentialChain.from_llm( llm, db, verbose=True, use_query_checker=True ) try: results = db_chain.run(QUESTION_01) print(results) except (ProgrammingError, ValueError, DataError) as exc: print(f"\n\n{exc}") ### Description I am using a Sagemaker Flan T5 Xl model to ask model and is ok, but when try to use langchain to query RedShift DB. I have this error. ----> " Error raised by inference endpoint: An error occurred (ValidationError) when calling the InvokeEndpoint operation: Inference Component Name header is required for endpoints to which you plan to deploy inference components. Please include Inference Component Name header or consider using SageMaker models." ### System Info model jumpstart-dft-hf-text2text-flan-t5 xxl Python 3.11.6 boto3 1.34.19 langchain 0.1.0 langchain-community 0.0.12 langchain-core 0.1.10 langchain-experimental 0.0.49 sentence-transformers 2.2.2 SQLAlchemy 1.4.48 ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5kU70D

Update gradient.ipynb

closed

CONTRIBUTOR

Enviroment -> Environment <!-- 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_kwDOIPDwls5kU6_j

langchain_community.vectorstores: Issue #16146. Fix: Missing unpack operator for or_clause in pgvector document filter

closed

CONTRIBUTOR

- Fix for #16146 - Adding unpack operation to "or" and "and" filter for pgvector retriever. #

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I_kwDOIPDwls58XCoo

Missing unpack operator for or_clause in pgvector document filter

closed

CONTRIBUTOR

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code The following code: ``` from langchain.vectorstores import PGVector filter = { "source": { "or": [{"contains": "xlsx"}, {"contains": "pdf"}] } } vector_db = PGVector(connection_string=connection_string, embedding_function=embeddings) vector_db.as_retriever(search_type="similarity", search_kwargs={"k": 5, "filter": filter}) ``` won't work because the list is not unpacked when passed to filter_by_metadata = sqlalchemy.or_(or_clauses) in https://github.com/langchain-ai/langchain/blob/e5cf1e2414a22c7465f0f47bd661ece183e88924/libs/community/langchain_community/vectorstores/pgvector.py#L548 The code throws the error `ArgumentError: SQL expression for WHERE/HAVING role expected, got [, ].` ### Description * I am trying to filter documents based on metadata during retrieval * I want to pass a list to filter metadata via an "or" statement. * I think it should be `filter_by_metadata = sqlalchemy.or_(*or_clauses)` in https://github.com/langchain-ai/langchain/blob/e5cf1e2414a22c7465f0f47bd661ece183e88924/libs/community/langchain_community/vectorstores/pgvector.py#L548 ### System Info langchain==0.0.353 langchain-community==0.0.13 langchain-core==0.1.11 Python 3.10.13 ### 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

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I_kwDOIPDwls58WzD7

DOC: No documentation about QAGenerationChain in langchain.evaluation.qa

open

NONE

### Issue with current documentation: This is no documentation about QAGenerationChain in langchain.evaluation.qa I used `from langchain.evaluation.qa import QAGenerateChain` to run evaluation using LLM-generated examples. I tried to find documentation regarding the QAGenerateChain and found none. Even in [api.python.langchain.com/](https://api.python.langchain.com/en/latest/search.html?q=QAGenerationChain#) site there is documentation regarding `langchain.chains.qa_generation.base.QAGenerationChain` but nothing about `from langchain.evaluation.qa import QAGenerateChain` ### Idea or request for content: I believe there is more work that needs to be done. Please fill a documentation or a use case of `QAGenerateChain`

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PR_kwDOIPDwls5kUd_Y

[community][cohere] Handle when documents are not provided in the Cohere response

closed

CONTRIBUTOR

- **Description:** This handles the cohere response when documents aren't included in the response - **Issue:** N/A - **Dependencies:** N/A - **Twitter handle:** N/A

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I_kwDOIPDwls58V2Dk

i want to add OutputFixingParser in LCEL chain

open

NONE

### Feature request like from langchain.output_parsers import OutputFixingParser fix_parser = OutputFixingParser.from_llm(llm, parser) chain= chat_template | llm | parser |fix_parser ### Motivation I think OutputFixing should be in the process of generating content in a specific format. ### Your contribution I'm sorry.

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PR_kwDOIPDwls5kTnbU

Add neo4j timeout and value sanitization option

closed

CONTRIBUTOR

The timeout function comes in handy when you want to kill longrunning queries. The value sanitization removes all lists that are larger than 128 elements. The idea here is to remove embedding properties from results.

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PR_kwDOIPDwls5kTcfg

Use dotenv in langchain-community's integration tests

closed

CONTRIBUTOR

* Removed some env vars not used in langchain package IT * Added Astra DB env vars in langchain package, used for cache tests * Added conftest.py to load env vars in langchain_community IT * Added .env.example in langchain_community IT

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I_kwDOIPDwls58VHJG

Return Source Documents in LECL

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code The chain is defined as: ```python retriever_chain = create_retriever_chain(llm, retriever, use_chat_history) _get_k_or_less_documents = partial(get_k_or_less_documents, k=k) context = ( RunnableMap( { "question": lambda x: x["question"], "memory": memory.load_memory_variables, } ) | RunnableMap( { "context": ( retriever_chain | _get_k_or_less_documents | reorder_documents | format_docs ), "question": lambda x: x["question"], "chat_history": lambda x: x["memory"]["chat_history"], } ) ) prompt = ChatPromptTemplate.from_messages( messages=[ ("system", SYSTEM_ANSWER_QUESTION_TEMPLATE), MessagesPlaceholder(variable_name="chat_history"), ("human", "{question}"), ] ) response_synthesizer = prompt | llm response_chain = context | response_synthesizer ``` ### Description I am trying to get back source documents used for my RAG application. However, I cannot find any ways to retrieve the document. I am using `chain.astream` to stream through my chain. The relevant code and chains are given below. ```python async def generate_response(): input = {"question": data.message} response = "" async for token in chain.astream(input=input): yield token.content response += token.content memory.save_context(input, {"output": response}) ``` The chain is defined as: ```python retriever_chain = create_retriever_chain(llm, retriever, use_chat_history) _get_k_or_less_documents = partial(get_k_or_less_documents, k=k) context = ( RunnableMap( { "question": lambda x: x["question"], "memory": memory.load_memory_variables, } ) | RunnableMap( { "context": ( retriever_chain | _get_k_or_less_documents | reorder_documents | format_docs ), "question": lambda x: x["question"], "chat_history": lambda x: x["memory"]["chat_history"], } ) ) prompt = ChatPromptTemplate.from_messages( messages=[ ("system", SYSTEM_ANSWER_QUESTION_TEMPLATE), MessagesPlaceholder(variable_name="chat_history"), ("human", "{question}"), ] ) response_synthesizer = prompt | llm response_chain = context | response_synthesizer ``` Any kind of help is highly appreciated. ### System Info langchain 0.1.0 langchain-community 0.0.11 langchain-core 0.1.9 langchain-openai 0.0.2 ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [X] Async

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I_kwDOIPDwls58U7jv

ElasticsearchStore does not accept proxy

open

NONE

### Feature request We should be able to pass some extra parameters to `ElasticsearchStore` in form of kwargs or predefined parameters. For example, to allow usage of `RequestsHttpNode` that takes `https_proxy` / `http_proxy` / `no_proxy` env variables. ### Motivation In some cases, all the external calls to internet/ELK (on cloud) must use a proxy, else it will be rejected by the firewall of the company. The default behavior of `Elasticsearch` class from https://github.com/elastic/elasticsearch-py does not take by default proxy env variable and you need to set a `node_class` to `RequestsHttpNode` ### Your contribution Instead of calling `ElasticsearchStore` from langchain, I duplicated locally the file and hardcoded in the `Elasticsearch` init the `node_class`. ``` python from elastic_transport import RequestsHttpNodes # https://github.com/langchain-ai/langchain/blob/e5cf1e2414a22c7465f0f47bd661ece183e88924/libs/community/langchain_community/vectorstores/elasticsearch.py#L593 es_client = elasticsearch.Elasticsearch( **connection_params, node_class=RequestsHttpNode, headers={"user-agent": ElasticsearchStore.get_user_agent()}, ) ``` I am sure there's a better way and more global way to pass parameters to `Elasticsearch()`

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I_kwDOIPDwls58Uzoe

How is multiquery retreiver implemented with RAG?

open

NONE

<peek> ``` below is my code :- def regenerate_custom_prompt(new_project_qa,chroma_db_path,query,name,not_uuid): check = query.lower() relevant_document = new_project_qa.get_relevant_documents(query=query) 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 = """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) print(formatted_prompt,"-------------------------------") embedding = OpenAIEmbeddings() vectordb = Chroma(persist_directory=chroma_db_path, embedding_function=embedding) llm = ChatOpenAI(model="gpt-3.5-turbo-16k",temperature=0.1) ### mmr stands for Maximum marginal relevance retriever = vectordb.as_retriever(search_type="similarity", search_kwargs={"k":2}) qa = MultiQueryRetriever.from_llm(llm=llm, retriever=retriever) return qa </peek>

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PR_kwDOIPDwls5kSrKn

Implement vector length definition at init time in PGVector for indexing

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Replace this entire comment with: - **Description:** allow user to define tVector length in PGVector when creating the embedding store, this allows for later indexing - **Issue:** #16132 - **Dependencies:** None

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I_kwDOIPDwls58UmhM

Implement vector length definition at init time in PGVector for indexing

open

CONTRIBUTOR

### Feature request Allow manual definition of embedding column length in PGVector ### Motivation Right now embeddings stores in PGVector are initialised with: `embedding: Vector = sqlalchemy.Column(Vector(None))` This won't allow indexing of the DB ([https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/howto-optimize-performance-pgvector](url)) ### Your contribution I can open a PR shortly to fix it

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I_kwDOIPDwls58Uk4R

DOC: Dead link in langgraph docs

open

NONE

### Issue with current documentation: https://python.langchain.com/docs/langgraph The paragraph under "Examples": > > Modifications > > We also have a lot of examples highlighting how to slightly modify the base chat agent executor. These all build off the [getting started notebook](https://python.langchain.com/docs/examples/chat_agent_executor_with_function_calling/base.ipynb) so it is recommended you start with that first. The link is not working. ### Idea or request for content: _No response_

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I_kwDOIPDwls58UcyS

SQLDatabase.from_uri Throws an error when used with Vertica

open

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### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code ``` db = SQLDatabase.from_uri( 'vertica+vertica_python://user:pwd@x.x.x.x:5433/verticadst', schema='myschema' ) ``` ### Description When I use the above snippet to create an SQLDatabase object for vertica, I get the below error: > sqlalchemy.exc.ProgrammingError: (vertica_python.errors.MissingSchema) Severity: ERROR, Message: Schema "pg_catalog" does not exist Which makes sense because Vertica does not have pg_catalog schema, but how can I create the object without the reflect part where it tries to read from pg_catalog ### System Info sqlalchemy-vertica-python~=0.6.3 langchain==0.1.0 ### 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

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I_kwDOIPDwls58TocJ

PydanticOutputParser ` ValidationError: Field required [type=missing,' for an Optional field

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### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code The following code follows the example from [langchain docs](https://python.langchain.com/docs/use_cases/extraction#option-2-parsing). ```python class Person(BaseModel): person_name: str person_height: int person_hair_color: str dog_breed: Optional[str] dog_name: Optional[str] class People(BaseModel): """Identifying information about all people in a text.""" people: Sequence[Person] query = """Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde. Alex has a poodle named Rex.""" # Set up a parser + inject instructions into the prompt template parser = PydanticOutputParser(pydantic_object=People) prompt = PromptTemplate( template="Follow the given instructions and perform the task mentioned in the context of given information. \n{format_instructions}\n Context is:\n{query}\n", input_variables=["query"], partial_variables={"format_instructions": parser.get_format_instructions()}, ) input_to_model=prompt.format_prompt(query=query) print(input_to_model.to_string()) # output=FLAN.generate_text(input_to_model.to_string()) output=LLAMA.generate_text(input_to_model.to_string()) print(output) parser.parse(output) ``` ### Description * I'm trying to use PydanticOutputParser for structured output. * Incase of an optional field missing from JSON, pydanticoutput parser raises a validation error saying missing a required field Output from model: ```json ``` { "people": [ { "person_name": "Alex", "person_height": 5, "person_hair_color": "blonde", "dog_breed": "poodle", "dog_name": "Rex" }, { "person_name": "Claudia", "person_height": 6, "person_hair_color": "brunette" } ] } ``` ``` Complete Error: ```json { "name": "ValidationError", "message": "2 validation errors for People\npeople.1.dog_breed\n Field required [type=missing, input_value={'person_name': 'Claudia'...hair_color': 'brunette'}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.5/v/missing\npeople.1.dog_name\n Field required [type=missing, input_value={'person_name': 'Claudia'...hair_color': 'brunette'}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.5/v/missing", "stack": "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValidationError\u001b[0m Traceback (most recent call last)\nCell \u001b[1;32mIn[70], line 33\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[38;5;66;03m# output=FLAN.generate_text(input_to_model.to_string())\u001b[39;00m\n\u001b[0;32m 32\u001b[0m \u001b[38;5;28mprint\u001b[39m(output)\n\u001b[1;32m---> 33\u001b[0m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 35\u001b[0m \u001b[38;5;66;03m# print(parser.get_format_instructions())\u001b[39;00m\n\nFile \u001b[1;32mc:\\Users\\user\\repos\\.venv\\Lib\\site-packages\\langchain\\output_parsers\\pydantic.py:30\u001b[0m, in \u001b[0;36mPydanticOutputParser.parse\u001b[1;34m(self, text)\u001b[0m\n\u001b[0;32m 28\u001b[0m json_str \u001b[38;5;241m=\u001b[39m match\u001b[38;5;241m.\u001b[39mgroup()\n\u001b[0;32m 29\u001b[0m json_object \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(json_str, strict\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m---> 30\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpydantic_object\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson_object\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 32\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (json\u001b[38;5;241m.\u001b[39mJSONDecodeError, ValidationError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 33\u001b[0m name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpydantic_object\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\nFile \u001b[1;32mc:\\Users\\user\\repos\\.venv\\Lib\\site-packages\\typing_extensions.py:2499\u001b[0m, in \u001b[0;36mdeprecated.__call__.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 2496\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(arg)\n\u001b[0;32m 2497\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 2498\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(msg, category\u001b[38;5;241m=\u001b[39mcategory, stacklevel\u001b[38;5;241m=\u001b[39mstacklevel \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m-> 2499\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marg\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\nFile \u001b[1;32mc:\\Users\\user\\repos\\.venv\\Lib\\site-packages\\pydantic\\main.py:1027\u001b[0m, in \u001b[0;36mBaseModel.parse_obj\u001b[1;34m(cls, obj)\u001b[0m\n\u001b[0;32m 1021\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[0;32m 1022\u001b[0m \u001b[38;5;129m@typing_extensions\u001b[39m\u001b[38;5;241m.\u001b[39mdeprecated(\n\u001b[0;32m 1023\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe `parse_obj` method is deprecated; use `model_validate` instead.\u001b[39m\u001b[38;5;124m'\u001b[39m, category\u001b[38;5;241m=\u001b[39mPydanticDeprecatedSince20\n\u001b[0;32m 1024\u001b[0m )\n\u001b[0;32m 1025\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mparse_obj\u001b[39m(\u001b[38;5;28mcls\u001b[39m: \u001b[38;5;28mtype\u001b[39m[Model], obj: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Model: \u001b[38;5;66;03m# noqa: D102\u001b[39;00m\n\u001b[0;32m 1026\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe `parse_obj` method is deprecated; use `model_validate` instead.\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m)\n\u001b[1;32m-> 1027\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_validate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\nFile \u001b[1;32mc:\\Users\\user\\repos\\.venv\\Lib\\site-packages\\pydantic\\main.py:503\u001b[0m, in \u001b[0;36mBaseModel.model_validate\u001b[1;34m(cls, obj, strict, from_attributes, context)\u001b[0m\n\u001b[0;32m 501\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[0;32m 502\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 503\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 504\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_attributes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_attributes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontext\u001b[49m\n\u001b[0;32m 505\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\n\u001b[1;31mValidationError\u001b[0m: 2 validation errors for People\npeople.1.dog_breed\n Field required [type=missing, input_value={'person_name': 'Claudia'...hair_color': 'brunette'}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.5/v/missing\npeople.1.dog_name\n Field required [type=missing, input_value={'person_name': 'Claudia'...hair_color': 'brunette'}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.5/v/missing" } ``` ### System Info Langchain: ``` langchain==0.1.0 langchain-community==0.0.10 langchain-core==0.1.8 ``` platform: ``` Windows 11 Enterprise Version 10.0.22621 Build 22621 ``` python : ``` Python 3.11.7 ``` ### 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

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PR_kwDOIPDwls5kRpOt

langchain_community: Replace azure_cosmos_db_vector_search with azure_cosmos_db in Cosmos DB Documentation

closed

CONTRIBUTOR

**Description**: This PR fixes an error in the documentation for Azure Cosmos DB Integration. **Issue**: The correct way to import `AzureCosmosDBVectorSearch` is ```python from langchain_community.vectorstores.azure_cosmos_db import ( AzureCosmosDBVectorSearch, ) ``` While the [documentation](https://python.langchain.com/docs/integrations/vectorstores/azure_cosmos_db) states it to be ```python from langchain_community.vectorstores.azure_cosmos_db_vector_search import ( AzureCosmosDBVectorSearch, CosmosDBSimilarityType, ) ``` As you can see in [azure_cosmos_db.py](https://github.com/langchain-ai/langchain/blob/c323742f4fda30e8bef5381f41f66192afb2b9d2/libs/langchain/langchain/vectorstores/azure_cosmos_db.py#L1C45-L2) **Dependencies:**: None **Twitter handle**: None

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I_kwDOIPDwls58Tfkf

Why can't test117 be retrieved by vector and provide replies for reference to achieve long-term memory?

open

NONE

### Issue with current documentation: import os from langchain_community.chat_models import ChatOpenAI from langchain.schema import Document from langchain_community.embeddings import HuggingFaceEmbeddings from langchain_community.vectorstores import Qdrant from langchain.chains import ConversationChain from langchain.memory import ConversationBufferMemory from qdrant_client import QdrantClient, models os.environ['OPENAI_API_KEY'] = "mykey" embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") client = QdrantClient(host="192.168.10.31", port=6333) collection_name = "test117" client.create_collection( collection_name=collection_name, vectors_config=models.VectorParams(size=384, distance=models.Distance.COSINE), optimizers_config=models.OptimizersConfigDiff(memmap_threshold=20000), ) vectorstore = Qdrant( client=client, embedding=embeddings, location=":memory:", collection_name=collection_name, ) llm = ChatOpenAI( temperature=0.7, model="gpt-3.5-turbo", max_tokens=100 ) memory = ConversationBufferMemory() conversation = ConversationChain(llm=llm, memory=memory, verbose=True,) collection_info = client.get_collection(collection_name) while True: chat_history = memory.load_memory_variables({}) question = input('ask：') question_vector = embeddings.embed_query(question)[0] search_results = client.search( collection_name=collection_name, query_vector=models.NamedSparseVector( name=collection_name, vector=models.SparseVector( indices=question_vector.indices, values=question_vector.values, ), ), limit=1, ) if search_results and search_results[0].payload: response_vector = search_results[0].payload.vector response_str = vectorstore.retrieve_text(response_vector) else: response = conversation.predict(input=f"Q: {question}\nA:") response_str = response.choices[0].text.strip() response_vector = embeddings.embed_query(response_str)[0] vectorstore.add_documents([Document(vector=response_vector, text=response_str)]) print("respones：", response_str) memory.add_memory_variables({"input": question, "output": response_str}) if question.lower() == 'bye': break ### Idea or request for content: Why can't test117 be retrieved by vector and provide replies for reference to achieve long-term memory? Is it that the retriever is not written well?

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PR_kwDOIPDwls5kRlrm

Add Tuple support for messages placeholder

open

COLLABORATOR

It's a bit annoying to work with LangChain messages on the frontend, which is why we [did things like this in chat langchain](https://github.com/langchain-ai/chat-langchain/blob/592d588a1bfe35fe03ed33ac8ba36afd9452bb6d/chain.py#L148). It would be convenient if the messages placeholder also accepted the same tuple format as the prompt template, so you could just pass the role and content values around. Maybe not as useful in a real memory setup, but simplifies the user code a bit in some situations.

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PR_kwDOIPDwls5kRJnO

community: Fix LlamaContentFormatter to match actual input/output formatting

open

NONE

**Description:** The `LlamaContentFormatter` in the `langchain_community.chat_models.azureml_endpoint` does not work because it doesn't actually comply to the output of Llama models. The input/output formats are documented here: [Deploy Llama2 Chat Models - Chat API](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-llama?tabs=azure-studio#chat-api) This PR creates a LlamaContentFormatter that works with the documented API. **Issue:** N/A **Dependencies:** N/A **Twitter handle:** parkerdhancock

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PR_kwDOIPDwls5kQ3Dk

partners: add license field

closed

COLLABORATOR

- bumps package post versions for packages without current unreleased updates - will bump package version in release prs associated with packages that do have changes (mistral, vertex)

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PR_kwDOIPDwls5kQ2OI

mistralai[patch]: release 0.0.3

closed

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embeddings

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PR_kwDOIPDwls5kQxUY

refactor `langchain.retrievers.self_query`

open

COLLABORATOR

**Issue:** all classes in `langchain.retrievers.self_query` namespace belong to the `community` package. **Following issue:** We cannot just move these classes to `community` because of the dependency on the Visitor class and other classes from the `langchain.chains.query_constructor` namespace. What is interesting is that `langchain.chains.query_constructor` artifacts are used ONLY in the `langchain.retrievers.self_query` artifacts. It happens that `langchain.chains.query_constructor` artifacts do not depend on the `langchain.chains`. Actually the `langchain.chains.query_constructor` is quite independent. Because of that we can move `langchain.chains.query_constructor` under `langchain_core` as the `langchain_core.sql_constructor` The reason for renaming `query_constructor` into `sql_constructor` is simple. It IS the SQL constructor. Moreover, the `query_..` is the ambiguous name because we use `query` in retrievers and many other places not as the SQL queries but as the queries in prompts. `sql` is unambiguous name here. NOTE: The `langchain.chains.query_constructor.base.load_query_constructor_chain` is not moved! This is the only dependency to `chains`. It is not important because Chains will be depricated soon.

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I_kwDOIPDwls58SWeg

structured output parser on ChatPromptTemplate.from_template()

closed

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code ```python llm = VertexAI(model_name='gemini-pro', max_output_tokens=512, temperature=0.2) data = loader.load() embeddings = VertexAIEmbeddings(model_name='textembedding-gecko@003') vectorstore = Chroma.from_documents(data, embeddings) retriever = vectorstore.as_retriever() template = """ Use the following pieces of context to answer the question at the end. - If you think you need more information to find proper answer, ask user to clarify. - If you cannot find a proper answer from the reference, just say we don't have a data from the reference. Use three sentences maximum and keep the answer as concise as possible. Context: {context} Question: {question} Helpful Answer: **Resource:** [reference source name] """ found_response_schemas = [ ResponseSchema(name="answer", description="answer to the user's question"), ResponseSchema( name="found", description="whether the model could find the proper answers or not.", ), ] found_output_parser = StructuredOutputParser.from_response_schemas(found_response_schemas) format_instructions = found_output_parser.get_format_instructions() rag_prompt_custom = ChatPromptTemplate.from_template(template) rag_chain = ( {"context": retriever, "question": RunnablePassthrough()} | rag_prompt_custom | llm | StrOutputParser() ) response = rag_chain.invoke(message) print(response) ``` ### Description I am trying to add StructuredOutputParser with a specific ResponseSchema. What I would like to add as a ResponseSchema is, adding boolean value to check whether the llm could find proper information from reference document in RAG. ### System Info langchain==0.0.353 ### 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 - [X] Chains - [ ] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5kPoV5

Community[Patch] Remove docs form bm25 repr

closed

COLLABORATOR

Not sure how helpful they are to include in the representation? Resolves: https://github.com/langchain-ai/langsmith-sdk/issues/356

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PR_kwDOIPDwls5kPekO

Community[patch]use secret str in Tavily and HuggingFaceInferenceEmbeddings

closed

COLLABORATOR

So the api keys don't show up in repr's Still need to do tests

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PR_kwDOIPDwls5kPRhM

core : in RunnableRetry, correct the example snippet that uses with_retry method on Runnable

closed

CONTRIBUTOR

The example code snippet for with_retry is using incorrect argument names. This PR fixes that

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PR_kwDOIPDwls5kO7Jm

docs: updated `Anyscale` page

closed

COLLABORATOR

- added description - fixed broken links - added setting instructions - added the Chat model reference

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PR_kwDOIPDwls5kOloi

WIP docs: agent prompt docs

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PR_kwDOIPDwls5kOjyd

langchain[patch]: bump core dep to 0.1.9

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PR_kwDOIPDwls5kObmT

langchain[patch]: Release 0.1.1

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I_kwDOIPDwls58P4sU

JSONDecodeError: Invalid control character at: line 2 column 631 (char 632) when using langchain new agent

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code This code: ```python def parse(output): # If no function was invoked, return to user if "function_call" not in output.additional_kwargs: return AgentFinish(return_values={"output": output.content}, log=output.content) # Parse out the function call function_call = output.additional_kwargs["function_call"] name = function_call["name"] inputs = json.loads(function_call["arguments"]) # If the Response function was invoked, return to the user with the function inputs if name == "Response": return AgentFinish(return_values=inputs, log=str(function_call)) # Otherwise, return an agent action else: return AgentActionMessageLog( tool=name, tool_input=inputs, log="", message_log=[output] ) def creat_ai_search_new_agent(embeddings, llm, class_name_rich): ai_search_endpoint = get_ai_search_endpoint() ai_search_admin_key = get_ai_search_admin_key() vector_store = AzureSearch( azure_search_endpoint=ai_search_endpoint, azure_search_key=ai_search_admin_key, index_name=class_name_rich, embedding_function=embeddings.embed_query, content_key=content_key ) """Retriever that uses `Azure Cognitive Search`.""" azure_search_retriever = AzureSearchVectorStoreRetriever( vectorstore=vector_store, search_type=search_type, k=k, top=n ) retriever_tool = create_retriever_tool( azure_search_retriever, "Retriever", "Useful when you need to retrieve information from documents", ) class Response(BaseModel): """Final response to the question being asked""" answer: str = Field(description="The final answer to respond to the user") sources: List[int] = Field( description="List of page chunks that contain answer to the question. Only include a page chunk if it contains relevant information" ) prompt = ChatPromptTemplate.from_messages( [ ("system", "You are a helpful assistant who retrieves information from documents"), ("user", "{input}"), MessagesPlaceholder(variable_name="agent_scratchpad"), ] ) llm_with_tools = llm.bind( functions=[ # The retriever tool format_tool_to_openai_function(retriever_tool), # Response schema convert_pydantic_to_openai_function(Response), ] ) try: agent = ( { "input": lambda x: x["input"], # Format agent scratchpad from intermediate steps "agent_scratchpad": lambda x: format_to_openai_function_messages( x["intermediate_steps"] ), } | prompt | llm_with_tools | parse ) agent_executor = AgentExecutor(tools=[retriever_tool], agent=agent, verbose=True, return_intermediate_steps=True) except Exception as e: print(e) print("error instanciating the agent") return agent_executor ``` Gives me the following error: JSONDecodeError Traceback (most recent call last) File <command-2290979546782169>, line 5 3 # get the start time 4 st = time.time() ----> 5 answer = agent_executor.invoke( 6 {"input": text}, 7 # return_only_outputs=True, 8 9 ) 10 # get the end time 11 et = time.time() File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain/chains/base.py:162, in Chain.invoke(self, input, config, **kwargs) 160 except BaseException as e: 161 run_manager.on_chain_error(e) --> 162 raise e 163 run_manager.on_chain_end(outputs) 164 final_outputs: Dict[str, Any] = self.prep_outputs( 165 inputs, outputs, return_only_outputs 166 ) File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain/chains/base.py:156, in Chain.invoke(self, input, config, **kwargs) 149 run_manager = callback_manager.on_chain_start( 150 dumpd(self), 151 inputs, 152 name=run_name, 153 ) 154 try: 155 outputs = ( --> 156 self._call(inputs, run_manager=run_manager) 157 if new_arg_supported 158 else self._call(inputs) 159 ) 160 except BaseException as e: 161 run_manager.on_chain_error(e) File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain/agents/agent.py:1329, in AgentExecutor._call(self, inputs, run_manager) 1327 # We now enter the agent loop (until it returns something). 1328 while self._should_continue(iterations, time_elapsed): -> 1329 next_step_output = self._take_next_step( 1330 name_to_tool_map, 1331 color_mapping, 1332 inputs, 1333 intermediate_steps, 1334 run_manager=run_manager, 1335 ) 1336 if isinstance(next_step_output, AgentFinish): 1337 return self._return( 1338 next_step_output, intermediate_steps, run_manager=run_manager 1339 ) File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain/agents/agent.py:1055, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager) 1046 def _take_next_step( 1047 self, 1048 name_to_tool_map: Dict[str, BaseTool], (...) 1052 run_manager: Optional[CallbackManagerForChainRun] = None, 1053 ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]: 1054 return self._consume_next_step( -> 1055 [ 1056 a 1057 for a in self._iter_next_step( 1058 name_to_tool_map, 1059 color_mapping, 1060 inputs, 1061 intermediate_steps, 1062 run_manager, 1063 ) 1064 ] 1065 ) File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain/agents/agent.py:1055, in <listcomp>(.0) 1046 def _take_next_step( 1047 self, 1048 name_to_tool_map: Dict[str, BaseTool], (...) 1052 run_manager: Optional[CallbackManagerForChainRun] = None, 1053 ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]: 1054 return self._consume_next_step( -> 1055 [ 1056 a 1057 for a in self._iter_next_step( 1058 name_to_tool_map, 1059 color_mapping, 1060 inputs, 1061 intermediate_steps, 1062 run_manager, 1063 ) 1064 ] 1065 ) File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain/agents/agent.py:1083, in AgentExecutor._iter_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager) 1080 intermediate_steps = self._prepare_intermediate_steps(intermediate_steps) 1082 # Call the LLM to see what to do. -> 1083 output = self.agent.plan( 1084 intermediate_steps, 1085 callbacks=run_manager.get_child() if run_manager else None, 1086 **inputs, 1087 ) 1088 except OutputParserException as e: 1089 if isinstance(self.handle_parsing_errors, bool): File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain/agents/agent.py:386, in RunnableAgent.plan(self, intermediate_steps, callbacks, **kwargs) 374 """Given input, decided what to do. 375 376 Args: (...) 383 Action specifying what tool to use. 384 """ 385 inputs = {**kwargs, **{"intermediate_steps": intermediate_steps}} --> 386 output = self.runnable.invoke(inputs, config={"callbacks": callbacks}) 387 return output File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain_core/runnables/base.py:1774, in RunnableSequence.invoke(self, input, config) 1772 try: 1773 for i, step in enumerate(self.steps): -> 1774 input = step.invoke( 1775 input, 1776 # mark each step as a child run 1777 patch_config( 1778 config, callbacks=run_manager.get_child(f"seq:step:{i+1}") 1779 ), 1780 ) 1781 # finish the root run 1782 except BaseException as e: File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain_core/runnables/base.py:3074, in RunnableLambda.invoke(self, input, config, **kwargs) 3072 """Invoke this runnable synchronously.""" 3073 if hasattr(self, "func"): -> 3074 return self._call_with_config( 3075 self._invoke, 3076 input, 3077 self._config(config, self.func), 3078 **kwargs, 3079 ) 3080 else: 3081 raise TypeError( 3082 "Cannot invoke a coroutine function synchronously." 3083 "Use `ainvoke` instead." 3084 ) File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain_core/runnables/base.py:975, in Runnable._call_with_config(self, func, input, config, run_type, **kwargs) 971 context = copy_context() 972 context.run(var_child_runnable_config.set, child_config) 973 output = cast( 974 Output, --> 975 context.run( 976 call_func_with_variable_args, 977 func, # type: ignore[arg-type] 978 input, # type: ignore[arg-type] 979 config, 980 run_manager, 981 **kwargs, 982 ), 983 ) 984 except BaseException as e: 985 run_manager.on_chain_error(e) File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain_core/runnables/config.py:323, in call_func_with_variable_args(func, input, config, run_manager, **kwargs) 321 if run_manager is not None and accepts_run_manager(func): 322 kwargs["run_manager"] = run_manager --> 323 return func(input, **kwargs) File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain_core/runnables/base.py:2950, in RunnableLambda._invoke(self, input, run_manager, config, **kwargs) 2948 output = chunk 2949 else: -> 2950 output = call_func_with_variable_args( 2951 self.func, input, config, run_manager, **kwargs 2952 ) 2953 # If the output is a runnable, invoke it 2954 if isinstance(output, Runnable): File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain_core/runnables/config.py:323, in call_func_with_variable_args(func, input, config, run_manager, **kwargs) 321 if run_manager is not None and accepts_run_manager(func): 322 kwargs["run_manager"] = run_manager --> 323 return func(input, **kwargs) File <command-2290979546782090>, line 11, in parse(output) 9 function_call = output.additional_kwargs["function_call"] 10 name = function_call["name"] ---> 11 inputs = json.loads(function_call["arguments"]) 13 # If the Response function was invoked, return to the user with the function inputs 14 if name == "Response": File /usr/lib/python3.10/json/__init__.py:346, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 341 s = s.decode(detect_encoding(s), 'surrogatepass') 343 if (cls is None and object_hook is None and 344 parse_int is None and parse_float is None and 345 parse_constant is None and object_pairs_hook is None and not kw): --> 346 return _default_decoder.decode(s) 347 if cls is None: 348 cls = JSONDecoder File /usr/lib/python3.10/json/decoder.py:337, in JSONDecoder.decode(self, s, _w) 332 def decode(self, s, _w=WHITESPACE.match): 333 """Return the Python representation of ``s`` (a ``str`` instance 334 containing a JSON document). 335 336 """ --> 337 obj, end = self.raw_decode(s, idx=_w(s, 0).end()) 338 end = _w(s, end).end() 339 if end != len(s): File /usr/lib/python3.10/json/decoder.py:353, in JSONDecoder.raw_decode(self, s, idx) 344 """Decode a JSON document from ``s`` (a ``str`` beginning with 345 a JSON document) and return a 2-tuple of the Python 346 representation and the index in ``s`` where the document ended. (...) 350 351 """ 352 try: --> 353 obj, end = self.scan_once(s, idx) 354 except StopIteration as err: 355 raise JSONDecodeError("Expecting value", s, err.value) from None JSONDecodeError: Invalid control character at: line 2 column 631 (char 632) Can someone help? ### Description Error half of the times ### System Info langchain==0.1.0 openai==1.7.0 ### Related Components - [X] LLMs/Chat Models - [X] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [X] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5kOXBW

core[patch]: fallbacks error chain

open

COLLABORATOR

Potential alternative to #16048 bit about last exception would need to be implemented for ainvoke too

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PR_kwDOIPDwls5kOSy2

core[patch]: Release 0.1.11

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I_kwDOIPDwls58Po6G

RedisVectorStoreRetriever similarity search similarity_distance_threshold is not implemented asyncronously

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code ```python from langchain_community.vectorstores.redis import Redis embedding = ... redis_url = ... schema_path = ... vectorstore = Redis.from_existing_index( embedding, index_name='index', redis_url=redis_url, schema=schema_path, ) retriever = vectorstore.as_retriever(search_type="similarity_distance_threshold", search_kwargs={"distance_threshold": 0.9, "k": 5}) retriever.aget_relevant_documents('query') ``` ### Description The asynchronous method `_aget_relevant_documents` is not defined for `RedisVectorStoreRetriever`. As such, the method of the parent of `RedisVectorStoreRetriever` (`VectorStoreRetriever`) is called. In `VectorStoreRetriever` however, `similarity_distance_threshold` is not available. This makes the chain crash when `RedisVectorStoreRetriever` is called asynchronously in a chain. I will make a PR to solve this ### System Info ``` langchain 0.1.0 langchain-community 0.0.12 langchain-core 0.1.10 ``` ### 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 - [x] Async

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PR_kwDOIPDwls5kOJtR

core:Add optional max_messages to MessagePlaceholder

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CONTRIBUTOR

- **Description:** Add optional max_messages to MessagePlaceholder - **Issue:** [16096](https://github.com/langchain-ai/langchain/issues/16096) - **Dependencies:** None - **Twitter handle:** @davedecaprio Sometimes it's better to limit the history in the prompt itself rather than the memory. This is needed if you want different prompts in the chain to have different history lengths.

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I_kwDOIPDwls58Pcit

Allow MessagesPlaceholder to include a maximum number of messages

open

CONTRIBUTOR

### Feature request When using `MessagesPlaceholder`, it always includes all messages. For some prompts I want the full message history, but for others, I want to limit it to just the most recent few messages. This can be accomplished with `ConversationBufferWindowMemory`, but that limits the memory storage which is used for all my LLM prompts in the chain. I currently accomplish this with a custom prompt generator, but it would be easier if there were an optional max_messages parameter to MessagesPlaceholder that limits the history to the given number of messages. ### Motivation I have chain with multiple LLM calls. For one, the routing prompt, it works best when passed the last few messages in the conversation. Right now I have to use a custom prompt generator to accomplish this. I'd really like to be able to just add a limit to MessagesPlaceholder. ### Your contribution I will submit a PR,

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I_kwDOIPDwls58PbqW

Embedding for NVIDIA NeMo

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NONE

### Feature request This feature request adds support for NVIDIA NeMo embedding models. ### Motivation I am constantly in need of custom embedding models like Embedding models from NeMo. ### Your contribution I will open a PR to add support for this.

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PR_kwDOIPDwls5kN7Wn

docs: Updated SQLite example to use LCEL and SQLChatMessageHistory

closed

CONTRIBUTOR

- **Description:** Updated the SQLite example integration notebook to latest standards - **Issue:** [15664](https://github.com/langchain-ai/langchain/issues/15664) - **Dependencies:** None - **Twitter handle:** @davedecaprio

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PR_kwDOIPDwls5kN4Hw

Add unit test for overridden lc_namespace

closed

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PR_kwDOIPDwls5kNwgQ

[community][document loaders][surrealdb] fix for asyncio

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Code fix for asyncio

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PR_kwDOIPDwls5kNu7L

docs:Updated integration docs structure for llm/google_vertex_ai_palm

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NONE

- **Description**: Updated doc for llm/google_vertex_ai_palm with new functions: `invoke`, `stream`... Changed structure of the document to match the required one. - **Issue**: #15664 - **Dependencies**: None - **Twitter handle**: None

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PR_kwDOIPDwls5kNTFp

Update elasticsearch.ipynb

closed

CONTRIBUTOR

Fixed a typo, the parameter used for the Elasticsearch API key was called api_key, but the parameter is called es_api_key. <!-- 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_kwDOIPDwls5kM0fm

community[patch]: release 0.0.13

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I_kwDOIPDwls58N0ht

PDFInfoNotInstalledError: Unable to get page count. Is poppler installed and in PATH?

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code ``` Python from langchain_community.document_loaders import DirectoryLoader directory = '/content/drive/MyDrive/History_QA_dataset' def load_docs(directory): return DirectoryLoader(directory).load() documents = load_docs(directory) ``` ### Description ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/pdf2image/pdf2image.py](https://localhost:8080/#) in pdfinfo_from_path(pdf_path, userpw, ownerpw, poppler_path, rawdates, timeout, first_page, last_page) 580 env["LD_LIBRARY_PATH"] = poppler_path + ":" + env.get("LD_LIBRARY_PATH", "") --> 581 proc = Popen(command, env=env, stdout=PIPE, stderr=PIPE) 582 19 frames FileNotFoundError: [Errno 2] No such file or directory: 'pdfinfo' During handling of the above exception, another exception occurred: PDFInfoNotInstalledError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/pdf2image/pdf2image.py](https://localhost:8080/#) in pdfinfo_from_path(pdf_path, userpw, ownerpw, poppler_path, rawdates, timeout, first_page, last_page) 605 606 except OSError: --> 607 raise PDFInfoNotInstalledError( 608 "Unable to get page count. Is poppler installed and in PATH?" 609 ) PDFInfoNotInstalledError: Unable to get page count. Is poppler installed and in PATH? ``` ### System Info Windows: 11 pip == 23.3.1 python == 3.10.10 langchain == 0.1.0 transformers == 4.36.2 sentence_transformers == 2.2.2 unstructured == 0.12.0 ### 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

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I_kwDOIPDwls58NLPP

ConversationalRetrievalChain with ChatGoogleGenerativeAI performs poorly

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code I have omitted as much as possible irrelevant portions: ```python def get_text(docs): if not docs: return None all_text = "" for doc in docs: reader = PdfReader(doc) for page in reader.pages: all_text += page.extract_text() return all_text def get_chunks(text): splitter = CharacterTextSplitter( separator='\n', chunk_size=1000, chunk_overlap=200, ) chunks = splitter.split_text(text) return chunks def get_vectorstore(chunks, model='models/embedding-001'): client = qdrant_client.QdrantClient( os.environ['QDRANT_HOST'], api_key=os.environ["QDRANT_API_KEY"], ) vectors_config = qdrant_client.http.models.VectorParams( size=768, distance=qdrant_client.http.models.Distance.COSINE ) client.recreate_collection( collection_name=os.environ["QDRANT_COLLECTION"], vectors_config=vectors_config ) embeddings = GoogleGenerativeAIEmbeddings(model=model) vectorstore = Qdrant( client=client, collection_name=os.environ["QDRANT_COLLECTION"], embeddings=embeddings, ) vectorstore.add_texts(chunks) return vectorstore def get_conversation_chain(vectorstore): llm = ChatGoogleGenerativeAI(model="gemini-pro", convert_system_message_to_human=True, temperature=0) memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) chain = ConversationalRetrievalChain.from_llm( llm=llm, retriever=vectorstore.as_retriever(), memory=memory, ) return chain docs = st.file_uploader("Upload your documents:", accept_multiple_files=True, type=["pdf", "txt", "csv"]) user_input = "Some question" if st.button("Proess"): with st.spinner("Processing..."): text = get_text(docs) chunks = get_chunks(text) vectorstore = get_vectorstore(chunks) chain = get_conversation_chain(vectorstore) response = chain({'question': user_input}) ``` ### Description I'm trying to use ``ConversationalRetrievalChain`` with the ``ChatGoogleGenerativeAI`` integration. This seems to perform rather poorly in several scenarios involving PDF documents, with 2 issues frequently arising: 1. The model seems to default quite often to "I don't know" or "I cannot find the information in the context provided", despite the fact that the context seems to have the requested information within (so it's unlikely to be a problem with the vector DB). 2. Some of the LangChain-built system prompts (converted to human messages via the recently-added hyperparameter) which ask the model to translate something to the "original language" completely confuse it, making it output its response in a random language. I have tried using the prompt created by LangChain with the Google API directly, and the response is also bad. Example prompt created by LangChain from my conversation: ``"Human: Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\n\nChat History:\n\nHuman: what does the third amendment say?\nAssistant: I do not have access to the context necessary to answer that question.\nFollow Up Input: what does the second amendment say?\nStandalone question:"``. This outputs a message in a random language on both the Google Gemini API and LangChain. Example document: <a href="https://www.archives.gov/files/legislative/resources/education/bill-of-rights/images/handout-3.pdf"> Bill of rights</a>. Example questions: (see example prompt above). ### System Info langchain==0.0.351 langchain-community==0.0.4 langchain-core==0.1.1 langchain-experimental==0.0.47 langchain-google-genai==0.0.5 ### 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

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I_kwDOIPDwls58Md2s

Make research assistant template work with OpenAI models on AzureAI

open

CONTRIBUTOR

### Feature request I would like to use the research assistant template with OpenAI models on Azure. Can this be integrated into the template somehow ? I can of course modify the code post template expansion (ChatOpenAI -> AzureChatOpenAI etc.), but that would be lost upon changes / enhancements to the templates. ### Motivation Cannot use OpenAI models directly due to company policy. ### Your contribution Happy to spend some effort if someone can point me to a good way of adding e.g. a switch to the template (don't want to duplicate the whole template).

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PR_kwDOIPDwls5kK31p

Add doc for AstraDB self retriever

closed

CONTRIBUTOR

Preview: https://langchain-git-fork-cbornet-astra-self-retriever-docs-langchain.vercel.app/docs/integrations/retrievers/self_query/astradb

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