Workflow for contributing to LangChain

[Jesse1786]

I'm new to contributing to open source, and I'm unsure if my procedures and setup is correct. So far what I've done is:

1. I forked and cloned the LangChain repo
2. I installed docker desktop and the Dev Containers VSCode extension
3. I opened my local repository in the dev container, and installed dependencies
4. I typed cd libs/langchain followed by pip install -e . to install my local version of LangChain instead of the main LangChain

First, please verify the above steps are correct. Then answer the following questions:

1. I created a new file in my LangChain repository (in the dev container) and I tried to run from langchain_openai import ChatOpenAI. However I get the error Import langchain_openai could not be resolved. This happens even if I run pip install langchain-openai. How do I fix this?
2. What is the standard way to develop LangChain code inside the provided dev container? Suppose I made some changes, and now I want to verify those changes by writing a short python script and running it. How would I use my own version of LangChain in my script? And where should I run the script from?

[dosubot[bot]]

Hey @Jesse1786! 👋 I'm here to help you with your LangChain questions. I'm a bot designed to assist you with bug fixes, answers, and contributing.

Your initial setup steps look mostly correct, but there are a few adjustments and additional steps you might consider:

1. Error Resolution:

   • The error "Import langchain_openai could not be resolved" can be fixed by ensuring you have the correct dependencies installed using Poetry. Instead of using pip, follow these steps:
     • Navigate to the relevant package directory (e.g., libs/community) and install dependencies with Poetry:

       cd libs/community
       poetry install --with lint,typing,test,test_integration

2. Standard Development Workflow:

   • To develop and test changes in LangChain using the dev container:
     • Set up the environment: Use the dev container setup to ensure all dependencies are correctly installed.
     • Run your script: You can write a Python script and run it from the root of your repository or any directory within the dev container. Make sure your script uses the local version of LangChain by ensuring it's installed in editable mode (pip install -e . should work if dependencies are correctly managed with Poetry).

3. Testing Changes:

   • To verify changes, you can run unit tests:

     make test

   • Alternatively, run tests in Docker:

     make docker_tests

This setup ensures your local changes are reflected and tested properly ^[1][2].

To continue talking to Dosu, mention @dosu.

[Jesse1786]

@dosu

I'm interested in contributing to LC by adding a tool. Can you show me the proper way to use the tools defined in langchain_community? I tried looking through the docs but I wasn't able to find this information.

For example, if I want to use the stackexchange tool, would I do something like:

from langchain_community import stackexchange or
'from langchain_community.agent_toolkits.load_tools import load_toolsfollowed bytools = load_tools("stackexchange", llm)`

[dosubot[bot]]

To use the stackexchange tool in langchain_community, you should use the load_tools function. Here's how you can do it:

1. Install the required package:
   Make sure you have the stackapi package installed:

   pip install --upgrade stackapi

2. Load the tool:
   Use the load_tools function to load the stackexchange tool. Here's an example:

   from langchain.agents import load_tools
   
   # Assuming you have an LLM instance
   tools = load_tools(["stackexchange"], llm=your_llm_instance)

This setup allows you to integrate the stackexchange tool with an Agent in LangChain ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[Jesse1786]

@dosu Could you please elaborate and provide an example? Suppose I want to ask about the question "What programming languages implement memory safety" using the stackexchange tool. Demonstrate how it's done and to get the response.

[dosubot[bot]]

To query StackExchange using the stackexchange tool in LangChain, follow these steps:

1. Install the Required Package:
   Ensure you have the stackapi package installed:

   pip install --upgrade stackapi

2. Import and Initialize the Wrapper:
   Use the StackExchangeAPIWrapper to perform the query.

   from langchain_community.utilities import StackExchangeAPIWrapper
   
   # Initialize the wrapper
   api_wrapper = StackExchangeAPIWrapper(max_results=5, query_type="title")

3. Run the Query:
   Use the run method to search for your question.

   # Run a query
   results = api_wrapper.run("What programming languages implement memory safety")
   
   # Print the results
   print(results)

This will search for questions with "What programming languages implement memory safety" in their titles and return up to 5 results. The output will include question and answer pairs formatted as:

Question: <question_title>
<question_excerpt>
Answer: <answer_excerpt>

This setup allows you to effectively query StackExchange and retrieve relevant information ^[1][2].

To continue talking to Dosu, mention @dosu.

[Jesse1786]

@dosu Just to clarify, I want a llm to use the tool to answer the question. I don't want to manually call the tool.

[dosubot[bot]]

To have an LLM automatically use the stackexchange tool to answer a question, follow these steps:

1. Install the Required Package:
   Ensure you have the stackapi package installed:

   pip install --upgrade stackapi

2. Load the Tool:
   Use the load_tools function to load the stackexchange tool and bind it to your LLM.

   from langchain_community.utilities import StackExchangeAPIWrapper
   from langchain.agents import load_tools
   
   # Initialize the StackExchange tool
   stackexchange = StackExchangeAPIWrapper()
   
   # Load the tool with your LLM
   tools = load_tools(["stackexchange"], llm=your_llm_instance)

3. Bind the Tool to the LLM:
   Use the .bind_tools() method to allow the LLM to generate arguments for the tool.

   llm_with_tools = your_llm_instance.bind_tools(tools)

4. Ask the Question:
   Now, you can ask the LLM to use the tool to answer your question.

   response = llm_with_tools("What programming languages implement memory safety?")
   print(response)

This setup allows the LLM to automatically use the stackexchange tool to find and provide an answer to your question ^[1][2].

To continue talking to Dosu, mention @dosu.