langchain/templates/extraction-openai-functions

extraction-openai-functions

This template uses OpenAI function calling for extraction of structured output from unstructured input text.

The extraction output schema can be set in chain.py.

Environment Setup

Set the OPENAI_API_KEY environment variable to access the OpenAI models.

Usage

To use this package, you should first have the LangChain CLI installed:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package extraction-openai-functions

If you want to add this to an existing project, you can just run:

langchain app add extraction-openai-functions

And add the following code to your server.py file:

from extraction_openai_functions import chain as extraction_openai_functions_chain

add_routes(app, extraction_openai_functions_chain, path="/extraction-openai-functions")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"

If you are inside this directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/extraction-openai-functions/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/extraction-openai-functions")

By default, this package is set to extract the title and author of papers, as specified in the chain.py file.

LLM is leveraged by the OpenAI function by default.