Llama.cpp functions

This template performs extraction of structured data from unstructured data using Llama.cpp package with the LLaMA2 model that supports a specified JSON output schema.

The extraction schema can be set in chain.py.

Environment Setup

This will use a LLaMA2-13b model hosted by Replicate.

Ensure that REPLICATE_API_TOKEN is set in your environment.

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 llama2-functions

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

langchain app add llama2-functions

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

from llama2_functions import chain as llama2_functions_chain

add_routes(app, llama2_functions_chain, path="/llama2-functions")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith 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/llama2-functions/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/llama2-functions")