langchain/templates/rag-jaguardb
2024-04-08 10:56:53 -05:00
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rag_jaguardb docs: use standard openai params (#20160) 2024-04-08 10:56:53 -05:00
tests templates: Added template for JaguarDB (#16757) 2024-03-19 02:36:24 +00:00
LICENSE templates: Added template for JaguarDB (#16757) 2024-03-19 02:36:24 +00:00
pyproject.toml templates: Added template for JaguarDB (#16757) 2024-03-19 02:36:24 +00:00
rag_jaguardb.ipynb templates: Added template for JaguarDB (#16757) 2024-03-19 02:36:24 +00:00
README.md templates: Added template for JaguarDB (#16757) 2024-03-19 02:36:24 +00:00

rag-jaguardb

This template performs RAG using JaguarDB and OpenAI.

Environment Setup

You should export two environment variables, one being your Jaguar URI, the other being your OpenAI API KEY. If you do not have JaguarDB set up, see the Setup Jaguar section at the bottom for instructions on how to do so.

export JAGUAR_API_KEY=...
export OPENAI_API_KEY=...

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 rag-jaguardb

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

langchain app add rag-jagaurdb

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

from rag_jaguardb import chain as rag_jaguardb

add_routes(app, rag_jaguardb_chain, path="/rag-jaguardb")

(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/rag-jaguardb/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/rag-jaguardb")

JaguarDB Setup

To utilize JaguarDB, you can use docker pull and docker run commands to quickly setup JaguarDB.

docker pull jaguardb/jaguardb 
docker run -d -p 8888:8888 --name jaguardb jaguardb/jaguardb

To launch the JaguarDB client terminal to interact with JaguarDB server:

docker exec -it jaguardb /home/jaguar/jaguar/bin/jag

Another option is to download an already-built binary package of JaguarDB on Linux, and deploy the database on a single node or in a cluster of nodes. The streamlined process enables you to quickly start using JaguarDB and leverage its powerful features and functionalities. here.