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| .. | ||
| rag_opensearch | ||
| tests | ||
| .gitignore | ||
| dummy_data.txt | ||
| dummy_index_setup.py | ||
| LICENSE | ||
| pyproject.toml | ||
| rag_opensearch.ipynb | ||
| README.md | ||
rag-opensearch
This Template performs RAG using OpenSearch.
Environment Setup
Set the following environment variables.
OPENAI_API_KEY- To access OpenAI Embeddings and Models.
And optionally set the OpenSearch ones if not using defaults:
OPENSEARCH_URL- URL of the hosted OpenSearch InstanceOPENSEARCH_USERNAME- User name for the OpenSearch instanceOPENSEARCH_PASSWORD- Password for the OpenSearch instanceOPENSEARCH_INDEX_NAME- Name of the index
To run the default OpenSearch instance in docker, you can use the command
docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" --name opensearch-node -d opensearchproject/opensearch:latest
Note: To load dummy index named langchain-test with dummy documents, run python dummy_index_setup.py in the package
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-opensearch
If you want to add this to an existing project, you can just run:
langchain app add rag-opensearch
And add the following code to your server.py file:
from rag_opensearch import chain as rag_opensearch_chain
add_routes(app, rag_opensearch_chain, path="/rag-opensearch")
(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/rag-opensearch/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-opensearch")