ec53d983a1
<!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> Adding rag-opensearch template. --------- Signed-off-by: kalyanr <kalyan.ben10@live.com> Co-authored-by: Erick Friis <erick@langchain.dev>
2.2 KiB
rag-opensearch
This Template performs RAG using OpenSearch.
Environment Setup
Set the following environment variables.
OPENAI_API_KEY- To access OpenAI Embeddings and Models.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
Note: To load dummy index named langchain-test with dummy documents, use dummy_index_setup.py script in the folder
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. 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-opensearch/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-opensearch")