# rag-opensearch This Template performs RAG using [OpenSearch](https://python.langchain.com/docs/integrations/vectorstores/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 Instance - `OPENSEARCH_USERNAME` - User name for the OpenSearch instance - `OPENSEARCH_PASSWORD` - Password for the OpenSearch instance - `OPENSEARCH_INDEX_NAME` - Name of the index To run the default OpenSearch instance in docker, you can use the command ```shell 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: ```shell pip install -U langchain-cli ``` To create a new LangChain project and install this as the only package, you can do: ```shell langchain app new my-app --package rag-opensearch ``` If you want to add this to an existing project, you can just run: ```shell langchain app add rag-opensearch ``` And add the following code to your `server.py` file: ```python 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](https://smith.langchain.com/). If you don't have access, you can skip this section ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_PROJECT= # if not specified, defaults to "default" ``` If you are inside this directory, then you can spin up a LangServe instance directly by: ```shell langchain serve ``` This will start the FastAPI app with a server is running locally at [http://localhost:8000](http://localhost:8000) We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) We can access the playground at [http://127.0.0.1:8000/rag-opensearch/playground](http://127.0.0.1:8000/rag-opensearch/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-opensearch") ```