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- [ ] **PR title**: "package: description"
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83 lines
2.5 KiB
Markdown
83 lines
2.5 KiB
Markdown
# rag-opensearch
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This Template performs RAG using [OpenSearch](https://python.langchain.com/docs/integrations/vectorstores/opensearch).
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## Environment Setup
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Set the following environment variables.
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- `OPENAI_API_KEY` - To access OpenAI Embeddings and Models.
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And optionally set the OpenSearch ones if not using defaults:
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- `OPENSEARCH_URL` - URL of the hosted OpenSearch Instance
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- `OPENSEARCH_USERNAME` - User name for the OpenSearch instance
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- `OPENSEARCH_PASSWORD` - Password for the OpenSearch instance
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- `OPENSEARCH_INDEX_NAME` - Name of the index
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To run the default OpenSearch instance in docker, you can use the command
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```shell
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docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" --name opensearch-node -d opensearchproject/opensearch:latest
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```
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Note: To load dummy index named `langchain-test` with dummy documents, run `python dummy_index_setup.py` in the package
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package rag-opensearch
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add rag-opensearch
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```
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And add the following code to your `server.py` file:
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```python
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from rag_opensearch import chain as rag_opensearch_chain
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add_routes(app, rag_opensearch_chain, path="/rag-opensearch")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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You can sign up for LangSmith [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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We can access the playground at [http://127.0.0.1:8000/rag-opensearch/playground](http://127.0.0.1:8000/rag-opensearch/playground)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-opensearch")
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```
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