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163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
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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