mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
95 lines
2.7 KiB
Markdown
95 lines
2.7 KiB
Markdown
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# rag-elasticsearch
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This template performs RAG using [Elasticsearch](https://python.langchain.com/docs/integrations/vectorstores/elasticsearch).
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It relies on sentence transformer `MiniLM-L6-v2` for embedding passages and questions.
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## Environment Setup
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
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To connect to your Elasticsearch instance, use the following environment variables:
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```bash
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export ELASTIC_CLOUD_ID = <ClOUD_ID>
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export ELASTIC_USERNAME = <ClOUD_USERNAME>
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export ELASTIC_PASSWORD = <ClOUD_PASSWORD>
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```
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For local development with Docker, use:
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```bash
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export ES_URL="http://localhost:9200"
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```
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And run an Elasticsearch instance in Docker with
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```bash
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docker run -p 9200:9200 -e "discovery.type=single-node" -e "xpack.security.enabled=false" -e "xpack.security.http.ssl.enabled=false" docker.elastic.co/elasticsearch/elasticsearch:8.9.0
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```
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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-elasticsearch
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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-elasticsearch
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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_elasticsearch import chain as rag_elasticsearch_chain
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add_routes(app, rag_elasticsearch_chain, path="/rag-elasticsearch")
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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-elasticsearch/playground](http://127.0.0.1:8000/rag-elasticsearch/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-elasticsearch")
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```
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For loading the fictional workplace documents, run the following command from the root of this repository:
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```bash
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python ingest.py
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```
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However, you can choose from a large number of document loaders [here](https://python.langchain.com/docs/integrations/document_loaders).
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