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https://github.com/hwchase17/langchain
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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.
94 lines
2.8 KiB
Markdown
94 lines
2.8 KiB
Markdown
# 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 `Hugging Face 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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