mirror of
https://github.com/hwchase17/langchain
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87 lines
2.8 KiB
Markdown
87 lines
2.8 KiB
Markdown
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# rag-self-query
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This template performs RAG using the self-query retrieval technique. The main idea is to let an LLM convert unstructured queries into structured queries. See the [docs for more on how this works](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query).
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## Environment Setup
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In this template we'll use OpenAI models and an Elasticsearch vector store, but the approach generalizes to all LLMs/ChatModels and [a number of vector stores](https://python.langchain.com/docs/integrations/retrievers/self_query/).
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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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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[serve]"
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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-self-query
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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-self-query
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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_self_query import chain
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add_routes(app, chain, path="/rag-elasticsearch")
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```
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To populate the vector store with the sample data, from the root of the directory run:
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```bash
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python ingest.py
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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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LangSmith is currently in private beta, you can sign up [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-self-query")
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```
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