mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
87 lines
2.8 KiB
Markdown
87 lines
2.8 KiB
Markdown
# rag-self-query
|
|
|
|
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).
|
|
|
|
## Environment Setup
|
|
|
|
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/).
|
|
|
|
Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
|
|
|
|
To connect to your Elasticsearch instance, use the following environment variables:
|
|
|
|
```bash
|
|
export ELASTIC_CLOUD_ID = <ClOUD_ID>
|
|
export ELASTIC_USERNAME = <ClOUD_USERNAME>
|
|
export ELASTIC_PASSWORD = <ClOUD_PASSWORD>
|
|
```
|
|
For local development with Docker, use:
|
|
|
|
```bash
|
|
export ES_URL = "http://localhost:9200"
|
|
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
|
|
```
|
|
|
|
## Usage
|
|
|
|
To use this package, you should first have the LangChain CLI installed:
|
|
|
|
```shell
|
|
pip install -U "langchain-cli[serve]"
|
|
```
|
|
|
|
To create a new LangChain project and install this as the only package, you can do:
|
|
|
|
```shell
|
|
langchain app new my-app --package rag-self-query
|
|
```
|
|
|
|
If you want to add this to an existing project, you can just run:
|
|
|
|
```shell
|
|
langchain app add rag-self-query
|
|
```
|
|
|
|
And add the following code to your `server.py` file:
|
|
```python
|
|
from rag_self_query import chain
|
|
|
|
add_routes(app, chain, path="/rag-elasticsearch")
|
|
```
|
|
|
|
To populate the vector store with the sample data, from the root of the directory run:
|
|
```bash
|
|
python ingest.py
|
|
```
|
|
|
|
(Optional) Let's now configure LangSmith.
|
|
LangSmith will help us trace, monitor and debug LangChain applications.
|
|
LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
|
|
If you don't have access, you can skip this section
|
|
|
|
```shell
|
|
export LANGCHAIN_TRACING_V2=true
|
|
export LANGCHAIN_API_KEY=<your-api-key>
|
|
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
|
|
```
|
|
|
|
If you are inside this directory, then you can spin up a LangServe instance directly by:
|
|
|
|
```shell
|
|
langchain serve
|
|
```
|
|
|
|
This will start the FastAPI app with a server is running locally at
|
|
[http://localhost:8000](http://localhost:8000)
|
|
|
|
We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
|
|
We can access the playground at [http://127.0.0.1:8000/rag-elasticsearch/playground](http://127.0.0.1:8000/rag-elasticsearch/playground)
|
|
|
|
We can access the template from code with:
|
|
|
|
```python
|
|
from langserve.client import RemoteRunnable
|
|
|
|
runnable = RemoteRunnable("http://localhost:8000/rag-self-query")
|
|
```
|