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# rag-redis | |
This template performs RAG using Redis (vector database) and OpenAI (LLM) on financial 10k filings docs for Nike. | |
It relies on the sentence transformer `all-MiniLM-L6-v2` for embedding chunks of the pdf and user questions. | |
## Environment Setup | |
Set the `OPENAI_API_KEY` environment variable to access the [OpenAI](https://platform.openai.com) models: | |
```bash | |
export OPENAI_API_KEY= <YOUR OPENAI API KEY> | |
``` | |
Set the following [Redis](https://redis.com/try-free) environment variables: | |
```bash | |
export REDIS_HOST = <YOUR REDIS HOST> | |
export REDIS_PORT = <YOUR REDIS PORT> | |
export REDIS_USER = <YOUR REDIS USER NAME> | |
export REDIS_PASSWORD = <YOUR REDIS PASSWORD> | |
``` | |
## Supported Settings | |
We use a variety of environment variables to configure this application | |
| Environment Variable | Description | Default Value | | |
|----------------------|-----------------------------------|---------------| | |
| `DEBUG` | Enable or disable Langchain debugging logs | True | | |
| `REDIS_HOST` | Hostname for the Redis server | "localhost" | | |
| `REDIS_PORT` | Port for the Redis server | 6379 | | |
| `REDIS_USER` | User for the Redis server | "" | | |
| `REDIS_PASSWORD` | Password for the Redis server | "" | | |
| `REDIS_URL` | Full URL for connecting to Redis | `None`, Constructed from user, password, host, and port if not provided | | |
| `INDEX_NAME` | Name of the vector index | "rag-redis" | | |
## Usage | |
To use this package, you should first have the LangChain CLI and Pydantic installed in a Python virtual environment: | |
```shell | |
pip install -U langchain-cli pydantic==1.10.13 | |
``` | |
To create a new LangChain project and install this as the only package, you can do: | |
```shell | |
langchain app new my-app --package rag-redis | |
``` | |
If you want to add this to an existing project, you can just run: | |
```shell | |
langchain app add rag-redis | |
``` | |
And add the following code snippet to your `app/server.py` file: | |
```python | |
from rag_redis.chain import chain as rag_redis_chain | |
add_routes(app, rag_redis_chain, path="/rag-redis") | |
``` | |
(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-redis/playground](http://127.0.0.1:8000/rag-redis/playground) | |
We can access the template from code with: | |
```python | |
from langserve.client import RemoteRunnable | |
runnable = RemoteRunnable("http://localhost:8000/rag-redis") | |
``` |