Spaces:
Runtime error
Runtime error
File size: 3,196 Bytes
42a588a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
# 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")
``` |