mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
94 lines
3.1 KiB
Markdown
94 lines
3.1 KiB
Markdown
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# rag-redis
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This template performs RAG using Redis (vector database) and OpenAI (LLM) on financial 10k filings docs for Nike.
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It relies on the sentence transformer `all-MiniLM-L6-v2` for embedding chunks of the pdf and user questions.
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## Environment Setup
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Set the `OPENAI_API_KEY` environment variable to access the [OpenAI](https://platform.openai.com) models:
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```bash
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export OPENAI_API_KEY= <YOUR OPENAI API KEY>
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```
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Set the following [Redis](https://redis.com/try-free) environment variables:
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```bash
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export REDIS_HOST = <YOUR REDIS HOST>
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export REDIS_PORT = <YOUR REDIS PORT>
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export REDIS_USER = <YOUR REDIS USER NAME>
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export REDIS_PASSWORD = <YOUR REDIS PASSWORD>
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```
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## Supported Settings
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We use a variety of environment variables to configure this application
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| Environment Variable | Description | Default Value |
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|----------------------|-----------------------------------|---------------|
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| `DEBUG` | Enable or disable Langchain debugging logs | True |
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| `REDIS_HOST` | Hostname for the Redis server | "localhost" |
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| `REDIS_PORT` | Port for the Redis server | 6379 |
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| `REDIS_USER` | User for the Redis server | "" |
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| `REDIS_PASSWORD` | Password for the Redis server | "" |
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| `REDIS_URL` | Full URL for connecting to Redis | `None`, Constructed from user, password, host, and port if not provided |
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| `INDEX_NAME` | Name of the vector index | "rag-redis" |
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## Usage
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To use this package, you should first have the LangChain CLI and Pydantic installed in a Python virtual environment:
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```shell
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pip install -U langchain-cli pydantic==1.10.13
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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-redis
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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-redis
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```
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And add the following code snippet to your `app/server.py` file:
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```python
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from rag_redis.chain import chain as rag_redis_chain
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add_routes(app, rag_redis_chain, path="/rag-redis")
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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-redis/playground](http://127.0.0.1:8000/rag-redis/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-redis")
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``` |