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163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
93 lines
3.1 KiB
Markdown
93 lines
3.1 KiB
Markdown
# 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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You can sign up for LangSmith [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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``` |