# LangChain RAG Demo

This repository demonstrates how to use LangChain for a Retrieval-Augmented Generation (RAG) application.
The code retrieves Hacker News front page stories, categorizes them, stores them in a vector store, and performs retrieval based on user preferences.

## Getting Started

1. Set the following environment variables:

   - `OPENAI_API_KEY`: Your OpenAI API key for chat and embedding models.
   - `JINA_AI_KEY`: Your [Jina AI Reader](https://jina.ai/reader/) key for text extraction.

2. Start local Weaviate vector store instance:

   ```bash
   docker compose up -d
   ```

3. Run the RAG application:

   ```bash
   uv run python indexing.py
   ```

Adjust the constants in `indexing.py` to change the number of stories to fetch and the categories to use.
You can optionally enable MLflow tracing by setting `ENABLE_MLFLOW_TRACING=True` there (make sure to run `mlflow server` first).