Add readme

README.md

@@ -0,0 +1,26 @@
+# 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).