28 lines
970 B
Markdown
28 lines
970 B
Markdown
# LangChain RAG Demo
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This repository demonstrates how to use LangChain for a Retrieval-Augmented Generation (RAG) application.
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The code retrieves Hacker News front page stories, categorizes them, stores them in a vector store, and performs retrieval based on user preferences.
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## Getting Started
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1. Set the following environment variables:
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- `OPENAI_API_KEY`: Your OpenAI API key for chat and embedding models.
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- `JINA_AI_KEY`: Your [Jina AI Reader](https://jina.ai/reader/) key for text extraction.
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- `SLACK_BOT_TOKEN`: Your Slack bot token for sending messages (optional).
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2. Start local Weaviate vector store instance:
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```bash
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docker compose up -d
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```
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3. Run the RAG application:
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```bash
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uv run python indexing.py
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```
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Adjust the constants in `indexing.py` to configure the behavior of the application.
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You can optionally enable MLflow tracing by setting `ENABLE_MLFLOW_TRACING=True` there (make sure to run `mlflow server` first).
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