ArXivChatGuru

ArXiv ChatGuru

ArXiv ChatGuru is a Streamlit app that turns a topic from arXiv into a topic-scoped Redis vector index. It fetches papers, chunks them, stores embeddings in Redis, and lets you ask grounded questions against the papers you loaded.

This app is a learning project for academic RAG. It is intentionally simple and is meant to show how Redis fits into a paper Q&A workflow, not to act as a production-ready research assistant.

What Redis does in this app

• Stores topic-specific paper chunks and embeddings
• Powers vector search for retrieval
• Lets you inspect the active index from the built-in stats page

How it works

1. Enter a topic and choose how many papers to load.
2. The app pulls papers from arXiv and splits them into chunks.
3. OpenAI generates embeddings for those chunks.
4. Redis stores the chunks and embeddings in a topic-scoped index.
5. LangChain retrieves the closest chunks for each user question and sends that context to the chat model.

Prerequisites

• Python 3.13 for local development
• Docker Desktop if you want the Docker-first flow
• An OpenAI API key

Environment setup

Create a .env file from the template:

cp .env.template .env

Then set at least:

OPENAI_API_KEY=your_key_here

The default template uses:

• OPENAI_CHAT_MODEL=gpt-4.1-mini
• OPENAI_EMBEDDING_MODEL=text-embedding-3-small
• REDIS_INDEX_BASENAME=arxiv
• REDIS_URL=redis://arxivchatguru-redis:6379

Run with Docker

Docker is the primary local path.

make docker-up

Then open:

http://localhost:8501

To stop the stack:

make docker-down

Run locally

Install Poetry if you do not already have it:

python3 -m pip install --user poetry

Use Python 3.13 for the project environment, install dependencies, and start the app:

python3 -m poetry env use python3.13
make install
make dev

Then open:

http://localhost:8501

If you run locally outside Docker, make sure REDIS_URL points at a reachable Redis instance such as redis://localhost:6379.

Developer commands

• make format formats the app and tests
• make test runs the test suite
• make build builds the Docker image
• make dev starts Streamlit locally
• make docker-up starts the app with Docker Compose

Stats page

After you load a topic from the main page, open the Streamlit stats page to inspect the active Redis index. It shows:

• Index metadata
• Indexed fields
• Query Engine stats for the active topic

Planned follow-ups

• Add better metadata filters such as year or author
• Improve chunking strategy for long papers
• Add chat history or memory features only if the tutorial needs them