OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
RAG & Knowledge Systems

Arxivchatguru

Arxivchatguru is a Python Streamlit application that fetches arXiv papers for a chosen topic, creates embeddings for paper chunks with OpenAI, and stores them in a topic-scoped Redis vector index. LangChain retrieves relevant passages to provide context for chat-based questions about the loaded papers.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Arxivchatguru — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it to explore a topic through questions grounded in papers loaded into the app, or to study a compact academic RAG workflow.

Who it is for

  • Readers exploring research papers on a particular arXiv topic.
  • Developers learning how retrieval-augmented generation can connect paper ingestion, vector search and chat.

Documented uses

  • Load a chosen number of arXiv papers for a topic.
  • Ask questions using retrieved passages from the loaded papers as context.
  • Inspect metadata, indexed fields and query-engine statistics for the active Redis index.
Before you choose

Requirements & limits

The repository documents local use through Docker or a Python development setup. An OpenAI API key is required; local development uses Python 3.13, and running outside Docker requires a reachable Redis instance. The repository lists an MIT license.

  • Paper chunks and embeddings are stored in a Redis index scoped to the selected topic.
  • OpenAI supplies embeddings and the chat model receives context retrieved by LangChain.
  • The repository presents the app as a learning project, not a production-ready research assistant.
  • Year or author metadata filters, improved long-paper chunking and chat history are described as planned follow-ups.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and start Arxivchatguru with Docker

Configure the documented Docker-first local environment and open the Streamlit app.

Before you start

  • Docker Desktop
  • An OpenAI API key

Steps

  1. Create a .env file from the template with cp .env.template .env.

  2. Set OPENAI_API_KEY in the .env file.

  3. Start the stack with the command below.

    Documented command
    make docker-up

  4. Open http://localhost:8501 in a browser.

Expected outcome

The app's local Streamlit interface is available at the documented address.

Guide limitations

  • This setup guide does not describe a production deployment.
  • The project is characterized by its repository as a learning project, not a production-ready research assistant.
Documentation 1 ↗
Source-based practical guide

Run the app locally for development

Prepare the documented Python development environment and start the local Streamlit app.

Before you start

  • Python 3.13
  • Poetry
  • An OpenAI API key
  • A reachable Redis instance

Steps

  1. Create .env from .env.template and set OPENAI_API_KEY.

  2. Install Poetry if needed with the command below.

    Documented command
    python3 -m pip install --user poetry

  3. Select Python 3.13 and install dependencies with the command below and the command below.

    Documented command
    python3 -m poetry env use python3.13
    Documented command
    make install

  4. Start the app with the command below and open http://localhost:8501.

    Documented command
    make dev

  5. If running outside Docker, set REDIS_URL to a reachable Redis instance, such as redis://localhost:6379.

Expected outcome

The documented local development flow starts the Streamlit app at the local address.

Guide limitations

  • The repository describes the project as a learning app rather than production-ready software.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
ArxivchatguruLoad a chosen number of arXiv papers for a topic.Docker, Self-hosted, WebThe repository documents local use through Docker or a Python development setup. An OpenAI API key is required; local development uses Python 3.13, and running outside Docker requires a reachable Redis instance. The repository lists an MIT license.
Chatbot with PDF for Semantic SearchPrepare document content as embeddings and save it to a supported vector database.WebThe repository is identified as open source and publicly visible on GitHub.
AI LLM PlaygroundAsk questions about PDF documents through an assistant interface.Web
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
9 October 2026
Original website title
redis-developer/ArXivChatGuru · Official homepage title · retained in its source language
How we review sourcesSuggest a correction