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arXiv Search API

arXiv Search API is a web-based arXiv paper discovery and document-retrieval demo that uses embeddings and Redis vector search to find papers through natural-language queries. Its public repository provides a React and TypeScript single-page app, a FastAPI backend, Redis Stack storage and RedisVL client, with Hugging Face, OpenAI and Cohere listed as embedding ecosystems.

Independent OpenViST project profile, based on linked sources.

arXiv Search API — OpenViST project profile in Search & Research AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the hosted demo to explore semantic search over arXiv papers, or the repository when you need a documented starting point for building or adapting a Redis-backed document-retrieval app.

Who it is for

  • Researchers exploring papers in the included arXiv collection through semantic search.
  • Developers studying or adapting a Redis-backed document-retrieval application.

Documented uses

  • Search for arXiv papers using natural-language queries and vector similarity.
  • Inspect a documented architecture for a web-based semantic document-search app.
  • Adapt the retrieval approach to a different document collection, with changes to ingestion or configuration as needed.
Before you choose

Requirements & limits

The README identifies a hosted demo at https://docsearch.redisvl.com and provides a local setup path. Local setup instructions request OpenAI and Cohere API keys.

  • The example dataset is arXiv papers sourced from Kaggle; other collections may require changes to data loading and configuration.
  • The documented stack combines Redis Stack and RedisVL with FastAPI and Pydantic on the backend and React with TypeScript on the frontend.
  • The README describes Hugging Face, OpenAI and Cohere embedding options and names environment variables for changing their models.
  • The repository is licensed under BSD-3-Clause.

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 the project locally

Prepare the documented local environment and launch the app using the repository's Docker-based workflow.

Before you start

  • Docker Desktop
  • A cloned project repository
  • A copy of .env.template named .env
  • OpenAI and Cohere API keys added to .env, as directed by the README

Steps

  1. Clone the repository and change into its directory.

  2. Copy .env.template to .env and add the specified API keys.

  3. Run make deploy to start the app locally with Redis 8 CE.

Expected outcome

The documented local deployment workflow is initiated with the application and Redis 8 CE.

Guide limitations

  • The instructions do not specify a verification procedure or guarantee that deployment will succeed in every environment.
  • The README lists Hugging Face, OpenAI and Cohere embedding options, while its setup instructions specifically request OpenAI and Cohere keys.
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
arXiv Search APISearch for arXiv papers using natural-language queries and vector similarity.Cloud, Server, WebThe README identifies a hosted demo at https://docsearch.redisvl.com and provides a local setup path. Local setup instructions request OpenAI and Cohere API keys.
AlisiaAsk questions in a chat interface and receive responses informed by web search.Cloud, Self-hosted, Web
Procurement AI AssistantFind products by combining criteria such as price, product type and supplier reliability.Cloud, Web
Evidence and transparency

Sources & review

Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
redis-developer/redis-arXiv-search · Official homepage title · retained in its source language