PrithivirajDamodaran/FlashRank

Health v2 · maintenance only

Adoption not part of the score

1003 stars · 72 forks observed · 2026-09-03

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

FlashRank is an ultra-light, fast Python library for re-ranking search and retrieval results using SoTA cross-encoders and listwise LLM-based rerankers. It requires no Torch or Transformers, runs on CPU, and includes models as small as ~4MB, making it well-suited for serverless and cost-sensitive deployments.

Use cases

• rerank search results in a RAG pipeline before feeding an LLM
• add re-ranking to a vector database or hybrid search pipeline
• rerank documents on CPU without heavy dependencies like torch
• improve relevance of semantic search results with a cross-encoder
• deploy a tiny reranker in serverless functions like AWS Lambda
• rerank multilingual search results
• boost retrieval quality in a full-text or lexical search system

When to choose

• you need fast, low-cost reranking on CPU with minimal dependencies
• you want to improve RAG retrieval quality with SoTA rerankers
• you're deploying to serverless environments where package size and cold start matter
• you need multilingual or listwise LLM-based reranking options

When to avoid

• you need GPU-accelerated reranking of very large document sets
• you need a full search engine or vector database rather than a reranking layer
• you require models with context beyond 8192 tokens
• you need tight integration with a specific framework's native reranker APIs

Facets

library · maturity active

search-engine rag machine-learning nlp machine-learning python cli cross-platform reranking cross-encoder listwise-reranker vector-search semantic-search lightweight cpu-only search retrieval-augmented-generation natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="PrithivirajDamodaran/FlashRank")