superlinear-ai/raglite

Health v2 · maintenance only

Adoption not part of the score

1207 stars · 109 forks observed · 2026-09-30

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

RAGLite is a Python toolkit for building Retrieval-Augmented Generation (RAG) pipelines backed by DuckDB or PostgreSQL, with hybrid keyword and vector search, reranking, and PDF-to-Markdown ingestion. It supports any LLM provider via LiteLLM, including local llama-cpp-python models, and uses lightweight permissive open-source dependencies.

Use cases

• build a RAG chatbot over PDF documents
• add hybrid keyword and vector search to a Postgres app
• run retrieval-augmented generation with local LLMs
• chunk and embed documents with late chunking
• evaluate and rerank RAG retrieval quality
• query documents with adaptive retrieval

When to choose

• you want a lightweight RAG toolkit without PyTorch or LangChain
• you prefer DuckDB or PostgreSQL as your vector store
• you need PDF ingestion, hybrid search, and reranking out of the box
• you want to run LLMs locally or swap providers via LiteLLM

When to avoid

• you need a fully managed RAG service
• you require a heavy multi-agent orchestration framework
• your stack is not Python

Facets

library · maturity active

rag search-engine vector-database llm-inference pdf nlp chatbot large-language-models databases pdf developer-tools python windows duckdb postgresql pgvector hybrid-search reranking late-chunking colbert litellm chainlit sqlite retrieval-augmented-generation natural-language-processing macos linux docker

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