raglite
Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL
GraphCanon updated 2d · GitHub synced 2d
Decision brief
RAGLite offers specialized capabilities for integrating Retrieval-Augmented Generation (RAG) models with DuckDB or PostgreSQL.
Good fit when
- - You need to leverage Retriever-Reader architectures specifically optimized for either DuckDB or PostgreSQL backend databases.
- - Your application requires a Python-based solution without the overhead of additional language binding requirements.
Avoid when
- - The project demands integration with RAG systems that natively support database backends other than DuckDB and PostgreSQL, as RAGLite is limited to these two options.
- - You are looking for a more generalized framework that supports multiple vector search engines besides those compatible with DuckDB or PostgreSQL.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (5d since push)
- As of 2d
- Provenance
- Not a fork · Organization account
- As of 2d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install ragliteSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
RAGLite is a Python-based toolkit facilitating Retrieval-Augmented Generation using either DuckDB or PostgreSQL as backend databases.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 23, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 23, 2026
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 23, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 23, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 23, 2026)
source dependencies (e.g., no [PyTorch](https://github.com/pytorch/pytorch) or [LangChain](https://github.com/langchain-ai/langchain))Source link
Source: README excerpt (regex_v1, Aug 23, 2026)
RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL.Source link
Source: README excerpt (regex_v1, Aug 23, 2026)
- 💬 Optional customizable ChatGPT-like frontend for [web](https://docs.chainlit.io/deploy/copilot), [Slack](httpsSource link
Source: README excerpt (regex_v1, Aug 23, 2026)
otocol](https://modelcontextprotocol.io) (MCP) server that any MCP client like [Claude desktop](https://claude.ai/download) can connect withSource link
Tags
README
🥤 RAGLite
RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL.
Features
Configurable
- 🧠 Choose any LLM provider with LiteLLM, including local llama-cpp-python models
- 💾 Choose either DuckDB or PostgreSQL as a keyword & vector search database
- 🥇 Choose any reranker with rerankers, including multilingual FlashRank as the default
Fast and permissive
- ❤️ Only lightweight and permissive open source dependencies (e.g., no PyTorch or LangChain)
- 🚀 Acceleration with Metal on macOS, and CUDA on Linux and Windows
Unhobbled
- 📖 PDF to Markdown conversion on top of pdftext and pypdfium2
- 🧬 Multi-vector chunk embedding with late chunking and contextual chunk headings
- ✏️ Optimal sentence splitting with wtpsplit-lite by solving a binary integer programming problem
- ✂️ Optimal semantic chunking by solving a binary integer programming problem
- 🔍 Hybrid search with the database's native keyword & vector search (FTS+VSS; tsvector+pgvector)
- 💭 Adaptive retrieval where the LLM decides whether to and what to retrieve based on the query
- 💰 Improved cost and latency with a prompt caching-aware message array structure
- 🍰 Improved output quality with Anthropic's long-context prompt format
- 🌀 Optimal closed-form linear query adapter by solving an orthogonal Procrustes problem
Extensible
- 🔌 A built-in Model Context Protocol (MCP) server that any MCP client like Claude desktop can connect with
- 💬 Optional customizable ChatGPT-like frontend for web, Slack, and Teams with Chainlit
- ✍️ Optional conversion of any input document to Markdown with Pandoc
- 🔎 Optional high-quality document processing with Mistral OCR for PDFs, images, DOCX, and PPTX with automatic image descriptions
- ✅ Optional evaluation of retrieval and generation performance with Ragas
Installing
[!TIP] 🚀 If you want to use local models, it is recommended to install an accelerated llama-cpp-python precompiled binary with:
# Configure which llama-cpp-python precompiled binary to install (⚠️ not every combination is available): LLAMA_CPP_PYTHON_VERSION=0.3.9 PYTHON_VERSION=310|311|312 ACCELERATOR=metal|cu121|cu122|cu123|cu124 PLATFORM=macos
For agents
This page has a .md twin and JSON over the API.