Vector Databases · superlinear-ai

raglite

RAGLite is a lightweight Python toolkit for building Retrieval-Augmented Generation (RAG) systems with minimal dependencies. It supports DuckDB or PostgreSQL as vector/keyword search databases and integrates with any LLM via LiteLLM, including local llama.cpp models.

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Source: GitHub — github.com/superlinear-ai/raglite

1.2k

GitHub stars

108

Forks

Python

Primary language

MPL-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	superlinear-ai/raglite
Owner	superlinear-ai
Primary language	Python
License	MPL-2.0 — OSI-approved
Stars	1.2k
Forks	108
Open issues	16
Latest release	v1.1.1 (2026-05-18)
Last updated	2026-05-18
Source	https://github.com/superlinear-ai/raglite

What raglite is

RAGLite provides modular RAG components including hybrid search (FTS + vector), late chunking, semantic chunking, adaptive retrieval, multi-vector embeddings, and query adaptation via Procrustes optimization. It avoids PyTorch/LangChain dependencies and offers native support for PDF-to-Markdown conversion, reranking, and prompt caching structures.

Quickstart

Get the raglite source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/superlinear-ai/raglite.git2cd raglite3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Cost-sensitive production RAG deployments

Organizations seeking to minimize dependencies, reduce compute footprint, and leverage local LLMs (llama.cpp) with permissive licenses for regulated/on-premises deployments.

Document ingestion pipelines requiring fine-grained control

Teams needing semantic chunking, late chunking, optimal sentence splitting, contextual chunk headings, and PDF-to-Markdown conversion without heavy ML frameworks.

Hybrid search and evaluation workflows

Projects combining keyword and vector search with reranking, adaptive retrieval, query adaptation, and built-in Ragas integration for retrieval/generation quality measurement.

Implementation considerations

Explicitly select and configure database (DuckDB for local/lightweight, PostgreSQL for production), LLM provider (via LiteLLM), embedder model, and optional reranker; no defaults apply automatically.
For local models, precompiled llama-cpp-python binaries are recommended; ensure correct CUDA/Metal accelerator, Python version, and platform match before installation.
PDF processing relies on pdftext and pypdfium2; optional Mistral OCR integration for higher-quality document extraction but requires separate API key.
Semantic and sentence-level chunking solve integer programming problems; performance impact at scale is not benchmarked in the provided data—test with real document volumes.
Query adaptation and adaptive retrieval require initial tuning; optimal results depend on domain-specific query patterns and retrieval quality metrics.

When to avoid it — and what to weigh

Need extensive pretrained embedding/reranking models out-of-box — RAGLite requires explicit configuration of embedders and rerankers; there is no single 'batteries-included' model. Integration with LiteLLM and rerankers library means you must select and manage model lifecycle.
Require advanced multi-agent orchestration or complex chains — RAGLite focuses on core RAG and retrieval; it does not provide workflow orchestration, agent frameworks, or complex task chaining comparable to LangChain or LlamaIndex.
Need extensive third-party integrations out-of-box — While extensible, RAGLite does not ship with pre-built connectors to dozens of data sources, knowledge bases, or API endpoints. Custom integration code is required for many sources.
Prefer rapid prototyping with minimal configuration — RAGLite requires explicit database, LLM, embedder, and reranker configuration; there is no quick-start with sensible cloud defaults like some all-in-one platforms.

License & commercial use

RAGLite is licensed under MPL-2.0 (Mozilla Public License 2.0), a copyleft license requiring source code disclosure for modifications but permitting commercial use in unmodified binaries and libraries. Derivative works must be published under MPL-2.0.

MPL-2.0 permits commercial use. However, any modifications to RAGLite itself must be released under MPL-2.0. If you deploy RAGLite unmodified or via a library/service, MPL-2.0 does not restrict commercial monetization. Review your specific deployment model and legal counsel to confirm compliance, particularly if you plan to modify the toolkit.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Standard RAG security concerns apply: database credentials in config require secure management (environment variables, secrets vaults); LLM API keys similarly require protection. DuckDB and PostgreSQL security postures depend on respective projects. PDF parsing and OCR introduce document processing surface; no exploit details or security audit data provided. Self-query feature allows LLM-generated metadata filters—verify filter logic to prevent injection. No security audit or vulnerability disclosure process information provided.

Alternatives to consider

LangChain + LlamaIndex

Industry-standard frameworks offering extensive pre-built integrations, agents, and chains. Trade-off: heavier dependencies (PyTorch, transformers), steeper learning curve, less granular control over chunking/retrieval.

Haystack (Deepset)

Modular pipeline framework with similar retrieval components (hybrid search, reranking, adaptive retrieval). Trade-off: different dependency stack, smaller community, less emphasis on local/lightweight deployment.

Anthropic Prompt Caching + Direct API

For Anthropic models, native prompt caching and long-context APIs reduce need for external RAG orchestration. Trade-off: vendor lock-in, limited to Anthropic, no hybrid search or reranking abstractions.

Software development agency

Build on raglite with DEV.co software developers

Explore a lightweight, modular RAG toolkit optimized for cost, control, and local deployment. Integrate with any LLM, embedder, and reranker via LiteLLM and configure DuckDB or PostgreSQL in minutes.

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raglite FAQ

Can I use RAGLite with OpenAI, Anthropic, and local llama.cpp models interchangeably?

Yes. RAGLite extends LiteLLM, so any LiteLLM-supported provider (OpenAI, Anthropic, Cohere, etc.) and local llama.cpp models via `llama-cpp-python/` identifiers work. Switch providers by changing the config; no code change required.

What databases does RAGLite support?

DuckDB (local/lightweight, with FTS + VSS extensions) and PostgreSQL (production, with pgvector + tsvector). No other databases (e.g., Pinecone, Weaviate, Elasticsearch) are natively supported; custom backends would require code changes.

Is RAGLite suitable for production deployments?

Yes, with caveats: PostgreSQL backend supports production workloads; semantic/sentence chunking optimization complexity and adaptive retrieval tuning require testing. Lightweight dependencies are an advantage for on-premises/regulated deployments. No SLA or production support information provided.

Do I need PyTorch to use RAGLite?

No. RAGLite explicitly avoids PyTorch and LangChain. Local rerankers (FlashRank) use lightweight models; remote rerankers (e.g., Cohere) use API calls. This reduces install size and deployment overhead significantly.

Work with a software development agency

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If raglite is part of your vector databases roadmap, our team can implement, customize, migrate, and maintain it.

Build efficient RAG systems with RAGLite

Explore a lightweight, modular RAG toolkit optimized for cost, control, and local deployment. Integrate with any LLM, embedder, and reranker via LiteLLM and configure DuckDB or PostgreSQL in minutes.

Talk to DEV.co Schedule a Consultation