Practical overview
When it fits
Use Raglite when building a Python RAG application and you want to choose the language-model provider and DuckDB or PostgreSQL search backend while using its documented document-processing and retrieval components.
Who it is for
- Developers building document-grounded question-answering or other RAG applications.
- Teams choosing between DuckDB and PostgreSQL for keyword and vector search.
- Builders who want optional MCP, chat-interface or retrieval-and-generation evaluation integrations.
Documented uses
- Connect a language model to retrieved passages from an indexed document collection.
- Convert PDFs to Markdown and, with the Pandoc extra, support other document file types.
- Combine keyword and vector search, then rerank retrieved results.
- Expose a RAG workflow through an MCP server or an optional Chainlit chat interface.
- Evaluate retrieval and generation performance with the optional Ragas extra.
Requirements & limits
- Raglite is a library toolkit, not a hosted end-user question-answering service.
- The supported database choice is DuckDB or PostgreSQL; PostgreSQL search uses native text search and pgvector.
- LiteLLM supports provider choice, including local llama-cpp-python models; the README recommends an accelerated precompiled binary for local models and notes not every binary combination is available.
- PDF conversion is included; Pandoc is an optional extra for other file types, while Mistral OCR is a separately installed option for PDFs, images, DOCX and PPTX.
- Chainlit and Ragas are optional extras for chat interfaces and evaluation, respectively.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Raglite and optional capabilities
Set up the Python package with only the documented extras needed for a frontend, broader file conversion, OCR or evaluation.
Before you start
- Python environment for installing Python packages.
- Choose any optional capability and its corresponding package from the project README.
Steps
Install the core package with the command below.
pip install ragliteFor a Chainlit frontend, install the command below; for Pandoc filetype support, install the command below.
pip install raglite[chainlit]pip install raglite[pandoc]For Mistral OCR document processing, install
mistralai; for Ragas evaluation, install the command below.pip install raglite[ragas]
Expected outcome
Raglite is installed, with the selected documented optional package available for the corresponding workflow.
Guide limitations
- The installation steps alone do not configure a model or database, ingest documents, or start an application.
- The README presents Mistral OCR as an optional processing capability; installing its package does not describe a complete OCR workflow.
Compare options for Retrieval-augmented applications
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.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Raglite | Connect a language model to retrieved passages from an indexed document collection. | Linux, macOS, Windows | |
| Langsearch | Crawl website or local data for a knowledge base. | CLI, Self-hosted, Server | |
| Hybrid Search Rag | Combine document retrieval and language-model generation in a configurable workflow. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 8 October 2026
- Original website title
- superlinear-ai/raglite · Official homepage title · retained in its source language