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Byaldi

Byaldi is an open-source Python library that wraps ColPali and related late-interaction multimodal models behind a familiar API for indexing and searching documents in retrieval-augmented generation workflows.

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Independent OpenViST project profile, based on linked sources.

Byaldi — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Byaldi when building a Python retrieval component that needs late-interaction multimodal search over PDFs or images, especially where page-level results can feed a separate generation step.

Who it is for

  • Python developers adding multimodal document retrieval to a RAG application.
  • Teams that need to search PDF pages or image collections with ColPali-style models.

Documented uses

  • Index a PDF, image, or directory of documents.
  • Search an index and retrieve relevant document pages with scores and metadata.
  • Provide retrieved document content to a later LLM step when encoded documents are stored with the index.
Before you choose

Requirements & limits

The public GitHub repository is licensed under Apache-2.0.

  • PDF indexing requires Poppler through the pdf2image conversion workflow.
  • The project recommends a GPU; encoding collections may perform poorly on CPU or MPS.
  • Indexes are uncompressed in this pre-release version.
  • Storing base64-encoded documents with the index simplifies passing retrieved content onward but raises memory and storage needs.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Index documents and search for relevant pages

Create a Byaldi index from PDFs or images and retrieve matching pages with a query.

Before you start

  • Python and the Byaldi library.
  • Poppler installed for PDF conversion; the README gives Homebrew and Debian/Ubuntu installation examples.
  • A supported model checkpoint, such as vidore/colqwen2-v1.0.
  • A GPU is recommended for smooth encoding; CPU or MPS may perform poorly.

Steps

  1. Install Poppler if indexing PDFs.

  2. Load a model with RAGMultiModalModel.from_pretrained("vidore/colqwen2-v1.0").

  3. Call RAG.index() with a PDF, image, or directory path and an index name.

  4. Search the loaded index with RAG.search(query, k=3) to retrieve matching results.

Expected outcome

A local index and search results containing document IDs, page numbers, scores and metadata.

Guide limitations

  • The README identifies Byaldi as pre-release and says its indexes are uncompressed.
  • Setting store_collection_with_index=True includes base64-encoded documents in results but increases memory and storage requirements; the default is False.
Documentation 1 ↗
COMPARE OPTIONS

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.

ProjectDocumented purposePlatformAccess and requirements
ByaldiIndex a PDF, image, or directory of documents.LinuxThe public GitHub repository is licensed under Apache-2.0.
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
AnswerDotAI/byaldi · Official homepage title · retained in its source language
How we review sourcesSuggest a correction