Morphik – An open-source multimodal retrieval-augmented generation tool

What is Morphik?

Morphik is an open-source multimodal retrieval-augmented generation (RAG) tool designed to handle highly technical and visually rich documents. It supports search across multiple formats such as images, PDFs, and videos, and leverages technologies like ColPali to understand visual content within documents. Morphik features fast metadata extraction capabilities, enabling the retrieval of bounding boxes, tags, classifications, and more from various documents.

Key Features of Morphik

• Multimodal Data Processing: Supports a wide range of file formats including text, PDFs, images, and videos.

• Intelligent File Parsing: Automatically splits files into smaller chunks and generates embeddings for efficient retrieval and processing.

• ColPali Multimodal Embeddings: Integrates both text and visual elements for effective search and comprehension of document visuals.

• Knowledge Graph Construction: With a single line of code, users can build domain-specific knowledge graphs, automatically extracting entities and relationships.

• Natural Language Rule Engine: Allows users to define rules in natural language to extract structured information from unstructured data.

• Data Management and Integration: Supports multi-user environments and folder-level data organization and isolation. Integrates with hundreds of AI models that can be registered and configured based on task requirements.

• Rapid Metadata Extraction: Quickly extracts metadata such as bounding boxes, tags, and classifications from documents.

Technical Principles of Morphik

• Multimodal Embedding with ColPali: Morphik uses ColPali to treat document pages as images and generate embeddings that capture layout, typography, and visual context, enabling unified retrieval across text and visuals—such as charts and diagrams.

• Enhanced Retrieval via Knowledge Graphs: The use of knowledge graphs improves retrieval accuracy and depth. Users can explore concept relationships through graph paths for a more intuitive understanding of document content.

• Preprocessing and Freezing: Morphik preprocesses data and “freezes” document states by creating persistent key-value caches. This reduces redundant computation, lowers costs, and significantly speeds up retrieval and generation.

• Hybrid Retrieval Architecture: Combines various technologies including vector-based semantic search, rule engine filtering, knowledge graph expansion, and relevance re-ranking algorithms to ensure precision and relevance in search results.

Project Links

• Official Website: morphik.ai

• GitHub Repository: github.com/morphik-org/morphik-core

Application Scenarios of Morphik

• Technical Document Processing: Ideal for handling complex documents like technical manuals, API documentation, and academic papers.

• Enterprise Knowledge Management: Functions as an enterprise-level knowledge base, helping researchers and professionals manage and retrieve knowledge efficiently.

• Intelligent Application Development: Provides developers with tools to quickly build AI-powered applications.