Morphik
Morphik is an open-source AI-native database and RAG system that enables fast, versatile AI applications by processing multi-modal data (text, images, PDFs, videos) with advanced retrieval and knowledge graph capabilities.
https://www.morphik.ai/?ref=aipure
Product Information
Updated:May 27, 2025
Morphik Monthly Traffic Trends
Morphik received 6.5k visits last month, demonstrating a Significant Growth of Infinity%. Based on our analysis, this trend aligns with typical market dynamics in the AI tools sector.
View history trafficWhat is Morphik
Morphik is a powerful open-source database designed specifically for AI applications, originally known as DataBridge Core. It serves as a comprehensive solution for organizations looking to build AI applications over private knowledge bases. The platform excels at handling unstructured and multi-modal documents, providing developers with tools to ingest, search, transform, and manage various types of content including complex technical documentation, diagrams, schematics, and datasheets. With both hosted and self-hosted options available, Morphik offers enterprise-grade features while maintaining its open-source nature.
Key Features of Morphik
Morphik is an open-source AI-native database and RAG (Retrieval Augmented Generation) system designed for building AI applications over private knowledge bases. It specializes in handling multimodal data, including text, images, PDFs, and videos, with advanced capabilities for visual content understanding and knowledge graph generation. The system offers enterprise-grade features like folder scoping, user permissions, and GPU acceleration while maintaining fast retrieval times even with millions of documents.
Multimodal Document Processing: Directly embeds whole pages including both text and images without relying solely on OCR, enabling better understanding of complex diagrams, schematics, and visual content
Persistent KV-caching: Allows processing documents once and freezing the LLM's internal state for future use, significantly reducing compute costs and speeding up model responses
Intelligent Research Agent: Autonomous agent that can chain multiple tools, analyze documents, and traverse knowledge graphs to find specific information or answer complex queries
Enterprise-Grade Security: Built-in folder scoping, user management, and fine-grained permissions for secure data organization and access control
Use Cases of Morphik
Technical Documentation Search: Enables engineers and researchers to quickly find relevant information from complex technical documents containing diagrams and schematics
Research Paper Analysis: Helps knowledge workers analyze and extract insights from large collections of research papers and reports, reducing research time by 70%
Enterprise Knowledge Management: Provides organizations with a secure, scalable solution for managing and retrieving information from their internal documents and knowledge bases
Pros
Open-source with active community support
Superior performance in technical and domain-specific search
Comprehensive multimodal understanding capabilities
Enterprise-ready with built-in security features
Cons
Certain features not available in open-source version
Requires PostgreSQL with pgvector extension
Limited to Python 3.12 compatibility
How to Use Morphik
Install Prerequisites: Install PostgreSQL and pgvector using Homebrew on macOS. Create a database named 'morphik' and a superuser named 'postgres'. Set up a virtual environment using venv, uv or poetry to manage dependencies.
Set Up Server: Follow the Getting Started guide to run the Morphik server. Verify the server is running by checking the [api] section in morphik.toml file in the morphik-core repository root. Access docs at http://localhost:8000/docs
Install UI Component: Install NVM, Node.js LTS version, and update npm. Run 'npm run dev' to start the UI server. Access the UI at http://localhost:3000
Configure Environment: Set up environment variables for secrets and API keys. Configure models in morphik.toml file. For memory constraints, either increase Docker memory allocation, use a smaller model, or switch to OpenAI API
Ingest Data: Import your documents (text, PDFs, images, videos) into Morphik. The system will automatically process and embed the content while preserving context from diagrams and visuals
Create Knowledge Graph (Optional): Review document collection and identify domains for relationship-aware retrieval. Use EntityResolver class for entity extraction and resolution. Customize entity recognition with examples
Set Up MCP Integration (Optional): Install @morphik/mcp package to enable AI assistants like Claude to access your Morphik knowledge base. Configure MCP servers in your environment
Use the System: Query your data through the UI, SDK, or REST API. Utilize features like multimodal search, metadata extraction, and cache-augmented generation for faster responses
Morphik FAQs
Morphik is an open-source multi-modal RAG (Retrieval Augmented Generation) platform for building AI applications over private knowledge, allowing users to ingest, search, transform, and manage unstructured and multimodal documents.
Morphik Video
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Analytics of Morphik Website
Morphik Traffic & Rankings
6.5K
Monthly Visits
#2745913
Global Rank
-
Category Rank
Traffic Trends: Feb 2025-Apr 2025
Morphik User Insights
00:01:36
Avg. Visit Duration
2.11
Pages Per Visit
65.97%
User Bounce Rate
Top Regions of Morphik
US: 100%
Others: 0%
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