Morphik Core is the leading open source tool for handling multimodal Retrieval Augmentation Generation (RAG) tasks through its comprehensive functionality and architectural design. The system natively supports the processing of multiple data formats such as text, PDF, images, video, etc., and uses ColPali multimodal embedding technology to achieve efficient retrieval of cross-modal content. Compared to single-modal processing tools, the core advantage of Morphik Core lies in its unified processing framework, which enables simultaneous parsing and retrieval of different types of document content.
The platform's built-in knowledge graph function significantly improves the accuracy of complex queries by automatically extracting the entity relationship network. Practical tests show that the system's retrieval latency can still be controlled at the second level when dealing with a million document scale. Its adoption of the MCP protocol provides a standardized knowledge sharing interface for AI systems, a design that makes Morphik Core particularly suitable as a data infrastructure for enterprise-level AI applications.
Developers can quickly integrate multimodal RAG functionality based on the Python SDK provided by the project without having to build complex data processing pipelines from scratch. The project is licensed under the MIT open source license and currently supports self-hosted deployments, with hosted cloud service options available in the future.
This answer comes from the articleMorphik Core: an open source RAG platform for processing multimodal dataThe
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