Solution: Utilizing ColPali Multimodal Embedding Technology
While traditional retrieval systems often treat graphic content in a fragmented manner, Morphik Core's ColPali technology enables federated retrieval through the following steps:
- pretreatment stage: Use
ingest_file()When importing a file adduse_colpali=Trueparameter, the system automatically parses the visual elements (diagrams/images) in the document with the corresponding descriptive text to generate the joint embedding vector. - retrieval stage: Implementation
retrieve_chunks()When querying, the system will match both textual semantic and visual features. For example, a query for "Sales Trend Chart" matches both the textual description and recognizes line graph features. - Optimization Tips: 1) Adding an image-intensive document to
metadata={'content_type':'multimodal'}Elevate the processing priority 2) PasskParameters control the number of returned results balancing accuracy and efficiency.
Experimental data show that the method improves the accuracy of mixed graphic and text retrieval by 47%, and the response time is controlled within 800ms (million-level document size).
This answer comes from the articleMorphik Core: an open source RAG platform for processing multimodal dataThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of efficient retrieval of multimodal documents (e.g., PDF with graphics)?
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