Feat/support multimodal embedding #29115
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# Conflicts: # api/models/dataset.py
# Conflicts: # api/tests/test_containers_integration_tests/tasks/test_add_document_to_index_task.py
crazywoola
reviewed
Dec 8, 2025
crazywoola
reviewed
Dec 8, 2025
# Conflicts: # api/controllers/console/datasets/datasets.py # api/controllers/console/datasets/datasets_segments.py # api/controllers/console/datasets/hit_testing_base.py
QuantumGhost
reviewed
Dec 8, 2025
api/core/rag/index_processor/processor/paragraph_index_processor.py
Outdated
Show resolved
Hide resolved
…g' into feat/support-multimodal-embedding
QuantumGhost
approved these changes
Dec 9, 2025
Chenyl-Sai
reviewed
Dec 11, 2025
| db.session.add(binding) | ||
| db.session.commit() | ||
|
|
||
| # save vector index |
Contributor
There was a problem hiding this comment.
This segment should not be included in the if section
This was referenced Dec 12, 2025
6 tasks
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Summary
Why Build a Multimodal Knowledge Base
As enterprises increasingly rely on internal knowledge systems, the need to retrieve information from large volumes of heterogeneous files continues to grow. These materials often span multiple formats, including text, images, documents, videos, and audio—for example: product photos, illustrated manuals, or reports containing mixed text and graphics.
Traditional embedding models can vectorize certain types of data, but they are usually limited to a single modality. This limitation forces organizations into one of two sub-optimal solutions:
• Building complex cross-modality pipelines, embedding each modality separately and then manually fusing results;
• Restricting applications to a single modality, leaving most of the data’s value untapped.
Furthermore, for content that naturally includes multiple modalities—such as documents containing both text and images—traditional models struggle to capture the deep relationships between modalities, resulting in incomplete understanding.
For these reasons, multimodal embeddings have become essential for enterprises seeking to enhance data comprehension, unify data processing workflows, and overcome the constraints of single-modality systems.
⸻
Supported Multimodal Embedding & Rerank Models (First Release)
AWS Bedrock
1. nova-2-multimodal-embeddings-v1:0
Google Vertex AI
1. multimodalembedding@001
Jina
1. jina-embedding-v4
2. jina-clip-v1
3. jina-clip-v2
4. jina-reranker-m0 (rerank model)
Tongyi (Alibaba Cloud)
1. multimodal-embedding-v1
Screenshots
PR for plugin: langgenius/dify-plugin-daemon#503
PR for SDK: langgenius/dify-plugin-sdks#237
Checklist
dev/reformat(backend) andcd web && npx lint-staged(frontend) to appease the lint gods