Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.
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1. Is this request related to a challenge you're experiencing?
Hello, would it be possible to automatically and comprehensively populate the metadata in the Dify knowledge base when embedding files, using the files' metadata? (see image)
Also, wouldn't it be more practical and relevant to integrate the knowledge graph for retrieval purposes?
Yes, the current manual process of filling in metadata can be time-consuming and error-prone. Additionally, the lack of structured knowledge representation makes it challenging to retrieve comprehensive and accurate information.
2. Describe the feature you'd like to see
Automatic Metadata Population
Automatically and comprehensively populate the metadata in the Dify knowledge base when embedding files, using the files' metadata.
Knowledge Graph Integration
Nodes (Entities): Objects, concepts, or people (e.g., "Tesla," "Elon Musk").
Edges (Relationships): Links between entities (e.g., "CEO_of," "founded").
Properties: Additional attributes for nodes and relationships.
Integrate a knowledge graph for comprehensive and efficient retrieval purposes.
3. How will this feature improve your workflow or experience?
Automatic Metadata Population
Efficiency: Saves time by automating metadata extraction.
Accuracy: Reduces errors and inconsistencies in metadata.
Comprehensive: Ensures all relevant metadata is captured.
Knowledge Graph Integration
Improved Retrieval: Enables more precise and comprehensive search results.
Explainability: Provides logical connections and explanations between entities.
Personalization: Allows for tailored responses based on user preferences.
Contextual Knowledge: Enhances relevance through contextual relationships.
4. Additional context or comments
Advantages of KGs in LLMs
Accuracy and Consistency: Structured information improves response quality.
Explainability: Logical connections between entities.
Personalization: Tailored answers based on user preferences.
Embeddings: Semantic similarity via numerical representations.
Indexing and Search:
Quick search via embeddings.
KGs provide an explanatory layer.
Practical Example
Graph Construction: Create a KG representing user data.
Embedding Creation: Generate embeddings for each entity and relationship.
Vector Database: Store these embeddings in a vector database.
LLM Interaction: Find relevant embeddings, formulate an explainable response, and generate answers.
Conclusion
Knowledge Graphs, combined with LLMs and vector databases, create a powerful search engine that offers structure, explainability, and contextual relevance, improving the quality of generated answers.
5. Can you help us with this feature?
[ ] I am interested in contributing to this feature.
MrBrabus75
commented
1 month ago
Self Checks
1. Is this request related to a challenge you're experiencing?
Hello, would it be possible to automatically and comprehensively populate the metadata in the Dify knowledge base when embedding files, using the files' metadata? (see image)
Also, wouldn't it be more practical and relevant to integrate the knowledge graph for retrieval purposes?
Yes, the current manual process of filling in metadata can be time-consuming and error-prone. Additionally, the lack of structured knowledge representation makes it challenging to retrieve comprehensive and accurate information.
2. Describe the feature you'd like to see
Automatic Metadata Population
Automatically and comprehensively populate the metadata in the Dify knowledge base when embedding files, using the files' metadata.
Knowledge Graph Integration
Integrate a knowledge graph for comprehensive and efficient retrieval purposes.
3. How will this feature improve your workflow or experience?
Automatic Metadata Population
Knowledge Graph Integration
4. Additional context or comments
Advantages of KGs in LLMs
Integration with Vector Databases
Combined Advantages:
Indexing and Search:
Practical Example
Conclusion
Knowledge Graphs, combined with LLMs and vector databases, create a powerful search engine that offers structure, explainability, and contextual relevance, improving the quality of generated answers.
5. Can you help us with this feature?