¶ Knowledge Embeddings
The Knowledge Embeddings feature in the Fuse platform enables semantic indexing and retrieval of knowledge content using high-dimensional vector representations.
¶ Overview
Embeddings allow Fuse to represent the meaning of documents, pages, or entities as numeric vectors. These vectors can then be used to retrieve semantically relevant content — even when there is no exact keyword match.
This capability enhances AI model performance, powers semantic search, and enables contextual prompt injection using meaning-based retrieval instead of raw text matching.
¶ Embedding Scope
Fuse can generate embeddings for:
- Entire Resources
- Individual KnowledgePages or sections
- Specific KnowledgeNodes or graph entities
- Workflow or pipeline outputs
- External documents and imported content
Embeddings are typically generated upon ingestion or on-demand, and can be updated when content or models change.
¶ Key Capabilities
| Capability | Description |
|---|---|
 Semantic Representation |
Converts text or structured knowledge into high-dimensional vectors |
 Vector-Based Retrieval |
Enables semantic similarity search across embedded content |
 Re-embedding |
Supports reprocessing content when embedding models are updated |
 Access-Aware Filtering |
Ensures that retrieved vectors respect resource-level access control |
 Storage Options |
Supports internal and external vector database backends (e.g., FAISS, Qdrant) |
¶ Embedding Lifecycle
- A knowledge artifact (page, node, section) is submitted for indexing
- An embedding model is applied to generate a vector
- The vector is stored and associated with the original object
- The vector becomes part of a searchable embedding index
- AI assistants, pipelines, or queries can retrieve related content by meaning
¶ Integration with Inference
During AI prompt composition:
- A query is embedded
- The vector is compared against indexed embeddings
- Top matches are retrieved and filtered by access and context
- Selected knowledge snippets are injected into the prompt
This enables retrieval-augmented generation (RAG) with governance-aware precision.
¶ Future Extensions
- Multi-model embedding pipelines
- Clustering and visualization of embedded knowledge
- Embedding explainability tools
- Cross-index search across KnowledgeBases or tenants
Next: Knowledge Provider Integration — Learn how to register and implement connectors for external knowledge systems.