Component Vector Retriever

Vector Retriever Component

Provide the retriever capability for an Agentic RAG slot.

Interface

Input RetrievalRequest:

• query: retrieval query
• documents: JSON-compatible documents containing id and text
• top_k: maximum returned documents

The default is top_k=10. The runtime embedding configuration used for the validated BGE run uses BAAI/bge-large-en-v1.5, normalized vectors, and batch_size=8; callers may supply another compatible embedding service.

Output RetrievalResult:

• documents: ranked document dictionaries with cosine score

Execution

Run scripts/component.py:run(inputs, context). The runtime must provide context.embed(texts). When the runtime additionally provides context.search_vector_index(...), the Component uses its persistent corpus index; otherwise it falls back to direct embedding and cosine scoring. This optional acceleration keeps the Skill executable in Claude Code and other Agent environments that only implement the basic embedding contract. This Component performs similarity retrieval only and does not select a model or another Skill.