knowledge-query

name	knowledge-query
description	Hybrid search (vector + BM25 via RRF + metadata boost) against the pgvector Knowledge base, with optional RAG synthesis. Use when the user asks factual questions that should be grounded in indexed documents (e.g., 'what do we know about X', 'search the knowledge base for Y', '@knowledge <query>'). Pass answer=true to synthesize a narrative response with citations instead of raw snippets.

knowledge-query

Group: Consumption. Hybrid search on pgvector + optional RAG (LLM synthesis with citations).

When to trigger

• "What do we know about X?"
• "Search the knowledge base for Y"
• "@knowledge "
• Any factual question that should be grounded in indexed documents

Arguments

Name	Type	Default	Description
query	str	required	Natural language question
connection	str	first ready	Connection slug (e.g., "academy", "acme")
space	str	null = all	Space slug within the connection
top_k	int	5	How many snippets to return
filters	dict	{}	{unit_id, content_type, topics, date_range}
answer	bool	false	If true, synthesize narrative answer with citations

Workflow

Step 1 — Identify active connection

If connection is not provided, call GET /api/knowledge/connections?status=ready and use the first one. If none ready: return actionable error: "No Knowledge connection configured. Run knowledge-admin action=connect first."

Step 2 — Hybrid search

from dashboard.backend.sdk_client import evo

hits = evo.post(
    "/api/knowledge/v1/search",
    {"query": query, "space": space, "top_k": top_k, "filters": filters},
    headers={"X-Knowledge-Connection": connection},
)

Response: list of {chunk_id, content, document_id, title, content_type, similarity_score, metadata: {page, section, heading_path}}.

Step 3a — Format snippets (if answer=false)

For each hit:

**[{content_type}]** {title} — p.{metadata.page or "?"}
> {content[:300]}...
Score: {similarity_score:.3f}

Separate with ---.

Step 3b — RAG synthesis (if answer=true)

1. Take top-5 snippets
2. Build prompt:

You are a factual assistant. Answer ONLY using the sources below.
Cite each fact with [source:page] right after the claim.
If sources don't cover the question: "The knowledge base contains no information on this."

### Question
{query}

### Sources
[1] {title_1} (p.{page_1}): {content_1}
[2] {title_2} (p.{page_2}): {content_2}
...

### Answer

3. Call Claude Haiku 4.5 via anthropic SDK (ANTHROPIC_API_KEY from .env). Model: claude-haiku-4-5-20251001. Max tokens: 800.
4. Render response + sources block at the end.

Output

• answer=false: markdown list of snippets with scores
• answer=true: narrative answer + sources
• Always: footer Searched {N} chunks in {connection}/{space or "all"} in {elapsed_ms}ms

Actionable failures

• Connection not found → "Connection X does not exist. Run knowledge-admin action=health."
• Space not found → list available spaces
• 0 hits → suggest relaxing filters
• ANTHROPIC_API_KEY missing with answer=true → fallback to raw snippets + warning