Agent Skills
› evolution-foundation/evo-nexus
› knowledge-query
knowledge-query
GitHub基于 pgvector 的知识库混合搜索与 RAG 合成技能，支持向量+BM25检索及可选的LLM摘要生成。
Trigger Scenarios
用户询问关于X的事实性问题
要求搜索知识库中的Y
使用 @knowledge <query> 指令
Install
npx skills add evolution-foundation/evo-nexus --skill knowledge-query -g -y
SKILL.md
Frontmatter
{
"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)
- Take top-5 snippets
- 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
- Call Claude Haiku 4.5 via
anthropicSDK (ANTHROPIC_API_KEYfrom.env). Model:claude-haiku-4-5-20251001. Max tokens: 800. - Render response + sources block at the end.
Output
answer=false: markdown list of snippets with scoresanswer=true: narrative answer + sources- Always: footer
Searched {N} chunks in {connection}/{space or "all"} in {elapsed_ms}ms
Actionable failures
- Connection not found → "Connection
Xdoes not exist. Runknowledge-admin action=health." - Space not found → list available spaces
- 0 hits → suggest relaxing filters
ANTHROPIC_API_KEYmissing withanswer=true→ fallback to raw snippets + warning
Version History
- 7f5dd76 Current 2026-07-25 04:55
