feat(agent-zero): add corpus_search + intranet_search to cluster configmaps
- Add corpus_search.py to bluejay-tools-c: semantic vector search over fleet SQLite-vec DBs (fleet-workstation-full, fleet-pi-edge, fleet-bmo-bot). Returns offline-friendly results for Bible/Greek/Hebrew/Strongs corpora. Cluster pod degrades gracefully (no DB mounted yet — BLUEJAY-WS only for now). - Add intranet_search.py to bluejay-tools-c: live RAG search over the intranet vector store via GET /api/search?q=...&topK=N. Uses in-cluster service URL (http://intranet-web.intranet.svc:5300) to bypass Traefik TLS and the private-range egress denylist. - Fix intranet_search.py param name: was 'limit', now 'topK' matching the SearchController's [FromQuery] parameter name. - NetworkPolicy: add egress rule for intranet namespace port 5300 (without this the pod's TCP connection to the search endpoint was dropped). - agent-zero.yaml: set FLOWERCORE_INTRANET_URL env var to in-cluster service URL so intranet_search uses internal routing, not the public Traefik VIP. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Andrew Stoltz
@@ -412,6 +412,12 @@ spec:
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secretKeyRef:
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name: print-web-api-keys
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key: password
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# Intranet search — use in-cluster HTTP (no step-ca TLS needed)
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# corpus_search.py reads FLOWERCORE_FLEET_VECTOR_DIR but that mount is not
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# on the cluster yet (BLUEJAY-WS only). The tool gracefully returns a
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# "no DB found" message with rebuild instructions rather than crashing.
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- name: FLOWERCORE_INTRANET_URL
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value: "http://intranet-web.intranet.svc:5300"
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# Kubernetes
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- name: KUBERNETES_SERVICE_HOST
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value: "kubernetes.default.svc"
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@@ -624,6 +630,15 @@ spec:
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protocol: TCP
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- port: 8080
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protocol: TCP
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# Intranet search API — use in-cluster svc so traffic stays inside
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# the cluster and is not blocked by the private-range egress denylist.
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- to:
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- namespaceSelector:
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matchLabels:
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kubernetes.io/metadata.name: intranet
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ports:
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- port: 5300
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protocol: TCP
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# Allow internet (for kubectl image pull, etc)
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- to:
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- ipBlock:
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@@ -13158,6 +13158,445 @@ metadata:
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---
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apiVersion: v1
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data:
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corpus_search.py: |
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# FlowerCore Fleet Corpus Vector Search Tool
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#
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# Queries the AiStation-built SqliteVecVectorStore DB at /a0/usr/vectors/fleet.db
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# (bind-mounted read-only from /var/lib/flowercore/vector-stores/ on the host).
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# Embeds the query through Ollama's nomic-embed-text model, computes cosine
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# similarity against every stored chunk in pure Python (no numpy — not present
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# in the container), and returns the top-K nearest neighbors with source metadata.
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#
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# This is the offline-friendly counterpart to `intranet_search` (which hits the
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# Intranet's live REST API). Use it for Bible/Greek/Hebrew/Strong's lookups and
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# anywhere the workstation has a newer DB than the Intranet one. The store is
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# refreshed by `aistation-indexer build <edition>` — see the FlowerCore.Knowledge
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# ADR at docs/ai-agents/flowercore-knowledge-service-plan.md.
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import json
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import math
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import os
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import sqlite3
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import urllib.request
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from pathlib import Path
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from python.helpers.tool import Tool, Response
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DEFAULT_VECTORS_DIR = os.environ.get(
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"FLOWERCORE_FLEET_VECTOR_DIR",
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"/a0/usr/vectors",
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)
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# When the caller doesn't pick an explicit DB, prefer the biggest fleet tier
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# present on disk. Workstation → pi-edge → bmo-bot.
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PREFERRED_DB_ORDER = [
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os.environ.get("FLOWERCORE_FLEET_VECTOR_DB", ""),
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"fleet-workstation-full.db",
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"fleet-pi-edge.db",
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"fleet-bmo-bot.db",
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]
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OLLAMA_BASE_URL = os.environ.get(
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"FLOWERCORE_AGENTZERO_OLLAMA_URL",
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"http://host.containers.internal:11434",
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)
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EMBEDDING_MODEL = os.environ.get(
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"FLOWERCORE_FLEET_EMBEDDING_MODEL",
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"nomic-embed-text",
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)
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class CorpusSearch(Tool):
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async def execute(self, **kwargs) -> Response:
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"""
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Semantic search over the FlowerCore fleet corpus (Bible texts, lexicons,
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dictionaries, morphology) pre-indexed by aistation-indexer.
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Args (via self.args):
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query (str): Search query text. Required unless action=stats.
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limit (int): Max results. Default 8.
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index (str): Optional index name filter ("bible-texts", "lexicons",
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"dictionaries", "morphology"). Default: all indexes.
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repo (str): Optional repo filter (e.g. "world-english-bible").
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db (str): Override DB path OR file name inside FLOWERCORE_FLEET_VECTOR_DIR
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(defaults to /a0/usr/vectors). If omitted, the largest
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fleet tier present on disk is picked automatically.
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action (str): Optional. "stats" returns an inventory of all fleet DBs
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visible to the tool (names, sizes, index counts, chunk
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counts, last-built timestamps). No embedding call.
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Returns:
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Response with ranked chunks (score, source, text preview) OR
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(when action=stats) a markdown inventory of available fleet DBs.
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"""
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query = (self.args.get("query") or "").strip()
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limit = int(self.args.get("limit") or 8)
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index_filter = (self.args.get("index") or "").strip()
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repo_filter = (self.args.get("repo") or "").strip()
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db_override = (self.args.get("db") or "").strip()
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action = (self.args.get("action") or "").strip().lower()
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if action == "stats":
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return Response(message=_render_stats(), break_loop=False)
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if not query:
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return Response(
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message=(
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"Error: 'query' is required unless action=stats.\n"
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"Example: query=\"what does Genesis 1:1 say\" limit=5\n"
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"Inventory: action=stats"
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),
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break_loop=False,
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)
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db = _resolve_db(db_override)
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if db is None:
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return Response(
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message=(
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f"Error: no fleet vector DB found under {DEFAULT_VECTORS_DIR}.\n"
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"Host side: run `aistation-indexer build fleet-workstation-full`\n"
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"(or `fleet-pi-edge`/`fleet-bmo-bot`) to produce\n"
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"`/var/lib/flowercore/vector-stores/<slug>.db`, then confirm the\n"
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"Podman unit mounts that directory into `/a0/usr/vectors:ro`."
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),
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break_loop=False,
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)
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try:
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query_vec = _embed(query)
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except Exception as e:
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return Response(
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message=f"Error: failed to embed query via Ollama at {OLLAMA_BASE_URL}: {e}",
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break_loop=False,
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)
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try:
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hits = _search(db, query_vec, index_filter, repo_filter, limit)
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except Exception as e:
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return Response(
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message=f"Error: corpus search failed: {e}",
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break_loop=False,
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)
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if not hits:
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return Response(
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message=(
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f"No matches for '{query}' in {db.name}.\n"
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f"Indexes available: " + _list_indexes_summary(db)
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),
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break_loop=False,
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)
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lines = [f"**Corpus search: `{query}`** (top {len(hits)} of {limit} requested, DB={db.name})", ""]
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for rank, h in enumerate(hits, 1):
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passage = h.get("passage") or ""
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lang = h.get("language") or ""
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meta_bits = [x for x in (h["index"], h["repo"], passage, lang) if x]
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meta = " · ".join(meta_bits)
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preview = h["text"]
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if len(preview) > 320:
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preview = preview[:320].rstrip() + "…"
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lines.append(f"{rank}. **{h['score']:.3f}** {meta}")
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lines.append(f" `{h['source']}`")
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lines.append(f" {preview}")
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lines.append("")
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return Response(message="\n".join(lines).rstrip() + "\n", break_loop=False)
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def _resolve_db(override: str) -> "Path | None":
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"""Pick a fleet DB by explicit path, explicit filename, or preferred order."""
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vectors_dir = Path(DEFAULT_VECTORS_DIR)
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if override:
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# Absolute or relative path that points at a real file wins outright.
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p = Path(override)
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if p.is_absolute() and p.exists():
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return p
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# Otherwise treat it as a filename within the vectors dir.
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candidate = vectors_dir / override
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if candidate.exists():
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return candidate
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return None
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for name in PREFERRED_DB_ORDER:
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if not name:
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continue
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p = Path(name) if Path(name).is_absolute() else vectors_dir / name
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if p.exists():
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return p
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# Fallback: any *.db in the dir, largest first.
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if vectors_dir.is_dir():
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candidates = sorted(vectors_dir.glob("*.db"), key=lambda p: p.stat().st_size, reverse=True)
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if candidates:
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return candidates[0]
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return None
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def _embed(text: str) -> list:
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"""Embed a query via Ollama's /api/embeddings. Single-vector response."""
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body = json.dumps({"model": EMBEDDING_MODEL, "prompt": text}).encode("utf-8")
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req = urllib.request.Request(
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f"{OLLAMA_BASE_URL.rstrip('/')}/api/embeddings",
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data=body,
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headers={"Content-Type": "application/json"},
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)
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with urllib.request.urlopen(req, timeout=60) as resp:
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data = json.loads(resp.read().decode("utf-8"))
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vec = data.get("embedding")
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if not isinstance(vec, list) or not vec:
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raise RuntimeError(f"Ollama returned no embedding: {data}")
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return [float(x) for x in vec]
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def _cosine(a: list, b: list) -> float:
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"""Cosine similarity in pure Python — no numpy in the A0 container."""
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# zip() stops at the shorter — AiStation DB guarantees same dim per index.
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dot = 0.0
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na = 0.0
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nb = 0.0
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for x, y in zip(a, b):
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dot += x * y
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na += x * x
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nb += y * y
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if na == 0.0 or nb == 0.0:
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return 0.0
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return dot / (math.sqrt(na) * math.sqrt(nb))
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def _search(db_path: Path, query_vec: list, index_filter: str, repo_filter: str, limit: int) -> list:
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"""Load entries, compute cosine, return top-K.
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SqliteVecVectorStore schema:
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VectorIndexes(IndexName, Dimensions, UpdatedAtUtc)
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VectorEntries(IndexName, ChunkId, TextContent, SourceRepo, SourceFile,
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Book, Chapter, VerseRange, Language, ContentType, License,
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EstimatedTokens, EmbeddingJson)
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Embeddings are stored as JSON arrays in EmbeddingJson; similarity is computed
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in Python. For ~100k chunks × 768 dims this takes a couple seconds on a
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workstation — acceptable for interactive A0 use.
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"""
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conn = sqlite3.connect(f"file:{db_path}?mode=ro", uri=True)
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try:
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sql = [
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"SELECT IndexName, ChunkId, TextContent, SourceRepo, SourceFile, ",
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" Book, Chapter, VerseRange, Language, EmbeddingJson ",
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"FROM VectorEntries",
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]
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where = []
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params = []
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if index_filter:
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where.append("IndexName = ?")
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params.append(index_filter)
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if repo_filter:
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where.append("SourceRepo LIKE ?")
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params.append(f"%{repo_filter}%")
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if where:
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sql.append(" WHERE " + " AND ".join(where))
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sql.append(";")
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cursor = conn.execute("".join(sql), params)
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# Min-heap by (score, ...) would be faster but for interactive use we
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# just sort at the end — simpler and readable.
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scored = []
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for row in cursor:
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idx, chunk_id, text, repo, source_file, book, chapter, verses, lang, emb_json = row
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try:
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vec = json.loads(emb_json)
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except (json.JSONDecodeError, TypeError):
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continue
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score = _cosine(query_vec, vec)
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passage = None
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if book and chapter:
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passage = f"{book} {chapter}"
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if verses:
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passage += f":{verses}"
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scored.append((score, {
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"index": idx,
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"chunk_id": chunk_id,
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"text": text,
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"repo": repo or "",
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"source": source_file or "",
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"passage": passage or "",
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"language": lang or "",
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}))
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scored.sort(key=lambda t: t[0], reverse=True)
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return [{"score": s, **meta} for s, meta in scored[:limit]]
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finally:
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conn.close()
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def _render_stats() -> str:
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"""Markdown inventory of every *.db in FLOWERCORE_FLEET_VECTOR_DIR."""
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vectors_dir = Path(DEFAULT_VECTORS_DIR)
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if not vectors_dir.is_dir():
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return f"No fleet vector dir mounted at {vectors_dir}. Ask the host operator to build an index with scripts/agent-zero/build-fleet-index.sh."
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dbs = sorted(vectors_dir.glob("*.db"))
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if not dbs:
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return f"No fleet DBs present under {vectors_dir}. Run `scripts/agent-zero/build-fleet-index.sh fleet-workstation-full` on the host."
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lines = [f"**Fleet vector DB inventory** ({vectors_dir})", ""]
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for db in dbs:
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size_mb = db.stat().st_size / (1024 * 1024)
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lines.append(f"### `{db.name}` ({size_mb:.1f} MB)")
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try:
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conn = sqlite3.connect(f"file:{db}?mode=ro", uri=True)
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try:
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idx_rows = conn.execute(
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"SELECT IndexName, Dimensions, UpdatedAtUtc FROM VectorIndexes ORDER BY IndexName;"
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).fetchall()
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if not idx_rows:
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lines.append("- (no indexes registered)")
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else:
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counts = dict(conn.execute(
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"SELECT IndexName, COUNT(*) FROM VectorEntries GROUP BY IndexName;"
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).fetchall())
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for name, dim, updated in idx_rows:
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count = counts.get(name, 0)
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lines.append(f"- **{name}** — {count:,} chunks × {dim}d (built {updated})")
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finally:
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conn.close()
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except Exception as e:
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lines.append(f"- (inspect failed: {e})")
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lines.append("")
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lines.append(f"**Tool defaults:** embedding model `{EMBEDDING_MODEL}`, Ollama at `{OLLAMA_BASE_URL}`. Pick a DB with `db=<filename>`; filter by `index=<name>`/`repo=<substring>`.")
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return "\n".join(lines).rstrip() + "\n"
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def _list_indexes_summary(db_path: Path) -> str:
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try:
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conn = sqlite3.connect(f"file:{db_path}?mode=ro", uri=True)
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try:
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rows = conn.execute(
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"SELECT IndexName, Dimensions, "
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" (SELECT COUNT(*) FROM VectorEntries WHERE VectorEntries.IndexName = VectorIndexes.IndexName) "
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"FROM VectorIndexes ORDER BY IndexName;"
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).fetchall()
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if not rows:
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return "(no indexes)"
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return ", ".join(f"{r[0]}({r[2]}×{r[1]}d)" for r in rows)
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finally:
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conn.close()
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except Exception as e:
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return f"(couldn't list: {e})"
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intranet_search.py: |
|
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# Intranet Vector Search Tool
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# Queries the Blue Jay Lab Intranet's Shared.Indexing RAG corpus over its
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# live REST API (https://intranet.iamworkin.lan/search). Returns ranked chunks
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# with source file paths and scores.
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import json
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||||
import os
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import ssl
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||||
import urllib.parse
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||||
import urllib.request
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||||
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from python.helpers.tool import Tool, Response
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||||
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||||
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||||
INTRANET_BASE_URL = os.environ.get(
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||||
"FLOWERCORE_INTRANET_URL",
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"https://intranet.iamworkin.lan",
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||||
)
|
||||
STEPCA_ROOT_CRT = "/a0/usr/ca/stepca-root.crt"
|
||||
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||||
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||||
def _ssl_ctx() -> ssl.SSLContext:
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||||
ctx = ssl.create_default_context()
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if os.path.exists(STEPCA_ROOT_CRT):
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ctx.load_verify_locations(cafile=STEPCA_ROOT_CRT)
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return ctx
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||||
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||||
|
||||
class IntranetSearch(Tool):
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async def execute(self, **kwargs) -> Response:
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"""
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||||
Search the Blue Jay Lab intranet corpus (docs, project notes, dashboards).
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||||
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||||
Args (via self.args):
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query (str): Search query. Required.
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||||
limit (int): Max chunks to return. Default 8.
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||||
corpus (str): Optional corpus filter (e.g. "notes", "docs").
|
||||
|
||||
Returns:
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||||
Response with ranked chunk text, source path, and score.
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||||
"""
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||||
query = self.args.get("query", "").strip()
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||||
limit = int(self.args.get("limit", 8))
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||||
corpus = self.args.get("corpus", "").strip()
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||||
|
||||
if not query:
|
||||
return Response(
|
||||
message="Error: 'query' is required.",
|
||||
break_loop=False,
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||||
)
|
||||
|
||||
params = {"q": query, "topK": str(limit)}
|
||||
if corpus:
|
||||
params["indexName"] = corpus
|
||||
url = f"{INTRANET_BASE_URL}/api/search?{urllib.parse.urlencode(params)}"
|
||||
|
||||
try:
|
||||
req = urllib.request.Request(url, headers={"Accept": "application/json"})
|
||||
with urllib.request.urlopen(req, timeout=20, context=_ssl_ctx()) as resp:
|
||||
raw = resp.read().decode("utf-8", errors="replace")
|
||||
except Exception as exc:
|
||||
return Response(
|
||||
message=f"Intranet search failed: {exc}\nURL: {url}",
|
||||
break_loop=False,
|
||||
)
|
||||
|
||||
try:
|
||||
data = json.loads(raw)
|
||||
except json.JSONDecodeError:
|
||||
return Response(
|
||||
message=f"Intranet returned non-JSON response:\n{raw[:500]}",
|
||||
break_loop=False,
|
||||
)
|
||||
|
||||
hits = data if isinstance(data, list) else (
|
||||
data.get("results") or data.get("hits") or data.get("chunks") or []
|
||||
)
|
||||
if not hits:
|
||||
return Response(
|
||||
message=f"No intranet results for query: {query!r}",
|
||||
break_loop=False,
|
||||
)
|
||||
|
||||
lines = [f"# Intranet search: {query} ({len(hits)} hits)\n"]
|
||||
for i, hit in enumerate(hits[:limit], 1):
|
||||
src = (
|
||||
hit.get("sourceFile")
|
||||
or hit.get("source")
|
||||
or hit.get("path")
|
||||
or hit.get("file")
|
||||
or "?"
|
||||
)
|
||||
repo = hit.get("sourceRepo") or ""
|
||||
idx = hit.get("indexName") or ""
|
||||
score = hit.get("score") or hit.get("similarity") or ""
|
||||
text = (
|
||||
hit.get("snippet")
|
||||
or hit.get("text")
|
||||
or hit.get("content")
|
||||
or hit.get("chunk")
|
||||
or ""
|
||||
).strip()
|
||||
if len(text) > 600:
|
||||
text = text[:600] + "..."
|
||||
header = f"## [{i}] {repo}/{src}" if repo else f"## [{i}] {src}"
|
||||
if idx:
|
||||
header += f" ({idx})"
|
||||
if score:
|
||||
header += f" score={score:.3f}" if isinstance(score, float) else f" score={score}"
|
||||
lines.append(header)
|
||||
lines.append(text)
|
||||
lines.append("")
|
||||
|
||||
return Response(message="\n".join(lines), break_loop=False)
|
||||
agent.json: |
|
||||
{
|
||||
"title": "Blue Jay",
|
||||
|
||||
Reference in New Issue
Block a user