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https://github.com/hansjone/oclaw.git
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oliver
420abac9f1
- Rename platform/ to svc/ to avoid shadowing stdlib platform. - Replace from oclaw.* with from svc/runtime/interfaces; update -m CLI paths. - tests/conftest: prepend repo root to sys.path (no parent-folder package name). - CI: paths and offline_eval script under repo root. - Ops scripts: PYTHONPATH must be repo root for python -m runtime.* (fixes gateway/WhatsApp sidecar startup). - Fix default oclaw.json path in tabular/file attachment limits; stabilize attachment test config. Co-authored-by: Cursor <cursoragent@cursor.com>
96 lines
4.2 KiB
Python
96 lines
4.2 KiB
Python
from __future__ import annotations
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import hashlib
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from typing import Any
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from svc.config.paths import db_path
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from svc.embeddings.embedding_client import build_default_embedding_client
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from svc.persistence.sqlite_store import SqliteStore
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from runtime.tools.base import ToolSpec
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def _chunk_id(source: str, text: str) -> str:
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raw = f"{source}\n{text}".encode("utf-8", errors="ignore")
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return hashlib.sha1(raw).hexdigest()
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def kb_add_tool() -> ToolSpec:
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def handler(args: dict[str, Any]) -> dict[str, Any]:
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try:
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tenant_id = str(args.get("tenant_id") or "").strip()
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user_id = str(args.get("user_id") or "").strip()
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text = str(args.get("text") or "").strip()
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title = str(args.get("title") or "").strip()
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if not tenant_id or not user_id or not text:
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return {"ok": False, "error": "tenant_id, user_id, text are required"}
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source = f"builtin:tenant:{tenant_id}:kb"
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if title:
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source = f"{source}:{title[:48]}"
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cid = _chunk_id(source, text)
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store = SqliteStore(db_path())
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store.upsert_knowledge_chunk(
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chunk_id=cid,
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source=source,
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content=text,
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metadata={"tenant_id": tenant_id, "user_id": user_id, "title": title, "source": source},
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)
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client = build_default_embedding_client()
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emb = client.embed(text[:8000])
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store.upsert_knowledge_embedding(chunk_id=cid, model=emb.model, vector=emb.vector)
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return {"ok": True, "chunk_id": cid, "source": source, "embedding_model": emb.model}
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except Exception as e:
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return {"ok": False, "error": f"{type(e).__name__}: {e}"}
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return ToolSpec(
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name="kb_add",
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description="Add a knowledge snippet for a tenant into the vector knowledge base.",
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parameters={
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"type": "object",
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"properties": {"tenant_id": {"type": "string"}, "user_id": {"type": "string"}, "title": {"type": "string", "description": "Optional title/label."}, "text": {"type": "string", "description": "Knowledge content to store."}},
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"required": ["tenant_id", "user_id", "text"],
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"additionalProperties": False,
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},
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handler=handler,
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tags=frozenset({"productivity", "rag", "write"}),
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)
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def kb_search_tool() -> ToolSpec:
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def handler(args: dict[str, Any]) -> dict[str, Any]:
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try:
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tenant_id = str(args.get("tenant_id") or "").strip()
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query = str(args.get("query") or "").strip()
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limit = int(args.get("limit") or 3)
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if not tenant_id or not query:
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return {"ok": False, "error": "tenant_id and query are required"}
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store = SqliteStore(db_path())
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from runtime.orchestration.memory import retrieve_context
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rows = retrieve_context(store, query, limit=max(1, min(limit, 6)))
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filtered = [r for r in rows if str(r.get("source") or "").startswith(f"builtin:tenant:{tenant_id}:")]
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hits = filtered[: max(1, min(limit, 6))]
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if not hits:
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like_rows = store.search_knowledge(query=query, limit=max(1, min(limit, 6)))
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hits = [r for r in like_rows if str(r.get("source") or "").startswith(f"builtin:tenant:{tenant_id}:")][: max(1, min(limit, 6))]
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refs = []
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for h in hits:
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refs.append({"source": str(h.get("source") or ""), "snippet": str(h.get("content") or "")[:240]})
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return {"ok": True, "hits": refs}
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except Exception as e:
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return {"ok": False, "error": f"{type(e).__name__}: {e}"}
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return ToolSpec(
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name="kb_search",
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description="Search tenant knowledge base and return citations/snippets.",
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parameters={
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"type": "object",
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"properties": {"tenant_id": {"type": "string"}, "query": {"type": "string"}, "limit": {"type": "integer", "default": 3}},
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"required": ["tenant_id", "query"],
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"additionalProperties": False,
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},
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handler=handler,
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tags=frozenset({"productivity", "rag"}),
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)
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__all__ = ["kb_add_tool", "kb_search_tool"]
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