- Tool-using playbooks: read-only tool registry (search_memory, search_history, search_documents, list_models, get_time), per-playbook allowlist, agentic loop, and a "running tool" status indicator. - Document ingest / RAG: documents table + chunker + embed/cosine retrieval reusing the existing stack; Documents page (upload/paste, viewer, delete); top-k chunks injected into the system prompt. - Vision chat: attach images, base64 into /api/chat. - Voice I/O: Web Speech dictation + read-aloud (browser-native, no backend). - Chat controls: stop, regenerate, edit-and-resend; num_ctx knob in Settings. - Per-playbook model override. - History polish: per-conversation + bulk ShareGPT export. - Faster ncp start: UI up first, Ollama warms in the background, with a per-phase timing readout. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
139 lines
4.6 KiB
Python
139 lines
4.6 KiB
Python
"""Read-only tools a playbook can call during chat.
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Ollama drives the calling: `/api/chat` with a `tools` param returns
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`message.tool_calls`, and this module is just the registry + dispatch. Every
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tool here only READS local state (SQLite, Ollama) — no side effects. The
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per-playbook allowlist (`PlaybookItem.tools`) is the security boundary; keep the
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registry read-only until the loop is trusted.
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"""
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from __future__ import annotations
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import json
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from typing import Awaitable, Callable
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from .memory.store import store
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from .ollama_manager import get_ollama_manager
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async def _search_memory(query: str = "", **_) -> str:
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q = (query or "").strip().lower()
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hits = [
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{"section": it.section, "text": it.text}
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for it in store.all()
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if not q
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or q in it.text.lower()
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or q in (it.section or "").lower()
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or any(q in t.lower() for t in it.tags)
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]
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return json.dumps(hits[:20])
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async def _search_history(query: str = "", **_) -> str:
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# Hybrid recall: semantic (embeddings) unioned with lexical, falls back to
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# lexical if embeddings are down. Same retrieval the chat endpoint uses.
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convs = await store.semantic_search_conversations(
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query or "", get_ollama_manager().embed, limit=3
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)
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return json.dumps([{"matches": c.get("matches", [])} for c in convs])
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async def _list_models(**_) -> str:
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return json.dumps(await get_ollama_manager().list_models())
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async def _search_documents(query: str = "", **_) -> str:
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hits = await store.search_documents(query or "", get_ollama_manager().embed, limit=3)
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return json.dumps([{"title": h["title"], "text": h["text"]} for h in hits])
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async def _get_time(**_) -> str:
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from datetime import datetime
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return json.dumps({"now": datetime.now().isoformat(timespec="seconds")})
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# name -> (schema, callable). Schema is the OpenAI/Ollama function-tool format.
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REGISTRY: dict[str, tuple[dict, Callable[..., Awaitable[str]]]] = {
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"search_memory": (
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{
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"type": "function",
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"function": {
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"name": "search_memory",
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"description": "Search the user's persistent memory facts. Empty query returns all facts.",
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"parameters": {
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"type": "object",
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"properties": {"query": {"type": "string", "description": "text to match"}},
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},
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},
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},
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_search_memory,
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),
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"search_history": (
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{
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"type": "function",
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"function": {
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"name": "search_history",
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"description": "Search past conversations for exchanges containing the query text.",
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"parameters": {
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"type": "object",
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"properties": {"query": {"type": "string"}},
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"required": ["query"],
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},
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},
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},
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_search_history,
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),
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"list_models": (
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{
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"type": "function",
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"function": {
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"name": "list_models",
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"description": "List the locally installed Ollama models.",
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"parameters": {"type": "object", "properties": {}},
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},
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},
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_list_models,
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),
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"search_documents": (
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{
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"type": "function",
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"function": {
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"name": "search_documents",
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"description": "Search the user's uploaded documents for relevant passages.",
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"parameters": {
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"type": "object",
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"properties": {"query": {"type": "string"}},
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"required": ["query"],
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},
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},
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},
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_search_documents,
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),
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"get_time": (
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{
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"type": "function",
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"function": {
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"name": "get_time",
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"description": "Get the current local date and time.",
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"parameters": {"type": "object", "properties": {}},
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},
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},
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_get_time,
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),
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}
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def schemas_for(names: list[str]) -> list[dict]:
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"""Tool schemas for a playbook's allowlist; unknown names are dropped."""
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return [REGISTRY[n][0] for n in (names or []) if n in REGISTRY]
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async def dispatch(name: str, args: dict | None) -> str:
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"""Run a tool by name. Never raises — returns an error string on failure."""
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entry = REGISTRY.get(name)
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if not entry:
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return json.dumps({"error": f"unknown tool: {name}"})
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try:
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return await entry[1](**(args or {}))
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except Exception as e: # a broken tool must not kill the chat loop
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return json.dumps({"error": f"{name} failed: {e}"})
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