61a756da7f
`Agent.from_spec` raised `TypeError` on `RAGCapability` and `AnalysisCapability`, whose constructors take a state class, packaged instruction text and a tool-name set, and silently omitted both from the generated spec schema. The two zero-configuration capabilities constructed but with `id=None`, so pydantic-ai's duplicate-id rejection no longer held and a spec could register two citation policies, defeating the single-decision-maker invariant. Override `from_spec` on all four, delegating to `create_capability()` so ids and instructions come from one place. The spec surface is `db_path`, `config`, `defer_loading`, `request_limit` and `vision`; a live `HaikuRAG` client stays out of it, and a `config` mapping is validated through `AppConfig`.
133 lines
4.1 KiB
Python
133 lines
4.1 KiB
Python
from dataclasses import dataclass
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from functools import cache
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from pathlib import Path
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from typing import TYPE_CHECKING, Any
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from pydantic_ai import RunContext
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from pydantic_ai.messages import ToolReturn
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from pydantic_ai.toolsets import FunctionToolset
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if TYPE_CHECKING:
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from haiku.rag.client import HaikuRAG
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from haiku.rag.capabilities._base import (
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EvidenceState,
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RAGCapabilityBase,
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resolve_db_path,
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)
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from haiku.rag.config.models import AppConfig
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AGENT_PREAMBLE = """You are a helpful research assistant powered by haiku.rag, a knowledge base system.
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CRITICAL RULES:
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1. For greetings or casual chat: respond directly WITHOUT using any tools
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2. NEVER make up information - always use capabilities to get facts from the knowledge base
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3. When a capability returns citations, always include them in your response
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"""
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STATE_NAMESPACE = "rag"
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_CAPABILITY_ID = "haiku-rag"
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_TOOL_NAMES = frozenset({"rag_search", "rag_cite"})
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_instructions_path = Path(__file__).parent / "instructions" / "rag.md"
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class RAGState(EvidenceState):
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"""The RAG capability carries nothing beyond the shared evidence fields."""
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@cache
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def instructions() -> str:
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return _instructions_path.read_text().strip()
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@dataclass
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class RAGCapability(RAGCapabilityBase[RAGState]):
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"""Deferred, native Pydantic AI capability for grounded RAG queries."""
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@classmethod
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def from_spec(
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cls,
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db_path: Path | None = None,
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config: AppConfig | None = None,
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*,
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defer_loading: bool = True,
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request_limit: int | None = 20,
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vision: bool | None = None,
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) -> "RAGCapability":
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"""Build from an agent spec, mirroring the factory's serializable arguments.
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A live ``HaikuRAG`` client cannot be written in a spec, so ``rag`` is
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absent here. ``config`` arrives as a mapping and is validated.
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"""
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return create_capability(
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db_path,
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AppConfig.model_validate(config) if config is not None else None,
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defer_loading=defer_loading,
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request_limit=request_limit,
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vision=vision,
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)
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def get_toolset(self) -> FunctionToolset[Any]:
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async def rag_search(
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ctx: RunContext[Any], query: str, limit: int | None = None
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) -> str | ToolReturn:
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"""Search the knowledge base using hybrid vector and full-text search."""
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return await self._with_state(self._search(query, limit))
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async def rag_cite(ctx: RunContext[Any], chunk_ids: list[str]) -> Any:
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"""Register exact search-result chunk IDs as citations for the answer."""
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return await self._with_state(self._cite(chunk_ids))
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return FunctionToolset(
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[rag_search, rag_cite],
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id=_CAPABILITY_ID,
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max_retries=3,
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sequential=True,
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)
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def create_capability(
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db_path: Path | str | None = None,
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config: AppConfig | None = None,
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*,
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defer_loading: bool = True,
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rag: "HaikuRAG | None" = None,
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request_limit: int | None = 20,
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vision: bool | None = None,
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) -> RAGCapability:
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"""Create a native Pydantic AI RAG capability.
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``vision`` gates whether picture chunks are attached to search results as
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images, and should reflect the model the hosting agent actually runs.
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Defaults to ``config.qa.model.vision``.
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"""
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if config is None:
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from haiku.rag.config import get_config
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config = get_config()
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return RAGCapability(
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db_path=resolve_db_path(db_path, config),
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config=config,
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borrowed_rag=rag,
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state_type=RAGState,
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state_namespace=STATE_NAMESPACE,
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instruction_text=instructions(),
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vision=config.qa.model.vision if vision is None else vision,
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tool_names=_TOOL_NAMES,
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request_limit=request_limit,
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id=_CAPABILITY_ID,
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description=(
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"Search the haiku.rag knowledge base and cite evidence for grounded answers."
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),
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defer_loading=defer_loading,
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)
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__all__ = [
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"AGENT_PREAMBLE",
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"RAGCapability",
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"RAGState",
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"STATE_NAMESPACE",
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"create_capability",
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"instructions",
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]
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