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`.
228 lines
7.9 KiB
Python
228 lines
7.9 KiB
Python
from dataclasses import dataclass, field
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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 import Field
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from pydantic_ai import RunContext, ToolFailed
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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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CodeExecutionEntry,
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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.capabilities._tools import merge_results
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from haiku.rag.config.models import AppConfig
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from haiku.rag.sandbox import AnalysisContext, Sandbox
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STATE_NAMESPACE = "analysis"
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_CAPABILITY_ID = "haiku-rag-analysis"
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_TOOL_NAMES = frozenset({"analysis_search", "analysis_execute_code", "analysis_cite"})
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_instructions_path = Path(__file__).parent / "instructions" / "analysis.md"
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class AnalysisState(EvidenceState):
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executions: list[CodeExecutionEntry] = Field(default_factory=list)
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def begin_invocation(self) -> None:
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super().begin_invocation()
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self.executions.clear()
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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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def _recovery_hint(stderr: str) -> str:
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"""Name the workaround for sandbox limits models trip over repeatedly.
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The instructions already say file objects are not iterable, and models write
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``for line in open(...)`` regardless. Carrying the fix in the error gives
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them something to act on for the retry.
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"""
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if "TextIOWrapper" in stderr and "not iterable" in stderr:
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return (
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"\n\nHint: file objects cannot be iterated here. Read lines with "
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'.readlines() or .read().split("\\n").'
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)
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return ""
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@dataclass
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class AnalysisCapability(RAGCapabilityBase[AnalysisState]):
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"""Deferred capability for sandboxed computation over a RAG corpus."""
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sandbox: Sandbox | None = field(default=None, repr=False)
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execute_count: int = field(default=0, repr=False)
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async def for_run(self, ctx: RunContext[Any]) -> "AnalysisCapability":
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capability = await super().for_run(ctx)
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assert isinstance(capability, AnalysisCapability)
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capability.sandbox = None
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capability.execute_count = 0
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return capability
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async def _ensure_sandbox(self) -> Sandbox:
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if self.sandbox is None:
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rag = await self._ensure_rag()
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assert self.state is not None
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self.sandbox = Sandbox(
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db_path=self.db_path,
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config=self.config,
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context=AnalysisContext(filter=self.state.document_filter),
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rag=rag,
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lock=self.rag_lock,
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)
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return self.sandbox
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async def _close(self) -> None:
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if self.sandbox is not None:
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await self.sandbox.close()
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self.sandbox = None
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await super()._close()
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def evidence_tool_names(self) -> set[str]:
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# Searching from inside the sandbox does not count against
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# `qa.max_searches`, so code execution outlives a spent search budget
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# as a way to reach new evidence.
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return super().evidence_tool_names() | {"analysis_execute_code"}
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def _spent_tool_names(self) -> set[str]:
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spent = super()._spent_tool_names()
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if self.execute_count >= self.config.analysis.max_executions:
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spent.add("analysis_execute_code")
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return spent
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async def _execute_code(self, code: str) -> str:
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assert self.state is not None
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self.execute_count += 1
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if self.execute_count > self.config.analysis.max_executions:
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raise ToolFailed(
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"Code-execution limit reached. Give your final answer now from what "
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"you already have; do not call analysis_execute_code again."
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)
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sandbox = await self._ensure_sandbox()
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result = await sandbox.execute(code)
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if result.success or result.stdout:
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self._note_evidence()
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if sandbox._search_results:
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merge_results(
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self.state.searches.setdefault("_sandbox", []),
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sandbox._search_results,
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)
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self.state.executions.append(
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CodeExecutionEntry(
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code=code,
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stdout=result.stdout,
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stderr=result.stderr,
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success=result.success,
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)
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)
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if not result.success:
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raise ToolFailed(
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f"{result.stderr}{_recovery_hint(result.stderr)}"
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f"\n\nOutput: {result.stdout}"
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)
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return result.stdout or "No output."
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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 = 30,
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vision: bool | None = None,
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) -> "AnalysisCapability":
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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 analysis_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 for evidence to analyze."""
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return await self._with_state(self._search(query, limit))
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async def analysis_execute_code(ctx: RunContext[Any], code: str) -> Any:
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"""Execute Python against the sandboxed document filesystem."""
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return await self._with_state(self._execute_code(code))
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async def analysis_cite(ctx: RunContext[Any], chunk_ids: list[str]) -> Any:
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"""Register exact retrieved 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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[analysis_search, analysis_execute_code, analysis_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 = 30,
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vision: bool | None = None,
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) -> AnalysisCapability:
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"""Create a native Pydantic AI analysis 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.analysis.model.vision`` (falling back to
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``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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analysis_model = config.analysis.model or config.qa.model
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return AnalysisCapability(
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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=AnalysisState,
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state_namespace=STATE_NAMESPACE,
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instruction_text=instructions(),
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vision=analysis_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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"Analyze the haiku.rag corpus with search and sandboxed Python code. "
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"Use for counting, aggregation, statistics, data traversal, comparison "
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"across documents, and other tasks best solved by writing Python code."
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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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"AnalysisCapability",
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"AnalysisState",
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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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