0882dc9fed
`client.ask` and `client.analyze` built their capability from a db_path, so the capability opened a second connection to the database the client already had open, once per call. Ownership is now explicit rather than inferred. `rag` stays the connection the capability opened and must close; `borrowed_rag` is a caller's, which `_ensure_rag` prefers and `_close` never touches. Two fields rather than a flag, so closing a borrowed connection is not expressible. `for_run` still clears `rag` per run, since a run owns what it opens. It leaves `borrowed_rag` alone: that connection belongs to the caller and outlives the run.
117 lines
3.8 KiB
Python
117 lines
3.8 KiB
Python
from dataclasses import dataclass
|
|
from functools import cache
|
|
from pathlib import Path
|
|
from typing import TYPE_CHECKING, Any
|
|
|
|
from pydantic import BaseModel, Field
|
|
from pydantic_ai import RunContext
|
|
from pydantic_ai.messages import ToolReturn
|
|
from pydantic_ai.toolsets import FunctionToolset
|
|
|
|
if TYPE_CHECKING:
|
|
from haiku.rag.client import HaikuRAG
|
|
|
|
from haiku.rag.capabilities._base import (
|
|
RAGCapabilityBase,
|
|
resolve_db_path,
|
|
)
|
|
from haiku.rag.capabilities.ledger import CapabilityEvidenceRecord
|
|
from haiku.rag.config.models import AppConfig
|
|
from haiku.rag.store.models.chunk import SearchResult
|
|
from haiku.rag.store.models.citation import Citation
|
|
|
|
AGENT_PREAMBLE = """You are a helpful research assistant powered by haiku.rag, a knowledge base system.
|
|
|
|
CRITICAL RULES:
|
|
1. For greetings or casual chat: respond directly WITHOUT using any tools
|
|
2. NEVER make up information - always use capabilities to get facts from the knowledge base
|
|
3. When a capability returns citations, always include them in your response
|
|
"""
|
|
|
|
STATE_NAMESPACE = "rag"
|
|
_CAPABILITY_ID = "haiku-rag"
|
|
_TOOL_NAMES = frozenset({"rag_search", "rag_cite"})
|
|
_instructions_path = Path(__file__).parent / "instructions" / "rag.md"
|
|
|
|
|
|
class RAGState(BaseModel):
|
|
citation_index: dict[str, Citation] = Field(default_factory=dict)
|
|
citations: list[str] = Field(default_factory=list)
|
|
evidence: CapabilityEvidenceRecord = Field(default_factory=CapabilityEvidenceRecord)
|
|
document_filter: str | None = None
|
|
searches: dict[str, list[SearchResult]] = Field(default_factory=dict)
|
|
|
|
|
|
@cache
|
|
def instructions() -> str:
|
|
return _instructions_path.read_text().strip()
|
|
|
|
|
|
@dataclass
|
|
class RAGCapability(RAGCapabilityBase[RAGState]):
|
|
"""Deferred, native Pydantic AI capability for grounded RAG queries."""
|
|
|
|
def get_toolset(self) -> FunctionToolset[Any]:
|
|
async def rag_search(
|
|
ctx: RunContext[Any], query: str, limit: int | None = None
|
|
) -> str | ToolReturn:
|
|
"""Search the knowledge base using hybrid vector and full-text search."""
|
|
return await self._with_state(self._search(query, limit))
|
|
|
|
async def rag_cite(ctx: RunContext[Any], chunk_ids: list[str]) -> Any:
|
|
"""Register exact search-result chunk IDs as citations for the answer."""
|
|
return await self._with_state(self._cite(chunk_ids))
|
|
|
|
return FunctionToolset(
|
|
[rag_search, rag_cite],
|
|
id=_CAPABILITY_ID,
|
|
max_retries=3,
|
|
sequential=True,
|
|
)
|
|
|
|
|
|
def create_capability(
|
|
db_path: Path | None = None,
|
|
config: AppConfig | None = None,
|
|
*,
|
|
defer_loading: bool = True,
|
|
rag: "HaikuRAG | None" = None,
|
|
request_limit: int | None = 20,
|
|
vision: bool | None = None,
|
|
) -> RAGCapability:
|
|
"""Create a native Pydantic AI RAG capability.
|
|
|
|
``vision`` gates whether picture chunks are attached to search results as
|
|
images, and should reflect the model the hosting agent actually runs.
|
|
Defaults to ``config.qa.model.vision``.
|
|
"""
|
|
if config is None:
|
|
from haiku.rag.config import get_config
|
|
|
|
config = get_config()
|
|
return RAGCapability(
|
|
db_path=resolve_db_path(db_path, config),
|
|
config=config,
|
|
borrowed_rag=rag,
|
|
state_type=RAGState,
|
|
state_namespace=STATE_NAMESPACE,
|
|
instruction_text=instructions(),
|
|
vision=config.qa.model.vision if vision is None else vision,
|
|
tool_names=_TOOL_NAMES,
|
|
request_limit=request_limit,
|
|
id=_CAPABILITY_ID,
|
|
description=(
|
|
"Search the haiku.rag knowledge base and cite evidence for grounded answers."
|
|
),
|
|
defer_loading=defer_loading,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"AGENT_PREAMBLE",
|
|
"RAGCapability",
|
|
"RAGState",
|
|
"STATE_NAMESPACE",
|
|
"create_capability",
|
|
"instructions",
|
|
]
|