83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
from pydantic import BaseModel, Field
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from pydantic_ai import Agent, RunContext
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from haiku.rag.client import HaikuRAG
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from haiku.rag.config import Config
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from haiku.rag.config.models import ModelConfig
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from haiku.rag.graph.common import get_model
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from haiku.rag.qa.prompts import QA_SYSTEM_PROMPT, QA_SYSTEM_PROMPT_WITH_CITATIONS
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class ToolSearchResult(BaseModel):
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"""Search result model exposed to the LLM tool."""
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content: str = Field(description="The document text content")
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score: float = Field(description="Relevance score (higher is more relevant)")
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document_uri: str = Field(description="The URI/path of the source document")
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document_title: str | None = Field(
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default=None, description="The title of the document (if available)"
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)
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page_numbers: list[int] = Field(
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default=[], description="Page numbers where this content appears"
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)
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headings: list[str] | None = Field(
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default=None, description="Section heading hierarchy for this content"
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)
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class Dependencies(BaseModel):
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model_config = {"arbitrary_types_allowed": True}
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client: HaikuRAG
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class QuestionAnswerAgent:
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def __init__(
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self,
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client: HaikuRAG,
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model_config: ModelConfig,
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use_citations: bool = False,
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q: float = 0.0,
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system_prompt: str | None = None,
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):
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self._client = client
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if system_prompt is None:
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system_prompt = (
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QA_SYSTEM_PROMPT_WITH_CITATIONS if use_citations else QA_SYSTEM_PROMPT
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)
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model_obj = get_model(model_config, Config)
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self._agent = Agent(
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model=model_obj,
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deps_type=Dependencies,
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system_prompt=system_prompt,
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retries=3,
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)
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@self._agent.tool
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async def search_documents(
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ctx: RunContext[Dependencies],
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query: str,
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limit: int = 5,
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) -> list[ToolSearchResult]:
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"""Search the knowledge base for relevant documents."""
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results = await ctx.deps.client.search(query, limit=limit)
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results = await ctx.deps.client.expand_context(results)
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return [
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ToolSearchResult(
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content=r.content,
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score=r.score,
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document_uri=(r.document_uri or ""),
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document_title=r.document_title,
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page_numbers=r.page_numbers,
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headings=r.headings,
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)
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for r in results
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]
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async def answer(self, question: str) -> str:
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"""Answer a question using the RAG system."""
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deps = Dependencies(client=self._client)
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result = await self._agent.run(question, deps=deps)
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return result.output
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