56 lines
2.2 KiB
Python
56 lines
2.2 KiB
Python
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from typing import Protocol
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from app.core.config import get_settings
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from app.core.contracts import RequestContext
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from app.core.knowledge_contracts import KnowledgeQuery, KnowledgeSearchResult
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from app.infrastructure.db import SessionFactory
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from app.infrastructure.milvus_knowledge_adapter import MilvusKnowledgeClient
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from app.service.knowledge_authority import KnowledgeMysqlAuthority
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from app.service.knowledge_config import KnowledgeRuntimeConfig
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from app.service.knowledge_retrieval_service import KnowledgeRetrievalService
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from app.service.model_gateway import DatabaseModelGateway
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class EmbeddingGateway(Protocol):
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async def embed(self, *, endpoint_code: str, text: str, timeout_ms: int) -> list[float]: ...
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class DatabaseEmbeddingAdapter:
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def __init__(
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self, endpoint_code: str, timeout_ms: int, *, gateway: EmbeddingGateway
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) -> None:
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self._endpoint_code = endpoint_code
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self._timeout_ms = timeout_ms
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self._gateway = gateway
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async def embed(self, text: str) -> list[float]:
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return await self._gateway.embed(
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endpoint_code=self._endpoint_code, text=text, timeout_ms=self._timeout_ms
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)
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async def query_knowledge_tool(
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arguments: KnowledgeQuery, context: RequestContext
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) -> KnowledgeSearchResult:
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settings = get_settings()
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if not settings.knowledge_embedding_endpoint_code:
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return KnowledgeSearchResult(
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degraded=True, degradation_reason="embedding_endpoint_unconfigured"
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)
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# 知识向量端点与默认聊天端点隔离，避免回答模型被误用于检索。
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embedder = DatabaseEmbeddingAdapter(
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settings.knowledge_embedding_endpoint_code,
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settings.knowledge_embedding_timeout_ms,
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gateway=DatabaseModelGateway(),
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)
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vector_store = MilvusKnowledgeClient(
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settings.milvus_uri, token=settings.milvus_token or None
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)
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# 权威元数据只读回查，确保对客答案始终来自已发布、有效的知识条目。
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async with SessionFactory() as session:
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authority = KnowledgeMysqlAuthority(session)
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service = KnowledgeRetrievalService(
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embedder, vector_store, KnowledgeRuntimeConfig(), authority
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)
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return await service.search(arguments, context)
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