85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
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from typing import Any, Dict, List
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from fastapi import APIRouter, Depends
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from sqlalchemy.orm import Session
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from app.core.config import settings
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from app.core.security import get_current_user
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from app.db.session import get_db
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from app.models.knowledge import Document, KnowledgeBase
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from app.models.user import User
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from app.schemas.testing import TestingPipelineRequest, TestingPipelineResponse
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from app.services.embedding.embedding_factory import EmbeddingsFactory
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from app.services.retrieval.multi_kb_retriever import MultiKBRetriever, format_retrieval_context
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from app.services.testing_pipeline import run_testing_pipeline
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from app.services.vector_store import VectorStoreFactory
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router = APIRouter()
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async def _build_kb_vector_stores(db: Session, knowledge_bases: List[KnowledgeBase]) -> List[Dict[str, Any]]:
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embeddings = EmbeddingsFactory.create()
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kb_vector_stores: List[Dict[str, Any]] = []
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for kb in knowledge_bases:
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documents = db.query(Document).filter(Document.knowledge_base_id == kb.id).all()
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if not documents:
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continue
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store = VectorStoreFactory.create(
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store_type=settings.VECTOR_STORE_TYPE,
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collection_name=f"kb_{kb.id}",
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embedding_function=embeddings,
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)
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kb_vector_stores.append({"kb_id": kb.id, "store": store})
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return kb_vector_stores
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@router.post("/generate", response_model=TestingPipelineResponse)
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async def generate_testing_content(
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*,
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payload: TestingPipelineRequest,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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) -> Any:
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_ = current_user
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knowledge_context = (payload.knowledge_context or "").strip()
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if payload.knowledge_base_ids:
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knowledge_bases = (
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db.query(KnowledgeBase)
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.filter(
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KnowledgeBase.id.in_(payload.knowledge_base_ids),
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KnowledgeBase.user_id == current_user.id,
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)
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.all()
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)
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kb_vector_stores = await _build_kb_vector_stores(db, knowledge_bases)
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if kb_vector_stores:
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retriever = MultiKBRetriever(
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reranker_weight=settings.RERANKER_WEIGHT,
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)
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retrieval_rows = await retriever.retrieve(
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query=payload.requirement_text,
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kb_vector_stores=kb_vector_stores,
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fetch_k_per_kb=max(12, payload.retrieval_top_k * 2),
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top_k=payload.retrieval_top_k,
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)
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if retrieval_rows:
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knowledge_context = format_retrieval_context(retrieval_rows)
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result = run_testing_pipeline(
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user_requirement_text=payload.requirement_text,
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requirement_type_input=payload.requirement_type,
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debug=payload.debug,
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knowledge_context=knowledge_context,
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use_model_generation=payload.use_model_generation,
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max_items_per_group=payload.max_items_per_group,
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cases_per_item=payload.cases_per_item,
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max_focus_points=payload.max_focus_points,
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max_llm_calls=payload.max_llm_calls,
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)
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return result
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