增加代码知识库；修复文档处理内容；增加API设置

rag-web-ui/backend/app/services/chat_service.py

@@ -17,6 +17,7 @@ from app.services.fusion_prompts import (
 from app.services.graph.graphrag_adapter import GraphRAGAdapter
 from app.services.intent_router import route_intent
 from app.services.llm.llm_factory import LLMFactory
+from app.services.model_config import ModelConfigService
 from app.services.reranker.external_api import ExternalRerankerClient
 from app.services.retrieval.multi_kb_retriever import MultiKBRetriever, format_retrieval_context
 from app.services.testing_pipeline.pipeline import run_testing_pipeline
@@ -202,8 +203,12 @@ def _build_global_community_context_fallback(rows: List[Dict[str, Any]]) -> str:
     return "\n\n".join(lines)
 
 
-async def _build_kb_vector_stores(db: Any, knowledge_bases: List[KnowledgeBase]) -> List[Dict[str, Any]]:
-    embeddings = EmbeddingsFactory.create()
+async def _build_kb_vector_stores(
+    db: Any,
+    knowledge_bases: List[KnowledgeBase],
+    model_profile: Any,
+) -> List[Dict[str, Any]]:
+    embeddings = EmbeddingsFactory.create(model_profile=model_profile)
     kb_vector_stores: List[Dict[str, Any]] = []
 
     for kb in knowledge_bases:
@@ -221,10 +226,13 @@ async def _build_kb_vector_stores(db: Any, knowledge_bases: List[KnowledgeBase])
     return kb_vector_stores
 
 
-def _build_reranker_client() -> ExternalRerankerClient:
+def _build_reranker_client(model_profile: Any = None) -> ExternalRerankerClient:
+    api_key = settings.RERANKER_API_KEY
+    if model_profile is not None and getattr(model_profile, "provider", "") == "dashscope":
+        api_key = getattr(model_profile, "api_key", "") or api_key
     return ExternalRerankerClient(
         api_url=settings.RERANKER_API_URL,
-        api_key=settings.RERANKER_API_KEY,
+        api_key=api_key,
         model=settings.RERANKER_MODEL,
         timeout_seconds=settings.RERANKER_TIMEOUT_SECONDS,
     )
@@ -287,6 +295,7 @@ async def generate_response(
     knowledge_base_ids: List[int],
     chat_id: int,
     db: Any,
+    user_id: int,
 ) -> AsyncGenerator[str, None]:
     try:
         user_message = Message(content=query, role="user", chat_id=chat_id)
@@ -297,6 +306,9 @@ async def generate_response(
         db.add(bot_message)
         db.commit()
 
+        model_profile = ModelConfigService.require_active_config(db, user_id)
+        ModelConfigService.touch_last_used(db, model_profile)
+
         if _is_testing_generation_request(query):
             explicit_type = _extract_requirement_type_from_query(query)
 
@@ -309,7 +321,7 @@ async def generate_response(
                     .filter(KnowledgeBase.id.in_(knowledge_base_ids))
                     .all()
                 )
-                kb_vector_stores = await _build_kb_vector_stores(db, testing_kbs)
+                kb_vector_stores = await _build_kb_vector_stores(db, testing_kbs, model_profile)
 
             if kb_vector_stores:
                 testing_retriever = MultiKBRetriever(
@@ -330,6 +342,7 @@ async def generate_response(
                 debug=True,
                 knowledge_context=knowledge_context,
                 use_model_generation=True,
+                llm_model=LLMFactory.create(streaming=False, model_profile=model_profile),
                 max_items_per_group=6,
                 cases_per_item=1,
                 max_focus_points=6,
@@ -391,11 +404,11 @@ async def generate_response(
         )
         kb_ids = [kb.id for kb in knowledge_bases]
 
-        llm = LLMFactory.create()
+        llm = LLMFactory.create(model_profile=model_profile)
         decision = await route_intent(llm=llm, query=query, messages=messages)
         intent = decision["intent"]
 
-        kb_vector_stores = await _build_kb_vector_stores(db, knowledge_bases)
+        kb_vector_stores = await _build_kb_vector_stores(db, knowledge_bases, model_profile)
         if intent in {"B", "C", "D"} and not kb_vector_stores:
             intent = "A"
             decision = {
@@ -403,7 +416,7 @@ async def generate_response(
                 "reason": "未发现可用知识库向量集合，已降级为通用对话路。",
             }
 
-        reranker_client = _build_reranker_client()
+        reranker_client = _build_reranker_client(model_profile)
         retriever = MultiKBRetriever(
             reranker_client=reranker_client,
             reranker_weight=settings.RERANKER_WEIGHT,
@@ -432,7 +445,7 @@ async def generate_response(
             used_kb_ids: List[int] = []
             if settings.GRAPHRAG_ENABLED and kb_ids:
                 try:
-                    adapter = GraphRAGAdapter()
+                    adapter = GraphRAGAdapter(model_profile=model_profile)
                     graph_context, used_kb_ids = await adapter.local_context_multi(
                         kb_ids,
                         query,
@@ -465,7 +478,7 @@ async def generate_response(
             community_context = ""
             if settings.GRAPHRAG_ENABLED and kb_ids:
                 try:
-                    adapter = GraphRAGAdapter()
+                    adapter = GraphRAGAdapter(model_profile=model_profile)
                     community_context, used_kb_ids = await adapter.global_context_multi(
                         kb_ids,
                         query,