feat(chat): 接入全局知识库上下文

review_agent/llm.py

@@ -16,7 +16,7 @@ class LLMRequestError(RuntimeError):
 logger = logging.getLogger(__name__)
 
 
-def generate_reply(conversation, user_message: str) -> str:
+def generate_reply(conversation, user_message: str, knowledge_context: str = "") -> str:
     """Calls the SiliconFlow OpenAI-compatible chat endpoint and returns assistant text."""
 
     if not settings.LLM_API_KEY:
@@ -26,7 +26,7 @@ def generate_reply(conversation, user_message: str) -> str:
 
     payload = {
         "model": settings.LLM_MODEL,
-        "messages": build_messages(conversation, user_message),
+        "messages": build_messages(conversation, user_message, knowledge_context=knowledge_context),
         "temperature": 0.3,
     }
     body = json.dumps(payload).encode("utf-8")
@@ -98,7 +98,7 @@ def generate_completion(messages: list[dict[str, str]], *, temperature: float =
         raise LLMRequestError("模型接口返回格式不符合预期。") from exc
 
 
-def stream_reply(conversation, user_message: str):
+def stream_reply(conversation, user_message: str, knowledge_context: str = ""):
     """Streams incremental assistant text from the SiliconFlow chat endpoint."""
 
     if not settings.LLM_API_KEY:
@@ -108,7 +108,7 @@ def stream_reply(conversation, user_message: str):
 
     payload = {
         "model": settings.LLM_MODEL,
-        "messages": build_messages(conversation, user_message),
+        "messages": build_messages(conversation, user_message, knowledge_context=knowledge_context),
         "temperature": 0.3,
         "stream": True,
     }
@@ -153,10 +153,21 @@ def stream_reply(conversation, user_message: str):
         raise LLMRequestError(f"模型接口调用失败：{exc.reason}") from exc
 
 
-def build_messages(conversation, latest_user_message: str) -> list[dict[str, str]]:
+def build_messages(conversation, latest_user_message: str, knowledge_context: str = "") -> list[dict[str, str]]:
     """Builds system and conversation history messages for the provider call."""
 
     messages = [{"role": "system", "content": system_prompt()}]
+    if knowledge_context.strip():
+        messages.append(
+            {
+                "role": "system",
+                "content": (
+                    "以下是全局知识库检索到的材料片段。回答用户时优先依据这些片段；"
+                    "如果片段不足以支持结论，请明确说明信息不足，不要编造。\n\n"
+                    f"{knowledge_context.strip()}"
+                ),
+            }
+        )
 
     for message in conversation.messages.all():
         messages.append({"role": message.role, "content": message.content})