Fix 法规对话

backend/app/infrastructure/llm/openai_compatible_answer_generator.py

@@ -12,7 +12,6 @@ from app.services.llm.llm_factory import get_llm_client
 # Keep adapter behavior explicit so integration details remain easy to audit.
 
 
-
 PROMPT_TEMPLATES = {
     "default": "你是法规知识问答助手。请仅依据提供的上下文回答；如果上下文不足，明确说明。",
     "compliance_qa": "你是法规合规问答助手。优先引用给定法规原文，回答要准确、克制，并注明依据来源。",
@@ -21,6 +20,17 @@ PROMPT_TEMPLATES = {
 
 class OpenAICompatibleAnswerGenerator(AnswerGenerator):
     """Represent the Open A I Compatible Answer Generator type."""
+
+    @staticmethod
+    def _estimate_tokens(text: str) -> int:
+        """Estimate token count for mixed Chinese/English text.
+
+        Chinese chars are ~1.5 chars/token; ASCII is ~4 chars/token.
+        """
+        chinese = sum(1 for c in text if "一" <= c <= "鿿")
+        other = len(text) - chinese
+        return int(chinese / 1.5 + other / 4) + 1
+
     def _build_messages(
         self,
         *,
@@ -40,9 +50,12 @@ class OpenAICompatibleAnswerGenerator(AnswerGenerator):
                 f"页码: {chunk.page_number}\n"
                 f"内容: {chunk.content}"
             )
-            context_tokens += len(block)
+            block_tokens = self._estimate_tokens(block)
+            if context_tokens + block_tokens > settings.rag_max_context_tokens:
+                break
+            context_tokens += block_tokens
             context_blocks.append(block)
-        context = "\n\n".join(context_blocks)[: settings.rag_max_context_tokens * 4]
+        context = "\n\n".join(context_blocks)
         messages = [{"role": "system", "content": system_prompt}]
         for item in history or []:
             messages.append({"role": item["role"], "content": item["content"]})
@@ -52,7 +65,7 @@ class OpenAICompatibleAnswerGenerator(AnswerGenerator):
                 "content": f"问题：{query}\n\n参考上下文：\n{context}\n\n请在回答后给出简要引用编号。",
             }
         )
-        return messages, min(context_tokens, settings.rag_max_context_tokens)
+        return messages, context_tokens
 
     def _sources(self, chunks: list[RetrievedChunk]) -> list[AnswerSource]:
         """Handle sources for this module for the Open A I Compatible Answer Generator instance."""
@@ -98,7 +111,7 @@ class OpenAICompatibleAnswerGenerator(AnswerGenerator):
             latency_ms=latency_ms,
             retrieved_count=len(retrieved_chunks),
             context_tokens=context_tokens,
-            truncated=False,
+            truncated=len(retrieved_chunks) > len(messages),
             error=response.error,
         )
 
@@ -124,15 +137,18 @@ class OpenAICompatibleAnswerGenerator(AnswerGenerator):
         yield {"event": "sources", "data": sources}
         client = get_llm_client(provider=provider or settings.llm_provider, model=model or settings.llm_model)
         answer_parts: list[str] = []
-        if hasattr(client, "stream_chat"):
-            for chunk in client.stream_chat(messages):
-                answer_parts.append(chunk)
-                yield {"event": "content", "data": chunk}
-        else:
-            response = client.chat(messages)
-            answer_parts.append(response.content)
-            yield {"event": "content", "data": response.content}
-        full_answer = "".join(answer_parts)
+        try:
+            if hasattr(client, "stream_chat"):
+                for chunk in client.stream_chat(messages):
+                    answer_parts.append(chunk)
+                    yield {"event": "content", "data": chunk}
+            else:
+                response = client.chat(messages)
+                answer_parts.append(response.content)
+                yield {"event": "content", "data": response.content}
+        except Exception as exc:
+            yield {"event": "error", "data": str(exc)}
+            return
         yield {
             "event": "done",
             "data": {