Fix SSE route dependency and align architecture docs

backend/app/infrastructure/llm/openai_compatible_answer_generator.py

@@ -0,0 +1,144 @@
+"""Implement infrastructure support for openai compatible answer generator."""
+
+from __future__ import annotations
+
+import time
+from typing import Generator
+
+from app.config.settings import settings
+from app.domain.conversation import AnswerGenerator, AnswerResult, AnswerSource
+from app.domain.retrieval import RetrievedChunk
+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。",
+}
+
+
+class OpenAICompatibleAnswerGenerator(AnswerGenerator):
+    """Represent the Open A I Compatible Answer Generator type."""
+    def _build_messages(
+        self,
+        *,
+        query: str,
+        retrieved_chunks: list[RetrievedChunk],
+        history: list[dict[str, str]] | None,
+        prompt_template: str | None,
+    ) -> tuple[list[dict[str, str]], int]:
+        """Handle build messages for this module for the Open A I Compatible Answer Generator instance."""
+        system_prompt = PROMPT_TEMPLATES.get(prompt_template or "compliance_qa", PROMPT_TEMPLATES["default"])
+        context_blocks = []
+        context_tokens = 0
+        for idx, chunk in enumerate(retrieved_chunks, start=1):
+            block = (
+                f"[{idx}] 文档: {chunk.doc_name}\n"
+                f"章节: {chunk.section_title or '未标注'}\n"
+                f"页码: {chunk.page_number}\n"
+                f"内容: {chunk.content}"
+            )
+            context_tokens += len(block)
+            context_blocks.append(block)
+        context = "\n\n".join(context_blocks)[: settings.rag_max_context_tokens * 4]
+        messages = [{"role": "system", "content": system_prompt}]
+        for item in history or []:
+            messages.append({"role": item["role"], "content": item["content"]})
+        messages.append(
+            {
+                "role": "user",
+                "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)
+
+    def _sources(self, chunks: list[RetrievedChunk]) -> list[AnswerSource]:
+        """Handle sources for this module for the Open A I Compatible Answer Generator instance."""
+        return [
+            AnswerSource(
+                doc_id=chunk.doc_id,
+                doc_name=chunk.doc_name,
+                chunk_id=chunk.chunk_id,
+                section_title=chunk.section_title,
+                page_number=chunk.page_number,
+                score=chunk.score,
+                content=chunk.content,
+                metadata=chunk.metadata,
+            )
+            for chunk in chunks
+        ]
+
+    def generate(
+        self,
+        *,
+        query: str,
+        retrieved_chunks: list[RetrievedChunk],
+        history: list[dict[str, str]] | None = None,
+        provider: str | None = None,
+        model: str | None = None,
+        prompt_template: str | None = None,
+    ) -> AnswerResult:
+        """Handle generate for the Open A I Compatible Answer Generator instance."""
+        start = time.time()
+        messages, context_tokens = self._build_messages(
+            query=query,
+            retrieved_chunks=retrieved_chunks,
+            history=history,
+            prompt_template=prompt_template,
+        )
+        client = get_llm_client(provider=provider or settings.llm_provider, model=model or settings.llm_model)
+        response = client.chat(messages)
+        latency_ms = int((time.time() - start) * 1000)
+        return AnswerResult(
+            answer=response.content if response.is_success else "",
+            sources=self._sources(retrieved_chunks),
+            model=response.model or (model or settings.llm_model),
+            latency_ms=latency_ms,
+            retrieved_count=len(retrieved_chunks),
+            context_tokens=context_tokens,
+            truncated=False,
+            error=response.error,
+        )
+
+    def stream_generate(
+        self,
+        *,
+        query: str,
+        retrieved_chunks: list[RetrievedChunk],
+        history: list[dict[str, str]] | None = None,
+        provider: str | None = None,
+        model: str | None = None,
+        prompt_template: str | None = None,
+    ) -> Generator[dict, None, AnswerResult]:
+        """Stream generate for the Open A I Compatible Answer Generator instance."""
+        start = time.time()
+        messages, context_tokens = self._build_messages(
+            query=query,
+            retrieved_chunks=retrieved_chunks,
+            history=history,
+            prompt_template=prompt_template,
+        )
+        sources = [source.__dict__ for source in self._sources(retrieved_chunks)]
+        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)
+        yield {
+            "event": "done",
+            "data": {
+                "latency_ms": int((time.time() - start) * 1000),
+                "retrieved_count": len(retrieved_chunks),
+                "context_tokens": context_tokens,
+                "model": model or settings.llm_model,
+            },
+        }