add rag server

backend/.env.example

@@ -56,8 +56,19 @@ ARK_TIMEOUT_SECONDS=1800
 LOCAL_LLM_SYSTEM_PROMPT= "你是一个测试助手。如果别人问你是谁，你就说你是哈哈哈。"
 LOCAL_LLM_TEMPERATURE=0.3
 
-# 可选 RAG 占位配置
-# 当前首版默认未启用主链路 RAG，如需后续接入，可再填写这两个配置
+# RAG 配置
+# 方式一：火山引擎知识库（语义检索）
+# 设置 VOLC_KB_ENABLED=true 后，每次对话前自动检索知识库并注入上下文
+VOLC_KB_ENABLED=false
+VOLC_KB_NAME=your_collection_name       # 知识库名称（与 VOLC_KB_RESOURCE_ID 二选一）
+VOLC_KB_RESOURCE_ID=                    # 知识库唯一 ID（优先级高于 NAME）
+VOLC_KB_PROJECT=default                 # 知识库所属项目
+VOLC_KB_ENDPOINT=https://api-knowledgebase.mlp.cn-beijing.volces.com
+VOLC_KB_TOP_K=5                         # 检索返回条数
+VOLC_KB_RERANK=false                    # 是否开启 rerank 重排
+VOLC_KB_ATTACHMENT_LINK=false           # 是否返回图片临时链接（图文混合场景开启，链接有效期 10 分钟）
+
+# 方式二：静态上下文占位（VOLC_KB_ENABLED=false 时生效）
 RAG_STATIC_CONTEXT=
 RAG_CONTEXT_FILE=
 

backend/routes/chat_callback.py

@@ -8,6 +8,7 @@ from fastapi import APIRouter, Request
 from fastapi.responses import StreamingResponse
 
 from services.local_llm_service import local_llm_service
+from services.rag_service import rag_service
 from services.scene_service import ensure_custom_llm_authorized, get_custom_llm_callback_settings
 from utils.responses import custom_llm_error_response
 
@@ -57,9 +58,20 @@ async def chat_callback(request: Request):
             status_code=400,
         )
 
+    import time as _time
+    user_query = last_message.get("content", "")
+    _rag_t0 = _time.monotonic()
+    try:
+        rag_context = await rag_service.retrieve(user_query)
+    except Exception as exc:
+        print(f"⚠️ RAG 检索异常，已跳过: {exc}")
+        rag_context = ""
+    print(f"⏱️ RAG 检索耗时: {_time.monotonic() - _rag_t0:.2f}s")
+
     try:
         stream_iterator = local_llm_service.chat_stream(
             history_messages=messages,
+            rag_context=rag_context,
             request_options={
                 "temperature": payload.get("temperature"),
                 "max_tokens": payload.get("max_tokens"),

backend/routes/debug.py

@@ -23,8 +23,10 @@ async def debug_chat(request: DebugChatRequest):
     current_messages.append({"role": "user", "content": request.question})
 
     start_time = time.time()
+    rag_context = await rag_service.retrieve(request.question)
     stream_iterator = local_llm_service.chat_stream(
         history_messages=current_messages,
+        rag_context=rag_context,
     )
 
     def generate_text():

backend/security/signer.py

@@ -47,6 +47,7 @@ class Signer:
         """
         self.region = request_data.get("region", "cn-north-1")
         self.method = request_data.get("method", "POST").upper()
+        self.path = request_data.get("path", "/")
         self.params = request_data.get("params", {})
         self.headers = request_data.get("headers", {})
         self.body = request_data.get("body", {})
@@ -84,7 +85,7 @@ class Signer:
         canonical_request = "\n".join(
             [
                 self.method,
-                "/",
+                self.path,
                 canonical_qs,
                 canonical_headers,
                 signed_headers_str,

backend/services/rag_service.py

@@ -1,30 +1,127 @@
 """
-最小可用的 RAG 服务占位实现。
+RAG 服务。
 
-当前版本支持两种简单来源：
-- RAG_STATIC_CONTEXT：直接写在环境变量中的固定知识
-- RAG_CONTEXT_FILE：读取本地文件全文作为知识上下文
+支持两种模式：
+- VOLC_KB_ENABLED=false（默认）：走旧的静态上下文逻辑（RAG_STATIC_CONTEXT / RAG_CONTEXT_FILE）
+- VOLC_KB_ENABLED=true：调用火山引擎知识库 API 进行语义检索
 
-后续如果要接真正的向量检索，可以直接替换 retrieve 方法实现。
+出错时自动降级为空字符串，不阻断主链路 LLM 调用。
 """
 
 from __future__ import annotations
 
+import json
 from pathlib import Path
 
-from utils.env import env_str
+import httpx
+
+from security.signer import Signer
+from utils.env import env_bool, env_int, env_str
+
+_KB_API_PATH = "/api/knowledge/collection/search_knowledge"
+_KB_SERVICE = "air"
+_KB_REGION = "cn-beijing"
+
+
+def _build_rag_context(result_list: list, with_attachment: bool) -> str:
+    parts = []
+    for i, chunk in enumerate(result_list, start=1):
+        title = chunk.get("chunk_title") or ""
+        content = chunk.get("content") or ""
+        header = f"[{i}] {title}" if title else f"[{i}]"
+        block = f"{header}\n{content}"
+
+        if with_attachment:
+            attachments = chunk.get("chunk_attachment") or []
+            image_links = [a["link"] for a in attachments if a.get("link")]
+            if image_links:
+                block += "\n" + "\n".join(f"![图片]({link})" for link in image_links)
+
+        parts.append(block)
+    return "\n\n".join(parts)
+
+
+async def _retrieve_from_kb(query: str) -> str:
+    access_key = env_str("CUSTOM_ACCESS_KEY_ID")
+    secret_key = env_str("CUSTOM_SECRET_KEY")
+    if not access_key or not secret_key:
+        print("⚠️ RAG: CUSTOM_ACCESS_KEY_ID / CUSTOM_SECRET_KEY 未配置，跳过知识库检索")
+        return ""
+
+    endpoint = env_str("VOLC_KB_ENDPOINT", "https://api-knowledgebase.mlp.cn-beijing.volces.com")
+    top_k = env_int("VOLC_KB_TOP_K", 5)
+    rerank = env_bool("VOLC_KB_RERANK", False)
+    attachment_link = env_bool("VOLC_KB_ATTACHMENT_LINK", False)
+
+    body: dict = {"query": query, "limit": top_k}
+
+    kb_name = env_str("VOLC_KB_NAME")
+    kb_resource_id = env_str("VOLC_KB_RESOURCE_ID")
+    kb_project = env_str("VOLC_KB_PROJECT", "default")
+
+    if kb_resource_id:
+        body["resource_id"] = kb_resource_id
+    elif kb_name:
+        body["name"] = kb_name
+        body["project"] = kb_project
+    else:
+        print("⚠️ RAG: VOLC_KB_NAME 或 VOLC_KB_RESOURCE_ID 未配置，跳过知识库检索")
+        return ""
+
+    post_processing: dict = {}
+    if rerank:
+        post_processing["rerank_switch"] = True
+    if attachment_link:
+        post_processing["get_attachment_link"] = True
+    if post_processing:
+        body["post_processing"] = post_processing
+
+    headers = {"Content-Type": "application/json"}
+    signer = Signer(
+        request_data={
+            "region": _KB_REGION,
+            "method": "POST",
+            "path": _KB_API_PATH,
+            "headers": headers,
+            "body": body,
+        },
+        service=_KB_SERVICE,
+    )
+    signer.add_authorization({"accessKeyId": access_key, "secretKey": secret_key})
+
+    # 签名与发送必须用完全相同的序列化（紧凑无空格）
+    body_bytes = json.dumps(body, separators=(",", ":"), ensure_ascii=False).encode("utf-8")
+    url = endpoint.rstrip("/") + _KB_API_PATH
+    async with httpx.AsyncClient(timeout=30) as client:
+        resp = await client.post(url, headers=headers, content=body_bytes)
+
+    resp_data = resp.json()
+    if resp_data.get("code") != 0:
+        print(f"⚠️ RAG: 知识库检索失败 code={resp_data.get('code')} message={resp_data.get('message')}")
+        return ""
+
+    result_list = resp_data.get("data", {}).get("result_list", [])
+    print(f"✅ RAG: 检索到 {len(result_list)} 条知识片段")
+    return _build_rag_context(result_list, with_attachment=attachment_link)
 
 
 class RagService:
     async def retrieve(self, query: str) -> str:
-        _ = query
+        if env_bool("VOLC_KB_ENABLED", False):
+            try:
+                return await _retrieve_from_kb(query)
+            except Exception as exc:
+                print(f"⚠️ RAG: 知识库检索异常，已降级: {exc}")
+                return ""
+
+        # 旧逻辑：静态上下文
         context_file = env_str("RAG_CONTEXT_FILE")
         if context_file:
             path = Path(context_file).expanduser()
             if path.exists() and path.is_file():
                 return path.read_text(encoding="utf-8")
 
-        return env_str("RAG_STATIC_CONTEXT")
+        return env_str("RAG_STATIC_CONTEXT") or ""
 
 
 rag_service = RagService()