Adjust the embedding rate to prevent triggering rate limiting.

backend/app/core/config.py

@@ -106,7 +106,15 @@ class Settings(BaseSettings):
     AGENT_TOKEN_BUDGET: int = 100000  # Agent Token 预算
     AGENT_TIMEOUT_SECONDS: int = 1800  # Agent 超时时间（30分钟）
     
+    # 嵌入并发布配置
+    EMBEDDING_CONCURRENCY: int = 2  # 经计算，Batch=10 时并发 2 最接近 1.2M TPM 限流线
+    EMBEDDING_RETRY_MAX: int = 10  # 嵌入模型最大重试次数
+
+
+
+    
     # 沙箱配置（必须）
+
     SANDBOX_IMAGE: str = "deepaudit/sandbox:latest"  # 沙箱 Docker 镜像
     SANDBOX_MEMORY_LIMIT: str = "512m"  # 沙箱内存限制
     SANDBOX_CPU_LIMIT: float = 1.0  # 沙箱 CPU 限制

backend/app/services/rag/embeddings.py

@@ -34,7 +34,7 @@ class EmbeddingProvider(ABC):
         pass
     
     @abstractmethod
-    async def embed_texts(self, texts: List[str]) -> List[EmbeddingResult]:
+    async def embed_texts(self, texts: List[str], client: Optional[httpx.AsyncClient] = None) -> List[EmbeddingResult]:
         """批量嵌入文本"""
         pass
     
@@ -90,7 +90,7 @@ class OpenAIEmbedding(EmbeddingProvider):
         results = await self.embed_texts([text])
         return results[0]
     
-    async def embed_texts(self, texts: List[str]) -> List[EmbeddingResult]:
+    async def embed_texts(self, texts: List[str], client: Optional[httpx.AsyncClient] = None) -> List[EmbeddingResult]:
         if not texts:
             return []
         
@@ -109,20 +109,25 @@ class OpenAIEmbedding(EmbeddingProvider):
         
         url = f"{self.base_url.rstrip('/')}/embeddings"
         
-        async with httpx.AsyncClient(timeout=60) as client:
+        if client:
             response = await client.post(url, headers=headers, json=payload)
             response.raise_for_status()
             data = response.json()
+        else:
+            async with httpx.AsyncClient(timeout=60) as client:
+                response = await client.post(url, headers=headers, json=payload)
+                response.raise_for_status()
+                data = response.json()
             
-            results = []
-            for item in data.get("data", []):
-                results.append(EmbeddingResult(
-                    embedding=item["embedding"],
-                    tokens_used=data.get("usage", {}).get("total_tokens", 0) // len(texts),
-                    model=self.model,
-                ))
-            
-            return results
+        results = []
+        for item in data.get("data", []):
+            results.append(EmbeddingResult(
+                embedding=item["embedding"],
+                tokens_used=data.get("usage", {}).get("total_tokens", 0) // len(texts),
+                model=self.model,
+            ))
+        
+        return results
 
 
 class AzureOpenAIEmbedding(EmbeddingProvider):
@@ -168,7 +173,7 @@ class AzureOpenAIEmbedding(EmbeddingProvider):
         results = await self.embed_texts([text])
         return results[0]
     
-    async def embed_texts(self, texts: List[str]) -> List[EmbeddingResult]:
+    async def embed_texts(self, texts: List[str], client: Optional[httpx.AsyncClient] = None) -> List[EmbeddingResult]:
         if not texts:
             return []
         
@@ -187,20 +192,25 @@ class AzureOpenAIEmbedding(EmbeddingProvider):
         # Azure URL 格式 - 使用最新 API 版本
         url = f"{self.base_url.rstrip('/')}/openai/deployments/{self.model}/embeddings?api-version={self.API_VERSION}"
         
-        async with httpx.AsyncClient(timeout=60) as client:
+        if client:
             response = await client.post(url, headers=headers, json=payload)
             response.raise_for_status()
             data = response.json()
+        else:
+            async with httpx.AsyncClient(timeout=60) as client:
+                response = await client.post(url, headers=headers, json=payload)
+                response.raise_for_status()
+                data = response.json()
             
-            results = []
-            for item in data.get("data", []):
-                results.append(EmbeddingResult(
-                    embedding=item["embedding"],
-                    tokens_used=data.get("usage", {}).get("total_tokens", 0) // len(texts),
-                    model=self.model,
-                ))
-            
-            return results
+        results = []
+        for item in data.get("data", []):
+            results.append(EmbeddingResult(
+                embedding=item["embedding"],
+                tokens_used=data.get("usage", {}).get("total_tokens", 0) // len(texts),
+                model=self.model,
+            ))
+        
+        return results
 
 
 class OllamaEmbedding(EmbeddingProvider):
@@ -238,7 +248,7 @@ class OllamaEmbedding(EmbeddingProvider):
         results = await self.embed_texts([text])
         return results[0]
     
-    async def embed_texts(self, texts: List[str]) -> List[EmbeddingResult]:
+    async def embed_texts(self, texts: List[str], client: Optional[httpx.AsyncClient] = None) -> List[EmbeddingResult]:
         if not texts:
             return []
         
@@ -250,23 +260,28 @@ class OllamaEmbedding(EmbeddingProvider):
             "input": texts,  # 新 API 使用 'input' 参数，支持批量
         }
         
-        async with httpx.AsyncClient(timeout=120) as client:
+        if client:
             response = await client.post(url, json=payload)
             response.raise_for_status()
             data = response.json()
+        else:
+            async with httpx.AsyncClient(timeout=120) as client:
+                response = await client.post(url, json=payload)
+                response.raise_for_status()
+                data = response.json()
             
-            # 新 API 返回格式: {"embeddings": [[...], [...], ...]}
-            embeddings = data.get("embeddings", [])
-            
-            results = []
-            for i, embedding in enumerate(embeddings):
-                results.append(EmbeddingResult(
-                    embedding=embedding,
-                    tokens_used=len(texts[i]) // 4,
-                    model=self.model,
-                ))
-            
-            return results
+        # 新 API 返回格式: {"embeddings": [[...], [...], ...]}
+        embeddings = data.get("embeddings", [])
+        
+        results = []
+        for i, embedding in enumerate(embeddings):
+            results.append(EmbeddingResult(
+                embedding=embedding,
+                tokens_used=len(texts[i]) // 4,
+                model=self.model,
+            ))
+        
+        return results
 
 
 class CohereEmbedding(EmbeddingProvider):
@@ -307,7 +322,7 @@ class CohereEmbedding(EmbeddingProvider):
         results = await self.embed_texts([text])
         return results[0]
     
-    async def embed_texts(self, texts: List[str]) -> List[EmbeddingResult]:
+    async def embed_texts(self, texts: List[str], client: Optional[httpx.AsyncClient] = None) -> List[EmbeddingResult]:
         if not texts:
             return []
         
@@ -326,24 +341,29 @@ class CohereEmbedding(EmbeddingProvider):
         
         url = f"{self.base_url.rstrip('/')}/embed"
         
-        async with httpx.AsyncClient(timeout=60) as client:
+        if client:
             response = await client.post(url, headers=headers, json=payload)
             response.raise_for_status()
             data = response.json()
+        else:
+            async with httpx.AsyncClient(timeout=60) as client:
+                response = await client.post(url, headers=headers, json=payload)
+                response.raise_for_status()
+                data = response.json()
             
-            results = []
-            # v2 API 返回格式: {"embeddings": {"float": [[...], [...]]}, ...}
-            embeddings_data = data.get("embeddings", {})
-            embeddings = embeddings_data.get("float", []) if isinstance(embeddings_data, dict) else embeddings_data
-            
-            for embedding in embeddings:
-                results.append(EmbeddingResult(
-                    embedding=embedding,
-                    tokens_used=data.get("meta", {}).get("billed_units", {}).get("input_tokens", 0) // max(len(texts), 1),
-                    model=self.model,
-                ))
-            
-            return results
+        results = []
+        # v2 API 返回格式: {"embeddings": {"float": [[...], [...]]}, ...}
+        embeddings_data = data.get("embeddings", {})
+        embeddings = embeddings_data.get("float", []) if isinstance(embeddings_data, dict) else embeddings_data
+        
+        for embedding in embeddings:
+            results.append(EmbeddingResult(
+                embedding=embedding,
+                tokens_used=data.get("meta", {}).get("billed_units", {}).get("input_tokens", 0) // max(len(texts), 1),
+                model=self.model,
+            ))
+        
+        return results
 
 
 class HuggingFaceEmbedding(EmbeddingProvider):
@@ -383,7 +403,7 @@ class HuggingFaceEmbedding(EmbeddingProvider):
         results = await self.embed_texts([text])
         return results[0]
     
-    async def embed_texts(self, texts: List[str]) -> List[EmbeddingResult]:
+    async def embed_texts(self, texts: List[str], client: Optional[httpx.AsyncClient] = None) -> List[EmbeddingResult]:
         if not texts:
             return []
         
@@ -393,7 +413,6 @@ class HuggingFaceEmbedding(EmbeddingProvider):
         }
         
         # 新的 HuggingFace Router URL 格式
-        # https://router.huggingface.co/hf-inference/models/{model}/pipeline/feature-extraction
         url = f"{self.base_url.rstrip('/')}/hf-inference/models/{self.model}/pipeline/feature-extraction"
         
         payload = {
@@ -403,27 +422,29 @@ class HuggingFaceEmbedding(EmbeddingProvider):
             }
         }
         
-        async with httpx.AsyncClient(timeout=120) as client:
+        if client:
             response = await client.post(url, headers=headers, json=payload)
             response.raise_for_status()
             data = response.json()
+        else:
+            async with httpx.AsyncClient(timeout=120) as client:
+                response = await client.post(url, headers=headers, json=payload)
+                response.raise_for_status()
+                data = response.json()
             
-            results = []
-            # HuggingFace 返回格式: [[embedding1], [embedding2], ...]
-            for embedding in data:
-                # 有时候返回的是嵌套的列表
-                if isinstance(embedding, list) and len(embedding) > 0:
-                    if isinstance(embedding[0], list):
-                        # 取平均或第一个
-                        embedding = embedding[0]
-                
-                results.append(EmbeddingResult(
-                    embedding=embedding,
-                    tokens_used=len(texts[len(results)]) // 4,
-                    model=self.model,
-                ))
+        results = []
+        for embedding in data:
+            if isinstance(embedding, list) and len(embedding) > 0:
+                if isinstance(embedding[0], list):
+                    embedding = embedding[0]
             
-            return results
+            results.append(EmbeddingResult(
+                embedding=embedding,
+                tokens_used=len(texts[len(results)]) // 4,
+                model=self.model,
+            ))
+        
+        return results
 
 
 class JinaEmbedding(EmbeddingProvider):
@@ -455,7 +476,7 @@ class JinaEmbedding(EmbeddingProvider):
         results = await self.embed_texts([text])
         return results[0]
     
-    async def embed_texts(self, texts: List[str]) -> List[EmbeddingResult]:
+    async def embed_texts(self, texts: List[str], client: Optional[httpx.AsyncClient] = None) -> List[EmbeddingResult]:
         if not texts:
             return []
         
@@ -471,20 +492,25 @@ class JinaEmbedding(EmbeddingProvider):
         
         url = f"{self.base_url.rstrip('/')}/embeddings"
         
-        async with httpx.AsyncClient(timeout=60) as client:
+        if client:
             response = await client.post(url, headers=headers, json=payload)
             response.raise_for_status()
             data = response.json()
+        else:
+            async with httpx.AsyncClient(timeout=60) as client:
+                response = await client.post(url, headers=headers, json=payload)
+                response.raise_for_status()
+                data = response.json()
             
-            results = []
-            for item in data.get("data", []):
-                results.append(EmbeddingResult(
-                    embedding=item["embedding"],
-                    tokens_used=data.get("usage", {}).get("total_tokens", 0) // len(texts),
-                    model=self.model,
-                ))
-            
-            return results
+        results = []
+        for item in data.get("data", []):
+            results.append(EmbeddingResult(
+                embedding=item["embedding"],
+                tokens_used=data.get("usage", {}).get("total_tokens", 0) // len(texts),
+                model=self.model,
+            ))
+        
+        return results
 
 
 class QwenEmbedding(EmbeddingProvider):
@@ -537,7 +563,7 @@ class QwenEmbedding(EmbeddingProvider):
         results = await self.embed_texts([text])
         return results[0]
     
-    async def embed_texts(self, texts: List[str]) -> List[EmbeddingResult]:
+    async def embed_texts(self, texts: List[str], client: Optional[httpx.AsyncClient] = None) -> List[EmbeddingResult]:
         if not texts:
             return []
 
@@ -557,30 +583,30 @@ class QwenEmbedding(EmbeddingProvider):
         url = f"{self.base_url.rstrip('/')}/embeddings"
 
         try:
-            async with httpx.AsyncClient(timeout=60.0) as client:
-                response = await client.post(
-                    url,
-                    headers=headers,
-                    json=payload,
-                )
-                response.raise_for_status()
-                data = response.json()
+            if client:
+                response = await client.post(url, headers=headers, json=payload)
+            else:
+                async with httpx.AsyncClient(timeout=60.0) as client:
+                    response = await client.post(url, headers=headers, json=payload)
+            
+            response.raise_for_status()
+            data = response.json()
 
-                usage = data.get("usage", {}) or {}
-                total_tokens = usage.get("total_tokens") or usage.get("prompt_tokens") or 0
+            usage = data.get("usage", {}) or {}
+            total_tokens = usage.get("total_tokens") or usage.get("prompt_tokens") or 0
 
-                results: List[EmbeddingResult] = []
-                for item in data.get("data", []):
-                    results.append(EmbeddingResult(
-                        embedding=item["embedding"],
-                        tokens_used=total_tokens // max(len(texts), 1),
-                        model=self.model,
-                    ))
+            results: List[EmbeddingResult] = []
+            for item in data.get("data", []):
+                results.append(EmbeddingResult(
+                    embedding=item["embedding"],
+                    tokens_used=total_tokens // max(len(texts), 1),
+                    model=self.model,
+                ))
 
-                return results
+            return results
         except httpx.HTTPStatusError as e:
             logger.error(f"Qwen embedding API error: {e.response.status_code} - {e.response.text}")
-            raise RuntimeError(f"Qwen embedding API failed: {e.response.status_code}") from e
+            raise
         except httpx.RequestError as e:
             logger.error(f"Qwen embedding network error: {e}")
             raise RuntimeError(f"Qwen embedding network error: {e}") from e
@@ -639,13 +665,32 @@ class EmbeddingService:
         )
 
         # 🔥 控制并发请求数 (RPS 限制)
-        self._semaphore = asyncio.Semaphore(30)
-        
-        # 🔥 设置默认批次大小 (对于 remote 模型，用户要求为 10)
         is_remote = self.provider.lower() in ["openai", "qwen", "azure", "cohere", "jina", "huggingface"]
+        self.concurrency = getattr(settings, "EMBEDDING_CONCURRENCY", 2 if is_remote else 10)
+        self._semaphore = asyncio.Semaphore(self.concurrency)
+        
+        # 🔥 设置默认批次大小 (DashScope text-embedding-v4 限制为 10)
         self.batch_size = 10 if is_remote else 100
 
-        logger.info(f"Embedding service initialized with {self.provider}/{self.model} (Batch size: {self.batch_size})")
+
+
+        
+        # 🔥 共享 HTTP 客户端
+        self._client: Optional[httpx.AsyncClient] = None
+
+        logger.info(f"Embedding service initialized with {self.provider}/{self.model} (Concurrency: {self.concurrency}, Batch size: {self.batch_size})")
+    
+    async def _get_client(self) -> httpx.AsyncClient:
+        """获取或创建共享的 AsyncClient"""
+        if self._client is None or self._client.is_closed:
+            self._client = httpx.AsyncClient(timeout=120.0)
+        return self._client
+
+    async def close(self):
+        """关闭共享客户端"""
+        if self._client and not self._client.is_closed:
+            await self._client.aclose()
+            self._client = None
     
     def _create_provider(
         self,
@@ -809,27 +854,34 @@ class EmbeddingService:
         batch: List[str], 
         indices: List[int], 
         cancel_check: Optional[callable] = None,
-        max_retries: int = 3
+        max_retries: Optional[int] = None
     ) -> List[EmbeddingResult]:
         """带重试机制的单批次处理"""
-        for attempt in range(max_retries):
+        # 优先使用配置中的重试次数
+        actual_max_retries = max_retries or getattr(settings, "EMBEDDING_RETRY_MAX", 5)
+        
+        client = await self._get_client()
+        
+        for attempt in range(actual_max_retries):
             if cancel_check and cancel_check():
                 raise asyncio.CancelledError("嵌入操作已取消")
 
             async with self._semaphore:
                 try:
-                    return await self._provider.embed_texts(batch)
+                    return await self._provider.embed_texts(batch, client=client)
                 except httpx.HTTPStatusError as e:
-                    if e.response.status_code == 429 and attempt < max_retries - 1:
-                        # 429 限流，指数级退避
-                        wait_time = (2 ** attempt) + 1
-                        logger.warning(f"Rate limited (429), retrying in {wait_time}s... (Attempt {attempt+1}/{max_retries})")
+                    if e.response.status_code == 429 and attempt < actual_max_retries - 1:
+                        # 429 限流，使用更保守的指数退避
+                        # 第一次重试等待 5s, 第二次 10s, 第三次 20s...
+                        wait_time = (2 ** attempt) * 5
+                        logger.warning(f"Rate limited (429), retrying in {wait_time}s... (Attempt {attempt+1}/{actual_max_retries})")
                         await asyncio.sleep(wait_time)
                         continue
                     raise
                 except Exception as e:
-                    if attempt < max_retries - 1:
-                        await asyncio.sleep(1)
+                    if attempt < actual_max_retries - 1:
+                        # 普通错误等待 2s
+                        await asyncio.sleep(2)
                         continue
                     raise
         return []

backend/docker-entrypoint.sh

@@ -55,6 +55,7 @@ exec .venv/bin/gunicorn app.main:app \
     --workers 4 \
     --worker-class uvicorn.workers.UvicornWorker \
     --bind 0.0.0.0:8000 \
-    --timeout 120 \
+    --timeout 1800 \
+
     --access-logfile - \
     --error-logfile -

backend/tests/test_indexer_isolated.py

@@ -29,8 +29,9 @@ class TestIndexerLogicIsolated(unittest.IsolatedAsyncioTestCase):
         
         # Mock methods that are called during smart_index_directory
         indexer.initialize = AsyncMock(return_value=(False, ""))
-        indexer._full_index = AsyncMock()
-        indexer._incremental_index = AsyncMock()
+        indexer._full_index = MagicMock()
+        indexer._incremental_index = MagicMock()
+
         
         async def mock_gen(*args, **kwargs):
             yield MagicMock()
@@ -69,10 +70,11 @@ class TestIndexerLogicIsolated(unittest.IsolatedAsyncioTestCase):
             vector_store=mock_vector_store
         )
         
-        indexer._full_index = AsyncMock()
-        indexer._incremental_index = AsyncMock()
+        indexer._full_index = MagicMock()
+        indexer._incremental_index = MagicMock()
         async def mock_gen(*args, **kwargs):
             yield MagicMock()
+
         indexer._full_index.side_effect = mock_gen
         
         # Test: Existing collection, but needs_rebuild is True (should be FULL)