Merge branch 'prod' into bot_manager

.circleci/config.yml

@@ -135,6 +135,18 @@ workflows:
             branches:
               only:
                 - prod
+      - build-and-push:
+          name: build-for-staging
+          context:
+            - ecr-new
+          path: .
+          dockerfile: Dockerfile
+          repo: catalog-agent
+          docker-tag: ''
+          filters:
+            branches:
+              only:
+                - staging
       - deploy:
           name: deploy-for-prod
           docker-tag: ''
@@ -149,6 +161,20 @@ workflows:
                 - prod
           requires:
             - build-for-prod
+      - deploy:
+          name: deploy-for-staging
+          docker-tag: ''
+          path: '/home/ubuntu/cluster-for-B/gbase-staging/catalog-agent/deploy.yaml'
+          deploy-name: catalog-agent
+          deploy-namespace: gbase-staging
+          context:
+            - ecr-new
+          filters:
+            branches:
+              only:
+                - staging
+          requires:
+            - build-for-staging
       - docker-hub-build-push:
           name: docker-hub-build-push
           repo: gptbasesparticle/catalog-agent

.gitignore

@@ -6,3 +6,6 @@ __pycache__
 models
 projects/queue_data
 worktree
+.idea/*
+
+.idea/

agent/deep_assistant.py

@@ -12,7 +12,8 @@ from deepagents.backends.sandbox import SandboxBackendProtocol
 from deepagents_cli.agent import create_cli_agent
 from langchain.agents import create_agent
 from langgraph.store.base import BaseStore
-from langchain.agents.middleware import SummarizationMiddleware
+from langchain.agents.middleware import SummarizationMiddleware as LangchainSummarizationMiddleware
+from .summarization_middleware import SummarizationMiddleware
 from langchain_mcp_adapters.client import MultiServerMCPClient
 from sympy.printing.cxx import none
 from utils.fastapi_utils import detect_provider
@@ -21,11 +22,13 @@ from .tool_output_length_middleware import ToolOutputLengthMiddleware
 from .tool_use_cleanup_middleware import ToolUseCleanupMiddleware
 from utils.settings import (
     SUMMARIZATION_MAX_TOKENS,
-    SUMMARIZATION_MESSAGES_TO_KEEP,
+    SUMMARIZATION_TOKENS_TO_KEEP,
     TOOL_OUTPUT_MAX_LENGTH,
     MCP_HTTP_TIMEOUT,
     MCP_SSE_READ_TIMEOUT,
+    DEFAULT_TRIM_TOKEN_LIMIT
 )
+from utils.token_counter import create_token_counter
 from agent.agent_config import AgentConfig
 from .mem0_manager import get_mem0_manager
 from .mem0_middleware import create_mem0_middleware
@@ -252,8 +255,9 @@ async def init_agent(config: AgentConfig):
             summarization_middleware = SummarizationMiddleware(
                 model=llm_instance,
                 trigger=('tokens', SUMMARIZATION_MAX_TOKENS),
-                keep=('messages', SUMMARIZATION_MESSAGES_TO_KEEP),
+                trim_tokens_to_summarize=DEFAULT_TRIM_TOKEN_LIMIT,
-                summary_prompt="请简洁地总结以上对话的要点，包括重要的用户信息、讨论过的话题和关键结论。"
+                keep=('tokens', SUMMARIZATION_TOKENS_TO_KEEP),
+                token_counter=create_token_counter(config.model_name)
             )
             middleware.append(summarization_middleware)
 

agent/summarization_middleware.py

@@ -0,0 +1,61 @@
+"""Custom Summarization middleware with summary tag support."""
+
+from typing import Any
+from collections.abc import Callable
+from langchain_core.messages import AIMessage, AnyMessage, HumanMessage
+from langgraph.runtime import Runtime
+from langchain.agents.middleware.summarization import SummarizationMiddleware as LangchainSummarizationMiddleware
+from langchain.agents.middleware.types import AgentState
+
+
+class SummarizationMiddleware(LangchainSummarizationMiddleware):
+    """Summarization middleware that outputs summary in <summary> tags instead of direct output."""
+
+    def _create_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
+        """Generate summary for the given messages with message_tag in metadata."""
+        if not messages_to_summarize:
+            return "No previous conversation history."
+
+        trimmed_messages = self._trim_messages_for_summary(messages_to_summarize)
+        if not trimmed_messages:
+            return "Previous conversation was too long to summarize."
+
+        try:
+            response = self.model.invoke(
+                self.summary_prompt.format(messages=trimmed_messages),
+                config={"metadata": {"message_tag": "SUMMARY"}}
+            )
+            return response.text.strip()
+        except Exception as e:  # noqa: BLE001
+            return f"Error generating summary: {e!s}"
+
+    async def _acreate_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
+        """Generate summary for the given messages with message_tag in metadata."""
+        if not messages_to_summarize:
+            return "No previous conversation history."
+
+        trimmed_messages = self._trim_messages_for_summary(messages_to_summarize)
+        if not trimmed_messages:
+            return "Previous conversation was too long to summarize."
+
+        try:
+            response = await self.model.ainvoke(
+                self.summary_prompt.format(messages=trimmed_messages),
+                config={"metadata": {"message_tag": "SUMMARY"}}
+            )
+            return response.text.strip()
+        except Exception as e:  # noqa: BLE001
+            return f"Error generating summary: {e!s}"
+
+    def _build_new_messages(self, summary: str) -> list[HumanMessage | AIMessage]:
+        """Build messages with summary wrapped in <summary> tags.
+
+        Similar to how GuidelineMiddleware wraps thinking content in <thinking> tags,
+        this wraps the summary in <summary> tags with message_tag set to "SUMMARY".
+        """
+        # Create an AIMessage with the summary wrapped in <summary> tags
+        content = f"<summary>\n{summary}\n</summary>"
+        message = AIMessage(content=content)
+        # Set message_tag so the frontend can identify and handle this message appropriately
+        message.additional_kwargs["message_tag"] = "SUMMARY"
+        return [message]

pyproject.toml

@@ -35,6 +35,7 @@ dependencies = [
     "mem0ai (>=0.1.50,<0.3.0)",
     "psycopg2-binary (>=2.9.11,<3.0.0)",
     "json-repair (>=0.29.0,<0.30.0)",
+    "tiktoken (>=0.5.0,<1.0.0)",
 ]
 
 [tool.poetry.requires-plugins]

routes/chat.py

@@ -96,6 +96,9 @@ async def enhanced_generate_stream_response(
                             preamble_completed.set()
                             await output_queue.put(("preamble_done", None))
                             meta_message_tag = metadata.get("message_tag", "ANSWER")
+                            # SUMMARY 不输出内容
+                            if meta_message_tag == "SUMMARY":
+                                continue
                             if meta_message_tag != message_tag:
                                 message_tag = meta_message_tag
                                 new_content = f"[{meta_message_tag}]\n"
@@ -234,6 +237,8 @@ async def create_agent_and_generate_response(
         if isinstance(msg,AIMessage):
             if len(msg.text)>0:
                 meta_message_tag = msg.additional_kwargs.get("message_tag", "ANSWER")
+                if meta_message_tag == "SUMMARY":
+                    continue
                 output_text = msg.text.replace("````","").replace("````","") if meta_message_tag == "THINK" else msg.text
                 response_text += f"[{meta_message_tag}]\n"+output_text+ "\n"
             if len(msg.tool_calls)>0 and config.tool_response:

utils/settings.py

@@ -2,18 +2,19 @@ import os
 
 # 必填参数
 # API Settings
-BACKEND_HOST = os.getenv("BACKEND_HOST", "https://api-dev.gptbase.ai")
+BACKEND_HOST = os.getenv("BACKEND_HOST", "https://api.gbase.ai")
 MASTERKEY = os.getenv("MASTERKEY", "master")
 FASTAPI_URL = os.getenv('FASTAPI_URL', 'http://127.0.0.1:8001')
 
 # LLM Token Settings
-MAX_CONTEXT_TOKENS = int(os.getenv("MAX_CONTEXT_TOKENS", 262144))
+MAX_CONTEXT_TOKENS = int(os.getenv("MAX_CONTEXT_TOKENS", 200000))
 MAX_OUTPUT_TOKENS = int(os.getenv("MAX_OUTPUT_TOKENS", 8000))
 
 # 可选参数
 # Summarization Settings
-SUMMARIZATION_MAX_TOKENS = MAX_CONTEXT_TOKENS - MAX_OUTPUT_TOKENS - 1000
+SUMMARIZATION_MAX_TOKENS = int(MAX_CONTEXT_TOKENS/3)
-SUMMARIZATION_MESSAGES_TO_KEEP = int(os.getenv("SUMMARIZATION_MESSAGES_TO_KEEP", 20))
+SUMMARIZATION_TOKENS_TO_KEEP = int(SUMMARIZATION_MAX_TOKENS/3)
+DEFAULT_TRIM_TOKEN_LIMIT = SUMMARIZATION_MAX_TOKENS - SUMMARIZATION_TOKENS_TO_KEEP + 5000
 
 # Agent Cache Settings
 TOOL_CACHE_MAX_SIZE = int(os.getenv("TOOL_CACHE_MAX_SIZE", 20))

utils/token_counter.py

@@ -0,0 +1,353 @@
+"""
+Token 计数工具模块
+
+使用 tiktoken 替代 LangChain 默认的 chars_per_token=3.3，
+支持中/日/英多语言的精确 token 计算。
+
+参考 langchain_core.messages.utils.count_tokens_approximately 的消息读取方式。
+"""
+import json
+import logging
+import math
+from typing import Any, Dict, Sequence
+from functools import lru_cache
+
+try:
+    import tiktoken
+    TIKTOKEN_AVAILABLE = True
+except ImportError:
+    TIKTOKEN_AVAILABLE = False
+
+# 尝试导入 LangChain 的消息类型
+try:
+    from langchain_core.messages import (
+        BaseMessage,
+        AIMessage,
+        HumanMessage,
+        SystemMessage,
+        ToolMessage,
+        FunctionMessage,
+        ChatMessage,
+        convert_to_messages,
+    )
+    from langchain_core.messages.utils import _get_message_openai_role
+    LANGCHAIN_AVAILABLE = True
+except ImportError:
+    LANGCHAIN_AVAILABLE = False
+    BaseMessage = None
+    AIMessage = None
+    HumanMessage = None
+    SystemMessage = None
+    ToolMessage = None
+    FunctionMessage = None
+    ChatMessage = None
+
+logger = logging.getLogger('app')
+
+
+# <20><>持的模型编码映射
+MODEL_TO_ENCODING: Dict[str, str] = {
+    # OpenAI 模型
+    "gpt-4o": "o200k_base",
+    "gpt-4o-mini": "o200k_base",
+    "gpt-4-turbo": "cl100k_base",
+    "gpt-4": "cl100k_base",
+    "gpt-3.5-turbo": "cl100k_base",
+    "gpt-3.5": "cl100k_base",
+    # Claude 使用 cl100k_base 作为近似
+    "claude": "cl100k_base",
+    # 其他模型默认使用 cl100k_base
+}
+
+
+@lru_cache(maxsize=128)
+def _get_encoding(model_name: str) -> Any:
+    """
+    获取模型的 tiktoken 编码器（带缓存）
+
+    Args:
+        model_name: 模型名称
+
+    Returns:
+        tiktoken.Encoding 实例
+    """
+    if not TIKTOKEN_AVAILABLE:
+        return None
+
+    # 标准化模型名称
+    model_lower = model_name.lower()
+
+    # 查找匹配的编码
+    encoding_name = None
+    for key, encoding in MODEL_TO_ENCODING.items():
+        if key in model_lower:
+            encoding_name = encoding
+            break
+
+    # 默认使用 cl100k_base（适用于大多数现代模型）
+    if encoding_name is None:
+        encoding_name = "cl100k_base"
+
+    try:
+        return tiktoken.get_encoding(encoding_name)
+    except Exception as e:
+        logger.warning(f"Failed to get tiktoken encoding {encoding_name}: {e}")
+        return None
+
+
+def count_tokens(text: str, model_name: str = "gpt-4o") -> int:
+    """
+    计算文本的 token 数量
+
+    Args:
+        text: 要计算的文本
+        model_name: 模型名称，用于选择合适的编码器
+
+    Returns:
+        token 数量
+    """
+    if not text:
+        return 0
+
+    encoding = _get_encoding(model_name)
+
+    if encoding is None:
+        # tiktoken 不可用时，回退到字符估算（保守估计）
+        # 中文/日文约 1.5 字符/token，英文约 4 字符/token
+        # 混合文本使用 2.5 作为中间值
+        return max(1, len(text) // 2)
+
+    try:
+        tokens = encoding.encode(text)
+        return len(tokens)
+    except Exception as e:
+        logger.warning(f"Failed to encode text: {e}")
+        return max(1, len(text) // 2)
+
+
+def _get_role(message: Dict[str, Any]) -> str:
+    """
+    获取消息的 role（参考 _get_message_openai_role）
+
+    Args:
+        message: 消息字典
+
+    Returns:
+        role 字符串
+    """
+    # 优先使用 type 字段
+    msg_type = message.get("type", "")
+
+    if msg_type == "ai" or msg_type == "AIMessage":
+        return "assistant"
+    elif msg_type == "human" or msg_type == "HumanMessage":
+        return "user"
+    elif msg_type == "tool" or msg_type == "ToolMessage":
+        return "tool"
+    elif msg_type == "system" or msg_type == "SystemMessage":
+        # 检查是否有 __openai_role__
+        additional_kwargs = message.get("additional_kwargs", {})
+        if isinstance(additional_kwargs, dict):
+            return additional_kwargs.get("__openai_role__", "system")
+        return "system"
+    elif msg_type == "function" or msg_type == "FunctionMessage":
+        return "function"
+    elif msg_type == "chat" or msg_type == "ChatMessage":
+        return message.get("role", "user")
+    else:
+        # 如果有 role 字段，直接使用
+        if "role" in message:
+            return message["role"]
+        return "user"
+
+
+def count_message_tokens(message: Dict[str, Any] | BaseMessage, model_name: str = "gpt-4o") -> int:
+    """
+    计算消息的 token 数量（参考 count_tokens_approximately 的消息读取方式）
+
+    包括:
+    - 消息内容 (content)
+    - 消息角色 (role)
+    - 消息名称 (name)
+    - AIMessage 的 tool_calls
+    - ToolMessage 的 tool_call_id
+
+    Args:
+        message: 消息对象（字典或 BaseMessage）
+        model_name: 模型名称
+
+    Returns:
+        token 数量
+    """
+    # 转换为字典格式处理
+    if LANGCHAIN_AVAILABLE and isinstance(message, BaseMessage):
+        # 将 BaseMessage 转换为字典
+        msg_dict = message.model_dump(exclude={"type"})
+    else:
+        msg_dict = message if isinstance(message, dict) else {}
+
+    token_count = 0
+    encoding = _get_encoding(model_name)
+
+    # 1. 处理 content
+    content = msg_dict.get("content", "")
+
+    if isinstance(content, str):
+        token_count += count_tokens(content, model_name)
+    elif isinstance(content, list):
+        # 处理多模态内容块
+        for block in content:
+            if isinstance(block, str):
+                token_count += count_tokens(block, model_name)
+            elif isinstance(block, dict):
+                block_type = block.get("type", "")
+
+                if block_type == "text":
+                    token_count += count_tokens(block.get("text", ""), model_name)
+                elif block_type == "image_url":
+                    # 图片 token 计算（OpenAI 标准：85 tokens/base + 每个 tile 170 tokens）
+                    token_count += 85
+                elif block_type == "tool_use":
+                    # tool_use 块
+                    token_count += count_tokens(block.get("name", ""), model_name)
+                    input_data = block.get("input", {})
+                    if isinstance(input_data, dict):
+                        token_count += count_tokens(json.dumps(input_data, ensure_ascii=False), model_name)
+                    elif isinstance(input_data, str):
+                        token_count += count_tokens(input_data, model_name)
+                elif block_type == "tool_result":
+                    # tool_result 块
+                    result_content = block.get("content", "")
+                    if isinstance(result_content, str):
+                        token_count += count_tokens(result_content, model_name)
+                    elif isinstance(result_content, list):
+                        for sub_block in result_content:
+                            if isinstance(sub_block, dict):
+                                if sub_block.get("type") == "text":
+                                    token_count += count_tokens(sub_block.get("text", ""), model_name)
+                    token_count += count_tokens(block.get("tool_use_id", ""), model_name)
+                elif block_type == "json":
+                    json_data = block.get("json", {})
+                    token_count += count_tokens(json.dumps(json_data, ensure_ascii=False), model_name)
+                else:
+                    # 其他类型，将整个 block 序列化
+                    token_count += count_tokens(repr(block), model_name)
+    else:
+        # 其他类型的 content，序列化后计算
+        token_count += count_tokens(repr(content), model_name)
+
+    # 2. 处理 tool_calls（仅当 content 不是 list 时）
+    if msg_dict.get("type") in ["ai", "AIMessage"] or isinstance(msg_dict.get("tool_calls"), list):
+        tool_calls = msg_dict.get("tool_calls", [])
+        # 只有在 content 不是 list 时才单独计算 tool_calls
+        # (因为 Anthropic 格式中 tool_calls 已包含在 content 的 tool_use 块中)
+        if not isinstance(content, list) and tool_calls:
+            tool_calls_str = repr(tool_calls)
+            token_count += count_tokens(tool_calls_str, model_name)
+
+    # 3. 处理 tool_call_id（ToolMessage）
+    tool_call_id = msg_dict.get("tool_call_id", "")
+    if tool_call_id:
+        token_count += count_tokens(tool_call_id, model_name)
+
+    # 4. 处理 role
+    role = _get_role(msg_dict)
+    token_count += count_tokens(role, model_name)
+
+    # 5. 处理 name
+    name = msg_dict.get("name", "")
+    if name:
+        token_count += count_tokens(name, model_name)
+
+    # 6. 添加每条消息的格式开销（参考 OpenAI 的计算方式）
+    # 每条消息约有 4 个 token 的格式开销
+    token_count += 4
+
+    return token_count
+
+
+def count_messages_tokens(messages: Sequence[Dict[str, Any]] | Sequence[BaseMessage], model_name: str = "gpt-4o") -> int:
+    """
+    计算消息列表的总 token 数量
+
+    Args:
+        messages: 消息列表（字典列表或 BaseMessage 列表）
+        model_name: 模型名称
+
+    Returns:
+        总 token 数量
+    """
+    if not messages:
+        return 0
+
+    total = 0
+    for message in messages:
+        total += count_message_tokens(message, model_name)
+
+    # 添加回复的估算（3 tokens）
+    total += 3
+
+    return int(math.ceil(total))
+
+
+def create_token_counter(model_name: str = "gpt-4o"):
+    """
+    创建 token 计数函数，用于传入 SummarizationMiddleware
+
+    Args:
+        model_name: 模型名称
+
+    Returns:
+        token 计数函数
+    """
+    if not TIKTOKEN_AVAILABLE:
+        logger.warning("tiktoken not available, falling back to character-based estimation")
+        # 回退到字符估算（参考 count_tokens_approximately 的方式）
+        def fallback_counter(messages) -> int:
+            token_count = 0.0
+            for message in messages:
+                # 转换为字典格式处理
+                if LANGCHAIN_AVAILABLE and isinstance(message, BaseMessage):
+                    msg_dict = message.model_dump(exclude={"type"})
+                else:
+                    msg_dict = message if isinstance(message, dict) else {}
+
+                message_chars = 0
+                content = msg_dict.get("content", "")
+
+                if isinstance(content, str):
+                    message_chars += len(content)
+                elif isinstance(content, list):
+                    message_chars += len(repr(content))
+
+                # 处理 tool_calls
+                if (msg_dict.get("type") in ["ai", "AIMessage"] and
+                        not isinstance(content, list) and
+                        msg_dict.get("tool_calls")):
+                    message_chars += len(repr(msg_dict.get("tool_calls")))
+
+                # 处理 tool_call_id
+                if msg_dict.get("tool_call_id"):
+                    message_chars += len(msg_dict.get("tool_call_id", ""))
+
+                # 处理 role
+                role = _get_role(msg_dict)
+                message_chars += len(role)
+
+                # 处理 name
+                if msg_dict.get("name"):
+                    message_chars += len(msg_dict.get("name", ""))
+
+                # 使用 2.5 作为 chars_per_token（适合中/日/英混合文本）
+                token_count += math.ceil(message_chars / 2.5)
+                token_count += 3.0  # extra_tokens_per_message
+
+            return int(math.ceil(token_count))
+
+        return fallback_counter
+
+    def token_counter(messages: Sequence[Dict[str, Any]] | Sequence[BaseMessage]) -> int:
+        """Token 计数函数"""
+        return count_messages_tokens(messages, model_name)
+
+    return token_counter