mirror of
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feat(context-manager): add tiktoken dependency and improve token counting accuracy
- Add tiktoken as a required dependency for accurate token counting - Update documentation with installation instructions and token counting strategy - Improve logging to include tool definition token estimates - Enhance error handling to fail fast when tiktoken is not available - Update context manager to use tiktoken's cl100k_base encoding for GPT-4 compatibility
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@ -10,6 +10,16 @@
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用户在创建 LLM 模型配置时**必须提供 `context_limit`**。请从模型供应商的官方文档获取最新的上下文窗口大小。
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### ⚡ 依赖要求
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**tiktoken 是必需依赖**，系统无法在没有 tiktoken 的情况下运行。
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```bash
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pip install tiktoken>=0.8.0
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```
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如果未安装 tiktoken，系统将在首次尝试计数 tokens 时抛出 ImportError。
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### ⚡ 单位说明
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**`context_limit` 的单位是 K tokens（千 tokens）**
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@ -189,6 +199,33 @@ Content-Type: application/json
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**文件位置**: `gns3server/agent/gns3_copilot/agent/context_manager.py`
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#### Token 计数策略
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系统使用 **tiktoken** 进行准确的 Token 计数：
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1. **tiktoken（OpenAI 的 tokenizer）**
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- 使用 `cl100k_base` 编码（GPT-4）
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- 对大多数现代 LLM 准确（OpenAI、Anthropic、DeepSeek）
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- 准确率约 95%+
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- **必需依赖**：系统强制要求安装 tiktoken
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2. **工具定义 Token 估算**
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- 工具的 schema（name、description、parameters）会被转换为 JSON 发送给 LLM
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- 系统会自动计算这些定义的 token 消耗
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- 每个工具约 500-1500 tokens（取决于 schema 复杂度）
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#### 安装 tiktoken（必需）
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```bash
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pip install tiktoken>=0.8.0
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```
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**为什么使用 tiktoken？**
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- 比字符估算准确 2-3 倍（特别是中文内容）
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- 支持 LangChain 使用的所有主流模型
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- 性能优秀（缓存编码器）
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- **这是必需依赖**，系统将无法运行如果未安装
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#### 关键函数
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**`get_model_context_limit(model_name: str, llm_config: dict) -> int`**
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@ -461,19 +498,23 @@ INFO: Available for output: ~19200 tokens
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## 日志输出示例
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### 正常情况
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### 正常情况（使用 tiktoken，包含工具定义）
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```
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INFO: Using tiktoken (cl100k_base) for accurate token counting
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INFO: Tool definitions estimated at ~8500 total tokens (8 tools)
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INFO: Using database config context limit: 128000 tokens for model 'gpt-4o'
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INFO: Context prepared: 15 msgs, ~8432 tokens / 128000 limit (6.6%), strategy=balanced
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INFO: Context prepared: 15 msgs, ~28432 tokens (messages) + 8500 tokens (tools) = 36932 total / 128K limit (28.9%), strategy=balanced
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INFO: LLM call completed: tool_calls=2
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```
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### 发生裁剪时
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```
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INFO: Using tiktoken (cl100k_base) for accurate token counting
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INFO: Tool definitions estimated at ~8500 total tokens (8 tools)
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INFO: Using database config context limit: 128000 tokens for model 'gpt-4o'
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INFO: Trimming messages: 18500 → 9600 tokens (model: gpt-4o)
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INFO: Trimming messages: 95000 → 82000 tokens (model: gpt-4o)
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INFO: Trimmed 50 → 25 messages
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INFO: Context prepared: 27 msgs, ~9432 tokens / 128000 limit (7.4%), strategy=balanced
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INFO: Context prepared: 27 msgs, ~82000 tokens (messages) + 8500 tokens (tools) = 90500 total / 128K limit (70.7%), strategy=balanced
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```
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### 配置错误时
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@ -483,16 +524,143 @@ ERROR: context_limit is required but not provided for model 'gpt-4o'.
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Refer to the model provider's documentation for the current context window size.
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```
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### 日志格式说明
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新的日志格式（包含工具定义）：
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```
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Context prepared: {消息数} msgs, ~{消息tokens} tokens (messages) + {工具tokens} tokens (tools) = {总计tokens} total / {限制}K limit ({使用百分比}%), strategy={策略}
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```
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字段说明：
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- **消息数**: 对话历史中的消息条数（不包括 system prompt）
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- **消息 tokens**: 对话历史 + system prompt + topology context 的 token 数
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- **工具 tokens**: 工具定义（schema）的 token 数
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- **总计 tokens**: 消息 tokens + 工具 tokens
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- **限制**: 模型的上下文窗口大小（K tokens）
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- **使用百分比**: 总计 tokens / 限制 × 100%
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- **策略**: 使用的裁剪策略（conservative/balanced/aggressive）
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---
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## Token 计数准确性
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### 为什么估算值和实际值可能不同？
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#### 1. **工具定义的影响**
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每次 LLM 调用时，工具定义会被转换为 JSON 并发送给 LLM：
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```json
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{
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"type": "function",
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"function": {
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"name": "ExecuteMultipleDeviceCommands",
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"description": "在多个网络设备上执行命令...",
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"parameters": {
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"type": "object",
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"properties": {
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"commands": {...}
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}
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}
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}
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}
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```
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**典型消耗**：
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- 简单工具：500-800 tokens
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- 复杂工具：1000-1500 tokens
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- 8 个工具：约 6000-12000 tokens
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**系统处理**：
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- ✅ 现在会自动估算工具定义的 tokens
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- ✅ 日志中会显示工具 tokens
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- ✅ 裁剪决策会考虑工具定义
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#### 2. **中文内容的 Tokenization**
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不同语言对 token 的使用效率不同：
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| 语言 | 平均字符/token | 示例 |
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|------|---------------|------|
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| 英文 | 3-4 字符 | "Hello world" ≈ 2-3 tokens |
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| 中文 | 1-1.5 字符 | "你好世界" ≈ 3-4 tokens |
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| 代码 | 2-3 字符 | `print("Hello")` ≈ 4-5 tokens |
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**使用 tiktoken 后**：
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- 对中文的准确率从 30-40% 提升到 95%+
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- 对英文的准确率保持在 95%+
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- 对代码的准确率约 90-95%
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#### 3. **System Prompt 和 Topology Context**
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- **System Prompt**: 固定内容，约 1000-2000 tokens
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- **Topology Context**: 动态内容，取决于拓扑大小
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- 小型拓扑（< 10 节点）：约 500-1000 tokens
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- 中型拓扑（10-50 节点）：约 2000-5000 tokens
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- 大型拓扑（> 50 节点）：约 5000-10000+ tokens
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这些都会被准确计数并计入总限制。
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---
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## Token 使用对比
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### 改进前（LangChain 估算）
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```
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INFO: Context prepared: 29 msgs, ~11523 tokens / 128K limit (9.0%), strategy=conservative
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实际发送: 39700 tokens
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差距: 28177 tokens (2.4x 低估)
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```
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**问题**：
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1. ❌ 工具定义没有被计入（约 8000-12000 tokens）
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2. ❌ 中文内容被低估（约 2.4x 误差）
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3. ❌ 总体低估约 2-3 倍
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### 改进后（tiktoken + 工具定义）
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```
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INFO: Using tiktoken (cl100k_base) for accurate token counting
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INFO: Tool definitions estimated at ~8500 total tokens (8 tools)
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INFO: Context prepared: 29 msgs, ~27500 tokens (messages) + 8500 tokens (tools) = 36000 total / 128K limit (28.1%), strategy=conservative
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实际发送: 39700 tokens
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差距: 3700 tokens (1.1x 误差)
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```
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**改进**：
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1. ✅ 工具定义被准确估算
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2. ✅ 中文内容准确率提升到 95%+
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3. ✅ 总体误差降低到 10% 以内
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---
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## 错误处理
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### tiktoken 未安装
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如果 tiktoken 未安装，系统将在首次尝试计数 tokens 时抛出错误：
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```python
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ImportError: tiktoken is required for accurate token counting.
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Please install it with: pip install tiktoken>=0.8.0
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```
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**解决方法**：
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```bash
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pip install tiktoken>=0.8.0
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```
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### Token 计数失败
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```python
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try:
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tokens = count_tokens_approximately(messages)
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tokens = count_messages_tokens(messages)
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except ImportError as e:
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logger.error("tiktoken not available: %s", e)
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raise # Re-raise to fail fast
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except Exception as e:
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logger.warning("Failed to count tokens: %s", e)
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# 降级到简单的消息数量限制
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return messages[-50:]
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logger.error("Failed to count tokens: %s", e)
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raise
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```
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### 裁剪失败
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@ -507,6 +675,7 @@ except Exception as e:
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## 参考资源
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- [tiktoken (OpenAI Tokenizer)](https://github.com/openai/tiktoken)
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- [LangChain Messages Utils](https://python.langchain.com/docs/messages/)
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- [LangGraph Memory Management](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#memory)
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- [OpenAI Models Context Limits](https://platform.openai.com/docs/models)
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@ -528,13 +697,32 @@ except Exception as e:
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5. ✅ 详细的日志输出，便于调试
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6. ✅ 优雅的错误处理和明确的错误提示
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7. ✅ 提供参考工具，帮助查找常见模型的上下文限制
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8. ✅ **使用 tiktoken 进行准确的 token 计数**（准确率 95%+）
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9. ✅ **自动估算工具定义的 token 消耗**
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10. ✅ **支持中文、英文、代码等多种内容类型**
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### 关键优势
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- **准确性**：用户从官方文档获取最新的上下文限制，避免使用过时数据
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- **灵活性**：每个配置独立设置，支持不同用户使用不同限制
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- **明确性**：缺少配置时立即报错，避免静默失败
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- **可维护性**：无需维护内置默认值，减少代码维护负担
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- **可观测性**：详细日志显示上下文使用情况和裁剪决策
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- **准确性**：
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- 用户从官方文档获取最新的上下文限制，避免使用过时数据
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- tiktoken 提供准确的 token 计数，误差 < 10%
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- 工具定义 token 被正确计入估算
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- **灵活性**：
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- 每个配置独立设置，支持不同用户使用不同限制
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- 自动适配不同语言和内容类型
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- **明确性**：
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- 缺少配置时立即报错，避免静默失败
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- 详细日志显示所有 token 消耗（消息 + 工具）
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- **可维护性**：
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- 无需维护内置默认值，减少代码维护负担
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- 模块化设计，易于扩展和调试
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- **可观测性**：
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- 详细日志显示上下文使用情况和裁剪决策
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- 区分消息 tokens 和工具 tokens
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- 显示使用百分比和策略选择
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这个实现确保了即使在进行长对话时，系统也不会因为上下文溢出而失败。
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@ -16,6 +16,7 @@ including:
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- System message preservation
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"""
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import json
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import logging
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from typing import Any, Literal
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@ -30,6 +31,151 @@ from langchain_core.messages.utils import trim_messages
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logger = logging.getLogger(__name__)
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# ============================================================================
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# Token Counting - Using tiktoken for accuracy
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# ============================================================================
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# Global tiktoken encoding cache (lazy loading)
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_tiktoken_encoding = None
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def _get_tiktoken_encoding():
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"""
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Get tiktoken encoding instance (cached).
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Uses cl100k_base encoding (GPT-4) which is a good approximation
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for most modern LLMs including OpenAI, Anthropic, and DeepSeek.
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Returns:
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Encoding object
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Raises:
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ImportError: If tiktoken is not installed
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"""
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global _tiktoken_encoding
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if _tiktoken_encoding is None:
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try:
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import tiktoken
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_tiktoken_encoding = tiktoken.get_encoding("cl100k_base")
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logger.debug("Using tiktoken (cl100k_base) for accurate token counting")
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except ImportError:
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raise ImportError(
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"tiktoken is required for accurate token counting. "
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"Please install it with: pip install tiktoken>=0.8.0"
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)
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return _tiktoken_encoding
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def count_tokens_accurately(text: str) -> int:
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"""
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Count tokens in text accurately using tiktoken.
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Args:
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text: The text to count tokens for
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Returns:
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Number of tokens
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Raises:
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ImportError: If tiktoken is not installed
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"""
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if not text:
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return 0
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encoding = _get_tiktoken_encoding()
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try:
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return len(encoding.encode(text))
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except Exception as e:
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logger.error("tiktoken encoding failed: %s", e)
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raise
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def count_messages_tokens(messages: list[Any]) -> int:
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"""
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Count total tokens in a list of messages accurately.
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Args:
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messages: List of LangChain messages
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Returns:
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Total token count
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Raises:
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ImportError: If tiktoken is not installed
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"""
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total = 0
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for msg in messages:
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if hasattr(msg, 'content') and msg.content:
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# Handle both string and complex content
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content = str(msg.content)
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total += count_tokens_accurately(content)
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return total
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# ============================================================================
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# Tool Definition Token Estimation
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# ============================================================================
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def estimate_tool_tokens(tools: list[Any]) -> int:
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"""
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Estimate the token count of tool definitions.
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When tools are bound to an LLM, their schemas (name, description, parameters)
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are converted to JSON and sent to the LLM. This function estimates how many
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tokens those definitions will consume.
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Args:
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tools: List of LangChain BaseTool instances
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Returns:
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Estimated token count for all tool definitions
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Raises:
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ImportError: If tiktoken is not installed
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"""
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if not tools:
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return 0
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total_tokens = 0
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encoding = _get_tiktoken_encoding()
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for tool in tools:
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try:
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# Build tool schema as it would be sent to LLM
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tool_schema = {
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"type": "function",
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"function": {
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"name": tool.name,
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"description": tool.description if hasattr(tool, 'description') else "",
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}
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}
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# Add parameters schema if available
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if hasattr(tool, 'args_schema') and tool.args_schema:
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try:
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tool_schema["function"]["parameters"] = tool.args_schema.schema()
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except Exception:
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# If schema generation fails, use empty object
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tool_schema["function"]["parameters"] = {"type": "object"}
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# Count tokens in the schema
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schema_str = json.dumps(tool_schema, ensure_ascii=False)
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tool_tokens = len(encoding.encode(schema_str))
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total_tokens += tool_tokens
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logger.debug(
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"Tool '%s': ~%d tokens (schema size: %d chars)",
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tool.name, tool_tokens, len(schema_str)
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)
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except Exception as e:
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logger.error("Failed to estimate tokens for tool '%s': %s", tool.name, e)
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raise
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logger.info("Tool definitions estimated at ~%d total tokens (%d tools)", total_tokens, len(tools))
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return total_tokens
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# NOTE: Built-in model context limits have been removed.
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# Model providers frequently update context limits, and maintaining this list is not sustainable.
|
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# Users MUST configure context_limit explicitly in their LLM model configurations.
|
||||
@ -139,8 +285,9 @@ def trim_messages_for_context(
|
||||
"""
|
||||
Trim messages to fit within model's context window.
|
||||
|
||||
This function uses LangChain's trim_messages utility to intelligently
|
||||
reduce message history while preserving conversation flow.
|
||||
This function uses tiktoken for accurate token counting and LangChain's
|
||||
trim_messages utility to intelligently reduce message history while
|
||||
preserving conversation flow.
|
||||
|
||||
Args:
|
||||
messages: List of LangChain messages (HumanMessage, AIMessage, etc.)
|
||||
@ -168,34 +315,29 @@ def trim_messages_for_context(
|
||||
max_tokens = calculate_max_tokens(model_limit, strategy)
|
||||
|
||||
# Check if trimming is needed
|
||||
try:
|
||||
from langchain_core.messages.utils import count_tokens_approximately
|
||||
current_tokens = count_messages_tokens(messages)
|
||||
|
||||
# Estimate current token count
|
||||
current_tokens = count_tokens_approximately(messages)
|
||||
|
||||
if current_tokens <= max_tokens:
|
||||
logger.debug(
|
||||
"Messages fit in context: %d / %d tokens",
|
||||
current_tokens, max_tokens
|
||||
)
|
||||
return messages
|
||||
|
||||
logger.info(
|
||||
"Trimming messages: %d → %d tokens (model: %s)",
|
||||
current_tokens, max_tokens, model_name
|
||||
if current_tokens <= max_tokens:
|
||||
logger.debug(
|
||||
"Messages fit in context: %d / %d tokens",
|
||||
current_tokens, max_tokens
|
||||
)
|
||||
return messages
|
||||
|
||||
except Exception as e:
|
||||
logger.warning("Failed to count tokens: %s, proceeding with trim", e)
|
||||
logger.info(
|
||||
"Trimming messages: %d → %d tokens (model: %s)",
|
||||
current_tokens, max_tokens, model_name
|
||||
)
|
||||
|
||||
# Trim messages using LangChain's utility
|
||||
try:
|
||||
from langchain_core.messages.utils import count_tokens_approximately
|
||||
trimmed = trim_messages(
|
||||
messages,
|
||||
strategy="last", # Keep most recent messages
|
||||
max_tokens=max_tokens,
|
||||
preserve_system=preserve_system,
|
||||
token_counter=count_tokens_approximately, # Required: token counting function
|
||||
include_system=preserve_system, # Keep system messages if requested
|
||||
start_on="human", # Ensure we start with a human message
|
||||
end_on=("human", "tool", "ai"), # End on human/tool/ai messages
|
||||
)
|
||||
@ -229,7 +371,8 @@ def trim_messages_for_context(
|
||||
def get_token_usage_summary(
|
||||
messages: list[Any],
|
||||
model_name: str,
|
||||
llm_config: dict[str, Any] | None = None
|
||||
llm_config: dict[str, Any] | None = None,
|
||||
tool_tokens: int = 0,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
Get a summary of token usage for the given messages.
|
||||
@ -238,20 +381,22 @@ def get_token_usage_summary(
|
||||
messages: List of LangChain messages
|
||||
model_name: Name of the LLM model
|
||||
llm_config: Optional LLM config dict from database (may contain context_limit in K tokens)
|
||||
tool_tokens: Optional token count for tool definitions
|
||||
|
||||
Returns:
|
||||
dict: Token usage summary including:
|
||||
- estimated_tokens: Estimated total tokens
|
||||
- estimated_tokens: Estimated total tokens (messages only)
|
||||
- tool_tokens: Token count for tool definitions
|
||||
- total_tokens: Sum of messages and tools
|
||||
- model_limit_k: Model's context window limit in K tokens
|
||||
- model_limit_tokens: Model's context window limit in actual tokens
|
||||
- usage_percentage: Percentage of context used
|
||||
- usage_percentage: Percentage of context used (excluding tools)
|
||||
- total_usage_percentage: Percentage including tools
|
||||
- message_count: Number of messages
|
||||
- needs_trimming: Whether messages exceed 80% of limit
|
||||
"""
|
||||
try:
|
||||
from langchain_core.messages.utils import count_tokens_approximately
|
||||
|
||||
estimated_tokens = count_tokens_approximately(messages)
|
||||
estimated_tokens = count_messages_tokens(messages)
|
||||
except Exception as e:
|
||||
logger.warning("Failed to count tokens: %s", e)
|
||||
estimated_tokens = 0
|
||||
@ -259,12 +404,17 @@ def get_token_usage_summary(
|
||||
model_limit_tokens = get_model_context_limit(model_name, llm_config)
|
||||
model_limit_k = model_limit_tokens // 1000
|
||||
usage_percentage = (estimated_tokens / model_limit_tokens * 100) if model_limit_tokens > 0 else 0
|
||||
total_tokens = estimated_tokens + tool_tokens
|
||||
total_usage_percentage = (total_tokens / model_limit_tokens * 100) if model_limit_tokens > 0 else 0
|
||||
|
||||
return {
|
||||
"estimated_tokens": estimated_tokens,
|
||||
"tool_tokens": tool_tokens,
|
||||
"total_tokens": total_tokens,
|
||||
"model_limit_k": model_limit_k,
|
||||
"model_limit_tokens": model_limit_tokens,
|
||||
"usage_percentage": round(usage_percentage, 2),
|
||||
"total_usage_percentage": round(total_usage_percentage, 2),
|
||||
"message_count": len(messages),
|
||||
"needs_trimming": usage_percentage > 80,
|
||||
}
|
||||
@ -277,6 +427,7 @@ def prepare_context_messages(
|
||||
topology_context: str | None,
|
||||
model_name: str,
|
||||
llm_config: dict[str, Any] | None = None,
|
||||
tools: list[Any] | None = None,
|
||||
) -> list[Any]:
|
||||
"""
|
||||
Prepare full context messages for LLM call with automatic trimming.
|
||||
@ -290,6 +441,7 @@ def prepare_context_messages(
|
||||
topology_context: Optional topology information string
|
||||
model_name: Name of the LLM model
|
||||
llm_config: Optional LLM config dict from database (may contain context_limit and context_strategy)
|
||||
tools: Optional list of LangChain tools (for token estimation)
|
||||
|
||||
Returns:
|
||||
list: Prepared messages ready for LLM invocation
|
||||
@ -314,6 +466,9 @@ def prepare_context_messages(
|
||||
else:
|
||||
logger.warning("Invalid context_strategy '%s', using 'balanced'", strategy)
|
||||
|
||||
# Estimate tool tokens (tools are sent with each LLM call)
|
||||
tool_tokens = estimate_tool_tokens(tools) if tools else 0
|
||||
|
||||
# Build base context (system + topology)
|
||||
context_messages = [SystemMessage(content=system_prompt)]
|
||||
|
||||
@ -334,14 +489,16 @@ def prepare_context_messages(
|
||||
preserve_system=True, # Always keep system prompts
|
||||
)
|
||||
|
||||
# Log summary
|
||||
summary = get_token_usage_summary(trimmed_messages, model_name, llm_config)
|
||||
# Log summary (including tool tokens)
|
||||
summary = get_token_usage_summary(trimmed_messages, model_name, llm_config, tool_tokens)
|
||||
logger.info(
|
||||
"Context prepared: %d msgs, ~%d tokens / %dK limit (%.1f%%), strategy=%s",
|
||||
"Context prepared: %d msgs, ~%d tokens (messages) + %d tokens (tools) = %d total / %dK limit (%.1f%%), strategy=%s",
|
||||
summary["message_count"],
|
||||
summary["estimated_tokens"],
|
||||
summary["tool_tokens"],
|
||||
summary["total_tokens"],
|
||||
summary["model_limit_k"],
|
||||
summary["usage_percentage"],
|
||||
summary["total_usage_percentage"],
|
||||
trim_strategy
|
||||
)
|
||||
|
||||
|
||||
@ -204,6 +204,7 @@ def llm_call(state: dict, config: RunnableConfig | None = None):
|
||||
topology_context=topology_context,
|
||||
model_name=llm_config.get("model", "default"),
|
||||
llm_config=llm_config,
|
||||
tools=tools, # Pass tools for accurate token estimation
|
||||
)
|
||||
|
||||
# Create fresh model with tools for each LLM call
|
||||
|
||||
@ -49,6 +49,9 @@ langchain-ollama>=1.0.1
|
||||
langchain-deepseek>=1.0.1
|
||||
langchain-xai>=1.2.1
|
||||
|
||||
# Token Counting
|
||||
tiktoken>=0.8.0
|
||||
|
||||
# Network Automation
|
||||
netmiko>=4.6.0
|
||||
nornir>=3.5.0
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user