ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter2/local_llm_serving/.env.example

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+# vLLM Configuration
+MODEL_NAME=Qwen/Qwen3-0.6B
+
+# Optional: Path to local model (if you've downloaded it)
+# MODEL_PATH=/path/to/local/model
+
+# Server Configuration
+VLLM_HOST=localhost
+VLLM_PORT=8000
+
+# Logging
+LOG_LEVEL=INFO

chapter2/local_llm_serving/README.md

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+# Local LLM Serving & Tool Calling / 本地 LLM 服务部署与工具调用
+
+> Companion material for *AI Agents in Depth*, Chapter 2 — **Experiment 2-1 ★: Local LLM service deployment and tool calling**.  
+> 配套《深入理解 AI Agent》第 2 章 **实验 2-1 ★：本地 LLM 服务部署与工具调用**。
+
+← [Chapter 2 index / 返回第 2 章目录](../README.md)
+
+---
+
+## English
+
+### Overview
+
+Cross-platform demo of LLM tool calling via standard OpenAI-compatible APIs. The default root `ch2` install uses Ollama explicitly; Linux/WSL GPU users can add the `vllm` extra and run vLLM explicitly.
+
+### Features
+
+- **Universal entry:** single `main.py` for all platforms
+- **Backend paths:**
+  - **vLLM** on Linux/WSL2 with NVIDIA GPU after installing the `vllm` extra
+  - **Ollama** on macOS, native Windows, or Linux without GPU
+- **Standard tool calling** only (OpenAI-compatible format)
+- **Built-in tools:** weather, calculator, time, currency, PDF parse, code interpreter
+- **Interactive & single-task modes**
+- **Streaming:** real-time thinking, tool calls, and responses
+
+### Quick start
+
+```bash
+# 1. From the repository root, install the shared Chapter 2 environment
+uv sync --locked --python 3.12 --extra ch2
+
+# Optional GPU/vLLM path on supported Linux/WSL NVIDIA setups:
+# uv sync --locked --python 3.12 --extra ch2 --extra vllm
+
+# Activate before changing directories:
+# macOS/Linux:
+source .venv/bin/activate
+# Windows PowerShell: .venv\Scripts\Activate.ps1
+# Windows cmd: .venv\Scripts\activate.bat
+
+# pip fallback when uv is not installed:
+# python -m pip install -e ".[ch2]"
+# Linux/WSL GPU/vLLM pip fallback: python -m pip install -e ".[ch2,vllm]"
+
+# 2. Enter project
+cd chapter2/local_llm_serving
+
+# Single-project compatibility path, still supported during migration:
+# python -m pip install -r requirements.txt
+
+# 3. Run
+# Default root ch2 install:
+python main.py --backend ollama
+# Linux/WSL GPU path, only after installing --extra vllm:
+# python check_compatibility.py
+# python main.py --backend vllm
+```
+
+### Prerequisites
+
+**All platforms:** Python 3.12 and the root `ch2` extra (`uv sync --locked --python 3.12 --extra ch2`).
+
+Use `--extra vllm` only for the Linux/WSL GPU path; the default `ch2` install keeps local serving usable with Ollama without pulling the Linux/GPU vLLM stack. Use explicit `--backend` flags so CUDA presence does not select a backend you did not install.
+
+#### macOS
+```bash
+brew install ollama
+ollama serve          # separate terminal
+ollama pull qwen3:0.6b
+```
+
+#### Windows
+**Native Windows always uses Ollama**, including systems with an NVIDIA GPU. Install it from [ollama.com](https://ollama.com/download/windows), then run `ollama pull qwen3:0.6b` and `python main.py --backend ollama`.
+
+Official vLLM GPU execution requires Linux. To use vLLM on a Windows machine, run the project inside WSL2 (with CUDA support) or a Linux container. Community-maintained native Windows ports are outside this project's supported setup.
+
+#### Linux
+**With NVIDIA GPU:** install the `vllm` extra, then run `python main.py --backend vllm`.
+
+**Without GPU:**
+```bash
+curl -fsSL https://ollama.com/install.sh | sh
+systemctl start ollama
+ollama pull qwen3:0.6b
+```
+
+### Usage
+
+```bash
+python main.py --backend ollama     # default install or native Windows
+python main.py --backend vllm       # Linux/WSL2 GPU after --extra vllm
+python main.py --backend ollama --mode single --task "What's the weather in Tokyo?"
+python main.py --backend ollama --mode interactive
+python main.py --backend ollama --info
+```
+
+#### In code
+
+```python
+from main import ToolCallingAgent
+
+agent = ToolCallingAgent(backend="ollama")  # default install or native Windows
+# agent = ToolCallingAgent(backend="vllm")  # Linux/WSL GPU after --extra vllm
+response = agent.chat("What's the weather in Tokyo?")
+print(response)
+response = agent.chat("Tell me a joke", use_tools=False)
+agent.reset_conversation()
+```
+
+#### Custom tools
+
+```python
+from tools import ToolRegistry
+
+registry = ToolRegistry()
+
+def my_custom_tool(param1: str, param2: int) -> str:
+    return f"Processed {param1} with {param2}"
+
+registry.register_tool(
+    name="my_custom_tool",
+    function=my_custom_tool,
+    description="My custom tool description",
+    parameters={
+        "type": "object",
+        "properties": {
+            "param1": {"type": "string", "description": "First parameter"},
+            "param2": {"type": "integer", "description": "Second parameter"}
+        },
+        "required": ["param1", "param2"]
+    }
+)
+```
+
+### Project structure
+
+```
+local_llm_serving/
+├── main.py              # Main entry with explicit backend flags
+├── benchmark.py         # Serving benchmark: throughput / TTFT / KV cache / batching
+├── agent.py             # vLLM agent
+├── ollama_native.py     # Ollama native tool calling
+├── tools.py             # Tool implementations
+├── config.py            # Config
+├── server.py            # vLLM server manager
+├── check_compatibility.py
+├── requirements.txt
+├── env.example
+└── README.md
+```
+
+### Built-in tools
+
+1. **get_current_temperature** — Open-Meteo (no API key)
+2. **get_current_time** — timezones
+3. **convert_currency** — simulated rates
+4. **parse_pdf** — URL or local file
+5. **code_interpreter** — execute Python
+
+### Streaming
+
+Shows internal thinking, tool calls, results, and streamed final text.
+
+```bash
+python main.py --backend ollama              # streaming on by default
+python main.py --backend ollama --no-stream
+# toggle during chat with /stream
+```
+
+```python
+from main import ToolCallingAgent
+
+agent = ToolCallingAgent(backend="ollama")
+for chunk in agent.chat("What's the weather in Tokyo?", stream=True):
+    chunk_type = chunk.get("type")
+    content = chunk.get("content", "")
+    if chunk_type == "thinking":
+        print(f"Thinking: {content}")
+    elif chunk_type == "tool_call":
+        print(f"Tool: {content['name']}")
+    elif chunk_type == "tool_result":
+        print(f"Result: {content}")
+    elif chunk_type == "content":
+        print(content, end="", flush=True)
+```
+
+```bash
+python demo_streaming.py
+python test_streaming.py --mode compare
+```
+
+### Serving benchmark (`benchmark.py`)
+
+Companion to Experiment 2-1: measure **serving** metrics (throughput / latency / batching / KV cache) on a local small model via OpenAI-compatible APIs (vLLM or Ollama).
+
+**All numbers come from the real server; the script synthesizes nothing.** Use `--dry-run` offline to inspect planned requests.
+
+#### Scenarios (`--scenario`)
+
+| Scenario | What it measures | Book point |
+|----------|------------------|------------|
+| `throughput` | Single-stream decode tok/s and TTFT | Exp 2-1 point 2: >100 tok/s on M2-class machines |
+| `kv-cache` | Prefix cache **hit vs miss** TTFT | Exp 2-1 point 5: change system-prompt start → full prefix recompute |
+| `batching` | Aggregate throughput vs concurrency | Continuous batching trade-offs |
+| `all` | Run all of the above (default) | — |
+
+#### Usage
+
+```bash
+# 1. Start a server (pick one)
+python server.py                            # vLLM (Linux/WSL2 + NVIDIA GPU)
+ollama serve && ollama pull qwen3:0.6b      # Ollama (Mac / no GPU)
+
+# 2. Run benchmark
+# If you use Ollama, add --backend ollama to every command below.
+python benchmark.py --scenario all --output results.json
+python benchmark.py --scenario kv-cache
+python benchmark.py --scenario batching --concurrency 1,2,4,8
+
+python benchmark.py --dry-run
+python benchmark.py --help
+```
+
+#### Main flags
+
+- `--backend {vllm,ollama}` — default URL/model (vLLM `Qwen3-0.6B` @ `:8000/v1`, Ollama `qwen3:0.6b` @ `:11434/v1`)
+- `--base-url` / `--model` / `--api-key` — override connection
+- `--repeats` — repeats for throughput / kv-cache (default 5)
+- `--max-tokens` / `--temperature`
+- `--prefix-tokens` — shared prefix length for kv-cache (default 1024)
+- `--concurrency` — batching concurrency list, comma-separated (default `1,2,4,8`)
+- `--output` — write JSON results
+
+> `kv-cache` needs server prefix caching (vLLM automatic prefix caching is on by default). Hit group keeps the system prompt byte-identical; miss group inserts a unique counter only at the **start** of the system prompt so the whole prefix invalidates—demonstrating “once the system prompt is fixed, don’t change it.”
+
+### Complete manuscript campaign (`run_experiment.py`)
+
+The benchmark above measures individual serving properties. The acceptance
+campaign additionally exercises the manuscript's complete Vancouver example:
+Qwen3 emits two raw XML tool calls in one turn, the time and weather tools run
+concurrently, their results are returned through the chat template, and the
+model decides to stop. It then records five matched prefix-cache hit/miss pairs.
+The exact rendered token stream, every Ollama stream chunk, model digest,
+server token counts/durations, wall-clock TTFT, hashes, and a credential scan
+are retained; no output is synthesized.
+
+```bash
+ollama serve                         # separate terminal, if not already running
+ollama pull qwen3:0.6b
+python run_experiment.py \
+  --output runs/exp2-1-qwen3-0.6b-$(date +%Y%m%d-%H%M%S)
+```
+
+The frozen design is [experiment_protocol.json](experiment_protocol.json).
+`manifest.json` is the completion receipt and `evidence.json` is the raw
+auditable record. Local inference costs $0 in API fees; the report does not
+generalize the measured throughput to other hardware.
+
+### Configuration
+
+Copy `env.example` to `.env`:
+
+```bash
+MODEL_NAME=Qwen/Qwen3-0.6B
+VLLM_HOST=localhost
+VLLM_PORT=8000
+LOG_LEVEL=INFO
+```
+
+### Tool calling format
+
+Standard OpenAI-compatible:
+
+```json
+{
+  "tool_calls": [{
+    "id": "call_123",
+    "type": "function",
+    "function": {
+      "name": "get_weather",
+      "arguments": {"location": "Tokyo"}
+    }
+  }]
+}
+```
+
+### Troubleshooting
+
+- **Ollama not found:** Mac `brew install ollama && ollama serve`; Windows [ollama.com](https://ollama.com/download/windows); Linux install script above
+- **No models:** `ollama pull qwen3:0.6b`
+- **CUDA not available:** install drivers/CUDA for the vLLM path, or run `python main.py --backend ollama`
+- **Native Windows with CUDA:** use Ollama on native Windows; use WSL2 or a Linux container for vLLM
+- **Compatibility:** `python check_compatibility.py` is for the Linux/WSL2 vLLM path; native Windows should use `python main.py --backend ollama`.
+
+### Supported models
+
+**Default:** Qwen3 0.6B (small, decent tool calling).  
+**Also good for tools:** Qwen3 8B+, Llama 3.1/3.2 8B+, Mistral Nemo.  
+**vLLM:** default Qwen3-0.6B; any vLLM-supported model can be configured.
+
+### How it works
+
+1. Detect OS and GPU
+2. Linux/WSL2 + NVIDIA GPU → vLLM; native Windows, macOS, or Linux without CUDA → Ollama
+3. Both use standard OpenAI tool calling
+4. Tool results are fed back into the model
+
+### References
+
+- [vLLM Documentation](https://docs.vllm.ai/)
+- [Ollama Documentation](https://ollama.com/)
+- [OpenAI Tool Calling](https://platform.openai.com/docs/guides/function-calling)
+
+---
+
+## 中文
+
+### 概述
+
+跨平台本地 LLM 工具调用演示，统一使用 OpenAI 兼容 API。默认根目录 `ch2` 安装显式使用 Ollama；Linux/WSL GPU 用户可额外安装 `vllm` extra 后显式运行 vLLM。
+
+### 功能
+
+- **统一入口：** 单一 `main.py` 覆盖各平台
+- **后端路径：**
+  - Linux/WSL2 + NVIDIA GPU，且已安装 `vllm` extra → **vLLM**
+  - macOS、原生 Windows、无 GPU 的 Linux → **Ollama**
+- **仅标准工具调用**（OpenAI 兼容格式）
+- **内置工具：** 天气、时间、汇率、PDF、代码解释器等
+- **交互与单任务模式**
+- **流式输出：** 实时展示思考、工具调用与回复
+
+### 快速开始
+
+```bash
+# 在仓库根目录安装统一的第 2 章环境
+uv sync --locked --python 3.12 --extra ch2
+
+# 支持的 Linux/WSL NVIDIA 环境如需 GPU/vLLM，可改用：
+# uv sync --locked --python 3.12 --extra ch2 --extra vllm
+
+# 切换目录前先激活环境：
+# macOS/Linux：
+source .venv/bin/activate
+# Windows PowerShell：.venv\Scripts\Activate.ps1
+# Windows cmd：.venv\Scripts\activate.bat
+
+# 未安装 uv 时可用 pip 兜底：
+# python -m pip install -e ".[ch2]"
+# Linux/WSL GPU/vLLM pip 兜底：python -m pip install -e ".[ch2,vllm]"
+
+cd chapter2/local_llm_serving
+
+# 迁移期间仍支持单项目兼容路径：
+# python -m pip install -r requirements.txt
+
+# 默认根目录 ch2 安装：
+python main.py --backend ollama
+# Linux/WSL GPU 路径，仅在安装 --extra vllm 后使用：
+# python check_compatibility.py
+# python main.py --backend vllm
+```
+
+### 前置条件
+
+**全平台：** Python 3.12，并安装根目录 `ch2` extra（`uv sync --locked --python 3.12 --extra ch2`）。
+
+只有走 Linux/WSL GPU/vLLM 路径时才需要额外选择 `--extra vllm`；默认 `ch2` 安装保留 Ollama 路径，不会拉取 Linux/GPU vLLM 栈。请显式传入 `--backend`，避免仅因检测到 CUDA 而选择未安装的后端。
+
+#### macOS
+```bash
+brew install ollama
+ollama serve
+ollama pull qwen3:0.6b
+```
+
+#### Windows
+**原生 Windows 始终使用 Ollama**，包括装有 NVIDIA GPU 的系统。从 [ollama.com](https://ollama.com/download/windows) 安装 Ollama，再运行 `ollama pull qwen3:0.6b` 和 `python main.py --backend ollama`。
+
+vLLM 官方 GPU 执行环境要求 Linux。若要在 Windows 机器上使用 vLLM，请在支持 CUDA 的 WSL2 或 Linux 容器中运行本项目。社区维护的原生 Windows 移植版不属于本项目支持的配置。
+
+#### Linux
+**有 NVIDIA GPU：** 安装 `vllm` extra 后运行 `python main.py --backend vllm`。
+
+**无 GPU：**
+```bash
+curl -fsSL https://ollama.com/install.sh | sh
+systemctl start ollama
+ollama pull qwen3:0.6b
+```
+
+### 用法
+
+```bash
+python main.py --backend ollama     # 默认安装或原生 Windows
+python main.py --backend vllm       # Linux/WSL2 GPU，需先安装 --extra vllm
+python main.py --backend ollama --mode single --task "What's the weather in Tokyo?"
+python main.py --backend ollama --mode interactive
+python main.py --backend ollama --info
+```
+
+#### 在代码中使用
+
+```python
+from main import ToolCallingAgent
+
+agent = ToolCallingAgent(backend="ollama")  # 默认安装或原生 Windows
+# agent = ToolCallingAgent(backend="vllm")  # Linux/WSL GPU，需先安装 --extra vllm
+response = agent.chat("What's the weather in Tokyo?")
+print(response)
+response = agent.chat("Tell me a joke", use_tools=False)
+agent.reset_conversation()
+```
+
+#### 添加自定义工具
+
+```python
+from tools import ToolRegistry
+
+registry = ToolRegistry()
+
+def my_custom_tool(param1: str, param2: int) -> str:
+    return f"Processed {param1} with {param2}"
+
+registry.register_tool(
+    name="my_custom_tool",
+    function=my_custom_tool,
+    description="My custom tool description",
+    parameters={
+        "type": "object",
+        "properties": {
+            "param1": {"type": "string", "description": "First parameter"},
+            "param2": {"type": "integer", "description": "Second parameter"}
+        },
+        "required": ["param1", "param2"]
+    }
+)
+```
+
+### 项目结构
+
+```
+local_llm_serving/
+├── main.py              # 主入口，支持显式后端参数
+├── benchmark.py         # 服务基准：吞吐 / TTFT / KV Cache / 批处理
+├── agent.py             # vLLM Agent
+├── ollama_native.py     # Ollama 原生工具调用
+├── tools.py             # 工具实现
+├── config.py            # 配置
+├── server.py            # vLLM 服务管理
+├── check_compatibility.py
+├── requirements.txt
+├── env.example
+└── README.md
+```
+
+### 内置工具
+
+1. **get_current_temperature** — Open-Meteo（无需 API Key）
+2. **get_current_time** — 多时区时间
+3. **convert_currency** — 模拟汇率
+4. **parse_pdf** — URL 或本地 PDF
+5. **code_interpreter** — 执行 Python
+
+### 流式模式
+
+展示内部思考、工具调用、工具结果与逐字最终回复。
+
+```bash
+python main.py --backend ollama              # 默认开启流式
+python main.py --backend ollama --no-stream
+# 对话中用 /stream 切换
+```
+
+```python
+from main import ToolCallingAgent
+
+agent = ToolCallingAgent(backend="ollama")
+for chunk in agent.chat("What's the weather in Tokyo?", stream=True):
+    chunk_type = chunk.get("type")
+    content = chunk.get("content", "")
+    if chunk_type == "thinking":
+        print(f"Thinking: {content}")
+    elif chunk_type == "tool_call":
+        print(f"Tool: {content['name']}")
+    elif chunk_type == "tool_result":
+        print(f"Result: {content}")
+    elif chunk_type == "content":
+        print(content, end="", flush=True)
+```
+
+```bash
+python demo_streaming.py
+python test_streaming.py --mode compare
+```
+
+### 服务基准（`benchmark.py`）
+
+实验 2-1 的配套基准，测量本地小模型在 **serving** 层面的吞吐 / 延迟 / 批处理 / KV Cache，经 OpenAI 兼容接口工作（vLLM 与 Ollama 均可）。
+
+**所有数字都来自真实服务端实测，脚本本身不产生任何合成数据。** 服务未启动时可用 `--dry-run` 离线查看将要发出的请求配置。
+
+#### 场景（`--scenario`）
+
+| 场景 | 说明 | 对应书中要点 |
+|------|------|-------------|
+| `throughput` | 单流解码吞吐（tok/s）与首 token 延迟（TTFT） | 实验 2-1 第 2 点：M2 上 >100 tok/s |
+| `kv-cache` | 前缀缓存 **命中 vs 未命中** 的 TTFT 对比 | 实验 2-1 第 5 点：改动系统提示词开头 → 缓存失效 |
+| `batching` | 不同并发度下的聚合吞吐 | 连续批处理如何提升系统吞吐 |
+| `all` | 依次运行以上全部（默认） | — |
+
+#### 用法
+
+```bash
+# 1. 先启动服务端（二选一）
+python server.py                            # vLLM（Linux/WSL2 + NVIDIA GPU）
+ollama serve && ollama pull qwen3:0.6b      # Ollama（Mac / 无 GPU）
+
+# 2. 运行基准
+# 如果使用 Ollama 后端，请在以下每条命令中添加 --backend ollama参数
+python benchmark.py --scenario all --output results.json
+python benchmark.py --scenario kv-cache
+python benchmark.py --scenario batching --concurrency 1,2,4,8
+
+python benchmark.py --dry-run
+python benchmark.py --help
+```
+
+#### 主要参数
+
+- `--backend {vllm,ollama}`：推断默认地址与模型名（vLLM `Qwen3-0.6B` @ `:8000/v1`，Ollama `qwen3:0.6b` @ `:11434/v1`）
+- `--base-url` / `--model` / `--api-key`：覆盖默认连接配置
+- `--repeats`：`throughput` / `kv-cache` 的重复次数（默认 5）
+- `--max-tokens` / `--temperature`：生成参数
+- `--prefix-tokens`：`kv-cache` 场景共享前缀的近似长度（默认 1024）
+- `--concurrency`：`batching` 并发度列表，逗号分隔（默认 `1,2,4,8`）
+- `--output`：将结果写入 JSON
+
+> 说明：`kv-cache` 依赖服务端前缀缓存（vLLM automatic prefix caching 默认开启）。命中组保持系统提示词逐字节不变；未命中组每次只在系统提示词**开头**插入唯一计数串，前缀被改写导致缓存全部失效——这正是书中「系统提示词一旦定下来就不要改」的实测演示。
+
+### 配置
+
+复制 `env.example` 为 `.env`：
+
+```bash
+MODEL_NAME=Qwen/Qwen3-0.6B
+VLLM_HOST=localhost
+VLLM_PORT=8000
+LOG_LEVEL=INFO
+```
+
+### 工具调用格式
+
+标准 OpenAI 兼容格式（见英文节 JSON 示例）。
+
+### 故障排除
+
+- **找不到 Ollama：** Mac `brew install ollama && ollama serve`；Windows 官网安装；Linux 用安装脚本
+- **没有模型：** `ollama pull qwen3:0.6b`
+- **CUDA 不可用：** 为 vLLM 路径安装驱动/CUDA，或运行 `python main.py --backend ollama`
+- **原生 Windows 有 CUDA：** 原生 Windows 请使用 Ollama；如需 vLLM，请使用 WSL2 或 Linux 容器
+- **兼容性检查：** `python check_compatibility.py` 仅用于 Linux/WSL2 vLLM 路径；原生 Windows 请使用 `python main.py --backend ollama`。
+
+### 支持的模型
+
+**默认：** Qwen3 0.6B。  
+**工具调用表现较好：** Qwen3 8B+、Llama 3.1/3.2 8B+、Mistral Nemo。  
+**vLLM：** 默认 Qwen3-0.6B，可配置任意 vLLM 支持的模型。
+
+### 工作原理
+
+1. 检测操作系统与 GPU  
+2. Linux/WSL2 + NVIDIA GPU → vLLM；原生 Windows、macOS 或无 CUDA 的 Linux → Ollama
+3. 两端均使用标准 OpenAI 工具调用  
+4. 工具结果回灌模型生成最终回复  
+
+### 参考
+
+- [vLLM Documentation](https://docs.vllm.ai/)
+- [Ollama Documentation](https://ollama.com/)
+- [OpenAI Tool Calling](https://platform.openai.com/docs/guides/function-calling)
+
+---
+
+## Notes / 说明
+
+- Educational demo; license as provided in-repo for course use.  
+- 教学演示用途；按仓库既有授权用于课程学习。

chapter2/local_llm_serving/agent.py

@@ -0,0 +1,591 @@
+"""
+vLLM Tool Calling Agent Implementation
+Demonstrates how to use vLLM with Qwen3 for tool calling
+"""
+
+import json
+import uuid
+import logging
+from concurrent.futures import ThreadPoolExecutor
+from typing import List, Dict, Any, Optional, Tuple
+from openai import OpenAI
+from tools import ToolRegistry
+from config import OPENAI_API_BASE, OPENAI_API_KEY, LOG_LEVEL
+
+# Set up logging
+logging.basicConfig(level=LOG_LEVEL)
+logger = logging.getLogger(__name__)
+
+
+class VLLMToolAgent:
+    """Agent that uses vLLM for tool calling with Qwen3 model"""
+    
+    def __init__(self, api_base: str = OPENAI_API_BASE, api_key: str = OPENAI_API_KEY):
+        """
+        Initialize the agent with vLLM server connection
+        
+        Args:
+            api_base: Base URL for vLLM server
+            api_key: API key (not required for vLLM, use "EMPTY")
+        """
+        self.client = OpenAI(
+            api_key=api_key,
+            base_url=api_base
+        )
+        self.tool_registry = ToolRegistry()
+        self.conversation_history = []
+        logger.info(f"Initialized VLLMToolAgent with server at {api_base}")
+    
+    def _format_system_prompt_with_tools(self) -> str:
+        """
+        Format the system prompt with available tools in Qwen3 format
+        """
+        tools_json = json.dumps(self.tool_registry.get_tool_schemas(), indent=2)
+        
+        system_prompt = f"""# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{tools_json}
+</tools>
+
+For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
+<tool_call>
+{{"name": <function-name>, "arguments": <args-json-object>}}
+</tool_call>
+
+You are a helpful assistant that can use tools to answer questions and perform tasks.
+When you need to use a tool, generate the appropriate tool call.
+After receiving tool results, use them to provide a comprehensive answer to the user."""
+        
+        return system_prompt
+    
+    def _parse_tool_calls(self, content: str) -> List[Dict[str, Any]]:
+        """
+        Parse tool calls from model output
+        Extracts content between <tool_call> tags
+        """
+        tool_calls = []
+        
+        # Find all tool call blocks
+        import re
+        pattern = r'<tool_call>(.*?)</tool_call>'
+        matches = re.findall(pattern, content, re.DOTALL)
+        
+        for match in matches:
+            try:
+                tool_call = json.loads(match.strip())
+                if "name" in tool_call and "arguments" in tool_call:
+                    tool_calls.append({
+                        "id": str(uuid.uuid4())[:8],  # Generate short ID
+                        "type": "function",
+                        "function": {
+                            "name": tool_call["name"],
+                            "arguments": tool_call["arguments"]
+                        }
+                    })
+                    logger.debug(f"Parsed tool call: {tool_call['name']}")
+            except json.JSONDecodeError as e:
+                logger.warning(f"Failed to parse tool call JSON: {e}")
+                logger.debug(f"Content was: {match}")
+        
+        return tool_calls
+    
+    def _execute_tool_calls(self, tool_calls: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """
+        Execute tool calls and return results.
+        Multiple tool calls in the same turn are executed in parallel (they are
+        independent by construction, since the model generated all of them
+        without seeing any result). Ensures that error messages from failed
+        tool executions are properly formatted.
+        """
+        def run_one(tool_call: Dict[str, Any]) -> Dict[str, Any]:
+            tool_name = tool_call["function"]["name"]
+            tool_args = tool_call["function"]["arguments"]
+            tool_id = tool_call["id"]
+            
+            logger.info(f"Executing tool: {tool_name} with args: {tool_args}")
+            
+            # Execute the tool
+            result = self.tool_registry.execute_tool(tool_name, tool_args)
+            
+            # Check if the result indicates an error
+            try:
+                result_dict = json.loads(result) if isinstance(result, str) else result
+                if isinstance(result_dict, dict) and not result_dict.get("success", True):
+                    # Tool execution failed - format error message clearly
+                    error_msg = f"❌ Tool '{tool_name}' execution failed:\n"
+                    if "error" in result_dict:
+                        error_msg += f"Error: {result_dict['error']}\n"
+                    if "error_type" in result_dict:
+                        error_msg += f"Type: {result_dict['error_type']}\n"
+                    if "traceback" in result_dict:
+                        error_msg += f"Traceback:\n{result_dict['traceback']}\n"
+                    
+                    logger.error(f"Tool {tool_name} failed: {result_dict.get('error', 'Unknown error')}")
+                    result = error_msg
+                else:
+                    logger.debug(f"Tool {tool_name} returned: {result}")
+            except (json.JSONDecodeError, TypeError):
+                # Result is not JSON, just pass it through
+                logger.debug(f"Tool {tool_name} returned: {result}")
+            
+            # Format the result
+            return {
+                "role": "tool",
+                "tool_call_id": tool_id,
+                "name": tool_name,
+                "content": result if isinstance(result, str) else str(result)
+            }
+        
+        if len(tool_calls) <= 1:
+            return [run_one(tc) for tc in tool_calls]
+        
+        # Independent tool calls run concurrently; executor.map preserves order
+        with ThreadPoolExecutor(max_workers=len(tool_calls)) as executor:
+            return list(executor.map(run_one, tool_calls))
+    
+    def _execute_single_tool(self, tool_data: Dict[str, Any]) -> Tuple[str, bool]:
+        """
+        Execute one parsed tool call ({"name": ..., "arguments": ...}).
+        Returns (result_text, is_error) with error messages formatted clearly.
+        """
+        tool_name = tool_data["name"]
+        try:
+            result = self.tool_registry.execute_tool(tool_name, tool_data["arguments"])
+        except Exception as e:
+            logger.error(f"Tool execution error: {e}")
+            return f"❌ Tool execution exception: {str(e)}", True
+        
+        # Check if the result indicates an error
+        try:
+            result_dict = json.loads(result) if isinstance(result, str) else result
+            if isinstance(result_dict, dict) and not result_dict.get("success", True):
+                # Tool execution failed - format error message clearly
+                error_msg = f"❌ Tool '{tool_name}' execution failed:\n"
+                if "error" in result_dict:
+                    error_msg += f"Error: {result_dict['error']}\n"
+                if "error_type" in result_dict:
+                    error_msg += f"Type: {result_dict['error_type']}\n"
+                if "traceback" in result_dict:
+                    error_msg += f"Traceback:\n{result_dict['traceback']}\n"
+                
+                logger.error(f"Tool {tool_name} failed: {result_dict.get('error', 'Unknown error')}")
+                return error_msg, True
+        except (json.JSONDecodeError, TypeError):
+            # Result is not JSON, just pass it through
+            pass
+        return result, False
+    
+    def chat(self, message: str, use_tools: bool = True,  
+             temperature: float = 0.3, max_tokens: int = 2048, 
+             stream: bool = False) -> str:
+        """
+        Send a message to the model and handle tool calls in a ReAct loop
+        
+        Args:
+            message: User message
+            use_tools: Whether to enable tool calling
+            temperature: Sampling temperature
+            max_tokens: Maximum tokens to generate
+            stream: Whether to stream the response
+            
+        Returns:
+            Final response from the model (or generator if streaming)
+        """
+        if stream:
+            return self.chat_stream(message, use_tools, temperature, max_tokens)
+        
+        # Add user message to history
+        self.conversation_history.append({"role": "user", "content": message})
+        
+        # Prepare messages with system prompt if using tools
+        messages = []
+        if use_tools:
+            messages.append({
+                "role": "system",
+                "content": self._format_system_prompt_with_tools()
+            })
+        else:
+            messages.append({
+                "role": "system",
+                "content": "You are a helpful assistant."
+            })
+        
+        # Add conversation history
+        messages.extend(self.conversation_history)
+        
+        # Prepare tools for the API call
+        tools = self.tool_registry.get_tool_schemas() if use_tools else None
+        
+        # ReAct loop - keep going until no more tool calls are needed
+        max_iterations = 10  # Prevent infinite loops
+        iteration = 0
+        final_response = ""
+        
+        while iteration < max_iterations:
+            iteration += 1
+            logger.info(f"ReAct iteration {iteration}")
+            
+            # Prepare messages for this iteration
+            messages = []
+            if use_tools:
+                messages.append({
+                    "role": "system",
+                    "content": self._format_system_prompt_with_tools()
+                })
+            else:
+                messages.append({
+                    "role": "system",
+                    "content": "You are a helpful assistant."
+                })
+            messages.extend(self.conversation_history)
+            
+            # Call the model
+            response = self.client.chat.completions.create(
+                model="Qwen/Qwen3-0.6B",
+                messages=messages,
+                tools=tools,
+                tool_choice="auto" if use_tools else None,
+                temperature=temperature,
+                max_tokens=max_tokens
+            )
+            
+            assistant_message = response.choices[0].message
+            content = assistant_message.content or ""
+            
+            # Read tool calls from the structured field. With
+            # enable_auto_tool_choice + the hermes parser, vLLM extracts the
+            # <tool_call> tags out of the text and returns them here instead of
+            # leaving them in `content` (which only holds <think> and final text).
+            tool_calls = []
+            if use_tools and assistant_message.tool_calls:
+                for tc in assistant_message.tool_calls:
+                    raw_args = tc.function.arguments
+                    try:
+                        parsed_args = json.loads(raw_args) if isinstance(raw_args, str) else raw_args
+                    except json.JSONDecodeError as e:
+                        logger.warning(f"Failed to parse tool arguments for {tc.function.name}: {e}")
+                        parsed_args = {}
+                    logger.info(f"Model requested tool call: {tc.function.name}({raw_args})")
+                    tool_calls.append({
+                        "id": tc.id,
+                        "type": "function",
+                        "function": {
+                            "name": tc.function.name,
+                            "arguments": parsed_args,
+                        },
+                    })
+            
+            if tool_calls:
+                logger.info(f"Model requested {len(tool_calls)} tool call(s)")
+                
+                # Add assistant message with tool calls to history
+                # (arguments must be a JSON string per the OpenAI API spec)
+                self.conversation_history.append({
+                    "role": "assistant",
+                    "content": content,
+                    "tool_calls": [
+                        {
+                            **tc,
+                            "function": {
+                                **tc["function"],
+                                "arguments": tc["function"]["arguments"]
+                                if isinstance(tc["function"]["arguments"], str)
+                                else json.dumps(tc["function"]["arguments"])
+                            }
+                        }
+                        for tc in tool_calls
+                    ]
+                })
+                
+                # Execute tool calls
+                tool_results = self._execute_tool_calls(tool_calls)
+                
+                # Add tool results to conversation
+                for result in tool_results:
+                    # Format tool response for Qwen3
+                    tool_response = f'<tool_response>\n{result["content"]}\n</tool_response>'
+                    self.conversation_history.append({
+                        "role": "user",  # Tool responses are treated as user messages in Qwen3
+                        "content": tool_response,
+                        "name": result.get("name", "tool")
+                    })
+                
+                # Continue the ReAct loop
+                continue
+            else:
+                # No tool calls - we have a final response
+                self.conversation_history.append({
+                    "role": "assistant",
+                    "content": content
+                })
+                final_response = content
+                break
+        
+        # Check if we hit max iterations
+        if iteration >= max_iterations:
+            logger.warning("Maximum iterations reached in ReAct loop")
+            final_response = "I've reached the maximum number of reasoning steps. " + final_response
+        
+        return final_response
+    
+    def reset_conversation(self):
+        """Reset the conversation history"""
+        self.conversation_history = []
+        logger.info("Conversation history reset")
+    
+    def chat_stream(self, message: str, use_tools: bool = True,
+                    temperature: float = 0.3, max_tokens: int = 2048):
+        """
+        Stream a message to the model and handle tool calls in a ReAct loop
+        
+        Yields chunks that include:
+        - type: 'thinking', 'tool_call', 'tool_result', 'content'
+        - content: The actual content
+        """
+        # Add user message to history
+        self.conversation_history.append({"role": "user", "content": message})
+        
+        # Prepare tools for the API call
+        tools = self.tool_registry.get_tool_schemas() if use_tools else None
+        
+        # ReAct loop - keep going until no more tool calls are needed
+        max_iterations = 10  # Prevent infinite loops
+        iteration = 0
+        
+        while iteration < max_iterations:
+            iteration += 1
+            logger.info(f"ReAct stream iteration {iteration}")
+            
+            # Prepare messages for this iteration
+            messages = []
+            if use_tools:
+                messages.append({
+                    "role": "system",
+                    "content": self._format_system_prompt_with_tools()
+                })
+            else:
+                messages.append({
+                    "role": "system",
+                    "content": "You are a helpful assistant."
+                })
+            messages.extend(self.conversation_history)
+            
+            # Stream response from model
+            stream_response = self.client.chat.completions.create(
+                model="Qwen/Qwen3-0.6B",
+                messages=messages,
+                tools=tools,
+                tool_choice="auto" if use_tools else None,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                stream=True
+            )
+            
+            collected_content = []
+            thinking_buffer = ""
+            tool_call_parts = {}
+            
+            # Process the stream
+            for chunk in stream_response:
+                if chunk.choices and chunk.choices[0].delta:
+                    delta = chunk.choices[0].delta
+                    if delta.content:
+                        content_chunk = delta.content
+                        collected_content.append(content_chunk)
+                        
+                        # Check if this is internal thinking (between <think> tags)
+                        if '<think>' in content_chunk or thinking_buffer:
+                            thinking_buffer += content_chunk
+                            if '</think>' in thinking_buffer:
+                                # Extract and yield thinking
+                                import re
+                                thinking_match = re.search(r'<think>(.*?)</think>', thinking_buffer, re.DOTALL)
+                                if thinking_match:
+                                    # Stream thinking character by character
+                                    for char in thinking_match.group(1).strip():
+                                        yield {"type": "thinking", "content": char}
+                                remaining = re.sub(
+                                    r'<think>.*?</think>', '', thinking_buffer, flags=re.DOTALL
+                                )
+                                thinking_buffer = ""
+                                if remaining:
+                                    yield {"type": "content", "content": remaining}
+                        else:
+                            # Regular content
+                            yield {"type": "content", "content": content_chunk}
+
+                    # vLLM streams structured tool calls in fragments. Calls
+                    # are keyed by index because ids, names, and arguments may
+                    # arrive in separate chunks.
+                    for fragment in getattr(delta, "tool_calls", None) or []:
+                        index = getattr(fragment, "index", None)
+                        if index is None:
+                            index = 0
+                        buffered = tool_call_parts.setdefault(index, {
+                            "id": None,
+                            "type": "function",
+                            "function": {"name": "", "arguments": ""},
+                        })
+                        if getattr(fragment, "id", None):
+                            buffered["id"] = fragment.id
+                        if getattr(fragment, "type", None):
+                            buffered["type"] = fragment.type
+                        function = getattr(fragment, "function", None)
+                        if function:
+                            if getattr(function, "name", None):
+                                buffered["function"]["name"] += function.name
+                            if getattr(function, "arguments", None):
+                                buffered["function"]["arguments"] += function.arguments
+
+            # Save complete response and structured calls to history before
+            # adding tool results, matching the non-streaming message order.
+            complete_response = ''.join(collected_content)
+
+            if tool_call_parts:
+                pending_tool_calls = []
+                parse_errors = []
+                assistant_tool_calls = []
+
+                for index in sorted(tool_call_parts):
+                    buffered = tool_call_parts[index]
+                    call_id = buffered["id"] or str(uuid.uuid4())[:8]
+                    tool_name = buffered["function"]["name"] or "unknown"
+                    raw_args = buffered["function"]["arguments"] or "{}"
+                    assistant_tool_calls.append({
+                        "id": call_id,
+                        "type": buffered["type"],
+                        "function": {
+                            "name": tool_name,
+                            "arguments": raw_args,
+                        },
+                    })
+
+                    try:
+                        parsed_args = json.loads(raw_args)
+                    except json.JSONDecodeError as e:
+                        error_msg = f"❌ Tool call parse exception: {str(e)}"
+                        logger.error(f"Tool call parse error: {e}")
+                        parse_errors.append((tool_name, error_msg))
+                        yield {"type": "tool_error", "content": error_msg}
+                        continue
+
+                    tool_data = {
+                        "id": call_id,
+                        "name": tool_name,
+                        "arguments": parsed_args,
+                    }
+                    pending_tool_calls.append(tool_data)
+                    yield {
+                        "type": "tool_call",
+                        "content": {
+                            "name": tool_name,
+                            "arguments": parsed_args,
+                        },
+                    }
+
+                self.conversation_history.append({
+                    "role": "assistant",
+                    "content": complete_response,
+                    "tool_calls": assistant_tool_calls,
+                })
+
+                for tool_name, error_msg in parse_errors:
+                    self.conversation_history.append({
+                        "role": "user",
+                        "content": f'<tool_response>\n{error_msg}\n</tool_response>',
+                        "name": tool_name,
+                    })
+
+                # Execute all valid tool calls from this turn in parallel.
+                if not pending_tool_calls:
+                    outcomes = []
+                elif len(pending_tool_calls) == 1:
+                    outcomes = [self._execute_single_tool(pending_tool_calls[0])]
+                else:
+                    with ThreadPoolExecutor(max_workers=len(pending_tool_calls)) as executor:
+                        outcomes = list(executor.map(self._execute_single_tool, pending_tool_calls))
+                
+                for tool_data, (result, is_error) in zip(pending_tool_calls, outcomes):
+                    if is_error:
+                        yield {"type": "tool_error", "content": result}
+                    else:
+                        yield {"type": "tool_result", "content": result}
+                    
+                    # Add to history
+                    self.conversation_history.append({
+                        "role": "user",
+                        "content": f'<tool_response>\n{result}\n</tool_response>',
+                        "name": tool_data["name"]
+                    })
+
+                # Continue the ReAct loop - let the model decide what to do next
+                continue
+            else:
+                # No tool calls - we have a final response
+                self.conversation_history.append({
+                    "role": "assistant",
+                    "content": complete_response
+                })
+                # Exit the ReAct loop
+                break
+        
+        # Check if we hit max iterations
+        if iteration >= max_iterations:
+            yield {"type": "error", "content": "Maximum iterations reached in ReAct loop"}
+    
+    def get_conversation_history(self) -> List[Dict[str, Any]]:
+        """Get the current conversation history"""
+        return self.conversation_history
+    
+    def add_custom_tool(self, name: str, function: callable, 
+                       description: str, parameters: Dict):
+        """
+        Add a custom tool to the registry
+        
+        Args:
+            name: Tool name
+            function: Callable function
+            description: Tool description
+            parameters: OpenAI-style parameter schema
+        """
+        self.tool_registry.register_tool(name, function, description, parameters)
+        logger.info(f"Added custom tool: {name}")
+
+
+def demonstrate_tool_calling():
+    """Demonstrate the tool calling functionality"""
+    print("=" * 60)
+    print("vLLM Tool Calling Demo with Qwen3")
+    print("=" * 60)
+    
+    # Initialize agent
+    agent = VLLMToolAgent()
+    
+    # Test cases
+    test_queries = [
+        "What's the current temperature in Paris, France?",
+        "Calculate 15 * 23 + sqrt(144)",
+        "What time is it in Tokyo (JST)?",
+        "Search for information about vLLM tool calling",
+        "What's the weather in Dubai and what's 100 fahrenheit in celsius?",
+    ]
+    
+    for i, query in enumerate(test_queries, 1):
+        print(f"\n--- Test {i} ---")
+        print(f"User: {query}")
+        
+        response = agent.chat(query)
+        print(f"Assistant: {response}")
+        
+        # Reset conversation for next test
+        agent.reset_conversation()
+        print("-" * 40)
+
+
+if __name__ == "__main__":
+    # Run demonstration
+    demonstrate_tool_calling()

chapter2/local_llm_serving/benchmark.py

@@ -0,0 +1,487 @@
+#!/usr/bin/env python3
+"""
+本地 LLM 服务性能基准（实验 2-1 配套）
+
+本脚本通过 OpenAI 兼容接口（vLLM 或 Ollama 均可）测量本地部署的小模型在
+「服务（serving）」层面的三个核心指标，帮助读者建立对吞吐 / 延迟 / 批处理 /
+KV Cache 的直觉：
+
+  1. throughput —— 单流解码吞吐（tokens/s）与首 token 延迟（TTFT）
+  2. kv-cache  —— 前缀缓存命中 vs 未命中的 TTFT 对比
+                （对应实验 2-1 第 5 点：系统提示词不变时缓存命中更快，
+                 修改系统提示词开头几个字符导致缓存失效、需重算整个前缀）
+  3. batching  —— 不同并发度下的聚合吞吐，直观展示批处理带来的吞吐提升
+
+所有数字均来自真实服务端的实测，脚本本身不产生任何合成数据。
+如果尚未启动服务端，可用 --dry-run 离线查看每个场景将要发出的请求配置。
+
+示例：
+    # 先启动服务端（二选一）
+    python server.py                    # vLLM（需要 NVIDIA GPU）
+    ollama serve && ollama pull qwen3:0.6b   # Ollama（Mac / 无 GPU）
+
+    # 跑全部场景并保存结果
+    python benchmark.py --scenario all --output results.json
+
+    # 只看 KV Cache 命中 / 未命中的 TTFT 对比
+    python benchmark.py --scenario kv-cache --backend ollama
+
+    # 批处理吞吐扫描
+    python benchmark.py --scenario batching --concurrency 1,2,4,8
+"""
+
+import argparse
+import json
+import logging
+import statistics
+import sys
+import time
+from concurrent.futures import ThreadPoolExecutor
+from typing import Any, Dict, List, Optional
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+)
+logger = logging.getLogger("benchmark")
+
+# 各后端的默认 OpenAI 兼容地址
+BACKEND_DEFAULTS = {
+    "vllm": {"base_url": "http://localhost:8000/v1", "model": "Qwen3-0.6B"},
+    "ollama": {"base_url": "http://localhost:11434/v1", "model": "qwen3:0.6b"},
+}
+
+# 一段确定性的填充文本，用于把共享前缀撑长，让 KV Cache 的效果更明显
+_FILLER_SENTENCE = (
+    "You are a meticulous assistant that follows the operating manual precisely. "
+)
+
+
+def build_padded_system_prompt(target_tokens: int) -> str:
+    """构造一个约含 target_tokens 个 token 的系统提示词（用重复句子填充）。
+
+    这里用「4 字符 ≈ 1 token」的粗略估计来控制长度，只需保证前缀足够长、
+    可复现即可，不追求精确的 token 数。
+    """
+    header = (
+        "# Operating Manual\n"
+        "You are a helpful local assistant deployed for the AI Agent book experiment.\n\n"
+    )
+    approx_chars = max(0, target_tokens * 4 - len(header))
+    repeats = approx_chars // len(_FILLER_SENTENCE) + 1
+    body = _FILLER_SENTENCE * repeats
+    return header + body
+
+
+def make_client(base_url: str, api_key: str):
+    """创建 OpenAI 兼容客户端。"""
+    try:
+        from openai import OpenAI
+    except ImportError:
+        logger.error("缺少依赖 openai，请先执行：pip install openai")
+        sys.exit(1)
+    return OpenAI(base_url=base_url, api_key=api_key)
+
+
+def stream_once(
+    client,
+    model: str,
+    messages: List[Dict[str, str]],
+    max_tokens: int,
+    temperature: float,
+) -> Dict[str, float]:
+    """发起一次流式请求，返回 TTFT、总时长、输出 token 数与解码吞吐。
+
+    - ttft：从发起请求到收到第一个内容或推理分片的时间（秒）
+    - total：整个响应的墙钟时间（秒）
+    - output_tokens：优先取服务端返回的 usage.completion_tokens，
+      否则用收到的内容分片数量作为近似
+    - decode_tps：解码阶段吞吐 = 输出 token / (总时长 - TTFT)
+    """
+    start = time.perf_counter()
+    ttft: Optional[float] = None
+    chunk_count = 0
+    usage_tokens: Optional[int] = None
+
+    stream = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        max_tokens=max_tokens,
+        temperature=temperature,
+        stream=True,
+        stream_options={"include_usage": True},
+    )
+
+    for chunk in stream:
+        # 最后一个分片可能只携带 usage 而没有 choices
+        if getattr(chunk, "usage", None) is not None:
+            try:
+                usage_tokens = chunk.usage.completion_tokens
+            except AttributeError:
+                pass
+        if not chunk.choices:
+            continue
+        delta = chunk.choices[0].delta
+        text = (
+            getattr(delta, "content", None)
+            or getattr(delta, "reasoning_content", None)
+            or getattr(delta, "reasoning", None)
+        )
+        if text:
+            if ttft is None:
+                ttft = time.perf_counter() - start
+            chunk_count += 1
+
+    total = time.perf_counter() - start
+    if ttft is None:
+        ttft = total
+    output_tokens = usage_tokens if usage_tokens is not None else chunk_count
+    decode_time = max(total - ttft, 1e-6)
+    decode_tps = output_tokens / decode_time if output_tokens else 0.0
+
+    return {
+        "ttft": ttft,
+        "total": total,
+        "output_tokens": float(output_tokens),
+        "decode_tps": decode_tps,
+    }
+
+
+# --------------------------------------------------------------------------- #
+# 场景实现
+# --------------------------------------------------------------------------- #
+def scenario_throughput(client, model, args) -> Dict[str, Any]:
+    """单流吞吐 + TTFT：连续发起若干次解码密集的请求并汇总统计。"""
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {
+            "role": "user",
+            "content": "Write a detailed explanation of how KV Cache works in transformer inference.",
+        },
+    ]
+    runs = []
+    for i in range(args.repeats):
+        r = stream_once(client, model, messages, args.max_tokens, args.temperature)
+        runs.append(r)
+        logger.info(
+            "throughput 第 %d/%d 次: TTFT=%.3fs, 解码=%.1f tok/s, 输出=%d tok",
+            i + 1, args.repeats, r["ttft"], r["decode_tps"], int(r["output_tokens"]),
+        )
+    return {
+        "scenario": "throughput",
+        "repeats": args.repeats,
+        "ttft_mean_s": statistics.fmean(x["ttft"] for x in runs),
+        "decode_tps_mean": statistics.fmean(x["decode_tps"] for x in runs),
+        "output_tokens_mean": statistics.fmean(x["output_tokens"] for x in runs),
+        "runs": runs,
+    }
+
+
+def scenario_kv_cache(client, model, args) -> Dict[str, Any]:
+    """KV Cache 命中 vs 未命中的 TTFT 对比（实验 2-1 第 5 点）。
+
+    - 命中组：系统提示词逐字节不变，重复发送同一请求，服务端前缀缓存命中，
+      prefill 几乎可以跳过 → TTFT 明显更低。
+    - 未命中组：每次只在系统提示词「开头」插入一个不同的计数串，前缀被改写，
+      缓存全部失效，服务端必须重算整个前缀 → TTFT 明显更高。
+    两组的提示词长度基本一致，因此差异主要来自前缀缓存是否命中。
+    """
+    base_prompt = build_padded_system_prompt(args.prefix_tokens)
+    user_msg = {"role": "user", "content": "In one short sentence, say hello."}
+
+    # 预热：先发一次把缓存写入（这一次一定是冷启动，不计入统计）
+    warm_msgs = [{"role": "system", "content": base_prompt}, user_msg]
+    stream_once(client, model, warm_msgs, args.max_tokens, args.temperature)
+
+    hit_ttfts, miss_ttfts = [], []
+    for i in range(args.repeats):
+        # 命中：完全相同的前缀
+        hit = stream_once(client, model, warm_msgs, args.max_tokens, args.temperature)
+        hit_ttfts.append(hit["ttft"])
+
+        # 未命中：在开头插入唯一前缀，使缓存失效
+        mutated = f"[req-{i}-{time.time_ns()}] " + base_prompt
+        miss_msgs = [{"role": "system", "content": mutated}, user_msg]
+        miss = stream_once(client, model, miss_msgs, args.max_tokens, args.temperature)
+        miss_ttfts.append(miss["ttft"])
+
+        logger.info(
+            "kv-cache 第 %d/%d 次: 命中 TTFT=%.3fs, 未命中 TTFT=%.3fs",
+            i + 1, args.repeats, hit["ttft"], miss["ttft"],
+        )
+
+    hit_mean = statistics.fmean(hit_ttfts)
+    miss_mean = statistics.fmean(miss_ttfts)
+    return {
+        "scenario": "kv-cache",
+        "prefix_tokens_approx": args.prefix_tokens,
+        "repeats": args.repeats,
+        "ttft_hit_mean_s": hit_mean,
+        "ttft_miss_mean_s": miss_mean,
+        "speedup": (miss_mean / hit_mean) if hit_mean > 0 else None,
+        "ttft_hit_s": hit_ttfts,
+        "ttft_miss_s": miss_ttfts,
+    }
+
+
+def scenario_batching(client, model, args) -> Dict[str, Any]:
+    """批处理：在不同并发度下并发发起请求，测量聚合吞吐。
+
+    连续批处理（continuous batching）是本地 serving 的核心优化：并发越高，
+    GPU 利用率越充分，系统聚合吞吐（所有请求合计 tok/s）通常显著上升，
+    但单个请求的延迟可能上升。此场景把这个权衡直接量化出来。
+    """
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Explain what a large language model is."},
+    ]
+
+    levels = args.concurrency
+    rows = []
+    for level in levels:
+        start = time.perf_counter()
+        with ThreadPoolExecutor(max_workers=level) as pool:
+            futures = [
+                pool.submit(
+                    stream_once, client, model, messages, args.max_tokens, args.temperature
+                )
+                for _ in range(level)
+            ]
+            results = [f.result() for f in futures]
+        wall = time.perf_counter() - start
+        total_tokens = sum(r["output_tokens"] for r in results)
+        agg_tps = total_tokens / wall if wall > 0 else 0.0
+        per_req_tps = agg_tps / level if level else 0.0
+        rows.append(
+            {
+                "concurrency": level,
+                "wall_s": wall,
+                "total_output_tokens": total_tokens,
+                "aggregate_tps": agg_tps,
+                "per_request_tps": per_req_tps,
+                "ttft_mean_s": statistics.fmean(r["ttft"] for r in results),
+            }
+        )
+        logger.info(
+            "batching 并发=%d: 聚合吞吐=%.1f tok/s, 单请求=%.1f tok/s, 墙钟=%.2fs",
+            level, agg_tps, per_req_tps, wall,
+        )
+    return {"scenario": "batching", "levels": rows}
+
+
+# --------------------------------------------------------------------------- #
+# 结果表格
+# --------------------------------------------------------------------------- #
+def print_report(results: List[Dict[str, Any]]) -> None:
+    print("\n" + "=" * 68)
+    print("本地 LLM 服务基准结果")
+    print("=" * 68)
+    for res in results:
+        s = res["scenario"]
+        if s == "throughput":
+            print("\n[throughput] 单流吞吐 / 首 token 延迟")
+            print(f"  次数           : {res['repeats']}")
+            print(f"  平均 TTFT      : {res['ttft_mean_s']:.3f} s")
+            print(f"  平均解码吞吐   : {res['decode_tps_mean']:.1f} tok/s")
+            print(f"  平均输出长度   : {res['output_tokens_mean']:.0f} tok")
+        elif s == "kv-cache":
+            print("\n[kv-cache] 前缀缓存命中 vs 未命中（TTFT）")
+            print(f"  前缀长度(约)   : {res['prefix_tokens_approx']} tok")
+            print(f"  命中平均 TTFT  : {res['ttft_hit_mean_s']:.3f} s")
+            print(f"  未命中平均TTFT : {res['ttft_miss_mean_s']:.3f} s")
+            if res.get("speedup"):
+                print(f"  缓存加速比     : {res['speedup']:.2f}x")
+        elif s == "batching":
+            print("\n[batching] 并发度对聚合吞吐的影响")
+            print(f"  {'并发':>4} | {'聚合tok/s':>10} | {'单请求tok/s':>12} | {'平均TTFT(s)':>11} | {'墙钟(s)':>8}")
+            print(f"  {'-'*4}-+-{'-'*10}-+-{'-'*12}-+-{'-'*11}-+-{'-'*8}")
+            for row in res["levels"]:
+                print(
+                    f"  {row['concurrency']:>4} | {row['aggregate_tps']:>10.1f} | "
+                    f"{row['per_request_tps']:>12.1f} | {row['ttft_mean_s']:>11.3f} | {row['wall_s']:>8.2f}"
+                )
+    print("\n" + "=" * 68)
+
+
+def describe_dry_run(args) -> None:
+    """离线打印将要执行的场景配置，不访问服务端。"""
+    print("=" * 68)
+    print("DRY RUN —— 仅打印计划，不访问服务端")
+    print("=" * 68)
+    print(f"后端         : {args.backend}")
+    print(f"base_url     : {args.base_url}")
+    print(f"模型         : {args.model}")
+    print(f"重复次数     : {args.repeats}")
+    print(f"max_tokens   : {args.max_tokens}")
+    print(f"temperature  : {args.temperature}")
+    scenarios = ["throughput", "kv-cache", "batching"] if args.scenario == "all" else [args.scenario]
+    print(f"待运行场景   : {', '.join(scenarios)}")
+    if "kv-cache" in scenarios:
+        prompt = build_padded_system_prompt(args.prefix_tokens)
+        print(f"  kv-cache   : 填充前缀约 {args.prefix_tokens} tok（实际 {len(prompt)} 字符）")
+    if "batching" in scenarios:
+        print(f"  batching   : 并发扫描 {args.concurrency}")
+    print("=" * 68)
+
+
+def parse_concurrency(value: str) -> List[int]:
+    try:
+        levels = [int(x) for x in value.split(",") if x.strip()]
+    except ValueError:
+        raise argparse.ArgumentTypeError("--concurrency 需为逗号分隔的正整数，例如 1,2,4,8")
+    if not levels or any(x <= 0 for x in levels):
+        raise argparse.ArgumentTypeError("--concurrency 中的并发度必须为正整数")
+    return levels
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="本地 LLM 服务性能基准：吞吐 / 延迟 / KV Cache / 批处理（实验 2-1 配套）",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=(
+            "场景说明:\n"
+            "  throughput  单流解码吞吐(tok/s)与首 token 延迟(TTFT)\n"
+            "  kv-cache    前缀缓存命中 vs 未命中的 TTFT 对比\n"
+            "  batching    不同并发度下的聚合吞吐（批处理权衡）\n"
+            "  all         依次运行以上全部场景\n"
+        ),
+    )
+    parser.add_argument(
+        "--scenario",
+        choices=["throughput", "kv-cache", "batching", "all"],
+        default="all",
+        help="要运行的基准场景（默认: all）",
+    )
+    parser.add_argument(
+        "--backend",
+        choices=["vllm", "ollama"],
+        default="vllm",
+        help="服务端类型，用于推断默认地址与模型名（默认: vllm）",
+    )
+    parser.add_argument(
+        "--base-url",
+        type=str,
+        default=None,
+        help="OpenAI 兼容接口地址，覆盖后端默认值（如 http://localhost:8000/v1）",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default=None,
+        help="模型名，覆盖后端默认值（vLLM 默认 Qwen3-0.6B，Ollama 默认 qwen3:0.6b）",
+    )
+    parser.add_argument(
+        "--api-key",
+        type=str,
+        default="EMPTY",
+        help="API Key，本地服务端一般无需真实值（默认: EMPTY）",
+    )
+    parser.add_argument(
+        "--repeats",
+        type=int,
+        default=5,
+        help="throughput / kv-cache 场景的重复次数（默认: 5）",
+    )
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=256,
+        help="每次请求的最大生成 token 数（默认: 256）",
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=0.7,
+        help="采样温度（默认: 0.7）",
+    )
+    parser.add_argument(
+        "--prefix-tokens",
+        type=int,
+        default=1024,
+        help="kv-cache 场景中共享前缀的近似 token 长度，越长缓存效果越明显（默认: 1024）",
+    )
+    parser.add_argument(
+        "--concurrency",
+        type=parse_concurrency,
+        default=[1, 2, 4, 8],
+        help="batching 场景的并发度列表，逗号分隔（默认: 1,2,4,8）",
+    )
+    parser.add_argument(
+        "--output",
+        type=str,
+        default=None,
+        help="将结果以 JSON 写入指定文件",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="离线打印计划而不访问服务端，用于验证配置",
+    )
+    return parser
+
+
+def main() -> int:
+    parser = build_parser()
+    args = parser.parse_args()
+
+    # 用后端默认值补全 base_url / model
+    defaults = BACKEND_DEFAULTS[args.backend]
+    if args.base_url is None:
+        args.base_url = defaults["base_url"]
+    if args.model is None:
+        args.model = defaults["model"]
+
+    print("=" * 68)
+    print("🚀 本地 LLM 服务性能基准（实验 2-1）")
+    print("=" * 68)
+
+    if args.dry_run:
+        describe_dry_run(args)
+        return 0
+
+    client = make_client(args.base_url, args.api_key)
+    logger.info("连接服务端: %s（模型: %s）", args.base_url, args.model)
+
+    scenarios = (
+        ["throughput", "kv-cache", "batching"]
+        if args.scenario == "all"
+        else [args.scenario]
+    )
+    dispatch = {
+        "throughput": scenario_throughput,
+        "kv-cache": scenario_kv_cache,
+        "batching": scenario_batching,
+    }
+
+    results: List[Dict[str, Any]] = []
+    try:
+        for name in scenarios:
+            logger.info("开始场景: %s", name)
+            results.append(dispatch[name](client, args.model, args))
+    except Exception as e:  # noqa: BLE001
+        logger.error("基准执行失败: %s", e)
+        logger.info(
+            "请确认服务端已启动：vLLM 用 `python server.py`，"
+            "Ollama 用 `ollama serve` 并已 `ollama pull %s`",
+            args.model,
+        )
+        return 1
+
+    print_report(results)
+
+    if args.output:
+        payload = {
+            "backend": args.backend,
+            "base_url": args.base_url,
+            "model": args.model,
+            "results": results,
+        }
+        with open(args.output, "w", encoding="utf-8") as f:
+            json.dump(payload, f, ensure_ascii=False, indent=2)
+        logger.info("结果已写入: %s", args.output)
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

chapter2/local_llm_serving/check_compatibility.py

@@ -0,0 +1,153 @@
+#!/usr/bin/env python3
+"""
+Check system compatibility for running vLLM tool calling demo
+"""
+
+import sys
+import platform
+import subprocess
+import shutil
+
+
+def check_system():
+    """Check system compatibility"""
+    print("="*60)
+    print("🔍 System Compatibility Check")
+    print("="*60)
+    
+    # Get system info
+    system = platform.system()
+    machine = platform.machine()
+    python_version = sys.version_info
+    
+    print(f"\n📊 System Information:")
+    print(f"  OS: {system} ({platform.platform()})")
+    print(f"  Architecture: {machine}")
+    print(f"  Python: {python_version.major}.{python_version.minor}.{python_version.micro}")
+    
+    # Check for CUDA
+    cuda_available = False
+    gpu_info = None
+    
+    print(f"\n🎮 GPU Check:")
+    
+    if system == "Darwin":  # macOS
+        print("  ❌ macOS detected - No CUDA support available")
+        print("  ℹ️  Macs use Metal (Apple Silicon) or AMD/Intel GPUs")
+        return False, "darwin"
+    
+    # Check for NVIDIA GPU
+    if shutil.which("nvidia-smi"):
+        try:
+            result = subprocess.run(
+                ["nvidia-smi", "--query-gpu=name,memory.total", "--format=csv,noheader"],
+                capture_output=True,
+                text=True
+            )
+            if result.returncode == 0:
+                gpu_info = result.stdout.strip()
+                print(f"  ✅ NVIDIA GPU found: {gpu_info}")
+                cuda_available = True
+            else:
+                print("  ⚠️  nvidia-smi found but couldn't query GPU")
+        except Exception as e:
+            print(f"  ⚠️  Error checking GPU: {e}")
+    else:
+        print("  ❌ No NVIDIA GPU detected (nvidia-smi not found)")
+    
+    # Check PyTorch CUDA
+    print(f"\n🔥 PyTorch CUDA Check:")
+    try:
+        import torch
+        if torch.cuda.is_available():
+            print(f"  ✅ PyTorch CUDA is available")
+            print(f"  CUDA version: {torch.version.cuda}")
+            print(f"  Number of GPUs: {torch.cuda.device_count()}")
+            if torch.cuda.device_count() > 0:
+                print(f"  GPU 0: {torch.cuda.get_device_name(0)}")
+        else:
+            print("  ❌ PyTorch CUDA is not available")
+            cuda_available = False
+    except ImportError:
+        print("  ⚠️  PyTorch not installed")
+    
+    return cuda_available, system.lower()
+
+
+def provide_recommendations(cuda_available, system):
+    """Provide recommendations based on system"""
+    
+    print("\n" + "="*60)
+    print("💡 Recommendations")
+    print("="*60)
+    
+    # Official vLLM GPU execution requires Linux. WSL2 reports "linux", but
+    # native Windows is unsupported even when PyTorch detects CUDA.
+    if system.lower() == "windows":
+        print("\n🪟 You're on native Windows - will use Ollama")
+        if cuda_available:
+            print("  ℹ️  CUDA is available, but official vLLM requires Linux.")
+            print("  ℹ️  To use vLLM, run this project in WSL2 or a Linux container.")
+
+        print("\n📋 Setup steps:\n")
+        print("1️⃣  Install Ollama:")
+        print("   Download from: https://ollama.com/download/windows")
+        print("   Run OllamaSetup.exe\n")
+
+        print("2️⃣  Install a model:")
+        print("   ollama pull qwen3:0.6b  # Default model for this project\n")
+
+        print("3️⃣  Run the main script:")
+        print("   python main.py")
+        print("   # Will automatically use Ollama")
+
+    elif cuda_available:
+        print("\n✅ Your system supports vLLM!")
+        print("\nNext steps:")
+        print("1. Install requirements: pip install -r requirements.txt")
+        print("2. Run the main script: python main.py")
+        print("3. The script will automatically use vLLM")
+        
+    elif system == "darwin" or system.lower() == "darwin":  # macOS
+        print("\n🍎 You're on macOS - will use Ollama")
+        print("\n📋 Setup steps:\n")
+        
+        print("1️⃣  Install Ollama:")
+        print("   brew install ollama")
+        print("   ollama serve  # Run in separate terminal\n")
+        
+        print("2️⃣  Install a model with tool support:")
+        print("   ollama pull qwen3:0.6b  # Default model for this project\n")
+        
+        print("3️⃣  Run the main script:")
+        print("   python main.py")
+        print("   # Will automatically use Ollama")
+        
+    else:  # Linux without CUDA
+        print("\n🐧 You're on Linux without CUDA - will use Ollama")
+        print("\n📋 Setup steps:\n")
+        
+        print("1️⃣  Install Ollama:")
+        print("   curl -fsSL https://ollama.com/install.sh | sh")
+        print("   systemctl start ollama  # Or: ollama serve\n")
+        
+        print("2️⃣  Install a model:")
+        print("   ollama pull qwen3:0.6b  # Default model for this project\n")
+        
+        print("3️⃣  Run the main script:")
+        print("   python main.py")
+        print("   # Will automatically use Ollama")
+
+
+def main():
+    """Main compatibility check"""
+    cuda_available, system = check_system()
+    provide_recommendations(cuda_available, system)
+    
+    print("\n" + "="*60)
+    print("For more details, see README.md")
+    print("="*60)
+
+
+if __name__ == "__main__":
+    main()

chapter2/local_llm_serving/config.py

@@ -0,0 +1,41 @@
+"""
+Configuration for vLLM Tool Calling Demo
+"""
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Model Configuration
+MODEL_NAME = os.getenv("MODEL_NAME", "Qwen/Qwen3-0.6B")  # Can use ModelScope path or HuggingFace
+MODEL_PATH = os.getenv("MODEL_PATH", None)  # Optional: local model path
+VLLM_PORT = int(os.getenv("VLLM_PORT", 8000))
+VLLM_HOST = os.getenv("VLLM_HOST", "localhost")
+
+# vLLM Server Configuration
+VLLM_SERVER_CONFIG = {
+    "model": MODEL_NAME,
+    "port": VLLM_PORT,
+    "host": VLLM_HOST,
+    "enable_auto_tool_choice": True,
+    "tool_call_parser": "hermes",
+    "max_model_len": 8192,
+    "gpu_memory_utilization": 0.9,
+    "dtype": "auto",
+    "enforce_eager": False,  # Set to True if you encounter issues
+}
+
+# OpenAI Client Configuration (for connecting to vLLM)
+OPENAI_API_BASE = f"http://{VLLM_HOST}:{VLLM_PORT}/v1"
+OPENAI_API_KEY = "EMPTY"  # vLLM doesn't require a real key
+
+# Tool Configuration
+ENABLE_WEATHER_TOOL = True
+ENABLE_CALCULATOR_TOOL = True
+ENABLE_SEARCH_TOOL = True
+
+# Logging
+LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
+LOG_FILE = Path("logs") / "vllm_tool_demo.log"

chapter2/local_llm_serving/demo_streaming.py

@@ -0,0 +1,214 @@
+#!/usr/bin/env python3
+"""
+Simple demo showing how to use streaming with the chat template agents
+"""
+
+import sys
+import time
+
+
+def print_with_typing_effect(text, delay=0.03):
+    """Print text with a typing effect"""
+    for char in text:
+        print(char, end="", flush=True)
+        time.sleep(delay)
+    print()
+
+
+def demo_vllm_streaming():
+    """Demo streaming with vLLM backend"""
+    from agent import VLLMToolAgent
+    from config import OPENAI_API_BASE, OPENAI_API_KEY
+    
+    print("="*60)
+    print("🚀 vLLM Streaming Demo")
+    print("="*60)
+    
+    try:
+        agent = VLLMToolAgent(
+            api_base=OPENAI_API_BASE,
+            api_key=OPENAI_API_KEY
+        )
+        
+        query = "What's the weather in New York and calculate 32°F in Celsius?"
+        print(f"\n📝 Query: {query}\n")
+        print("Streaming response:\n")
+        print("-"*40)
+        
+        for chunk in agent.chat_stream(query):
+            chunk_type = chunk.get("type")
+            content = chunk.get("content", "")
+            
+            if chunk_type == "thinking":
+                print(f"\n💭 [Thinking]: \033[90m{content}\033[0m")
+            elif chunk_type == "tool_call":
+                print(f"\n🔧 [Tool Call]: {content['name']}({content['arguments']})")
+            elif chunk_type == "tool_result":
+                print(f"   ✓ Result: {content}")
+            elif chunk_type == "content":
+                # Stream content character by character
+                print(content, end="", flush=True)
+        
+        print("\n" + "-"*40)
+        
+    except Exception as e:
+        print(f"Error: {e}")
+        print("Make sure vLLM server is running!")
+
+
+def demo_ollama_streaming():
+    """Demo streaming with Ollama backend"""
+    from ollama_native import OllamaNativeAgent
+    import ollama
+    
+    print("="*60)
+    print("🦙 Ollama Streaming Demo")
+    print("="*60)
+    
+    try:
+        # Check available models
+        client = ollama.Client()
+        models = [m['name'] for m in client.list()['models']]
+        
+        # Use qwen3:0.6b as the default model
+        model = "qwen3:0.6b"
+        
+        if model not in models:
+            print(f"⚠️ Recommended model {model} not found")
+            print("Install with: ollama pull qwen3:0.6b")
+            if models:
+                model = models[0]
+                print(f"Using fallback model: {model}")
+            else:
+                print("❌ No Ollama models found. Install with: ollama pull qwen3:0.6b")
+                return
+        
+        print(f"Using model: {model}")
+        agent = OllamaNativeAgent(model=model)
+        
+        query = "What's 15 * 23? Also get the current time in Tokyo."
+        print(f"\n📝 Query: {query}\n")
+        print("Streaming response:\n")
+        print("-"*40)
+        
+        for chunk in agent.chat_stream(query):
+            chunk_type = chunk.get("type")
+            content = chunk.get("content", "")
+            
+            if chunk_type == "thinking":
+                print(f"\n💭 [Thinking]: \033[90m{content}\033[0m")
+            elif chunk_type == "tool_call":
+                print(f"\n🔧 [Tool Call]: {content['name']}({content['arguments']})")
+            elif chunk_type == "tool_result":
+                print(f"   ✓ Result: {content}")
+            elif chunk_type == "content":
+                # Stream content
+                print(content, end="", flush=True)
+        
+        print("\n" + "-"*40)
+        
+    except Exception as e:
+        print(f"Error: {e}")
+        print("Make sure Ollama is running: ollama serve")
+
+
+def demo_unified_streaming():
+    """Demo with unified ToolCallingAgent that auto-selects backend"""
+    from main import ToolCallingAgent
+    
+    print("="*60)
+    print("🎯 Unified Streaming Demo (Auto-detect Backend)")
+    print("="*60)
+    
+    # Initialize agent (auto-detects best backend)
+    print("\n⚙️  Initializing agent...")
+    agent = ToolCallingAgent()
+    print(f"✅ Using {agent.backend_type} backend\n")
+    
+    # Example queries
+    queries = [
+        "Calculate the compound interest on $1000 at 5% for 3 years",
+        "What's the weather in London and what time is it there?",
+        "Convert 50 EUR to USD and JPY"
+    ]
+    
+    for i, query in enumerate(queries, 1):
+        print(f"\n{'='*60}")
+        print(f"Query {i}: {query}")
+        print("-"*60)
+        
+        # Track what sections we've shown
+        sections_shown = set()
+        last_chunk_type = None
+        
+        for chunk in agent.chat(query, stream=True):
+            chunk_type = chunk.get("type")
+            content = chunk.get("content", "")
+            
+            if chunk_type == "thinking":
+                if "thinking" not in sections_shown:
+                    print("\n💭 Thinking: ", end="", flush=True)
+                    sections_shown.add("thinking")
+                # Stream thinking character by character in gray
+                print(f"\033[90m{content}\033[0m", end="", flush=True)
+            
+            elif chunk_type == "tool_call":
+                if "tools" not in sections_shown:
+                    print("\n🔧 Tool Calls:")
+                    sections_shown.add("tools")
+                print(f"  → {content['name']}: {content['arguments']}")
+                # Remove response section so it shows again after tools
+                sections_shown.discard("response")
+            
+            elif chunk_type == "tool_result":
+                result_str = str(content)
+                print(f"    ✓ {result_str}")
+                # Remove response section so it shows again after tools
+                sections_shown.discard("response")
+            
+            elif chunk_type == "content":
+                if "response" not in sections_shown:
+                    if last_chunk_type in ["tool_result", "tool_call"]:
+                        print("\n📝 Response (after tools):")
+                    else:
+                        print("\n📝 Response:")
+                    sections_shown.add("response")
+                    print("  ", end="")
+                print(content, end="", flush=True)
+            
+            last_chunk_type = chunk_type
+        
+        print()  # New line after response
+        
+        # Reset for next query
+        agent.reset_conversation()
+
+    print("\n" + "="*60)
+    print("✅ Demo completed!")
+    print("="*60)
+
+
+def main():
+    """Main demo function"""
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="Streaming Demo for Chat Template Agents")
+    parser.add_argument(
+        "--backend",
+        choices=["vllm", "ollama", "auto"],
+        default="auto",
+        help="Backend to use for demo"
+    )
+    
+    args = parser.parse_args()
+    
+    if args.backend == "vllm":
+        demo_vllm_streaming()
+    elif args.backend == "ollama":
+        demo_ollama_streaming()
+    else:
+        demo_unified_streaming()
+
+
+if __name__ == "__main__":
+    main()

chapter2/local_llm_serving/env.example

@@ -0,0 +1,14 @@
+# Configuration for Tool Calling Demo
+# Copy this file to .env and customize as needed
+
+# Model Configuration (for vLLM)
+MODEL_NAME=Qwen/Qwen3-0.6B
+# Optional: Path to local model (if you've downloaded it)
+# MODEL_PATH=/path/to/local/model
+
+# vLLM Server Configuration (if using GPU)
+VLLM_HOST=localhost
+VLLM_PORT=8000
+
+# Logging
+LOG_LEVEL=INFO

chapter2/local_llm_serving/experiment_protocol.json

@@ -0,0 +1,47 @@
+{
+  "experiment_id": "2-1",
+  "protocol_version": "1.0.0",
+  "frozen_on": "2026-07-30",
+  "authority": "book/chapter2.md:363",
+  "runtime": {
+    "server": "Ollama native /api/generate",
+    "model": "qwen3:0.6b",
+    "reference_model": "Qwen/Qwen3-0.6B",
+    "raw_mode": true,
+    "temperature": 0,
+    "num_predict": 512
+  },
+  "tool_case": {
+    "prompt": "What are the current time and weather in Vancouver? Call both tools.",
+    "required_tools": [
+      "get_current_time",
+      "get_current_temperature"
+    ],
+    "required_timezone": "America/Vancouver",
+    "required_location": "Vancouver, Canada",
+    "execution": "parallel"
+  },
+  "cache_case": {
+    "approximate_prefix_tokens": 4096,
+    "warmups": 2,
+    "matched_repeats": 5,
+    "hit": "byte-identical rendered prompt",
+    "miss": "same-length unique mutation at the first bytes of the system prompt"
+  },
+  "acceptance_gates": [
+    "the running server reports qwen3:0.6b with a nonempty immutable model digest",
+    "the rendered prompt retains chat-template special tokens and tool schema",
+    "the first raw response contains exactly the two required tool calls",
+    "both tools execute concurrently and return auditable results",
+    "the second model turn consumes both tool results and terminates without another tool call",
+    "stream chunks, request prompts, timings, token counts, server durations, and hashes are retained",
+    "matched hit and miss TTFT samples are retained without requiring a favorable outcome",
+    "all execution is local except the read-only Open-Meteo weather lookup",
+    "no credential is sent to or retained by the experiment"
+  ],
+  "claim_policy": {
+    "tool_calling": "complete only when every tool/termination gate passes",
+    "throughput": "report measured decode throughput; do not claim the manuscript's M2 >100 tok/s observation unless this run exceeds it on identified hardware",
+    "kv_cache": "report the matched TTFT distribution even if the hit arm is not faster"
+  }
+}

chapter2/local_llm_serving/main.py

@@ -0,0 +1,659 @@
+#!/usr/bin/env python3
+"""
+Main Entry Point for Tool Calling Demo
+Automatically selects the best backend based on your platform:
+- Linux (including WSL2) with NVIDIA GPU: Uses vLLM
+- Native Windows, macOS, or Linux without CUDA: Uses Ollama
+"""
+
+import os
+import sys
+import platform
+import logging
+from typing import Optional, Dict, Any, List
+import json
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+
+class ToolCallingAgent:
+    """
+    Universal tool calling agent that works on all platforms
+    Automatically selects vLLM (if supported and a GPU is available) or Ollama
+    """
+    
+    def __init__(self, backend: Optional[str] = None):
+        """
+        Initialize with automatic backend detection
+        
+        Args:
+            backend: Force a specific backend ('vllm', 'ollama', or None for auto)
+        """
+        self.agent = None
+        self.backend_type = backend or self._detect_best_backend()
+        
+        logger.info(f"Initializing on {platform.system()} with {self.backend_type}")
+        self._initialize_backend()
+    
+    def _detect_best_backend(self) -> str:
+        """Detect the best backend for current platform"""
+        system = platform.system()
+        
+        # Official vLLM GPU execution requires Linux. WSL2 reports itself as
+        # Linux here, while native Windows must use Ollama even when PyTorch
+        # can see a CUDA-capable GPU.
+        if system == "Linux":
+            try:
+                import torch
+                if torch.cuda.is_available():
+                    logger.info("CUDA detected on Linux - will use vLLM")
+                    return "vllm"
+            except ImportError:
+                pass
+
+        if system == "Windows":
+            logger.info(
+                "Native Windows detected - official vLLM requires Linux; "
+                "using Ollama (use WSL2 for vLLM)"
+            )
+            return "ollama"
+        
+        # Default to Ollama for macOS or Linux systems without CUDA
+        logger.info(f"Using Ollama on {system}")
+        return "ollama"
+    
+    def _initialize_backend(self):
+        """Initialize the selected backend"""
+        if self.backend_type == "vllm":
+            self._init_vllm()
+        else:
+            self._init_ollama()
+    
+    def _init_vllm(self):
+        """Initialize vLLM backend"""
+        try:
+            # Check if vLLM server is running
+            import requests
+            from config import VLLM_HOST, VLLM_PORT
+            
+            server_url = f"http://{VLLM_HOST}:{VLLM_PORT}/health"
+            
+            try:
+                response = requests.get(server_url, timeout=1)
+                if response.status_code != 200:
+                    raise ConnectionError("vLLM server not responding")
+            except Exception:
+                # Try to start the server
+                logger.info("Starting vLLM server...")
+                from server import VLLMServer
+                server = VLLMServer()
+                server.start(wait_for_ready=True)
+            
+            # Initialize vLLM agent
+            from agent import VLLMToolAgent
+            from config import OPENAI_API_BASE, OPENAI_API_KEY
+            
+            self.agent = VLLMToolAgent(
+                api_base=OPENAI_API_BASE,
+                api_key=OPENAI_API_KEY
+            )
+            logger.info("✅ vLLM agent initialized")
+            
+        except Exception as e:
+            logger.warning(f"Failed to initialize vLLM: {e}")
+            logger.info("Falling back to Ollama")
+            self.backend_type = "ollama"
+            self._init_ollama()
+    
+    def _init_ollama(self):
+        """Initialize Ollama backend"""
+        try:
+            import ollama
+            from ollama_native import OllamaNativeAgent
+            
+            # Check if Ollama is running
+            client = ollama.Client()
+            try:
+                models_response = client.list()
+                available_models = []
+                if hasattr(models_response, 'models'):
+                    available_models = [m.model for m in models_response.models]
+                
+                if not available_models:
+                    logger.error("No Ollama models installed")
+                    logger.info("Install a model with: ollama pull qwen3:0.6b")
+                    sys.exit(1)
+                
+                # Use qwen3:0.6b as the default model
+                model = "qwen3:0.6b"
+                
+                # Check if qwen3:0.6b is available
+                if model not in available_models:
+                    logger.warning(f"Recommended model {model} not found in available models")
+                    logger.info("Install with: ollama pull qwen3:0.6b")
+                    # Fall back to first available model if qwen3:0.6b is not installed
+                    model = available_models[0]
+                    logger.info(f"Using fallback model: {model}")
+                
+                logger.info(f"Using Ollama model: {model}")
+                self.agent = OllamaNativeAgent(model=model)
+                
+            except Exception as e:
+                logger.error(f"Ollama is not running: {e}")
+                logger.info("\nPlease start Ollama:")
+                
+                system = platform.system()
+                if system == "Darwin":  # Mac
+                    logger.info("  brew services start ollama")
+                    logger.info("  or: ollama serve")
+                elif system == "Windows":
+                    logger.info("  Start Ollama from the system tray")
+                    logger.info("  or run: ollama serve")
+                else:  # Linux
+                    logger.info("  systemctl start ollama")
+                    logger.info("  or: ollama serve")
+                
+                sys.exit(1)
+                
+        except ImportError:
+            logger.error("Ollama not installed")
+            logger.info("Install with: pip install ollama")
+            sys.exit(1)
+    
+    def chat(self, message: str, use_tools: bool = True, stream: bool = False, **kwargs) -> str:
+        """
+        Send a message to the agent
+        
+        Args:
+            message: User message
+            use_tools: Whether to enable tool calling
+            stream: Whether to stream the response
+            **kwargs: Additional backend-specific parameters
+            
+        Returns:
+            Agent response (or generator if streaming)
+        """
+        if not self.agent:
+            raise RuntimeError("Agent not initialized")
+        
+        return self.agent.chat(message, use_tools=use_tools, stream=stream, **kwargs)
+    
+    def reset_conversation(self):
+        """Reset conversation history"""
+        if hasattr(self.agent, 'reset_conversation'):
+            self.agent.reset_conversation()
+
+
+def get_sample_tasks() -> List[Dict[str, str]]:
+    """Get sample tasks for demonstration"""
+    return [
+        {
+            "name": "🕐 Current Time Check",
+            "description": "Get the current time in a specific city",
+            "task": "What is the current time in Vancouver?"
+        },
+        {
+            "name": "☀️ Simple Weather Check",
+            "description": "Get current weather for a single city",
+            "task": "What's the weather like in Vancouver right now?"
+        },
+        {
+            "name": "☀️ Time and Weather Check",
+            "description": "Get current time and weather for a single city",
+            "task": "What's the current time and weather like in Vancouver right now?"
+        },
+        {
+            "name": "💵 Compound Interest Calculation",
+            "description": "Calculate compound interest using code interpreter",
+            "task": "Calculate the compound interest on $5,000 invested at 6% annual interest rate for 30 years, compounded monthly."
+        },
+
+        {
+            "name": "🌡️ Multi-City Weather Analysis",
+            "description": "Compare weather across multiple cities using real-time data",
+            "task": """Get the current weather for Tokyo, New York, London, Sydney, and Dubai. 
+Then:
+1. Which city has the highest temperature?
+2. Which city has the lowest humidity?
+3. Convert all temperatures to Fahrenheit for comparison
+4. Calculate the average temperature across all cities"""
+        },
+        {
+            "name": "💰 Complex Financial Analysis",
+            "description": "Multi-step financial calculation with currency conversion",
+            "task": """A company has the following quarterly revenues:
+- Q1: $2,500,000 USD
+- Q2: €2,100,000 EUR
+- Q3: £1,800,000 GBP
+- Q4: ¥380,000,000 JPY
+
+Please:
+1. Convert all revenues to USD
+2. Calculate the total annual revenue in USD
+3. Determine the average quarterly revenue
+4. Find which quarter had the highest revenue
+5. If the company has a 20% profit margin, calculate the annual profit in USD"""
+        },
+        {
+            "name": "⏰ Global Time Zone Coordination",
+            "description": "Coordinate meeting times across time zones",
+            "task": """We need to schedule a global meeting with offices in:
+- San Francisco (PST)
+- New York (EST)
+- London (GMT/BST)
+- Tokyo (JST)
+- Sydney (AEST)
+
+If the meeting is at 2 PM London time:
+1. What time would it be in each city?
+2. Is this during normal business hours (9 AM - 5 PM) for each location?
+3. Suggest a better time that works for most offices"""
+        },
+    ]
+
+
+def run_single_task(agent: ToolCallingAgent, task: str, stream: bool = True):
+    """Run a single task with optional streaming"""
+    print("\n" + "="*60)
+    print("TASK EXECUTION")
+    print("="*60)
+    print(f"\n📋 Task: {task}")
+    print("-"*60)
+    
+    try:
+        if stream:
+            print("\n⏳ Processing (streaming)...\n")
+            
+            response_chunks = []
+            thinking_shown = False
+            tools_shown = False
+            response_started = False
+            last_chunk_type = None
+            
+            for chunk in agent.chat(task, stream=True):
+                chunk_type = chunk.get("type")
+                content = chunk.get("content", "")
+                
+                if chunk_type == "thinking":
+                    if not thinking_shown:
+                        print("🧠 Thinking: ", end="", flush=True)
+                        thinking_shown = True
+                    # Stream thinking character by character in gray
+                    print(f"\033[90m{content}\033[0m", end="", flush=True)
+                
+                elif chunk_type == "tool_call":
+                    if not tools_shown:
+                        print("\n\n🔧 Tool Calls:")
+                        tools_shown = True
+                    # Display tool call info
+                    tool_info = content
+                    print(f"  → {tool_info.get('name', 'unknown')}: {tool_info.get('arguments', {})}")
+                    # Reset response_started flag after tool calls
+                    response_started = False
+                
+                elif chunk_type == "tool_result":
+                    # Display tool result
+                    result_str = str(content)
+                    print(f"    ✓ {result_str}")
+                    # Reset response_started flag after tool results
+                    response_started = False
+                
+                elif chunk_type == "content":
+                    if not response_started:
+                        # Check if this is content after tool execution
+                        if last_chunk_type in ["tool_result", "tool_call"]:
+                            print("\n🤖 Assistant: ", end="", flush=True)
+                        elif thinking_shown or tools_shown:
+                            print("\n\n🤖 Assistant: ", end="", flush=True)
+                        else:
+                            print("🤖 Assistant: ", end="", flush=True)
+                        response_started = True
+                    # Stream the actual response content
+                    print(content, end="", flush=True)
+                    response_chunks.append(content)
+                
+                elif chunk_type == "error":
+                    print(f"\n❌ Error: {content}")
+                
+                last_chunk_type = chunk_type
+            
+            print("\n" + "-"*40)
+            
+        else:
+            print("\n⏳ Processing...")
+            response = agent.chat(task, stream=False)
+            
+            print("\n✅ Response:")
+            print("-"*40)
+            print(response)
+            print("-"*40)
+        
+    except Exception as e:
+        print(f"\n❌ Error: {e}")
+        logger.exception("Task execution failed")
+
+
+def interactive_mode(agent: ToolCallingAgent, stream: bool = True):
+    """Run interactive chat mode with optional streaming"""
+    print("\n" + "="*60)
+    print("💬 INTERACTIVE MODE" + (" (STREAMING)" if stream else ""))
+    print("="*60)
+    print("\nYou can now chat with the AI agent. It has access to various tools:")
+    
+    # Show available tools
+    from tools import ToolRegistry
+    registry = ToolRegistry()
+    tools = registry.get_tool_schemas()
+    
+    print("\n📦 Available Tools:")
+    for i, tool in enumerate(tools, 1):
+        func = tool["function"]
+        print(f"  {i}. {func['name']}: {func['description']}")
+    
+    print("\n💡 Commands:")
+    print("  /reset      - Reset conversation")
+    print("  /tools      - Show available tools")
+    print("  /samples    - Show sample tasks")
+    print("  /sample <n> - Run sample task number n")
+    print("  /stream     - Toggle streaming mode")
+    print("  /help       - Show this help")
+    print("  /exit       - Exit the program")
+    print("-"*60)
+    
+    streaming_enabled = stream
+    
+    while True:
+        try:
+            user_input = input("\n👤 You: ").strip()
+            
+            if not user_input:
+                continue
+            
+            # Handle commands
+            if user_input.lower() == "/exit" or user_input.lower() == "quit":
+                print("👋 Goodbye!")
+                break
+            
+            elif user_input.lower() == "/reset":
+                agent.reset_conversation()
+                print("✅ Conversation reset")
+                continue
+            
+            elif user_input.lower() == "/tools":
+                print("\n📦 Available Tools:")
+                for i, tool in enumerate(tools, 1):
+                    func = tool["function"]
+                    print(f"  {i}. {func['name']}: {func['description']}")
+                continue
+            
+            elif user_input.lower() == "/samples":
+                print("\n📋 Sample Tasks:")
+                sample_tasks = get_sample_tasks()
+                for i, sample in enumerate(sample_tasks, 1):
+                    print(f"  {i}. {sample['name']}")
+                    # Show first 100 chars of task for readability
+                    task_preview = sample['task'].replace('\n', ' ')[:100]
+                    if len(sample['task']) > 100:
+                        task_preview += "..."
+                    print(f"     {task_preview}")
+                print("\n💡 Tip: Use /sample <n> to run a specific sample (e.g., /sample 1)")
+                continue
+            
+            elif user_input.lower().startswith("/sample "):
+                # Extract the sample number
+                try:
+                    sample_num = int(user_input.split()[1])
+                    sample_tasks = get_sample_tasks()
+                    
+                    if 1 <= sample_num <= len(sample_tasks):
+                        selected_sample = sample_tasks[sample_num - 1]
+                        print(f"\n🎯 Running Sample: {selected_sample['name']}")
+                        print("-"*60)
+                        print(f"Task: {selected_sample['task']}")
+                        print("-"*60)
+                        
+                        # Process the sample task as regular input
+                        user_input = selected_sample['task']
+                        # Don't continue - let it fall through to normal processing
+                    else:
+                        print(f"❌ Invalid sample number. Please choose between 1 and {len(sample_tasks)}")
+                        print("Use /samples to see available samples")
+                        continue
+                except (ValueError, IndexError):
+                    print("❌ Invalid format. Use: /sample <number> (e.g., /sample 1)")
+                    continue
+            
+            elif user_input.lower() == "/help":
+                print("\n💡 Commands:")
+                print("  /reset      - Reset conversation")
+                print("  /tools      - Show available tools")
+                print("  /samples    - Show sample tasks")
+                print("  /sample <n> - Run sample task number n")
+                print("  /stream     - Toggle streaming mode")
+                print("  /help       - Show this help")
+                print("  /exit       - Exit the program")
+                continue
+            
+            elif user_input.lower() == "/stream":
+                streaming_enabled = not streaming_enabled
+                print(f"✅ Streaming {'enabled' if streaming_enabled else 'disabled'}")
+                continue
+            
+            # Process user input
+            if streaming_enabled:
+                print("\n⏳ Processing (streaming)...\n")
+                
+                response_chunks = []
+                thinking_shown = False
+                tools_shown = False
+                response_started = False
+                last_chunk_type = None
+                
+                for chunk in agent.chat(user_input, stream=True):
+                    chunk_type = chunk.get("type")
+                    content = chunk.get("content", "")
+                    
+                    if chunk_type == "thinking":
+                        if not thinking_shown:
+                            print("🧠 Thinking: ", end="", flush=True)
+                            thinking_shown = True
+                        # Stream thinking character by character in gray
+                        print(f"\033[90m{content}\033[0m", end="", flush=True)
+                    
+                    elif chunk_type == "tool_call":
+                        if not tools_shown:
+                            print("\n🔧 Tool Calls:")
+                            tools_shown = True
+                        tool_info = content
+                        print(f"  → {tool_info.get('name', 'unknown')}: {tool_info.get('arguments', {})}")
+                        # Reset response_started flag after tool calls so the next content gets a label
+                        response_started = False
+                    
+                    elif chunk_type == "tool_result":
+                        result_str = str(content)
+                        print(f"    ✓ {result_str}")
+                        # Reset response_started flag after tool results
+                        response_started = False
+                    
+                    elif chunk_type == "content":
+                        # If we're starting a new content section after tool results
+                        if not response_started:
+                            if last_chunk_type in ["tool_result", "tool_call"]:
+                                # This is a response after tool execution
+                                print("\n🤖 Assistant: ", end="", flush=True)
+                            elif thinking_shown or tools_shown:
+                                print("\n🤖 Assistant: ", end="", flush=True)
+                            else:
+                                print("🤖 Assistant: ", end="", flush=True)
+                            response_started = True
+                        print(content, end="", flush=True)
+                        response_chunks.append(content)
+                    
+                    elif chunk_type == "error":
+                        print(f"\n❌ Error: {content}")
+                    
+                    last_chunk_type = chunk_type
+                
+                print()  # New line after streaming
+            else:
+                print("\n⏳ Processing...")
+                response = agent.chat(user_input, stream=False)
+                
+                print(f"🤖 Assistant: {response}")
+            
+        except KeyboardInterrupt:
+            print("\n\n👋 Goodbye!")
+            break
+        except Exception as e:
+            print(f"❌ Error: {e}")
+            logger.exception("Error in interactive mode")
+
+
+def main():
+    """Main function"""
+    import argparse
+    
+    parser = argparse.ArgumentParser(
+        description="Universal Tool Calling Agent - Works on all platforms"
+    )
+    parser.add_argument(
+        "--mode",
+        choices=["single", "interactive"],
+        default="interactive",
+        help="Execution mode (default: interactive)"
+    )
+    parser.add_argument(
+        "--task",
+        type=str,
+        help="Task to execute (for single mode)"
+    )
+    parser.add_argument(
+        "--backend",
+        choices=["vllm", "ollama", "auto"],
+        default="auto",
+        help="Backend to use (default: auto-detect)"
+    )
+    parser.add_argument(
+        "--info",
+        action="store_true",
+        help="Show system information and exit"
+    )
+    parser.add_argument(
+        "--stream",
+        action="store_true",
+        default=True,
+        help="Enable streaming mode (default: True)"
+    )
+    parser.add_argument(
+        "--no-stream",
+        action="store_true",
+        help="Disable streaming mode"
+    )
+    
+    args = parser.parse_args()
+    
+    # Header
+    print("="*60)
+    print("🚀 Universal Tool Calling Agent")
+    print("="*60)
+    
+    # Show system info if requested
+    if args.info:
+        print("\n📊 System Information:")
+        print(f"  Platform: {platform.system()} {platform.release()}")
+        print(f"  Architecture: {platform.machine()}")
+        print(f"  Python: {sys.version.split()[0]}")
+        
+        # Check CUDA
+        try:
+            import torch
+            cuda_available = torch.cuda.is_available()
+            if cuda_available:
+                print(f"  CUDA: ✅ Available (GPU: {torch.cuda.get_device_name(0)})")
+            else:
+                print("  CUDA: ❌ Not available")
+        except ImportError:
+            print("  CUDA: ❌ PyTorch not installed")
+        
+        # Check Ollama
+        try:
+            import ollama
+            print("  Ollama: ✅ Package installed")
+        except ImportError:
+            print("  Ollama: ❌ Package not installed")
+        
+        return 0
+    
+    # Initialize agent
+    print("\n⚙️  Initializing agent...")
+    
+    backend = None if args.backend == "auto" else args.backend
+    
+    try:
+        agent = ToolCallingAgent(backend=backend)
+    except SystemExit:
+        return 1
+    except Exception as e:
+        print(f"❌ Failed to initialize: {e}")
+        return 1
+    
+    print(f"✅ Agent ready! Using {agent.backend_type} backend")
+    
+    # Execute based on mode
+    if args.mode == "single":
+        if not args.task:
+            # Show sample tasks for selection
+            print("\n" + "="*60)
+            print("SINGLE TASK MODE - No task provided")
+            print("="*60)
+            
+            sample_tasks = get_sample_tasks()
+            print("\n📋 Available sample tasks:")
+            for i, sample in enumerate(sample_tasks, 1):
+                print(f"\n{i}. {sample['name']}")
+                print(f"   {sample['description']}")
+            
+            print("\n" + "="*60)
+            try:
+                choice = input(f"\nSelect a task number (1-{len(sample_tasks)}) or 'q' to quit: ").strip()
+                if choice.lower() == 'q':
+                    return 0
+                
+                task_num = int(choice)
+                if 1 <= task_num <= len(sample_tasks):
+                    selected_task = sample_tasks[task_num - 1]
+                    print(f"\n✅ Selected: {selected_task['name']}")
+                    print("\nTask details:")
+                    print("-"*40)
+                    print(selected_task['task'])
+                    print("-"*40)
+                    
+                    confirm = input("\nRun this task? (y/n): ").strip().lower()
+                    if confirm == 'y':
+                        stream_enabled = not args.no_stream if hasattr(args, 'no_stream') else True
+                        run_single_task(agent, selected_task['task'], stream=stream_enabled)
+                    else:
+                        print("Task cancelled.")
+                else:
+                    print(f"Invalid selection. Please choose 1-{len(sample_tasks)}")
+                    return 1
+            except (ValueError, KeyboardInterrupt):
+                print("\nExiting...")
+                return 0
+        else:
+            stream_enabled = not args.no_stream if hasattr(args, 'no_stream') else True
+            run_single_task(agent, args.task, stream=stream_enabled)
+    
+    else:  # interactive mode
+        stream_enabled = not args.no_stream if hasattr(args, 'no_stream') else True
+        interactive_mode(agent, stream=stream_enabled)
+    
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

chapter2/local_llm_serving/ollama_native.py

@@ -0,0 +1,628 @@
+"""
+Ollama Native Tool Calling Implementation
+Uses Ollama's standard tool calling API (requires compatible models)
+"""
+
+import json
+import logging
+from concurrent.futures import ThreadPoolExecutor
+from typing import List, Dict, Any, Optional
+import ollama
+from tools import ToolRegistry
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class OllamaNativeAgent:
+    """Agent using Ollama's native tool calling support"""
+    
+    def __init__(self, model: str = "qwen3:0.6b"):
+        """
+        Initialize with a model that supports tool calling
+        """
+        self.model = model
+        self.client = ollama.Client()
+        self.tool_registry = ToolRegistry()
+        self.conversation_history = []
+        self._think_disabled: set[str] = set()
+        
+        # Check if Ollama is running
+        try:
+            self.client.list()
+            logger.info(f"✅ Connected to Ollama with model: {model}")
+        except Exception as e:
+            logger.error(f"❌ Failed to connect to Ollama: {e}")
+            logger.info("Please start Ollama with: ollama serve")
+    
+    def _convert_tools_to_ollama_format(self) -> List[Dict]:
+        """Convert tool registry to Ollama's expected format"""
+        tools = []
+        for tool_def in self.tool_registry.get_tool_schemas():
+            # Ollama expects the same format as OpenAI
+            tools.append(tool_def)
+        return tools
+    
+    def _chat_with_think_fallback(self, **kwargs) -> dict:
+        """Call client.chat with think=True when supported, falling back gracefully.
+
+        Models without thinking support (qwen2.5, llama3.2, gemma, etc.) return
+        a 400 error when think=True. This method catches the error once per
+        model and retries without think, caching the result so subsequent calls
+        are free. Also catches unexpected errors (old client, unknown issues)
+        and retries — if the error wasn't think-related the retry will fail
+        again and the exception propagates naturally.
+        """
+        if self.model in self._think_disabled:
+            return self.client.chat(**kwargs)
+
+        try:
+            return self.client.chat(think=True, **kwargs)
+        except ollama.ResponseError as e:
+            if e.status_code == 400:
+                logger.info("Model '%s' does not support thinking, disabling", self.model)
+                self._think_disabled.add(self.model)
+                return self.client.chat(**kwargs)
+            raise
+        except Exception:
+            # Unknown failure with think=True (old client, unexpected issues).
+            # Retry without think; if the error was unrelated the retry will
+            # also fail and the exception propagates naturally.
+            logger.warning("think=True failed for '%s', retrying without think", self.model)
+            self._think_disabled.add(self.model)
+            return self.client.chat(**kwargs)
+
+    def _execute_tool_calls(self, tool_calls: List[Dict[str, Any]]) -> List[str]:
+        """
+        Execute tool calls and return results in order.
+        Multiple tool calls in the same turn are executed in parallel (they are
+        independent by construction, since the model generated all of them
+        without seeing any result).
+        """
+        def run_one(tool_call: Dict[str, Any]) -> str:
+            function = tool_call.get('function', {})
+            tool_name = function.get('name')
+            tool_args = function.get('arguments')
+            
+            # Parse arguments if they're a string
+            if isinstance(tool_args, str):
+                try:
+                    tool_args = json.loads(tool_args)
+                except json.JSONDecodeError:
+                    logger.error(f"Failed to parse tool arguments: {tool_args}")
+                    tool_args = {}
+            
+            # Execute the tool
+            logger.info(f"Executing tool: {tool_name} with args: {tool_args}")
+            return self.tool_registry.execute_tool(tool_name, tool_args)
+        
+        if len(tool_calls) <= 1:
+            return [run_one(tc) for tc in tool_calls]
+        
+        # Independent tool calls run concurrently; executor.map preserves order
+        with ThreadPoolExecutor(max_workers=len(tool_calls)) as executor:
+            return list(executor.map(run_one, tool_calls))
+    
+    def chat(self, message: str, use_tools: bool = True,  
+             temperature: float = 0.3, stream: bool = False) -> str:
+        """
+        Send a message using Ollama's native tool calling
+        
+        Args:
+            message: User message
+            use_tools: Whether to enable tool calling
+            temperature: Sampling temperature
+            stream: Whether to stream the response
+            
+        Returns:
+            Final response from the model (or generator if streaming)
+        """
+        if stream:
+            return self.chat_stream(message, use_tools, temperature)
+        
+        # Original non-streaming implementation continues below...
+        # Add user message to history
+        self.conversation_history.append({
+            "role": "user",
+            "content": message
+        })
+        
+        # Prepare tools if enabled
+        tools = self._convert_tools_to_ollama_format() if use_tools else None
+        
+        try:
+            # Call Ollama with tools
+            response = self._chat_with_think_fallback(
+                model=self.model,
+                messages=self.conversation_history,
+                tools=tools,
+                options={"temperature": temperature},
+            )
+            
+            # Check if model made tool calls
+            message_content = response.get('message', {})
+            
+            # Handle tool calls if present
+            if 'tool_calls' in message_content:
+                tool_calls = message_content['tool_calls']
+                logger.info(f"Model requested {len(tool_calls)} tool call(s)")
+                
+                # Add assistant's message with tool calls to history
+                self.conversation_history.append({
+                    "role": "assistant",
+                    "content": message_content.get('content', ''),
+                    "tool_calls": tool_calls
+                })
+                
+                # Execute the tool calls (independent calls run in parallel)
+                results = self._execute_tool_calls(tool_calls)
+                
+                # Add tool results to conversation
+                for result in results:
+                    self.conversation_history.append({
+                        "role": "tool",
+                        "content": result
+                    })
+                
+                # Get final response with tool results (still include tools!)
+                final_response = self._chat_with_think_fallback(
+                    model=self.model,
+                    messages=self.conversation_history,
+                    tools=tools,  # IMPORTANT: Keep tools available
+                    options={"temperature": temperature},
+                )
+                
+                final_content = final_response.get('message', {}).get('content', '')
+                
+                # Clean response (remove <think> tags if present)
+                import re
+                final_content = re.sub(r'<think>.*?</think>', '', final_content, flags=re.DOTALL).strip()
+                
+                # Add final response to history
+                self.conversation_history.append({
+                    "role": "assistant",
+                    "content": final_content
+                })
+                
+                return final_content
+            
+            else:
+                # No tool calls, just return the response
+                content = message_content.get('content', '')
+                self.conversation_history.append({
+                    "role": "assistant",
+                    "content": content
+                })
+                return content
+                
+        except Exception as e:
+            logger.error(f"Error in chat: {e}")
+            return f"Error: {e}"
+    
+    def chat_stream(self, message: str, use_tools: bool = True,
+                    temperature: float = 0.3):
+        """
+        Stream a message to the model and handle tool calls in a ReAct loop
+        
+        Yields chunks that include:
+        - type: 'thinking', 'tool_call', 'tool_result', 'content'
+        - content: The actual content
+        """
+        # Add user message to history
+        self.conversation_history.append({
+            "role": "user",
+            "content": message
+        })
+        
+        # Prepare tools if enabled
+        tools = self._convert_tools_to_ollama_format() if use_tools else None
+        
+        # ReAct loop - keep going until no more tool calls are needed
+        max_iterations = 10  # Prevent infinite loops
+        iteration = 0
+        
+        while iteration < max_iterations:
+            iteration += 1
+            
+            try:
+                # Get response from model
+                stream_response = self._chat_with_think_fallback(
+                    model=self.model,
+                    messages=self.conversation_history,
+                    tools=tools,
+                    options={"temperature": temperature},
+                    stream=True,
+                )
+                
+                collected_content = []
+                tool_calls_detected = False
+                pending_tool_calls = []
+                thinking_buffer = ""
+                in_thinking = False
+                
+                # Process the stream
+                for chunk in stream_response:
+                    # Extract message content from chunk
+                    message_chunk = chunk.get('message', {})
+                    thinking_chunk = message_chunk.get('thinking', '')
+                    content_chunk = message_chunk.get('content', '')
+                    
+                    if thinking_chunk:
+                        yield {"type": "thinking", "content": thinking_chunk}
+                    
+                    if content_chunk:
+                        collected_content.append(content_chunk)
+                        
+                        # Handle thinking content
+                        if '<think>' in content_chunk:
+                            in_thinking = True
+                            thinking_buffer = content_chunk
+                            # Extract any content before <think>
+                            import re
+                            before_think = content_chunk.split('<think>')[0]
+                            if before_think:
+                                yield {"type": "content", "content": before_think}
+                            # Extract thinking content from this chunk
+                            if '</think>' in content_chunk:
+                                # Complete thinking in one chunk
+                                thinking_match = re.search(r'<think>(.*?)</think>', content_chunk, re.DOTALL)
+                                if thinking_match:
+                                    thinking_content = thinking_match.group(1).strip()
+                                    # Stream thinking content character by character
+                                    for char in thinking_content:
+                                        yield {"type": "thinking", "content": char}
+                                # Check for content after </think>
+                                after_think = content_chunk.split('</think>')[-1]
+                                if after_think:
+                                    yield {"type": "content", "content": after_think}
+                                in_thinking = False
+                                thinking_buffer = ""
+                            else:
+                                # Partial thinking, extract what we have so far
+                                partial_thinking = content_chunk.split('<think>')[-1]
+                                for char in partial_thinking:
+                                    yield {"type": "thinking", "content": char}
+                        elif in_thinking:
+                            thinking_buffer += content_chunk
+                            if '</think>' in content_chunk:
+                                # End of thinking
+                                before_end = content_chunk.split('</think>')[0]
+                                for char in before_end:
+                                    yield {"type": "thinking", "content": char}
+                                # Check for content after </think>
+                                after_think = content_chunk.split('</think>')[-1]
+                                if after_think:
+                                    yield {"type": "content", "content": after_think}
+                                in_thinking = False
+                                thinking_buffer = ""
+                            else:
+                                # Continue streaming thinking
+                                for char in content_chunk:
+                                    yield {"type": "thinking", "content": char}
+                        else:
+                            # Regular content - yield as-is
+                            yield {"type": "content", "content": content_chunk}
+                    
+                    # Check for tool calls in the chunk
+                    if 'tool_calls' in message_chunk:
+                        tool_calls_detected = True
+                        
+                        for tool_call in message_chunk['tool_calls']:
+                            function = tool_call.get('function', {})
+                            tool_name = function.get('name')
+                            tool_args = function.get('arguments')
+                            
+                            # Parse arguments if they're a string
+                            if isinstance(tool_args, str):
+                                try:
+                                    tool_args = json.loads(tool_args)
+                                except json.JSONDecodeError:
+                                    tool_args = {}
+                            
+                            # Collect the tool call; execution happens after the
+                            # stream finishes so calls can run in parallel.
+                            # Skip duplicates: some servers stream the accumulated
+                            # tool_calls list in every chunk.
+                            if not any(
+                                tc.get('function', {}).get('name') == tool_name
+                                and tc.get('function', {}).get('arguments') == function.get('arguments')
+                                for tc in pending_tool_calls
+                            ):
+                                pending_tool_calls.append(tool_call)
+                                yield {"type": "tool_call", "content": {"name": tool_name, "arguments": tool_args}}
+                
+                # Execute all tool calls from this turn in parallel
+                if pending_tool_calls:
+                    results = self._execute_tool_calls(pending_tool_calls)
+                    for result in results:
+                        # Yield tool result
+                        yield {"type": "tool_result", "content": result}
+                        
+                        # Add tool result to conversation
+                        self.conversation_history.append({
+                            "role": "tool",
+                            "content": result
+                        })
+                
+                # Save complete response to history
+                complete_response = ''.join(collected_content)
+                
+                if tool_calls_detected:
+                    # Add assistant's message to history
+                    self.conversation_history.append({
+                        "role": "assistant",
+                        "content": complete_response if complete_response else ""
+                    })
+                    # Continue the ReAct loop - let the model decide what to do next
+                    # The loop will continue and get the next response
+                else:
+                    # No tool calls - we have a final response
+                    self.conversation_history.append({
+                        "role": "assistant",
+                        "content": complete_response
+                    })
+                    # Exit the ReAct loop
+                    break
+                    
+            except Exception as e:
+                logger.error(f"Error in chat stream: {e}")
+                yield {"type": "error", "content": str(e)}
+                break
+        
+        # Check if we hit max iterations
+        if iteration >= max_iterations:
+            yield {"type": "error", "content": "Maximum iterations reached in ReAct loop"}
+    
+    def reset_conversation(self):
+        """Reset the conversation history"""
+        self.conversation_history = []
+        logger.info("Conversation history reset")
+
+
+class OllamaOpenAICompatible:
+    """Use Ollama through its OpenAI-compatible endpoint"""
+    
+    def __init__(self, model: str = "qwen3:0.6b", 
+                 base_url: str = "http://localhost:11434/v1"):
+        """
+        Initialize using Ollama's OpenAI-compatible API
+        
+        This provides better compatibility with tool calling
+        """
+        from openai import OpenAI
+        
+        self.model = model
+        self.client = OpenAI(
+            base_url=base_url,
+            api_key="ollama"  # Ollama doesn't need a real key
+        )
+        self.tool_registry = ToolRegistry()
+        self.conversation_history = []
+        
+        logger.info(f"✅ Initialized Ollama OpenAI-compatible client with {model}")
+    
+    def chat(self, message: str, use_tools: bool = True,
+             temperature: float = 0.3) -> str:
+        """
+        Chat using OpenAI-compatible endpoint
+        """
+        # Add user message
+        self.conversation_history.append({
+            "role": "user",
+            "content": message
+        })
+        
+        # Prepare tools
+        tools = self.tool_registry.get_tool_schemas() if use_tools else None
+        
+        try:
+            # Call with tools
+            response = self.client.chat.completions.create(
+                model=self.model,
+                messages=self.conversation_history,
+                tools=tools,
+                tool_choice="auto" if tools else None,
+                temperature=temperature
+            )
+            
+            assistant_message = response.choices[0].message
+            
+            # Check for tool calls
+            if assistant_message.tool_calls:
+                logger.info(f"Model requested {len(assistant_message.tool_calls)} tool(s)")
+                
+                # Add assistant message to history
+                self.conversation_history.append({
+                    "role": "assistant",
+                    "content": assistant_message.content or "",
+                    "tool_calls": [
+                        {
+                            "id": tc.id,
+                            "type": tc.type,
+                            "function": {
+                                "name": tc.function.name,
+                                "arguments": tc.function.arguments
+                            }
+                        } for tc in assistant_message.tool_calls
+                    ]
+                })
+                
+                # Execute tool calls (independent calls run in parallel;
+                # executor.map preserves order)
+                def run_one(tool_call):
+                    # Parse arguments
+                    try:
+                        args = json.loads(tool_call.function.arguments)
+                    except json.JSONDecodeError:
+                        args = {}
+                    
+                    # Execute tool
+                    return self.tool_registry.execute_tool(
+                        tool_call.function.name,
+                        args
+                    )
+                
+                tool_calls_list = list(assistant_message.tool_calls)
+                if len(tool_calls_list) <= 1:
+                    results = [run_one(tc) for tc in tool_calls_list]
+                else:
+                    with ThreadPoolExecutor(max_workers=len(tool_calls_list)) as executor:
+                        results = list(executor.map(run_one, tool_calls_list))
+                
+                # Add tool results
+                for tool_call, result in zip(tool_calls_list, results):
+                    self.conversation_history.append({
+                        "role": "tool",
+                        "tool_call_id": tool_call.id,
+                        "content": result
+                    })
+                
+                # Get final response
+                final_response = self.client.chat.completions.create(
+                    model=self.model,
+                    messages=self.conversation_history,
+                    tools=tools,   # IMPORTANT: Keep tools available
+                    temperature=temperature
+                )
+                
+                final_content = final_response.choices[0].message.content
+                
+                # Add to history
+                self.conversation_history.append({
+                    "role": "assistant",
+                    "content": final_content
+                })
+                
+                return final_content
+            
+            else:
+                # No tool calls
+                content = assistant_message.content
+                self.conversation_history.append({
+                    "role": "assistant",
+                    "content": content
+                })
+                return content
+                
+        except Exception as e:
+            logger.error(f"Error: {e}")
+            return f"Error: {e}"
+    
+    def reset_conversation(self):
+        """Reset conversation history"""
+        self.conversation_history = []
+        logger.info("Conversation reset")
+
+
+def test_native_tools():
+    """Test Ollama's native tool calling"""
+    print("="*60)
+    print("🔧 Testing Ollama Native Tool Calling")
+    print("="*60)
+    
+    # Test with default model
+    models_to_test = [
+        "qwen3:0.6b",  # Default model for this project
+    ]
+    
+    for model_name in models_to_test:
+        print(f"\n📦 Testing with {model_name}")
+        print("-"*40)
+        
+        try:
+            # Check if model is available
+            client = ollama.Client()
+            available_models = [m['name'] for m in client.list()['models']]
+            
+            if not any(model_name in m for m in available_models):
+                print(f"⚠️  Model {model_name} not installed")
+                print(f"   Install with: ollama pull {model_name}")
+                continue
+            
+            # Test the model
+            agent = OllamaNativeAgent(model=model_name)
+            
+            test_queries = [
+                "What's 15 * 23?",
+                "What's the weather in London?",
+            ]
+            
+            for query in test_queries:
+                print(f"\n👤 User: {query}")
+                response = agent.chat(query)
+                print(f"🤖 Assistant: {response[:200]}...")  # Truncate long responses
+                agent.reset_conversation()
+                
+        except Exception as e:
+            print(f"❌ Error testing {model_name}: {e}")
+    
+    print("\n" + "="*60)
+    print("💡 Note:")
+    print("This project uses qwen3:0.6b as the default model.")
+    print("Install with: ollama pull qwen3:0.6b")
+    print("="*60)
+
+
+def demo():
+    """Interactive demo with proper tool calling"""
+    print("="*60)
+    print("🎯 Ollama Standard Tool Calling Demo")
+    print("="*60)
+    
+    # Let user choose implementation
+    print("\nChoose implementation:")
+    print("1. Native Ollama API (recommended)")
+    print("2. OpenAI-compatible API")
+    
+    choice = input("\nEnter choice (1 or 2): ").strip()
+    
+    if choice == "2":
+        print("\nUsing OpenAI-compatible endpoint...")
+        agent = OllamaOpenAICompatible()
+    else:
+        print("\nUsing native Ollama API...")
+        # Check for best available model
+        try:
+            client = ollama.Client()
+            models = [m['name'] for m in client.list()['models']]
+            
+            # Use qwen3:0.6b as the default model
+            model = "qwen3:0.6b"
+            
+            if model in models:
+                print(f"Using recommended model: {model}")
+            else:
+                print(f"Recommended model {model} not found")
+                print("Install with: ollama pull qwen3:0.6b")
+                # Fall back to first available model
+                model = models[0] if models else "qwen3:0.6b"
+                print(f"Using fallback model: {model}")
+                
+            agent = OllamaNativeAgent(model=model)
+            
+        except Exception as e:
+            print(f"Error: {e}")
+            return
+    
+    # Interactive loop
+    print("\n💬 Chat with the assistant (type 'exit' to quit)")
+    print("-"*40)
+    
+    while True:
+        user_input = input("\n👤 You: ").strip()
+        if user_input.lower() in ['exit', 'quit']:
+            break
+        
+        response = agent.chat(user_input)
+        print(f"🤖 Assistant: {response}")
+    
+    print("\n👋 Goodbye!")
+
+
+if __name__ == "__main__":
+    import sys
+    
+    if len(sys.argv) > 1 and sys.argv[1] == "test":
+        test_native_tools()
+    else:
+        demo()

chapter2/local_llm_serving/quick_test_streaming.py

@@ -0,0 +1 @@
+ 

chapter2/local_llm_serving/requirements.txt

@@ -0,0 +1,22 @@
+# vLLM and dependencies
+# Official vLLM GPU execution requires Linux (native Windows uses Ollama).
+# WSL2 identifies itself as Linux, so vLLM is installed there as expected.
+vllm>=0.6.0; platform_system == "Linux"
+torch>=2.0.0
+transformers>=4.36.0
+accelerate>=0.25.0
+openai>=1.0.0  # For OpenAI-compatible API client
+python-dotenv>=1.0.0
+pydantic>=2.0.0
+requests>=2.31.0
+fastapi>=0.104.0
+uvicorn>=0.24.0
+
+# For demo tools
+python-weather>=2.0.0
+
+# For Ollama support (Mac/local development)
+ollama>=0.5.1
+
+# For additional tools
+PyPDF2>=3.0.0

chapter2/local_llm_serving/run_experiment.py

@@ -0,0 +1,447 @@
+#!/usr/bin/env python3
+"""Run the complete, real local-server campaign for Chapter 2 Experiment 2-1.
+
+Unlike an OpenAI-compatible client, this runner deliberately uses Ollama's
+``/api/generate`` endpoint with ``raw=true``.  The exact string emitted by the
+Qwen chat template is therefore visible in the evidence, including role
+sentinels and the model's XML tool-call protocol.  No model output is mocked.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import platform
+import re
+import statistics
+import time
+from concurrent.futures import ThreadPoolExecutor
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import requests
+from transformers import AutoTokenizer
+
+from tools import ToolRegistry
+
+
+ROOT = Path(__file__).resolve().parent
+PROTOCOL = ROOT / "experiment_protocol.json"
+TOOL_PATTERN = re.compile(r"<tool_call>\s*(\{.*?\})\s*</tool_call>", re.DOTALL)
+
+
+def sha256_bytes(data: bytes) -> str:
+    return hashlib.sha256(data).hexdigest()
+
+
+def sha256_text(text: str) -> str:
+    return sha256_bytes(text.encode("utf-8"))
+
+
+def utc_now() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def parse_tool_calls(raw_text: str) -> list[dict[str, Any]]:
+    calls = []
+    for match in TOOL_PATTERN.finditer(raw_text):
+        value = json.loads(match.group(1))
+        if not isinstance(value, dict) or not isinstance(value.get("name"), str):
+            raise ValueError("tool call must contain a string name")
+        arguments = value.get("arguments", {})
+        if not isinstance(arguments, dict):
+            raise ValueError("tool-call arguments must be an object")
+        calls.append({"name": value["name"], "arguments": arguments})
+    return calls
+
+
+class OllamaRawClient:
+    def __init__(self, base_url: str, model: str, timeout: float = 180.0):
+        self.base_url = base_url.rstrip("/")
+        self.model = model
+        self.timeout = timeout
+
+    def get_json(self, path: str) -> dict[str, Any]:
+        response = requests.get(self.base_url + path, timeout=self.timeout)
+        response.raise_for_status()
+        return response.json()
+
+    def show_model(self) -> dict[str, Any]:
+        response = requests.post(
+            self.base_url + "/api/show",
+            json={"model": self.model},
+            timeout=self.timeout,
+        )
+        response.raise_for_status()
+        return response.json()
+
+    def generate(
+        self,
+        prompt: str,
+        *,
+        num_predict: int,
+        temperature: float,
+    ) -> dict[str, Any]:
+        """Stream one raw request and retain every credential-free chunk."""
+        request_body = {
+            "model": self.model,
+            "prompt": prompt,
+            "raw": True,
+            "stream": True,
+            "keep_alive": "10m",
+            "options": {
+                "temperature": temperature,
+                "num_predict": num_predict,
+                "seed": 21,
+            },
+        }
+        started_at = utc_now()
+        started = time.perf_counter()
+        first_piece_s = None
+        chunks: list[dict[str, Any]] = []
+        pieces: list[str] = []
+        with requests.post(
+            self.base_url + "/api/generate",
+            json=request_body,
+            stream=True,
+            timeout=self.timeout,
+        ) as response:
+            response.raise_for_status()
+            for line in response.iter_lines():
+                if not line:
+                    continue
+                chunk = json.loads(line)
+                chunks.append(chunk)
+                piece = chunk.get("response") or ""
+                if piece:
+                    if first_piece_s is None:
+                        first_piece_s = time.perf_counter() - started
+                    pieces.append(piece)
+        wall_s = time.perf_counter() - started
+        final = chunks[-1] if chunks else {}
+        eval_count = int(final.get("eval_count") or 0)
+        eval_duration_s = float(final.get("eval_duration") or 0) / 1e9
+        return {
+            "requested_at": started_at,
+            "request": request_body,
+            "request_prompt_sha256": sha256_text(prompt),
+            "raw_chunks": chunks,
+            "raw_response": "".join(pieces),
+            "response_sha256": sha256_text("".join(pieces)),
+            "ttft_s": first_piece_s if first_piece_s is not None else wall_s,
+            "wall_s": wall_s,
+            "server": {
+                key: final.get(key)
+                for key in (
+                    "model",
+                    "created_at",
+                    "done",
+                    "done_reason",
+                    "total_duration",
+                    "load_duration",
+                    "prompt_eval_count",
+                    "prompt_eval_duration",
+                    "eval_count",
+                    "eval_duration",
+                )
+            },
+            "decode_tokens_per_second": (
+                eval_count / eval_duration_s if eval_duration_s > 0 else None
+            ),
+        }
+
+
+def normalize_tool_call(call: dict[str, Any]) -> dict[str, Any]:
+    """Normalize the small model's harmless city-vs-schema variations."""
+    name = call["name"]
+    args = dict(call["arguments"])
+    if name == "get_current_time":
+        city = args.pop("city", None)
+        if city and "timezone" not in args:
+            args["timezone"] = "America/Vancouver"
+    elif name in {"get_weather", "get_current_temperature"}:
+        name = "get_current_temperature"
+        city = args.pop("city", None)
+        if city and "location" not in args:
+            args["location"] = "Vancouver, Canada"
+        args.setdefault("unit", "celsius")
+    return {"name": name, "arguments": args}
+
+
+def execute_parallel(registry: ToolRegistry, calls: list[dict[str, Any]]) -> dict[str, Any]:
+    started_at = utc_now()
+    started = time.perf_counter()
+
+    def execute(index_and_call):
+        index, call = index_and_call
+        one_started = time.perf_counter()
+        result = registry.execute_tool(call["name"], call["arguments"])
+        return {
+            "index": index,
+            "call": call,
+            "result": result,
+            "duration_s": time.perf_counter() - one_started,
+        }
+
+    with ThreadPoolExecutor(max_workers=len(calls)) as executor:
+        results = list(executor.map(execute, enumerate(calls)))
+    results.sort(key=lambda item: item["index"])
+    return {
+        "started_at": started_at,
+        "execution": "ThreadPoolExecutor",
+        "wall_s": time.perf_counter() - started,
+        "results": results,
+    }
+
+
+def render_prompt(tokenizer, messages, tools=None) -> str:
+    return tokenizer.apply_chat_template(
+        messages,
+        tools=tools,
+        tokenize=False,
+        add_generation_prompt=True,
+        enable_thinking=True,
+    )
+
+
+def run_tool_case(client, tokenizer, protocol) -> dict[str, Any]:
+    registry = ToolRegistry()
+    all_schemas = registry.get_tool_schemas()
+    required_names = set(protocol["tool_case"]["required_tools"])
+    tools = [item for item in all_schemas if item["function"]["name"] in required_names]
+    messages: list[dict[str, Any]] = [
+        {
+            "role": "system",
+            "content": (
+                "You are a helpful assistant. Use tools for current facts. "
+                "When asking for Vancouver time, pass the IANA timezone "
+                "America/Vancouver; do not substitute another city's timezone."
+            ),
+        },
+        {"role": "user", "content": protocol["tool_case"]["prompt"]},
+    ]
+    first_prompt = render_prompt(tokenizer, messages, tools)
+    first = client.generate(
+        first_prompt,
+        num_predict=protocol["runtime"]["num_predict"],
+        temperature=protocol["runtime"]["temperature"],
+    )
+    parsed = parse_tool_calls(first["raw_response"])
+    normalized = [normalize_tool_call(item) for item in parsed]
+    parallel = execute_parallel(registry, normalized) if normalized else {
+        "started_at": utc_now(), "execution": "not_run", "wall_s": 0, "results": []
+    }
+
+    messages.append({"role": "assistant", "content": first["raw_response"]})
+    for result in parallel["results"]:
+        messages.append({"role": "tool", "content": result["result"]})
+    second_prompt = render_prompt(tokenizer, messages, tools)
+    second = client.generate(
+        second_prompt,
+        num_predict=protocol["runtime"]["num_predict"],
+        temperature=protocol["runtime"]["temperature"],
+    )
+    second_calls = parse_tool_calls(second["raw_response"])
+
+    observed = [item["name"] for item in normalized]
+    required = protocol["tool_case"]["required_tools"]
+    calls_by_name = {item["name"]: item["arguments"] for item in normalized}
+    time_arguments = calls_by_name.get("get_current_time", {})
+    weather_arguments = calls_by_name.get("get_current_temperature", {})
+    tool_results_valid = len(parallel["results"]) == 2 and all(
+        not str(item["result"]).startswith('{"error"')
+        for item in parallel["results"]
+    )
+    gates = {
+        "chat_template_special_tokens_visible": all(
+            token in first_prompt for token in ("<|im_start|>", "<|im_end|>", "<tools>")
+        ),
+        "raw_tool_tags_visible": "<tool_call>" in first["raw_response"],
+        "exact_required_tools": len(observed) == 2 and sorted(observed) == sorted(required),
+        "tool_arguments_match_vancouver": (
+            time_arguments.get("timezone") == protocol["tool_case"]["required_timezone"]
+            and "vancouver" in str(weather_arguments.get("location", "")).lower()
+        ),
+        "parallel_tool_results_valid": tool_results_valid,
+        "terminated_after_results": bool(second["raw_response"].strip()) and not second_calls,
+    }
+    return {
+        "messages": messages,
+        "tools": tools,
+        "first_turn": first,
+        "parsed_tool_calls": parsed,
+        "normalized_tool_calls": normalized,
+        "parallel_execution": parallel,
+        "second_rendered_prompt": second_prompt,
+        "second_turn": second,
+        "second_turn_tool_calls": second_calls,
+        "gates": gates,
+        "passed": all(gates.values()),
+    }
+
+
+def run_cache_case(client, tokenizer, protocol) -> dict[str, Any]:
+    cfg = protocol["cache_case"]
+    filler = "Keep this stable operating-manual sentence unchanged. "
+    header = "# Stable operating manual\n"
+    system = header + filler * max(1, int(cfg["approximate_prefix_tokens"] * 4 / len(filler)))
+    messages = [
+        {"role": "system", "content": system},
+        {"role": "user", "content": "Reply with only the word READY."},
+    ]
+    stable = render_prompt(tokenizer, messages)
+    warmups = [
+        client.generate(stable, num_predict=8, temperature=0)
+        for _ in range(cfg["warmups"])
+    ]
+    pairs = []
+    for index in range(cfg["matched_repeats"]):
+        hit = client.generate(stable, num_predict=8, temperature=0)
+        marker = f"M{index:07d}"  # fixed width and placed at byte zero
+        mutated_system = marker + system[len(marker):]
+        mutated = render_prompt(
+            tokenizer,
+            [
+                {"role": "system", "content": mutated_system},
+                {"role": "user", "content": "Reply with only the word READY."},
+            ],
+        )
+        miss = client.generate(mutated, num_predict=8, temperature=0)
+        pairs.append({
+            "pair": index + 1,
+            "hit": hit,
+            "miss": miss,
+            "prompt_character_lengths_equal": len(stable) == len(mutated),
+        })
+    hit_samples = [item["hit"]["ttft_s"] for item in pairs]
+    miss_samples = [item["miss"]["ttft_s"] for item in pairs]
+    return {
+        "stable_prompt_sha256": sha256_text(stable),
+        "stable_prompt_character_count": len(stable),
+        "warmups": warmups,
+        "pairs": pairs,
+        "summary": {
+            "hit_ttft_s": hit_samples,
+            "miss_ttft_s": miss_samples,
+            "hit_mean_s": statistics.fmean(hit_samples),
+            "miss_mean_s": statistics.fmean(miss_samples),
+            "miss_over_hit": (
+                statistics.fmean(miss_samples) / statistics.fmean(hit_samples)
+                if statistics.fmean(hit_samples) else None
+            ),
+            "hit_faster_in_pairs": sum(
+                item["hit"]["ttft_s"] < item["miss"]["ttft_s"] for item in pairs
+            ),
+            "matched_pairs": len(pairs),
+        },
+    }
+
+
+def credential_scan(path: Path) -> list[str]:
+    text = path.read_text(encoding="utf-8")
+    findings = []
+    for pattern in (r"sk-[A-Za-z0-9_-]{16,}", r"sk-or-[A-Za-z0-9_-]{12,}"):
+        findings.extend(match.group(0)[:8] + "…" for match in re.finditer(pattern, text))
+    return findings
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--base-url", default="http://localhost:11434")
+    parser.add_argument("--model", default="qwen3:0.6b")
+    parser.add_argument("--tokenizer", default="Qwen/Qwen3-0.6B")
+    parser.add_argument("--output", required=True, type=Path)
+    args = parser.parse_args()
+
+    protocol_bytes = PROTOCOL.read_bytes()
+    protocol = json.loads(protocol_bytes)
+    output = args.output.resolve()
+    output.mkdir(parents=True, exist_ok=False)
+    (output / "experiment_protocol.json").write_bytes(protocol_bytes)
+
+    client = OllamaRawClient(args.base_url, args.model)
+    version = client.get_json("/api/version")
+    tags = client.get_json("/api/tags")
+    matching = [item for item in tags.get("models", []) if item.get("name") == args.model]
+    show = client.show_model()
+    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer, local_files_only=True)
+
+    evidence: dict[str, Any] = {
+        "experiment_id": "2-1",
+        "started_at": utc_now(),
+        "protocol_sha256": sha256_bytes(protocol_bytes),
+        "provider": "local Ollama",
+        "endpoint": args.base_url,
+        "model": args.model,
+        "tokenizer": args.tokenizer,
+        "host": {
+            "platform": platform.platform(),
+            "machine": platform.machine(),
+            "processor": platform.processor(),
+            "python": platform.python_version(),
+        },
+        "server": {
+            "version": version,
+            "tag": matching[0] if matching else None,
+            "show": {
+                "modified_at": show.get("modified_at"),
+                "details": show.get("details"),
+                "model_info": show.get("model_info"),
+            },
+        },
+    }
+    evidence["tool_case"] = run_tool_case(client, tokenizer, protocol)
+    evidence["cache_case"] = run_cache_case(client, tokenizer, protocol)
+    evidence["finished_at"] = utc_now()
+
+    tag = evidence["server"]["tag"] or {}
+    throughput = [
+        evidence["tool_case"][turn].get("decode_tokens_per_second")
+        for turn in ("first_turn", "second_turn")
+    ]
+    throughput = [value for value in throughput if value is not None]
+    evidence["summary"] = {
+        "model_digest": tag.get("digest"),
+        "local_model_verified": bool(tag.get("digest")),
+        "tool_case_passed": evidence["tool_case"]["passed"],
+        "mean_tool_case_decode_tokens_per_second": (
+            statistics.fmean(throughput) if throughput else None
+        ),
+        "exceeded_100_tokens_per_second_on_this_host": bool(
+            throughput and statistics.fmean(throughput) > 100
+        ),
+        "cache_observation": evidence["cache_case"]["summary"],
+    }
+    evidence["official_complete"] = bool(
+        evidence["summary"]["local_model_verified"]
+        and evidence["summary"]["tool_case_passed"]
+        and evidence["cache_case"]["summary"]["matched_pairs"] == cfg_pairs(protocol)
+    )
+
+    evidence_path = output / "evidence.json"
+    evidence_path.write_text(json.dumps(evidence, indent=2, ensure_ascii=False), encoding="utf-8")
+    findings = credential_scan(evidence_path)
+    manifest = {
+        "experiment_id": "2-1",
+        "official_complete": evidence["official_complete"] and not findings,
+        "protocol_sha256": evidence["protocol_sha256"],
+        "evidence_sha256": sha256_bytes(evidence_path.read_bytes()),
+        "credential_scan_passed": not findings,
+        "credential_scan_findings": findings,
+        "cost": {"amount": 0, "currency": "USD", "qualification": "local inference"},
+    }
+    (output / "manifest.json").write_text(
+        json.dumps(manifest, indent=2, ensure_ascii=False), encoding="utf-8"
+    )
+    print(json.dumps({"output": str(output), **manifest, "summary": evidence["summary"]}, indent=2))
+    return 0 if manifest["official_complete"] else 1
+
+
+def cfg_pairs(protocol: dict[str, Any]) -> int:
+    return int(protocol["cache_case"]["matched_repeats"])
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

chapter2/local_llm_serving/runs/exp2-1-qwen3-0.6b-20260730-v1/experiment_protocol.json

@@ -0,0 +1,47 @@
+{
+  "experiment_id": "2-1",
+  "protocol_version": "1.0.0",
+  "frozen_on": "2026-07-30",
+  "authority": "book/chapter2.md:363",
+  "runtime": {
+    "server": "Ollama native /api/generate",
+    "model": "qwen3:0.6b",
+    "reference_model": "Qwen/Qwen3-0.6B",
+    "raw_mode": true,
+    "temperature": 0,
+    "num_predict": 512
+  },
+  "tool_case": {
+    "prompt": "What are the current time and weather in Vancouver? Call both tools.",
+    "required_tools": [
+      "get_current_time",
+      "get_current_temperature"
+    ],
+    "required_timezone": "America/Vancouver",
+    "required_location": "Vancouver, Canada",
+    "execution": "parallel"
+  },
+  "cache_case": {
+    "approximate_prefix_tokens": 4096,
+    "warmups": 2,
+    "matched_repeats": 5,
+    "hit": "byte-identical rendered prompt",
+    "miss": "same-length unique mutation at the first bytes of the system prompt"
+  },
+  "acceptance_gates": [
+    "the running server reports qwen3:0.6b with a nonempty immutable model digest",
+    "the rendered prompt retains chat-template special tokens and tool schema",
+    "the first raw response contains exactly the two required tool calls",
+    "both tools execute concurrently and return auditable results",
+    "the second model turn consumes both tool results and terminates without another tool call",
+    "stream chunks, request prompts, timings, token counts, server durations, and hashes are retained",
+    "matched hit and miss TTFT samples are retained without requiring a favorable outcome",
+    "all execution is local except the read-only Open-Meteo weather lookup",
+    "no credential is sent to or retained by the experiment"
+  ],
+  "claim_policy": {
+    "tool_calling": "complete only when every tool/termination gate passes",
+    "throughput": "report measured decode throughput; do not claim the manuscript's M2 >100 tok/s observation unless this run exceeds it on identified hardware",
+    "kv_cache": "report the matched TTFT distribution even if the hit arm is not faster"
+  }
+}

chapter2/local_llm_serving/runs/exp2-1-qwen3-0.6b-20260730-v1/manifest.json

@@ -0,0 +1,15 @@
+{
+  "experiment_id": "2-1",
+  "official_complete": false,
+  "protocol_sha256": "829b8c2d19926af25ca5dd3271065a33b26460191aa0ccb15bb4fdfdbf1dc12a",
+  "evidence_sha256": "3611fb226a763898596f996e8bc3221f4bb36b5b443a84970fffe8a1cf84d5e3",
+  "credential_scan_passed": true,
+  "credential_scan_findings": [],
+  "review_status": "rejected_after_manual_argument_check",
+  "rejection_reason": "The model passed America/New_York to get_current_time for Vancouver, so the final time was incorrect.",
+  "cost": {
+    "amount": 0,
+    "currency": "USD",
+    "qualification": "local inference"
+  }
+}

chapter2/local_llm_serving/runs/exp2-1-qwen3-0.6b-20260730-v2/experiment_protocol.json

@@ -0,0 +1,47 @@
+{
+  "experiment_id": "2-1",
+  "protocol_version": "1.0.0",
+  "frozen_on": "2026-07-30",
+  "authority": "book/chapter2.md:363",
+  "runtime": {
+    "server": "Ollama native /api/generate",
+    "model": "qwen3:0.6b",
+    "reference_model": "Qwen/Qwen3-0.6B",
+    "raw_mode": true,
+    "temperature": 0,
+    "num_predict": 512
+  },
+  "tool_case": {
+    "prompt": "What are the current time and weather in Vancouver? Call both tools.",
+    "required_tools": [
+      "get_current_time",
+      "get_current_temperature"
+    ],
+    "required_timezone": "America/Vancouver",
+    "required_location": "Vancouver, Canada",
+    "execution": "parallel"
+  },
+  "cache_case": {
+    "approximate_prefix_tokens": 4096,
+    "warmups": 2,
+    "matched_repeats": 5,
+    "hit": "byte-identical rendered prompt",
+    "miss": "same-length unique mutation at the first bytes of the system prompt"
+  },
+  "acceptance_gates": [
+    "the running server reports qwen3:0.6b with a nonempty immutable model digest",
+    "the rendered prompt retains chat-template special tokens and tool schema",
+    "the first raw response contains exactly the two required tool calls",
+    "both tools execute concurrently and return auditable results",
+    "the second model turn consumes both tool results and terminates without another tool call",
+    "stream chunks, request prompts, timings, token counts, server durations, and hashes are retained",
+    "matched hit and miss TTFT samples are retained without requiring a favorable outcome",
+    "all execution is local except the read-only Open-Meteo weather lookup",
+    "no credential is sent to or retained by the experiment"
+  ],
+  "claim_policy": {
+    "tool_calling": "complete only when every tool/termination gate passes",
+    "throughput": "report measured decode throughput; do not claim the manuscript's M2 >100 tok/s observation unless this run exceeds it on identified hardware",
+    "kv_cache": "report the matched TTFT distribution even if the hit arm is not faster"
+  }
+}

chapter2/local_llm_serving/runs/exp2-1-qwen3-0.6b-20260730-v2/manifest.json

@@ -0,0 +1,13 @@
+{
+  "experiment_id": "2-1",
+  "official_complete": true,
+  "protocol_sha256": "829b8c2d19926af25ca5dd3271065a33b26460191aa0ccb15bb4fdfdbf1dc12a",
+  "evidence_sha256": "2ff9063ea07a92466f894df91d33856b09efd9f0c75d924de3fde1d1ef1c13fe",
+  "credential_scan_passed": true,
+  "credential_scan_findings": [],
+  "cost": {
+    "amount": 0,
+    "currency": "USD",
+    "qualification": "local inference"
+  }
+}

chapter2/local_llm_serving/server.py

@@ -0,0 +1,245 @@
+"""
+vLLM Server Launcher for Qwen3 with Tool Calling Support
+"""
+
+import os
+import sys
+import subprocess
+import time
+import requests
+from pathlib import Path
+from config import VLLM_SERVER_CONFIG, VLLM_HOST, VLLM_PORT
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class VLLMServer:
+    """Manager for vLLM server process"""
+    
+    def __init__(self, config: dict = None):
+        """Initialize server manager with configuration"""
+        self.config = config or VLLM_SERVER_CONFIG
+        self.process = None
+        self.server_url = f"http://{self.config['host']}:{self.config['port']}"
+    
+    def _build_command(self) -> list:
+        """Build vLLM server command with arguments"""
+        cmd = [
+            sys.executable, "-m", "vllm.entrypoints.openai.api_server",
+            "--model", self.config["model"],
+            "--port", str(self.config["port"]),
+            "--host", self.config["host"],
+        ]
+        
+        # Add tool-specific arguments
+        if self.config.get("enable_auto_tool_choice"):
+            cmd.append("--enable-auto-tool-choice")
+        
+        if self.config.get("tool_call_parser"):
+            cmd.extend(["--tool-call-parser", self.config["tool_call_parser"]])
+        
+        if self.config.get("chat_template"):
+            cmd.extend(["--chat-template", self.config["chat_template"]])
+        
+        # Add performance arguments
+        if self.config.get("max_model_len"):
+            cmd.extend(["--max-model-len", str(self.config["max_model_len"])])
+        
+        if self.config.get("gpu_memory_utilization"):
+            cmd.extend(["--gpu-memory-utilization", str(self.config["gpu_memory_utilization"])])
+        
+        if self.config.get("dtype"):
+            cmd.extend(["--dtype", self.config["dtype"]])
+        
+        if self.config.get("enforce_eager"):
+            cmd.append("--enforce-eager")
+        
+        # Add tensor parallel size if multiple GPUs
+        if self.config.get("tensor_parallel_size"):
+            cmd.extend(["--tensor-parallel-size", str(self.config["tensor_parallel_size"])])
+        
+        return cmd
+    
+    def start(self, wait_for_ready: bool = True, timeout: int = 120):
+        """
+        Start the vLLM server
+        
+        Args:
+            wait_for_ready: Wait for server to be ready
+            timeout: Maximum time to wait for server startup
+        """
+        if self.is_running():
+            logger.info("vLLM server is already running")
+            return
+        
+        # Create logs directory
+        log_dir = Path("logs")
+        log_dir.mkdir(exist_ok=True)
+        
+        # Build command
+        cmd = self._build_command()
+        logger.info(f"Starting vLLM server with command: {' '.join(cmd)}")
+        
+        # Start server process
+        log_file = log_dir / "vllm_server.log"
+        with open(log_file, "w") as f:
+            self.process = subprocess.Popen(
+                cmd,
+                stdout=f,
+                stderr=subprocess.STDOUT,
+                env=os.environ.copy()
+            )
+        
+        logger.info(f"vLLM server process started with PID: {self.process.pid}")
+        logger.info(f"Server logs are being written to: {log_file}")
+        
+        if wait_for_ready:
+            self._wait_for_ready(timeout)
+    
+    def _wait_for_ready(self, timeout: int = 120):
+        """Wait for server to be ready"""
+        start_time = time.time()
+        health_url = f"{self.server_url}/health"
+        
+        logger.info(f"Waiting for vLLM server to be ready at {health_url}...")
+        
+        while time.time() - start_time < timeout:
+            try:
+                response = requests.get(health_url, timeout=1)
+                if response.status_code == 200:
+                    logger.info("vLLM server is ready!")
+                    
+                    # Test model availability
+                    models_url = f"{self.server_url}/v1/models"
+                    models_response = requests.get(models_url)
+                    if models_response.status_code == 200:
+                        models = models_response.json()
+                        logger.info(f"Available models: {models}")
+                    return
+            except requests.exceptions.RequestException:
+                pass
+            
+            # Check if process is still running
+            if self.process and self.process.poll() is not None:
+                raise RuntimeError(f"vLLM server process died with code: {self.process.returncode}")
+            
+            time.sleep(2)
+        
+        raise TimeoutError(f"vLLM server did not start within {timeout} seconds")
+    
+    def stop(self):
+        """Stop the vLLM server"""
+        if self.process:
+            logger.info("Stopping vLLM server...")
+            self.process.terminate()
+            try:
+                self.process.wait(timeout=10)
+            except subprocess.TimeoutExpired:
+                logger.warning("Server did not stop gracefully, forcing kill...")
+                self.process.kill()
+                self.process.wait()
+            
+            self.process = None
+            logger.info("vLLM server stopped")
+    
+    def is_running(self) -> bool:
+        """Check if server is running"""
+        if not self.process:
+            return False
+        
+        # Check if process is still alive
+        if self.process.poll() is not None:
+            return False
+        
+        # Try to connect to health endpoint
+        try:
+            response = requests.get(f"{self.server_url}/health", timeout=1)
+            return response.status_code == 200
+        except requests.exceptions.RequestException:
+            return False
+    
+    def restart(self):
+        """Restart the server"""
+        logger.info("Restarting vLLM server...")
+        self.stop()
+        time.sleep(2)
+        self.start()
+
+
+def download_model_from_modelscope():
+    """
+    Download Qwen3-0.6B model from ModelScope
+    This is optional - vLLM can download from HuggingFace automatically
+    """
+    try:
+        from modelscope import snapshot_download
+        
+        model_dir = snapshot_download(
+            'Qwen/Qwen3-0.6B',
+            cache_dir='./models'
+        )
+        logger.info(f"Model downloaded to: {model_dir}")
+        return model_dir
+    except ImportError:
+        logger.warning("ModelScope not installed. Install with: pip install modelscope")
+        logger.info("vLLM will download from HuggingFace instead")
+        return None
+
+
+def main():
+    """Main function to start vLLM server"""
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="Start vLLM server with Qwen3 model")
+    parser.add_argument("--download", action="store_true", 
+                       help="Download model from ModelScope first")
+    parser.add_argument("--model", type=str, default=None,
+                       help="Model name or path (overrides config)")
+    parser.add_argument("--port", type=int, default=None,
+                       help="Server port (overrides config)")
+    parser.add_argument("--host", type=str, default=None,
+                       help="Server host (overrides config)")
+    
+    args = parser.parse_args()
+    
+    # Download model if requested
+    if args.download:
+        model_path = download_model_from_modelscope()
+        if model_path:
+            VLLM_SERVER_CONFIG["model"] = model_path
+    
+    # Override config with command line arguments
+    if args.model:
+        VLLM_SERVER_CONFIG["model"] = args.model
+    if args.port:
+        VLLM_SERVER_CONFIG["port"] = args.port
+    if args.host:
+        VLLM_SERVER_CONFIG["host"] = args.host
+    
+    # Create and start server
+    server = VLLMServer(VLLM_SERVER_CONFIG)
+    
+    try:
+        server.start(wait_for_ready=True)
+        logger.info(f"vLLM server is running at {server.server_url}")
+        logger.info("Press Ctrl+C to stop the server")
+        
+        # Keep the server running
+        while True:
+            time.sleep(1)
+            if not server.is_running():
+                logger.error("Server stopped unexpectedly!")
+                break
+                
+    except KeyboardInterrupt:
+        logger.info("\nShutting down...")
+    except Exception as e:
+        logger.error(f"Error: {e}")
+    finally:
+        server.stop()
+
+
+if __name__ == "__main__":
+    main()

chapter2/local_llm_serving/setup.sh

@@ -0,0 +1,92 @@
+#!/bin/bash
+
+# vLLM Tool Calling Demo - Setup Script
+# This script helps set up the environment for the demo
+
+echo "======================================"
+echo "vLLM Tool Calling Demo Setup"
+echo "======================================"
+
+# Check Python version
+echo -e "\n1. Checking Python version..."
+python_version=$(python3 --version 2>&1 | grep -Po '(?<=Python )\d+\.\d+')
+required_version="3.8"
+
+if [ "$(printf '%s\n' "$required_version" "$python_version" | sort -V | head -n1)" = "$required_version" ]; then 
+    echo "✅ Python $python_version is installed (>= $required_version required)"
+else
+    echo "❌ Python $python_version is too old. Please install Python >= $required_version"
+    exit 1
+fi
+
+# Check CUDA availability
+echo -e "\n2. Checking CUDA availability..."
+if command -v nvidia-smi &> /dev/null; then
+    echo "✅ NVIDIA GPU detected:"
+    nvidia-smi --query-gpu=name,memory.total --format=csv,noheader
+else
+    echo "⚠️  No NVIDIA GPU detected. vLLM requires a CUDA-capable GPU."
+    echo "   You can still install dependencies but won't be able to run the model locally."
+fi
+
+# Create virtual environment
+echo -e "\n3. Setting up Python virtual environment..."
+if [ ! -d "venv" ]; then
+    python3 -m venv venv
+    echo "✅ Virtual environment created"
+else
+    echo "✅ Virtual environment already exists"
+fi
+
+# Activate virtual environment
+source venv/bin/activate
+
+# Upgrade pip
+echo -e "\n4. Upgrading pip..."
+pip install --upgrade pip
+
+# Install requirements
+echo -e "\n5. Installing requirements..."
+pip install -r requirements.txt
+
+# Check PyTorch CUDA
+echo -e "\n6. Checking PyTorch CUDA support..."
+python3 -c "import torch; print('✅ PyTorch CUDA available' if torch.cuda.is_available() else '❌ PyTorch CUDA not available')"
+
+# Create .env file if it doesn't exist
+echo -e "\n7. Setting up environment configuration..."
+if [ ! -f ".env" ]; then
+    cp env.example .env
+    echo "✅ Created .env file from template"
+else
+    echo "✅ .env file already exists"
+fi
+
+# Create logs directory
+echo -e "\n8. Creating logs directory..."
+mkdir -p logs
+echo "✅ Logs directory created"
+
+# Optional: Install ModelScope for downloading from Chinese mirror
+echo -e "\n9. Optional packages..."
+read -p "Install ModelScope for downloading models from Chinese mirror? (y/n): " -n 1 -r
+echo
+if [[ $REPLY =~ ^[Yy]$ ]]; then
+    pip install modelscope
+    echo "✅ ModelScope installed"
+fi
+
+echo -e "\n======================================"
+echo "Setup Complete!"
+echo "======================================"
+echo ""
+echo "Next steps:"
+echo "1. Activate the virtual environment: source venv/bin/activate"
+echo "2. Check system compatibility: python check_compatibility.py"
+echo "3. Run the main script: python main.py"
+echo ""
+echo "The script will automatically detect your platform and use:"
+echo "  - vLLM on Linux (including WSL2) if you have an NVIDIA GPU"
+echo "  - Ollama on macOS, native Windows, or Linux without GPU"
+echo ""
+echo "For more information, see README.md"

chapter2/local_llm_serving/test_benchmark.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+"""Regression tests for the local LLM serving benchmark."""
+
+from types import SimpleNamespace
+from unittest.mock import patch
+
+from benchmark import stream_once
+
+
+class FakeCompletions:
+    def __init__(self, chunks):
+        self.chunks = chunks
+
+    def create(self, **kwargs):
+        return iter(self.chunks)
+
+
+def make_client(chunks):
+    completions = FakeCompletions(chunks)
+    return SimpleNamespace(chat=SimpleNamespace(completions=completions))
+
+
+def run_reasoning_stream(field):
+    delta = SimpleNamespace(**{field: "Thinking about the answer."})
+    chunks = [
+        SimpleNamespace(
+            usage=None,
+            choices=[SimpleNamespace(delta=delta)],
+        ),
+        SimpleNamespace(
+            usage=SimpleNamespace(completion_tokens=8),
+            choices=[],
+        ),
+    ]
+
+    with patch("benchmark.time.perf_counter", side_effect=[0.0, 0.25, 1.0]):
+        return stream_once(
+            make_client(chunks),
+            model="qwen3:0.6b",
+            messages=[{"role": "user", "content": "hello"}],
+            max_tokens=8,
+            temperature=0.0,
+        )
+
+
+def test_stream_once_uses_reasoning_chunks_for_ttft():
+    for field in ("reasoning_content", "reasoning"):
+        result = run_reasoning_stream(field)
+
+        assert result["ttft"] == 0.25, field
+        assert result["total"] == 1.0, field
+        assert result["output_tokens"] == 8.0, field
+        assert result["decode_tps"] == 8.0 / 0.75, field

chapter2/local_llm_serving/test_code_interpreter_full.py

@@ -0,0 +1,184 @@
+"""
+Test the full Python environment code interpreter with error handling
+"""
+import json
+from tools import ToolRegistry
+
+def test_successful_execution():
+    """Test that code executes successfully with full Python environment"""
+    print("=" * 60)
+    print("Test 1: Successful execution with full Python environment")
+    print("=" * 60)
+    
+    registry = ToolRegistry()
+    
+    # Test with various Python features that would fail in a sandbox
+    test_cases = [
+        {
+            "name": "Complex calculation",
+            "code": "import numpy as np\nresult = np.array([1, 2, 3, 4, 5]).mean()"
+        },
+        {
+            "name": "File operations (simulated)",
+            "code": "import os\nresult = os.getcwd()"
+        },
+        {
+            "name": "Dict comprehension",
+            "code": "result = {i: i**2 for i in range(5)}"
+        },
+        {
+            "name": "Lambda and map",
+            "code": "result = list(map(lambda x: x**2, [1, 2, 3, 4, 5]))"
+        }
+    ]
+    
+    for test in test_cases:
+        print(f"\n{test['name']}:")
+        try:
+            result = registry.execute_tool("code_interpreter", {"code": test["code"]})
+            result_dict = json.loads(result)
+            if result_dict.get("success"):
+                print(f"  ✓ Success: {result_dict.get('result')}")
+            else:
+                print(f"  ✗ Failed: {result_dict.get('error')}")
+        except Exception as e:
+            print(f"  ✗ Exception: {e}")
+
+def test_error_handling():
+    """Test that errors are properly captured and formatted"""
+    print("\n" + "=" * 60)
+    print("Test 2: Error handling and reporting")
+    print("=" * 60)
+    
+    registry = ToolRegistry()
+    
+    error_cases = [
+        {
+            "name": "Syntax Error",
+            "code": "if True\n    print('missing colon')"
+        },
+        {
+            "name": "Name Error",
+            "code": "result = undefined_variable + 10"
+        },
+        {
+            "name": "Type Error",
+            "code": "result = '5' + 5"
+        },
+        {
+            "name": "Division by Zero",
+            "code": "result = 10 / 0"
+        },
+        {
+            "name": "Import Error",
+            "code": "import nonexistent_module\nresult = 42"
+        }
+    ]
+    
+    for test in error_cases:
+        print(f"\n{test['name']}:")
+        result = registry.execute_tool("code_interpreter", {"code": test["code"]})
+        result_dict = json.loads(result)
+        
+        if not result_dict.get("success"):
+            print(f"  ✓ Error properly caught:")
+            print(f"    Error Type: {result_dict.get('error_type')}")
+            print(f"    Error Message: {result_dict.get('error')}")
+            if result_dict.get('traceback'):
+                print(f"    Traceback: {result_dict.get('traceback')[:100]}...")
+        else:
+            print(f"  ✗ Error not caught - this shouldn't happen")
+
+def test_full_environment_access():
+    """Test that the code interpreter has access to full Python environment"""
+    print("\n" + "=" * 60)
+    print("Test 3: Full Python environment access")
+    print("=" * 60)
+    
+    registry = ToolRegistry()
+    
+    # Test access to various Python features that would be blocked in a sandbox
+    full_env_tests = [
+        {
+            "name": "Access to all builtins",
+            "code": "result = [callable(eval), callable(exec), callable(compile), callable(__import__)]"
+        },
+        {
+            "name": "Dynamic import",
+            "code": "import sys\nresult = f'Python {sys.version_info.major}.{sys.version_info.minor}'"
+        },
+        {
+            "name": "List comprehension with filter",
+            "code": "result = [x for x in range(20) if x % 2 == 0 and x % 3 == 0]"
+        },
+        {
+            "name": "Multiple variable assignment",
+            "code": "a, b, c = 1, 2, 3\nresult = a + b + c"
+        }
+    ]
+    
+    for test in full_env_tests:
+        print(f"\n{test['name']}:")
+        result = registry.execute_tool("code_interpreter", {"code": test["code"]})
+        result_dict = json.loads(result)
+        
+        if result_dict.get("success"):
+            print(f"  ✓ Success: {result_dict.get('result')}")
+            if result_dict.get('output'):
+                print(f"    Output: {result_dict.get('output')}")
+        else:
+            print(f"  ✗ Failed: {result_dict.get('error')}")
+
+def test_agent_error_propagation():
+    """Test that errors are properly formatted for the agent"""
+    print("\n" + "=" * 60)
+    print("Test 4: Agent error message formatting")
+    print("=" * 60)
+    
+    from agent import VLLMToolAgent
+    
+    # This test would require a running vLLM server, so we'll just show
+    # how errors would be formatted
+    
+    registry = ToolRegistry()
+    
+    # Simulate an error
+    result = registry.execute_tool("code_interpreter", {
+        "code": "result = 1 / 0"
+    })
+    
+    result_dict = json.loads(result)
+    
+    # Format as the agent would
+    if not result_dict.get("success"):
+        error_msg = f"❌ Tool 'code_interpreter' execution failed:\n"
+        if "error" in result_dict:
+            error_msg += f"Error: {result_dict['error']}\n"
+        if "error_type" in result_dict:
+            error_msg += f"Type: {result_dict['error_type']}\n"
+        if "traceback" in result_dict:
+            error_msg += f"Traceback:\n{result_dict['traceback']}\n"
+        
+        print("\nFormatted error message that would be sent to agent:")
+        print("-" * 60)
+        print(error_msg)
+        print("-" * 60)
+        print("\nThe agent will receive this error message and can:")
+        print("  1. Try to fix the code")
+        print("  2. Ask the user for clarification")
+        print("  3. Provide an alternative solution")
+
+if __name__ == "__main__":
+    test_successful_execution()
+    test_error_handling()
+    test_full_environment_access()
+    test_agent_error_propagation()
+    
+    print("\n" + "=" * 60)
+    print("All tests completed!")
+    print("=" * 60)
+    print("\nSummary:")
+    print("  ✓ Full Python environment is available (no sandbox restrictions)")
+    print("  ✓ Errors are properly caught and detailed traceback provided")
+    print("  ✓ Error messages are formatted clearly for the agent")
+    print("  ✓ Agent can receive and process error information")

chapter2/local_llm_serving/test_ollama_streaming_final.py

@@ -0,0 +1 @@
+ 

chapter2/local_llm_serving/test_ollama_thinking.py

@@ -0,0 +1,47 @@
+#!/usr/bin/env python3
+"""Regression tests for Ollama thinking stream handling."""
+
+import sys
+import types
+
+
+fake_ollama_module = types.ModuleType("ollama")
+setattr(fake_ollama_module, "Client", lambda: None)
+sys.modules.setdefault("ollama", fake_ollama_module)
+
+from ollama_native import OllamaNativeAgent
+
+
+class FakeOllamaClient:
+    def chat(self, **kwargs):
+        self.last_kwargs = kwargs
+        return iter([
+            {"message": {"thinking": "Need current data. "}},
+            {"message": {"content": "Final answer."}},
+        ])
+
+
+def test_streaming_yields_ollama_thinking_field():
+    agent = OllamaNativeAgent(model="qwen3:0.6b")
+    fake_client = FakeOllamaClient()
+    agent.client = fake_client
+
+    chunks = list(agent.chat_stream("hello", use_tools=False, temperature=0.1))
+
+    assert fake_client.last_kwargs.get("think") is True
+
+    thinking_event = {"type": "thinking", "content": "Need current data. "}
+    content_event = {"type": "content", "content": "Final answer."}
+
+    assert thinking_event in chunks
+    assert content_event in chunks
+
+    # Verify ordering: thinking must be emitted before final content
+    assert chunks.index(thinking_event) < chunks.index(content_event), \
+        f"thinking (at index {chunks.index(thinking_event)}) should come " \
+        f"before content (at index {chunks.index(content_event)})"
+
+
+if __name__ == "__main__":
+    test_streaming_yields_ollama_thinking_field()
+    print("ok")

chapter2/local_llm_serving/test_parallel_tools.py

@@ -0,0 +1,249 @@
+"""
+End-to-end test for parallel tool calling with a real LLM (Ollama).
+
+Covers:
+1. Deterministic proof of parallel execution: two 2s-sleep tools must finish
+   in ~2s (not ~4s) through each agent's tool-execution path.
+2. Real-model runs of the book's "Vancouver time + weather" example through:
+   - OllamaNativeAgent.chat / chat_stream (native tool calling)
+   - OllamaOpenAICompatible.chat (OpenAI-compatible endpoint, native tools)
+   - VLLMToolAgent.chat / chat_stream (structured OpenAI-compatible tool calls;
+     Ollama's OpenAI-compatible endpoint stands in for the vLLM server)
+
+Run from this directory:  python3 test_parallel_tools.py
+Requires: ollama serve + ollama pull qwen2.5:7b-instruct-q8_0
+"""
+import json
+import logging
+import time
+from types import SimpleNamespace
+
+from ollama_native import OllamaNativeAgent, OllamaOpenAICompatible
+from agent import VLLMToolAgent
+
+logging.basicConfig(level=logging.WARNING)
+
+SLEEP = 2
+QUERY = "What time is it in Vancouver and what's the current weather in Vancouver?"
+# qwen3:0.6b is the project default but flaky at native tool calling;
+# qwen2.5:7b-instruct-q8_0 (also pulled locally) calls tools reliably.
+REAL_MODEL = "qwen2.5:7b-instruct-q8_0"
+MAX_ATTEMPTS = 4  # small models occasionally skip tool calling; retry
+
+
+# ---------------------------------------------------------------- helpers
+
+def _sleep_tool(name):
+    def fn(tag: str = "") -> dict:
+        time.sleep(SLEEP)
+        return {"tool": name, "slept": SLEEP, "success": True}
+    return fn
+
+
+SLEEP_SCHEMA = {
+    "type": "object",
+    "properties": {"tag": {"type": "string", "description": "optional tag"}},
+    "required": [],
+}
+
+
+def _register_sleep_tools(registry):
+    registry.register_tool("sleep_a", _sleep_tool("sleep_a"), "Sleep tool A", SLEEP_SCHEMA)
+    registry.register_tool("sleep_b", _sleep_tool("sleep_b"), "Sleep tool B", SLEEP_SCHEMA)
+
+
+def _check_parallel(label, elapsed, n=2):
+    status = "PARALLEL OK" if elapsed < SLEEP * 1.8 else "NOT PARALLEL"
+    print(f"  [{label}] {n} x {SLEEP}s tools finished in {elapsed:.1f}s -> {status}")
+    assert elapsed < SLEEP * 1.8, f"{label}: tool calls were not executed in parallel"
+
+
+# ------------------------------------- 1. deterministic parallel checks
+
+def test_native_execute_parallel():
+    print("\n== OllamaNativeAgent._execute_tool_calls (sleep tools) ==")
+    agent = OllamaNativeAgent(model=REAL_MODEL)
+    _register_sleep_tools(agent.tool_registry)
+    calls = [
+        {"function": {"name": "sleep_a", "arguments": {}}},
+        {"function": {"name": "sleep_b", "arguments": {}}},
+    ]
+    start = time.time()
+    results = agent._execute_tool_calls(calls)
+    _check_parallel("native _execute_tool_calls", time.time() - start)
+    assert all(json.loads(r)["success"] for r in results)
+
+
+def _make_chunk(text=None, tool_calls=None):
+    delta = SimpleNamespace(content=text, tool_calls=tool_calls or [])
+    return SimpleNamespace(choices=[SimpleNamespace(delta=delta)])
+
+
+def _tool_fragment(index, *, call_id=None, name=None, arguments=None):
+    return SimpleNamespace(
+        index=index,
+        id=call_id,
+        type="function" if call_id else None,
+        function=SimpleNamespace(name=name, arguments=arguments),
+    )
+
+
+def test_vllm_stream_parallel():
+    print("\n== VLLMToolAgent.chat_stream (fake stream with 2 tool calls) ==")
+    agent = VLLMToolAgent(api_base="http://localhost:11434/v1", api_key="ollama")
+    _register_sleep_tools(agent.tool_registry)
+
+    state = {"n": 0}
+
+    def fake_create(**kwargs):
+        state["n"] += 1
+        if state["n"] == 1:
+            # Split arguments across chunks and interleave two call indexes.
+            return iter([
+                _make_chunk(tool_calls=[
+                    _tool_fragment(
+                        0, call_id="call_a", name="sleep_a", arguments='{"tag":'
+                    ),
+                    _tool_fragment(
+                        1, call_id="call_b", name="sleep_b", arguments='{"tag":'
+                    ),
+                ]),
+                _make_chunk(tool_calls=[
+                    _tool_fragment(0, arguments='"a"}'),
+                    _tool_fragment(1, arguments='"b"}'),
+                ]),
+            ])
+        return iter([_make_chunk("done")])
+
+    agent.client = SimpleNamespace(
+        chat=SimpleNamespace(completions=SimpleNamespace(create=fake_create))
+    )
+
+    start = time.time()
+    events = list(agent.chat_stream("run both sleep tools"))
+    elapsed = time.time() - start
+
+    types = [e["type"] for e in events]
+    print(f"  event types: {types}")
+    assert types.count("tool_call") == 2, f"expected 2 tool_call events: {types}"
+    assert types.count("tool_result") == 2, f"expected 2 tool_result events: {types}"
+    _check_parallel("vllm chat_stream", elapsed)
+
+
+# ------------------------------------- 2. real-model end-to-end runs
+
+def test_native_real_model():
+    print(f"\n== OllamaNativeAgent.chat (real {REAL_MODEL}) ==")
+    agent = OllamaNativeAgent(model=REAL_MODEL)
+    response, tool_msgs = "", []
+    for attempt in range(1, MAX_ATTEMPTS + 1):
+        agent.reset_conversation()
+        response = agent.chat(QUERY)
+        tool_msgs = [m for m in agent.conversation_history if m.get("role") == "tool"]
+        if tool_msgs:
+            break
+        print(f"  attempt {attempt}: model made no tool call, retrying")
+    assistant_tc = [m for m in agent.conversation_history if m.get("tool_calls")]
+    batch = len(assistant_tc[0]["tool_calls"]) if assistant_tc else 0
+    print(f"  tool calls in one turn: {batch} ({'PARALLEL BATCH' if batch > 1 else 'single'})")
+    for m in tool_msgs:
+        print(f"    - {m['content'][:120]}")
+    print(f"  final response: {response[:300]}")
+    assert tool_msgs, "model did not call any tool"
+
+    print(f"\n== OllamaNativeAgent.chat_stream (real {REAL_MODEL}) ==")
+    events = []
+    for attempt in range(1, MAX_ATTEMPTS + 1):
+        agent.reset_conversation()
+        events = list(agent.chat_stream(QUERY))
+        if any(e["type"] == "tool_result" for e in events):
+            break
+        print(f"  attempt {attempt}: no tool call, retrying")
+    tool_calls = [e for e in events if e["type"] == "tool_call"]
+    tool_results = [e for e in events if e["type"] == "tool_result"]
+    print(f"  tool_call events: {len(tool_calls)}, tool_result events: {len(tool_results)}")
+    for e in tool_calls:
+        print(f"    - {e['content']['name']}({e['content']['arguments']})")
+    final = "".join(e["content"] for e in events if e["type"] == "content")
+    print(f"  final content: {final[:300]}")
+    assert tool_results, "streaming path produced no tool results"
+
+
+def test_openai_compat_real_model():
+    print(f"\n== OllamaOpenAICompatible.chat (real {REAL_MODEL}) ==")
+    agent = OllamaOpenAICompatible(model=REAL_MODEL)
+    response, tool_msgs = "", []
+    for attempt in range(1, MAX_ATTEMPTS + 1):
+        agent.reset_conversation()
+        response = agent.chat(QUERY)
+        tool_msgs = [m for m in agent.conversation_history if m.get("role") == "tool"]
+        if tool_msgs:
+            break
+        print(f"  attempt {attempt}: model made no tool call, retrying")
+    print(f"  tool results in history: {len(tool_msgs)}")
+    print(f"  final response: {response[:300]}")
+    assert tool_msgs, "model did not call any tool"
+
+
+def test_vllm_agent_real_model():
+    print(f"\n== VLLMToolAgent.chat (real {REAL_MODEL} via OpenAI endpoint) ==")
+    agent = VLLMToolAgent(api_base="http://localhost:11434/v1", api_key="ollama")
+
+    # Ollama's OpenAI endpoint stands in for the vLLM server. Keep the native
+    # tools payload so both chat paths receive structured tool_calls.
+    real_create = agent.client.chat.completions.create
+
+    def create_structured_mode(**kwargs):
+        kwargs["model"] = REAL_MODEL
+        return real_create(**kwargs)
+
+    agent.client = SimpleNamespace(
+        chat=SimpleNamespace(completions=SimpleNamespace(create=create_structured_mode))
+    )
+
+    response, tool_msgs, batch = "", [], 0
+    for attempt in range(1, MAX_ATTEMPTS + 1):
+        agent.reset_conversation()
+        # temperature 0.7 sampling can degenerate into a tool-call loop with
+        # this handcrafted XML format; 0.3 is stable
+        response = agent.chat(QUERY, temperature=0.3)
+        tool_msgs = [m for m in agent.conversation_history if m.get("name")]
+        assistant_tc = [m for m in agent.conversation_history if m.get("tool_calls")]
+        batch = len(assistant_tc[0]["tool_calls"]) if assistant_tc else 0
+        if tool_msgs and batch <= 10:
+            break
+        print(f"  attempt {attempt}: no tool call or degenerate run ({batch} calls), retrying")
+    print(f"  tool calls in one turn: {batch} ({'PARALLEL BATCH' if batch > 1 else 'single'})")
+    for m in tool_msgs:
+        print(f"    - {m['name']}: {m['content'][:100]}")
+    print(f"  final response: {response[:300]}")
+    assert tool_msgs, "model did not emit a structured tool call"
+
+    print(f"\n== VLLMToolAgent.chat_stream (real {REAL_MODEL}) ==")
+    events = []
+    for attempt in range(1, MAX_ATTEMPTS + 1):
+        agent.reset_conversation()
+        # temperature 0.7 sampling can degenerate into a tool-call loop with
+        # this handcrafted XML format; 0.3 is stable
+        events = list(agent.chat_stream(QUERY, temperature=0.3))
+        n_calls = sum(1 for e in events if e["type"] == "tool_call")
+        if any(e["type"] == "tool_result" for e in events) and n_calls <= 10:
+            break
+        print(f"  attempt {attempt}: no tool call or degenerate run ({n_calls} calls), retrying")
+    tool_calls = [e for e in events if e["type"] == "tool_call"]
+    tool_results = [e for e in events if e["type"] == "tool_result"]
+    print(f"  tool_call events: {len(tool_calls)}, tool_result events: {len(tool_results)}")
+    for e in tool_calls:
+        print(f"    - {e['content']}")
+    final = "".join(e["content"] for e in events if e["type"] == "content")
+    print(f"  final content: {final[:300]}")
+    assert tool_results, "streaming path produced no tool results"
+
+
+if __name__ == "__main__":
+    test_native_execute_parallel()
+    test_vllm_stream_parallel()
+    test_native_real_model()
+    test_openai_compat_real_model()
+    test_vllm_agent_real_model()
+    print("\nALL TESTS PASSED")

chapter2/local_llm_serving/test_platform_detection.py

@@ -0,0 +1,60 @@
+"""Regression tests for platform-specific backend selection."""
+
+import contextlib
+import io
+import sys
+import unittest
+from types import SimpleNamespace
+from unittest import mock
+
+from check_compatibility import provide_recommendations
+from main import ToolCallingAgent
+
+
+class BackendDetectionTests(unittest.TestCase):
+    def setUp(self):
+        self.agent = ToolCallingAgent.__new__(ToolCallingAgent)
+
+    def test_native_windows_uses_ollama_even_when_cuda_is_available(self):
+        fake_torch = SimpleNamespace(
+            cuda=SimpleNamespace(is_available=lambda: True)
+        )
+
+        with mock.patch("main.platform.system", return_value="Windows"):
+            with mock.patch.dict(sys.modules, {"torch": fake_torch}):
+                self.assertEqual(self.agent._detect_best_backend(), "ollama")
+
+    def test_linux_with_cuda_uses_vllm(self):
+        fake_torch = SimpleNamespace(
+            cuda=SimpleNamespace(is_available=lambda: True)
+        )
+
+        with mock.patch("main.platform.system", return_value="Linux"):
+            with mock.patch.dict(sys.modules, {"torch": fake_torch}):
+                self.assertEqual(self.agent._detect_best_backend(), "vllm")
+
+    def test_linux_without_cuda_uses_ollama(self):
+        fake_torch = SimpleNamespace(
+            cuda=SimpleNamespace(is_available=lambda: False)
+        )
+
+        with mock.patch("main.platform.system", return_value="Linux"):
+            with mock.patch.dict(sys.modules, {"torch": fake_torch}):
+                self.assertEqual(self.agent._detect_best_backend(), "ollama")
+
+
+class CompatibilityRecommendationTests(unittest.TestCase):
+    def test_native_windows_with_cuda_recommends_ollama(self):
+        output = io.StringIO()
+
+        with contextlib.redirect_stdout(output):
+            provide_recommendations(cuda_available=True, system="windows")
+
+        recommendations = output.getvalue()
+        self.assertIn("native Windows - will use Ollama", recommendations)
+        self.assertIn("official vLLM requires Linux", recommendations)
+        self.assertNotIn("Your system supports vLLM!", recommendations)
+
+
+if __name__ == "__main__":
+    unittest.main()

chapter2/local_llm_serving/test_run_experiment.py

@@ -0,0 +1,42 @@
+import json
+
+from run_experiment import normalize_tool_call, parse_tool_calls, sha256_text
+
+
+def test_parse_multiple_raw_tool_calls():
+    raw = (
+        '<tool_call>\n{"name":"get_current_time","arguments":{"city":"Vancouver"}}\n</tool_call>'
+        '<tool_call>\n{"name":"get_weather","arguments":{"city":"Vancouver"}}\n</tool_call>'
+    )
+    calls = parse_tool_calls(raw)
+    assert [item["name"] for item in calls] == ["get_current_time", "get_weather"]
+
+
+def test_normalize_small_model_city_arguments():
+    time_call = normalize_tool_call(
+        {"name": "get_current_time", "arguments": {"city": "Vancouver"}}
+    )
+    weather_call = normalize_tool_call(
+        {"name": "get_weather", "arguments": {"city": "Vancouver"}}
+    )
+    assert time_call == {
+        "name": "get_current_time",
+        "arguments": {"timezone": "America/Vancouver"},
+    }
+    assert weather_call == {
+        "name": "get_current_temperature",
+        "arguments": {"location": "Vancouver, Canada", "unit": "celsius"},
+    }
+
+
+def test_normalize_does_not_hide_an_explicitly_wrong_timezone():
+    call = normalize_tool_call(
+        {"name": "get_current_time", "arguments": {"timezone": "America/New_York"}}
+    )
+    assert call["arguments"]["timezone"] == "America/New_York"
+
+
+def test_protocol_is_valid_json_and_hashable():
+    raw = b'{"experiment_id":"2-1"}'
+    assert json.loads(raw)["experiment_id"] == "2-1"
+    assert len(sha256_text(raw.decode())) == 64

chapter2/local_llm_serving/test_sample_command.py

@@ -0,0 +1 @@
+ 

chapter2/local_llm_serving/test_streaming.py

@@ -0,0 +1,173 @@
+#!/usr/bin/env python3
+"""
+Test script to demonstrate streaming functionality
+for both vLLM and Ollama backends
+"""
+
+import sys
+import platform
+from main import ToolCallingAgent
+
+
+def test_streaming():
+    """Test streaming functionality with various queries"""
+    print("="*60)
+    print("🚀 STREAMING TEST DEMO")
+    print("="*60)
+    print(f"Platform: {platform.system()}")
+    print("="*60)
+    
+    # Initialize agent
+    print("\n⚙️  Initializing agent...")
+    agent = ToolCallingAgent()
+    print(f"✅ Using {agent.backend_type} backend")
+    
+    # Test queries that will demonstrate streaming features
+    test_queries = [
+        {
+            "name": "Simple Calculation with Thinking",
+            "query": "Calculate 15 * 23 + sqrt(144). Think through the steps."
+        },
+        {
+            "name": "Tool Usage with Weather",
+            "query": "What's the weather in Tokyo? If it's hot (above 25°C), suggest some cooling tips."
+        },
+        {
+            "name": "Multiple Tools",
+            "query": "Convert 100 USD to EUR and tell me the current time in London."
+        }
+    ]
+    
+    for test in test_queries:
+        print("\n" + "="*60)
+        print(f"📋 Test: {test['name']}")
+        print("="*60)
+        print(f"Query: {test['query']}")
+        print("-"*60)
+        
+        try:
+            print("\n🔄 Streaming response:\n")
+            
+            thinking_shown = False
+            tools_shown = False
+            response_shown = False
+            
+            # Stream the response
+            for chunk in agent.chat(test['query'], stream=True):
+                chunk_type = chunk.get("type")
+                content = chunk.get("content", "")
+                
+                if chunk_type == "thinking":
+                    if not thinking_shown:
+                        print("🧠 Internal Thinking:")
+                        print("  ", end="")
+                        thinking_shown = True
+                    # Show thinking in gray/dim text
+                    print(f"\033[90m{content}\033[0m")
+                
+                elif chunk_type == "tool_call":
+                    if not tools_shown:
+                        print("\n🔧 Tool Calls:")
+                        tools_shown = True
+                    tool_info = content
+                    print(f"  📦 Calling: {tool_info.get('name', 'unknown')}")
+                    print(f"     Arguments: {tool_info.get('arguments', {})}")
+                
+                elif chunk_type == "tool_result":
+                    result_str = str(content)
+                    print(f"     ✓ Result: {result_str}")
+                
+                elif chunk_type == "content":
+                    if not response_shown:
+                        print("\n🤖 Assistant Response:")
+                        print("  ", end="")
+                        response_shown = True
+                    # Stream the content character by character
+                    print(content, end="", flush=True)
+                
+                elif chunk_type == "error":
+                    print(f"\n❌ Error: {content}")
+            
+            print("\n")  # New line after response
+            
+        except Exception as e:
+            print(f"\n❌ Error during test: {e}")
+        
+        # Reset conversation for next test
+        agent.reset_conversation()
+        
+        # Ask user if they want to continue
+        if test != test_queries[-1]:
+            cont = input("\nPress Enter to continue to next test (or 'q' to quit): ")
+            if cont.lower() == 'q':
+                break
+    
+    print("\n" + "="*60)
+    print("✅ Streaming test completed!")
+    print("="*60)
+
+
+def compare_streaming_vs_regular():
+    """Compare streaming vs regular responses"""
+    print("="*60)
+    print("📊 STREAMING VS REGULAR COMPARISON")
+    print("="*60)
+    
+    # Initialize agent
+    agent = ToolCallingAgent()
+    
+    test_query = "What's the weather in Paris and convert 20°C to Fahrenheit?"
+    
+    print(f"\n📋 Test Query: {test_query}")
+    print("="*60)
+    
+    # Regular mode
+    print("\n1️⃣ REGULAR MODE (No Streaming):")
+    print("-"*40)
+    print("⏳ Processing...")
+    response = agent.chat(test_query, stream=False)
+    print(f"🤖 Response: {response}")
+    
+    agent.reset_conversation()
+    
+    # Streaming mode
+    print("\n2️⃣ STREAMING MODE:")
+    print("-"*40)
+    print("⏳ Processing (you'll see content as it arrives)...\n")
+    
+    for chunk in agent.chat(test_query, stream=True):
+        chunk_type = chunk.get("type")
+        content = chunk.get("content", "")
+        
+        if chunk_type == "thinking":
+            print(f"[THINKING] \033[90m{content}\033[0m")
+        elif chunk_type == "tool_call":
+            print(f"[TOOL CALL] {content}")
+        elif chunk_type == "tool_result":
+            print(f"[TOOL RESULT] {content}")
+        elif chunk_type == "content":
+            print(content, end="", flush=True)
+    
+    print("\n\n" + "="*60)
+    print("✅ Comparison complete!")
+    print("💡 Streaming mode shows intermediate steps in real-time")
+    print("="*60)
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="Test streaming functionality")
+    parser.add_argument(
+        "--mode",
+        choices=["demo", "compare"],
+        default="demo",
+        help="Test mode: demo (full demo) or compare (streaming vs regular)"
+    )
+    
+    args = parser.parse_args()
+    
+    if args.mode == "demo":
+        test_streaming()
+    else:
+        compare_streaming_vs_regular()

chapter2/local_llm_serving/test_streaming_fix.py

@@ -0,0 +1 @@
+ 

chapter2/local_llm_serving/test_vllm_structured_streaming.py

@@ -0,0 +1,102 @@
+"""Focused tests for fragmented structured tool calls in VLLMToolAgent.chat_stream."""
+
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+
+from agent import VLLMToolAgent
+
+
+def _chunk(content=None, tool_calls=None):
+    delta = SimpleNamespace(content=content, tool_calls=tool_calls or [])
+    return SimpleNamespace(choices=[SimpleNamespace(delta=delta)])
+
+
+def _fragment(index, *, call_id=None, name=None, arguments=None):
+    return SimpleNamespace(
+        index=index,
+        id=call_id,
+        type="function" if call_id else None,
+        function=SimpleNamespace(name=name, arguments=arguments),
+    )
+
+
+def _agent_with_streams(*streams):
+    agent = VLLMToolAgent.__new__(VLLMToolAgent)
+    agent.conversation_history = []
+    agent.tool_registry = MagicMock()
+    agent.tool_registry.get_tool_schemas.return_value = []
+    agent._format_system_prompt_with_tools = MagicMock(return_value="system")
+    agent.client = MagicMock()
+    agent.client.chat.completions.create.side_effect = [iter(stream) for stream in streams]
+    return agent
+
+
+def test_stream_assembles_fragmented_parallel_tool_calls():
+    agent = _agent_with_streams(
+        [
+            _chunk(tool_calls=[
+                _fragment(0, call_id="call_weather", name="get_", arguments='{"city":'),
+                _fragment(1, call_id="call_time", name="get_time", arguments="{"),
+            ]),
+            _chunk(tool_calls=[
+                _fragment(0, name="weather", arguments='"Paris"}'),
+                _fragment(1, arguments="}"),
+            ]),
+        ],
+        [_chunk(content="Done")],
+    )
+    agent._execute_single_tool = MagicMock(
+        side_effect=lambda call: (f'{call["name"]} result', False)
+    )
+
+    events = list(agent.chat_stream("Use both tools"))
+
+    assert events == [
+        {"type": "tool_call", "content": {"name": "get_weather", "arguments": {"city": "Paris"}}},
+        {"type": "tool_call", "content": {"name": "get_time", "arguments": {}}},
+        {"type": "tool_result", "content": "get_weather result"},
+        {"type": "tool_result", "content": "get_time result"},
+        {"type": "content", "content": "Done"},
+    ]
+    assert agent._execute_single_tool.call_count == 2
+
+    create_calls = agent.client.chat.completions.create.call_args_list
+    assert [call.kwargs["model"] for call in create_calls] == [
+        "Qwen/Qwen3-0.6B",
+        "Qwen/Qwen3-0.6B",
+    ]
+    second_turn_messages = create_calls[1].kwargs["messages"]
+    assistant_message = next(message for message in second_turn_messages if message.get("tool_calls"))
+    assert assistant_message["tool_calls"] == [
+        {
+            "id": "call_weather",
+            "type": "function",
+            "function": {"name": "get_weather", "arguments": '{"city":"Paris"}'},
+        },
+        {
+            "id": "call_time",
+            "type": "function",
+            "function": {"name": "get_time", "arguments": "{}"},
+        },
+    ]
+
+
+def test_stream_reports_malformed_arguments_and_continues():
+    agent = _agent_with_streams(
+        [_chunk(tool_calls=[
+            _fragment(0, call_id="call_bad", name="bad_tool", arguments="{bad")
+        ])],
+        [_chunk(content="Recovered")],
+    )
+    agent._execute_single_tool = MagicMock()
+
+    events = list(agent.chat_stream("Try the tool"))
+
+    assert [event["type"] for event in events] == ["tool_error", "content"]
+    assert events[-1] == {"type": "content", "content": "Recovered"}
+    agent._execute_single_tool.assert_not_called()
+    error_message = next(
+        message for message in agent.conversation_history
+        if message.get("name") == "bad_tool"
+    )
+    assert "Tool call parse exception" in error_message["content"]

chapter2/local_llm_serving/test_weather.py

@@ -0,0 +1 @@
+ 

chapter2/local_llm_serving/tools.py

@@ -0,0 +1,502 @@
+"""
+Sample tools for demonstrating vLLM tool calling functionality
+"""
+import json
+import math
+import random
+import io
+import contextlib
+from typing import Dict, Any, List
+from datetime import datetime
+import requests
+from io import BytesIO
+import PyPDF2
+
+
+class ToolRegistry:
+    """Registry for managing available tools"""
+    
+    def __init__(self):
+        self.tools = {}
+        self._register_default_tools()
+    
+    def _register_default_tools(self):
+        """Register default tools"""
+        self.register_tool(
+            name="get_current_temperature",
+            function=self.get_current_temperature,
+            description="Get the current temperature for a specific location",
+            parameters={
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and country, e.g., 'Paris, France'"
+                    },
+                    "unit": {
+                        "type": "string",
+                        "enum": ["celsius", "fahrenheit"],
+                        "description": "The temperature unit to use (by default, celsius)"
+                    }
+                },
+                "required": ["location", "unit"]
+            }
+        )
+        
+        self.register_tool(
+            name="get_current_time",
+            function=self.get_current_time,
+            description="Get the current date and time in a specific timezone",
+            parameters={
+                "type": "object",
+                "properties": {
+                    "timezone": {
+                        "type": "string",
+                        "description": "Timezone name (e.g., 'America/New_York', 'Europe/London', 'Asia/Tokyo'). Use standard IANA timezone names.",
+                        "default": "UTC"
+                    }
+                },
+                "required": []
+            }
+        )
+        
+        self.register_tool(
+            name="convert_currency",
+            function=self.convert_currency,
+            description="Convert an amount from one currency to another. You MUST use this tool to convert currencies in order to get the latest exchange rate.",
+            parameters={
+                "type": "object",
+                "properties": {
+                    "amount": {
+                        "type": "number",
+                        "description": "Amount to convert"
+                    },
+                    "from_currency": {
+                        "type": "string",
+                        "description": "Source currency code (e.g., 'USD', 'EUR')"
+                    },
+                    "to_currency": {
+                        "type": "string",
+                        "description": "Target currency code (e.g., 'USD', 'EUR')"
+                    }
+                },
+                "required": ["amount", "from_currency", "to_currency"]
+            }
+        )
+
+        self.register_tool(
+            name="code_interpreter",
+            function=self.code_interpreter,
+            description="Execute Python code for calculations and data processing. You MUST use this tool to perform any complex calculations or data processing.",
+            parameters={
+                "type": "object",
+                "properties": {
+                    "code": {
+                        "type": "string",
+                        "description": "Python code to execute. Use Python operators: ** for exponentiation (2 ** 10), not ^ — in Python ^ is bitwise XOR."
+                    }
+                },
+                "required": ["code"]
+            }
+        )
+    
+    def register_tool(self, name: str, function: callable, description: str, parameters: Dict):
+        """Register a new tool"""
+        self.tools[name] = {
+            "function": function,
+            "description": description,
+            "parameters": parameters
+        }
+    
+    def get_tool_schemas(self) -> List[Dict]:
+        """Get OpenAI-compatible tool schemas"""
+        schemas = []
+        for name, tool in self.tools.items():
+            schemas.append({
+                "type": "function",
+                "function": {
+                    "name": name,
+                    "description": tool["description"],
+                    "parameters": tool["parameters"]
+                }
+            })
+        return schemas
+    
+    def execute_tool(self, name: str, arguments: Dict[str, Any]) -> str:
+        """Execute a tool by name with given arguments"""
+        if name not in self.tools:
+            return json.dumps({"error": f"Tool '{name}' not found"})
+        
+        try:
+            result = self.tools[name]["function"](**arguments)
+            return json.dumps(result) if isinstance(result, (dict, list)) else str(result)
+        except Exception as e:
+            return json.dumps({"error": str(e)})
+    
+    # Tool implementations
+    @staticmethod
+    def get_current_temperature(location: str, unit: str = "celsius") -> Dict:
+        """
+        Get current temperature using Open-Meteo free weather API
+        No API key required - https://open-meteo.com/
+        """
+        try:
+            # First, geocode the location to get coordinates
+            geocoding_url = "https://geocoding-api.open-meteo.com/v1/search"
+            geo_params = {
+                "name": location,
+                "count": 1,
+                "language": "en",
+                "format": "json"
+            }
+            
+            geo_response = requests.get(geocoding_url, params=geo_params, timeout=5)
+            geo_data = geo_response.json()
+            
+            if not geo_data.get("results"):
+                return {
+                    "location": location,
+                    "error": f"Location '{location}' not found",
+                    "timestamp": datetime.now().isoformat()
+                }
+            
+            # Get coordinates from first result
+            result = geo_data["results"][0]
+            latitude = result["latitude"]
+            longitude = result["longitude"]
+            location_name = f"{result.get('name', location)}, {result.get('country', '')}"
+            
+            # Get current weather from Open-Meteo
+            weather_url = "https://api.open-meteo.com/v1/forecast"
+            
+            # Determine temperature unit
+            temp_unit = "fahrenheit" if unit.lower() == "fahrenheit" else "celsius"
+            
+            weather_params = {
+                "latitude": latitude,
+                "longitude": longitude,
+                "current": "temperature_2m,relative_humidity_2m,weather_code,wind_speed_10m",
+                "temperature_unit": temp_unit,
+                "timezone": "auto"
+            }
+            
+            weather_response = requests.get(weather_url, params=weather_params, timeout=5)
+            weather_data = weather_response.json()
+            
+            if "current" not in weather_data:
+                return {
+                    "location": location_name,
+                    "error": "Weather data not available",
+                    "timestamp": datetime.now().isoformat()
+                }
+            
+            current = weather_data["current"]
+            
+            # Map weather codes to conditions
+            weather_codes = {
+                0: "clear sky",
+                1: "mainly clear", 2: "partly cloudy", 3: "overcast",
+                45: "foggy", 48: "foggy",
+                51: "light drizzle", 53: "moderate drizzle", 55: "dense drizzle",
+                61: "light rain", 63: "moderate rain", 65: "heavy rain",
+                71: "light snow", 73: "moderate snow", 75: "heavy snow",
+                77: "snow grains",
+                80: "light rain showers", 81: "moderate rain showers", 82: "heavy rain showers",
+                85: "light snow showers", 86: "heavy snow showers",
+                95: "thunderstorm", 96: "thunderstorm with light hail", 99: "thunderstorm with heavy hail"
+            }
+            
+            weather_code = current.get("weather_code", 0)
+            conditions = weather_codes.get(weather_code, "unknown")
+            
+            unit_symbol = "°F" if unit.lower() == "fahrenheit" else "°C"
+            
+            return {
+                "location": location_name,
+                "temperature": round(current["temperature_2m"], 1),
+                "unit": unit_symbol,
+                "conditions": conditions,
+                "humidity": current.get("relative_humidity_2m"),
+                "wind_speed": round(current.get("wind_speed_10m", 0), 1),
+                "wind_unit": "km/h",
+                "coordinates": {"latitude": latitude, "longitude": longitude},
+                "timestamp": current.get("time", datetime.now().isoformat()),
+                "source": "Open-Meteo"
+            }
+            
+        except requests.RequestException as e:
+            # Fallback to simulated data if API fails
+            import logging
+            logging.warning(f"Open-Meteo API error: {e}. Using simulated data.")
+            
+            # Simulated fallback
+            base_temp = 20 + random.uniform(-10, 10)
+            
+            if unit == "fahrenheit":
+                temp = base_temp * 9/5 + 32
+                unit_symbol = "°F"
+            else:
+                temp = base_temp
+                unit_symbol = "°C"
+            
+            return {
+                "location": location,
+                "temperature": round(temp, 1),
+                "unit": unit_symbol,
+                "conditions": random.choice(["sunny", "cloudy", "partly cloudy", "rainy"]),
+                "timestamp": datetime.now().isoformat(),
+                "note": "Simulated data (API unavailable)"
+            }
+        except Exception as e:
+            return {
+                "location": location,
+                "error": str(e),
+                "timestamp": datetime.now().isoformat()
+            }
+    
+    @staticmethod
+    def get_current_time(timezone: str = "UTC") -> Dict:
+        """
+        Get current date and time in specified timezone using zoneinfo (Python 3.9+)
+        """
+        from datetime import datetime
+        from zoneinfo import ZoneInfo
+        
+        # Common abbreviation mappings to IANA timezone names
+        timezone_aliases = {
+            "EST": "America/New_York",
+            "EDT": "America/New_York",
+            "PST": "America/Los_Angeles",
+            "PDT": "America/Los_Angeles",
+            "CST": "America/Chicago",
+            "CDT": "America/Chicago",
+            "MST": "America/Denver",
+            "MDT": "America/Denver",
+            "GMT": "Europe/London",
+            "BST": "Europe/London",
+            "CET": "Europe/Paris",
+            "CEST": "Europe/Paris",
+            "JST": "Asia/Tokyo",
+            "IST": "Asia/Kolkata",
+            "AEST": "Australia/Sydney",
+            "AEDT": "Australia/Sydney",
+            "SGT": "Asia/Singapore",
+            "HKT": "Asia/Hong_Kong",
+            "UTC+1": "Etc/GMT-1",  # Note: signs are inverted in Etc/GMT
+            "UTC-1": "Etc/GMT+1",
+            "UTC+8": "Etc/GMT-8",
+            "UTC-8": "Etc/GMT+8"
+        }
+        
+        # Convert abbreviation to IANA name if needed
+        tz_name = timezone_aliases.get(timezone.upper(), timezone)
+        
+        try:
+            tz = ZoneInfo(tz_name)
+            current_time = datetime.now(tz)
+            
+            return {
+                "timezone": tz_name,
+                "datetime": current_time.strftime("%Y-%m-%d %H:%M:%S"),
+                "date": current_time.strftime("%Y-%m-%d"),
+                "time": current_time.strftime("%H:%M:%S"),
+                "day_of_week": current_time.strftime("%A"),
+                "utc_offset": current_time.strftime("%z"),
+                "timestamp": current_time.isoformat()
+            }
+        except Exception as e:
+            # Fallback to UTC if timezone not found
+            try:
+                tz_utc = ZoneInfo("UTC")
+                current_time = datetime.now(tz_utc)
+                return {
+                    "timezone": "UTC",
+                    "datetime": current_time.strftime("%Y-%m-%d %H:%M:%S"),
+                    "date": current_time.strftime("%Y-%m-%d"),
+                    "time": current_time.strftime("%H:%M:%S"),
+                    "day_of_week": current_time.strftime("%A"),
+                    "utc_offset": "+0000",
+                    "timestamp": current_time.isoformat(),
+                    "note": f"Invalid timezone '{timezone}', using UTC as fallback"
+                }
+            except Exception as fallback_error:
+                return {
+                    "error": str(e),
+                    "fallback_error": str(fallback_error),
+                    "timezone": timezone,
+                    "timestamp": datetime.utcnow().isoformat()
+                }
+    
+    @staticmethod
+    def convert_currency(amount: float, from_currency: str, to_currency: str) -> Dict:
+        """
+        Convert currency using live exchange rates (simulated)
+        """
+        # Normalize currency codes
+        from_currency = from_currency.upper().replace("S$", "SGD").replace("$", "USD")
+        to_currency = to_currency.upper().replace("S$", "SGD").replace("$", "USD")
+        
+        # Simulated exchange rates
+        exchange_rates = {
+            "USD": 1.0,
+            "EUR": 0.92,
+            "GBP": 0.79,
+            "JPY": 149.50,
+            "CNY": 7.24,
+            "CAD": 1.36,
+            "AUD": 1.53,
+            "CHF": 0.88,
+            "INR": 83.12,
+            "SGD": 1.34,
+            "KRW": 1330.50,
+            "MXN": 17.10
+        }
+        
+        if from_currency not in exchange_rates or to_currency not in exchange_rates:
+            return {"error": f"Unsupported currency: {from_currency} or {to_currency}"}
+        
+        # Convert to USD first, then to target currency
+        usd_amount = amount / exchange_rates[from_currency]
+        converted_amount = usd_amount * exchange_rates[to_currency]
+        
+        return {
+            "original_amount": amount,
+            "from_currency": from_currency,
+            "to_currency": to_currency,
+            "converted_amount": round(converted_amount, 2),
+            "exchange_rate": round(exchange_rates[to_currency] / exchange_rates[from_currency], 4),
+            "timestamp": datetime.now().isoformat()
+        }
+    
+    @staticmethod
+    def parse_pdf(url: str) -> Dict:
+        """
+        Parse a PDF document from URL or local file
+        """
+        try:
+            # Check if it's a local file
+            if url.startswith('file://') or url.startswith('/') or url.startswith('./'):
+                # Local file
+                file_path = url.replace('file://', '')
+                with open(file_path, 'rb') as f:
+                    pdf_content = f.read()
+            else:
+                # Remote URL
+                response = requests.get(url, timeout=30)
+                response.raise_for_status()
+                pdf_content = response.content
+            
+            # Parse PDF
+            pdf_file = BytesIO(pdf_content)
+            pdf_reader = PyPDF2.PdfReader(pdf_file)
+            
+            text_content = []
+            for page_num, page in enumerate(pdf_reader.pages, 1):
+                text = page.extract_text()
+                text_content.append({
+                    "page": page_num,
+                    "text": text[:1000]  # Limit text per page
+                })
+            
+            return {
+                "url": url,
+                "num_pages": len(pdf_reader.pages),
+                "content": text_content[:5],  # Limit to first 5 pages
+                "success": True
+            }
+        except Exception as e:
+            return {"error": str(e), "success": False}
+    
+    @staticmethod
+    def code_interpreter(code: str) -> Dict:
+        """
+        Execute Python code in a full Python environment.
+        This provides unrestricted access to Python's built-in functions and standard library.
+        """
+        try:
+            # Strip markdown code blocks and other formatting
+            import re
+            
+            # Remove ```python or ```py or ``` blocks
+            code = re.sub(r'^```(?:python|py)?\s*\n', '', code.strip())
+            code = re.sub(r'\n```\s*$', '', code)
+            code = re.sub(r'^```\s*', '', code)
+            code = re.sub(r'\s*```$', '', code)
+            
+            # Also strip any leading/trailing whitespace
+            code = code.strip()
+            
+            # NOTE: we deliberately do NOT rewrite '^' to '**' here. '^' is a
+            # valid Python operator (bitwise XOR), so a blanket substitution
+            # silently changes the meaning of correct code -- 5 ^ 3 is 6, but
+            # rewritten as 5 ** 3 it returns 125 with no error. It also broke
+            # anchored regexes (r'^a.*' -> r'**a.*' raises "nothing to repeat")
+            # and corrupted carets inside string literals. The two meanings of
+            # '^' cannot be told apart from the source, so the convention is
+            # stated in the tool description instead.
+
+            # Create a full Python namespace with all builtins available
+            # This gives the agent access to the complete Python environment
+            import sys
+            namespace = {
+                '__builtins__': __builtins__,
+                'math': math,
+                'random': random,
+                'datetime': datetime,
+                'sys': sys,
+                're': re,
+                'json': json
+            }
+            
+            # Capture both stdout and stderr
+            output_buffer = io.StringIO()
+            error_buffer = io.StringIO()
+            
+            with contextlib.redirect_stdout(output_buffer), contextlib.redirect_stderr(error_buffer):
+                exec(code, namespace)
+            
+            # Get output and any error messages
+            printed_output = output_buffer.getvalue()
+            error_output = error_buffer.getvalue()
+            
+            # Try to get result from common variable names
+            result = namespace.get('result', None)
+            if result is None:
+                for var_name in ['A', 'total', 'sum', 'output', 'answer', 'final', 'value']:
+                    if var_name in namespace:
+                        result = namespace[var_name]
+                        break
+            
+            response = {
+                "result": result,
+                "output": printed_output if printed_output else None,
+                "stderr": error_output if error_output else None,
+                "success": True
+            }
+            
+            return response
+            
+        except SyntaxError as e:
+            error_msg = f"Syntax Error on line {e.lineno}: {e.msg}\n{e.text}"
+            return {
+                "error": error_msg,
+                "error_type": "SyntaxError",
+                "success": False
+            }
+        except Exception as e:
+            import traceback
+            error_trace = traceback.format_exc()
+            return {
+                "error": str(e),
+                "error_type": type(e).__name__,
+                "traceback": error_trace,
+                "success": False
+            }
+    
+def format_tool_response(tool_name: str, tool_result: str) -> Dict:
+    """Format tool response for the chat model"""
+    return {
+        "role": "tool",
+        "name": tool_name,
+        "content": tool_result
+    }