Add chapter7 Agent code

docs/chapter7/Agent/src/core.py

@@ -0,0 +1,76 @@
+from openai import OpenAI
+import json
+from typing import List, Dict, Any
+from src.utils import function_to_json
+from src.tools import get_current_datetime, add, compare, count_letter_in_string
+
+import pprint
+
+SYSREM_PROMPT = """
+你是一个叫不要葱姜蒜的人工智能助手。你的输出应该与用户的语言保持一致。
+当用户的问题需要调用工具时，你可以从提供的工具列表中调用适当的工具函数。
+"""
+
+class Agent:
+    def __init__(self, client: OpenAI, model: str = "Qwen/Qwen2.5-32B-Instruct", tools: List=[], verbose : bool = True):
+        self.client = client
+        self.tools = tools
+        self.model = model
+        self.messages = [
+            {"role": "system", "content": SYSREM_PROMPT},
+        ]
+        self.verbose = verbose
+
+    def get_tool_schema(self) -> List[Dict[str, Any]]:
+        # 获取所有工具的 JSON 模式
+        return [function_to_json(tool) for tool in self.tools]
+
+    def handle_tool_call(self, tool_call):
+        # 处理工具调用
+        function_name = tool_call.function.name
+        function_args = tool_call.function.arguments
+        function_id = tool_call.id
+
+        function_call_content = eval(f"{function_name}(**{function_args})")
+
+        return {
+            "role": "tool",
+            "content": function_call_content,
+            "tool_call_id": function_id,
+        }
+
+    def get_completion(self, prompt) -> str:
+
+        self.messages.append({"role": "user", "content": prompt})
+
+        # 获取模型的完成响应
+        response = self.client.chat.completions.create(
+            model=self.model,
+            messages=self.messages,
+            tools=self.get_tool_schema(),
+            stream=False,
+        )
+        if response.choices[0].message.tool_calls:
+            # 处理工具调用
+            tool_list = []
+            for tool_call in response.choices[0].message.tool_calls:
+                # 处理工具调用并将结果添加到消息列表中
+                self.messages.append(self.handle_tool_call(tool_call))
+                tool_list.append(tool_call.function.name)
+            if self.verbose:
+                print("调用工具：", tool_list)
+            # 再次获取模型的完成响应，这次包含工具调用的结果
+            response = self.client.chat.completions.create(
+                model=self.model,
+                messages=self.messages,
+                tools=self.get_tool_schema(),
+                stream=False,
+            )
+
+        # 将模型的完成响应添加到消息列表中
+        self.messages.append({"role": "assistant", "content": response.choices[0].message.content})
+        return response.choices[0].message.content
+
+
+    
+