#### Step 1 : 初始化客户端和模型
@@ -602,7 +624,8 @@ class Agent:
return response.choices[0].message.content
```
-这个 Agent 的工作流程如下:
+Agent 的工作流程如下:
+
1. 接收用户输入。
2. 调用大模型(如 Qwen),并告知其可用的工具及其 Schema。
3. 如果模型决定调用工具,Agent 会解析请求,执行相应的 Python 函数。
@@ -610,8 +633,11 @@ class Agent:
5. 模型根据工具结果生成最终回复。
6. Agent 将最终回复返回给用户。
-
-

+如图7.9所示,Agent 调用工具流程:
+
+
+

+
图7.9 Agent 工作流程
#### Step 4: 运行 Agent
@@ -644,7 +670,7 @@ if __name__ == "__main__":
print("\033[92mAssistant: \033[0m", response) # 绿色显示AI助手回答
```
-运行 `python src/core.py` 后,你可以开始提问。如果问题需要调用工具,Agent 会自动处理。
+运行 `python demo.py` 后,你可以开始提问。如果问题需要调用工具,Agent 会自动处理。
**示例交互:**
@@ -670,15 +696,26 @@ User: exit
**参考文献**
-- [Open LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
-- [lmsys Chatbot Arena Leaderboard](https://huggingface.co/spaces/awacke1/lmsys-chatbot-arena-leaderboard)
-- [OpenCompass](https://rank.opencompass.org.cn/home)
-- [金融榜 CFBenchmark](https://specialist.opencompass.org.cn/CFBenchmark)
-- [安全榜 Flames](https://flames.opencompass.org.cn/leaderboard)
-- [通识榜 BotChat](https://botchat.opencompass.org.cn/?lang=zh-CN)
-- [法律榜 LawBench](https://lawbench.opencompass.org.cn/leaderboard?lang=en-US?lang=zh-CN)
-- [医疗榜 MedBench](https://medbench.opencompass.org.cn/leaderboard?lang=zh-CN?lang=zh-CN)
-- [When Large Language Models Meet Vector Databases: A Survey ](http://arxiv.org/abs/2402.01763)
-- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://arxiv.org/abs/2312.10997)
-- [Learning to Filter Context for Retrieval-Augmented Generation](http://arxiv.org/abs/2311.08377)
-- [In-Context Retrieval-Augmented Language Models](https://arxiv.org/abs/2302.00083)
\ No newline at end of file
+[1] Hugging Face. (2023). *Open LLM Leaderboard: 开源大语言模型基准测试平台*. https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
+
+[2] awacke1. (2023). *LMSYS Chatbot Arena Leaderboard: 大型语言模型竞技场评估平台*. https://huggingface.co/spaces/awacke1/lmsys-chatbot-arena-leaderboard
+
+[3] OpenCompass 团队. (2023). *OpenCompass: 大模型统一评测平台*. https://rank.opencompass.org.cn/home
+
+[4] OpenCompass 金融榜团队. (2024). *CFBENCHMARK: 金融领域大模型评测榜单*. https://specialist.opencompass.org.cn/CFBenchmark
+
+[5] OpenCompass 安全榜团队. (2024). *Flames: 大模型安全评测榜单*. https://flames.opencompass.org.cn/leaderboard
+
+[6] OpenCompass 通识榜团队. (2024). *BotChat: 大模型通用对话能力评测*. https://botchat.opencompass.org.cn/
+
+[7] OpenCompass 法律榜团队. (2024). *LawBench: 法律领域大模型评测*. https://lawbench.opencompass.org.cn/leaderboard
+
+[8] OpenCompass 医疗榜团队. (2024). *MedBench: 医疗领域大模型评测*. https://medbench.opencompass.org.cn/leaderboard
+
+[9] Zhi Jing, Yongye Su, and Yikun Han. (2024). *When Large Language Models Meet Vector Databases: A Survey.* arXiv preprint arXiv:2402.01763.
+
+[10] Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. (2024). *Retrieval-Augmented Generation for Large Language Models: A Survey.* arXiv preprint arXiv:2312.10997.
+
+[11] Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, 和 Graham Neubig. (2023). *Learning to Filter Context for Retrieval-Augmented Generation.* arXiv preprint arXiv:2311.08377.
+
+[12] Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown 和 Yoav Shoham. (2023). *In-Context Retrieval-Augmented Language Models.* arXiv preprint arXiv:2302.00083.
\ No newline at end of file