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znt_F?k^lLrRybe(vVJ)>FID^EN}ol(F4>b9EI0fRshh?i?SG&9e?~!jm=A!>K{MV( T?7&};f0vAeqIj*SQSko(LD_Po literal 0 HcmV?d00001 diff --git a/public/principle-of-ai-agent/index.md b/public/principle-of-ai-agent/index.md new file mode 100644 index 0000000..398dd84 --- /dev/null +++ b/public/principle-of-ai-agent/index.md @@ -0,0 +1,195 @@ +--- +title: "AI 思考的快与慢" +date: "2024-06-12" +cta: "lowcode" +spoiler: "AI 思考的快与慢" +--- + +## 前言 + +2024 年,一位以心理学家身份获得诺贝尔经济学家的传奇人物——丹尼尔·卡尼曼去世,牵动了整个国外科技圈的关注。 + +卡尼曼教授在在著作《思考,快与慢》中提出了两种思考模式: + +- 系统 1:快思考。依赖直觉,反应快速,更偏向于感性给出反馈 +- 系统 2:慢思考。深思熟虑,反馈时间长,基于逻辑和理性给出答案 + +我们每天都在不停地切换这两种思考模式,比如我们可以毫不费力在人潮中识别到朋友(系统 1)。而在撰写这篇文章时,我需要思考主题,搜集材料,整理结构,并持续调整(系统 2)。 + +Open AI 创始成员 Karpathy 在一次公开演讲中提到,基于大模型的思维链(Chain Of Thought)模式背后的思想正是来源于「慢思考」理论,要想让大模型表现得更好,就需要引导大模型一步一步思考,循环往复,直到达成预定结束条件。 + +比如,即使是很简单的数学题,如果要求大模型一步到位给出答案,也很难获得理想的结果。 + +有研究人员给出一道数学题,定义了 ”Q 运算”,规则是 `a Q b = a + b - 1`(未在 prompt 中声明),要求大模型在按规律进行运算。结果 GPT4、Claude、Germini 这些先进大模型全军覆没。 + +![image](https://www.qbitai.com/wp-content/uploads/replace/5e68afd060e111d7f4db0d0f5cc17ef1.png) + +只有让模型一步步推理,甚至需要不断地给出引导,才能让它输出正确结果。 + +![image](https://www.qbitai.com/wp-content/uploads/replace/fb95a68643a74dd0ac7ba5e1f1d2c225.png) + +像这样一步一步处理方式正是区别于 Agent(智能体)和 LLM 的核心,背后的原理也正是卡尼曼的「慢思考」理论。 + +## Agent 是什么 + +简而言之,AI agent 是有能力主动思考和行动的智能体。当我们提出需求时,agent 有能力自行感知环境、形成记忆、规划和决策行动,甚至与别的 agent 合作实现任务。对比之下,LLM 是被动的推理引擎,用户每 prompt 点拨一次才会回复一次。 + +具体到 AI Agent 的产品形态的可能性,目前社区主流有三种理解方式: + +### 1.AI Agent 是 AI Native 的开发范式 + +在 AI Native 之前,云原生(Cloud Native)的概念炙手可热。云原生并非指代具体的某一项技术,而是一套指导软件架构设计的方法论。首先软件天然就“生在云上,长在云上”,其次软件遵循一种新的开发、发布、运维模式,能够最大化发挥“云”的价值。 + +AI Native 软件的设计架构也是类似,软件原生为 AI 而设计,更好地利用并适应 AI,而非传统的 AI 来适应软件。 + +未来 AI Native 应用的核心思路都有 Agent 的影子。Agent 是能够更好的辅助 AI 扬长避短,结构化思考的设计思想。 + +### 2. AI Agent 比传统软件更灵活,比 LLM 更可靠 + +传统软件基于规则和逻辑,带来的是高可靠性和确定性,但也使其难以解决灵活的长尾问题。AI Agent 可以基于用户的需求灵活定制,真正实现千人千面的定制化能力。优秀的 Agent 要充分发挥 LLM 的推理(Reasoning)、角色(Actor)和交互能力(Interact)。 + +短期内要实现灵活性,Agent 还是要牺牲一定的可靠性,对于容错度较低的场景,短期内并不推荐直接上 Agent。但在可预期未来,Agent 能够达到类似于传统软件的可靠性。 + +### 3. AI Agent 和 LLM-based 应用相比,存在一些不同点: + +- 3.1 编排能力(Orchestration)。不同于 LLM 的一锤子买卖,Agent 可以实现多 Agent 编排,共同完成一个复杂的工作流场景。比如在编程场景下,有开发、测试角色。 + +- 3.2 工具使用(Tool)。LLM 只适合文字补全(completion)的工作,而 Agent 基于 LLM 的理解能力,可以调用合适的外部工具,解决对应的问题。比如 LLM 并不擅长解决数学问题,而 Agent 基于 LLM 的理解,调用合适的外部数学工具,就能很好地解决数学难题。 + +- 3.3 记忆能力(Memory)。LLM 的记忆能力只和上下文窗口(context window)相关。而 Agent 的记忆能力可以将 context window 和长期记忆结合,类似于内存(memory)和存储(Storage)的关系。未来的 Agent 能够记录用户偏好和使用习惯,使用越多就越了解用户。作为人类值得信任的工作伙伴。 + +- 3.4 推理能力(Reasoning)。LLM 是一种建立在 next token 预测引擎之上的概率工具,这和人类的思考方式是截然相反的。比如我们在思考数学问题时,会对一系列的假设方案进行实验并得出最佳答案,而不可能从一开始就能获取最优解。Agent 的推理能力,就是模拟人类思考方式,通过不断地假设、试验、求证,并持续迭代这一个过程,最终获得最优解。 + +短期来看,一个优秀的 AI Agent 应用至少要具备 Orchestration 和 Tool 能力。LLM 应用还不够强大,需要在合适的场景下寻找合适的工具(Tool)来解决问题,这个过程需要人类专家参与工作流,进行设计和编排,使其成为可靠的合作伙伴。 + +中长期来看,Memory 是下一个需要突破的方向,如何让人机协助积累的数据,带来新的数据飞轮,是一个相当重要的问题,有了个性化的记忆,就是 Gen AI 时代个性化的推荐引擎。 + +Reasoning 能力则是一个长期目标,需要大模型产品持续去提升,当前基于 next token 预测模型,很难让 AI 真正学会探索、试错。这方面需要 LLM 和 Agent 分别从模型推理和产品形态的双重进化。 + +## Agent 设计模式 + +![agent](https://api.ibos.cn/v4/weapparticle/accesswximg?aid=79580&url=aHR0cHM6Ly9tbWJpei5xcGljLmNuL3N6X21tYml6X3BuZy9uUDNPbXBma0ZqWDRIZmpTRW04Ukl1aDZXaWNieDZOVUE5Q003RmljRjJyd1M2TG1IWFBQOG43UmljQm1pYWE5RjFPUFVoaWJPeTY4eE1UeHRpYnpnT3ZSOGx1dy82NDA/d3hfZm10PXBuZyZhbXA=;from=appmsg) + +### 1. ReAct + +ReAct 是 Agent 设计模式中最早提出的,发表于 2022 年 10 月,在 ChatGPT 发布之前就提出了让 LLM 学习外公不公使用,具有相当高的开创性。 + +![react](https://wangwei1237.github.io/LLM_in_Action/images/react.png) + +ReAct 模式,是把 Reasoning(推理),Action(行动) 与 LLM 结合起来的 Agent 开发范式。 + +前面我们提到过 LLM 实际上是「系统 1」的思考方式,基于用户提问,快速给出答案,然后给出证明答案的证据。事实上,很多问题需要思考,提出若干解决思路,并一步一步得出最终答案,也就是「系统 2」的思考方式。 + +ReAct 属于典型的「系统 2」思考方式。 + +### Prompt + +以 `langchain` 为例,官方提供了一套 ReAct system prompt 模板,[链接](https://smith.langchain.com/hub/hwchase17/react?organizationId=ebebe8d7-2f89-597e-90fa-f331000bbce3) + +``` +Answer the following questions as best you can. You have access to the following tools: + +{tools} + +Use the following format: + +Question: the input question you must answer +Thought: you should always think about what to do +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action +Observation: the result of the action +... (this Thought/Action/Action Input/Observation can repeat N times) +Thought: I now know the final answer +Final Answer: the final answer to the original input question + +Begin! + +Question: {input} +Thought:{agent_scratchpad} +``` + +首先,因为是一个通用的模板,所以给出了一个通用的解决问题的目标。 + +其次,定义`{tools}`,明确告诉 LLM 选择给定外部工具来执行 action。 + +然后又给出了 LLM 执行步骤: + +- Question: 用户提问 +- Thought: 这是大模型擅长的领域,基于一个问题,给出解决问题的思考方向 +- Action: 从 `{tools}` 选择对应的工具 +- Action Input: 从问题中提取参数作为 `tool` 的输出 +- Observation: 观察 Action 输出 + ... (Thought/Action/Action Input/Observation 重复执行 N 次) +- Final Answer: 基于 Observation, LLM 判断任务已完成,输出结果 + +最后将 `input(用户提问)`和 `agent_scratchpad(agent 执行记录)` 传递给大模型,开始本次命运的齿轮 ⚙ 转动。 + +![agent](https://api.ibos.cn/v4/weapparticle/accesswximg?aid=79580&url=aHR0cHM6Ly9tbWJpei5xcGljLmNuL3N6X21tYml6X3BuZy9uUDNPbXBma0ZqWDRIZmpTRW04Ukl1aDZXaWNieDZOVUFXSUZsSzJvcFZuejRhV0ZQVndpYk5qSm91aWFOaEdjMlpJQ2ljNTVDNmRjUmY1RGliQWpUM1FpY0lady82NDA/d3hfZm10PXBuZyZhbXA=;from=appmsg) + +### 使用场景 + +``` +Question: 中国哪个省份面积最大,哪个面积最小,最大和最小差几倍 +--- +action:: I should use the search engine to find the largest and smallest provinces in China by area, and then use the calculator to find the difference in their sizes. +Action: tavily_search_results_json +Action Input: "largest province in China by area" +Observation: The largest province in China by area is Xinjiang +--- +Action: tavily_search_results_json +Action Input: "smallest province in China by area" +Observation: The smallest province in China by area is Macau +--- +Action: calculator +Action Input: area of Xinjiang / area of Macau +Observation: The area of Xinjiang is about 166 times larger than the area of Macau +--- +Thought: I now know the final answer +--- +Final Answer: Xinjiang is the largest province in China by area, Macau is the smallest, and Xinjiang's area is about 166 times larger than Macau's. +``` + +接下来我们基于 `langchainjs` 是实现 ReAct: + +```typescript +import { AzureChatOpenAI } from "@langchain/azure-openai"; +import { AgentExecutor, createReactAgent } from "langchain/agents"; +import { pull } from "langchain/hub"; +import type { PromptTemplate } from "@langchain/core/prompts"; +import { TavilySearchResults } from "@langchain/community/tools/tavily_search"; +import { Calculator } from "@langchain/community/tools/calculator"; + +// 定义模型 +const llm = new AzureChatOpenAI({ + temperature: 0, +}); + +// 定义工具 +const tools = [new TavilySearchResults({ maxResults: 1 }), new Calculator()]; + +// 拉取 prompt +const prompt = await pull("hwchase17/react"); + +// 定义 agent +const agent = await createReactAgent({ + llm, + tools, + prompt, +}); +// 生成 agent 实例 +const agentExecutor = new AgentExecutor({ + agent, + tools, +}); + +const result = await agentExecutor.invoke({ + input: "中国哪个省份面积最大,哪个面积最小,最大和最小差几倍", +}); +``` + +### 小结 + +从以上示例可以看出,ReAct 设计的核心有两点: + +1. 基于上下文推理能力,包括选择合适的工具,并提取出工具参数 +2. 预设工具的丰富程度,能否和推理阶段相匹配 diff --git a/public/rag-from-scratch-overview/index.md b/public/rag-from-scratch-overview/index.md index 09eb646..e07d07b 100644 --- a/public/rag-from-scratch-overview/index.md +++ b/public/rag-from-scratch-overview/index.md @@ -1,5 +1,5 @@ --- -title: "RAG 实践入门(01)——RAG 概览" +title: "RAG 实践入门" date: "2024-04-26" cta: "lowcode" spoiler: "RAG 实践入门" @@ -86,7 +86,7 @@ text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20 splits = text_splitter.split_documents(docs) # Embed -vectorstore = Chroma.from_documents(documents=splits, +vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings()) retriever = vectorstore.as_retriever() @@ -138,7 +138,7 @@ HNSW 全称 Hierarchical Navigable Small World graphs(分层-可导航-小世 # Index from langchain_openai import OpenAIEmbeddings from langchain_community.vectorstores import Chroma -vectorstore = Chroma.from_documents(documents=splits, +vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings()) @@ -177,4 +177,4 @@ rag_chain.invoke("What is Task Decomposition?") ### 下一章 -Query Transformations,如何对输入查询(Query)进行转换,以更好的获取检索(retrieve)召回结果 \ No newline at end of file +Query Transformations,如何对输入查询(Query)进行转换,以更好的获取检索(retrieve)召回结果