分类目录：《大模型从入门到应用》总目录
LangChain系列文章：
- 基础知识
- 快速入门
- 安装与环境配置
- 链（Chains）、代理（Agent:）和记忆（Memory）
- 快速开发聊天模型
- 模型（Models）
- 基础知识
- 大型语言模型（LLMs）
- 基础知识
- LLM的异步API、自定义LLM包装器、虚假LLM和人类输入LLM（Human Input LLM）
- 缓存LLM的调用结果
- 加载与保存LLM类、流式传输LLM与Chat Model响应和跟踪tokens使用情况
- 聊天模型（Chat Models）
- 基础知识
- 使用少量示例和响应流式传输
- 文本嵌入模型
- Aleph Alpha、Amazon Bedrock、Azure OpenAI、Cohere等
- Embaas、Fake Embeddings、Google Vertex AI PaLM等
- 提示（Prompts）
- 基础知识
- 提示模板
- 基础知识
- 连接到特征存储
- 创建自定义提示模板和含有Few-Shot示例的提示模板
- 部分填充的提示模板和提示合成
- 序列化提示信息
- 示例选择器（Example Selectors）
- 输出解析器（Output Parsers）
- 记忆（Memory）
- 基础知识
- 记忆的类型
- 会话缓存记忆、会话缓存窗口记忆和实体记忆
- 对话知识图谱记忆、对话摘要记忆和会话摘要缓冲记忆
- 对话令牌缓冲存储器和基于向量存储的记忆
- 将记忆添加到LangChain组件中
- 自定义对话记忆与自定义记忆类
- 聊天消息记录
- 记忆的存储与应用
- 索引（Indexes）
- 基础知识
- 文档加载器（Document Loaders）
- 文本分割器（Text Splitters）
- 向量存储器（Vectorstores）
- 检索器（Retrievers）
- 链（Chains）
- 基础知识
- 通用功能
- 自定义Chain和Chain的异步API
- LLMChain和RouterChain
- SequentialChain和TransformationChain
- 链的保存（序列化）与加载（反序列化）
- 链与索引
- 文档分析和基于文档的聊天
- 问答的基础知识
- 图问答（Graph QA）和带来源的问答（Q&A with Sources）
- 检索式问答
- 文本摘要（Summarization）、HyDE和向量数据库的文本生成
- 代理（Agents）
- 基础知识
- 代理类型
- 自定义代理（Custom Agent）
- 自定义MRKL代理
- 带有ChatModel的LLM聊天自定义代理和自定义多操作代理（Custom MultiAction Agent）
- 工具
- 基础知识
- 自定义工具（Custom Tools）
- 多输入工具和工具输入模式
- 人工确认工具验证和Tools作为OpenAI函数
- 工具包（Toolkit）
- 代理执行器（Agent Executor）
- 结合使用Agent和VectorStore
- 使用Agents的异步API和创建ChatGPT克隆
- 处理解析错误、访问中间步骤和限制最大迭代次数
- 为代理程序设置超时时间和限制最大迭代次数和为代理程序和其工具添加共享内存
- 计划与执行
- 回调函数（Callbacks）
计划与执行代理通过首先规划要做的事情，然后执行子任务来实现目标。这个想法在很大程度上受到了BabyAGI以及《Plan-and-Solve》论文的启发。
- 规划几乎总是由一个LLM（语言模型）来完成。
- 执行通常由一个单独的代理（配备工具）来完成。
# 导入模块
from langchain.chat_models import ChatOpenAI
from langchain.experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner
from langchain.llms import OpenAI
from langchain import SerpAPIWrapper
from langchain.agents.tools import Tool
from langchain import LLMMathChain# 工具
search = SerpAPIWrapper()
llm = OpenAI(temperature=0)
llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)
tools = [Tool(name = "Search",func=search.run,description="useful for when you need to answer questions about current events"),Tool(name="Calculator",func=llm_math_chain.run,description="useful for when you need to answer questions about math"),
]# 规划器（Planner）、执行器（Executor）和代理（Agent）
model = ChatOpenAI(temperature=0)
planner = load_chat_planner(model)
executor = load_agent_executor(model, tools, verbose=True)
agent = PlanAndExecute(planner=planner, executor=executor, verbose=True)# 执行示例
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
Entering new PlanAndExecute chain...
steps=[Step(value="Search for Leo DiCaprio's girlfriend on the internet."), Step(value='Find her current age.'), Step(value='Raise her current age to the 0.43 power using a calculator or programming language.'), Step(value='Output the result.'), Step(value="Given the above steps taken, respond to the user's original question.\n\n")]
日志输出：
日志输出：
Entering new AgentExecutor chain...
Action:```{"action": "Search","action_input": "Who is Leo DiCaprio's girlfriend?"
}```Observation: DiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years. He's since been linked to another famous supermodel – Gigi Hadid. The power couple were first supposedly an item in September after being spotted getting cozy during a party at New York Fashion Week.
Thought:Based on the previous observation, I can provide the answer to the current objective.
Action:
```{"action": "Final Answer","action_input": "Leo DiCaprio is currently linked to Gigi Hadid."
}```Finished chain.
*****Step: Search for Leo DiCaprio's girlfriend on the internet.Response: Leo DiCaprio is currently linked to Gigi Hadid.Entering new AgentExecutor chain...
Action:
```{"action": "Search","action_input": "What is Gigi Hadid's current age?"
}```Observation: 28 years
Thought:Previous steps: steps=[(Step(value="Search for Leo DiCaprio's girlfriend on the internet."), StepResponse(response='Leo DiCaprio is currently linked to Gigi Hadid.'))]Current objective: value='Find her current age.'Action:
```{"action": "Search","action_input": "What is Gigi Hadid's current age?"
}```Observation: 28 years
Thought:Previous steps: steps=[(Step(value="Search for Leo DiCaprio's girlfriend on the internet."), StepResponse(response='Leo DiCaprio is currently linked to Gigi Hadid.')), (Step(value='Find her current age.'), StepResponse(response='28 years'))]Current objective: NoneAction:
```{"action": "Final Answer","action_input": "Gigi Hadid's current age is 28 years."
}```Finished chain.
*****Step: Find her current age.Response: Gigi Hadid's current age is 28 years.Entering new AgentExecutor chain...
Action:
```{"action": "Calculator","action_input": "28 ** 0.43"
}```Entering new LLMMathChain chain...
28 ** 0.43
```text28 ** 0.43```
...numexpr.evaluate("28 ** 0.43")...Answer: 4.1906168361987195
Finished chain.Observation: Answer: 4.1906168361987195
Thought:The next step is to provide the answer to the user's question.Action:
```{"action": "Final Answer","action_input": "Gigi Hadid's current age raised to the 0.43 power is approximately 4.19."
}```Finished chain.
*****Step: Raise her current age to the 0.43 power using a calculator or programming language.Response: Gigi Hadid's current age raised to the 0.43 power is approximately 4.19.Entering new AgentExecutor chain...
Action:
```{"action": "Final Answer","action_input": "The result is approximately 4.19."
}```Finished chain.
*****Step: Output the result.Response: The result is approximately 4.19.Entering new AgentExecutor chain...
Action:
```{"action": "Final Answer","action_input": "Gigi Hadid's current age raised to the 0.43 power is approximately 4.19."
}```Finished chain.
*****Step: Given the above steps taken, respond to the user's original question.Response: Gigi Hadid's current age raised to the 0.43 power is approximately 4.19.
Finished chain.
输出：
"Gigi Hadid's current age raised to the 0.43 power is approximately 4.19."
参考文献：
[1] LangChain官方网站：https://www.langchain.com/
[2] LangChain 🦜️🔗 中文网，跟着LangChain一起学LLM/GPT开发：https://www.langchain.com.cn/
[3] LangChain中文网 - LangChain 是一个用于开发由语言模型驱动的应用程序的框架：http://www.cnlangchain.com/
