### [AI / ML領域相關學習筆記入口頁面](https://hackmd.io/@YungHuiHsu/BySsb5dfp)
#### [Deeplearning.ai GenAI/LLM系列課程筆記](https://learn.deeplearning.ai/)
##### GenAI
- [Large Language Models with Semantic Search。大型語言模型與語義搜索 ](https://hackmd.io/@YungHuiHsu/rku-vjhZT)
- [LangChain for LLM Application Development。使用LangChain進行LLM應用開發](https://hackmd.io/1r4pzdfFRwOIRrhtF9iFKQ)
- [Finetuning Large Language Models。微調大型語言模型](https://hackmd.io/@YungHuiHsu/HJ6AT8XG6)
##### RAG
- [Preprocessing Unstructured Data for LLM Applications。大型語言模型(LLM)應用的非結構化資料前處理](https://hackmd.io/@YungHuiHsu/BJDAbgpgR)
- [Building and Evaluating Advanced RAG。建立與評估進階RAG](https://hackmd.io/@YungHuiHsu/rkqGpCDca)
- [[GenAI][RAG] Multi-Modal Retrieval-Augmented Generation and Evaluaion。多模態的RAG與評估
](https://hackmd.io/@YungHuiHsu/B1LJcOlfA)

##### AI Agents
原理可參考這篇綜論: [How Agents for LLM Perform Task Planning。大型語言模型的代理如何進行任務規劃](https://hackmd.io/@YungHuiHsu/rkK52BkQp)

- 相關framework選擇
- [AI Agents in LangGraph](https://hackmd.io/@YungHuiHsu/BJTKpkEHC)
- [Building Agentic RAG with LlamaIndex](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/1/introduction)
- [Multi AI Agent Systems with crewAI](https://learn.deeplearning.ai/courses/multi-ai-agent-systems-with-crewai/lesson/1/introduction)
- [Functions, Tools and Agents with LangChain](https://learn.deeplearning.ai/courses/functions-tools-agents-langchain/lesson/1/introduction)

---

# [AI Agents in LangGraph](https://learn.deeplearning.ai/courses/ai-agents-in-langgraph/lesson/1/introduction)
* [Build an Agent from Scratch。從零開始建立AI代理](https://hackmd.io/@YungHuiHsu/BJTKpkEHC)
* [LangGraph Components](https://hackmd.io/@YungHuiHsu/BySTFuhSC)
* Agentic Search Tools
* Persistence and Streaming
* Human in the loop
* Essay Writer

---
## LanaGraph Components。 LanaGraph組件

回顧上一小節的流程

- LangChain的PromptTemplate
- [hub/hwchase17/react](https://smith.langchain.com/hub/hwchase17/react)
- 使用方式

        # set the LANGCHAIN_API_KEY environment variable (create key in settings)
        from langchain import hub
        prompt = hub.pull("hwchase17/react")

- 提示模板
- `{tools}`可動態帶入指定工具
- `[{tool_names}]`

        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}

#### LangChain: Tools

- 從langchain_community中調用tools

# get a tool from the library 
from langchain_community.tools.tavily_search import \ 

TavilySearchResults 
tool = TavilySearchResults(max_results=2) 

self.tools = {t.name: t for t in tools} 
self.model = model.bind_tools([tool])

#### New in LangGraph
- **Cyclic Graphs**
- **Persistence**
- **Human-in-the-loop**


* LangGraph is an extension of LangChain that supports graphs.
* Single and Multi-agent flows are described and represented as graphs.
* Allows for extremely controlled “flows”
* Built-in persistence allows for human-in-the-loop workflows

### Graph 基本組件
* Nodes。節點： 代理或功能
* Edges。邊： 連接節點
* Conditional edges。條件邊： 決定

- 極簡範例

#### Data/State
* **代理狀態**可被圖的所有部分訪問
Agent state Is accessible to all parts of the graph
* 是圖的本地狀態
it is local to the graph
* 可存儲在持久層中
Can be stored in a persistence layer


- 範例
- Simple
- 當 `messages` 欄位被更新時，它不是覆蓋原來的值，而是將新的消息添加到已有的消息中。 通過 `Annotated` 和 `operator.add` 來實現的。
- Complex
- 更複雜的 `AgentState` 結構，包括 `input`、`chat_history`、`agent_outcome` 和 `intermediate_steps`
- `input`、`chat_history` 和 `agent_outcome` 這三個欄位都沒有進行特別的註釋，這意味著當這些欄位被更新時，新值會覆蓋現有的值
- 唯一例外的是 `intermediate_steps` 欄位，它被註釋了 `operator.add`，這意味著當更新這個欄位時，新的值會添加到現有的值中，而不是覆蓋它
- 為什麼 `intermediate_steps` 要添加而不是覆蓋
- `intermediate_steps` 欄位被設計為追蹤代理行動和觀察的過程
- 這些步驟在代理執行過程中不斷累加，所以每次更新時需要將新步驟添加到現有步驟中，而不是覆蓋掉
- 保留完整的執行記錄，幫助追蹤代理的行動和決策過程

:::info
類似RL中記錄所有STATE
:::

# Simple 
class AgentState(TypedDict): 
    messages: Annotated[Sequence[BaseMessage], operator.add] 

# Complex 
class AgentState(TypedDict): 
    input: str 
    chat_history: list[BaseMessage] 
    agent_outcome: Union[AgentAction, AgentFinish, None] 
    intermediate_steps: Annotatedf[list[tuple[AgentAction, str]], operator.add]

- State

    # State 
    class AgentState(TypedDict): 
        messages: Annotated[list[AnyMessage], operator.add]