# On macOS and Linux.
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows.
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

# 建立專案
uv init agent-project

# Mac or Linux: 將$HOME/.local/bin 加入PATH
export PATH="$HOME/.local/bin:$PATH"

# 重新啟動
source $HOME/.local/bin/env (sh, bash, zsh)

# On macOS and Linux.
source .venv/bin/activate

# 可指定python版本，例如3.12
cd agent-project
uv sync --python 3.12

# On Windows
.venv\Scripts\activate

def main():
    print("Hello from agent-project!")


if __name__ == "__main__":
    main()

python -m pip install langchain langchain-community langchain-ollama ollama

python -m ensurepip --upgrade

# 下載LLM model
ollama pull orieg/gemma3-tools:4b-ft

irm https://ollama.com/install.ps1 | iex

irm https://ollama.com/install.ps1 | iex

# 檢視有哪些model
ollama list
# 背景服務
ollama serve

from langchain_ollama import ChatOllama
def main(): 
    MODEL = "orieg/gemma3-tools:4b-ft"
    
    model = ChatOllama(
        model=MODEL,
        base_url="http://localhost:11434",
        temperature=0.2,
        top_k=25
    )
    
    prompt = "可以介紹一下你自己嗎？"    
    response = model.invoke(prompt)    
    print(type(response))
    print(response)

if __name__ == "__main__":
    main()

from langchain_ollama import ChatOllama
from langchain_core.messages import HumanMessage, SystemMessage
from langgraph.prebuilt import create_react_agent
import pandas as pd
import duckdb as ddb

model = ChatOllama(
    model="orieg/gemma3-tools:4b-ft",
    base_url="http://localhost:11434"
    )

def querycsv(query:str) -> str:
    """
    Execute a SQL query against a table named 'df' which represents resource.csv/
    The table has two columns: 'subject' (text) and 'marks' (numeric).
    
    Args:
        query: A valid SQL string (e.g., "SELECT AVG(marks) FROM df")
    """
    df = pd.read_csv("resource.csv")
    
    # Ensuring the agent doesn't have to worry about the filename vs table name
    query = query.replace("resource.csv", "df")

    try:
        mod_df = ddb.query(query).df()
        return mod_df.to_string()
    except Exception as e:
        return f"Error executing SQL: {str(e)}"

def main():
    tools = [querycsv]
    # 定義你的系統指令
    system_msg = SystemMessage(
        content="""You are an AI Agent that queries resource.csv file and give information. 
        You always call tools with SQL Query"""
        )
    agent_executor = create_react_agent(
        model, 
        tools=tools,
        )
    
    config = {"configurable": {"thread_id": "test"}}

    # Running the stream
    inputs = {"messages": [
        system_msg,
        HumanMessage(content="Get average over marks column. Query resource.csv")]}
        # HumanMessage(content="Subjects with marks less than 80. Query resource.csv")]}


    for step in agent_executor.stream(inputs, config, stream_mode="values"):
        step["messages"][-1].pretty_print()

if __name__ == "__main__":
    main()

def querycsv(query:str) -> str:
    """
    Execute a SQL query against a table named 'df' which represents resource.csv/
    The table has two columns: 'subject' (text) and 'marks' (numeric).
    Args:
        query: A valid SQL string (e.g., "SELECT AVG(marks) FROM df")
    """
    df = pd.read_csv("resource.csv")
    
    # Ensuring the agent doesn't have to worry about the filename vs table name
    query = query.replace("resource.csv", "df")

    try:
        mod_df = ddb.query(query).df()
        return mod_df.to_string()
    except Exception as e:
        return f"Error executing SQL: {str(e)}"

model = ChatOllama(
    model="orieg/gemma3-tools:4b-ft",
    base_url="http://localhost:11434"
    )

    tools = [querycsv]
    ...
    
    agent_executor = create_react_agent(
        model, 
        tools=tools,
        )

    # 定義你的系統指令
    system_msg = SystemMessage(
        content="""You are an AI Agent that queries resource.csv file and give information. 
        You always call tools with SQL Query"""
        )    
    ...
    config = {"configurable": {"thread_id": "test"}}
    ...
    # Running the stream
    inputs = {"messages": [
        system_msg,
        HumanMessage(content="Get average over marks column. Query resource.csv")]}
    
    for step in agent_executor.stream(inputs, config, stream_mode="values"):
        step["messages"][-1].pretty_print()

from langchain_ollama import ChatOllama
from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition
from tools.tool import querycsv # 直接匯入被 @tool 裝飾過的函式

class State(TypedDict):
    messages: Annotated[list, add_messages]

graph_builder = StateGraph(State)
    
tools = [querycsv]
llm = ChatOllama(
    model="orieg/gemma3-tools:4b-ft",
    base_url="http://localhost:11434"
)
llm_with_tools = llm.bind_tools(tools)  

def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"]) ]}

graph_builder.add_node("chatbot", chatbot)

tool_node = ToolNode(tools=tools)
graph_builder.add_node("tools", tool_node)

graph_builder.add_conditional_edges(
    "chatbot",
    tools_condition
)
# Any time a tool is called, we return to chatbot to decide the next step
graph_builder.add_edge("tools", "chatbot")
graph_builder.set_entry_point("chatbot")

graph = graph_builder.compile()

def stream_graph_updates(user_input: str):
    for event in graph.stream({"messages":[{"role": "user", "content": user_input}] }):
        for node, value in event.items():
            last_msg = value["messages"][-1]
            print(f"Node: {node}")
            # 檢查是否有 tool_calls 產生
            if hasattr(last_msg, 'tool_calls') and last_msg.tool_calls:
                print("Tool Calls detected:", last_msg.tool_calls)
            else:
                print("Assistant content:", last_msg.content)

while True:
    try:
        user_input = input("User: ")
        if user_input.lower() in ["exit", "quit", "bye"]:
            print("Goodbye")
            break
        
        stream_graph_updates(user_input)
    except KeyboardInterrupt:
        # fallback if input() is not available
        user_input = "What do you know about LangGraph?"
        print("User: " + user_input)
        stream_graph_updates(user_input)
        break

import pandas as pd
import duckdb as ddb
from langchain_core.tools import tool

@tool
def querycsv(query:str) -> str:
    """
    USE THIS TOOL TO READ DATA FROM resource.csv
    Execute a SQL query against a table named 'df' which represents resource.csv/
    The table has two columns: 'subject' (text) and 'marks' (numeric).
    當詢問關於 resource.csv 或成績 (marks) 時，必須呼叫此工具
    
    Args:
        query: A valid SQL string (e.g., "SELECT AVG(marks) FROM df")
    """
    try:
        df = pd.read_csv("resources/resource.csv")
        
        # 將 pandas 檔案註冊成 DuckDB 看得到的臨時視圖
        # 這樣無論模型寫 "resource.csv"、'resource.csv' 還是 resource.csv 
        # DuckDB 都能正確對應
        con = ddb.connect(database=':memory:')
        con.register('resource.csv', df)
        con.register('df', df) # 同時保留 df 作為備案
        
        result_df = con.execute(query).df()
        return result_df.to_string()
    except Exception as e:
        return f"Error executing SQL: {str(e)}"