Delete agent.py

agent.py

@@ -1,198 +0,0 @@
-from llama_index.core import SimpleDirectoryReader
-from langgraph.prebuilt import ToolNode
-from langchain_community.document_loaders import WikipediaLoader
-from langchain_community.tools import DuckDuckGoSearchRun
-from langchain_core.tools import tool
-from typing import TypedDict, Annotated, List, Any, Dict
-from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, ToolMessage, AnyMessage, SystemMessage
-from langgraph.graph.message import add_messages
-from langchain_groq import ChatGroq
-from langgraph.graph import StateGraph, START, END
-from langgraph.prebuilt import ToolNode, tools_condition
-import os
-
-@tool
-def read_file(directory:str)->str:
-    """
-    Reads text from files in a directory
-    Args:
-        directory (string): Takes in a directory name.
-    output_type: text
-    """
-    docs = SimpleDirectoryReader(directory).load_data()
-    return ' '.join([doc.text for doc in docs])
-
-@tool
-def web_search(query:str)->str:
-    """
-    Inputs a query and use DuckDuckGoSearchRun to search and fetch information from web.
-    Args:
-        query (string): Takes a query
-    output_type: text
-    """
-    search = DuckDuckGoSearchRun()
-    return search.invoke(query)
-
-@tool
-def multiply(a:float, b:float)->float:
-    """
-    Multiplies two numbers.
-    Args:
-        a (float): the first number
-        b (float): the second number
-    output_type: float
-    """
-    return a*b
-
-@tool
-def divide(a:float, b:float)->float:
-    """
-    Divides two numbers.
-    Args:
-        a (float): the first number
-        b (float): the second number
-    output_type: float
-    """
-    if b != 0:
-        return a / b
-    else:
-        raise ValueError('Cannot divide a number by zero')
-    
-@tool
-def subtract(a:float, b:float)->float:
-    """
-    Subtracts two numbers.
-    Args:
-        a (float): the first number
-        b (float): the second number
-    output_type: float
-    """
-    return a - b
-
-@tool
-def add(a:float, b:float)->float:
-    """
-    Adds two numbers.
-    Args:
-        a (float): the first number
-        b (float): the second number
-    output_type: float
-    """
-    return a + b
-
-@tool
-def modulus(a:int, b:int)->int:
-    """
-    Get the modulus of two numbers.
-    Args:
-        a (int): the first number
-        b (int): the second number
-    output_type: int
-    """
-    return a % b
-
-@tool
-def power(a:float, b:float)->float:
-    """
-    Get the power of two numbers.
-    Args:
-        a (float): the first number
-        b (float): the second number
-    output_type: float
-    """
-    return a**b
-
-@tool
-def square_root(a:float)->float | complex:
-    """
-    Get the square root of a numbers.
-    Args:
-        a (float): the first number
-    output_type: float
-    """
-    if a >= 0:
-        return a**0.5
-    return cmath.sqrt(a)
-
-@tool
-def wikipedia_fetcher(query:str)->str:
-    """
-    Inputs a query and use WikipediaLoader to fetch query realted information.
-    Args:
-        query (string): Takes a query
-    output_type: text
-    """
-    docs = WikipediaLoader(query=query, load_max_docs=3).load()
-    doc = docs[0].page_content
-    return doc
-
-@tool
-def query_csv_first_match(csv_file: str, filters: dict, column: str) -> str:
-    """
-    Returns the first value from `column` in CSV matching the filters.
-
-    Args:
-        csv_file (str): Path to CSV
-        filters (dict): Column-value filters, e.g. {"Year": 1983, "Country": "SU"}
-        column (str): Column to return value from
-
-    Returns:
-        str: First matching value
-    """
-    df = pd.read_csv(csv_file)
-    for key, val in filters.items():
-        if val is not None:
-            df = df[df[key] == val]
-    if not df.empty:
-        return str(df.iloc[0][column])
-    return "Unknown"
-
-@tool
-def query_csv_min(csv_file: str, column_min: str, return_column: str, tie_break_column: str = None) -> str:
-    """
-    Returns the value of `return_column` from the row with the minimum `column_min`.
-    If tie, use `tie_break_column` to select first alphabetically.
-
-    Args:
-        csv_file (str): Path to CSV
-        column_min (str): Column to find minimum
-        return_column (str): Column to return
-        tie_break_column (str, optional): Column to break tie alphabetically
-
-    Returns:
-        str: Result value
-    """
-    df = pd.read_csv(csv_file)
-    min_value = df[column_min].min()
-    candidates = df[df[column_min] == min_value]
-    if tie_break_column:
-        candidates = candidates.sort_values(tie_break_column)
-    return str(candidates.iloc[0][return_column])
-
-tools = [read_file, wikipedia_fetcher, web_search, add, subtract, divide, multiply, modulus, power, square_root, query_csv_first_match, query_csv_min]
-
-class AgentState(TypedDict):
-    messages: Annotated[List[AnyMessage], add_messages]
-
-api_key = os.getenv('GROQ_API_KEY')
-
-def call_model(state: AgentState):
-    llm = ChatGroq(model="qwen/qwen3-32b", temperature=0, api_key=api_key)
-    llm_with_tools = llm.bind_tools(tools)
-    response = llm_with_tools.invoke(state['messages'])
-    return {'messages': [response]}
-
-def build_graph():
-    workflow = StateGraph(AgentState)
-    workflow.add_node('llm', call_model)
-    workflow.add_node("call_tool", ToolNode(tools))
-    workflow.add_edge(START, 'llm')
-    workflow.add_conditional_edges(
-        'llm',
-        tools_condition,
-        {'tools':'call_tool', '__end__':END}
-    )
-    workflow.add_edge('call_tool', 'llm')
-
-    app = workflow.compile()
-    return app