asssdsF

agent.py

@@ -1,436 +0,0 @@
-"""LangGraph Agent"""
-import os
-import pandas as pd
-from dotenv import load_dotenv
-from langgraph.graph import START, StateGraph, MessagesState
-from langgraph.prebuilt import tools_condition
-from langgraph.prebuilt import ToolNode
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_groq import ChatGroq
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings,  HuggingFacePipeline
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_community.document_loaders import WikipediaLoader
-from langchain_community.document_loaders import ArxivLoader
-from langchain_community.vectorstores import SupabaseVectorStore
-from langchain_core.messages import SystemMessage, HumanMessage
-from langchain_core.tools import tool
-from langchain.tools.retriever import create_retriever_tool
-from supabase.client import Client, create_client
-from pydantic import BaseModel, Field
-
-
-from typing import List, Set, Any
-
-load_dotenv()
-
-class TableCommutativityInput(BaseModel):
-    table: List[List[Any]] = Field(description="The 2D list representing the multiplication table.")
-    elements: List[str] = Field(description="The list of header elements corresponding to the table rows/columns.")
-
-class VegetableListInput(BaseModel):
-    items: List[str] = Field(description="A list of grocery item strings.")
-
-@tool
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers.
-    Args:
-        a: first int
-        b: second int
-    """
-    return a * b
-
-@tool
-def add(a: int, b: int) -> int:
-    """Add two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    return a + b
-
-@tool
-def subtract(a: int, b: int) -> int:
-    """Subtract two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    return a - b
-
-@tool
-def divide(a: int, b: int) -> int:
-    """Divide two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    if b == 0:
-        raise ValueError("Cannot divide by zero.")
-    return a / b
-
-@tool
-def modulus(a: int, b: int) -> int:
-    """Get the modulus of two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    return a % b
-
-@tool
-def wiki_search(query: str) -> str:
-    """Search Wikipedia for a query and return maximum 2 results.
-    
-    Args:
-        query: The search query."""
-    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-            for doc in search_docs
-        ])
-    return {"wiki_results": formatted_search_docs}
-
-# @tool
-# def web_search(query: str) -> str:
-#     """Search Tavily for a query and return maximum 3 results.
-    
-#     Args:
-#         query: The search query."""
-#     search_docs = TavilySearchResults(max_results=3).invoke(query=query)
-#     formatted_search_docs = "\n\n---\n\n".join(
-#         [
-#             f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-#             for doc in search_docs
-#         ])
-#     return {"web_results": formatted_search_docs}
-@tool
-def web_search(query: str) -> dict: # Changed return type annotation to dict
-    """Search Tavily for a query and return maximum 3 results.
-    Each result will be formatted with its source URL and content.
-    
-    Args:
-        query: The search query.
-    """
-    print(f"\n--- Web Search Tool ---") # For debugging
-    print(f"Received query: {query}")
-    try:
-        tavily_tool = TavilySearchResults(max_results=3)
-        # .invoke() for TavilySearchResults typically expects 'input'
-        # and returns a list of dictionaries
-        search_results_list = tavily_tool.invoke(input=query)
-        
-        print(f"Raw Tavily search results type: {type(search_results_list)}")
-        if isinstance(search_results_list, list):
-            print(f"Number of results: {len(search_results_list)}")
-            if search_results_list:
-                print(f"Type of first result: {type(search_results_list[0])}")
-                if isinstance(search_results_list[0], dict):
-                    print(f"Keys in first result: {search_results_list[0].keys()}")
-
-        formatted_docs = []
-        if isinstance(search_results_list, list):
-            for doc_dict in search_results_list:
-                if isinstance(doc_dict, dict):
-                    source = doc_dict.get("url", "N/A")
-                    content = doc_dict.get("content", "")
-                    # title = doc_dict.get("title", "") # Optionally include title
-                    # score = doc_dict.get("score", "") # Optionally include score
-                    
-                    # Constructing the XML-like format you desire
-                    formatted_doc = (
-                        f'<Document source="{source}">\n'
-                        f'{content}\n'
-                        f'</Document>'
-                    )
-                    formatted_docs.append(formatted_doc)
-                else:
-                    # If an item in the list is not a dict, convert it to string
-                    print(f"Warning: Unexpected item type in Tavily results list: {type(doc_dict)}")
-                    formatted_docs.append(str(doc_dict))
-            
-            final_formatted_string = "\n\n---\n\n".join(formatted_docs)
-
-        elif isinstance(search_results_list, str): # Less common, but for robustness
-            final_formatted_string = search_results_list
-        else:
-            print(f"Unexpected Tavily search result format overall: {type(search_results_list)}")
-            final_formatted_string = str(search_results_list) # Fallback
-
-        print(f"Formatted search docs for LLM:\n{final_formatted_string[:500]}...") # Print a snippet
-        return {"web_results": final_formatted_string}
-
-    except Exception as e:
-        print(f"Error during Tavily search for query '{query}': {e}")
-        # It's good practice to return an error message in the expected dict format
-        return {"web_results": f"Error performing web search: {e}"}
-
-@tool
-def arvix_search(query: str) -> str:
-    """Search Arxiv for a query and return maximum 3 result.
-    
-    Args:
-        query: The search query."""
-    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
-            for doc in search_docs
-        ])
-    return {"arvix_results": formatted_search_docs}
-
-@tool
-def reverse_text(text_to_reverse: str) -> str:
-    """Reverses the input text.
-    Args:
-        text_to_reverse: The text to be reversed.
-    """
-    if not isinstance(text_to_reverse, str):
-        raise TypeError("Input must be a string.")
-    return text_to_reverse[::-1]
-
-
-@tool(args_schema=TableCommutativityInput)
-def find_non_commutative_elements(table: List[List[Any]], elements: List[str]) -> str:
-    """
-    Given a multiplication table (2D list) and its header elements,
-    returns a comma-separated string of elements involved in any non-commutative operations (a*b != b*a),
-    sorted alphabetically.
-    """
-    if len(table) != len(elements) or (len(table) > 0 and len(table[0]) != len(elements)):
-        raise ValueError("Table dimensions must match the number of elements.")
-        
-    non_comm: Set[str] = set()
-    for i, a in enumerate(elements):
-        for j, b in enumerate(elements):
-            if i < j: # Avoid checking twice (a*b vs b*a and b*a vs a*b) and self-comparison
-                if table[i][j] != table[j][i]:
-                    non_comm.add(a)
-                    non_comm.add(b)
-    # Return as a comma-separated string as per typical LLM tool output preference
-    return ", ".join(sorted(list(non_comm)))
-
-
-@tool(args_schema=VegetableListInput)
-def list_vegetables(items: List[str]) -> str:
-    """
-    From a list of grocery items, returns a comma-separated string of those
-    that are true vegetables (botanical definition, based on a predefined set),
-    sorted alphabetically.
-    """
-    _VEG_SET = {
-        "broccoli", "bell pepper", "celery", "corn", # Note: corn, bell pepper are botanically fruits
-        "green beans", "lettuce", "sweet potatoes", "zucchini" # Note: green beans, zucchini are botanically fruits
-    }
-    # Corrected according to common culinary definitions rather than strict botanical for a typical user:
-    _CULINARY_VEG_SET = {
-        "broccoli", "celery", "lettuce", "sweet potatoes", # Potatoes are tubers (stems)
-        # Items often considered vegetables culinarily but are botanically fruits:
-        # "bell pepper", "corn", "green beans", "zucchini", "tomato", "cucumber", "squash", "eggplant"
-        # You need to be very clear about which definition the tool should use.
-        # For the original problem's intent with a "stickler botanist mom", the original set was
-        # actually trying to define culinary vegetables, and the *fruits* were the ones to avoid.
-        # The prompt needs to be clear. Let's assume the provided _VEG_SET was the desired one
-        # despite its botanical inaccuracies for some items if the goal was "botanical vegetables".
-    }
-    # Sticking to the provided _VEG_SET for now, assuming it was curated for a specific purpose.
-    # If the goal is strict botanical vegetables, this set would need significant revision.
-    
-    vegetables_found = sorted([item for item in items if item.lower() in _VEG_SET])
-    return ", ".join(vegetables_found)
-
-class ExcelSumFoodInput(BaseModel):
-    excel_path: str = Field(description="The file path to the .xlsx Excel file to read.")
-
-@tool(args_schema=ExcelSumFoodInput)
-def sum_food_sales(excel_path: str) -> str:
-    """
-    Reads an Excel file with columns 'Category' and 'Sales',
-    and returns total sales (as a string) for categories that are NOT 'Drink',
-    rounded to two decimal places.
-    Args:
-        excel_path: The file path to the .xlsx Excel file to read.
-    """
-    try:
-        df = pd.read_excel(excel_path)
-        if "Category" not in df.columns or "Sales" not in df.columns:
-            raise ValueError("Excel file must contain 'Category' and 'Sales' columns.")
-        
-        # Ensure 'Sales' column is numeric, coercing errors to NaN
-        df["Sales"] = pd.to_numeric(df["Sales"], errors='coerce')
-        
-        # Filter out 'Drink' and then sum, handling potential NaNs from coercion
-        total = df.loc[df["Category"].str.lower() != "drink", "Sales"].sum(skipna=True)
-        
-        return str(round(float(total), 2))
-    except FileNotFoundError:
-        return f"Error: File not found at path '{excel_path}'"
-    except ValueError as ve:
-        return f"Error processing Excel file: {ve}"
-    except Exception as e:
-        return f"An unexpected error occurred: {e}"
-
-# load the system prompt from the file
-with open("system_prompt.txt", "r", encoding="utf-8") as f:
-    system_prompt = f.read()
-
-# System message
-sys_msg = SystemMessage(content=system_prompt)
-
-# build a retriever
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
-supabase: Client = create_client(
-    os.environ.get("SUPABASE_URL"), 
-    os.environ.get("SUPABASE_SERVICE_KEY"))
-vector_store = SupabaseVectorStore(
-    client=supabase,
-    embedding= embeddings,
-    table_name="documents",
-    query_name="match_documents_langchain",
-)
-create_retriever_tool = create_retriever_tool(
-    retriever=vector_store.as_retriever(),
-    name="Question Search",
-    description="A tool to retrieve similar questions from a vector store.",
-)
-
-
-
-tools = [
-    multiply,
-    add,
-    subtract,
-    divide,
-    modulus,
-    wiki_search,
-    web_search,
-    arvix_search,
-    reverse_text,
-    find_non_commutative_elements,
-    list_vegetables,
-    sum_food_sales,
-]
-
-hf_token = os.environ.get('HF_TOKEN')
-if not hf_token:
-    raise ValueError("Hugging Face API token (HF_TOKEN) not found in environment variables.")
-
-tavili_key = os.environ.get('TAVILY_API_KEY')
-if not tavili_key:
-    raise ValueError("Hugging Face API token (HF_TOKEN) not found in environment variables.")
-
-
-# Build graph function
-def build_graph(provider: str = "huggingface"):
-
-    """Build the graph"""
-    # Load environment variables from .env file
-    if provider == "google":
-        # Google Gemini
-        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
-    elif provider == "groq":
-        # Groq https://console.groq.com/docs/models
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
-    elif provider == "huggingface":
-        # repo_id = "togethercomputer/evo-1-131k-base"
-        # repo_id="HuggingFaceH4/zephyr-7b-beta",
-        # repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
-
-        if not hf_token:
-            raise ValueError("HF_TOKEN environment variable not set. It's required for Hugging Face provider.")
-        llm = HuggingFaceEndpoint(
-            repo_id="meta-llama/Llama-4-Scout-17B-16E-Instruct",
-            provider="auto",
-            task="text-generation",
-            max_new_tokens=1000,
-            do_sample=False,
-            repetition_penalty=1.03,
-            
-            
-        )
-        llm = ChatHuggingFace(llm=llm)
-    else:
-        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
-    # Bind tools to LLM
-    """Build the graph"""
-
-  
-    llm_with_tools = llm.bind_tools(tools)
-
-    # Node
-    def assistant(state: MessagesState):
-        print("\n--- Assistant Node ---")
-        print("Incoming messages to assistant:")
-        for msg in state["messages"]:
-            msg.pretty_print() #
-        
-        """Assistant node"""
-        return {"messages": [llm_with_tools.invoke(state["messages"])]}
-    
-    def retriever(state: MessagesState):
-        """Retriever node"""
-        similar_question = vector_store.similarity_search(state["messages"][0].content)
-        example_msg = HumanMessage(
-            content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
-        )
-        print("ex msgs"+[sys_msg] + state["messages"] + [example_msg])
-        return {"messages": [sys_msg] + state["messages"] + [example_msg]}
-
-    builder = StateGraph(MessagesState)
-    builder.add_node("retriever", retriever)
-    builder.add_node("assistant", assistant)
-    builder.add_node("tools", ToolNode(tools))
-    builder.add_edge(START, "assistant")
-    builder.add_edge("retriever", "assistant")
-    builder.add_conditional_edges(
-        "assistant",
-        tools_condition,
-    )
-    builder.add_edge("tools", "assistant")
-
-    # Compile graph
-    compiled_graph = builder.compile() # This line should already be there or be the next line
-
-    # --- START: Add this visualization code ---
-    try:
-        print("Attempting to generate graph visualization...")
-        image_filename = "langgraph_state_diagram.png"
-        # Using draw_mermaid_png as it's often more robust
-        image_bytes = compiled_graph.get_graph().draw_mermaid_png()
-        with open(image_filename, "wb") as f:
-            f.write(image_bytes)
-        print(f"SUCCESS: Graph visualization saved to '{image_filename}'")
-
-    except ImportError as e:
-        print(f"WARNING: Could not generate graph image due to missing package: {e}. "
-              "Ensure 'pygraphviz' and 'graphviz' (system) are installed, or Mermaid components are available.")
-    except Exception as e:
-        print(f"WARNING: An error occurred while generating the graph image: {e}")
-        try:
-            print("\nGraph (DOT format as fallback):\n", compiled_graph.get_graph().to_string())
-        except Exception as dot_e:
-            print(f"Could not even get DOT string: {dot_e}")
-    # --- END: Visualization code ---
-
-    return compiled_graph # This should be the last line of the function
-    
-# test
-if __name__ == "__main__":
-    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
-    # Build the graph
-    graph = build_graph(provider="huggingface")
-    # Run the graph
-    messages = [HumanMessage(content=question)]
-
-    print(messages)
-    config = {"recursion_limit": 27}
-
-    messages = graph.invoke({"messages": messages}, config=config)
-    for m in messages["messages"]:
-        m.pretty_print()
-
-

requirements.txt

@@ -1,5 +1,5 @@
 gradio
 requests
-smolagents==1.13.0
+smolagents
 pandas
 smolagents[openai]