Delete utils/agent2.py

utils/agent2.py

@@ -1,259 +0,0 @@
-import os
-from dotenv import load_dotenv
-
-from langchain_core.tools import tool
-from langgraph.prebuilt import tools_condition, ToolNode
-from langgraph.graph import START, StateGraph, MessagesState
-
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
-from langchain_core.messages import SystemMessage, HumanMessage
-from langchain_community.vectorstores import FAISS 
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_groq import ChatGroq
-from langchain.tools.retriever import create_retriever_tool
-
-"""LangGraph Agent"""
-import os
-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
-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,FAISS 
-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
-
-# ──────────────────────────────────────────────────────────
-# ENV
-# ──────────────────────────────────────────────────────────
-load_dotenv()
-# API Keys from .env file
-os.environ.setdefault("OPENAI_API_KEY", "<YOUR_OPENAI_KEY>")  # Set your own key or env var
-os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY", "default_key_or_placeholder")
-os.environ["MISTRAL_API_KEY"] = os.getenv("MISTRAL_API_KEY", "default_key_or_placeholder")
-
-# Tavily API Key
-TAVILY_API_KEY = os.getenv("TAVILY_API_KEY", "default_key_or_placeholder")
-_forbidden = ["nsfw", "porn", "sex", "explicit"]
-_playwright_available = True  # set False to disable Playwright
-
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")  # dim = 768
-
-@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 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}
-
-
-
-# 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
-
-INDEX_PATH = "faiss_index"
-if os.path.exists(INDEX_PATH):
-    vector_store = FAISS.load_local(INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
-else:
-    vector_store = FAISS.from_texts(["__init__"], embeddings)
-    vector_store.save_local(INDEX_PATH)
-
-create_retriever_tool = create_retriever_tool(
-    retriever=vector_store.as_retriever(),
-    name="Question Search",
-    description="A tool to retrieve similar questions from a local FAISS vector store."
-)
-
-
-
-tools = [
-    multiply,
-    add,
-    subtract,
-    divide,
-    modulus,
-    wiki_search,
-    web_search,
-    arvix_search,
-]
-
-# Build graph function
-def build_graph(provider: str = "groq"):
-    """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
-        try:
-            llm = ChatGroq(model="deepseek-r1-distill-llama-70b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
-        except Exception as e:
-            print(f"Error initializing Groq: {str(e)}")
-            raise
-    elif provider == "huggingface":
-        # TODO: Add huggingface endpoint
-        llm = ChatHuggingFace(
-            llm=HuggingFaceEndpoint(
-                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
-                temperature=0,
-            ),
-        )
-    else:
-        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
-    
-    # Bind tools to LLM
-    llm_with_tools = llm.bind_tools(tools)
-
-    # Node
-    def assistant(state: MessagesState):
-        """Assistant node"""
-        try:
-            if not state["messages"] or not state["messages"][-1].content:
-                raise ValueError("Empty message content")
-            return {"messages": [llm_with_tools.invoke(state["messages"])]}
-        except Exception as e:
-            print(f"Error in assistant node: {str(e)}")
-            raise
-    
-    def retriever(state: MessagesState):
-        """Retriever node"""
-        try:
-            if not state["messages"] or not state["messages"][0].content:
-                raise ValueError("Empty message content")
-            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}",
-            )
-            return {"messages": [sys_msg] + state["messages"] + [example_msg]}
-        except Exception as e:
-            print(f"Error in retriever node: {str(e)}")
-            raise
-
-    builder = StateGraph(MessagesState)
-    builder.add_node("retriever", retriever)
-    builder.add_node("assistant", assistant)
-    builder.add_node("tools", ToolNode(tools))
-    builder.add_edge(START, "retriever")
-    builder.add_edge("retriever", "assistant")
-    builder.add_conditional_edges(
-        "assistant",
-        tools_condition,
-    )
-    builder.add_edge("tools", "assistant")
-
-    # Compile graph
-    return builder.compile()
-
-# 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="groq")
-    # Run the graph
-    messages = [HumanMessage(content=question)]
-    messages = graph.invoke({"messages": messages})
-    for m in messages["messages"]:
-        m.pretty_print()