Delete app.py

app.py

@@ -1,128 +0,0 @@
-"""LangGraph Agent (GROQ version without Supabase)"""
-import os
-from langgraph.graph import START, StateGraph, MessagesState
-from langgraph.prebuilt import tools_condition, ToolNode
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_groq import ChatGroq
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
-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_core.tools import tool
-
-@tool
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers."""
-    return a * b
-
-@tool
-def add(a: int, b: int) -> int:
-    """Add two numbers."""
-    return a + b
-
-@tool
-def subtract(a: int, b: int) -> int:
-    """Subtract second number from the first."""
-    return a - b
-
-@tool
-def divide(a: int, b: int) -> float:
-    """Divide two numbers."""
-    if b == 0:
-        raise ValueError("Cannot divide by zero.")
-    return a / b
-
-@tool
-def modulus(a: int, b: int) -> int:
-    """Get the modulus (remainder) of two numbers."""
-    return a % b
-
-@tool
-def wiki_search(query: str) -> dict:
-    """Search Wikipedia for a query and return up to 2 results."""
-    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) -> dict:
-    """Search Tavily for a query and return up to 3 results."""
-    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) -> dict:
-    """Search Arxiv and return up to 3 truncated results."""
-    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 system prompt
-with open("system_prompt.txt", "r", encoding="utf-8") as f:
-    system_prompt = f.read()
-
-sys_msg = SystemMessage(content=system_prompt)
-
-tools = [
-    multiply, add, subtract, divide, modulus,
-    wiki_search, web_search, arvix_search
-]
-
-def build_graph(provider: str = "groq"):
-    """Build the LangGraph agent using specified LLM provider."""
-
-    if provider == "google":
-        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
-
-    elif provider == "groq":
-        groq_api_key = os.getenv("GROQ_API_KEY")
-        if not groq_api_key:
-            raise ValueError("GROQ_API_KEY environment variable not set.")
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0, api_key=groq_api_key)
-
-    elif provider == "huggingface":
-        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'.")
-
-    llm_with_tools = llm.bind_tools(tools)
-
-    def assistant(state: MessagesState):
-        return {"messages": [llm_with_tools.invoke(state["messages"])]}
-
-    builder = StateGraph(MessagesState)
-    builder.add_node("assistant", assistant)
-    builder.add_node("tools", ToolNode(tools))
-    builder.add_edge(START, "assistant")
-    builder.add_conditional_edges("assistant", tools_condition)
-    builder.add_edge("tools", "assistant")
-
-    return builder.compile()
-
-# For testing purposes
-if __name__ == "__main__":
-    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
-    graph = build_graph(provider="groq")
-    messages = [HumanMessage(content=question)]
-    result = graph.invoke({"messages": messages})
-    for msg in result["messages"]:
-        print(msg.content)