Delete model.py

model.py

@@ -1,141 +0,0 @@
-# ============================
-# model.py
-# ============================
-
-import os
-from dotenv import load_dotenv
-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, HuggingFaceEmbeddings
-from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
-from langchain_community.vectorstores import SupabaseVectorStore
-from langchain_core.messages import SystemMessage, HumanMessage
-from langchain_core.tools import tool
-from langchain_tavily import TavilySearch
-from langchain.tools.retriever import create_retriever_tool
-from supabase.client import Client, create_client
-
-load_dotenv()
-
-# Setup Supabase
-url = os.getenv("SUPABASE_URL")
-key = os.getenv("SUPABASE_KEY")
-supabase: Client = create_client(url, key)
-
-# Tools
-@tool
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers and return the result."""
-    return a * b
-
-@tool
-def add(a: int, b: int) -> int:
-    """Add two numbers and return the result."""
-    return a + b
-
-@tool
-def subtract(a: int, b: int) -> int:
-    """Subtract second number from first and return the result."""
-    return a - b
-
-@tool
-def divide(a: int, b: int) -> float:
-    """Divide first number by second and return the result."""
-    if b == 0:
-        raise ValueError("Cannot divide by zero.")
-    return a / b
-
-@tool
-def modulus(a: int, b: int) -> int:
-    """Return the modulus (remainder) of two numbers."""
-    return a % b
-
-@tool
-def wiki_search(query: str) -> str:
-    """Search Wikipedia and return 2 results."""
-    docs = WikipediaLoader(query=query, load_max_docs=2).load()
-    return "\n\n---\n\n".join(doc.page_content for doc in docs)
-
-@tool
-def web_search(query: str) -> str:
-    """Search the web using Tavily and return 3 results."""
-    docs = TavilySearch(max_results=3).invoke(query)
-    return "\n\n---\n\n".join(doc.page_content for doc in docs)
-
-@tool
-def arvix_search(query: str) -> str:
-    """Search Arxiv for academic papers and return 3 results."""
-    docs = ArxivLoader(query=query, load_max_docs=3).load()
-    return "\n\n---\n\n".join(doc.page_content[:1000] for doc in docs)
-
-# Load system prompt
-with open("system_prompt.txt", "r") as f:
-    system_prompt = f.read()
-
-sys_msg = SystemMessage(content=system_prompt)
-
-# Vector search setup
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
-vector_store = SupabaseVectorStore(
-    client=supabase,
-    embedding=embeddings,
-    table_name="documents",
-    query_name="match_documents_langchain",
-)
-
-retriever_tool = create_retriever_tool(
-    retriever=vector_store.as_retriever(),
-    name="Question Search",
-    description="Retrieve similar questions from vector DB.",
-)
-
-# Tools list
-tools = [
-    multiply, add, subtract, divide, modulus,
-    wiki_search, web_search, arvix_search,
-    retriever_tool,
-]
-
-# Build LangGraph
-
-def build_graph(provider: str = "groq"):
-    if provider == "google":
-        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
-    elif provider == "groq":
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0, api_key=os.getenv("GROQ_API"))
-    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")
-
-    llm_with_tools = llm.bind_tools(tools)
-
-    def assistant(state: MessagesState):
-        return {"messages": [llm_with_tools.invoke(state["messages"])]}
-
-    def retriever(state: MessagesState):
-        docs = vector_store.similarity_search(state["messages"][0].content)
-        if not docs:
-            return {"messages": [sys_msg] + state["messages"]}
-        similar_msg = HumanMessage(content=f"Reference: {docs[0].page_content}")
-        return {"messages": [sys_msg] + state["messages"] + [similar_msg]}
-
-    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")
-
-    return builder.compile()
-
-
-# ============================
-# Save this as model.py and let me know when you want full app.py regenerated to match
-# ============================