Update agent.py

agent.py

@@ -1,479 +1,492 @@
-# """LangGraph Agent"""
-# 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.tools.tavily_search import TavilySearchResults
-# 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.tools.retriever import create_retriever_tool
-# from supabase.client import create_client
-# import re
-# import traceback
+"""LangGraph Agent"""
+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.tools.tavily_search import TavilySearchResults
+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.tools.retriever import create_retriever_tool
+from supabase.client import create_client
+import re
+import traceback
 
-# load_dotenv()
+load_dotenv()
 
-# # ------------------ Arithmetic Tools ------------------
+# ------------------ Arithmetic Tools ------------------
 
-# @tool
-# def multiply(a: int, b: int) -> str:
-#     """
-#     Multiply two integers and return the result as a string.
+@tool
+def multiply(a: int, b: int) -> str:
+    """
+    Multiply two integers and return the result as a string.
 
-#     Args:
-#         a (int): The first integer.
-#         b (int): The second integer.
+    Args:
+        a (int): The first integer.
+        b (int): The second integer.
 
-#     Returns:
-#         str: The product of a and b, as a string.
-#     """
-#     return str(a * b)
+    Returns:
+        str: The product of a and b, as a string.
+    """
+    return str(a * b)
 
 
-# @tool
-# def add(a: int, b: int) -> str:
-#     """
-#     Add two integers and return the result as a string.
+@tool
+def add(a: int, b: int) -> str:
+    """
+    Add two integers and return the result as a string.
 
-#     Args:
-#         a (int): The first integer.
-#         b (int): The second integer.
+    Args:
+        a (int): The first integer.
+        b (int): The second integer.
 
-#     Returns:
-#         str: The sum of a and b, as a string.
-#     """
-#     return str(a + b)
+    Returns:
+        str: The sum of a and b, as a string.
+    """
+    return str(a + b)
 
 
-# @tool
-# def subtract(a: int, b: int) -> str:
-#     """
-#     Subtract one integer from another and return the result as a string.
+@tool
+def subtract(a: int, b: int) -> str:
+    """
+    Subtract one integer from another and return the result as a string.
 
-#     Args:
-#         a (int): The minuend.
-#         b (int): The subtrahend.
+    Args:
+        a (int): The minuend.
+        b (int): The subtrahend.
 
-#     Returns:
-#         str: The difference (a - b), as a string.
-#     """
-#     return str(a - b)
+    Returns:
+        str: The difference (a - b), as a string.
+    """
+    return str(a - b)
 
 
-# @tool
-# def divide(a: int, b: int) -> str:
-#     """
-#     Divide one integer by another and return the result as a string.
+@tool
+def divide(a: int, b: int) -> str:
+    """
+    Divide one integer by another and return the result as a string.
 
-#     Args:
-#         a (int): The numerator.
-#         b (int): The denominator. Must not be zero.
+    Args:
+        a (int): The numerator.
+        b (int): The denominator. Must not be zero.
 
-#     Returns:
-#         str: The result of the division (a / b), as a string. Returns an error message if b is zero.
-#     """
-#     if b == 0:
-#         return "Error: Cannot divide by zero."
-#     return str(a / b)
+    Returns:
+        str: The result of the division (a / b), as a string. Returns an error message if b is zero.
+    """
+    if b == 0:
+        return "Error: Cannot divide by zero."
+    return str(a / b)
 
 
-# @tool
-# def modulus(a: int, b: int) -> str:
-#     """
-#     Compute the modulus (remainder) of two integers and return the result as a string.
+@tool
+def modulus(a: int, b: int) -> str:
+    """
+    Compute the modulus (remainder) of two integers and return the result as a string.
 
-#     Args:
-#         a (int): The numerator.
-#         b (int): The denominator.
+    Args:
+        a (int): The numerator.
+        b (int): The denominator.
 
-#     Returns:
-#         str: The remainder when a is divided by b, as a string.
-#     """
-#     return str(a % b)
+    Returns:
+        str: The remainder when a is divided by b, as a string.
+    """
+    return str(a % b)
 
 
-# # ------------------ Retrieval Tools ------------------
+# ------------------ Retrieval Tools ------------------
 
-# @tool
-# def wiki_search(query: str) -> str:
-#     """
-#     Search Wikipedia for a given query and return text from up to two matching articles.
+@tool
+def wiki_search(query: str) -> str:
+    """
+    Search Wikipedia for a given query and return text from up to two matching articles.
 
-#     Args:
-#         query (str): A string query to search on Wikipedia.
+    Args:
+        query (str): A string query to search on Wikipedia.
 
-#     Returns:
-#         str: Combined content from up to two relevant articles, separated by dividers.
-#     """
-#     docs = WikipediaLoader(query=query, load_max_docs=2).load()
-#     return "\n\n---\n\n".join(doc.page_content for doc in docs)
+    Returns:
+        str: Combined content from up to two relevant articles, separated by dividers.
+    """
+    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:
-#     """
-#     Perform a web search using Tavily and return content from the top three results.
+@tool
+def web_search(query: str) -> str:
+    """
+    Perform a web search using Tavily and return content from the top three results.
 
-#     Args:
-#         query (str): A string representing the web search topic.
+    Args:
+        query (str): A string representing the web search topic.
 
-#     Returns:
-#         str: Combined content from up to three top results, separated by dividers.
-#     """
-#     docs = TavilySearchResults(max_results=3).invoke(query)
-#     return "\n\n---\n\n".join(doc.page_content for doc in docs)
+    Returns:
+        str: Combined content from up to three top results, separated by dividers.
+    """
+    docs = TavilySearchResults(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 related to the query and return excerpts.
+@tool
+def arvix_search(query: str) -> str:
+    """
+    Search arXiv for academic papers related to the query and return excerpts.
 
-#     Args:
-#         query (str): The search query string.
+    Args:
+        query (str): The search query string.
 
-#     Returns:
-#         str: Excerpts (up to 1000 characters each) from up to three relevant arXiv papers, separated by dividers.
-#     """
-#     docs = ArxivLoader(query=query, load_max_docs=3).load()
-#     return "\n\n---\n\n".join(doc.page_content[:1000] for doc in docs)
+    Returns:
+        str: Excerpts (up to 1000 characters each) from up to three relevant arXiv papers, separated by dividers.
+    """
+    docs = ArxivLoader(query=query, load_max_docs=3).load()
+    return "\n\n---\n\n".join(doc.page_content[:1000] for doc in docs)
 
 
 
-# # ------------------ System Prompt ------------------
-# with open("system_prompt.txt", "r", encoding="utf-8") as f:
-#     system_prompt = f.read().strip()
+# ------------------ System Prompt ------------------
+with open("system_prompt.txt", "r", encoding="utf-8") as f:
+    system_prompt = f.read().strip()
 
-# # ------------------ Supabase Setup ------------------
-# url = os.environ["SUPABASE_URL"].strip()
-# key = os.environ["SUPABASE_SERVICE_KEY"].strip()
-# client = create_client(url, key)
+# ------------------ Supabase Setup ------------------
+url = os.environ["SUPABASE_URL"].strip()
+key = os.environ["SUPABASE_SERVICE_KEY"].strip()
+client = create_client(url, key)
 
-# embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
 
-# # Embed improved QA docs
-# qa_examples = [
-#     {"content": "Q: What is the capital of Vietnam?\nA: FINAL ANSWER: Hanoi"},
-#     {"content": "Q: Alphabetize: lettuce, broccoli, basil\nA: FINAL ANSWER: basil,broccoli,lettuce"},
-#     {"content": "Q: What is 42 multiplied by 8?\nA: FINAL ANSWER: three hundred thirty six"},
-# ]
-# vector_store = SupabaseVectorStore(
-#     client=client,
-#     embedding=embeddings,
-#     table_name="documents",
-#     query_name="match_documents_langchain"
-# )
-# vector_store.add_texts([doc["content"] for doc in qa_examples])
-# print("✅ QA documents embedded into Supabase.")
+# Embed improved QA docs
+qa_examples = [
+    {"content": "Q: What is the capital of Vietnam?\nA: FINAL ANSWER: Hanoi"},
+    {"content": "Q: Alphabetize: lettuce, broccoli, basil\nA: FINAL ANSWER: basil,broccoli,lettuce"},
+    {"content": "Q: What is 42 multiplied by 8?\nA: FINAL ANSWER: three hundred thirty six"},
+]
+vector_store = SupabaseVectorStore(
+    client=client,
+    embedding=embeddings,
+    table_name="documents",
+    query_name="match_documents_langchain"
+)
+vector_store.add_texts([doc["content"] for doc in qa_examples])
+print("✅ QA documents embedded into Supabase.")
 
-# retriever_tool = create_retriever_tool(
-#     retriever=vector_store.as_retriever(),
-#     name="Question Search",
-#     description="Retrieve similar questions from vector DB."
-# )
+retriever_tool = create_retriever_tool(
+    retriever=vector_store.as_retriever(),
+    name="Question Search",
+    description="Retrieve similar questions from vector DB."
+)
 
-# tools = [multiply, add, subtract, divide, modulus, wiki_search, web_search, arvix_search]
+tools = [multiply, add, subtract, divide, modulus, wiki_search, web_search, arvix_search]
 
-# # ------------------ Build Agent Graph ------------------
-# class VerboseToolNode(ToolNode):
-#     def invoke(self, state):
-#         print("🔧 ToolNode evaluating:", [m.content for m in state["messages"]])
-#         return super().invoke(state)
+# ------------------ Build Agent Graph ------------------
+class VerboseToolNode(ToolNode):
+    def invoke(self, state):
+        print("🔧 ToolNode evaluating:", [m.content for m in state["messages"]])
+        return super().invoke(state)
 
-# def build_graph(provider: str = "groq"):
-#     if provider == "google":
-#         llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0.3)
-#     elif provider == "groq":
-#         llm = ChatGroq(model="qwen-qwq-32b", temperature=0.3)
-#     elif provider == "huggingface":
-#         llm = ChatHuggingFace(
-#             llm=HuggingFaceEndpoint(
-#                 url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
-#                 temperature=0.3
-#             )
-#         )
-#     else:
-#         raise ValueError("Invalid provider.")
+def build_graph(provider: str = "groq"):
+    if provider == "google":
+        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0.3)
+    elif provider == "groq":
+        llm = ChatGroq(model="qwen-qwq-32b", temperature=0.3)
+    elif provider == "huggingface":
+        llm = ChatHuggingFace(
+            llm=HuggingFaceEndpoint(
+                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
+                temperature=0.3
+            )
+        )
+    else:
+        raise ValueError("Invalid provider.")
 
-#     llm_with_tools = llm.bind_tools(tools)
+    llm_with_tools = llm.bind_tools(tools)
 
-#     def retriever(state: MessagesState):
-#         query = state["messages"][0].content
-#         similar = vector_store.similarity_search_with_score(query)
-#         threshold = 0.7
-#         examples = [
-#             HumanMessage(content=f"Similar QA:\n{doc.page_content}")
-#             for doc, score in similar if score >= threshold
-#         ]
-#         return {"messages": state["messages"] + examples}
+    def retriever(state: MessagesState):
+        query = state["messages"][0].content
+        similar = vector_store.similarity_search_with_score(query)
+        threshold = 0.7
+        examples = [
+            HumanMessage(content=f"Similar QA:\n{doc.page_content}")
+            for doc, score in similar if score >= threshold
+        ]
+        return {"messages": state["messages"] + examples}
 
     
 
-#     def assistant(state: MessagesState):
-#         try:
-#             messages = [SystemMessage(content=system_prompt.strip())] + state["messages"]
-#             result = llm_with_tools.invoke(messages)
+    def assistant(state: MessagesState):
+        try:
+            messages = [SystemMessage(content=system_prompt.strip())] + state["messages"]
+            result = llm_with_tools.invoke(messages)
 
-#             # Handle different return types gracefully
-#             if hasattr(result, "content"):
-#                 raw_output = result.content.strip()
-#             elif isinstance(result, dict) and "content" in result:
-#                 raw_output = result["content"].strip()
-#             else:
-#                 raise ValueError(f"Unexpected result format: {repr(result)}")
+            # Handle different return types gracefully
+            if hasattr(result, "content"):
+                raw_output = result.content.strip()
+            elif isinstance(result, dict) and "content" in result:
+                raw_output = result["content"].strip()
+            else:
+                raise ValueError(f"Unexpected result format: {repr(result)}")
 
-#             print("🤖 Raw LLM output:", repr(raw_output))
+            print("🤖 Raw LLM output:", repr(raw_output))
 
-#             match = re.search(r"FINAL ANSWER:\s*(.+)", raw_output, re.IGNORECASE)
-#             if match:
-#                 final_output = f"FINAL ANSWER: {match.group(1).strip()}"
-#             else:
-#                 print("⚠️ 'FINAL ANSWER:' not found. Raw content will be used as fallback.")
-#                 final_output = f"FINAL ANSWER: {raw_output or 'Unable to determine answer'}"
+            match = re.search(r"FINAL ANSWER:\s*(.+)", raw_output, re.IGNORECASE)
+            if match:
+                final_output = f"FINAL ANSWER: {match.group(1).strip()}"
+            else:
+                print("⚠️ 'FINAL ANSWER:' not found. Raw content will be used as fallback.")
+                final_output = f"FINAL ANSWER: {raw_output or 'Unable to determine answer'}"
 
-#             return {"messages": [HumanMessage(content=final_output)]}
+            return {"messages": [HumanMessage(content=final_output)]}
 
-#         except Exception as e:
-#             print(f"🔥 Exception: {e}")
-#             traceback.print_exc()
-#             return {"messages": [HumanMessage(content=f"FINAL ANSWER: AGENT ERROR: {type(e).__name__}: {e}")]}
+        except Exception as e:
+            print(f"🔥 Exception: {e}")
+            traceback.print_exc()
+            return {"messages": [HumanMessage(content=f"FINAL ANSWER: AGENT ERROR: {type(e).__name__}: {e}")]}
 
 
-#     builder = StateGraph(MessagesState)
-#     builder.add_node("retriever", retriever)
-#     builder.add_node("assistant", assistant)
-#     builder.add_node("tools", VerboseToolNode(tools))
-#     builder.add_edge(START, "retriever")
-#     builder.add_edge("retriever", "assistant")
-#     builder.add_conditional_edges("assistant", tools_condition)
-#     builder.add_edge("tools", "assistant")
+    builder = StateGraph(MessagesState)
+    builder.add_node("retriever", retriever)
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", VerboseToolNode(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()
+    return builder.compile()
 
-# # ------------------ Local Test Harness ------------------
-# if __name__ == "__main__":
-#     graph = build_graph(provider="groq")
-#     question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
-#     messages = [HumanMessage(content=question)]
-#     result = graph.invoke({"messages": messages})
-#     print(result["messages"][-1].content)
+# ------------------ Local Test Harness ------------------
+if __name__ == "__main__":
+    graph = build_graph(provider="groq")
+    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
+    messages = [HumanMessage(content=question)]
+    result = graph.invoke({"messages": messages})
+    print(result["messages"][-1].content)
 
-"""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
-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
 
-load_dotenv()
 
-@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.
+
+
+
+
+
+
+
+
+
+
+# """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
+# 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
+
+# load_dotenv()
+
+# @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
+#     Args:
+#         a: first int
+#         b: second int
+#     """
+#     return a + b
 
-@tool
-def subtract(a: int, b: int) -> int:
-    """Subtract two numbers.
+# @tool
+# def subtract(a: int, b: int) -> int:
+#     """Subtract two numbers.
     
-    Args:
-        a: first int
-        b: second int
-    """
-    return a - b
+#     Args:
+#         a: first int
+#         b: second int
+#     """
+#     return a - b
 
-@tool
-def divide(a: int, b: int) -> int:
-    """Divide two numbers.
+# @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
+#     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.
+# @tool
+# def modulus(a: int, b: int) -> int:
+#     """Get the modulus of two numbers.
     
-    Args:
-        a: first int
-        b: second int
-    """
-    return a % b
+#     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.
+# @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}
+#     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.
+# @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}
+#     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.
+# @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}
+#     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()
+# # 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)
+# # 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.",
-)
+# # 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,
-]
+# 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
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
-    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)
+# # 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
+#         llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
+#     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"""
-        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+#     # Node
+#     def assistant(state: MessagesState):
+#         """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}",
-        )
-        return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+#     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}",
+#         )
+#         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, "retriever")
-    builder.add_edge("retriever", "assistant")
-    builder.add_conditional_edges(
-        "assistant",
-        tools_condition,
-    )
-    builder.add_edge("tools", "assistant")
+#     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()
+#     # 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()
+# # 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()