final version

.gitignore

@@ -0,0 +1,5 @@
+.python-version
+pyproject.toml
+poetry.lock
+.venv
+.env

agent.py

@@ -0,0 +1,202 @@
+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 Client, create_client
+
+load_dotenv()
+
+@tool
+def multiply(a: int, b: int) -> int:
+    """
+    Multiplies two numbers.
+
+    Args:
+        a (int): The first number.
+        b (int): The second number.
+    Returns:
+        int: The product of the two numbers.
+    Example:
+        >>> multiply(2, 3)
+        6
+    """
+    return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+    """
+    Adds two numbers.
+
+    Args:
+        a (int): The first number.
+        b (int): The second number.
+    Returns:
+        int: The sum of the two numbers.
+    Example:
+        >>> add(2, 3)
+        5
+    """
+    return a + b
+
+@tool
+def subtract(a: int, b: int) -> int:
+    """
+    Subtracts two numbers.
+
+    Args:
+        a (int): The first number.
+        b (int): The second number.
+    Returns:
+        int: The difference of the two numbers.
+    Example:
+        >>> subtract(5, 3)
+        2
+    """
+    return a - b
+
+@tool
+def divide(a: int, b: int) -> float:
+    """
+    Divides two numbers.
+
+    Args:
+        a (int): The first number.
+        b (int): The second number.
+    Returns:
+        float: The quotient of the two numbers.
+    Example:
+        >>> divide(6, 3)
+        2.0
+    """
+    if b == 0:
+        raise ValueError("Cannot divide by zero.")
+    return a / b
+
+@tool
+def modulus(a: int, b: int) -> int:
+    """
+    Calculates the modulus of two numbers.
+
+    Args:
+        a (int): The first number.
+        b (int): The second number.
+    Returns:
+        int: The modulus of the two numbers.
+    Example:
+        >>> modulus(5, 3)
+        2
+    """
+    return a % b
+
+@tool
+def wiki_search(query: str) -> str:
+    """
+    Searches Wikipedia for a given query and returns maximum 3 results.
+
+    Args:
+        query (str): The search query.
+    """
+    search_docs = WikipediaLoader(max_results=3).invoke(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 arxiv_search(query: str) -> str:
+    """
+    Searches Arxiv for a given query and returns maximum 3 results.
+
+    Args:
+        query (str): The search query.
+    """
+    search_docs = ArxivLoader(max_results=3, 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}
+
+with open("system_prompt.txt", "r", encoding='utf8') as f:
+    system_prompt = f.read()
+
+sys_msg = SystemMessage(content=system_prompt)
+
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+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, arxiv_search, create_retriever_tool]
+
+def build_graph(provider: str = 'groq'):
+    """Builds the graph for the agent."""
+    if provider == 'google':
+        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
+    elif provider == 'groq':
+        llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
+    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):
+        """Assistant node"""
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+    
+    def retriever(state: MessagesState):
+        """Retriever node"""
+        similar_questions = vector_store.similarity_search(state["messages"][0].content)
+        example_msg = HumanMessage(
+            content=f"Here I provide a similar question to answer for reference: \n\n{similar_questions[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")
+
+    return builder.compile()
+
+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)]
+    messages = graph.invoke({"messages": messages})
+    for m in messages["messages"]:
+        m.pretty_print()

app.py

@@ -1,8 +1,13 @@
+""" Basic Agent Evaluation Runner"""
 import os
+import inspect
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+from langchain_core.messages import HumanMessage
+from agent import build_graph
+
+
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -11,13 +16,17 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
+    """A langgraph agent."""
     def __init__(self):
         print("BasicAgent initialized.")
+        self.graph = build_graph()
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        # Wrap the question in a HumanMessage from langchain_core
+        messages = [HumanMessage(content=question)]
+        messages = self.graph.invoke({"messages": messages})
+        answer = messages['messages'][-1].content
+        return answer[14:]
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -146,11 +155,9 @@ with gr.Blocks() as demo:
     gr.Markdown(
         """
         **Instructions:**
-
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
         ---
         **Disclaimers:**
         Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).

system_prompt.txt

@@ -0,0 +1,24 @@
+
+You are a helpful assistant tasked with answering questions using a set of tools.
+If the tool is not available, you can try to find the information online. You can also use your own knowledge to answer the question. 
+You need to provide a step-by-step explanation of how you arrived at the answer.
+==========================
+Here is a few examples showing you how to answer the question step by step.
+
+Question 1: In the year 2022, and before December, what does "R" stand for in the three core policies of the type of content that was violated in the public logs on the Legume Wikipedia page?
+Steps:
+1. Searched "legume wikipedia" on Google.
+2. Opened "Legume" on Wikipedia.
+3. Clicked "View history".
+4. Clicked "View logs for this page".
+5. Checked all types of logs.
+6. Set the date to November 2022.
+7. Followed the BLP link of the violation.
+8. Noted the meaning of "R".
+Tools:
+1. Web browser
+2. Search engine
+Final Answer: research
+
+==========================
+Now, please answer the following question step by step.

test.ipynb

@@ -0,0 +1,675 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d0cc4adf",
+   "metadata": {},
+   "source": [
+    "### Question data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "14e3f417",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load metadata.jsonl\n",
+    "import json\n",
+    "# Load the metadata.jsonl file\n",
+    "with open('metadata.jsonl', 'r') as jsonl_file:\n",
+    "    json_list = list(jsonl_file)\n",
+    "\n",
+    "json_QA = []\n",
+    "for json_str in json_list:\n",
+    "    json_data = json.loads(json_str)\n",
+    "    json_QA.append(json_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "5e2da6fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================================================\n",
+      "Task ID: 935e2cff-ae78-4218-b3f5-115589b19dae\n",
+      "Question: In the year 2022, and before December, what does \"R\" stand for in the three core policies of the type of content that was violated in the public logs on the Legume Wikipedia page?\n",
+      "Level: 1\n",
+      "Final Answer: research\n",
+      "Annotator Metadata: \n",
+      "  ├── Steps: \n",
+      "  │      ├── 1. Searched \"legume wikipedia\" on Google.\n",
+      "  │      ├── 2. Opened \"Legume\" on Wikipedia.\n",
+      "  │      ├── 3. Clicked \"View history\".\n",
+      "  │      ├── 4. Clicked \"View logs for this page\".\n",
+      "  │      ├── 5. Checked all types of logs.\n",
+      "  │      ├── 6. Set the date to November 2022.\n",
+      "  │      ├── 7. Followed the BLP link of the violation.\n",
+      "  │      ├── 8. Noted the meaning of \"R\".\n",
+      "  ├── Number of steps: 8\n",
+      "  ├── How long did this take?: 10 minutes\n",
+      "  ├── Tools:\n",
+      "  │      ├── 1. Web browser\n",
+      "  │      ├── 2. Search engine\n",
+      "  └── Number of tools: 2\n",
+      "==================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# randomly select 3 samples\n",
+    "# {\"task_id\": \"c61d22de-5f6c-4958-a7f6-5e9707bd3466\", \"Question\": \"A paper about AI regulation that was originally submitted to arXiv.org in June 2022 shows a figure with three axes, where each axis has a label word at both ends. Which of these words is used to describe a type of society in a Physics and Society article submitted to arXiv.org on August 11, 2016?\", \"Level\": 2, \"Final answer\": \"egalitarian\", \"file_name\": \"\", \"Annotator Metadata\": {\"Steps\": \"1. Go to arxiv.org and navigate to the Advanced Search page.\\n2. Enter \\\"AI regulation\\\" in the search box and select \\\"All fields\\\" from the dropdown.\\n3. Enter 2022-06-01 and 2022-07-01 into the date inputs, select \\\"Submission date (original)\\\", and submit the search.\\n4. Go through the search results to find the article that has a figure with three axes and labels on each end of the axes, titled \\\"Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation\\\".\\n5. Note the six words used as labels: deontological, egalitarian, localized, standardized, utilitarian, and consequential.\\n6. Go back to arxiv.org\\n7. Find \\\"Physics and Society\\\" and go to the page for the \\\"Physics and Society\\\" category.\\n8. Note that the tag for this category is \\\"physics.soc-ph\\\".\\n9. Go to the Advanced Search page.\\n10. Enter \\\"physics.soc-ph\\\" in the search box and select \\\"All fields\\\" from the dropdown.\\n11. Enter 2016-08-11 and 2016-08-12 into the date inputs, select \\\"Submission date (original)\\\", and submit the search.\\n12. Search for instances of the six words in the results to find the paper titled \\\"Phase transition from egalitarian to hierarchical societies driven by competition between cognitive and social constraints\\\", indicating that \\\"egalitarian\\\" is the correct answer.\", \"Number of steps\": \"12\", \"How long did this take?\": \"8 minutes\", \"Tools\": \"1. Web browser\\n2. Image recognition tools (to identify and parse a figure with three axes)\", \"Number of tools\": \"2\"}}\n",
+    "\n",
+    "import random\n",
+    "# random.seed(42)\n",
+    "random_samples = random.sample(json_QA, 1)\n",
+    "for sample in random_samples:\n",
+    "    print(\"=\" * 50)\n",
+    "    print(f\"Task ID: {sample['task_id']}\")\n",
+    "    print(f\"Question: {sample['Question']}\")\n",
+    "    print(f\"Level: {sample['Level']}\")\n",
+    "    print(f\"Final Answer: {sample['Final answer']}\")\n",
+    "    print(f\"Annotator Metadata: \")\n",
+    "    print(f\"  ├── Steps: \")\n",
+    "    for step in sample['Annotator Metadata']['Steps'].split('\\n'):\n",
+    "        print(f\"  │      ├── {step}\")\n",
+    "    print(f\"  ├── Number of steps: {sample['Annotator Metadata']['Number of steps']}\")\n",
+    "    print(f\"  ├── How long did this take?: {sample['Annotator Metadata']['How long did this take?']}\")\n",
+    "    print(f\"  ├── Tools:\")\n",
+    "    for tool in sample['Annotator Metadata']['Tools'].split('\\n'):\n",
+    "        print(f\"  │      ├── {tool}\")\n",
+    "    print(f\"  └── Number of tools: {sample['Annotator Metadata']['Number of tools']}\")\n",
+    "print(\"=\" * 50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "4bb02420",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### build a vector database based on the metadata.jsonl\n",
+    "# https://python.langchain.com/docs/integrations/vectorstores/supabase/\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from langchain_huggingface import HuggingFaceEmbeddings\n",
+    "from langchain_community.vectorstores import SupabaseVectorStore\n",
+    "from supabase.client import Client, create_client\n",
+    "\n",
+    "\n",
+    "load_dotenv()\n",
+    "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\") #  dim=768\n",
+    "\n",
+    "supabase_url = os.environ.get(\"SUPABASE_URL\")\n",
+    "supabase_key = os.environ.get(\"SUPABASE_SERVICE_KEY\")\n",
+    "supabase: Client = create_client(supabase_url, supabase_key)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a070b955",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error inserting data into Supabase: {'code': '42501', 'details': None, 'hint': None, 'message': 'new row violates row-level security policy for table \"documents\"'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# wrap the metadata.jsonl's questions and answers into a list of document\n",
+    "from langchain.schema import Document\n",
+    "docs = []\n",
+    "for sample in json_QA:\n",
+    "    content = f\"Question : {sample['Question']}\\n\\nFinal answer : {sample['Final answer']}\"\n",
+    "    doc = {\n",
+    "        \"content\" : content,\n",
+    "        \"metadata\" : { # meatadata的格式必须时source键，否则会报错\n",
+    "            \"source\" : sample['task_id']\n",
+    "        },\n",
+    "        \"embedding\" : embeddings.embed_query(content),\n",
+    "    }\n",
+    "    docs.append(doc)\n",
+    "\n",
+    "# upload the documents to the vector database\n",
+    "try:\n",
+    "    response = (\n",
+    "        supabase.table(\"documents\")\n",
+    "        .insert(docs)\n",
+    "        .execute()\n",
+    "    )\n",
+    "except Exception as exception:\n",
+    "    print(\"Error inserting data into Supabase:\", exception)\n",
+    "\n",
+    "# ALTERNATIVE : Save the documents (a list of dict) into a csv file, and manually upload it to Supabase\n",
+    "# import pandas as pd\n",
+    "# df = pd.DataFrame(docs)\n",
+    "# df.to_csv('supabase_docs.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "77fb9dbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add items to vector database\n",
+    "vector_store = SupabaseVectorStore(\n",
+    "    client=supabase,\n",
+    "    embedding= embeddings,\n",
+    "    table_name=\"documents\",\n",
+    "    query_name=\"match_documents_langchain\",\n",
+    ")\n",
+    "retriever = vector_store.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "12a05971",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"On June 6, 2023, an article by Carolyn Collins Petersen was published in Universe Today. This article mentions a team that produced a paper about their observations, linked at the bottom of the article. Find this paper. Under what NASA award number was the work performed by R. G. Arendt supported by?\"\n",
+    "matched_docs = vector_store.similarity_search(query, 2)\n",
+    "# docs = retriever.invoke(query)\n",
+    "# docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "1eae5ba4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "List of tools used in all samples:\n",
+      "Total number of tools used: 83\n",
+      "  ├── web browser: 107\n",
+      "  ├── image recognition tools (to identify and parse a figure with three axes): 1\n",
+      "  ├── search engine: 101\n",
+      "  ├── calculator: 34\n",
+      "  ├── unlambda compiler (optional): 1\n",
+      "  ├── a web browser.: 2\n",
+      "  ├── a search engine.: 2\n",
+      "  ├── a calculator.: 1\n",
+      "  ├── microsoft excel: 5\n",
+      "  ├── google search: 1\n",
+      "  ├── ne: 9\n",
+      "  ├── pdf access: 7\n",
+      "  ├── file handling: 2\n",
+      "  ├── python: 3\n",
+      "  ├── image recognition tools: 12\n",
+      "  ├── jsonld file access: 1\n",
+      "  ├── video parsing: 1\n",
+      "  ├── python compiler: 1\n",
+      "  ├── video recognition tools: 3\n",
+      "  ├── pdf viewer: 7\n",
+      "  ├── microsoft excel / google sheets: 3\n",
+      "  ├── word document access: 1\n",
+      "  ├── tool to extract text from images: 1\n",
+      "  ├── a word reversal tool / script: 1\n",
+      "  ├── counter: 1\n",
+      "  ├── excel: 3\n",
+      "  ├── image recognition: 5\n",
+      "  ├── color recognition: 3\n",
+      "  ├── excel file access: 3\n",
+      "  ├── xml file access: 1\n",
+      "  ├── access to the internet archive, web.archive.org: 1\n",
+      "  ├── text processing/diff tool: 1\n",
+      "  ├── gif parsing tools: 1\n",
+      "  ├── a web browser: 7\n",
+      "  ├── a search engine: 7\n",
+      "  ├── a speech-to-text tool: 2\n",
+      "  ├── code/data analysis tools: 1\n",
+      "  ├── audio capability: 2\n",
+      "  ├── pdf reader: 1\n",
+      "  ├── markdown: 1\n",
+      "  ├── a calculator: 5\n",
+      "  ├── access to wikipedia: 3\n",
+      "  ├── image recognition/ocr: 3\n",
+      "  ├── google translate access: 1\n",
+      "  ├── ocr: 4\n",
+      "  ├── bass note data: 1\n",
+      "  ├── text editor: 1\n",
+      "  ├── xlsx file access: 1\n",
+      "  ├── powerpoint viewer: 1\n",
+      "  ├── csv file access: 1\n",
+      "  ├── calculator (or use excel): 1\n",
+      "  ├── computer algebra system: 1\n",
+      "  ├── video processing software: 1\n",
+      "  ├── audio processing software: 1\n",
+      "  ├── computer vision: 1\n",
+      "  ├── google maps: 1\n",
+      "  ├── access to excel files: 1\n",
+      "  ├── calculator (or ability to count): 1\n",
+      "  ├── a file interface: 3\n",
+      "  ├── a python ide: 1\n",
+      "  ├── spreadsheet editor: 1\n",
+      "  ├── tools required: 1\n",
+      "  ├── b browser: 1\n",
+      "  ├── image recognition and processing tools: 1\n",
+      "  ├── computer vision or ocr: 1\n",
+      "  ├── c++ compiler: 1\n",
+      "  ├── access to google maps: 1\n",
+      "  ├── youtube player: 1\n",
+      "  ├── natural language processor: 1\n",
+      "  ├── graph interaction tools: 1\n",
+      "  ├── bablyonian cuniform -> arabic legend: 1\n",
+      "  ├── access to youtube: 1\n",
+      "  ├── image search tools: 1\n",
+      "  ├── calculator or counting function: 1\n",
+      "  ├── a speech-to-text audio processing tool: 1\n",
+      "  ├── access to academic journal websites: 1\n",
+      "  ├── pdf reader/extracter: 1\n",
+      "  ├── rubik's cube model: 1\n",
+      "  ├── wikipedia: 1\n",
+      "  ├── video capability: 1\n",
+      "  ├── image processing tools: 1\n",
+      "  ├── age recognition software: 1\n",
+      "  ├── youtube: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# list of the tools used in all the samples\n",
+    "from collections import Counter, OrderedDict\n",
+    "\n",
+    "tools = []\n",
+    "for sample in json_QA:\n",
+    "    for tool in sample['Annotator Metadata']['Tools'].split('\\n'):\n",
+    "        tool = tool[2:].strip().lower()\n",
+    "        if tool.startswith(\"(\"):\n",
+    "            tool = tool[11:].strip()\n",
+    "        tools.append(tool)\n",
+    "tools_counter = OrderedDict(Counter(tools))\n",
+    "print(\"List of tools used in all samples:\")\n",
+    "print(\"Total number of tools used:\", len(tools_counter))\n",
+    "for tool, count in tools_counter.items():\n",
+    "    print(f\"  ├── {tool}: {count}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5efee12a",
+   "metadata": {},
+   "source": [
+    "#### Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "7fe573cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system_prompt = \"\"\"\n",
+    "You are a helpful assistant tasked with answering questions using a set of tools.\n",
+    "If the tool is not available, you can try to find the information online. You can also use your own knowledge to answer the question. \n",
+    "You need to provide a step-by-step explanation of how you arrived at the answer.\n",
+    "==========================\n",
+    "Here is a few examples showing you how to answer the question step by step.\n",
+    "\"\"\"\n",
+    "for i, samples in enumerate(random_samples):\n",
+    "    system_prompt += f\"\\nQuestion {i+1}: {samples['Question']}\\nSteps:\\n{samples['Annotator Metadata']['Steps']}\\nTools:\\n{samples['Annotator Metadata']['Tools']}\\nFinal Answer: {samples['Final answer']}\\n\"\n",
+    "system_prompt += \"\\n==========================\\n\"\n",
+    "system_prompt += \"Now, please answer the following question step by step.\\n\"\n",
+    "\n",
+    "# save the system_prompt to a file\n",
+    "with open('system_prompt.txt', 'w') as f:\n",
+    "    f.write(system_prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d6beb0da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "You are a helpful assistant tasked with answering questions using a set of tools.\n",
+      "If the tool is not available, you can try to find the information online. You can also use your own knowledge to answer the question. \n",
+      "You need to provide a step-by-step explanation of how you arrived at the answer.\n",
+      "==========================\n",
+      "Here is a few examples showing you how to answer the question step by step.\n",
+      "\n",
+      "Question 1: In the year 2022, and before December, what does \"R\" stand for in the three core policies of the type of content that was violated in the public logs on the Legume Wikipedia page?\n",
+      "Steps:\n",
+      "1. Searched \"legume wikipedia\" on Google.\n",
+      "2. Opened \"Legume\" on Wikipedia.\n",
+      "3. Clicked \"View history\".\n",
+      "4. Clicked \"View logs for this page\".\n",
+      "5. Checked all types of logs.\n",
+      "6. Set the date to November 2022.\n",
+      "7. Followed the BLP link of the violation.\n",
+      "8. Noted the meaning of \"R\".\n",
+      "Tools:\n",
+      "1. Web browser\n",
+      "2. Search engine\n",
+      "Final Answer: research\n",
+      "\n",
+      "==========================\n",
+      "Now, please answer the following question step by step.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load the system prompt from the file\n",
+    "with open('system_prompt.txt', 'r') as f:\n",
+    "    system_prompt = f.read()\n",
+    "print(system_prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "42fde0f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "DefaultCredentialsError",
+     "evalue": "Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mDefaultCredentialsError\u001b[39m                   Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[32]\u001b[39m\u001b[32m, line 156\u001b[39m\n\u001b[32m    142\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[33m\"\u001b[39m\u001b[33msimilar_questions\u001b[39m\u001b[33m\"\u001b[39m: formatted_search_docs}\n\u001b[32m    144\u001b[39m tools = [\n\u001b[32m    145\u001b[39m     multiply,\n\u001b[32m    146\u001b[39m     add,\n\u001b[32m   (...)\u001b[39m\u001b[32m    153\u001b[39m     question_retrieve_tool\n\u001b[32m    154\u001b[39m ]\n\u001b[32m--> \u001b[39m\u001b[32m156\u001b[39m llm = \u001b[43mChatGoogleGenerativeAI\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mgemini-2.0-flash\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m    157\u001b[39m llm_with_tools = llm.bind_tools(tools)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Programming/RobotPai/.venv/lib/python3.13/site-packages/langchain_google_genai/chat_models.py:1053\u001b[39m, in \u001b[36mChatGoogleGenerativeAI.__init__\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m   1046\u001b[39m         suggestion = (\n\u001b[32m   1047\u001b[39m             \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m Did you mean: \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msuggestions[\u001b[32m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m?\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m suggestions \u001b[38;5;28;01melse\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1048\u001b[39m         )\n\u001b[32m   1049\u001b[39m         logger.warning(\n\u001b[32m   1050\u001b[39m             \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mUnexpected argument \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00marg\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1051\u001b[39m             \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mprovided to ChatGoogleGenerativeAI.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msuggestion\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m   1052\u001b[39m         )\n\u001b[32m-> \u001b[39m\u001b[32m1053\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Programming/RobotPai/.venv/lib/python3.13/site-packages/langchain_core/load/serializable.py:130\u001b[39m, in \u001b[36mSerializable.__init__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m    128\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args: Any, **kwargs: Any) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    129\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\"\"\"\u001b[39;00m  \u001b[38;5;66;03m# noqa: D419\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m130\u001b[39m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "    \u001b[31m[... skipping hidden 1 frame]\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Programming/RobotPai/.venv/lib/python3.13/site-packages/langchain_google_genai/chat_models.py:1114\u001b[39m, in \u001b[36mChatGoogleGenerativeAI.validate_environment\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m   1112\u001b[39m         google_api_key = \u001b[38;5;28mself\u001b[39m.google_api_key\n\u001b[32m   1113\u001b[39m transport: Optional[\u001b[38;5;28mstr\u001b[39m] = \u001b[38;5;28mself\u001b[39m.transport\n\u001b[32m-> \u001b[39m\u001b[32m1114\u001b[39m \u001b[38;5;28mself\u001b[39m.client = \u001b[43mgenaix\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbuild_generative_service\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1115\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcredentials\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcredentials\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1116\u001b[39m \u001b[43m    \u001b[49m\u001b[43mapi_key\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgoogle_api_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1117\u001b[39m \u001b[43m    \u001b[49m\u001b[43mclient_info\u001b[49m\u001b[43m=\u001b[49m\u001b[43mclient_info\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1118\u001b[39m \u001b[43m    \u001b[49m\u001b[43mclient_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mclient_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1119\u001b[39m \u001b[43m    \u001b[49m\u001b[43mtransport\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtransport\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1120\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1121\u001b[39m \u001b[38;5;28mself\u001b[39m.async_client_running = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1122\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Programming/RobotPai/.venv/lib/python3.13/site-packages/langchain_google_genai/_genai_extension.py:276\u001b[39m, in \u001b[36mbuild_generative_service\u001b[39m\u001b[34m(credentials, api_key, client_options, client_info, transport)\u001b[39m\n\u001b[32m    262\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mbuild_generative_service\u001b[39m(\n\u001b[32m    263\u001b[39m     credentials: Optional[credentials.Credentials] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m    264\u001b[39m     api_key: Optional[\u001b[38;5;28mstr\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m   (...)\u001b[39m\u001b[32m    267\u001b[39m     transport: Optional[\u001b[38;5;28mstr\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m    268\u001b[39m ) -> v1betaGenerativeServiceClient:\n\u001b[32m    269\u001b[39m     config = _prepare_config(\n\u001b[32m    270\u001b[39m         credentials=credentials,\n\u001b[32m    271\u001b[39m         api_key=api_key,\n\u001b[32m   (...)\u001b[39m\u001b[32m    274\u001b[39m         client_info=client_info,\n\u001b[32m    275\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m276\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mv1betaGenerativeServiceClient\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Programming/RobotPai/.venv/lib/python3.13/site-packages/google/ai/generativelanguage_v1beta/services/generative_service/client.py:697\u001b[39m, in \u001b[36mGenerativeServiceClient.__init__\u001b[39m\u001b[34m(self, credentials, transport, client_options, client_info)\u001b[39m\n\u001b[32m    688\u001b[39m     transport_init: Union[\n\u001b[32m    689\u001b[39m         Type[GenerativeServiceTransport],\n\u001b[32m    690\u001b[39m         Callable[..., GenerativeServiceTransport],\n\u001b[32m   (...)\u001b[39m\u001b[32m    694\u001b[39m         \u001b[38;5;28;01melse\u001b[39;00m cast(Callable[..., GenerativeServiceTransport], transport)\n\u001b[32m    695\u001b[39m     )\n\u001b[32m    696\u001b[39m     \u001b[38;5;66;03m# initialize with the provided callable or the passed in class\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m697\u001b[39m     \u001b[38;5;28mself\u001b[39m._transport = \u001b[43mtransport_init\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    698\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcredentials\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcredentials\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    699\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcredentials_file\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_client_options\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcredentials_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    700\u001b[39m \u001b[43m        \u001b[49m\u001b[43mhost\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_api_endpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    701\u001b[39m \u001b[43m        \u001b[49m\u001b[43mscopes\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_client_options\u001b[49m\u001b[43m.\u001b[49m\u001b[43mscopes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    702\u001b[39m \u001b[43m        \u001b[49m\u001b[43mclient_cert_source_for_mtls\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_client_cert_source\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    703\u001b[39m \u001b[43m        \u001b[49m\u001b[43mquota_project_id\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_client_options\u001b[49m\u001b[43m.\u001b[49m\u001b[43mquota_project_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    704\u001b[39m \u001b[43m        \u001b[49m\u001b[43mclient_info\u001b[49m\u001b[43m=\u001b[49m\u001b[43mclient_info\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    705\u001b[39m \u001b[43m        \u001b[49m\u001b[43malways_use_jwt_access\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    706\u001b[39m \u001b[43m        \u001b[49m\u001b[43mapi_audience\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_client_options\u001b[49m\u001b[43m.\u001b[49m\u001b[43mapi_audience\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    707\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    709\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33masync\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m._transport):\n\u001b[32m    710\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m CLIENT_LOGGING_SUPPORTED \u001b[38;5;129;01mand\u001b[39;00m _LOGGER.isEnabledFor(\n\u001b[32m    711\u001b[39m         std_logging.DEBUG\n\u001b[32m    712\u001b[39m     ):  \u001b[38;5;66;03m# pragma: NO COVER\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Programming/RobotPai/.venv/lib/python3.13/site-packages/google/ai/generativelanguage_v1beta/services/generative_service/transports/grpc.py:234\u001b[39m, in \u001b[36mGenerativeServiceGrpcTransport.__init__\u001b[39m\u001b[34m(self, host, credentials, credentials_file, scopes, channel, api_mtls_endpoint, client_cert_source, ssl_channel_credentials, client_cert_source_for_mtls, quota_project_id, client_info, always_use_jwt_access, api_audience)\u001b[39m\n\u001b[32m    229\u001b[39m             \u001b[38;5;28mself\u001b[39m._ssl_channel_credentials = grpc.ssl_channel_credentials(\n\u001b[32m    230\u001b[39m                 certificate_chain=cert, private_key=key\n\u001b[32m    231\u001b[39m             )\n\u001b[32m    233\u001b[39m \u001b[38;5;66;03m# The base transport sets the host, credentials and scopes\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m234\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m    235\u001b[39m \u001b[43m    \u001b[49m\u001b[43mhost\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    236\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcredentials\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcredentials\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    237\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcredentials_file\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcredentials_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    238\u001b[39m \u001b[43m    \u001b[49m\u001b[43mscopes\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscopes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    239\u001b[39m \u001b[43m    \u001b[49m\u001b[43mquota_project_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mquota_project_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    240\u001b[39m \u001b[43m    \u001b[49m\u001b[43mclient_info\u001b[49m\u001b[43m=\u001b[49m\u001b[43mclient_info\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    241\u001b[39m \u001b[43m    \u001b[49m\u001b[43malways_use_jwt_access\u001b[49m\u001b[43m=\u001b[49m\u001b[43malways_use_jwt_access\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    242\u001b[39m \u001b[43m    \u001b[49m\u001b[43mapi_audience\u001b[49m\u001b[43m=\u001b[49m\u001b[43mapi_audience\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    243\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    245\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._grpc_channel:\n\u001b[32m    246\u001b[39m     \u001b[38;5;66;03m# initialize with the provided callable or the default channel\u001b[39;00m\n\u001b[32m    247\u001b[39m     channel_init = channel \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m).create_channel\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Programming/RobotPai/.venv/lib/python3.13/site-packages/google/ai/generativelanguage_v1beta/services/generative_service/transports/base.py:100\u001b[39m, in \u001b[36mGenerativeServiceTransport.__init__\u001b[39m\u001b[34m(self, host, credentials, credentials_file, scopes, quota_project_id, client_info, always_use_jwt_access, api_audience, **kwargs)\u001b[39m\n\u001b[32m     96\u001b[39m     credentials, _ = google.auth.load_credentials_from_file(\n\u001b[32m     97\u001b[39m         credentials_file, **scopes_kwargs, quota_project_id=quota_project_id\n\u001b[32m     98\u001b[39m     )\n\u001b[32m     99\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m credentials \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._ignore_credentials:\n\u001b[32m--> \u001b[39m\u001b[32m100\u001b[39m     credentials, _ = \u001b[43mgoogle\u001b[49m\u001b[43m.\u001b[49m\u001b[43mauth\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdefault\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    101\u001b[39m \u001b[43m        \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mscopes_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquota_project_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mquota_project_id\u001b[49m\n\u001b[32m    102\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    103\u001b[39m     \u001b[38;5;66;03m# Don't apply audience if the credentials file passed from user.\u001b[39;00m\n\u001b[32m    104\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(credentials, \u001b[33m\"\u001b[39m\u001b[33mwith_gdch_audience\u001b[39m\u001b[33m\"\u001b[39m):\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Programming/RobotPai/.venv/lib/python3.13/site-packages/google/auth/_default.py:685\u001b[39m, in \u001b[36mdefault\u001b[39m\u001b[34m(scopes, request, quota_project_id, default_scopes)\u001b[39m\n\u001b[32m    677\u001b[39m             _LOGGER.warning(\n\u001b[32m    678\u001b[39m                 \u001b[33m\"\u001b[39m\u001b[33mNo project ID could be determined. Consider running \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    679\u001b[39m                 \u001b[33m\"\u001b[39m\u001b[33m`gcloud config set project` or setting the \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    680\u001b[39m                 \u001b[33m\"\u001b[39m\u001b[33menvironment variable\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    681\u001b[39m                 environment_vars.PROJECT,\n\u001b[32m    682\u001b[39m             )\n\u001b[32m    683\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m credentials, effective_project_id\n\u001b[32m--> \u001b[39m\u001b[32m685\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exceptions.DefaultCredentialsError(_CLOUD_SDK_MISSING_CREDENTIALS)\n",
+      "\u001b[31mDefaultCredentialsError\u001b[39m: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information."
+     ]
+    }
+   ],
+   "source": [
+    "import dotenv\n",
+    "from langgraph.graph import MessagesState, START, StateGraph\n",
+    "from langgraph.prebuilt import tools_condition\n",
+    "from langgraph.prebuilt import ToolNode\n",
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "from langchain_huggingface import HuggingFaceEmbeddings\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_community.document_loaders import WikipediaLoader\n",
+    "from langchain_community.document_loaders import ArxivLoader\n",
+    "from langchain_community.vectorstores import SupabaseVectorStore\n",
+    "from langchain.tools.retriever import create_retriever_tool\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "from langchain_core.tools import tool\n",
+    "from supabase.client import Client, create_client\n",
+    "\n",
+    "# Define the retriever from supabase\n",
+    "load_dotenv()\n",
+    "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\") #  dim=768\n",
+    "\n",
+    "supabase_url = os.environ.get(\"SUPABASE_URL\")\n",
+    "supabase_key = os.environ.get(\"SUPABASE_SERVICE_KEY\")\n",
+    "supabase: Client = create_client(supabase_url, supabase_key)\n",
+    "vector_store = SupabaseVectorStore(\n",
+    "    client=supabase,\n",
+    "    embedding= embeddings,\n",
+    "    table_name=\"documents\",\n",
+    "    query_name=\"match_documents_langchain\",\n",
+    ")\n",
+    "\n",
+    "question_retrieve_tool = create_retriever_tool(\n",
+    "    vector_store.as_retriever(),\n",
+    "    \"Question Retriever\",\n",
+    "    \"Find similar questions in the vector database for the given question.\",\n",
+    ")\n",
+    "\n",
+    "@tool\n",
+    "def multiply(a: int, b: int) -> int:\n",
+    "    \"\"\"Multiply two numbers.\n",
+    "\n",
+    "    Args:\n",
+    "        a: first int\n",
+    "        b: second int\n",
+    "    \"\"\"\n",
+    "    return a * b\n",
+    "\n",
+    "@tool\n",
+    "def add(a: int, b: int) -> int:\n",
+    "    \"\"\"Add two numbers.\n",
+    "    \n",
+    "    Args:\n",
+    "        a: first int\n",
+    "        b: second int\n",
+    "    \"\"\"\n",
+    "    return a + b\n",
+    "\n",
+    "@tool\n",
+    "def subtract(a: int, b: int) -> int:\n",
+    "    \"\"\"Subtract two numbers.\n",
+    "    \n",
+    "    Args:\n",
+    "        a: first int\n",
+    "        b: second int\n",
+    "    \"\"\"\n",
+    "    return a - b\n",
+    "\n",
+    "@tool\n",
+    "def divide(a: int, b: int) -> int:\n",
+    "    \"\"\"Divide two numbers.\n",
+    "    \n",
+    "    Args:\n",
+    "        a: first int\n",
+    "        b: second int\n",
+    "    \"\"\"\n",
+    "    if b == 0:\n",
+    "        raise ValueError(\"Cannot divide by zero.\")\n",
+    "    return a / b\n",
+    "\n",
+    "@tool\n",
+    "def modulus(a: int, b: int) -> int:\n",
+    "    \"\"\"Get the modulus of two numbers.\n",
+    "    \n",
+    "    Args:\n",
+    "        a: first int\n",
+    "        b: second int\n",
+    "    \"\"\"\n",
+    "    return a % b\n",
+    "\n",
+    "@tool\n",
+    "def wiki_search(query: str) -> str:\n",
+    "    \"\"\"Search Wikipedia for a query and return maximum 2 results.\n",
+    "    \n",
+    "    Args:\n",
+    "        query: The search query.\"\"\"\n",
+    "    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()\n",
+    "    formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
+    "        [\n",
+    "            f'<Document source=\"{doc.metadata[\"source\"]}\" page=\"{doc.metadata.get(\"page\", \"\")}\"/>\\n{doc.page_content}\\n</Document>'\n",
+    "            for doc in search_docs\n",
+    "        ])\n",
+    "    return {\"wiki_results\": formatted_search_docs}\n",
+    "\n",
+    "@tool\n",
+    "def web_search(query: str) -> str:\n",
+    "    \"\"\"Search Tavily for a query and return maximum 3 results.\n",
+    "    \n",
+    "    Args:\n",
+    "        query: The search query.\"\"\"\n",
+    "    search_docs = TavilySearchResults(max_results=3).invoke(query=query)\n",
+    "    formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
+    "        [\n",
+    "            f'<Document source=\"{doc.metadata[\"source\"]}\" page=\"{doc.metadata.get(\"page\", \"\")}\"/>\\n{doc.page_content}\\n</Document>'\n",
+    "            for doc in search_docs\n",
+    "        ])\n",
+    "    return {\"web_results\": formatted_search_docs}\n",
+    "\n",
+    "@tool\n",
+    "def arvix_search(query: str) -> str:\n",
+    "    \"\"\"Search Arxiv for a query and return maximum 3 result.\n",
+    "    \n",
+    "    Args:\n",
+    "        query: The search query.\"\"\"\n",
+    "    search_docs = ArxivLoader(query=query, load_max_docs=3).load()\n",
+    "    formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
+    "        [\n",
+    "            f'<Document source=\"{doc.metadata[\"source\"]}\" page=\"{doc.metadata.get(\"page\", \"\")}\"/>\\n{doc.page_content[:1000]}\\n</Document>'\n",
+    "            for doc in search_docs\n",
+    "        ])\n",
+    "    return {\"arvix_results\": formatted_search_docs}\n",
+    "\n",
+    "@tool\n",
+    "def similar_question_search(question: str) -> str:\n",
+    "    \"\"\"Search the vector database for similar questions and return the first results.\n",
+    "    \n",
+    "    Args:\n",
+    "        question: the question human provided.\"\"\"\n",
+    "    matched_docs = vector_store.similarity_search(query, 3)\n",
+    "    formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
+    "        [\n",
+    "            f'<Document source=\"{doc.metadata[\"source\"]}\" page=\"{doc.metadata.get(\"page\", \"\")}\"/>\\n{doc.page_content[:1000]}\\n</Document>'\n",
+    "            for doc in matched_docs\n",
+    "        ])\n",
+    "    return {\"similar_questions\": formatted_search_docs}\n",
+    "\n",
+    "tools = [\n",
+    "    multiply,\n",
+    "    add,\n",
+    "    subtract,\n",
+    "    divide,\n",
+    "    modulus,\n",
+    "    wiki_search,\n",
+    "    web_search,\n",
+    "    arvix_search,\n",
+    "    question_retrieve_tool\n",
+    "]\n",
+    "\n",
+    "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\")\n",
+    "llm_with_tools = llm.bind_tools(tools)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7dd0716c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the system prompt from the file\n",
+    "with open('system_prompt.txt', 'r') as f:\n",
+    "    system_prompt = f.read()\n",
+    "\n",
+    "\n",
+    "# System message\n",
+    "sys_msg = SystemMessage(content=system_prompt)\n",
+    "\n",
+    "# Node\n",
+    "def assistant(state: MessagesState):\n",
+    "    \"\"\"Assistant node\"\"\"\n",
+    "    return {\"messages\": [llm_with_tools.invoke([sys_msg] + state[\"messages\"])]}\n",
+    "\n",
+    "# Build graph\n",
+    "builder = StateGraph(MessagesState)\n",
+    "builder.add_node(\"assistant\", assistant)\n",
+    "builder.add_node(\"tools\", ToolNode(tools))\n",
+    "builder.add_edge(START, \"assistant\")\n",
+    "builder.add_conditional_edges(\n",
+    "    \"assistant\",\n",
+    "    # If the latest message (result) from assistant is a tool call -> tools_condition routes to tools\n",
+    "    # If the latest message (result) from assistant is a not a tool call -> tools_condition routes to END\n",
+    "    tools_condition,\n",
+    ")\n",
+    "builder.add_edge(\"tools\", \"assistant\")\n",
+    "\n",
+    "# Compile graph\n",
+    "graph = builder.compile()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4e77216",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image, display\n",
+    "\n",
+    "display(Image(graph.get_graph(xray=True).draw_mermaid_png()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5987d58c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "question = \"\"\n",
+    "messages = [HumanMessage(content=question)]\n",
+    "messages = graph.invoke({\"messages\": messages})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "330cbf17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for m in messages['messages']:\n",
+    "    m.pretty_print()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}