Update agent.py

agent.py

@@ -1,15 +1,36 @@
-"""LangGraph Agent"""
 import os
 from dotenv import load_dotenv
+from typing import List, Dict, Any, Optional
+import tempfile
+import re
+import json
+import requests
+from urllib.parse import urlparse
+import pytesseract
+from PIL import Image, ImageDraw, ImageFont, ImageEnhance, ImageFilter
+import cmath
+import pandas as pd
+import uuid
+import numpy as np
+from code_interpreter import CodeInterpreter
+
+interpreter_instance = CodeInterpreter()
+
+from image_processing import *
+
+"""Langraph"""
 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 langgraph.prebuilt import ToolNode, tools_condition
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_groq import ChatGroq
+from langchain_huggingface import (
+    ChatHuggingFace,
+    HuggingFaceEndpoint,
+    HuggingFaceEmbeddings,
+)
 from langchain_community.vectorstores import SupabaseVectorStore
 from langchain_core.messages import SystemMessage, HumanMessage
 from langchain_core.tools import tool
@@ -18,117 +39,664 @@ from supabase.client import Client, create_client
 
 load_dotenv()
 
+### =============== BROWSER TOOLS =============== ###
+
+
+@tool
+def wiki_search(query: str) -> str:
+    """Search Wikipedia for a query and return maximum 2 results.
+
+    Args:
+        query: The search query."""
+    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
+            for doc in search_docs
+        ]
+    )
+    return {"wiki_results": formatted_search_docs}
+
+
+@tool
+def web_search(query: str) -> str:
+    """Search Tavily for a query and return maximum 3 results.
+
+    Args:
+        query: The search query."""
+    search_docs = TavilySearchResults(max_results=3).invoke(query=query)
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
+            for doc in search_docs
+        ]
+    )
+    return {"web_results": formatted_search_docs}
+
+
 @tool
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers.
+def arxiv_search(query: str) -> str:
+    """Search Arxiv for a query and return maximum 3 result.
 
     Args:
-        a: first int
-        b: second int
+        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 {"arxiv_results": formatted_search_docs}
+
+
+### =============== CODE INTERPRETER TOOLS =============== ###
+
+
+@tool
+def execute_code_multilang(code: str, language: str = "python") -> str:
+    """Execute code in multiple languages (Python, Bash, SQL, C, Java) and return results.
+
+    Args:
+        code (str): The source code to execute.
+        language (str): The language of the code. Supported: "python", "bash", "sql", "c", "java".
+
+    Returns:
+        A string summarizing the execution results (stdout, stderr, errors, plots, dataframes if any).
+    """
+    supported_languages = ["python", "bash", "sql", "c", "java"]
+    language = language.lower()
+
+    if language not in supported_languages:
+        return f"❌ Unsupported language: {language}. Supported languages are: {', '.join(supported_languages)}"
+
+    result = interpreter_instance.execute_code(code, language=language)
+
+    response = []
+
+    if result["status"] == "success":
+        response.append(f"✅ Code executed successfully in **{language.upper()}**")
+
+        if result.get("stdout"):
+            response.append(
+                "\n**Standard Output:**\n```\n" + result["stdout"].strip() + "\n```"
+            )
+
+        if result.get("stderr"):
+            response.append(
+                "\n**Standard Error (if any):**\n```\n"
+                + result["stderr"].strip()
+                + "\n```"
+            )
+
+        if result.get("result") is not None:
+            response.append(
+                "\n**Execution Result:**\n```\n"
+                + str(result["result"]).strip()
+                + "\n```"
+            )
+
+        if result.get("dataframes"):
+            for df_info in result["dataframes"]:
+                response.append(
+                    f"\n**DataFrame `{df_info['name']}` (Shape: {df_info['shape']})**"
+                )
+                df_preview = pd.DataFrame(df_info["head"])
+                response.append("First 5 rows:\n```\n" + str(df_preview) + "\n```")
+
+        if result.get("plots"):
+            response.append(
+                f"\n**Generated {len(result['plots'])} plot(s)** (Image data returned separately)"
+            )
+
+    else:
+        response.append(f"❌ Code execution failed in **{language.upper()}**")
+        if result.get("stderr"):
+            response.append(
+                "\n**Error Log:**\n```\n" + result["stderr"].strip() + "\n```"
+            )
+
+    return "\n".join(response)
+
+
+### =============== MATHEMATICAL TOOLS =============== ###
+
+
+@tool
+def multiply(a: float, b: float) -> float:
+    """
+    Multiplies two numbers.
+
+    Args:
+        a (float): the first number
+        b (float): the second number
     """
     return a * b
 
+
 @tool
-def add(a: int, b: int) -> int:
-    """Add two numbers.
-    
+def add(a: float, b: float) -> float:
+    """
+    Adds two numbers.
+
     Args:
-        a: first int
-        b: second int
+        a (float): the first number
+        b (float): the second number
     """
     return a + b
 
+
 @tool
-def subtract(a: int, b: int) -> int:
-    """Subtract two numbers.
-    
+def subtract(a: float, b: float) -> int:
+    """
+    Subtracts two numbers.
+
     Args:
-        a: first int
-        b: second int
+        a (float): the first number
+        b (float): the second number
     """
     return a - b
 
+
 @tool
-def divide(a: int, b: int) -> int:
-    """Divide two numbers.
-    
+def divide(a: float, b: float) -> float:
+    """
+    Divides two numbers.
+
     Args:
-        a: first int
-        b: second int
+        a (float): the first float number
+        b (float): the second float number
     """
     if b == 0:
-        raise ValueError("Cannot divide by zero.")
+        raise ValueError("Cannot divided by zero.")
     return a / b
 
+
 @tool
 def modulus(a: int, b: int) -> int:
-    """Get the modulus of two numbers.
-    
+    """
+    Get the modulus of two numbers.
+
     Args:
-        a: first int
-        b: second int
+        a (int): the first number
+        b (int): the second number
     """
     return a % b
 
+
 @tool
-def wiki_search(query: str) -> str:
-    """Search Wikipedia for a query and return maximum 2 results.
-    
+def power(a: float, b: float) -> float:
+    """
+    Get the power of two numbers.
+
     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}
+        a (float): the first number
+        b (float): the second number
+    """
+    return a**b
+
 
 @tool
-def web_search(query: str) -> str:
-    """Search Tavily for a query and return maximum 3 results.
-    
+def square_root(a: float) -> float | complex:
+    """
+    Get the square root of a number.
+
     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}
+        a (float): the number to get the square root of
+    """
+    if a >= 0:
+        return a**0.5
+    return cmath.sqrt(a)
+
+
+### =============== DOCUMENT PROCESSING TOOLS =============== ###
+
 
 @tool
-def arvix_search(query: str) -> str:
-    """Search Arxiv for a query and return maximum 3 result.
-    
+def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
+    """
+    Save content to a file and return the path.
+
     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}
+        content (str): the content to save to the file
+        filename (str, optional): the name of the file. If not provided, a random name file will be created.
+    """
+    temp_dir = tempfile.gettempdir()
+    if filename is None:
+        temp_file = tempfile.NamedTemporaryFile(delete=False, dir=temp_dir)
+        filepath = temp_file.name
+    else:
+        filepath = os.path.join(temp_dir, filename)
+
+    with open(filepath, "w") as f:
+        f.write(content)
+
+    return f"File saved to {filepath}. You can read this file to process its contents."
+
+
+@tool
+def download_file_from_url(url: str, filename: Optional[str] = None) -> str:
+    """
+    Download a file from a URL and save it to a temporary location.
+
+    Args:
+        url (str): the URL of the file to download.
+        filename (str, optional): the name of the file. If not provided, a random name file will be created.
+    """
+    try:
+        # Parse URL to get filename if not provided
+        if not filename:
+            path = urlparse(url).path
+            filename = os.path.basename(path)
+            if not filename:
+                filename = f"downloaded_{uuid.uuid4().hex[:8]}"
+
+        # Create temporary file
+        temp_dir = tempfile.gettempdir()
+        filepath = os.path.join(temp_dir, filename)
+
+        # Download the file
+        response = requests.get(url, stream=True)
+        response.raise_for_status()
+
+        # Save the file
+        with open(filepath, "wb") as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+
+        return f"File downloaded to {filepath}. You can read this file to process its contents."
+    except Exception as e:
+        return f"Error downloading file: {str(e)}"
+
+
+@tool
+def extract_text_from_image(image_path: str) -> str:
+    """
+    Extract text from an image using OCR library pytesseract (if available).
+
+    Args:
+        image_path (str): the path to the image file.
+    """
+    try:
+        # Open the image
+        image = Image.open(image_path)
+
+        # Extract text from the image
+        text = pytesseract.image_to_string(image)
+
+        return f"Extracted text from image:\n\n{text}"
+    except Exception as e:
+        return f"Error extracting text from image: {str(e)}"
+
+
+@tool
+def analyze_csv_file(file_path: str, query: str) -> str:
+    """
+    Analyze a CSV file using pandas and answer a question about it.
+
+    Args:
+        file_path (str): the path to the CSV file.
+        query (str): Question about the data
+    """
+    try:
+        # Read the CSV file
+        df = pd.read_csv(file_path)
+
+        # Run various analyses based on the query
+        result = f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
+        result += f"Columns: {', '.join(df.columns)}\n\n"
+
+        # Add summary statistics
+        result += "Summary statistics:\n"
+        result += str(df.describe())
+
+        return result
+
+    except Exception as e:
+        return f"Error analyzing CSV file: {str(e)}"
+
+
+@tool
+def analyze_excel_file(file_path: str, query: str) -> str:
+    """
+    Analyze an Excel file using pandas and answer a question about it.
+
+    Args:
+        file_path (str): the path to the Excel file.
+        query (str): Question about the data
+    """
+    try:
+        # Read the Excel file
+        df = pd.read_excel(file_path)
+
+        # Run various analyses based on the query
+        result = (
+            f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
+        )
+        result += f"Columns: {', '.join(df.columns)}\n\n"
+
+        # Add summary statistics
+        result += "Summary statistics:\n"
+        result += str(df.describe())
+
+        return result
+
+    except Exception as e:
+        return f"Error analyzing Excel file: {str(e)}"
+
+
+### ============== IMAGE PROCESSING AND GENERATION TOOLS =============== ###
+
+
+@tool
+def analyze_image(image_base64: str) -> Dict[str, Any]:
+    """
+    Analyze basic properties of an image (size, mode, color analysis, thumbnail preview).
+
+    Args:
+        image_base64 (str): Base64 encoded image string
+
+    Returns:
+        Dictionary with analysis result
+    """
+    try:
+        img = decode_image(image_base64)
+        width, height = img.size
+        mode = img.mode
 
+        if mode in ("RGB", "RGBA"):
+            arr = np.array(img)
+            avg_colors = arr.mean(axis=(0, 1))
+            dominant = ["Red", "Green", "Blue"][np.argmax(avg_colors[:3])]
+            brightness = avg_colors.mean()
+            color_analysis = {
+                "average_rgb": avg_colors.tolist(),
+                "brightness": brightness,
+                "dominant_color": dominant,
+            }
+        else:
+            color_analysis = {"note": f"No color analysis for mode {mode}"}
+
+        thumbnail = img.copy()
+        thumbnail.thumbnail((100, 100))
+        thumb_path = save_image(thumbnail, "thumbnails")
+        thumbnail_base64 = encode_image(thumb_path)
+
+        return {
+            "dimensions": (width, height),
+            "mode": mode,
+            "color_analysis": color_analysis,
+            "thumbnail": thumbnail_base64,
+        }
+    except Exception as e:
+        return {"error": str(e)}
+
+
+@tool
+def transform_image(
+    image_base64: str, operation: str, params: Optional[Dict[str, Any]] = None
+) -> Dict[str, Any]:
+    """
+    Apply transformations: resize, rotate, crop, flip, brightness, contrast, blur, sharpen, grayscale.
+
+    Args:
+        image_base64 (str): Base64 encoded input image
+        operation (str): Transformation operation
+        params (Dict[str, Any], optional): Parameters for the operation
+
+    Returns:
+        Dictionary with transformed image (base64)
+    """
+    try:
+        img = decode_image(image_base64)
+        params = params or {}
+
+        if operation == "resize":
+            img = img.resize(
+                (
+                    params.get("width", img.width // 2),
+                    params.get("height", img.height // 2),
+                )
+            )
+        elif operation == "rotate":
+            img = img.rotate(params.get("angle", 90), expand=True)
+        elif operation == "crop":
+            img = img.crop(
+                (
+                    params.get("left", 0),
+                    params.get("top", 0),
+                    params.get("right", img.width),
+                    params.get("bottom", img.height),
+                )
+            )
+        elif operation == "flip":
+            if params.get("direction", "horizontal") == "horizontal":
+                img = img.transpose(Image.FLIP_LEFT_RIGHT)
+            else:
+                img = img.transpose(Image.FLIP_TOP_BOTTOM)
+        elif operation == "adjust_brightness":
+            img = ImageEnhance.Brightness(img).enhance(params.get("factor", 1.5))
+        elif operation == "adjust_contrast":
+            img = ImageEnhance.Contrast(img).enhance(params.get("factor", 1.5))
+        elif operation == "blur":
+            img = img.filter(ImageFilter.GaussianBlur(params.get("radius", 2)))
+        elif operation == "sharpen":
+            img = img.filter(ImageFilter.SHARPEN)
+        elif operation == "grayscale":
+            img = img.convert("L")
+        else:
+            return {"error": f"Unknown operation: {operation}"}
+
+        result_path = save_image(img)
+        result_base64 = encode_image(result_path)
+        return {"transformed_image": result_base64}
+
+    except Exception as e:
+        return {"error": str(e)}
+
+
+@tool
+def draw_on_image(
+    image_base64: str, drawing_type: str, params: Dict[str, Any]
+) -> Dict[str, Any]:
+    """
+    Draw shapes (rectangle, circle, line) or text onto an image.
+
+    Args:
+        image_base64 (str): Base64 encoded input image
+        drawing_type (str): Drawing type
+        params (Dict[str, Any]): Drawing parameters
+
+    Returns:
+        Dictionary with result image (base64)
+    """
+    try:
+        img = decode_image(image_base64)
+        draw = ImageDraw.Draw(img)
+        color = params.get("color", "red")
+
+        if drawing_type == "rectangle":
+            draw.rectangle(
+                [params["left"], params["top"], params["right"], params["bottom"]],
+                outline=color,
+                width=params.get("width", 2),
+            )
+        elif drawing_type == "circle":
+            x, y, r = params["x"], params["y"], params["radius"]
+            draw.ellipse(
+                (x - r, y - r, x + r, y + r),
+                outline=color,
+                width=params.get("width", 2),
+            )
+        elif drawing_type == "line":
+            draw.line(
+                (
+                    params["start_x"],
+                    params["start_y"],
+                    params["end_x"],
+                    params["end_y"],
+                ),
+                fill=color,
+                width=params.get("width", 2),
+            )
+        elif drawing_type == "text":
+            font_size = params.get("font_size", 20)
+            try:
+                font = ImageFont.truetype("arial.ttf", font_size)
+            except IOError:
+                font = ImageFont.load_default()
+            draw.text(
+                (params["x"], params["y"]),
+                params.get("text", "Text"),
+                fill=color,
+                font=font,
+            )
+        else:
+            return {"error": f"Unknown drawing type: {drawing_type}"}
+
+        result_path = save_image(img)
+        result_base64 = encode_image(result_path)
+        return {"result_image": result_base64}
+
+    except Exception as e:
+        return {"error": str(e)}
+
+
+@tool
+def generate_simple_image(
+    image_type: str,
+    width: int = 500,
+    height: int = 500,
+    params: Optional[Dict[str, Any]] = None,
+) -> Dict[str, Any]:
+    """
+    Generate a simple image (gradient, noise, pattern, chart).
+
+    Args:
+        image_type (str): Type of image
+        width (int), height (int)
+        params (Dict[str, Any], optional): Specific parameters
+
+    Returns:
+        Dictionary with generated image (base64)
+    """
+    try:
+        params = params or {}
+
+        if image_type == "gradient":
+            direction = params.get("direction", "horizontal")
+            start_color = params.get("start_color", (255, 0, 0))
+            end_color = params.get("end_color", (0, 0, 255))
+
+            img = Image.new("RGB", (width, height))
+            draw = ImageDraw.Draw(img)
+
+            if direction == "horizontal":
+                for x in range(width):
+                    r = int(
+                        start_color[0] + (end_color[0] - start_color[0]) * x / width
+                    )
+                    g = int(
+                        start_color[1] + (end_color[1] - start_color[1]) * x / width
+                    )
+                    b = int(
+                        start_color[2] + (end_color[2] - start_color[2]) * x / width
+                    )
+                    draw.line([(x, 0), (x, height)], fill=(r, g, b))
+            else:
+                for y in range(height):
+                    r = int(
+                        start_color[0] + (end_color[0] - start_color[0]) * y / height
+                    )
+                    g = int(
+                        start_color[1] + (end_color[1] - start_color[1]) * y / height
+                    )
+                    b = int(
+                        start_color[2] + (end_color[2] - start_color[2]) * y / height
+                    )
+                    draw.line([(0, y), (width, y)], fill=(r, g, b))
+
+        elif image_type == "noise":
+            noise_array = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
+            img = Image.fromarray(noise_array, "RGB")
+
+        else:
+            return {"error": f"Unsupported image_type {image_type}"}
+
+        result_path = save_image(img)
+        result_base64 = encode_image(result_path)
+        return {"generated_image": result_base64}
+
+    except Exception as e:
+        return {"error": str(e)}
+
+
+@tool
+def combine_images(
+    images_base64: List[str], operation: str, params: Optional[Dict[str, Any]] = None
+) -> Dict[str, Any]:
+    """
+    Combine multiple images (collage, stack, blend).
+
+    Args:
+        images_base64 (List[str]): List of base64 images
+        operation (str): Combination type
+        params (Dict[str, Any], optional)
+
+    Returns:
+        Dictionary with combined image (base64)
+    """
+    try:
+        images = [decode_image(b64) for b64 in images_base64]
+        params = params or {}
+
+        if operation == "stack":
+            direction = params.get("direction", "horizontal")
+            if direction == "horizontal":
+                total_width = sum(img.width for img in images)
+                max_height = max(img.height for img in images)
+                new_img = Image.new("RGB", (total_width, max_height))
+                x = 0
+                for img in images:
+                    new_img.paste(img, (x, 0))
+                    x += img.width
+            else:
+                max_width = max(img.width for img in images)
+                total_height = sum(img.height for img in images)
+                new_img = Image.new("RGB", (max_width, total_height))
+                y = 0
+                for img in images:
+                    new_img.paste(img, (0, y))
+                    y += img.height
+        else:
+            return {"error": f"Unsupported combination operation {operation}"}
+
+        result_path = save_image(new_img)
+        result_base64 = encode_image(result_path)
+        return {"combined_image": result_base64}
+
+    except Exception as e:
+        return {"error": str(e)}
 
 
 # load the system prompt from the file
 with open("system_prompt.txt", "r", encoding="utf-8") as f:
     system_prompt = f.read()
+print(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
+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"))
+    os.environ.get("SUPABASE_URL"), os.environ.get("SUPABASE_SERVICE_ROLE_KEY")
+)
 vector_store = SupabaseVectorStore(
     client=supabase,
-    embedding= embeddings,
+    embedding=embeddings,
     table_name="documents",
     query_name="match_documents_langchain",
 )
@@ -139,38 +707,53 @@ create_retriever_tool = create_retriever_tool(
 )
 
 
-
 tools = [
+    web_search,
+    wiki_search,
+    arxiv_search,
     multiply,
     add,
     subtract,
     divide,
     modulus,
-    wiki_search,
-    web_search,
-    arvix_search,
+    power,
+    square_root,
+    save_and_read_file,
+    download_file_from_url,
+    extract_text_from_image,
+    analyze_csv_file,
+    analyze_excel_file,
+    execute_code_multilang,
+    analyze_image,
+    transform_image,
+    draw_on_image,
+    generate_simple_image,
+    combine_images,
 ]
 
+
 # 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":
+    if provider == "groq":
         # Groq https://console.groq.com/docs/models
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
+        llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
     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",
+                repo_id="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+                task="text-generation",  # for chat‐style use “text-generation”
+                max_new_tokens=1024,
+                do_sample=False,
+                repetition_penalty=1.03,
                 temperature=0,
             ),
+            verbose=True,
         )
     else:
-        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
+        raise ValueError("Invalid provider. Choose 'groq' or 'huggingface'.")
     # Bind tools to LLM
     llm_with_tools = llm.bind_tools(tools)
 
@@ -178,14 +761,19 @@ def build_graph(provider: str = "groq"):
     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]}
+
+        if similar_question:  # Check if the list is not empty
+            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]}
+        else:
+            # Handle the case when no similar questions are found
+            return {"messages": [sys_msg] + state["messages"]}
 
     builder = StateGraph(MessagesState)
     builder.add_node("retriever", retriever)
@@ -202,13 +790,12 @@ def build_graph(provider: str = "groq"):
     # 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()
+        m.pretty_print()