diff --git a/docs/tutorials/agents/multimodal-agent/README.md b/docs/tutorials/agents/multimodal-agent/README.md deleted file mode 100644 index 2ff652d..0000000 --- a/docs/tutorials/agents/multimodal-agent/README.md +++ /dev/null @@ -1,142 +0,0 @@ -# ๐Ÿณ Multimodal Recipe Agent - -A complete AI-powered recipe search application that understands both text and images using LanceDB, PydanticAI, and Streamlit. - -## Features - -### Colab Tutorial -- **Interactive Learning**: Step-by-step notebook with sample recipes -- **Core Concepts**: Learn multimodal agent development -- **No Setup Required**: Run directly in your browser - -### Full Demo Application -- **Semantic Recipe Search**: Find recipes by describing what you want to cook -- **Visual Recipe Discovery**: Upload a photo to find similar recipes -- **Conversational Interface**: Chat with an AI agent about cooking -- **Multimodal Storage**: Recipe text, images, and vectors stored together in LanceDB -- **Production Ready**: Complete with error handling and logging - -## Quick Start - -### Option 1: Interactive Tutorial (Google Colab) -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pxavAGoXa-KSh_4HxNpvP2AjHPcIRpbq?usp=sharing) - -**Perfect for learning!** This Colab notebook provides a step-by-step tutorial with sample data. No setup required - just click and start learning about multimodal agents. - -### Option 2: Full Demo Application (Local Setup) - -#### 1. Download and Setup - -```bash -# Download the tutorial files from GitHub -# Extract all files to a folder named 'multimodal-recipe-agent' -# Navigate to the folder -cd multimodal-recipe-agent -``` - -#### 2. Install Dependencies - -```bash -uv sync -``` - -#### 3. Download and Import Full Dataset - -**First, download the dataset:** -1. Visit [Kaggle Recipe Dataset](https://www.kaggle.com/datasets/pes12017000148/food-ingredients-and-recipe-dataset-with-images) -2. Download the dataset and extract it to your `multimodal-recipe-agent` folder -3. Ensure the `recipes.csv` file is in the `data/` directory - -**Then run the import script:** -```bash -uv run python import.py -``` - -This will: -- Process the downloaded recipe dataset from Kaggle -- Generate text and image embeddings for thousands of recipes -- Store everything in a LanceDB database - -#### 4. Run the Complete Application - -**Streamlit Chat App:** -```bash -uv run streamlit run app.py -``` - -**Jupyter Notebook Tutorial:** -```bash -uv run jupyter notebook multimodal-recipe-agent.ipynb -``` - -## Project Structure - -``` -multimodal-recipe-agent/ -โ”œโ”€โ”€ multimodal-recipe-agent.ipynb # Interactive tutorial -โ”œโ”€โ”€ agent.py # PydanticAI agent implementation -โ”œโ”€โ”€ app.py # Streamlit chat interface -โ”œโ”€โ”€ import.py # Data import and processing -โ”œโ”€โ”€ pyproject.toml # Modern Python project configuration -โ”œโ”€โ”€ uv.lock # Locked dependency versions -โ”œโ”€โ”€ README.md # This file -โ””โ”€โ”€ data/ # Generated data directory (created after import) - โ”œโ”€โ”€ recipes.csv # Recipe dataset - โ”œโ”€โ”€ images/ # Recipe images - โ””โ”€โ”€ recipes.lance # LanceDB database -``` - -## Download Instructions - -1. **Download the tutorial files** from the GitHub repository -2. **Extract all files** to a folder named `multimodal-recipe-agent` -3. **Ensure all files are in the same directory** - this is important for imports to work -4. **Navigate to the folder** in your terminal before running commands - -## Usage - -### Text Search -- Ask questions like "Find me healthy pasta recipes with chicken" -- Search by ingredients: "What can I make with eggs, flour, and milk?" - -### Image Search -- Upload a photo of a dish in the Streamlit sidebar -- The AI will find similar recipes based on visual similarity - -### Chat Interface -- Have a conversation with the recipe assistant -- Ask follow-up questions about ingredients or cooking methods -- Get detailed recipe information with images - -## Key Technologies - -- **LanceDB**: Multimodal vector database for efficient storage and retrieval -- **PydanticAI**: Modern AI agent framework with type safety -- **Sentence Transformers**: Text embeddings for semantic search -- **CLIP**: Vision-language model for image understanding -- **Streamlit**: Interactive web application framework - -## Requirements - -- Python 3.8+ -- CUDA (optional, for GPU acceleration) - -## How It Works - -1. **Data Import**: `import.py` processes recipe data, generates embeddings, and stores everything in LanceDB -2. **AI Agent**: `agent.py` creates a PydanticAI agent with tools for searching recipes -3. **Web Interface**: `app.py` provides a Streamlit chat interface for interacting with the agent -4. **Tutorial**: `multimodal-recipe-agent.ipynb` walks through the implementation step-by-step - -## Development - -This project demonstrates: -- Building AI agents with multimodal capabilities -- Using LanceDB for vector storage and retrieval -- Creating custom tools for PydanticAI agents -- Building conversational interfaces with Streamlit -- Handling both text and image inputs in a single agent - -## License - -This project is part of the LanceDB tutorials and follows the same license terms. diff --git a/docs/tutorials/agents/multimodal-agent/agent.py b/docs/tutorials/agents/multimodal-agent/agent.py deleted file mode 100644 index c330fe7..0000000 --- a/docs/tutorials/agents/multimodal-agent/agent.py +++ /dev/null @@ -1,379 +0,0 @@ -#!/usr/bin/env python3 -""" -Multimodal Recipe Agent with LanceDB and PydanticAI - -A complete AI agent for recipe search with multimodal capabilities. -Uses LanceDB for data storage and PydanticAI for intelligent reasoning. -""" - -import base64 -from typing import Any, Dict, List, Optional - -import lancedb -import torch -from dotenv import load_dotenv -from PIL import Image -from pydantic_ai import Agent -from sentence_transformers import SentenceTransformer -from transformers import CLIPModel, CLIPProcessor - -# Load environment variables -load_dotenv() - -# Configure Logfire with PydanticAI integration -try: - import os - - import logfire - - # Check if Logfire environment variables are set - logfire_token = os.getenv("LOGFIRE_TOKEN") - logfire_project = os.getenv("LOGFIRE_PROJECT") - - if logfire_token and logfire_project: - # Configure Logfire (will use environment variables) - logfire.configure() - - # Instrument PydanticAI for automatic logging of agent calls, tool calls, and LLM calls - logfire.instrument_pydantic_ai() - - print("โœ… Logfire configured and PydanticAI instrumented") - print( - "๐Ÿ“Š All agent calls, tool calls, and LLM interactions will be automatically logged" - ) - else: - print( - "โš ๏ธ Logfire environment variables not set (LOGFIRE_TOKEN, LOGFIRE_PROJECT)" - ) - print("๐Ÿ“ Logfire logging disabled - agent will run without logging") - logfire = None -except Exception as e: - print(f"โš ๏ธ Logfire configuration failed: {e}") - print("๐Ÿ“ Logfire logging disabled - agent will run without logging") - logfire = None - -# Configuration -LANCEDB_PATH = "data/recipes.lance" -TEXT_MODEL = "all-MiniLM-L6-v2" -IMAGE_MODEL = "openai/clip-vit-base-patch32" -MAX_RESULTS = 10 - - -class RecipeSearchTools: - """Tools for the PydanticAI agent""" - - def _safe_convert(self, value): - """Safely convert numpy types to Python types for JSON serialization""" - import numpy as np - - if isinstance(value, np.ndarray): - if value.size == 1: # scalar array - return value.item() - else: # multi-element array - return value.tolist() - elif ( - hasattr(value, "item") and hasattr(value, "size") and value.size == 1 - ): # numpy scalar - return value.item() - elif hasattr(value, "tolist"): # other numpy array-like - return value.tolist() - elif isinstance(value, (list, tuple)): - return [self._safe_convert(item) for item in value] - else: - return value - - def __init__(self): - self.device = "cuda" if torch.cuda.is_available() else "cpu" - print(f"๐Ÿ”ง Using device: {self.device}") - - # Load models - print("๐Ÿ“ฅ Loading models...") - self.text_model = SentenceTransformer(TEXT_MODEL) - self.text_model.to(self.device) - - self.image_model = CLIPModel.from_pretrained(IMAGE_MODEL) - self.image_processor = CLIPProcessor.from_pretrained(IMAGE_MODEL) - self.image_model.to(self.device) - - # Connect to database - self.db = lancedb.connect(LANCEDB_PATH) - self.table = self.db.open_table("recipes") - - print("โœ… Tools ready!") - - def search_recipes_by_text( - self, query: str, limit: int = MAX_RESULTS - ) -> List[Dict[str, Any]]: - """Search recipes by text query""" - # Generate query embedding - query_embedding = self.text_model.encode( - [query], convert_to_tensor=True, device=self.device - ) - query_vector = query_embedding.cpu().numpy().flatten() - - # Search in LanceDB using text embeddings - results = ( - self.table.search(query_vector, vector_column_name="text_embedding") - .limit(limit) - .to_pandas() - ) - - # Convert to list of dicts for PydanticAI - recipes = [] - for _, row in results.iterrows(): - recipe = { - "id": self._safe_convert(row["id"]), - "title": self._safe_convert(row["title"]), - "ingredients": self._safe_convert(row["ingredients"]), - "ingredient_tags": self._safe_convert(row.get("ingredient_tags", [])), - "instructions": self._safe_convert(row["instructions"]), - "num_ingredients": self._safe_convert(row["num_ingredients"]), - "image_name": self._safe_convert(row["image_name"]), - "score": self._safe_convert(row.get("_distance", 0)), - } - recipes.append(recipe) - - return recipes - - def search_recipes_by_image( - self, image_path: str, limit: int = MAX_RESULTS - ) -> List[Dict[str, Any]]: - """Search recipes by image""" - try: - # Load and process image - image = Image.open(image_path).convert("RGB") - inputs = self.image_processor( - images=image, return_tensors="pt", padding=True - ) - inputs = {k: v.to(self.device) for k, v in inputs.items()} - - # Generate image embedding - with torch.no_grad(): - image_features = self.image_model.get_image_features(**inputs) - image_features = image_features / image_features.norm( - dim=-1, keepdim=True - ) - - query_vector = image_features.cpu().numpy().flatten() - - # Search in LanceDB using image embeddings - results = ( - self.table.search(query_vector, vector_column_name="image_embedding") - .limit(limit) - .to_pandas() - ) - - # Convert to list of dicts for PydanticAI - recipes = [] - for _, row in results.iterrows(): - recipe = { - "id": self._safe_convert(row["id"]), - "title": self._safe_convert(row["title"]), - "ingredients": self._safe_convert(row["ingredients"]), - "ingredient_tags": self._safe_convert( - row.get("ingredient_tags", []) - ), - "instructions": self._safe_convert(row["instructions"]), - "num_ingredients": self._safe_convert(row["num_ingredients"]), - "image_name": self._safe_convert(row["image_name"]), - "score": self._safe_convert(row.get("_distance", 0)), - } - recipes.append(recipe) - - return recipes - - except Exception as e: - print(f"Error processing image {image_path}: {e}") - return [] - - def get_available_ingredients(self) -> List[str]: - """Get all unique ingredients in the dataset""" - try: - # Get all ingredient tags - results = self.table.search().select(["ingredients"]).to_pandas() - all_ingredients = set() - - for _, row in results.iterrows(): - if row["ingredients"]: - all_ingredients.update(row["ingredients"]) - - return sorted(list(all_ingredients)) - except Exception as e: - print(f"Error getting ingredients: {e}") - return [] - - def browse_recipes(self, limit: int = MAX_RESULTS) -> List[Dict[str, Any]]: - """Browse all recipes""" - try: - results = self.table.search().limit(limit).to_pandas() - - recipes = [] - for _, row in results.iterrows(): - recipe = { - "id": self._safe_convert(row["id"]), - "title": self._safe_convert(row["title"]), - "ingredients": self._safe_convert(row["ingredients"]), - "ingredient_tags": self._safe_convert( - row.get("ingredient_tags", []) - ), - "instructions": self._safe_convert(row["instructions"]), - "num_ingredients": self._safe_convert(row["num_ingredients"]), - "image_name": self._safe_convert(row["image_name"]), - } - recipes.append(recipe) - - return recipes - except Exception as e: - print(f"Error browsing recipes: {e}") - return [] - - def get_recipe_image_base64_from_binary(self, recipe_id: str) -> Optional[str]: - """Get recipe image as base64 from stored binary data in database""" - try: - result = self.table.search().where(f"id = '{recipe_id}'").to_pandas() - if len(result) > 0: - row = result.iloc[0] - image_binary = row.get("image_binary") - if image_binary is not None: - return base64.b64encode(image_binary).decode("utf-8") - except Exception as e: - print(f"Error getting image binary for recipe {recipe_id}: {e}") - return None - - def get_recipes_with_images(self, query: str, limit: int = MAX_RESULTS) -> str: - """Search recipes and return formatted response with image placeholders""" - try: - recipes = self.search_recipes_by_text(query, limit) - - if not recipes: - return "No recipes found matching your query." - - response_parts = [] - response_parts.append( - f"Here are {len(recipes)} recipes that match your query:\n" - ) - - for recipe in recipes: - response_parts.append(f"## {recipe['title']}") - response_parts.append( - f"**Ingredients:** {recipe['num_ingredients']} ingredients" - ) - - # Add ingredients list - if recipe.get("ingredients"): - ingredients_text = ", ".join( - recipe["ingredients"][:5] - ) # Show first 5 ingredients - if len(recipe["ingredients"]) > 5: - ingredients_text += "..." - response_parts.append(f"*{ingredients_text}*") - - # Add instructions preview - if recipe.get("instructions"): - instructions = recipe["instructions"] - if len(instructions) > 200: - instructions = instructions[:200] + "..." - response_parts.append(f"**Instructions:** {instructions}") - - # Use placeholder for image - will be replaced by app - response_parts.append(f"![Recipe Image]({recipe['id']})") - - response_parts.append("---\n") - - return "\n".join(response_parts) - - except Exception as e: - return f"Error searching recipes: {str(e)}" - - def get_recipes_with_images_from_image( - self, image_path: str, limit: int = MAX_RESULTS - ) -> str: - """Search recipes by image and return formatted response with image placeholders""" - try: - recipes = self.search_recipes_by_image(image_path, limit) - - if not recipes: - return "No recipes found matching the uploaded image." - - response_parts = [] - response_parts.append( - f"Here are {len(recipes)} recipes that match your uploaded image:\n" - ) - - for recipe in recipes: - response_parts.append(f"## {recipe['title']}") - response_parts.append( - f"**Ingredients:** {recipe['num_ingredients']} ingredients" - ) - - # Add ingredients list - if recipe.get("ingredients"): - ingredients_text = ", ".join( - recipe["ingredients"][:5] - ) # Show first 5 ingredients - if len(recipe["ingredients"]) > 5: - ingredients_text += "..." - response_parts.append(f"*{ingredients_text}*") - - # Add instructions preview - if recipe.get("instructions"): - instructions = recipe["instructions"] - if len(instructions) > 200: - instructions = instructions[:200] + "..." - response_parts.append(f"**Instructions:** {instructions}") - - # Use placeholder for image - will be replaced by app - response_parts.append(f"![Recipe Image]({recipe['id']})") - - response_parts.append("---\n") - - return "\n".join(response_parts) - - except Exception as e: - return f"Error searching recipes by image: {str(e)}" - - -# Initialize tools -tools_instance = RecipeSearchTools() - -# Create PydanticAI agent -agent = Agent( - "gpt-4o-mini", - tools=[ - tools_instance.get_recipes_with_images, - tools_instance.get_recipes_with_images_from_image, - tools_instance.get_available_ingredients, - ], - system_prompt="""You are a helpful recipe assistant. You can search for recipes by text or image. - -CRITICAL RULES - FOLLOW THESE EXACTLY: -1. ALWAYS use the provided tools to search for recipes - NEVER generate recipe responses manually -2. When user provides an image path (e.g., "path: /path/to/image.jpg"), use get_recipes_with_images_from_image tool with that exact path -3. For ANY text-based recipe search request, use get_recipes_with_images tool -4. These tools automatically format recipes with image placeholders that will be replaced by the app -5. NEVER use external image URLs like https://via.placeholder.com or any other external image services -6. ONLY use the exact format: ![Recipe Image](recipe_id) for image placeholders -7. The app will automatically replace these placeholders with actual recipe images -8. DO NOT generate your own recipe responses - always use the tools - -Be helpful and provide detailed recipe information with proper markdown formatting.""", -) - - -def main(): - """Test the agent""" - print("๐Ÿณ Multimodal Recipe Agent") - print("=" * 40) - - # Test the agent - print("\n๐Ÿ” Testing agent...") - - # Test text search - result = agent.run_sync("Find me some chicken pasta recipes") - print(f"Agent response: {result.data}") - - print("\nโœ… Agent test completed!") - - -if __name__ == "__main__": - main() diff --git a/docs/tutorials/agents/multimodal-agent/app.py b/docs/tutorials/agents/multimodal-agent/app.py deleted file mode 100644 index 08909a2..0000000 --- a/docs/tutorials/agents/multimodal-agent/app.py +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python3 -""" -Streamlit Recipe Chat App - -A complete Streamlit chat application for recipe search using the multimodal agent. -""" - -import io -from typing import Any, Dict - -import streamlit as st -from agent import agent -from PIL import Image - -# Page config -st.set_page_config(page_title="Multimodal Recipe Chat", page_icon="๐Ÿณ", layout="wide") - -# Initialize session state -if "messages" not in st.session_state: - st.session_state.messages = [] - -if "uploaded_image" not in st.session_state: - st.session_state.uploaded_image = None - - -def display_image(image_binary: bytes) -> None: - """Display image from binary data""" - if image_binary: - try: - image = Image.open(io.BytesIO(image_binary)) - st.image(image, width=200) - except Exception as e: - st.error(f"Error displaying image: {e}") - else: - st.info("No image available") - - -def display_recipe(recipe: Dict[str, Any]) -> None: - """Display a single recipe in chat""" - with st.container(): - col1, col2 = st.columns([1, 3]) - - with col1: - if recipe.get("image_binary"): - display_image(recipe["image_binary"]) - else: - st.info("No image") - - with col2: - st.write(f"**{recipe['title']}**") - st.write(f"*{recipe['num_ingredients']} ingredients*") - - # Show ingredient tags - if recipe.get("ingredient_tags"): - tags = ", ".join(recipe["ingredient_tags"][:8]) - if len(recipe["ingredient_tags"]) > 8: - tags += "..." - st.write(f"*Ingredients: {tags}*") - - # Show instructions preview - if recipe.get("instructions"): - instructions = recipe["instructions"] - if len(instructions) > 150: - instructions = instructions[:150] + "..." - st.write(f"*Instructions: {instructions}*") - - -def replace_image_placeholders(response: str) -> str: - """Replace recipe ID placeholders with actual base64 encoded images""" - import re - - # Pattern to match ![any text](recipe_id) where recipe_id starts with "recipe_" - placeholder_pattern = r"!\[.*?\]\((recipe_[^)]+)\)" - - def replace_placeholder(match): - recipe_id = match.group(1) - try: - # Get base64 image data for this recipe - from agent import tools_instance - - image_base64 = tools_instance.get_recipe_image_base64_from_binary(recipe_id) - if image_base64: - return f"data:image/jpeg;base64,{image_base64}" - else: - return "*No image available*" - except Exception as e: - print(f"Error loading image for recipe {recipe_id}: {e}") - return "*No image available*" - - return re.sub(placeholder_pattern, replace_placeholder, response) - - -def display_enhanced_response(response: str) -> None: - """Display agent response with enhanced formatting and image support""" - import re - - # First replace placeholders with actual base64 images - response = replace_image_placeholders(response) - - # Look for base64 image patterns in the response - base64_pattern = r"data:image/[^;]+;base64,([A-Za-z0-9+/=]+)" - matches = re.findall(base64_pattern, response) - - # Debug: Check if there are placeholder images that need to be removed - placeholder_pattern = r"data:image/[^;]+;base64,\{base64_encoded_image\}" - if re.search(placeholder_pattern, response): - st.warning( - "โš ๏ธ Agent included placeholder image text instead of actual base64 data. This is a known issue." - ) - # Remove the placeholder image text - response = re.sub(placeholder_pattern, "", response) - - if matches: - # If there are base64 images, process them - parts = re.split(base64_pattern, response) - - for i, part in enumerate(parts): - if i % 2 == 0: # Text parts - if part.strip(): - st.markdown(part) - else: # Base64 image parts - try: - # Decode base64 image - import base64 - - image_data = base64.b64decode(part) - image = Image.open(io.BytesIO(image_data)) - st.image(image, width=300) - except Exception as e: - st.error(f"Error displaying image: {e}") - else: - # No images, just display as markdown - st.markdown(response) - - -def main(): - """Main app function""" - st.title("๐Ÿณ Multimodal Recipe Chat") - st.write("Chat with the recipe assistant to find recipes!") - - # Sidebar for image upload - with st.sidebar: - st.header("Upload Image") - uploaded_file = st.file_uploader( - "Upload an image for recipe search", - type=["jpg", "jpeg", "png"], - help="Upload an image to search for similar recipes", - ) - - if uploaded_file: - # Save uploaded file temporarily - temp_path = f"temp_{uploaded_file.name}" - with open(temp_path, "wb") as f: - f.write(uploaded_file.getbuffer()) - st.session_state.uploaded_image = temp_path - - # Display uploaded image - image = Image.open(uploaded_file) - st.image(image, caption="Uploaded Image", use_container_width=True) - - # Display chat messages - for message in st.session_state.messages: - with st.chat_message(message["role"]): - if message["role"] == "assistant": - display_enhanced_response(message["content"]) - else: - st.markdown(message["content"]) - - # Chat input - if prompt := st.chat_input("Ask about recipes..."): - # Add user message to chat history - st.session_state.messages.append({"role": "user", "content": prompt}) - - # Display user message - with st.chat_message("user"): - st.markdown(prompt) - - # Generate assistant response - with st.chat_message("assistant"): - with st.spinner("Thinking..."): - try: - # If there's an uploaded image, pass the image path to the agent - context = prompt - if st.session_state.uploaded_image: - context = f"{prompt} (I have uploaded an image at path: {st.session_state.uploaded_image})" - - # Prepare conversation history for the agent using PydanticAI message types - from pydantic_ai.messages import ( - ModelRequest, - ModelResponse, - TextPart, - UserPromptPart, - ) - - message_history = [] - for msg in st.session_state.messages: - if msg["role"] == "user": - # Create a ModelRequest for user messages - user_request = ModelRequest.user_text_prompt(msg["content"]) - message_history.append(user_request) - elif msg["role"] == "assistant": - # Create a ModelResponse for assistant messages - assistant_response = ModelResponse( - parts=[TextPart(content=msg["content"])] - ) - message_history.append(assistant_response) - - # Get response from agent with conversation history - result = agent.run_sync(context, message_history=message_history) - - # Extract the actual response text from AgentRunResult - response = result.output - - # Display the response with enhanced formatting - display_enhanced_response(response) - - # Add assistant response to chat history - st.session_state.messages.append( - {"role": "assistant", "content": response} - ) - - except Exception as e: - error_msg = f"Sorry, I encountered an error: {str(e)}" - st.error(error_msg) - st.session_state.messages.append( - {"role": "assistant", "content": error_msg} - ) - - # Clear chat button in sidebar - with st.sidebar: - st.header("Chat Controls") - if st.button("Clear Chat"): - st.session_state.messages = [] - st.session_state.uploaded_image = None - st.rerun() - - -if __name__ == "__main__": - main() diff --git a/docs/tutorials/agents/multimodal-agent/import.py b/docs/tutorials/agents/multimodal-agent/import.py deleted file mode 100644 index fb24a30..0000000 --- a/docs/tutorials/agents/multimodal-agent/import.py +++ /dev/null @@ -1,290 +0,0 @@ -#!/usr/bin/env python3 -""" -Recipe Data Import Script - -This script imports recipe data, cleans it, generates embeddings, and stores it in LanceDB. -Based on the recipe-final project but simplified for the tutorial. -""" - -import ast -import os -from pathlib import Path -from typing import Any, Dict, List, Optional - -import lancedb -import numpy as np -import pandas as pd -import pyarrow as pa -import torch -from PIL import Image -from sentence_transformers import SentenceTransformer -from tqdm import tqdm -from transformers import CLIPModel, CLIPProcessor - -# Configuration -DATA_DIR = "data" -CSV_FILE = "recipes.csv" -IMAGES_DIR = "data/images" -LANCEDB_PATH = "data/recipes.lance" - -# Model settings -TEXT_MODEL = "all-MiniLM-L6-v2" # Fast, efficient text embeddings -IMAGE_MODEL = "openai/clip-vit-base-patch32" # Good image embeddings -BATCH_SIZE = 32 - - -def load_data() -> pd.DataFrame: - """Step 1: Load the CSV data""" - print("๐Ÿ“Š Step 1: Loading data...") - print("๐Ÿ“ฅ Make sure you've downloaded the dataset from:") - print( - " https://www.kaggle.com/datasets/pes12017000148/food-ingredients-and-recipe-dataset-with-images" - ) - print(" and placed recipes.csv in the current directory") - - csv_path = os.path.join(DATA_DIR, CSV_FILE) - if not os.path.exists(csv_path): - raise FileNotFoundError( - f"CSV file not found at {csv_path}\n" - "Please download the dataset from Kaggle and place recipes.csv in the data/ directory" - ) - - df = pd.read_csv(csv_path) - print(f"โœ… Loaded {len(df):,} recipes from CSV") - return df - - -def clean_ingredients(ingredients_str: str) -> List[str]: - """Clean and parse ingredients from string format""" - if pd.isna(ingredients_str) or ingredients_str == "": - return [] - - try: - # Try to parse as Python list - if isinstance(ingredients_str, str) and ingredients_str.startswith("["): - ingredients = ast.literal_eval(ingredients_str) - else: - # Split by comma and clean - ingredients = [ing.strip().lower() for ing in ingredients_str.split(",")] - - # Filter out empty strings and clean - ingredients = [ing for ing in ingredients if ing and len(ing) > 1] - return ingredients - except Exception as e: - print(f"Error parsing ingredients '{ingredients_str}': {e}") - return [] - - -def clean_data(df: pd.DataFrame) -> pd.DataFrame: - """Step 2: Clean and prepare the data""" - print("๐Ÿงน Step 2: Cleaning data...") - - # Remove rows with missing essential data - initial_count = len(df) - df = df.dropna(subset=["title", "ingredients", "instructions"]) - df = df[df["title"].str.strip() != ""] - df = df[df["ingredients"].str.strip() != ""] - df = df[df["instructions"].str.strip() != ""] - - print(f"โœ… Removed {initial_count - len(df):,} invalid recipes") - - # Clean ingredients - df["ingredients"] = df["ingredients"].apply(clean_ingredients) - - # Add ingredient tags (simplified version) - df["ingredient_tags"] = df["ingredients"].apply(lambda x: x[:10] if x else []) - - # Add num_ingredients - df["num_ingredients"] = df["ingredients"].apply(len) - - # Add image_name if not present - if "image_name" not in df.columns: - df["image_name"] = df["id"].apply(lambda x: f"{x}.jpg") - - print(f"โœ… Cleaned data: {len(df):,} recipes remaining") - return df - - -def create_sample_images(df: pd.DataFrame) -> None: - """Step 3: Create sample images for demonstration""" - print("๐Ÿ–ผ๏ธ Step 3: Creating sample images...") - - # Create images directory - os.makedirs(IMAGES_DIR, exist_ok=True) - - # Create sample images with different colors - colors = [ - (255, 200, 150), # Orange - (200, 255, 200), # Green - (200, 200, 255), # Blue - (255, 255, 200), # Yellow - (255, 200, 255), # Pink - ] - - for i, (_, row) in enumerate(df.iterrows()): - # Create a simple colored image - color = colors[i % len(colors)] - image = Image.new("RGB", (224, 224), color) - - # Save the image - image_path = os.path.join(IMAGES_DIR, row["image_name"]) - image.save(image_path, "JPEG") - - print(f"โœ… Created {len(df)} sample images") - - -def load_models(): - """Step 4: Load embedding models""" - print("๐Ÿค– Step 4: Loading models...") - - device = "cuda" if torch.cuda.is_available() else "cpu" - print(f"Using device: {device}") - - # Load text model - text_model = SentenceTransformer(TEXT_MODEL) - text_model.to(device) - - # Load image model - image_model = CLIPModel.from_pretrained(IMAGE_MODEL) - image_processor = CLIPProcessor.from_pretrained(IMAGE_MODEL) - image_model.to(device) - - print("โœ… Models loaded successfully") - return text_model, image_model, image_processor, device - - -def generate_embeddings( - df: pd.DataFrame, text_model, image_model, image_processor, device -): - """Step 5: Generate embeddings for all recipes""" - print("๐Ÿ”ข Step 5: Generating embeddings...") - - text_embeddings = [] - image_embeddings = [] - image_binaries = [] - - for i, (_, row) in tqdm(df.iterrows(), total=len(df), desc="Processing recipes"): - # Generate text embedding - text_content = ( - f"{row['title']} {' '.join(row['ingredients'])} {row['instructions']}" - ) - text_embedding = text_model.encode( - [text_content], convert_to_tensor=True, device=device - ) - text_vector = text_embedding.cpu().numpy().flatten() - text_embeddings.append(text_vector) - - # Load and process image - image_path = os.path.join(IMAGES_DIR, row["image_name"]) - try: - image = Image.open(image_path).convert("RGB") - - # Convert to binary for storage - img_buffer = io.BytesIO() - image.save(img_buffer, format="JPEG") - image_binary = img_buffer.getvalue() - image_binaries.append(image_binary) - - # Generate image embedding - inputs = image_processor(images=image, return_tensors="pt", padding=True) - inputs = {k: v.to(device) for k, v in inputs.items()} - - with torch.no_grad(): - image_features = image_model.get_image_features(**inputs) - image_features = image_features / image_features.norm( - dim=-1, keepdim=True - ) - - image_vector = image_features.cpu().numpy().flatten() - image_embeddings.append(image_vector) - - except Exception as e: - print(f"Error processing image {image_path}: {e}") - # Create a zero vector as fallback - image_embeddings.append(np.zeros(512)) # CLIP base model has 512 dims - image_binaries.append(b"") - - print("โœ… Embeddings generated successfully") - return text_embeddings, image_embeddings, image_binaries - - -def create_lancedb_table( - df: pd.DataFrame, text_embeddings, image_embeddings, image_binaries -): - """Step 6: Create LanceDB table with all data""" - print("๐Ÿ’พ Step 6: Creating LanceDB table...") - - # Prepare data for LanceDB - recipes_data = [] - - for i, (_, row) in enumerate(df.iterrows()): - recipe_data = { - "id": row["id"], - "title": row["title"], - "ingredients": row["ingredients"], - "ingredient_tags": row["ingredient_tags"], - "instructions": row["instructions"], - "image_name": row["image_name"], - "text_embedding": text_embeddings[i], - "image_embedding": image_embeddings[i], - "image_binary": image_binaries[i], - "num_ingredients": row["num_ingredients"], - } - recipes_data.append(recipe_data) - - # Connect to LanceDB - db = lancedb.connect(LANCEDB_PATH) - - # Drop existing table if it exists - if "recipes" in db.table_names(): - db.drop_table("recipes") - - # Create new table - table = db.create_table("recipes", recipes_data) - - print(f"โœ… LanceDB table created with {len(recipes_data):,} recipes") - print(f"Table schema: {table.schema}") - - return table - - -def main(): - """Main import process""" - print("๐Ÿณ Recipe Data Import Process") - print("=" * 50) - - try: - # Step 1: Load data - df = load_data() - - # Step 2: Clean data - df = clean_data(df) - - # Step 3: Create sample images - create_sample_images(df) - - # Step 4: Load models - text_model, image_model, image_processor, device = load_models() - - # Step 5: Generate embeddings - text_embeddings, image_embeddings, image_binaries = generate_embeddings( - df, text_model, image_model, image_processor, device - ) - - # Step 6: Create LanceDB table - table = create_lancedb_table( - df, text_embeddings, image_embeddings, image_binaries - ) - - print("\n๐ŸŽ‰ Import process completed successfully!") - print(f"๐Ÿ“Š Total recipes imported: {len(df):,}") - print(f"๐Ÿ’พ Database location: {LANCEDB_PATH}") - print(f"๐Ÿ–ผ๏ธ Images location: {IMAGES_DIR}") - - except Exception as e: - print(f"โŒ Error during import: {e}") - raise - - -if __name__ == "__main__": - main() diff --git a/docs/tutorials/agents/multimodal-agent/index.mdx b/docs/tutorials/agents/multimodal-agent/index.mdx index 5c951d7..bea11ab 100644 --- a/docs/tutorials/agents/multimodal-agent/index.mdx +++ b/docs/tutorials/agents/multimodal-agent/index.mdx @@ -38,10 +38,8 @@ of items, run a Streamlit chat interface that supports image upload, and follow production-minded touches like error handling, logging, and monitoring. Everything you need to deploy is included. -#### Download the Complete Tutorial - - -Download the tutorial files from GitHub. + +Download the files for the full demo application here. ### Dataset Information diff --git a/docs/tutorials/agents/multimodal-agent/multimodal-recipe-agent.ipynb b/docs/tutorials/agents/multimodal-agent/multimodal-recipe-agent.ipynb deleted file mode 100644 index 249aca3..0000000 --- a/docs/tutorials/agents/multimodal-agent/multimodal-recipe-agent.ipynb +++ /dev/null @@ -1,487 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ๐Ÿณ Multimodal Recipe Agent with LanceDB and PydanticAI\n", - "\n", - "In this tutorial, you'll build an intelligent AI agent that can understand both text and images to help users discover recipes. The agent uses LanceDB for multimodal data storage and PydanticAI for intelligent reasoning.\n", - "\n", - "## What You'll Learn\n", - "\n", - "- How to build AI agents with multimodal capabilities\n", - "- Using LanceDB for efficient vector storage and retrieval\n", - "- Creating custom tools for PydanticAI agents\n", - "- Building conversational interfaces with Streamlit\n", - "- Handling both text and image inputs in a single agent\n", - "\n", - "## Prerequisites\n", - "\n", - "This tutorial assumes you have:\n", - "- Python 3.8+ installed\n", - "- Basic understanding of vector databases\n", - "- Familiarity with AI/ML concepts (helpful but not required)\n", - "\n", - "Let's get started!\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Setup and Installation\n", - "\n", - "First, let's install the required dependencies:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Install required packages\n", - "%pip install lancedb pydantic-ai sentence-transformers transformers pillow streamlit pandas numpy tqdm python-dotenv logfire\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Data Preparation\n", - "\n", - "For this tutorial, we'll use a recipe dataset with both text and images. Let's start by setting up our data directory and downloading a sample dataset:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "from pathlib import Path\n", - "import requests\n", - "import zipfile\n", - "\n", - "# Create data directory\n", - "data_dir = Path(\"data\")\n", - "data_dir.mkdir(exist_ok=True)\n", - "\n", - "print(\"๐Ÿ“ Data directory created\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For this tutorial, we'll create a sample dataset\n", - "# In practice, you would load your actual recipe data\n", - "sample_recipes = [\n", - " {\n", - " \"id\": \"recipe_1\",\n", - " \"title\": \"Classic Spaghetti Carbonara\",\n", - " \"ingredients\": [\"pasta\", \"eggs\", \"pancetta\", \"parmesan\", \"black pepper\"],\n", - " \"instructions\": \"Cook pasta according to package directions. In a bowl, whisk eggs with parmesan. Cook pancetta until crispy. Toss hot pasta with pancetta, then with egg mixture. Serve immediately.\",\n", - " \"image_name\": \"carbonara.jpg\"\n", - " },\n", - " {\n", - " \"id\": \"recipe_2\",\n", - " \"title\": \"Chocolate Chip Cookies\",\n", - " \"ingredients\": [\"flour\", \"butter\", \"sugar\", \"chocolate chips\", \"vanilla\", \"baking soda\"],\n", - " \"instructions\": \"Preheat oven to 375ยฐF. Mix dry ingredients. Cream butter and sugars. Add eggs and vanilla. Combine wet and dry ingredients. Fold in chocolate chips. Bake 9-11 minutes.\",\n", - " \"image_name\": \"cookies.jpg\"\n", - " },\n", - " {\n", - " \"id\": \"recipe_3\",\n", - " \"title\": \"Grilled Salmon with Herbs\",\n", - " \"ingredients\": [\"salmon fillets\", \"olive oil\", \"dill\", \"lemon\", \"garlic\", \"salt\", \"pepper\"],\n", - " \"instructions\": \"Preheat grill. Season salmon with salt, pepper, and herbs. Brush with olive oil. Grill 4-5 minutes per side. Serve with lemon wedges.\",\n", - " \"image_name\": \"salmon.jpg\"\n", - " }\n", - "]\n", - "\n", - "# Create sample CSV\n", - "df = pd.DataFrame(sample_recipes)\n", - "df.to_csv(\"data/recipes.csv\", index=False)\n", - "\n", - "print(f\"โœ… Created sample dataset with {len(sample_recipes)} recipes\")\n", - "print(\"\\nSample recipes:\")\n", - "for recipe in sample_recipes:\n", - " print(f\"- {recipe['title']}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Setting Up LanceDB\n", - "\n", - "Now let's set up LanceDB to store our recipe data with both text and image embeddings:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import lancedb\n", - "import numpy as np\n", - "import torch\n", - "from sentence_transformers import SentenceTransformer\n", - "from transformers import CLIPModel, CLIPProcessor\n", - "from PIL import Image\n", - "import io\n", - "import base64\n", - "\n", - "# Configuration\n", - "LANCEDB_PATH = \"data/recipes.lance\"\n", - "TEXT_MODEL = \"all-MiniLM-L6-v2\"\n", - "IMAGE_MODEL = \"openai/clip-vit-base-patch32\"\n", - "\n", - "print(\"๐Ÿ”ง Setting up models and database...\")\n", - "\n", - "# Set device\n", - "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "print(f\"Using device: {device}\")\n", - "\n", - "# Load models\n", - "text_model = SentenceTransformer(TEXT_MODEL)\n", - "text_model.to(device)\n", - "\n", - "image_model = CLIPModel.from_pretrained(IMAGE_MODEL)\n", - "image_processor = CLIPProcessor.from_pretrained(IMAGE_MODEL)\n", - "image_model.to(device)\n", - "\n", - "print(\"โœ… Models loaded successfully\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Connect to LanceDB\n", - "db = lancedb.connect(LANCEDB_PATH)\n", - "\n", - "# Create sample image data (in practice, you'd load actual images)\n", - "def create_sample_image(width=224, height=224, color=(100, 150, 200)):\n", - " \"\"\"Create a sample image for demonstration\"\"\"\n", - " image = Image.new('RGB', (width, height), color)\n", - " return image\n", - "\n", - "# Process recipes and create embeddings\n", - "recipes_data = []\n", - "\n", - "for i, recipe in enumerate(sample_recipes):\n", - " # Create text embedding\n", - " text_content = f\"{recipe['title']} {' '.join(recipe['ingredients'])} {recipe['instructions']}\"\n", - " text_embedding = text_model.encode([text_content], convert_to_tensor=True, device=device)\n", - " text_vector = text_embedding.cpu().numpy().flatten()\n", - " \n", - " # Create sample image and embedding\n", - " sample_image = create_sample_image()\n", - " \n", - " # Convert image to bytes for storage\n", - " img_buffer = io.BytesIO()\n", - " sample_image.save(img_buffer, format='JPEG')\n", - " image_binary = img_buffer.getvalue()\n", - " \n", - " # Create image embedding\n", - " inputs = image_processor(images=sample_image, return_tensors=\"pt\", padding=True)\n", - " inputs = {k: v.to(device) for k, v in inputs.items()}\n", - " \n", - " with torch.no_grad():\n", - " image_features = image_model.get_image_features(**inputs)\n", - " image_features = image_features / image_features.norm(dim=-1, keepdim=True)\n", - " \n", - " image_vector = image_features.cpu().numpy().flatten()\n", - " \n", - " # Prepare recipe data\n", - " recipe_data = {\n", - " \"id\": recipe[\"id\"],\n", - " \"title\": recipe[\"title\"],\n", - " \"ingredients\": recipe[\"ingredients\"],\n", - " \"instructions\": recipe[\"instructions\"],\n", - " \"image_name\": recipe[\"image_name\"],\n", - " \"text_embedding\": text_vector,\n", - " \"image_embedding\": image_vector,\n", - " \"image_binary\": image_binary,\n", - " \"num_ingredients\": len(recipe[\"ingredients\"])\n", - " }\n", - " \n", - " recipes_data.append(recipe_data)\n", - "\n", - "print(f\"โœ… Processed {len(recipes_data)} recipes with embeddings\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create LanceDB table\n", - "if \"recipes\" in db.table_names():\n", - " db.drop_table(\"recipes\")\n", - "\n", - "table = db.create_table(\"recipes\", recipes_data)\n", - "print(\"โœ… LanceDB table created successfully\")\n", - "print(f\"Table schema: {table.schema}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Building the AI Agent\n", - "\n", - "Now let's create our PydanticAI agent with custom tools for recipe search:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pydantic_ai import Agent\n", - "from typing import List, Dict, Any, Optional\n", - "import base64\n", - "\n", - "class RecipeSearchTools:\n", - " \"\"\"Tools for the PydanticAI agent\"\"\"\n", - " \n", - " def __init__(self, db_path: str, text_model, image_model, image_processor, device):\n", - " self.db = lancedb.connect(db_path)\n", - " self.table = self.db.open_table(\"recipes\")\n", - " self.text_model = text_model\n", - " self.image_model = image_model\n", - " self.image_processor = image_processor\n", - " self.device = device\n", - " \n", - " def _safe_convert(self, value):\n", - " \"\"\"Safely convert numpy types to Python types for JSON serialization\"\"\"\n", - " import numpy as np\n", - " \n", - " if isinstance(value, np.ndarray):\n", - " if value.size == 1:\n", - " return value.item()\n", - " else:\n", - " return value.tolist()\n", - " elif hasattr(value, \"item\") and hasattr(value, \"size\") and value.size == 1:\n", - " return value.item()\n", - " elif hasattr(value, \"tolist\"):\n", - " return value.tolist()\n", - " elif isinstance(value, (list, tuple)):\n", - " return [self._safe_convert(item) for item in value]\n", - " else:\n", - " return value\n", - " \n", - " def search_recipes_by_text(self, query: str, limit: int = 5) -> List[Dict[str, Any]]:\n", - " \"\"\"Search recipes by text query\"\"\"\n", - " # Generate query embedding\n", - " query_embedding = self.text_model.encode(\n", - " [query], convert_to_tensor=True, device=self.device\n", - " )\n", - " query_vector = query_embedding.cpu().numpy().flatten()\n", - " \n", - " # Search in LanceDB\n", - " results = (\n", - " self.table.search(query_vector, vector_column_name=\"text_embedding\")\n", - " .limit(limit)\n", - " .to_pandas()\n", - " )\n", - " \n", - " # Convert to list of dicts\n", - " recipes = []\n", - " for _, row in results.iterrows():\n", - " recipe = {\n", - " \"id\": self._safe_convert(row[\"id\"]),\n", - " \"title\": self._safe_convert(row[\"title\"]),\n", - " \"ingredients\": self._safe_convert(row[\"ingredients\"]),\n", - " \"instructions\": self._safe_convert(row[\"instructions\"]),\n", - " \"num_ingredients\": self._safe_convert(row[\"num_ingredients\"]),\n", - " \"score\": self._safe_convert(row.get(\"_distance\", 0)),\n", - " }\n", - " recipes.append(recipe)\n", - " \n", - " return recipes\n", - " \n", - " def get_available_ingredients(self) -> List[str]:\n", - " \"\"\"Get all unique ingredients in the dataset\"\"\"\n", - " try:\n", - " results = self.table.search().select([\"ingredients\"]).to_pandas()\n", - " all_ingredients = set()\n", - " \n", - " for _, row in results.iterrows():\n", - " if row[\"ingredients\"]:\n", - " all_ingredients.update(row[\"ingredients\"])\n", - " \n", - " return sorted(list(all_ingredients))\n", - " except Exception as e:\n", - " print(f\"Error getting ingredients: {e}\")\n", - " return []\n", - " \n", - " def get_recipes_with_images(self, query: str, limit: int = 5) -> str:\n", - " \"\"\"Search recipes and return formatted response\"\"\"\n", - " try:\n", - " recipes = self.search_recipes_by_text(query, limit)\n", - " \n", - " if not recipes:\n", - " return \"No recipes found matching your query.\"\n", - " \n", - " response_parts = []\n", - " response_parts.append(f\"Here are {len(recipes)} recipes that match your query:\\\\n\")\n", - " \n", - " for recipe in recipes:\n", - " response_parts.append(f\"## {recipe['title']}\")\n", - " response_parts.append(f\"**Ingredients:** {recipe['num_ingredients']} ingredients\")\n", - " \n", - " # Add ingredients list\n", - " if recipe.get(\"ingredients\"):\n", - " ingredients_text = \", \".join(recipe[\"ingredients\"][:5])\n", - " if len(recipe[\"ingredients\"]) > 5:\n", - " ingredients_text += \"...\"\n", - " response_parts.append(f\"*{ingredients_text}*\")\n", - " \n", - " # Add instructions preview\n", - " if recipe.get(\"instructions\"):\n", - " instructions = recipe[\"instructions\"]\n", - " if len(instructions) > 200:\n", - " instructions = instructions[:200] + \"...\"\n", - " response_parts.append(f\"**Instructions:** {instructions}\")\n", - " \n", - " response_parts.append(\"---\\\\n\")\n", - " \n", - " return \"\\\\n\".join(response_parts)\n", - " \n", - " except Exception as e:\n", - " return f\"Error searching recipes: {str(e)}\"\n", - "\n", - "print(\"โœ… Recipe search tools defined\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize tools\n", - "tools_instance = RecipeSearchTools(\n", - " LANCEDB_PATH, text_model, image_model, image_processor, device\n", - ")\n", - "\n", - "# Create PydanticAI agent\n", - "agent = Agent(\n", - " \"gpt-4o-mini\",\n", - " tools=[\n", - " tools_instance.get_recipes_with_images,\n", - " tools_instance.search_recipes_by_text,\n", - " tools_instance.get_available_ingredients,\n", - " ],\n", - " system_prompt=\"\"\"You are a helpful recipe assistant. You can search for recipes by text.\n", - "\n", - "CRITICAL RULES - FOLLOW THESE EXACTLY:\n", - "1. ALWAYS use the provided tools to search for recipes - NEVER generate recipe responses manually\n", - "2. For ANY text-based recipe search request, use get_recipes_with_images tool\n", - "3. These tools automatically format recipes with proper markdown\n", - "4. DO NOT generate your own recipe responses - always use the tools\n", - "\n", - "Be helpful and provide detailed recipe information with proper markdown formatting.\"\"\",\n", - ")\n", - "\n", - "print(\"โœ… PydanticAI agent created successfully\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Testing the Agent\n", - "\n", - "Let's test our agent with some sample queries:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import asyncio # Import asyncio to run the async agent\n", - "\n", - "# Test the agent with different queries\n", - "query = \"Find me some pasta recipes\"\n", - "print(f\"\\n๐Ÿ” Query: {query}\")\n", - "print(\"-\" * 50)\n", - "\n", - "# In Colab, an asyncio event loop is already running.\n", - "# We can directly await the async method of the agent.\n", - "if 'agent' in locals(): # Check if agent was successfully created\n", - " result = await agent.run(query)\n", - " # Print the entire result object to inspect its structure\n", - " print(result.output)\n", - "else:\n", - " print(\"Agent could not be initialized. Please ensure your OpenAI API key is set in Colab secrets.\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Summary and Next Steps\n", - "\n", - "Congratulations! You've built a complete multimodal recipe agent with the following features:\n", - "\n", - "### What You've Accomplished\n", - "\n", - "1. **Multimodal Data Storage**: Used LanceDB to store both text and image embeddings\n", - "2. **AI Agent Development**: Created a PydanticAI agent with custom tools\n", - "3. **Semantic Search**: Implemented text-based recipe search using vector similarity\n", - "4. **Production Features**: Added proper error handling and data conversion\n", - "\n", - "### Key Technologies Used\n", - "\n", - "- **LanceDB**: Multimodal vector database for efficient storage and retrieval\n", - "- **PydanticAI**: Modern AI agent framework with type safety\n", - "- **Sentence Transformers**: Text embeddings for semantic search\n", - "- **CLIP**: Vision-language model for image understanding\n", - "\n", - "### Next Steps\n", - "\n", - "1. **Add Image Search**: Implement the image search functionality\n", - "2. **Scale Up**: Use a larger recipe dataset\n", - "3. **Deploy**: Deploy your agent to a cloud platform\n", - "4. **Enhance UI**: Add more interactive features\n", - "5. **Add More Tools**: Extend the agent with additional capabilities\n", - "\n", - "### Running Your Agent\n", - "\n", - "To run your complete recipe agent, you can create a simple script:\n", - "\n", - "```python\n", - "# Simple test script\n", - "result = agent.run_sync(\"Find me some dessert recipes\")\n", - "print(result.data)\n", - "```\n", - "\n", - "Your agent is now ready to help users discover recipes through natural language conversations!\n" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/tutorials/agents/multimodal-agent/pyproject.toml b/docs/tutorials/agents/multimodal-agent/pyproject.toml deleted file mode 100644 index a319a5b..0000000 --- a/docs/tutorials/agents/multimodal-agent/pyproject.toml +++ /dev/null @@ -1,34 +0,0 @@ -[project] -name = "recipe-agent" -version = "0.1.0" -description = "Multimodal recipe search and recommendation agent using LanceDB" -requires-python = ">=3.12" -dependencies = [ - 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