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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/RobertoBarrosoLuque/scout-claims/blob/main/notebooks/2-Exercises.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"id": "0",
"metadata": {
"id": "0"
},
"source": [
"# Exercises: Putting the Building Blocks into Practice\n",
"\n",
"Welcome to the hands-on portion of the workshop! In these exercises, you will apply the concepts we've learned to solve a few practical problems.\n",
"\n",
"**Your goals will be to:**\n",
"1. **Extend Function Calling**: Add a new tool for the LLM to use.\n",
"2. **Modify Structured Output**: Change a Pydantic schema to extract additional structured information from an image.\n",
"3. **Bonus! Use Grammar Mode**: Force the LLM to respond in a highly specific, token-efficient format.\n",
"\n",
"Look out for the lines marked \"TODO\" in each cell; those are where you will write your code. Let's get started!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e966e0b4",
"metadata": {
"id": "e966e0b4"
},
"outputs": [],
"source": [
"#\n",
"# SETUP CELL #1: PLEASE RUN THIS BEFORE CONTINUING WITH THE EXERCISES.\n",
"# RESTART THE RUNTIME AFTER RUNNING THIS CELL IF PROMPTED TO DO SO.\n",
"#\n",
"!pip install pydantic requests Pillow python-dotenv"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eac6208b",
"metadata": {
"id": "eac6208b"
},
"outputs": [],
"source": [
"#\n",
"# SETUP CELL #2: PLEASE RUN THIS BEFORE CONTINUING WITH THE EXERCISES\n",
"#\n",
"import os\n",
"import io\n",
"import base64\n",
"from dotenv import load_dotenv\n",
"import requests\n",
"import json\n",
"load_dotenv()\n",
"\n",
"MODEL_ID = \"accounts/fireworks/models/llama4-scout-instruct-basic\"\n",
"\n",
"# This pattern is for Google Colab.\n",
"# If running locally, set the FIREWORKS_API_KEY environment variable.\n",
"try:\n",
" from google.colab import userdata\n",
" FIREWORKS_API_KEY = userdata.get('FIREWORKS_API_KEY')\n",
"except ImportError:\n",
" FIREWORKS_API_KEY = os.getenv(\"FIREWORKS_API_KEY\")\n",
"\n",
"# Make sure to set your FIREWORKS_API_KEY\n",
"if not FIREWORKS_API_KEY:\n",
" print(\"⚠️ Warning: FIREWORKS_API_KEY not set. The following cells will not run without it.\")\n",
"\n",
"# Helper function to prepare images for VLMs.\n",
"# It is defined here to be available for later exercises.\n",
"def pil_to_base64_dict(pil_image):\n",
" \"\"\"Convert PIL image to the format expected by VLMs\"\"\"\n",
" if pil_image is None:\n",
" return None\n",
"\n",
" buffered = io.BytesIO()\n",
" if pil_image.mode != \"RGB\":\n",
" pil_image = pil_image.convert(\"RGB\")\n",
"\n",
" pil_image.save(buffered, format=\"JPEG\")\n",
" img_base64 = base64.b64encode(buffered.getvalue()).decode(\"utf-8\")\n",
"\n",
" return {\"image\": pil_image, \"path\": \"uploaded_image.jpg\", \"base64\": img_base64}\n",
"\n",
"# Helper function to make api calls with requests\n",
"def make_api_call(payload, tools=None, model_id=None, base_url=None):\n",
" \"\"\"Make API call with requests\"\"\"\n",
" # Use defaults if not provided\n",
" final_model_id = model_id or MODEL_ID\n",
" final_base_url = base_url or \"https://api.fireworks.ai/inference/v1\"\n",
"\n",
" # Add model to payload\n",
" payload[\"model\"] = final_model_id\n",
"\n",
" # Add tools if provided\n",
" if tools:\n",
" payload[\"tools\"] = tools\n",
" payload[\"tool_choice\"] = \"auto\"\n",
"\n",
" headers = {\n",
" \"Authorization\": f\"Bearer {FIREWORKS_API_KEY}\",\n",
" \"Content-Type\": \"application/json\"\n",
" }\n",
"\n",
" response = requests.post(\n",
" f\"{final_base_url}/chat/completions\",\n",
" headers=headers,\n",
" json=payload\n",
" )\n",
"\n",
" if response.status_code == 200:\n",
" return response.json()\n",
" else:\n",
" raise Exception(f\"API Error: {response.status_code} - {response.text}\")\n",
"\n",
"print(\"✅ Setup complete. Helper function and API key are ready.\")"
]
},
{
"cell_type": "markdown",
"id": "09bc4200",
"metadata": {
"id": "09bc4200"
},
"source": [
"## Exercise 1: Extending Function Calling\n",
"\n",
"[Function calling](https://docs.fireworks.ai/guides/function-calling) allows an LLM to use external tools. Your first task is to give the LLM a new tool.\n",
"\n",
"**Goal**: Define a new function called `count_letter` that counts the occurrences of a specific letter in a word. You will then define its schema and make it available to the LLM.\n",
"\n",
"**Your Steps:**\n",
"1. Define the Python function `count_letter`.\n",
"2. Add it to the `available_functions` dictionary.\n",
"3. Define its schema and add it to the `tools` list.\n",
"4. Write a prompt to test your new function"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99c48d84",
"metadata": {
"id": "99c48d84"
},
"outputs": [],
"source": [
"###\n",
"### EXERCISE 1: WRITE YOUR CODE IN THIS CELL\n",
"###\n",
"import json\n",
"\n",
"# --- Step 1: Define the Python function and the available functions mapping ---\n",
"\n",
"# Base function from the previous notebook\n",
"def get_weather(location: str) -> str:\n",
" \"\"\"Get current weather for a location\"\"\"\n",
" weather_data = {\"New York\": \"Sunny, 72°F\", \"London\": \"Cloudy, 15°C\", \"Tokyo\": \"Rainy, 20°C\"}\n",
" return weather_data.get(location, \"Weather data not available\")\n",
"\n",
"# ---TODO Block start---- #\n",
"# Define a new function `count_letter` that takes a `word` and a `letter`\n",
"# and returns the number of times the letter appears in the word.\n",
"def count_letter(): # TODO: Add your function header here\n",
" # TODO: Add your function body here\n",
" pass\n",
"# ---TODO Block end---- #\n",
"\n",
"available_functions = {\n",
" \"get_weather\": get_weather,\n",
" # TODO: Add your new function to this dictionary\n",
"}\n",
"\n",
"\n",
"# --- Step 2: Define the function schemas for the LLM ---\n",
"\n",
"# Base tool schema from the previous notebook\n",
"tools = [\n",
" {\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"get_weather\",\n",
" \"description\": \"Get current weather for a location\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"location\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city name\"\n",
" }\n",
" },\n",
" \"required\": [\"location\"]\n",
" }\n",
" }\n",
" },\n",
" # TODO: Add the JSON schema for your `count_letter` function here.\n",
" # It should have two parameters: \"word\" and \"letter\", both are required strings.\n",
"]\n",
"\n",
"\n",
"# --- Step 3: Build your input to the LLM ---\n",
"\n",
"# Initialize the messages list\n",
"messages = [\n",
" {\n",
" \"role\": \"system\",\n",
" \"content\": \"You are a helpful assistant. You have access to a couple of tools, use them when needed.\"\n",
" },\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"\" #TODO: Add your user prompt here\n",
" }\n",
"]\n",
"\n",
"# Create payload\n",
"payload = {\n",
" \"messages\": messages,\n",
" \"tools\": tools,\n",
" \"model\": \"accounts/fireworks/models/llama4-maverick-instruct-basic\"\n",
"}\n",
"\n",
"# Get response from LLM\n",
"response = make_api_call(payload=payload)\n",
"\n",
"# Check if the model wants to call a tool/function\n",
"if response[\"choices\"][0][\"message\"][\"tool_calls\"]:\n",
" tool_call = response[\"choices\"][0][\"message\"][\"tool_calls\"][0]\n",
" function_name = tool_call[\"function\"][\"name\"]\n",
" function_args = json.loads(tool_call[\"function\"][\"arguments\"])\n",
"\n",
" print(f\"LLM wants to call: {function_name}\")\n",
" print(f\"With arguments: {function_args}\")\n",
"\n",
" # Execute the function\n",
" function_response = available_functions[function_name](**function_args)\n",
" print(f\"Function result: {function_response}\")\n",
"\n",
" # Add the assistant's tool call to the conversation\n",
" messages.append({\n",
" \"role\": \"assistant\",\n",
" \"content\": \"\",\n",
" \"tool_calls\": response[\"choices\"][0][\"message\"][\"tool_calls\"]\n",
" })\n",
"\n",
" # Add the function result to the conversation\n",
" messages.append({\n",
" \"role\": \"tool\",\n",
" \"content\": json.dumps(function_response) if isinstance(function_response, dict) else str(function_response)\n",
" })\n",
"\n",
" # Create the final payload\n",
" final_payload = {\n",
" \"messages\": messages,\n",
" \"tools\": tools,\n",
" \"model\": \"accounts/fireworks/models/llama4-maverick-instruct-basic\"\n",
" }\n",
"\n",
" # Get final response from LLM\n",
" final_response = make_api_call(payload=payload)\n",
"\n",
" print(f'Final response: {final_response[\"choices\"][0][\"message\"][\"content\"]}')"
]
},
{
"cell_type": "markdown",
"id": "4d198002",
"metadata": {
"id": "4d198002"
},
"source": [
"## Exercise 2: Modifying Structured Outputs (JSON Mode)\n",
"\n",
"Structured output is critical for building reliable applications. Here, you'll modify an existing schema to extract more information from an image.\n",
"\n",
"**Goal**: Update the `IncidentAnalysis` Pydantic model to also extract the `make` and `model` of the vehicle in the image.\n",
"\n",
"**Your Steps:**\n",
"1. Add the `make` and `model` fields to the `IncidentAnalysis` Pydantic class.\n",
"2. Run the VLM call using [JSON mode](https://docs.fireworks.ai/structured-responses/structured-response-formatting) to see the new structured output."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1dc5d727",
"metadata": {
"id": "1dc5d727"
},
"outputs": [],
"source": [
"###\n",
"### EXERCISE 2: WRITE YOUR CODE IN THIS CELL\n",
"###\n",
"import requests\n",
"import io\n",
"from PIL import Image\n",
"from pydantic import BaseModel, Field\n",
"from typing import Literal\n",
"\n",
"# --- Step 1: Download a sample image ---\n",
"url = \"https://raw.githubusercontent.com/RobertoBarrosoLuque/scout-claims/main/images/back_rhs_damage.png\"\n",
"response = requests.get(url)\n",
"image = Image.open(io.BytesIO(response.content))\n",
"print(\"Image downloaded.\")\n",
"\n",
"\n",
"# --- Step 2: Define the output schema ---\n",
"# ---TODO Block start---- #\n",
"# Add two new string fields to this Pydantic model:\n",
"# - `make`: To store the make of the car (e.g., \"Ford\")\n",
"# - `model`: To store the model of the car (e.g., \"Mustang\")\n",
"class IncidentAnalysis(BaseModel):\n",
" description: str = Field(description=\"A description of the damage to the vehicle.\")\n",
" location: Literal[\"front-left\", \"front-right\", \"back-left\", \"back-right\", \"front\", \"side\"]\n",
" severity: Literal[\"minor\", \"moderate\", \"major\"]\n",
" license_plate: str | None = Field(description=\"The license plate of the vehicle, if visible.\")\n",
"# ---TODO Block end---- #\n",
"\n",
"# --- Step 3: Call the VLM with the new schema ---\n",
"# The 'pil_to_base64_dict' function was defined in the setup cell\n",
"image_for_llm = pil_to_base64_dict(image)\n",
"\n",
"# Create payload\n",
"prompt = \"Describe the car damage in this image and extract all useful information.\" # TODO: modify the prompt to include the new fields\n",
"messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{image_for_llm['base64']}\"}},\n",
" {\"type\": \"text\", \"text\": prompt},\n",
" ],\n",
" }\n",
"]\n",
"response_format={\n",
" \"type\": \"json_object\",\n",
" \"schema\": IncidentAnalysis.model_json_schema(),\n",
"}\n",
"\n",
"payload = {\n",
" \"messages\": messages,\n",
" \"response_format\": response_format,\n",
" \"model\": \"accounts/fireworks/models/llama4-maverick-instruct-basic\"\n",
"}\n",
"\n",
"# Get response from LLM\n",
"response = make_api_call(payload=payload)\n",
"\n",
"\n",
"result = json.loads(response[\"choices\"][0][\"message\"][\"content\"])\n",
"print(json.dumps(result, indent=2))"
]
},
{
"cell_type": "markdown",
"id": "8e5a2e3d",
"metadata": {
"id": "8e5a2e3d"
},
"source": [
"## Bonus Exercise: Constrained Output with Grammar Mode\n",
"\n",
"Sometimes you need the model to respond in a very specific, non-JSON format. This is where [Grammar Mode](https://docs.fireworks.ai/structured-responses/structured-output-grammar-based) excels. It forces the model's output to conform to a strict pattern you define, which can also save output tokens vs. JSON mode and offer even more granular control.\n",
"\n",
"**Goal**: Use grammar mode to force the model to output *only* the make and model of the car as a single lowercase string (e.g., \"ford mustang\").\n",
"\n",
"**Your Steps:**\n",
"1. Define a GBNF grammar string.\n",
"2. Call the model using `response_format={\"type\": \"grammar\", \"grammar\": ...}`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1ea8cec3",
"metadata": {
"id": "1ea8cec3"
},
"outputs": [],
"source": [
"###\n",
"### BONUS EXERCISE: WRITE YOUR CODE IN THIS CELL\n",
"###\n",
"\n",
"# The 'image' variable and 'pil_to_base64_dict' helper function from previous\n",
"# cells are used here. Make sure those cells have been run.\n",
"# This assumes the image from Exercise 2 is still loaded.\n",
"image_for_llm = pil_to_base64_dict(image)\n",
"\n",
"\n",
"# --- Step 1: Define the GBNF grammar ---\n",
"# Define a grammar that forces the output to be:\n",
"# 1. A 'make' (one or more lowercase letters).\n",
"# 2. Followed by a single space.\n",
"# 3. Followed by a 'model' (one or more lowercase letters).\n",
"car_grammar = r'''\n",
"# TODO: define a grammar that forces the output to satisfy the format specified above (example output: \"ford mustang\")\n",
"'''\n",
"\n",
"# --- Step 2: Define the prompt ---\n",
"# Update the prompt to ask the model to identify the make and model and to respond only in the format specified above\n",
"prompt = \"\" # TODO: write your prompt here\n",
"\n",
"\n",
"# --- Step 3: Call the VLM with grammar mode ---\n",
"messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{image_for_llm['base64']}\"}},\n",
" {\"type\": \"text\", \"text\": prompt},\n",
" ],\n",
" }\n",
"]\n",
"response_format={\n",
" # TODO: define the response format to use the grammar defined above\n",
"}\n",
"\n",
"# Define payload\n",
"payload = {\n",
" \"messages\": messages,\n",
" \"response_format\": response_format,\n",
" \"model\": \"accounts/fireworks/models/llama4-maverick-instruct-basic\"\n",
"}\n",
"\n",
"# Get response from LLM\n",
"response = make_api_call(payload=payload)\n",
"\n",
"print(f'Constrained output from model: {response[\"choices\"][0][\"message\"][\"content\"]}')"
]
}
],
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