evaluate_agent_tool_calling_classifications.ipynb•207 kB
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "aEJv_ZkwEGba"
},
"source": [
"<center>\n",
" <p style=\"text-align:center\">\n",
" <img alt=\"phoenix logo\" src=\"https://storage.googleapis.com/arize-assets/arize-logo-white.jpg\" width=\"200\"/>\n",
" <br>\n",
" <img alt=\"phoenix logo\" src=\"https://storage.googleapis.com/arize-assets/phoenix/assets/phoenix-logo-light.svg\" width=\"200\"/>\n",
" <br>\n",
" <a href=\"https://arize.com/docs/phoenix/\">Docs</a>\n",
" |\n",
" <a href=\"https://github.com/Arize-ai/phoenix\">GitHub</a>\n",
" |\n",
" <a href=\"https://arize-ai.slack.com/join/shared_invite/zt-2w57bhem8-hq24MB6u7yE_ZF_ilOYSBw#/shared-invite/email\">Community</a>\n",
" </p>\n",
"</center>\n",
"<h1 align=\"center\">Agent Tool Calling Classification Evals</h1>\n",
"\n",
"The purpose of this notebook is:\n",
"\n",
"- to evaluate the performance of an LLM-assisted approach to evaluating agent tool calling,\n",
"- to provide an experimental framework for users to iterate and improve on an agent tool calling template.\n",
"\n",
"## Install Dependencies and Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0la5vhfxEGbb",
"outputId": "b309cf0d-7132-47bc-8039-bdb858ca1d73"
},
"outputs": [],
"source": [
"!pip install -qq \\\n",
" \"arize-phoenix-evals>=2.0\" \\\n",
" anthropic \\\n",
" openai \\\n",
" matplotlib \\\n",
" pandas \\\n",
" pyarrow \\\n",
" pycm \\\n",
" scikit-learn \\\n",
" tiktoken \\\n",
" seaborn"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5N4dMv_1EGbc"
},
"outputs": [],
"source": [
"import json\n",
"import os\n",
"from getpass import getpass\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import openai\n",
"import pandas as pd\n",
"from pycm import ConfusionMatrix\n",
"\n",
"from phoenix.evals.llm import LLM\n",
"\n",
"pd.set_option(\"display.max_colwidth\", None)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Gthqcv8xEGbc"
},
"source": [
"## Download Benchmark Dataset\n",
"\n",
"We'll evaluate the evaluation system consisting of a few example user prompts, with corresponding tool choices and ground truth answers, in addition to an evaluation prompt template against benchmark datasets of user queries and the corresponding tools called with ground-truth labels. We will be using the Berkeley Function-Calling Leaderboard dataset. This dataset is commonly used for agent applications that call tools as a benchmark."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "FW2qxwM2P0sh",
"outputId": "99345321-86a8-4e3a-ff0f-70f45f8a8bd5"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import requests\n",
"\n",
"# Load dataset\n",
"url = \"https://huggingface.co/datasets/gorilla-llm/Berkeley-Function-Calling-Leaderboard/resolve/main/BFCL_v3_exec_multiple.json\"\n",
"response = requests.get(url)\n",
"dataset = [json.loads(line) for line in response.text.strip().splitlines()]\n",
"\n",
"# Collect all unique tool definitions (these are all the tools the agent\n",
"# can choose from, as well as their parameters)\n",
"unique_tools = {}\n",
"for entry in dataset:\n",
" for tool in entry.get(\"function\", []):\n",
" if tool[\"name\"] not in unique_tools:\n",
" unique_tools[tool[\"name\"]] = tool\n",
"\n",
"tool_definitions_text = \"\\n\".join(\n",
" f\"{tool['name']}: {tool['description']} | Parameters: {json.dumps(tool['parameters'])}\"\n",
" for tool in unique_tools.values()\n",
")\n",
"\n",
"\n",
"# Prepare data for evaluation\n",
"eval_data = []\n",
"for entry in dataset:\n",
" question = entry[\"question\"][0][0][\"content\"]\n",
" ground_truths = entry.get(\"ground_truth\", [])\n",
" for gt in ground_truths:\n",
" eval_data.append(\n",
" {\"question\": question, \"tool_call\": gt, \"tool_definitions\": tool_definitions_text}\n",
" )\n",
"\n",
"df_eval = pd.DataFrame(eval_data)\n",
"df_eval.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eO7_Lz5iSxnt"
},
"source": [
"## Modify Benchmark Dataset\n",
"\n",
"The BFCL Dataset does not have any `negative` examples, i.e. only `question`, `available_tools`, and `ground_truth` are present. In order to accurately benchmark our LLM-as-a-Judge, this code snippet appends some `incorrect` examples to our dataset, by adding 20 rows of data where the `incorrect` tool is selected with correspondingly incorrect parameters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TnUiOiwYS31x"
},
"outputs": [],
"source": [
"import json\n",
"import random\n",
"import re\n",
"\n",
"\n",
"# Parse tool definitions into a dict: tool_name -> list of required parameters\n",
"def extract_tool_param_templates(tool_definitions):\n",
" tools = {}\n",
" pattern = r\"^(\\w+):.*?\\| Parameters: (.*)$\"\n",
" for line in tool_definitions.strip().split(\"\\n\"):\n",
" match = re.match(pattern, line.strip())\n",
" if match:\n",
" name, param_json = match.groups()\n",
" try:\n",
" param_schema = json.loads(param_json)\n",
" required = param_schema.get(\"required\", [])\n",
" tools[name] = required\n",
" except json.JSONDecodeError:\n",
" continue\n",
" return tools\n",
"\n",
"\n",
"tool_param_templates = extract_tool_param_templates(tool_definitions_text)\n",
"tool_names = list(tool_param_templates.keys())\n",
"\n",
"# Sample 20 wrong examples\n",
"wrong_examples = []\n",
"for i in range(20):\n",
" row = df_eval.iloc[i]\n",
" question = row[\"question\"]\n",
" correct_tool_call_str = row[\"tool_call\"]\n",
"\n",
" # Get correct tool name (assume it's before the first '(')\n",
" try:\n",
" correct_tool_name = correct_tool_call_str.split(\"(\")[0]\n",
" except Exception as e:\n",
" print(f\"Skipping row {i} due to error: {e}\")\n",
" continue\n",
"\n",
" # Get a different tool\n",
" incorrect_tools = [tool for tool in tool_names if tool != correct_tool_name]\n",
" if not incorrect_tools:\n",
" continue\n",
"\n",
" wrong_tool = random.choice(incorrect_tools)\n",
" required_params = tool_param_templates.get(wrong_tool, [])\n",
"\n",
" # Build dummy argument string\n",
" dummy_args = []\n",
" for param in required_params:\n",
" dummy_value = random.choice(\n",
" [42, 3.14, '\"example\"', \"[1, 2, 3]\", \"True\"]\n",
" ) # random but plausible\n",
" dummy_args.append(f\"{param}={dummy_value}\")\n",
"\n",
" wrong_tool_call = f\"{wrong_tool}({', '.join(dummy_args)})\"\n",
"\n",
" wrong_examples.append(\n",
" {\n",
" \"question\": question,\n",
" \"tool_call\": wrong_tool_call,\n",
" \"tool_definitions\": tool_definitions_text,\n",
" }\n",
" )\n",
"\n",
"# Create and label the correct examples\n",
"df_eval[\"true_label\"] = \"correct\"\n",
"\n",
"# Create and label the incorrect examples\n",
"df_wrong = pd.DataFrame(wrong_examples)\n",
"df_wrong[\"true_label\"] = \"incorrect\"\n",
"\n",
"# Combine both datasets\n",
"df_combined = pd.concat([df_eval, df_wrong], ignore_index=True)\n",
"\n",
"# OPTIONAL: Shuffle AFTER labeling\n",
"df_eval_final = df_combined.sample(frac=1).reset_index(drop=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1BsKhwkqEGbc"
},
"source": [
"## Define Agent Tool Calling Prompt Template\n",
"\n",
"Define the default template used to evaluate agent tool calling.\n",
"You can tweak this template and evaluate its performance."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mbuxlKUqlvk0",
"outputId": "92a45605-3b3e-4d2f-9679-5518bc7c4d58"
},
"outputs": [],
"source": [
"TOOL_CALLING_PROMPT_TEMPLATE = \"\"\"\n",
"You are an evaluation assistant evaluating questions and tool calls to\n",
"determine whether the tool called would answer the question. The tool\n",
"calls have been generated by a separate agent, and chosen from the list of\n",
"tools provided below. It is your job to decide whether that agent chose\n",
"the right tool to call.\n",
"\n",
" [BEGIN DATA]\n",
" ************\n",
" [Question]: {question}\n",
" ************\n",
" [Tool Called]: {tool_call}\n",
" [END DATA]\n",
"\n",
"\"incorrect\" means that the chosen tool would not answer the question,\n",
"the tool includes information that is not presented in the question,\n",
"or that the tool signature includes parameter values that don't match\n",
"the formats specified in the tool signatures below.\n",
"\n",
"\"correct\" means the correct tool call was chosen, the correct parameters\n",
"were extracted from the question, the tool call generated is runnable and correct,\n",
"and that no outside information not present in the question was used\n",
"in the generated question.\n",
"\n",
" [Tool Definitions]: {tool_definitions}\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oR2oNMS6EGbc"
},
"source": [
"Eval template variables:\n",
"\n",
"- **question** : The question the user poses\n",
"- **tool_call** : The tool that is actually called (ground truth)\n",
"- **tool_definitions** : All the available tools to the agent"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tV3bLwrAEGbd"
},
"source": [
"## Configure the LLM\n",
"\n",
"Configure your API keys."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dlm4HdAGEGbd",
"outputId": "642fad4d-23d3-4309-a3f2-749b9509abe0"
},
"outputs": [],
"source": [
"if not (openai_api_key := os.getenv(\"OPENAI_API_KEY\")):\n",
" openai_api_key = getpass(\"🔑 Enter your OpenAI API key: \")\n",
"\n",
"openai.api_key = openai_api_key\n",
"os.environ[\"OPENAI_API_KEY\"] = openai_api_key\n",
"\n",
"\n",
"# Set Anthropic API key\n",
"if not (anthropic_api_key := os.getenv(\"ANTHROPIC_API_KEY\")):\n",
" anthropic_api_key = getpass(\"🔑 Enter your Anthropic API key: \")\n",
"\n",
"os.environ[\"ANTHROPIC_API_KEY\"] = anthropic_api_key"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9NJpkArqgrmE"
},
"source": [
"## Evaluate the Dataset Using Phoenix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The follow cell will evaluate a dataset using multiple models and may take a while to run."
]
},
{
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"id": "q5zDDCXqPwN0",
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"outputs": [],
"source": [
"from tqdm import tqdm\n",
"\n",
"from phoenix.evals import ClassificationEvaluator, async_evaluate_dataframe\n",
"\n",
"# List of OpenAI model codes to benchmark\n",
"open_ai_models = [\n",
" \"gpt-5\",\n",
" \"gpt-4-omni\",\n",
" \"gpt-4o-mini\",\n",
" \"gpt-4o\",\n",
"]\n",
"\n",
"\n",
"all_results = []\n",
"\n",
"# ---------- Evaluate OpenAI Models ----------\n",
"for model_name in tqdm(open_ai_models):\n",
" print(f\"\\n🧪 Evaluating OpenAI model: {model_name}\")\n",
"\n",
" llm = LLM(provider=\"openai\", model=model_name)\n",
" tool_call_evaluator = ClassificationEvaluator(\n",
" name=\"tool_call_evaluator\",\n",
" llm=llm,\n",
" prompt_template=TOOL_CALLING_PROMPT_TEMPLATE,\n",
" choices={\"correct\": 1, \"incorrect\": 0},\n",
" )\n",
" results = await async_evaluate_dataframe(\n",
" dataframe=df_eval,\n",
" evaluators=[tool_call_evaluator],\n",
" )\n",
"\n",
" all_results.append(results)\n",
"\n",
"# Show the results for the last OpenAI model\n",
"results.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sNF3fP4QcFs7"
},
"source": [
"## Evaluate Anthropic Models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 373,
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"source": [
"# List of Claude (Anthropic) models to benchmark\n",
"anthropic_models = [\n",
" \"claude-3-opus-20240229\", # Claude 3 Opus\n",
" \"claude-3-sonnet-20240229\", # Claude 3 Sonnet\n",
" \"claude-3-haiku-20240307\", # Claude 3 Haiku\n",
" \"claude-opus-4-20250514\", # Claude Opus 4\n",
" \"claude-sonnet-4-20250514\", # Claude Sonnet 4\n",
"]\n",
"\n",
"\n",
"# ---------- Evaluate Anthropic Models ----------\n",
"for model_name in tqdm(anthropic_models):\n",
" print(f\"\\n🧪 Evaluating Anthropic model: {model_name}\")\n",
"\n",
" llm = LLM(provider=\"anthropic\", model=model_name)\n",
" tool_call_evaluator = ClassificationEvaluator(\n",
" name=\"tool_call_evaluator\",\n",
" llm=llm,\n",
" prompt_template=TOOL_CALLING_PROMPT_TEMPLATE,\n",
" choices={\"correct\": 1, \"incorrect\": 0},\n",
" )\n",
" results = await async_evaluate_dataframe(\n",
" dataframe=df_eval,\n",
" evaluators=[tool_call_evaluator],\n",
" )\n",
"\n",
" all_results.append(results)\n",
"\n",
"# Show the results for the last OpenAI model\n",
"results.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z7W3RD-yUrvR"
},
"source": [
"## Show Results\n",
"\n",
"Let's see how well different models performed as a Judge! We'll show:\n",
"- Confusion Matrix for one model (GPT 4o)\n",
"- F1 score per model benchmarked, to compare how different models perform as a judge vs the ground truth\n",
"- An agreement matrix to see which models agree with each other as judges."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "MEZEr0cVUu9Z",
"outputId": "a5da2d90-5dc0-42f1-a9db-632b44b2be9f"
},
"outputs": [],
"source": [
"import json\n",
"\n",
"import seaborn as sns\n",
"from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
"\n",
"# Combine results and save\n",
"df_all_models = pd.concat(all_results, ignore_index=True)\n",
"df_all_models = df_all_models.dropna(subset=[\"tool_call_evaluator_score\"])\n",
"df_all_models[\"label\"] = df_all_models[\"tool_call_evaluator_score\"].apply(\n",
" lambda score: json.loads(score)[\"label\"]\n",
")\n",
"df_all_models[\"model\"] = df_all_models[\"tool_call_evaluator_score\"].apply(\n",
" lambda score: json.loads(score)[\"metadata\"][\"model\"]\n",
")\n",
"df_all_models.to_csv(\"judge_results_all_models.csv\", index=False)\n",
"\n",
"# Compute metrics\n",
"metrics = []\n",
"models = df_all_models[\"model\"].unique()\n",
"\n",
"for model_name in models:\n",
" df_model = df_all_models[df_all_models[\"model\"] == model_name]\n",
"\n",
" # Keep only valid predictions and labels\n",
" df_model = df_model[\n",
" df_model[\"label\"].isin([\"correct\", \"incorrect\"])\n",
" & df_model[\"true_label\"].isin([\"correct\", \"incorrect\"])\n",
" ]\n",
"\n",
" # Use real ground truth, given we added some incorrect examples\n",
" true_labels = df_model[\"true_label\"].tolist()\n",
" pred_labels = df_model[\"label\"].tolist()\n",
"\n",
" accuracy = accuracy_score(true_labels, pred_labels)\n",
" precision = precision_score(true_labels, pred_labels, pos_label=\"correct\", zero_division=0)\n",
" recall = recall_score(true_labels, pred_labels, pos_label=\"correct\", zero_division=0)\n",
" f1 = f1_score(true_labels, pred_labels, pos_label=\"correct\", zero_division=0)\n",
"\n",
" metrics.append(\n",
" {\n",
" \"Model\": model_name,\n",
" \"Accuracy\": accuracy,\n",
" \"Precision\": precision,\n",
" \"Recall\": recall,\n",
" \"F1 Score\": f1,\n",
" }\n",
" )\n",
"\n",
"# Create dataframe and sort it\n",
"df_metrics = pd.DataFrame(metrics)\n",
"df_metrics_sorted = df_metrics.sort_values(\"F1 Score\", ascending=False)\n",
"\n",
"# Preserve order of models for plotting\n",
"model_order = df_metrics_sorted[\"Model\"].tolist()\n",
"\n",
"# Plot with fixed order\n",
"plt.figure(figsize=(10, 6))\n",
"ax = sns.barplot(\n",
" data=df_metrics_sorted, x=\"F1 Score\", y=\"Model\", palette=\"Blues_d\", order=model_order\n",
")\n",
"plt.title(\"F1 Score by Model\")\n",
"plt.xlim(0, 1)\n",
"\n",
"# Add labels at correct y-axis positions\n",
"for i, model in enumerate(model_order):\n",
" score = df_metrics_sorted[df_metrics_sorted[\"Model\"] == model][\"F1 Score\"].values[0]\n",
" ax.text(score + 0.01, i, f\"{score:.2f}\", va=\"center\")\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"# ---- Generate agreement Matrix for models -------\n",
"\n",
"# Generate a consistent ID for each example\n",
"df_all_models[\"example_id\"] = df_all_models.groupby(\"model\").cumcount()\n",
"\n",
"# Pivot to wide format: one row per example, one column per model\n",
"df_pivot = df_all_models.pivot(index=\"example_id\", columns=\"model\", values=\"label\")\n",
"\n",
"# Compute agreement rates between models\n",
"agreement_matrix = pd.DataFrame(index=model_order, columns=model_order)\n",
"\n",
"for m1 in model_order:\n",
" for m2 in model_order:\n",
" agreement = (df_pivot[m1] == df_pivot[m2]).mean()\n",
" agreement_matrix.loc[m1, m2] = agreement\n",
"\n",
"agreement_matrix = agreement_matrix.astype(float)\n",
"\n",
"# ----------------------------------------\n",
"# Plot Agreement Heatmap\n",
"# ----------------------------------------\n",
"\n",
"plt.figure(figsize=(10, 8))\n",
"sns.heatmap(agreement_matrix, annot=True, cmap=\"Blues\", fmt=\".2f\", cbar=True)\n",
"plt.title(\"Model Agreement Matrix (Label Agreement Rate)\")\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"# ---------------- Confusion Matrix for GPT-4o ----------------\n",
"\n",
"# Filter to GPT-4o predictions and valid labels\n",
"df_4o = df_all_models[\n",
" (df_all_models[\"model\"] == \"gpt-4o\")\n",
" & (df_all_models[\"label\"].isin([\"correct\", \"incorrect\"]))\n",
" & (df_all_models[\"true_label\"].isin([\"correct\", \"incorrect\"]))\n",
"]\n",
"\n",
"true_labels_4o = df_4o[\"true_label\"].tolist()\n",
"pred_labels_4o = df_4o[\"label\"].tolist()\n",
"choices = [\"correct\", \"incorrect\"] # class order\n",
"\n",
"# Generate and plot confusion matrix\n",
"confusion_matrix_4o = ConfusionMatrix(\n",
" actual_vector=true_labels_4o,\n",
" predict_vector=pred_labels_4o,\n",
" classes=choices,\n",
")\n",
"confusion_matrix_4o.plot(\n",
" cmap=plt.colormaps[\"Blues\"],\n",
" number_label=True,\n",
" normalized=True,\n",
")\n",
"plt.title(\"GPT-4o Confusion Matrix\")\n",
"plt.tight_layout()\n",
"plt.show()"
]
}
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