repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
ai-agent-book | chapter2/local_llm_serving/test_vllm_structured_streaming.py | .py | """Focused tests for fragmented structured tool calls in VLLMToolAgent.chat_stream."""
from types import SimpleNamespace
from unittest.mock import MagicMock
from agent import VLLMToolAgent
def _chunk(content=None, tool_calls=None):
delta = SimpleNamespace(content=content, tool_calls=tool_calls or [])
return... | 103 | 3,714 |
ai-agent-book | chapter2/local_llm_serving/test_ollama_thinking.py | .py | #!/usr/bin/env python3
"""Regression tests for Ollama thinking stream handling."""
import sys
import types
fake_ollama_module = types.ModuleType("ollama")
setattr(fake_ollama_module, "Client", lambda: None)
sys.modules.setdefault("ollama", fake_ollama_module)
from ollama_native import OllamaNativeAgent
class Fake... | 48 | 1,456 |
ai-agent-book | chapter2/local_llm_serving/test_run_experiment.py | .py | import json
from run_experiment import normalize_tool_call, parse_tool_calls, sha256_text
def test_parse_multiple_raw_tool_calls():
raw = (
'<tool_call>\n{"name":"get_current_time","arguments":{"city":"Vancouver"}}\n</tool_call>'
'<tool_call>\n{"name":"get_weather","arguments":{"city":"Vancouver"... | 43 | 1,459 |
ai-agent-book | chapter2/local_llm_serving/ollama_native.py | .py | """
Ollama Native Tool Calling Implementation
Uses Ollama's standard tool calling API (requires compatible models)
"""
import json
import logging
from concurrent.futures import ThreadPoolExecutor
from typing import List, Dict, Any, Optional
import ollama
from tools import ToolRegistry
logging.basicConfig(level=loggin... | 629 | 25,790 |
ai-agent-book | chapter2/local_llm_serving/demo_streaming.py | .py | #!/usr/bin/env python3
"""
Simple demo showing how to use streaming with the chat template agents
"""
import sys
import time
def print_with_typing_effect(text, delay=0.03):
"""Print text with a typing effect"""
for char in text:
print(char, end="", flush=True)
time.sleep(delay)
print()
... | 215 | 6,986 |
ai-agent-book | chapter2/local_llm_serving/test_code_interpreter_full.py | .py | """
Test the full Python environment code interpreter with error handling
"""
import json
from tools import ToolRegistry
def test_successful_execution():
"""Test that code executes successfully with full Python environment"""
print("=" * 60)
print("Test 1: Successful execution with full Python environment"... | 185 | 6,352 |
ai-agent-book | chapter2/local_llm_serving/config.py | .py | """
Configuration for vLLM Tool Calling Demo
"""
import os
from pathlib import Path
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Model Configuration
MODEL_NAME = os.getenv("MODEL_NAME", "Qwen/Qwen3-0.6B") # Can use ModelScope path or HuggingFace
MODEL_PATH = os.getenv("MODEL_PATH", Non... | 42 | 1,170 |
ai-agent-book | chapter2/local_llm_serving/test_benchmark.py | .py | #!/usr/bin/env python3
"""Regression tests for the local LLM serving benchmark."""
from types import SimpleNamespace
from unittest.mock import patch
from benchmark import stream_once
class FakeCompletions:
def __init__(self, chunks):
self.chunks = chunks
def create(self, **kwargs):
return i... | 54 | 1,486 |
ai-agent-book | chapter2/local_llm_serving/tools.py | .py | """
Sample tools for demonstrating vLLM tool calling functionality
"""
import json
import math
import random
import io
import contextlib
from typing import Dict, Any, List
from datetime import datetime
import requests
from io import BytesIO
import PyPDF2
class ToolRegistry:
"""Registry for managing available tool... | 503 | 19,571 |
ai-agent-book | chapter2/local_llm_serving/run_experiment.py | .py | #!/usr/bin/env python3
"""Run the complete, real local-server campaign for Chapter 2 Experiment 2-1.
Unlike an OpenAI-compatible client, this runner deliberately uses Ollama's
``/api/generate`` endpoint with ``raw=true``. The exact string emitted by the
Qwen chat template is therefore visible in the evidence, includi... | 448 | 16,801 |
ai-agent-book | chapter2/local_llm_serving/test_platform_detection.py | .py | """Regression tests for platform-specific backend selection."""
import contextlib
import io
import sys
import unittest
from types import SimpleNamespace
from unittest import mock
from check_compatibility import provide_recommendations
from main import ToolCallingAgent
class BackendDetectionTests(unittest.TestCase):... | 61 | 2,158 |
ai-agent-book | chapter2/local_llm_serving/agent.py | .py | """
vLLM Tool Calling Agent Implementation
Demonstrates how to use vLLM with Qwen3 for tool calling
"""
import json
import uuid
import logging
from concurrent.futures import ThreadPoolExecutor
from typing import List, Dict, Any, Optional, Tuple
from openai import OpenAI
from tools import ToolRegistry
from config impor... | 592 | 24,906 |
ai-agent-book | chapter2/prompt-engineering/test_tau_bench_empty_results.py | .py | """
display_metrics must handle empty results list without raising ZeroDivisionError.
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent / "tau_bench"))
from run import display_metrics
def test_display_metrics_empty_results():
"""Ensure display_metrics gracefully hand... | 15 | 371 |
ai-agent-book | chapter2/prompt-engineering/test_ablation.py | .py | #!/usr/bin/env python3
"""
Test script to demonstrate all ablation modes
Runs a small subset of tasks with different ablation settings
"""
import subprocess
import json
import time
from pathlib import Path
from typing import Dict, List, Tuple
import sys
try:
from dotenv import load_dotenv
load_dotenv()
except... | 297 | 9,096 |
ai-agent-book | chapter2/prompt-engineering/ablation_utils.py | .py | """
Ablation utilities for prompt engineering experiments
"""
import random
import re
from enum import Enum
from typing import List, Dict, Any, Optional
import copy
class ToneStyle(Enum):
"""Different tone styles for the agent"""
DEFAULT = "default"
TRUMP = "trump"
CASUAL = "casual"
# Tone style in... | 164 | 5,399 |
ai-agent-book | chapter2/prompt-engineering/setup.py | .py | # Copyright Sierra
from setuptools import find_packages, setup
setup(
name="tau_bench",
version="0.1.0",
description="The Tau-Bench package",
long_description=open("README.md").read(),
packages=find_packages(),
include_package_data=True,
install_requires=[
"openai>=1.13.3",
... | 23 | 524 |
ai-agent-book | chapter2/prompt-engineering/run_ablation.py | .py | #!/usr/bin/env python3
"""
Ablation Study Runner for Tau-Bench Framework
Demonstrates the importance of prompt engineering by testing different variations:
1. Tone variations (Trump style, Casual style, Default style)
2. Wiki rule randomization
3. Tool description removal
"""
import argparse
import copy
import hashlib... | 777 | 31,769 |
ai-agent-book | chapter2/prompt-engineering/run.py | .py | # Copyright Sierra
import argparse
try:
from dotenv import load_dotenv
load_dotenv()
except ImportError:
pass
from tau_bench.types import RunConfig
from tau_bench.run import run
from litellm import provider_list
from tau_bench.envs.user import UserStrategy
def parse_args() -> RunConfig:
parser = ar... | 110 | 3,340 |
ai-agent-book | chapter2/prompt-engineering/test_user_empty_response.py | .py | from types import SimpleNamespace
from tau_bench.envs import user as user_module
from tau_bench.envs.user import LLMUserSimulationEnv
from ablation_agent import completion_token_limit
class Message:
def __init__(self, content):
self.content = content
def model_dump(self):
return {"role": "as... | 57 | 2,021 |
ai-agent-book | chapter2/prompt-engineering/test_openrouter_direct.py | .py | #!/usr/bin/env python3
"""
Direct test of OpenRouter API connection with openai/gpt-5
This helps isolate API connection issues from tau-bench logic
"""
import os
import sys
try:
from dotenv import load_dotenv
load_dotenv()
except ImportError:
pass
from litellm import completion
def test_openrouter():
... | 90 | 3,012 |
ai-agent-book | chapter2/prompt-engineering/analyze_results.py | .py | #!/usr/bin/env python3
"""
Analyze and visualize ablation study results
"""
import argparse
import json
import glob
import re
from pathlib import Path
from collections import defaultdict
from typing import Dict, List, Tuple
import sys
def _extract_experiment_name(filename: str) -> str:
"""
Recover the ablati... | 394 | 13,852 |
ai-agent-book | chapter2/prompt-engineering/test_hash_item_dict_collision.py | .py | import pytest
from tau_bench.model_utils.api.cache import (
cache,
cache_call_w_dedup,
conditions,
hash_item,
)
class SameReprKey:
"""Hashable key whose representation intentionally carries no identity."""
def __init__(self, value):
self.value = value
def __hash__(self):
... | 144 | 3,998 |
ai-agent-book | chapter2/prompt-engineering/ablation_agent.py | .py | """
Custom Agent for Ablation Study
Extends ToolCallingAgent to support tone modifications
"""
import json
import os
import time
import copy
import traceback
from datetime import datetime, timezone
from litellm import completion
from typing import List, Optional, Dict, Any
from tau_bench.agents.base import Agent
from... | 396 | 17,318 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/types.py | .py | # Copyright Sierra
from pydantic import BaseModel
from typing import List, Dict, Any, Optional, Union
RESPOND_ACTION_NAME = "respond"
RESPOND_ACTION_FIELD_NAME = "content"
class Action(BaseModel):
name: str
kwargs: Dict[str, Any]
class Task(BaseModel):
user_id: str
actions: List[Action]
instru... | 91 | 1,823 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/run.py | .py | # Copyright Sierra
import os
import json
import random
import traceback
from math import comb
import multiprocessing
from typing import List, Dict, Any
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor
from tau_bench.envs import get_env
from tau_bench.agents.base import Agent
from tau_be... | 208 | 7,728 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/user.py | .py | # Copyright Sierra
import abc
import copy
import enum
import time
from datetime import datetime, timezone
from litellm import completion
from typing import Optional, List, Dict, Any, Union
class BaseUserSimulationEnv(abc.ABC):
metadata = {}
@abc.abstractmethod
def reset(self, instruction: Optional[str]... | 435 | 17,466 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/__init__.py | .py | # Copyright Sierra
from typing import Optional, Union
from tau_bench.envs.base import Env
from tau_bench.envs.user import UserStrategy
def get_env(
env_name: str,
user_strategy: Union[str, UserStrategy],
user_model: str,
task_split: str,
user_provider: Optional[str] = None,
task_index: Option... | 41 | 1,176 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/tool.py | .py | import abc
from typing import Any
class Tool(abc.ABC):
@staticmethod
def invoke(*args, **kwargs):
raise NotImplementedError
@staticmethod
def get_info() -> dict[str, Any]:
raise NotImplementedError
| 13 | 233 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/base.py | .py | # Copyright Sierra
import random
from hashlib import sha256
from tau_bench.envs.tool import Tool
from typing import Any, Callable, Dict, List, Type, Optional, Set, Union, Tuple
from tau_bench.envs.user import load_user, UserStrategy
from tau_bench.types import (
Action,
Task,
EnvInfo,
EnvResetResponse... | 167 | 5,756 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/wiki.py | .py | # Copyright Sierra
import os
FOLDER_PATH = os.path.dirname(__file__)
with open(os.path.join(FOLDER_PATH, "wiki.md"), "r") as f:
WIKI = f.read()
| 9 | 151 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tasks_test.py | .py | from tau_bench.types import Action, Task
TASKS = [
Task(
annotator="0",
user_id="mia_li_3668",
instruction="Your user id is mia_li_3668. You want to fly from New York to Seattle on May 20 (one way). You do not want to fly before 11am est. You want to fly in economy. You prefer direct flight... | 1,211 | 67,250 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/rules.py | .py | # Copyright Sierra
RULES = []
| 4 | 31 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/env.py | .py | # Copyright Sierra
from tau_bench.envs.airline.data import load_data
from tau_bench.envs.airline.rules import RULES
from tau_bench.envs.airline.tools import ALL_TOOLS
from tau_bench.envs.airline.wiki import WIKI
from tau_bench.envs.base import Env
from typing import Optional, Union
from tau_bench.envs.user import User... | 40 | 1,308 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tasks.py | .py | # Copyright Sierra
tasks = [
{
"annotator": 0,
"user_id": "mia_li_3668",
"instruction": "Your user id is mia_li_3668. You want to fly from New York to Seattle on May 20 (one way). You do not want to fly before 11am est. You want to fly in economy. You prefer direct flights but one stopover ... | 1,457 | 72,697 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/search_onestop_flight.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class SearchOnestopFlight(Tool):
@staticmethod
def invoke(data: Dict[str, Any], origin: str, destination: str, date: str) -> str:
flights = data["flights"]
results = []
for flight1 in flig... | 75 | 3,240 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/think.py | .py | # Copyright Sierra
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class Think(Tool):
@staticmethod
def invoke(data: Dict[str, Any], thought: str) -> str:
return ""
@staticmethod
def get_info() -> Dict[str, Any]:
return {
"type": "function",
... | 31 | 980 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/search_direct_flight.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class SearchDirectFlight(Tool):
@staticmethod
def invoke(data: Dict[str, Any], origin: str, destination: str, date: str) -> str:
flights = data["flights"]
results = []
for flight in flight... | 51 | 2,014 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/send_certificate.py | .py | # Copyright Sierra
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class SendCertificate(Tool):
@staticmethod
def invoke(
data: Dict[str, Any],
user_id: str,
amount: int,
) -> str:
users = data["users"]
if user_id not in users:
return ... | 53 | 1,765 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/update_reservation_flights.py | .py | # Copyright Sierra
import json
from copy import deepcopy
from typing import Any, Dict, List
from tau_bench.envs.tool import Tool
class UpdateReservationFlights(Tool):
@staticmethod
def invoke(
data: Dict[str, Any],
reservation_id: str,
cabin: str,
flights: List[Dict[str, Any]]... | 139 | 6,070 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/transfer_to_human_agents.py | .py | # Copyright Sierra
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class TransferToHumanAgents(Tool):
@staticmethod
def invoke(
data: Dict[str, Any],
summary: str,
) -> str:
return "Transfer successful"
@staticmethod
def get_info() -> Dict[str, Any]:
... | 36 | 1,144 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/__init__.py | .py | # Copyright Sierra
from .book_reservation import BookReservation
from .calculate import Calculate
from .cancel_reservation import CancelReservation
from .get_reservation_details import GetReservationDetails
from .get_user_details import GetUserDetails
from .list_all_airports import ListAllAirports
from .search_direct_... | 34 | 1,088 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/list_all_airports.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class ListAllAirports(Tool):
@staticmethod
def invoke(data: Dict[str, Any]) -> str:
airports = [
"SFO",
"JFK",
"LAX",
"ORD",
"DFW",
... | 71 | 1,633 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/calculate.py | .py | # Copyright Sierra
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class Calculate(Tool):
@staticmethod
def invoke(data: Dict[str, Any], expression: str) -> str:
if not all(char in "0123456789+-*/(). " for char in expression):
return "Error: invalid characters in express... | 36 | 1,266 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/get_reservation_details.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class GetReservationDetails(Tool):
@staticmethod
def invoke(data: Dict[str, Any], reservation_id: str) -> str:
reservations = data["reservations"]
if reservation_id in reservations:
re... | 35 | 1,082 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/cancel_reservation.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class CancelReservation(Tool):
@staticmethod
def invoke(
data: Dict[str, Any],
reservation_id: str,
) -> str:
reservations = data["reservations"]
if reservation_id not in reser... | 51 | 1,528 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/get_user_details.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class GetUserDetails(Tool):
@staticmethod
def invoke(data: Dict[str, Any], user_id: str) -> str:
users = data["users"]
if user_id in users:
return json.dumps(users[user_id])
re... | 35 | 1,028 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/update_reservation_baggages.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class UpdateReservationBaggages(Tool):
@staticmethod
def invoke(
data: Dict[str, Any],
reservation_id: str,
total_baggages: int,
nonfree_baggages: int,
payment_id: str,
... | 85 | 3,268 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/book_reservation.py | .py | # Copyright Sierra
import json
from copy import deepcopy
from typing import Any, Dict, List
from tau_bench.envs.tool import Tool
class BookReservation(Tool):
@staticmethod
def invoke(
data: Dict[str, Any],
user_id: str,
origin: str,
destination: str,
flight_type: str,
... | 227 | 10,282 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/tools/update_reservation_passengers.py | .py | # Copyright Sierra
import json
from typing import Any, Dict, List
from tau_bench.envs.tool import Tool
class UpdateReservationPassengers(Tool):
@staticmethod
def invoke(
data: Dict[str, Any],
reservation_id: str,
passengers: List[Dict[str, Any]],
) -> str:
reservations = d... | 65 | 2,712 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/airline/data/__init__.py | .py | # Copyright Sierra
import json
import os
from typing import Any
FOLDER_PATH = os.path.dirname(__file__)
def load_data() -> dict[str, Any]:
with open(os.path.join(FOLDER_PATH, "flights.json")) as f:
flight_data = json.load(f)
with open(os.path.join(FOLDER_PATH, "reservations.json")) as f:
res... | 22 | 564 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tasks_test.py | .py | from tau_bench.types import Task, Action
TASKS_TEST = [
Task(
annotator="0",
user_id="yusuf_rossi_9620",
instruction="You are Yusuf Rossi in 19122. You received your order #W2378156 and wish to exchange the mechanical keyboard for a similar one but with clicky switches and the smart thermos... | 3,359 | 169,476 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/rules.py | .py | # Copyright Sierra
RULES = [
"You are a customer service representative for an online retail company. You are chatting with a customer, and you can call tools or respond to the user.",
"The agent should always first confirm the user id by email or name+zip before proceeding with any task.",
"The agent shou... | 12 | 970 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tasks_train.py | .py | from tau_bench.types import Task, Action
TASKS_TRAIN = [
Task(
annotator="synthetic",
user_id="omar_anderson_3203",
instruction="Your name is Omar Anderson and your zip code is 19031. You are logical, independent, relaxing, polite. Return #W6067464 via credit_card_4190576: Electric Kettle; ... | 10,019 | 471,974 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tasks_dev.py | .py | from tau_bench.types import Task, Action
TASKS_DEV = [
Task(
annotator="",
user_id="olivia_ito_3591",
instruction="Your name is Olivia Ito and your zip code is 80218. You are outgoing, flexible, pessimistic, organized, logical. You've ordered an item (#W5442520) from this shop. You've reali... | 354 | 19,326 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/env.py | .py | # Copyright Sierra
from tau_bench.envs.base import Env
from tau_bench.envs.retail.data import load_data
from tau_bench.envs.retail.rules import RULES
from tau_bench.envs.retail.tools import ALL_TOOLS
from tau_bench.envs.retail.wiki import WIKI
from typing import Optional, Union
from tau_bench.envs.user import UserStra... | 44 | 1,519 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tasks.py | .py | # Copyright Sierra
tasks = [
{
"annotator": 0,
"user_id": "yusuf_rossi_9620",
"instruction": "You are Yusuf Rossi in 19122. You received your order #W2378156 and wish to exchange the mechanical keyboard for a similar one but with clicky switches and the smart thermostat for one compatible w... | 3,077 | 178,995 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/exchange_delivered_order_items.py | .py | # Copyright Sierra
import json
from typing import Any, Dict, List
from tau_bench.envs.tool import Tool
class ExchangeDeliveredOrderItems(Tool):
@staticmethod
def invoke(
data: Dict[str, Any],
order_id: str,
item_ids: List[str],
new_item_ids: List[str],
payment_method_... | 127 | 5,370 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/modify_pending_order_payment.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class ModifyPendingOrderPayment(Tool):
@staticmethod
def invoke(
data: Dict[str, Any],
order_id: str,
payment_method_id: str,
) -> str:
orders = data["orders"]
# Check... | 112 | 4,364 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/think.py | .py | # Copyright Sierra
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class Think(Tool):
@staticmethod
def invoke(data: Dict[str, Any], thought: str) -> str:
# This method does not change the state of the data; it simply returns an empty string.
return ""
@staticmethod
... | 35 | 1,160 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/return_delivered_order_items.py | .py | # Copyright Sierra
import json
from typing import Any, Dict, List
from tau_bench.envs.tool import Tool
class ReturnDeliveredOrderItems(Tool):
@staticmethod
def invoke(
data: Dict[str, Any], order_id: str, item_ids: List[str], payment_method_id: str
) -> str:
orders = data["orders"]
... | 83 | 3,632 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/modify_pending_order_items.py | .py | # Copyright Sierra
import json
from typing import Any, Dict, List
from tau_bench.envs.tool import Tool
class ModifyPendingOrderItems(Tool):
@staticmethod
def invoke(
data: Dict[str, Any],
order_id: str,
item_ids: List[str],
new_item_ids: List[str],
payment_method_id: s... | 130 | 5,707 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/transfer_to_human_agents.py | .py | # Copyright Sierra
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class TransferToHumanAgents(Tool):
@staticmethod
def invoke(data: Dict[str, Any], summary: str) -> str:
# This method simulates the transfer to a human agent.
return "Transfer successful"
@staticmethod
... | 35 | 1,200 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/__init__.py | .py | # Copyright Sierra
from .calculate import Calculate
from .cancel_pending_order import CancelPendingOrder
from .exchange_delivered_order_items import ExchangeDeliveredOrderItems
from .find_user_id_by_email import FindUserIdByEmail
from .find_user_id_by_name_zip import FindUserIdByNameZip
from .get_order_details import ... | 39 | 1,302 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/modify_pending_order_address.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class ModifyPendingOrderAddress(Tool):
@staticmethod
def invoke(
data: Dict[str, Any],
order_id: str,
address1: str,
address2: str,
city: str,
state: str,
c... | 90 | 3,212 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/list_all_product_types.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class ListAllProductTypes(Tool):
@staticmethod
def invoke(data: Dict[str, Any]) -> str:
products = data["products"]
product_dict = {
product["name"]: product["product_id"] for product ... | 32 | 1,035 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/get_product_details.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class GetProductDetails(Tool):
@staticmethod
def invoke(data: Dict[str, Any], product_id: str) -> str:
products = data["products"]
if product_id in products:
return json.dumps(products... | 35 | 1,104 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/find_user_id_by_email.py | .py | # Copyright Sierra
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class FindUserIdByEmail(Tool):
@staticmethod
def invoke(data: Dict[str, Any], email: str) -> str:
users = data["users"]
for user_id, profile in users.items():
if profile["email"].lower() == email.... | 35 | 1,132 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/calculate.py | .py | # Copyright Sierra
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class Calculate(Tool):
@staticmethod
def invoke(data: Dict[str, Any], expression: str) -> str:
if not all(char in "0123456789+-*/(). " for char in expression):
return "Error: invalid characters in express... | 37 | 1,324 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/get_user_details.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class GetUserDetails(Tool):
@staticmethod
def invoke(data: Dict[str, Any], user_id: str) -> str:
users = data["users"]
if user_id in users:
return json.dumps(users[user_id])
re... | 35 | 1,021 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/get_order_details.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class GetOrderDetails(Tool):
@staticmethod
def invoke(data: Dict[str, Any], order_id: str) -> str:
orders = data["orders"]
if order_id in orders:
return json.dumps(orders[order_id])
... | 35 | 1,087 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/modify_user_address.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class ModifyUserAddress(Tool):
@staticmethod
def invoke(
data: Dict[str, Any],
user_id: str,
address1: str,
address2: str,
city: str,
state: str,
country: s... | 85 | 2,920 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/find_user_id_by_name_zip.py | .py | # Copyright Sierra
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class FindUserIdByNameZip(Tool):
@staticmethod
def invoke(data: Dict[str, Any], first_name: str, last_name: str, zip: str) -> str:
users = data["users"]
for user_id, profile in users.items():
if (... | 51 | 2,013 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/tools/cancel_pending_order.py | .py | # Copyright Sierra
import json
from typing import Any, Dict
from tau_bench.envs.tool import Tool
class CancelPendingOrder(Tool):
@staticmethod
def invoke(data: Dict[str, Any], order_id: str, reason: str) -> str:
# check order exists and is pending
orders = data["orders"]
if order_id n... | 79 | 3,320 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/envs/retail/data/__init__.py | .py | # Copyright Sierra
import json
import os
from typing import Any
FOLDER_PATH = os.path.dirname(__file__)
def load_data() -> dict[str, Any]:
with open(os.path.join(FOLDER_PATH, "orders.json")) as f:
order_data = json.load(f)
with open(os.path.join(FOLDER_PATH, "products.json")) as f:
product_d... | 22 | 544 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/args.py | .py | import argparse
from tau_bench.model_utils.model.model import Platform
def api_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str)
parser.add_argument("--base-url", type=str)
parser.add_argument("--platform", type=str, required=True, choice... | 12 | 369 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/func_tools/filter.py | .py | from typing import Callable, Iterable, TypeVar
from tau_bench.model_utils.func_tools.map import map
T = TypeVar("T")
builtin_filter = filter
def filter(
func: Callable[[T], bool],
iterable: Iterable[T],
max_concurrency: int | None = None,
) -> Iterable[T]:
assert max_concurrency is None or max_conc... | 18 | 457 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/func_tools/map.py | .py | from concurrent.futures import ThreadPoolExecutor
from typing import Callable, Iterable, TypeVar
T = TypeVar("T")
U = TypeVar("U")
def map(
func: Callable[[T], U],
iterable: Iterable[T],
max_concurrency: int | None = None,
use_tqdm: bool = False,
) -> Iterable[U]:
assert max_concurrency is None o... | 21 | 593 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/openai.py | .py | import os
from tau_bench.model_utils.api.datapoint import Datapoint
from tau_bench.model_utils.model.chat import ChatModel, Message
from tau_bench.model_utils.model.completion import approx_cost_for_datapoint, approx_prompt_str
from tau_bench.model_utils.model.general_model import wrap_temperature
from tau_bench.model... | 124 | 4,133 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/utils.py | .py | import enum
import json
import re
from typing import Any, Optional, TypeVar
from pydantic import BaseModel, Field
from tau_bench.model_utils.api.types import PartialObj
T = TypeVar("T", bound=BaseModel)
class InputType(enum.Enum):
CHAT = "chat"
COMPLETION = "completion"
def display_choices(choices: list[... | 151 | 4,250 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/vllm_completion.py | .py | import os
from typing import Any
from pydantic import BaseModel
from tau_bench.model_utils.api.datapoint import Datapoint
from tau_bench.model_utils.model.completion import (
CompletionModel,
approx_cost_for_datapoint,
approx_prompt_str,
)
from tau_bench.model_utils.model.utils import approx_num_tokens
fr... | 122 | 4,467 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/vllm_utils.py | .py | from typing import Any
import requests
from tau_bench.model_utils.model.general_model import wrap_temperature
def generate_request(
url: str,
prompt: str,
temperature: float = 0.0,
force_json: bool = False,
**req_body_kwargs: Any,
) -> str:
args = {
"prompt": prompt,
"tempera... | 37 | 980 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/vllm_chat.py | .py | from tau_bench.model_utils.api.datapoint import Datapoint
from tau_bench.model_utils.model.chat import ChatModel, Message
from tau_bench.model_utils.model.completion import approx_cost_for_datapoint, approx_prompt_str
from tau_bench.model_utils.model.general_model import wrap_temperature
from tau_bench.model_utils.mode... | 130 | 4,759 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/outlines_completion.py | .py | from typing import Any
from pydantic import BaseModel
from tau_bench.model_utils.api.datapoint import Datapoint
from tau_bench.model_utils.model.vllm_completion import VLLMCompletionModel
from tau_bench.model_utils.model.vllm_utils import generate_request
class OutlinesCompletionModel(VLLMCompletionModel):
def ... | 37 | 1,186 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/model.py | .py | import abc
import enum
from typing import Any, TypeVar
from pydantic import BaseModel
from tau_bench.model_utils.api.datapoint import (
BinaryClassifyDatapoint,
ClassifyDatapoint,
Datapoint,
GenerateDatapoint,
ParseDatapoint,
ParseForceDatapoint,
ScoreDatapoint,
)
from tau_bench.model_util... | 131 | 3,154 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/completion.py | .py | import abc
import json
from typing import Any, TypeVar
from pydantic import BaseModel
from tau_bench.model_utils.api.datapoint import (
BinaryClassifyDatapoint,
ClassifyDatapoint,
Datapoint,
GenerateDatapoint,
ParseDatapoint,
ParseForceDatapoint,
ScoreDatapoint,
)
from tau_bench.model_util... | 539 | 18,672 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/chat.py | .py | import abc
import enum
import json
from typing import Any, TypeVar
from pydantic import BaseModel
from tau_bench.model_utils.api.datapoint import (
BinaryClassifyDatapoint,
ClassifyDatapoint,
Datapoint,
GenerateDatapoint,
ParseDatapoint,
ParseForceDatapoint,
ScoreDatapoint,
)
from tau_benc... | 609 | 21,999 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/exception.py | .py | from dataclasses import dataclass
from typing import Generic, TypeVar
T = TypeVar("T")
class ModelError(Exception):
def __init__(
self,
short_message: str,
prompt: str | list[dict[str, str]] | None = None,
response: str | None = None,
) -> None:
super().__init__(short_... | 24 | 522 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/mistral.py | .py | import os
from tau_bench.model_utils.api.datapoint import Datapoint
from tau_bench.model_utils.model.chat import ChatModel, Message
from tau_bench.model_utils.model.completion import approx_cost_for_datapoint, approx_prompt_str
from tau_bench.model_utils.model.general_model import wrap_temperature
from tau_bench.model... | 90 | 3,170 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/anyscale.py | .py | import os
from tau_bench.model_utils.api.datapoint import Datapoint
from tau_bench.model_utils.model.chat import ChatModel, Message
from tau_bench.model_utils.model.completion import approx_cost_for_datapoint, approx_prompt_str
from tau_bench.model_utils.model.general_model import wrap_temperature
from tau_bench.model... | 90 | 3,229 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/claude.py | .py | import json
import os
from tau_bench.model_utils.api.datapoint import Datapoint
from tau_bench.model_utils.model.chat import ChatModel, Message
from tau_bench.model_utils.model.completion import approx_cost_for_datapoint, approx_prompt_str
from tau_bench.model_utils.model.general_model import wrap_temperature
from tau... | 139 | 5,102 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/model/general_model.py | .py | import abc
from typing import Any, TypeVar
from pydantic import BaseModel
from tau_bench.model_utils.api.datapoint import (
BinaryClassifyDatapoint,
ClassifyDatapoint,
GenerateDatapoint,
ParseDatapoint,
ParseForceDatapoint,
ScoreDatapoint,
)
from tau_bench.model_utils.api.types import PartialO... | 188 | 5,529 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/api/types.py | .py | from typing import Any
PartialObj = dict[str, Any]
| 4 | 52 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/api/logging.py | .py | import functools
import inspect
import json
from multiprocessing import Lock
from typing import Any
from pydantic import BaseModel
from tau_bench.model_utils.api.sample import SamplingStrategy
from tau_bench.model_utils.model.utils import optionalize_type
log_files = {}
def prep_for_json_serialization(obj: Any, fr... | 75 | 2,692 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/api/_model_methods.py | .py | MODEL_METHODS = [
"classify",
"binary_classify",
"parse",
"generate",
"parse_force",
"score",
]
| 9 | 120 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/api/exception.py | .py | import json
import os
import time
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Callable, TypeVar
from tau_bench.model_utils.model.exception import ModelError, Result
T = TypeVar("T")
_REPORT_DIR = os.path.expanduser("~/.llm-primitives/log")
def set_report_dir(path: str) -> None:
gl... | 70 | 2,042 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/api/router.py | .py | import abc
from pydantic import BaseModel
from tau_bench.model_utils.api.datapoint import Datapoint, ScoreDatapoint
from tau_bench.model_utils.model.model import Model
class RequestRouter(abc.ABC):
@abc.abstractmethod
def route(self, dp: Datapoint, available_models: list[Model]) -> Model:
raise NotI... | 93 | 3,536 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/api/sample.py | .py | import abc
import functools
from multiprocessing import Lock
from typing import Any, Callable, TypeVar
from pydantic import BaseModel
from tau_bench.model_utils.api.exception import APIError, execute_and_filter_model_errors
from tau_bench.model_utils.model.exception import ModelError
from tau_bench.model_utils import... | 234 | 8,204 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/api/datapoint.py | .py | from __future__ import annotations
import abc
import json
from typing import Any, Callable, TypeVar
from pydantic import BaseModel
import tau_bench.model_utils
from tau_bench.model_utils.api._model_methods import MODEL_METHODS
from tau_bench.model_utils.api.exception import APIError
from tau_bench.model_utils.api.ty... | 300 | 9,514 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/api/api.py | .py | from __future__ import annotations
import argparse
from typing import Any, TypeVar
from pydantic import BaseModel
from tau_bench.model_utils.api._model_methods import MODEL_METHODS
from tau_bench.model_utils.api.cache import cache_call_w_dedup
from tau_bench.model_utils.api.datapoint import (
BinaryClassifyDatap... | 433 | 15,432 |
ai-agent-book | chapter2/prompt-engineering/tau_bench/model_utils/api/cache.py | .py | import functools
import inspect
import threading
from collections import defaultdict
from multiprocessing import Lock
from typing import Any, Callable, TypeVar
from pydantic import BaseModel
T = TypeVar("T")
class _CallableIdentity:
__slots__ = ("func",)
def __init__(self, func: Callable[..., Any]):
... | 116 | 3,225 |
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