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 | chapter7/elo-leaderboard/tests/test_optimize_empty.py | .py | """Regression: optimize_dataframe must tolerate empty object columns."""
import pandas as pd
from parallel_processing import optimize_dataframe
def test_optimize_empty_object_columns():
df = pd.DataFrame({
"model_a": pd.Series([], dtype=object),
"model_b": pd.Series([], dtype=object),
"win... | 14 | 420 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_load_battles_empty.py | .py | """Empty battles JSON array [] must load as an empty battle frame."""
import json
from pathlib import Path
import cli
def test_load_battles_empty_json_array(tmp_path):
path = tmp_path / "battles.json"
path.write_text("[]", encoding="utf-8")
df = cli._load_battles(str(path))
assert list(df.columns) ==... | 35 | 1,004 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_elo.py | .py | """
Unit tests for Elo rating system
"""
import math
import pytest
from _bootstrap import bootstrap_experiment_root
bootstrap_experiment_root()
from elo_rating import EloRatingSystem
def test_initial_rating():
"""Test that models start with initial rating."""
elo = EloRatingSystem(initial_rating=1000.0)
... | 171 | 5,154 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_filter_data_parallel_small.py | .py | """Regression: filter_data_parallel must tolerate n_jobs > len(df)."""
from unittest.mock import MagicMock, patch
import pandas as pd
from parallel_processing import filter_data_parallel
def test_n_jobs_larger_than_rows():
df = pd.DataFrame({"anony": [True, False, True], "turn": [1, 2, 1]})
def map_inline(... | 22 | 656 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_analyze_empty_history.py | .py | """Empty rating history must not crash analyze_rating_changes / get_rating_history."""
import pandas as pd
from animation import prepare_animation_data
from leaderboard import (
analyze_rating_changes,
build_historical_leaderboards,
get_rating_history,
)
def test_get_rating_history_empty_keeps_columns():... | 40 | 1,396 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_tie_bothbad.py | .py | """
Regression test for 'tie (bothbad)' handling in optimized_elo (实验 6-6 排行榜).
Chatbot Arena battle data has four outcomes; 'tie (bothbad)' was missing from
the outcome map, so Series.map produced NaN. NaN then propagated through the
rating updates and spread to every model that later faced an affected one,
leaving t... | 50 | 1,709 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_animation_empty_history.py | .py | """Regression: prepare_animation_data must tolerate empty history."""
import pandas as pd
from animation import prepare_animation_data
def test_empty_history_returns_empty_frames():
df = pd.DataFrame(columns=["date", "model", "rating", "rank", "matches", "wins"])
data = prepare_animation_data(df)
assert d... | 12 | 415 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_benchmark_zero_div.py | .py | """
Test suite locking out ZeroDivisionError in benchmark summary print logic
when time_basic is 0.0 or df_sample is empty.
"""
def test_benchmark_pct_reduction_zero_division():
"""
Ensure zero time_basic does not raise ZeroDivisionError during benchmark calculation.
"""
time_basic = 0.0
time_optim... | 24 | 767 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_zero_battles_bt.py | .py | """Empty battle DataFrame must not crash Bradley-Terry LogisticRegression."""
import pandas as pd
from bradley_terry import compute_bradley_terry_leaderboard, compute_mle_elo
def test_compute_mle_elo_empty_battles():
df = pd.DataFrame(columns=["model_a", "model_b", "winner"])
ratings = compute_mle_elo(df)
... | 33 | 1,101 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_filter_empty.py | .py | """
Regression test for filter_data on empty input (实验 6-6 排行榜).
An empty arena data file (e.g. a failed/truncated download saved as `[]`) used to
crash with ZeroDivisionError at the "After filtering" percentage print.
"""
import pandas as pd
import pytest
from _bootstrap import bootstrap_experiment_root
bootstrap_e... | 38 | 1,139 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_ch6_animation_string_date.py | .py | """Regression test for prepare_animation_data with string or date objects in history_df."""
import pandas as pd
from animation import prepare_animation_data
def test_prepare_animation_data_string_date():
"""prepare_animation_data must handle string dates without raising AttributeError."""
history = pd.DataFra... | 33 | 1,039 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_animation_tie_wins.py | .py | """prepare_animation_data must keep fractional wins from Elo ties."""
import pandas as pd
from animation import prepare_animation_data
def test_tie_half_wins_are_not_truncated():
history = pd.DataFrame(
{
"date": pd.to_datetime(["2024-01-07", "2024-01-07"]),
"model": ["A", "B"],
... | 36 | 1,040 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_validation_runner.py | .py | import json
from pathlib import Path
def test_canonical_manifest_is_hash_complete():
run_dir = Path(__file__).resolve().parents[1] / "validation" / "runs" / "exp6-6-arena-20260731-v1"
manifest_path = run_dir / "manifest.json"
assert manifest_path.exists()
manifest = json.loads(manifest_path.read_text(... | 21 | 705 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_ch6_compare_win_rates_empty.py | .py | """Regression test for compare_win_rates when comparisons list is empty."""
import numpy as np
import pandas as pd
from elo_rating import EloRatingSystem
from leaderboard import compare_win_rates
def test_compare_win_rates_empty_has_required_columns():
"""compare_win_rates must return a DataFrame with required co... | 18 | 794 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_bootstrap_reproducibility.py | .py | import pandas as pd
from bradley_terry import compute_mle_elo, get_bootstrap_result
def test_bootstrap_is_reproducible():
battles = pd.DataFrame(
[
{"model_a": "a", "model_b": "b", "winner": "model_a"},
{"model_a": "a", "model_b": "b", "winner": "model_b"},
{"model_a": ... | 17 | 636 |
ai-agent-book | chapter7/elo-leaderboard/tests/_bootstrap.py | .py | """Helpers for direct execution of tests moved under tests/."""
from pathlib import Path
import sys
def bootstrap_experiment_root() -> None:
experiment_root = Path(__file__).resolve().parents[1]
if str(experiment_root) not in sys.path:
sys.path.insert(0, str(experiment_root))
| 11 | 296 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_bt_tie_pivot.py | .py | """Ties must contribute to Bradley-Terry weights (not be zeroed by pivot+T)."""
import pandas as pd
from bradley_terry import compute_mle_elo
def test_all_ties_rates_models_instead_of_sample_weight_error():
df = pd.DataFrame(
[
{"model_a": "A", "model_b": "B", "winner": "tie"},
{"... | 42 | 1,413 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_bt_small_sample.py | .py | """Regression: compute_mle_elo must work on small Arena-shaped battle sets."""
import pandas as pd
from battle_simulator import simulate_battles
from bradley_terry import compute_mle_elo
def test_small_two_model_sample():
df = pd.DataFrame(simulate_battles({"gpt-4": 1200.0, "llama-3": 1000.0}, 10, seed=1))
ra... | 12 | 431 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_time_slices_monthly_interval.py | .py | """Regression: documented interval='M' must work on modern pandas."""
import pandas as pd
from data_loader import get_time_slices
def test_monthly_interval_alias():
df = pd.DataFrame({"tstamp": [1_700_000_000, 1_710_000_000]})
slices = get_time_slices(df, interval="M")
assert len(slices) >= 1
| 10 | 308 |
ai-agent-book | chapter7/elo-leaderboard/tests/test_time_slices_short_span.py | .py | """
Regression: get_time_slices must not IndexError when the tstamp span is
shorter than the requested interval (default weekly).
Chatbot Arena samples, same-second dumps, and single-row demos all produce an
empty pd.date_range for freq='W'; the old code then crashed on date_ranges[-1].
"""
import pandas as pd
from _... | 56 | 1,713 |
ai-agent-book | chapter7/elo-leaderboard/tests/conftest.py | .py | """Test import bootstrap for the elo-leaderboard experiment."""
from pathlib import Path
import sys
EXPERIMENT_ROOT = Path(__file__).resolve().parents[1]
if str(EXPERIMENT_ROOT) not in sys.path:
sys.path.insert(0, str(EXPERIMENT_ROOT))
| 10 | 243 |
ai-agent-book | chapter7/elo-leaderboard/validation/validate_evidence.py | .py | """Fail-closed verifier for a saved Experiment 6-6 run."""
from __future__ import annotations
import argparse
import hashlib
import json
from pathlib import Path
def sha256_file(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
while chunk := handle.read(8 * 1024 * 1024):... | 67 | 2,378 |
ai-agent-book | chapter7/elo-leaderboard/validation/run_experiment.py | .py | """Run the complete, evidence-producing Experiment 6-6 campaign.
The public Arena file is deliberately not copied into git. A canonical run
binds the exact input by URL, size, record count, and SHA-256, then retains all
derived tables, visualizations, the D3 history animation, and a manifest that
hashes every output ... | 390 | 15,148 |
ai-agent-book | chapter7/public-health-reporting-eval/evaluator.py | .py | """Objective structured scoring for public-health reporting agent traces."""
from __future__ import annotations
import json
import math
from pathlib import Path
from typing import Any
MAX_SCORE = 6
def load_json(path: str | Path) -> Any:
return json.loads(Path(path).read_text(encoding="utf-8"))
def expected... | 107 | 4,037 |
ai-agent-book | chapter7/public-health-reporting-eval/reporting_tools.py | .py | """Deterministic tools over synthetic DHIS2-style aggregate reports."""
from __future__ import annotations
import csv
from pathlib import Path
from typing import Any
INTEGER_FIELDS = {
"tests",
"confirmed_cases",
"deaths",
"report_expected",
"report_submitted",
"stockout_days",
}
class Rep... | 149 | 6,069 |
ai-agent-book | chapter7/public-health-reporting-eval/demo.py | .py | """Run the deterministic reference agent or evaluate external structured predictions."""
from __future__ import annotations
import argparse
import json
import os
from pathlib import Path
try:
from dotenv import load_dotenv
load_dotenv()
except ImportError:
pass
from agent import DeterministicReportingAg... | 65 | 2,001 |
ai-agent-book | chapter7/public-health-reporting-eval/agent.py | .py | """A deterministic reference agent for the reporting evaluation environment."""
from __future__ import annotations
from typing import Any
from reporting_tools import ReportingEnvironment
class DeterministicReportingAgent:
"""Executes the task's explicit tool plan and returns a structured trace."""
def __i... | 27 | 892 |
ai-agent-book | chapter7/public-health-reporting-eval/tests/test_offline.py | .py | """Offline regression tests; no model, API key or network access required."""
from __future__ import annotations
from copy import deepcopy
from pathlib import Path
from agent import DeterministicReportingAgent
from evaluator import MAX_SCORE, evaluate, expected_by_task, load_json, score_prediction
from reporting_too... | 69 | 2,655 |
ai-agent-book | chapter7/public-health-reporting-eval/tests/test_null_result.py | .py | """score_prediction must tolerate result:null like missing/empty result."""
from __future__ import annotations
from copy import deepcopy
from pathlib import Path
from agent import DeterministicReportingAgent
from evaluator import MAX_SCORE, expected_by_task, load_json, score_prediction
from reporting_tools import Re... | 72 | 2,466 |
ai-agent-book | chapter7/public-health-reporting-eval/tests/test_blank_csv_int.py | .py | """Blank integer CSV cells must load as 0, not ValueError from int('')."""
import csv
from pathlib import Path
from reporting_tools import ReportingEnvironment
def _write_csv(path: Path, tests_value: str) -> None:
with path.open("w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(
... | 77 | 2,316 |
ai-agent-book | chapter7/public-health-reporting-eval/tests/test_excel_float_int.py | .py | """Excel-style whole floats in integer CSV cells must load (10.0 -> 10)."""
import csv
from pathlib import Path
from reporting_tools import ReportingEnvironment
def _write_csv(path: Path, tests_value: str) -> None:
with path.open("w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(
... | 72 | 2,022 |
ai-agent-book | chapter7/public-health-reporting-eval/tests/conftest.py | .py | """Test import bootstrap for the public-health-reporting-eval experiment."""
from pathlib import Path
import sys
EXPERIMENT_ROOT = Path(__file__).resolve().parents[1]
if str(EXPERIMENT_ROOT) not in sys.path:
sys.path.insert(0, str(EXPERIMENT_ROOT))
| 10 | 256 |
ai-agent-book | chapter7/model-action-threshold/experiment.py | .py | #!/usr/bin/env python3
"""Controlled model action-threshold experiment for Chapter 6.
The experiment holds the coding harness, prompt, tools, task repositories, and
sampling order fixed while swapping only the model identifier. It measures
how much evidence a model gathers before its first edit and whether acting
ear... | 719 | 28,581 |
ai-agent-book | chapter7/model-action-threshold/tasks/crosscutting_identity/repo/accounts/models.py | .py | from dataclasses import dataclass
@dataclass(frozen=True)
class Profile:
username: str
email: str
password: str
| 9 | 126 |
ai-agent-book | chapter7/model-action-threshold/tasks/crosscutting_identity/repo/accounts/authentication.py | .py | def authenticate(store, username, password):
profile = store.find(username.strip())
if profile is None or profile.password != password:
return None
return profile
| 6 | 183 |
ai-agent-book | chapter7/model-action-threshold/tasks/crosscutting_identity/repo/accounts/directory.py | .py | def lookup_profile(store, username):
return store.find(username)
| 3 | 69 |
ai-agent-book | chapter7/model-action-threshold/tasks/crosscutting_identity/repo/accounts/store.py | .py | class AccountStore:
def __init__(self):
self._profiles = {}
def save(self, key, profile):
if key in self._profiles:
raise ValueError("username already exists")
self._profiles[key] = profile
def find(self, key):
return self._profiles.get(key)
| 12 | 300 |
ai-agent-book | chapter7/model-action-threshold/tasks/crosscutting_identity/repo/accounts/registration.py | .py | from accounts.models import Profile
def register(store, username, email, password):
display_name = username.strip()
key = display_name.lower()
profile = Profile(display_name, email, password)
store.save(key, profile)
return profile
| 10 | 254 |
ai-agent-book | chapter7/model-action-threshold/tasks/crosscutting_identity/repo/tests/test_accounts.py | .py | import unittest
from accounts.authentication import authenticate
from accounts.directory import lookup_profile
from accounts.registration import register
from accounts.store import AccountStore
class AccountIdentityTests(unittest.TestCase):
def setUp(self):
self.store = AccountStore()
def test_regis... | 36 | 1,357 |
ai-agent-book | chapter7/model-action-threshold/tasks/cache_contract/repo/cachelib/catalog.py | .py | class Catalog:
def __init__(self, cache, fetch_product):
self.cache = cache
self.fetch_product = fetch_product
def product(self, product_id):
return self.cache.get_or_load(product_id, self.fetch_product)
| 8 | 237 |
ai-agent-book | chapter7/model-action-threshold/tasks/cache_contract/repo/cachelib/__init__.py | .py | from cachelib.cache import Cache
__all__ = ["Cache"]
| 4 | 54 |
ai-agent-book | chapter7/model-action-threshold/tasks/cache_contract/repo/cachelib/cache.py | .py | class Cache:
def __init__(self):
self._values = {}
def get(self, key, default=None):
return self._values.get(key, default)
def put(self, key, value):
self._values[key] = value
def get_or_load(self, key, loader):
cached = self.get(key)
if cached is not None:
... | 18 | 421 |
ai-agent-book | chapter7/model-action-threshold/tasks/cache_contract/repo/tests/test_cache.py | .py | import unittest
from cachelib import Cache
class CacheContractTests(unittest.TestCase):
def test_negative_lookup_is_loaded_once(self):
cache = Cache()
calls = []
def loader(key):
calls.append(key)
return None
self.assertIsNone(cache.get_or_load("missing",... | 41 | 1,104 |
ai-agent-book | chapter7/model-action-threshold/tasks/localized_timeout/repo/tests/test_config.py | .py | import unittest
from app.config import DEFAULT_TIMEOUT, resolve_timeout
class ResolveTimeoutTests(unittest.TestCase):
def test_explicit_value_wins_over_environment(self):
self.assertEqual(resolve_timeout(12, {"AGENT_TIMEOUT": "45"}), 12)
def test_environment_is_used_without_explicit_value(self):
... | 21 | 695 |
ai-agent-book | chapter7/model-action-threshold/tasks/localized_timeout/repo/app/worker.py | .py | """Worker construction kept separate from configuration parsing."""
from app.config import resolve_timeout
def worker_options(timeout=None, env=None):
return {"timeout": resolve_timeout(timeout, env), "retries": 2}
| 8 | 222 |
ai-agent-book | chapter7/model-action-threshold/tasks/localized_timeout/repo/app/config.py | .py | """Runtime timeout configuration."""
DEFAULT_TIMEOUT = 30
def resolve_timeout(explicit=None, env=None):
"""Return a positive timeout using explicit > environment > default."""
env = env or {}
raw = env.get("AGENT_TIMEOUT", explicit)
try:
value = int(raw)
except (TypeError, ValueError):
... | 15 | 400 |
ai-agent-book | chapter7/model-action-threshold/tests/test_experiment.py | .py | import importlib.util
import sys
import tempfile
import unittest
from pathlib import Path
MODULE_PATH = Path(__file__).resolve().parents[1] / "experiment.py"
SPEC = importlib.util.spec_from_file_location("action_threshold_experiment", MODULE_PATH)
experiment = importlib.util.module_from_spec(SPEC)
sys.modules[SPEC.na... | 118 | 5,027 |
ai-agent-book | chapter7/tts-quality-eval/pipeline.py | .py | """TTS 质量评估流水线的核心步骤。
一条评估链路：
合成(OpenAI TTS) -> 时长探测(ffprobe) -> 回译(Whisper) -> 计算 CER/字准确率
-> LLM Rubric 打分(gpt-5.6-luna) [可选: Gemini 音频评审 gemini-3.5-flash]
说明：TTS 合成与 Whisper 回译必须走 OpenAI 直连；文本 Rubric 与直接听音频的
多模态 Rubric 支持 Google Gemini、OpenRouter 与 Mistral Voxtral。每条路径都把
两段真实音频交给音频模型，不会退化成转写文本评审。
所有对外函数都做了... | 826 | 33,722 |
ai-agent-book | chapter7/tts-quality-eval/test_minimax_t2a_refresh.py | .py | """
Regression tests for the Minimax t2a_v2 synthesis adapter (实验 6-5 TTS 质量评估).
Locks in the refreshed contract:
- the request targets the /v1/t2a_v2 endpoint with Bearer auth and no GroupId
query parameter, on the global host by default and the mainland-China host
when MINIMAX_REGION selects it;
- the de... | 110 | 3,963 |
ai-agent-book | chapter7/tts-quality-eval/demo.py | .py | """实验 6-5：全自动 TTS 质量评估流水线 —— 一条命令跑通。
python demo.py # 默认 4 个 OpenAI 配置 x 4 条语料
python demo.py --providers openai,minimax # 跨服务商横向对比
python demo.py --text '一段话' # 自定义文本
python demo.py --gemini # 评审改用多模态模型直接听两段音频
python demo.py --quick # 只用前 2 条语料... | 419 | 17,662 |
ai-agent-book | chapter7/tts-quality-eval/config.py | .py | """实验 6-5：全自动 TTS 质量评估流水线 —— 配置与测试语料。
本模块集中管理：
- 用到的 OpenAI 模型名与计费单价（仅供参考成本估算）；
- 多个 TTS「配置」（model / voice / speed 的组合，作为待对比的对象）；
- 一组带挑战性的参考文本（数字 / 多音字 / 长句 / 专有名词 + 情感）。
"""
import os
from dataclasses import dataclass, field
from dotenv import load_dotenv
load_dotenv()
# ------------------------------------... | 204 | 8,717 |
ai-agent-book | chapter7/tts-quality-eval/tests/test_resolve_gemini_model_null.py | .py | """
Test suite locking out TypeError in _resolve_gemini_model
when API returns data with models: None or non-dict items.
"""
import json
from unittest.mock import MagicMock
from pipeline import _resolve_gemini_model, config
def test_resolve_gemini_model_handles_null_models():
"""
Ensure _resolve_gemini_mode... | 26 | 798 |
ai-agent-book | chapter7/tts-quality-eval/tests/test_judge_robustness.py | .py | """
Regression tests for judge-response robustness (实验 6-5 TTS 质量评估).
Covers two failure classes on LLM/Gemini judge responses:
- judge_rubric: judge returns "score": null (or a bare null dimension) -> int(None) TypeError
- judge_gemini_audio: safety-blocked Gemini responses have no
candidates/content/parts ->... | 255 | 10,164 |
ai-agent-book | chapter7/tts-quality-eval/tests/conftest.py | .py | """Test import bootstrap for the tts-quality-eval experiment."""
from pathlib import Path
import sys
EXPERIMENT_ROOT = Path(__file__).resolve().parents[1]
if str(EXPERIMENT_ROOT) not in sys.path:
sys.path.insert(0, str(EXPERIMENT_ROOT))
| 10 | 244 |
ai-agent-book | chapter3/memory_rag_campaign.py | .py | #!/usr/bin/env python3
"""Shared canonical campaign for Experiments 3-9 and 3-11.
Experiment 3-9 is the plain fixed-window, agent-searched arm. Experiment
3-11 replays the exact same live agent-generated queries against plain and
live-contextualized indexes, then adds the live Advanced JSON Cards produced by
Experime... | 615 | 29,616 |
ai-agent-book | chapter3/experiment_utils.py | .py | """Shared evidence helpers for the Chapter 3 experiment campaigns.
The helpers deliberately never read or serialize credential values. Provider
keys are supplied directly to SDK clients by each campaign; receipts contain
only the provider name, endpoint, model, request payload, raw response, usage,
and latency needed... | 212 | 7,084 |
ai-agent-book | chapter3/user-memory-evaluation/evaluator.py | .py | """LLM-based evaluator for agent responses."""
import json
from typing import Dict, Any, Optional
from tenacity import retry, stop_after_attempt, wait_exponential
import openai
from config import Config
from models import (
TestCase,
EvaluationResult,
RubricDimensionResult,
RubricGrade,
Hallucinati... | 448 | 19,291 |
ai-agent-book | chapter3/user-memory-evaluation/main.py | .py | """Main script to run user memory evaluation tests."""
import argparse
import json
from typing import Dict, Optional
from pathlib import Path
from rich.console import Console
from rich.prompt import Prompt, Confirm
from framework import UserMemoryEvaluationFramework, TestCaseExporter
from evaluator import LLMEvaluato... | 472 | 19,376 |
ai-agent-book | chapter3/user-memory-evaluation/models.py | .py | """Data models for the User Memory Evaluation Framework."""
from typing import List, Dict, Any, Optional
from pydantic import BaseModel, Field
from enum import Enum
class MessageRole(str, Enum):
"""Message roles in conversation."""
USER = "user"
ASSISTANT = "assistant"
SYSTEM = "system"
class Rubri... | 151 | 6,184 |
ai-agent-book | chapter3/user-memory-evaluation/validate_rubric.py | .py | #!/usr/bin/env python3
"""Run and persist a live Experiment 6-3 structured-rubric validation."""
import argparse
import json
import time
from pathlib import Path
from evaluator import LLMEvaluator
from framework import UserMemoryEvaluationFramework
def main() -> int:
parser = argparse.ArgumentParser()
parse... | 56 | 2,135 |
ai-agent-book | chapter3/user-memory-evaluation/comparison.py | .py | """Cross-system comparison for the User Memory Evaluation Framework.
Experiment 3-1 evaluates *memory systems*: the same three-layer test suite is run
against several memory configurations (e.g. Simple Notes vs. Advanced JSON Cards)
and their scores are compared side by side. This module takes a mapping of
{syste... | 205 | 8,449 |
ai-agent-book | chapter3/user-memory-evaluation/__init__.py | .py | """User Memory Evaluation Framework.
The companion is intentionally executed from this directory (``python main.py``)
because the historical directory name contains a hyphen. Keep this initializer
free of eager relative imports so pytest and importlib can collect the directory
without pretending it is a normally name... | 10 | 356 |
ai-agent-book | chapter3/user-memory-evaluation/metrics.py | .py | """Offline, deterministic metrics for the User Memory Evaluation Framework.
The LLM-as-judge evaluator in ``evaluator.py`` requires an API key and network
access. This module provides a complementary metric that runs fully offline on
canned data, so the benchmark can produce a scored comparison across memory
systems w... | 125 | 4,643 |
ai-agent-book | chapter3/user-memory-evaluation/test_structured_rubric.py | .py | """Deterministic acceptance tests for book Experiment 6-3."""
import json
from evaluator import LLMEvaluator
def evaluator_without_network():
evaluator = object.__new__(LLMEvaluator)
return evaluator
def response(*, hallucination=False):
return json.dumps(
{
"dimensions": {
... | 103 | 4,186 |
ai-agent-book | chapter3/user-memory-evaluation/generate_test_cases.py | .py | #!/usr/bin/env python3
"""
Generate remaining test cases for the user memory evaluation framework.
This script creates properly structured YAML test cases for layers 1, 2, and 3.
"""
import yaml
import os
from typing import Dict, List, Any
# Layer 2 test case templates (disambiguation scenarios)
LAYER2_TEMPLATES = [
... | 330 | 11,395 |
ai-agent-book | chapter3/user-memory-evaluation/config.py | .py | """Configuration module for User Memory Evaluation Framework."""
import os
from typing import Optional
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
def _openrouter_model_id(model) -> str:
"""Map a provider-native model name to an OpenRouter model id, used by the
universal OpenRo... | 107 | 4,356 |
ai-agent-book | chapter3/user-memory-evaluation/framework.py | .py | """Main framework for User Memory Evaluation."""
import os
import yaml
from typing import List, Dict, Optional, Any
from pathlib import Path
from rich.console import Console
from rich.table import Table
from rich.panel import Panel
from rich import print as rprint
from config import Config
from models import (
Te... | 429 | 16,534 |
ai-agent-book | chapter3/dense-embedding/quick_demo.py | .py | #!/usr/bin/env python3
"""Quick demo script to showcase the vector similarity search service."""
import time
import sys
def print_section(title):
"""Print a formatted section header."""
print("\n" + "=" * 60)
print(f" {title}")
print("=" * 60)
def main():
"""Run a quick demo of the service."""... | 134 | 3,764 |
ai-agent-book | chapter3/dense-embedding/logger.py | .py | """Educational logging configuration with extensive debug information."""
import logging
import sys
import time
from typing import Optional
import colorlog
from functools import wraps
def setup_logger(name: str = "vector_search", level: str = "DEBUG") -> logging.Logger:
"""
Set up a colorful and informative ... | 158 | 6,179 |
ai-agent-book | chapter3/dense-embedding/test_client.py | .py | """Test client for the vector similarity search service."""
import requests
import json
import time
from typing import List, Dict, Any
class VectorSearchClient:
"""Client for testing the vector search service."""
def __init__(self, base_url: str = "http://localhost:8000"):
"""Initialize the clie... | 253 | 9,852 |
ai-agent-book | chapter3/dense-embedding/main.py | .py | """Main FastAPI application for vector similarity search service."""
import time
import argparse
from typing import List, Optional, Dict, Any
from contextlib import asynccontextmanager
import uvicorn
from fastapi import FastAPI, HTTPException, Query
from fastapi.middleware.cors import CORSMiddleware
from pydantic impo... | 437 | 13,327 |
ai-agent-book | chapter3/dense-embedding/indexing.py | .py | """Vector index implementations using ANNOY and HNSW."""
from abc import ABC, abstractmethod
from typing import List, Tuple, Dict, Optional
import numpy as np
import annoy
import hnswlib
import time
from logger import VectorSearchLogger
class VectorIndex(ABC):
"""Abstract base class for vector indexes."""
... | 391 | 14,977 |
ai-agent-book | chapter3/dense-embedding/embedding_service.py | .py | """Embedding service using BGE-M3 model."""
import time
import numpy as np
from typing import List, Dict, Optional
from FlagEmbedding import BGEM3FlagModel
from logger import VectorSearchLogger, log_execution_time
import logging
class EmbeddingService:
"""Service for generating embeddings using BGE-M3 model."""
... | 219 | 8,191 |
ai-agent-book | chapter3/dense-embedding/benchmark.py | .py | #!/usr/bin/env python3
"""Real-embedding ANNOY vs HNSW benchmark for Experiment 3-4."""
from __future__ import annotations
import argparse
import json
import os
import statistics
import subprocess
import sys
import tempfile
import time
from pathlib import Path
from typing import Any, Dict, List, Sequence
import nump... | 304 | 13,899 |
ai-agent-book | chapter3/dense-embedding/test_empty_batch_encode.py | .py | """
Test suite locking out ZeroDivisionError in EmbeddingService.encode_batch
when an empty texts list is provided.
"""
import os
import sys
from unittest.mock import MagicMock
# Mock third-party dependencies before importing embedding_service
sys.modules['FlagEmbedding'] = MagicMock()
sys.modules['colorlog'] = Magic... | 35 | 981 |
ai-agent-book | chapter3/dense-embedding/docker_annoy_runner.py | .py | #!/usr/bin/env python3
"""Linux-isolated ANNOY measurement used when the host ARM wheel is broken."""
import json
import os
import statistics
import sys
import tempfile
import time
import numpy as np
from annoy import AnnoyIndex
def latency_stats(values):
return {
"mean": statistics.mean(values),
... | 88 | 2,980 |
ai-agent-book | chapter3/dense-embedding/cli.py | .py | #!/usr/bin/env python3
"""
稠密检索命令行工具（实验 3-4）
在一个小型示例语料上运行稠密嵌入检索，支持：
- 自定义语料 / 查询 / top-k / 输出文件
- --eval：在带标注的小型评测集上计算 recall@k / precision@k / MRR，
直观展示"稠密嵌入读得懂同义表达"这一核心卖点
- --compare-ann：复现书中实验 3-4 的重点——对比 ANNOY 与 HNSW 两种 ANN 后端
相对精确暴力检索的召回率、建索引耗时与查询延迟（复用服务端 indexing.py）
- --embedding... | 453 | 22,866 |
ai-agent-book | chapter3/dense-embedding/config.py | .py | """Configuration for the dense embedding service."""
import os
from enum import Enum
from dataclasses import dataclass, field
from typing import Optional
class IndexType(Enum):
"""Supported index types."""
ANNOY = "annoy"
HNSW = "hnsw"
@dataclass
class ServiceConfig:
"""Service configuration."""
... | 59 | 1,646 |
ai-agent-book | chapter3/dense-embedding/document_store.py | .py | """In-memory document store for managing documents."""
from typing import Dict, Optional, List
from dataclasses import dataclass, field
from datetime import datetime
import uuid
from logger import VectorSearchLogger
@dataclass
class Document:
"""Document data class."""
id: str
text: str
metadata: Dic... | 194 | 5,746 |
ai-agent-book | chapter3/retrieval-pipeline/test_client.py | .py | """Test client with educational test cases for dense vs sparse retrieval."""
import asyncio
import httpx
import json
from typing import List, Dict, Any
import logging
from datetime import datetime
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class TestClient:
"""Test client for th... | 498 | 18,816 |
ai-agent-book | chapter3/retrieval-pipeline/main.py | .py | """FastAPI server for the retrieval pipeline."""
import logging
import sys
from typing import Dict, Any, Optional, List
from contextlib import asynccontextmanager
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
import uvicorn
import as... | 290 | 9,895 |
ai-agent-book | chapter3/retrieval-pipeline/retrieval_pipeline.py | .py | """Main retrieval pipeline combining dense, sparse, and reranking."""
import asyncio
import logging
from typing import Dict, Any, List, Optional, Tuple
from datetime import datetime
import uuid
from config import PipelineConfig, SearchMode
from document_store import DocumentStore
from retrieval_client import Retrieva... | 422 | 16,288 |
ai-agent-book | chapter3/retrieval-pipeline/campaign.py | .py | #!/usr/bin/env python3
"""Canonical real-model campaign for Chapter 3 Experiment 3-6."""
from __future__ import annotations
import json
import sys
from pathlib import Path
PROJECT_DIR = Path(__file__).resolve().parent
CHAPTER_DIR = PROJECT_DIR.parent
sys.path.insert(0, str(CHAPTER_DIR))
from experiment_utils import... | 137 | 5,019 |
ai-agent-book | chapter3/retrieval-pipeline/stage_evaluator.py | .py | """Stage-level evaluation for the hybrid retrieval pipeline.
The book's 第3章「混合检索流水线」(experiment 3-6) chains four retrieval
stages — dense retrieval, sparse retrieval, fusion of the two, and neural
reranking — and judges the whole pipeline with aggregate metrics. What it does
not provide is an *automated, stage-level* ... | 418 | 17,194 |
ai-agent-book | chapter3/retrieval-pipeline/reranker.py | .py | """Reranker module using BGE-Reranker-v2 model."""
import torch
from typing import List, Tuple, Dict, Any, Optional
from dataclasses import dataclass
from FlagEmbedding import FlagReranker
import logging
import time
import numpy as np
import os
import sys
from pathlib import Path
from huggingface_hub import snapshot_d... | 268 | 9,960 |
ai-agent-book | chapter3/retrieval-pipeline/test_weighted_fusion_dedup.py | .py | import pytest
from fusion import weighted_score_fusion
def test_weighted_score_fusion_preserves_top_score_on_duplicate_doc_id():
"""Verify weighted score fusion preserves top score when duplicate doc_ids exist in ranked list."""
ranked_lists = {
"dense": [("doc1", 0.95), ("doc2", 0.80), ("doc1", 0.10)]... | 16 | 541 |
ai-agent-book | chapter3/retrieval-pipeline/retrieval_client.py | .py | """Client for communicating with dense and sparse embedding services."""
import httpx
import asyncio
from typing import Dict, Any, List, Optional, Tuple
import logging
from dataclasses import dataclass
logger = logging.getLogger(__name__)
@dataclass
class SearchResult:
"""Unified search result from embedding ser... | 202 | 9,004 |
ai-agent-book | chapter3/retrieval-pipeline/test_pipeline.py | .py | #!/usr/bin/env python3
"""Test script for the retrieval pipeline with external doc_id support."""
import httpx
import asyncio
import json
import logging
from datetime import datetime
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger... | 201 | 9,057 |
ai-agent-book | chapter3/retrieval-pipeline/evaluate.py | .py | """混合检索流水线离线评测 CLI。
本脚本把整条检索流水线——分块(chunk) → 嵌入(embed) → 检索(retrieve) →
融合(fuse) → 重排(rerank)——完整地跑在**单进程、可离线**的环境里，并在一个带
标注答案的小型评测集上，逐阶段对比各方法的检索质量。它不依赖 dense/sparse 微服务
（4240/4241/4242 端口），因此可以脱离服务、直接用本地模型复现「每加一个阶段、指标如何
提升」这一核心结论。
各阶段使用的本地组件：
- 稀疏检索(sparse) : BM25（纯 Python，rank_bm25，无需下载模型）
- 稠密检索(dense) : 本地... | 772 | 37,506 |
ai-agent-book | chapter3/retrieval-pipeline/demo.py | .py | """Demo script showcasing dense vs sparse embedding strengths.
Service Configuration:
- Dense Embedding: http://localhost:4240
- Sparse Embedding: http://localhost:4241
- Retrieval Pipeline: http://localhost:4242
"""
import asyncio
import httpx
from typing import Dict, List
import json
class RetrievalDemo:
"""... | 282 | 11,260 |
ai-agent-book | chapter3/retrieval-pipeline/fusion.py | .py | """Result fusion for hybrid retrieval.
This module implements the *fusion* stage of the hybrid retrieval pipeline —
the step that merges the separately-ranked dense and sparse candidate lists into
a single, unified candidate pool before neural reranking.
Two production-grade fusion strategies are provided, matching t... | 152 | 5,718 |
ai-agent-book | chapter3/retrieval-pipeline/test_improvements.py | .py | #!/usr/bin/env python3
"""Test script to verify the improvements made to the retrieval pipeline."""
import subprocess
import time
import requests
import sys
import signal
def test_server_startup():
"""Test that the server starts without deprecation warnings."""
print("=" * 60)
print("Testing Server Startu... | 81 | 2,561 |
ai-agent-book | chapter3/retrieval-pipeline/config.py | .py | """Configuration for the retrieval pipeline."""
import os
from dataclasses import dataclass, field
from enum import Enum
from typing import Optional
class SearchMode(str, Enum):
"""Search mode for retrieval."""
DENSE = "dense"
SPARSE = "sparse"
HYBRID = "hybrid" # Both dense and sparse
@dataclass
cl... | 71 | 2,593 |
ai-agent-book | chapter3/retrieval-pipeline/document_store.py | .py | """Document store for the retrieval pipeline."""
from typing import Dict, Any, List, Optional
from datetime import datetime
import logging
logger = logging.getLogger(__name__)
class DocumentStore:
"""In-memory document store for educational purposes."""
def __init__(self):
self.documents: Dict[s... | 99 | 3,765 |
ai-agent-book | chapter3/contextual-retrieval-for-user-memory/quickstart.py | .py | #!/usr/bin/env python3
"""Quick start script for Agentic RAG User Memory Evaluation
This script provides a simple demo to get started with the system.
"""
import os
import sys
from pathlib import Path
from rich.console import Console
from rich.panel import Panel
# Check for required environment variables
console = C... | 163 | 5,673 |
ai-agent-book | chapter3/contextual-retrieval-for-user-memory/evaluator.py | .py | """Evaluation Framework Integration for User Memory RAG Agent
This module integrates with the user-memory-evaluation framework to load
test cases and evaluate the agent's performance.
"""
import os
import sys
import json
import yaml
import logging
from typing import List, Dict, Any, Optional, Tuple
from pathlib impor... | 646 | 26,230 |
ai-agent-book | chapter3/contextual-retrieval-for-user-memory/contextual_indexer.py | .py | """Contextual RAG Indexer with Advanced Memory Cards
This module combines contextual chunking for conversation histories with
advanced JSON cards for structured user memory.
"""
import os
import json
import logging
import requests
import time
from datetime import datetime
from typing import List, Dict, Any, Optional,... | 673 | 29,137 |
ai-agent-book | chapter3/contextual-retrieval-for-user-memory/test_top_k.py | .py | #!/usr/bin/env python3
"""Test that top_k parameter works correctly with the retrieval pipeline"""
import os
import logging
# Set up logging
logging.basicConfig(level=logging.INFO)
# Set dummy API key
os.environ["KIMI_API_KEY"] = "test-kimi-key"
from config import IndexConfig
from indexer import MemoryIndexer
from ... | 108 | 3,741 |
ai-agent-book | chapter3/contextual-retrieval-for-user-memory/indexer.py | .py | """RAG Indexer for User Memory Conversations
This module handles indexing of conversation chunks using the retrieval pipeline service.
Interfaces with the existing retrieval pipeline on port 4242.
"""
import os
import json
import logging
import requests
from typing import List, Dict, Any, Optional, Tuple
from datacla... | 458 | 17,816 |
ai-agent-book | chapter3/contextual-retrieval-for-user-memory/main.py | .py | #!/usr/bin/env python3
"""Main entry point for Contextual Retrieval + Advanced Memory Cards System
This demonstrates the dual-layer memory system combining:
1. Contextual chunking for conversation history
2. Advanced JSON cards for structured facts
"""
import argparse
import json
import logging
import sys
from pathli... | 895 | 36,092 |
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