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chore: import upstream snapshot with attribution
2026-07-13 11:56:03 +08:00

141 行
5.8 KiB
Python

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"""Tests for image-token accounting in the context compressor.
Covers the native-image-routing PR's companion change: the compressor's
multimodal message length counter now charges ~1600 tokens per attached
image part instead of 0, so tail-cut / prune decisions are accurate for
creative workflows that iterate on images across many turns.
"""
from __future__ import annotations
from agent.context_compressor import (
_CHARS_PER_TOKEN,
_IMAGE_CHAR_EQUIVALENT,
_IMAGE_TOKEN_ESTIMATE,
_content_length_for_budget,
)
class TestContentLengthForBudget:
def test_plain_string(self):
assert _content_length_for_budget("hello world") == 11
def test_empty_string(self):
assert _content_length_for_budget("") == 0
def test_none_coerces_to_zero(self):
assert _content_length_for_budget(None) == 0
def test_text_only_list(self):
content = [
{"type": "text", "text": "first"},
{"type": "text", "text": "second"},
]
assert _content_length_for_budget(content) == 5 + 6
def test_single_image_part_charges_fixed_budget(self):
content = [
{"type": "text", "text": "look"},
{"type": "image_url", "image_url": {"url": "data:image/png;base64,XXXX"}},
]
# 4 chars of text + 1 image at fixed char-equivalent
assert _content_length_for_budget(content) == 4 + _IMAGE_CHAR_EQUIVALENT
def test_image_url_raw_base64_is_not_counted_as_chars(self):
"""A 1MB base64 blob inside an image_url must NOT inflate token count.
The flat image estimate is what the provider actually bills; the raw
base64 is transport payload, not context tokens.
"""
huge_url = "data:image/png;base64," + ("A" * 1_000_000)
content = [
{"type": "image_url", "image_url": {"url": huge_url}},
]
# Exactly one image's worth, not 1M + something.
assert _content_length_for_budget(content) == _IMAGE_CHAR_EQUIVALENT
def test_multiple_image_parts(self):
content = [
{"type": "text", "text": "compare"},
{"type": "image_url", "image_url": {"url": "data:image/png;base64,AAA"}},
{"type": "image_url", "image_url": {"url": "data:image/png;base64,BBB"}},
{"type": "image_url", "image_url": {"url": "data:image/png;base64,CCC"}},
]
assert _content_length_for_budget(content) == 7 + 3 * _IMAGE_CHAR_EQUIVALENT
def test_openai_responses_input_image_shape(self):
"""Responses API uses type=input_image with top-level image_url string."""
content = [
{"type": "input_text", "text": "hey"},
{"type": "input_image", "image_url": "data:image/png;base64,XX"},
]
# input_text has .text "hey" (3 chars) + 1 image
assert _content_length_for_budget(content) == 3 + _IMAGE_CHAR_EQUIVALENT
def test_anthropic_native_image_shape(self):
"""Anthropic native shape: {type: image, source: {...}}."""
content = [
{"type": "text", "text": "hi"},
{"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": "XX"}},
]
assert _content_length_for_budget(content) == 2 + _IMAGE_CHAR_EQUIVALENT
def test_bare_string_part_in_list(self):
"""Older code paths sometimes produce mixed list-of-strings content."""
content = ["hello", {"type": "text", "text": "world"}]
assert _content_length_for_budget(content) == 5 + 5
def test_image_estimate_constant_is_reasonable(self):
"""Sanity-check the estimate aligns with real provider billing.
Anthropic ≈ width*height/750 → ~1600 for 1000×1200.
OpenAI GPT-4o high-detail 2048×2048 ≈ 1445.
Gemini 258/tile × 6 tiles for a 2048×2048 ≈ 1548.
Anything in the 800-2000 range is defensible. Enforce bounds so an
accidental edit doesn't drop it to e.g. 16.
"""
assert 800 <= _IMAGE_TOKEN_ESTIMATE <= 2500
assert _IMAGE_CHAR_EQUIVALENT == _IMAGE_TOKEN_ESTIMATE * _CHARS_PER_TOKEN
class TestTokenBudgetWithImages:
"""Integration: the compressor's tail-cut decision now respects image cost."""
def test_image_heavy_turns_count_toward_budget(self):
"""A tail with 5 image-bearing turns should blow past a 5K token budget."""
from agent.context_compressor import ContextCompressor
# Minimal compressor fixture — just enough to call _find_tail_cut_by_tokens
cc = object.__new__(ContextCompressor)
cc.tail_token_budget = 5000
# Build 10 messages: 5 with images, 5 with short text. Without the
# image-tokens fix, the compressor would think all 10 fit in 5K and
# protect them all. With the fix, images alone cost 5 × 1600 = 8K,
# so the tail should be trimmed.
messages = [{"role": "system", "content": "sys"}]
for i in range(5):
messages.append({
"role": "user",
"content": [
{"type": "text", "text": f"turn {i}"},
{"type": "image_url", "image_url": {"url": "data:image/png;base64,AAA"}},
],
})
messages.append({
"role": "assistant",
"content": f"response {i}",
})
cut = cc._find_tail_cut_by_tokens(messages, head_end=0, token_budget=5000)
# Budget is 5K, soft ceiling 7.5K. 5 images alone = 8000 image-tokens.
# Walking backward, the compressor should stop before including all 5.
# Exact cut depends on text lengths and min_tail, but it MUST be > 1
# (at least some head-side messages should be compressible).
assert cut > 1, (
f"Expected image-heavy tail to be trimmed; compressor placed cut at "
f"{cut} out of {len(messages)} (image tokens were likely ignored)."
)