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dc1b199 b76f199 dc1b199 b76f199 dc1b199 faa8fb3 dc1b199 b76f199 dc1b199 da8a68d 62f2173 da8a68d 62f2173 da8a68d 62f2173 da8a68d 62f2173 da8a68d 62f2173 da8a68d dc1b199 b76f199 dc1b199 b76f199 dc1b199 62f2173 dc1b199 752c1cd 865bc90 dc1b199 62f2173 dc1b199 752c1cd 62f2173 dc1b199 b76f199 dc1b199 b76f199 dc1b199 752c1cd dc1b199 752c1cd dc1b199 0136798 b76f199 0136798 2cfebbc 0136798 3c31a2a b76f199 3c31a2a faa8fb3 3c31a2a b76f199 3c31a2a faa8fb3 3c31a2a 2cfebbc 3c31a2a 2cfebbc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 | """Tests for the LLM adapter, prompt building, and post-processor.
All OpenAI calls are mocked. These tests verify:
1. The prompt context stays within MAX_CONTEXT_TOKENS.
2. The post-processor replaces invented facts with approved missing-info phrases.
3. Verifiable facts are NOT replaced.
"""
import pytest
from app.chunking.splitter import count_tokens
from app.generator.adapter import MockLLMAdapter
from app.generator.postprocess import enforce_verify, strip_verify_tags
from app.generator.prompts import _trim_snippets, build_user_prompt
# ββ Prompt building tests βββββββββββββββββββββββββββββββββββββββββββββββββββββ
def test_build_user_prompt_contains_skeleton() -> None:
"""The assembled prompt should include the skeleton text."""
prompt = build_user_prompt(
skeleton="[Location]: [description].",
bullets=["Semi-detached, NW3"],
snippets=[],
max_context_tokens=400,
)
assert "[Location]" in prompt
assert "DOCUMENT-LEVEL CONTEXT" in prompt
assert "SECTION-LEVEL CONTEXT" in prompt
assert "PARAGRAPH-LEVEL EVIDENCE" in prompt
assert "PROPERTY IDENTITY" in prompt
def test_build_user_prompt_contains_bullets() -> None:
"""Bullets should appear in the assembled prompt."""
prompt = build_user_prompt(
skeleton="skeleton",
bullets=["Semi-detached", "95 sqm"],
snippets=[],
max_context_tokens=400,
)
assert "Semi-detached" in prompt
assert "95 sqm" in prompt
def test_trim_snippets_respects_token_limit() -> None:
"""_trim_snippets should not exceed max_tokens."""
long_snippets = ["word " * 100 for _ in range(10)]
result = _trim_snippets(long_snippets, max_tokens=400)
assert count_tokens(result) <= 400 + 20 # small tolerance for label overhead
def test_trim_snippets_empty() -> None:
"""_trim_snippets with empty list should return empty string."""
assert _trim_snippets([], max_tokens=400) == ""
def test_context_tokens_within_limit() -> None:
"""Total context tokens in the examples block must not exceed MAX_CONTEXT_TOKENS."""
from app.config import settings
snippets = ["This is a retrieved snippet. " * 20 for _ in range(5)]
result = _trim_snippets(snippets, max_tokens=settings.max_context_tokens)
assert count_tokens(result) <= settings.max_context_tokens + 30
def test_max_output_tokens_for_survey_level_scales_with_tier() -> None:
"""Per-section token floors should reflect whole-report totals.
Because L1 has far fewer sections overall, its per-section allowance can
be higher than L2 while still yielding a smaller full report.
"""
from app.generator.prompts import max_output_tokens_for_survey_level
l1 = max_output_tokens_for_survey_level(1)
l2 = max_output_tokens_for_survey_level(2)
l3 = max_output_tokens_for_survey_level(3)
# Expected profile: L1 > L2, and L3 > L2.
assert l1 > l2
assert l3 > l2
assert l1 >= 900
assert l2 >= 650
assert l3 >= 850
# None defaults to L3 (matches resolve_generate_system_prompt fallback).
assert max_output_tokens_for_survey_level(None) == l3
def test_max_context_tokens_for_survey_level_scales_with_tier() -> None:
"""Same shape: L3 needs more retrieved evidence than L1. Without this,
only ~400 tokens of RAG snippets reach the LLM, and it falls back to
pre-training defaults β that's where invented addresses come from.
"""
from app.generator.prompts import max_context_tokens_for_survey_level
l1 = max_context_tokens_for_survey_level(1)
l2 = max_context_tokens_for_survey_level(2)
l3 = max_context_tokens_for_survey_level(3)
assert l1 <= l2 <= l3
assert l3 >= 1500
def test_word_target_for_survey_level_matches_full_report_totals() -> None:
"""Word targets are per-section budgets aligned to user-provided full totals."""
from app.generator.prompts import _word_target_for_survey_level
min_l1, max_l1 = _word_target_for_survey_level(1)
min_l2, max_l2 = _word_target_for_survey_level(2)
min_l3, max_l3 = _word_target_for_survey_level(3)
# L1 (5 sections total) can be longer per section than L2 (~33 sections).
assert min_l1 > min_l2
assert max_l1 > max_l2
# L3 should stay slightly above L2 per section for diagnostic depth.
assert min_l3 >= min_l2
assert max_l3 >= max_l2
# Sanity windows
assert 250 <= min_l1 <= 320 and 480 <= max_l1 <= 560
assert 90 <= min_l2 <= 130 and 160 <= max_l2 <= 220
assert 120 <= min_l3 <= 170 and 210 <= max_l3 <= 280
# ββ Post-processor tests ββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def test_enforce_verify_passes_through_verified_number() -> None:
"""A number present in the bullets should NOT be replaced."""
result = enforce_verify(
text="The property has 95 sqm floor area.",
bullets=["Floor area is 95 sqm"],
snippets=[],
)
assert "[VERIFY" not in result
assert "Information not provided in source document." not in result
assert "We were unable to verify this during inspection." not in result
def test_enforce_verify_wraps_invented_number() -> None:
"""An invented number should be removed from the output."""
result = enforce_verify(
text="The property has 120 sqm floor area.",
bullets=["Floor area is 95 sqm"],
snippets=[],
)
assert "120" not in result
# enforce_verify collapses the whitespace left where the invented number
# was removed (\s{2,} -> single space), so no double space survives.
assert result == "The property has floor area."
def test_enforce_verify_wraps_invented_entity() -> None:
"""An invented named entity should be removed from the output."""
result = enforce_verify(
text="The property is located near Hampstead Heath.",
bullets=["Property in NW3"],
snippets=[],
)
assert "Hampstead Heath" not in result
assert result == "The property is located near ."
def test_enforce_verify_passes_through_entity_in_snippets() -> None:
"""A named entity present in a retrieved snippet should NOT be replaced."""
result = enforce_verify(
text="The property is located near Hampstead Heath.",
bullets=["Property in NW3"],
snippets=["This area is adjacent to Hampstead Heath park."],
)
assert "Hampstead Heath" in result
assert "Information not provided in source document." not in result
assert "We were unable to verify this during inspection." not in result
def test_strip_verify_tags_extracts_values() -> None:
"""strip_verify_tags is a legacy stub β always returns empty list."""
text = "Area [VERIFY: 120 sqm] near [VERIFY: Canary Wharf]."
flagged = strip_verify_tags(text)
assert flagged == []
def test_strip_verify_tags_empty_when_no_tags() -> None:
"""strip_verify_tags on clean text should return an empty list."""
assert strip_verify_tags("Clean output with no issues.") == []
# ββ MockLLMAdapter tests ββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def test_mock_adapter_uses_override() -> None:
"""MockLLMAdapter with response_override should return exactly that string."""
adapter = MockLLMAdapter(response_override="FIXTURE output text.")
result = adapter.generate_section("skeleton", ["bullet"], ["snippet"])
assert result == "FIXTURE output text."
def test_mock_adapter_default_uses_bullets() -> None:
"""MockLLMAdapter without override should include bullet content in output."""
adapter = MockLLMAdapter()
result = adapter.generate_section("skeleton", ["semi-detached house"], [])
assert "semi-detached house" in result
def test_mock_adapter_includes_style_note_when_profile_given() -> None:
"""generate_section with a style_profile should embed the tone in the output."""
from app.models.schemas import WritingStyleProfile
profile = WritingStyleProfile(tone="technical")
adapter = MockLLMAdapter()
result = adapter.generate_section("skeleton", ["bullet"], [], style_profile=profile)
assert "technical" in result
# ββ build_user_prompt β style-aware vs plain ββββββββββββββββββββββββββββββββββ
def test_build_user_prompt_plain_when_no_style_profile() -> None:
"""Without a style_profile the plain template must be used (no WRITING STYLE PROFILE header)."""
prompt = build_user_prompt(
skeleton="[Location]: [description].",
bullets=["Semi-detached, NW3"],
snippets=[],
max_context_tokens=400,
)
assert "WRITING STYLE PROFILE" not in prompt
assert "[Location]" in prompt
def test_build_user_prompt_includes_style_anchor() -> None:
"""Optional draft paragraph should appear in the user prompt."""
prompt = build_user_prompt(
skeleton="[E4]: [content].",
bullets=["Brick cavity walls"],
snippets=["Localised cracking to the flank elevation."],
max_context_tokens=400,
style_anchor="The external walls are of cavity brick construction with some localised cracking.",
)
assert "SURVEYOR'S DRAFT PARAGRAPH" in prompt
assert "cavity brick" in prompt
def test_build_user_prompt_style_aware_when_profile_given() -> None:
"""With a style_profile the style-aware template must inject profile fields."""
from app.models.schemas import WritingStyleProfile
profile = WritingStyleProfile(
tone="semi-formal",
formality_level="academic",
writing_style_summary="Concise academic prose.",
)
prompt = build_user_prompt(
skeleton="[Location]: [description].",
bullets=["Semi-detached, NW3"],
snippets=[],
max_context_tokens=400,
style_profile=profile,
)
assert "polished RICS report section text of" in prompt # word target injected
assert "WRITING STYLE PROFILE" in prompt
assert "semi-formal" in prompt
assert "academic" in prompt
assert "Concise academic prose." in prompt
# ββ build_proofread_prompt ββββββββββββββββββββββββββββββββββββββββββββββββββββ
def test_build_proofread_prompt_contains_text_and_bullets() -> None:
"""The proofread prompt must contain the text to review and the bullets."""
from app.generator.prompts import build_proofread_prompt
prompt = build_proofread_prompt(
text="The property is a semi-detached house.",
bullets=["Semi-detached, NW3"],
)
assert "semi-detached house" in prompt
assert "Semi-detached, NW3" in prompt
def test_build_proofread_prompt_uses_style_profile() -> None:
"""When a style_profile is supplied its tone must appear in the proofread prompt."""
from app.generator.prompts import build_proofread_prompt
from app.models.schemas import WritingStyleProfile
profile = WritingStyleProfile(tone="casual", writing_style_summary="Conversational style.")
prompt = build_proofread_prompt(
text="Some text.",
bullets=["fact one"],
style_profile=profile,
)
assert "casual" in prompt
assert "Conversational style." in prompt
def test_build_proofread_prompt_falls_back_to_mock_profile() -> None:
"""Without a style_profile the mock profile defaults should still appear."""
from app.generator.prompts import build_proofread_prompt
prompt = build_proofread_prompt(text="Some text.", bullets=[])
assert "formal" in prompt # mock profile default tone
# ββ build_enhance_prompt ββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def test_build_enhance_prompt_contains_current_text() -> None:
"""The enhance prompt must contain the existing section text."""
from app.generator.prompts import build_enhance_prompt
prompt = build_enhance_prompt(
text="The roof is in poor condition.",
bullets=["Roof: felt tiles, poor condition"],
snippets=["Felt tiles typically last 15β20 years."],
max_context_tokens=400,
)
assert "roof is in poor condition" in prompt
assert "felt tiles" in prompt.lower()
def test_build_enhance_prompt_includes_evidence_snippets() -> None:
"""Retrieved evidence snippets must appear in the enhance prompt."""
from app.generator.prompts import build_enhance_prompt
prompt = build_enhance_prompt(
text="Short text.",
bullets=["bullet"],
snippets=["UNIQUE_SNIPPET_MARKER structural survey evidence"],
max_context_tokens=400,
)
assert "UNIQUE_SNIPPET_MARKER" in prompt
def test_build_enhance_prompt_uses_style_profile() -> None:
"""A supplied style_profile must inject its fields into the enhance prompt."""
from app.generator.prompts import build_enhance_prompt
from app.models.schemas import WritingStyleProfile
profile = WritingStyleProfile(tone="technical", writing_style_summary="Expert surveyor voice.")
prompt = build_enhance_prompt(
text="Existing text.",
bullets=["fact"],
snippets=[],
max_context_tokens=400,
style_profile=profile,
)
assert "technical" in prompt
assert "Expert surveyor voice." in prompt
# ββ MockLLMAdapter β proofread and enhance ββββββββββββββββββββββββββββββββββββ
def test_mock_proofread_contains_notes_separator() -> None:
"""MockLLMAdapter.proofread must include the ---NOTES--- separator."""
adapter = MockLLMAdapter()
result = adapter.proofread("Original text.", bullets=["fact"])
assert "---NOTES---" in result
assert "Original text." in result
def test_mock_enhance_appends_no_key_notice() -> None:
"""MockLLMAdapter.enhance must append a notice about the missing API key."""
adapter = MockLLMAdapter()
result = adapter.enhance("Existing text.", bullets=["fact"], snippets=["snippet"])
assert "Existing text." in result
assert "OPENAI_API_KEY" in result
def test_mock_enhance_notes_snippet_count() -> None:
"""MockLLMAdapter.enhance should mention the number of retrieved snippets."""
adapter = MockLLMAdapter()
result = adapter.enhance("text", bullets=[], snippets=["s1", "s2", "s3"])
assert "3" in result
# ββ GenerateRequest schema validators βββββββββββββββββββββββββββββββββββββββββ
def test_generate_request_rejects_invalid_template_id() -> None:
"""template_id must be a valid RICS section code (union of all product tiers)."""
from pydantic import ValidationError
from app.models.schemas import GenerateRequest
with pytest.raises(ValidationError, match="not a valid RICS section code"):
GenerateRequest(template_id="B1", bullets=["a fact"])
def test_generate_request_accepts_valid_template_id() -> None:
"""A correct RICS section code (e.g. E4) must pass validation."""
from app.models.schemas import GenerateRequest
req = GenerateRequest(template_id="E4", bullets=["Solid brick walls, DPC visible"])
assert req.template_id == "E4"
def test_generate_request_rejects_blank_template_id() -> None:
"""An empty or whitespace-only template_id must be rejected."""
from pydantic import ValidationError
from app.models.schemas import GenerateRequest
with pytest.raises(ValidationError):
GenerateRequest(template_id=" ", bullets=["a fact"])
def test_generate_request_allows_empty_bullets() -> None:
"""Empty bullets are allowed (backend may persist a blank section)."""
from app.models.schemas import GenerateRequest
req = GenerateRequest(template_id="D", bullets=[" ", ""])
assert req.bullets == []
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