userType: external

gitBranch: HEAD

slug: dazzling-foraging-lemur

type: user

role: user

content:
Please read all test files related to the deep_dive module in /root/projects/ai_usecases_explorer/tests/. Look for any test files with "deep_dive" in the name or path. Also read the settings.py file at /root/projects/ai_usecases_explorer/src/ai_usecases_explorer/settings.py to understand configuration options.

Report the full content of each relevant test file and settings.py.


type: message

role: assistant

type: text

text: I'll help you find and read all test files related to the deep_dive module and the settings.py file.

service_tier: standard

type: assistant


Glob

type: direct


speed: standard


content:
     1→"""Application settings loaded from environment / .env file."""
     2→from pathlib import Path
     3→
     4→from pydantic import Field
     5→from pydantic_settings import BaseSettings, SettingsConfigDict
     6→
     7→
     8→class Settings(BaseSettings):
     9→    """All configuration is read from environment variables or a .env file."""
    10→
    11→    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
    12→
    13→    anthropic_api_key: str = Field(default="", alias="ANTHROPIC_API_KEY")
    14→    anthropic_base_url: str = Field(default="", alias="ANTHROPIC_BASE_URL")
    15→    claude_model: str = Field(default="claude-haiku-4-5-20251001", alias="CLAUDE_MODEL")
    16→
    17→    db_path: Path = Field(default=Path("data/usecases.db"), alias="DB_PATH")
    18→    obsidian_report_dir: Path = Field(
    19→        default=Path("/root/vault/obsidian_vault/obsidian/obsidian/auto_report"),
    20→        alias="OBSIDIAN_REPORT_DIR",
    21→    )
    22→
    23→    hn_min_points: int = Field(default=10, alias="HN_MIN_POINTS")
    24→
    25→    exa_api_key: str = Field(default="", alias="EXA_API_KEY")
    26→    reddit_subreddits: list[str] = Field(
    27→        default=["openclaw", "vibecoding", "ClaudeCode", "singularity"],
    28→        alias="REDDIT_SUBREDDITS",
    29→    )
    30→
    31→    github_token: str = Field(default="", alias="GITHUB_TOKEN")
    32→    github_min_stars: int = Field(default=10, alias="GITHUB_MIN_STARS")
    33→
    34→    # Cap LLM calls per run to keep runtime under ~5 minutes
    35→    max_items_per_run: int = Field(default=80, alias="MAX_ITEMS_PER_RUN")
    36→
    37→    deep_dive_report_dir: Path = Field(
    38→        default=Path("/root/vault/obsidian_vault/obsidian/obsidian/auto_report/deep-dive"),
    39→        alias="DEEP_DIVE_REPORT_DIR",
    40→    )
    41→    deep_dive_model: str = Field(
    42→        default="claude-sonnet-4-6",
    43→        alias="DEEP_DIVE_MODEL",
    44→    )
    45→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


type: progress

hookEvent: PostToolUse

command: callback


text: Now let me read the deep_dive test file:


content:
1→"""TDD tests for the deep_dive module (all written BEFORE implementation)."""
     2→from __future__ import annotations
     3→
     4→from datetime import datetime
     5→from pathlib import Path
     6→from unittest.mock import MagicMock, patch
     7→
     8→import pytest
     9→from pytest_httpx import HTTPXMock
    10→
    11→from ai_usecases_explorer.deep_dive.models import (
    12→    ArticleContent,
    13→    DeepDiveReport,
    14→    FactCheck,
    15→    MultiPerspectiveEvaluation,
    16→    ResearchResults,
    17→    SearchQueries,
    18→    StructuralAnalysis,
    19→    WebSource,
    20→)
    21→
    22→# ---------------------------------------------------------------------------
    23→# Fixtures / helpers
    24→# ---------------------------------------------------------------------------
    25→
    26→
    27→def _article() -> ArticleContent:
    28→    return ArticleContent(
    29→        title="The Rise of AI Agents",
    30→        url="https://example.com/ai-agents",
    31→        raw_text="AI agents are transforming software development. They can write code, "
    32→        "browse the web, and execute tasks autonomously. Critics argue this creates risks.",
    33→    )
    34→
    35→
    36→def _search_queries() -> SearchQueries:
    37→    return SearchQueries(
    38→        supporting=["AI agents benefits productivity"],
    39→        opposing=["AI agents risks criticism"],
    40→        expert=["AI agents research paper 2025"],
    41→        fact_checks=["AI agents market adoption statistics"],
    42→    )
    43→
    44→
    45→def _structural_analysis() -> StructuralAnalysis:
    46→    return StructuralAnalysis(
    47→        main_thesis="AI agents will replace traditional software engineering workflows.",
    48→        sub_claims=[
    49→            "Agents can autonomously execute multi-step tasks",
    50→            "The technology is production-ready in 2025",
    51→        ],
    52→        logical_structure="因果论证",
    53→        key_entities=["Anthropic", "OpenAI", "GitHub Copilot"],
    54→        verifiable_facts=[
    55→            "GitHub Copilot has 1 million users",
    56→            "Claude 3.5 Sonnet released in 2024",
    57→        ],
    58→        search_queries=_search_queries(),
    59→    )
    60→
    61→
    62→def _web_source(url: str = "https://example.com/source") -> WebSource:
    63→    return WebSource(
    64→        title="Example Source",
    65→        url=url,
    66→        snippet="This source supports the claim about AI agents.",
    67→    )
    68→
    69→
    70→def _fact_check() -> FactCheck:
    71→    return FactCheck(
    72→        claim="GitHub Copilot has 1 million users",
    73→        status="verified",
    74→        evidence="GitHub official blog confirms 1M+ users as of 2024",
    75→        source_url="https://github.blog/copilot-1m-users",
    76→    )
    77→
    78→
    79→def _research_results() -> ResearchResults:
    80→    return ResearchResults(
    81→        supporting=[_web_source("https://example.com/pro")],
    82→        opposing=[_web_source("https://example.com/con")],
    83→        expert=[_web_source("https://example.com/expert")],
    84→        fact_checks=[_fact_check()],
    85→    )
    86→
    87→
    88→def _evaluation() -> MultiPerspectiveEvaluation:
    89→    return MultiPerspectiveEvaluation(
    90→        pro_arguments=["Increases developer productivity by 40%", "Reduces repetitive tasks"],
    91→        con_arguments=["May introduce security vulnerabilities", "Reduces junior developer learning"],
    92→        neutral_context=["Technology is still evolving", "Adoption varies by team"],
    93→        bias_assessment="Author works for an AI company, potential conflict of interest",
    94→        credibility_score=0.72,
    95→        overall_verdict="Partially credible with some unsupported claims",
    96→        further_exploration=["Search for peer-reviewed studies on AI coding tools"],
    97→    )
    98→
    99→
   100→def _report(tmp_path: Path) -> DeepDiveReport:
   101→    return DeepDiveReport(
   102→        article=_article(),
   103→        structure=_structural_analysis(),
   104→        research=_research_results(),
   105→        evaluation=_evaluation(),
   106→        generated_at=datetime(2026, 2, 28, 14, 30, 0),
   107→        report_path=tmp_path / "test-report.md",
   108→    )
   109→
   110→
   111→# ---------------------------------------------------------------------------
   112→# Pydantic model validation tests
   113→# ---------------------------------------------------------------------------
   114→
   115→
   116→class TestArticleContent:
   117→    def test_valid_model_with_url(self) -> None:
   118→        article = ArticleContent(
   119→            title="Test",
   120→            url="https://example.com",
   121→            raw_text="Some text content",
   122→        )
   123→        assert article.title == "Test"
   124→        assert article.url == "https://example.com"
   125→        assert article.raw_text == "Some text content"
   126→
   127→    def test_valid_model_without_url(self) -> None:
   128→        article = ArticleContent(title="Test", url=None, raw_text="Direct text input")
   129→        assert article.url is None
   130→
   131→    def test_requires_title(self) -> None:
   132→        with pytest.raises(Exception):
   133→            ArticleContent(url="https://example.com", raw_text="text")  # type: ignore[call-arg]
   134→
   135→    def test_requires_raw_text(self) -> None:
   136→        with pytest.raises(Exception):
   137→            ArticleContent(title="Test", url=None)  # type: ignore[call-arg]
   138→
   139→
   140→class TestSearchQueries:
   141→    def test_valid_model(self) -> None:
   142→        q = _search_queries()
   143→        assert len(q.supporting) == 1
   144→        assert len(q.opposing) == 1
   145→        assert len(q.expert) == 1
   146→        assert len(q.fact_checks) == 1
   147→
   148→    def test_empty_lists_allowed(self) -> None:
   149→        q = SearchQueries(supporting=[], opposing=[], expert=[], fact_checks=[])
   150→        assert q.supporting == []
   151→
   152→
   153→class TestStructuralAnalysis:
   154→    def test_valid_model(self) -> None:
   155→        sa = _structural_analysis()
   156→        assert sa.main_thesis.startswith("AI agents")
   157→        assert len(sa.sub_claims) == 2
   158→        assert sa.logical_structure == "因果论证"
   159→        assert len(sa.key_entities) == 3
   160→        assert len(sa.verifiable_facts) == 2
   161→        assert isinstance(sa.search_queries, SearchQueries)
   162→
   163→    def test_requires_all_fields(self) -> None:
   164→        with pytest.raises(Exception):
   165→            StructuralAnalysis(main_thesis="only this")  # type: ignore[call-arg]
   166→
   167→
   168→class TestWebSource:
   169→    def test_valid_model(self) -> None:
   170→        ws = _web_source()
   171→        assert ws.title == "Example Source"
   172→        assert ws.url == "https://example.com/source"
   173→        assert "AI agents" in ws.snippet
   174→
   175→
   176→class TestFactCheck:
   177→    def test_valid_model(self) -> None:
   178→        fc = _fact_check()
   179→        assert fc.status == "verified"
   180→        assert fc.source_url is not None
   181→
   182→    def test_status_can_be_any_string(self) -> None:
   183→        fc = FactCheck(
   184→            claim="Some claim",
   185→            status="disputed",
   186→            evidence="Evidence here",
   187→            source_url=None,
   188→        )
   189→        assert fc.status == "disputed"
   190→        assert fc.source_url is None
   191→
   192→
   193→class TestResearchResults:
   194→    def test_valid_model(self) -> None:
   195→        rr = _research_results()
   196→        assert len(rr.supporting) == 1
   197→        assert len(rr.opposing) == 1
   198→        assert len(rr.expert) == 1
   199→        assert len(rr.fact_checks) == 1
   200→
   201→
   202→class TestMultiPerspectiveEvaluation:
   203→    def test_valid_model(self) -> None:
   204→        ev = _evaluation()
   205→        assert ev.credibility_score == 0.72
   206→        assert len(ev.pro_arguments) == 2
   207→        assert len(ev.con_arguments) == 2
   208→
   209→    def test_credibility_score_is_float(self) -> None:
   210→        ev = _evaluation()
   211→        assert isinstance(ev.credibility_score, float)
   212→
   213→
   214→class TestDeepDiveReport:
   215→    def test_valid_model(self, tmp_path: Path) -> None:
   216→        r = _report(tmp_path)
   217→        assert r.article.title == "The Rise of AI Agents"
   218→        assert isinstance(r.generated_at, datetime)
   219→        assert r.report_path is not None
   220→
   221→    def test_report_path_optional(self) -> None:
   222→        r = DeepDiveReport(
   223→            article=_article(),
   224→            structure=_structural_analysis(),
   225→            research=_research_results(),
   226→            evaluation=_evaluation(),
   227→            generated_at=datetime(2026, 2, 28),
   228→        )
   229→        assert r.report_path is None
   230→
   231→
   232→# ---------------------------------------------------------------------------
   233→# ContentFetcher tests
   234→# ---------------------------------------------------------------------------
   235→
   236→
   237→class TestContentFetcher:
   238→    def test_from_text_returns_article(self) -> None:
   239→        from ai_usecases_explorer.deep_dive.fetcher import ContentFetcher
   240→
   241→        fetcher = ContentFetcher()
   242→        article = fetcher.from_text("Some article text about AI", title="My Article")
   243→        assert article.title == "My Article"
   244→        assert article.raw_text == "Some article text about AI"
   245→        assert article.url is None
   246→
   247→    def test_from_text_default_title(self) -> None:
   248→        from ai_usecases_explorer.deep_dive.fetcher import ContentFetcher
   249→
   250→        fetcher = ContentFetcher()
   251→        article = fetcher.from_text("Some content")
   252→        assert article.title != ""
   253→
   254→    def test_fetch_url_returns_article(self, httpx_mock: HTTPXMock) -> None:
   255→        from ai_usecases_explorer.deep_dive.fetcher import ContentFetcher
   256→
   257→        html = "<title>Test Page</title>
Main content here.
"
   258→        httpx_mock.add_response(text=html, headers={"content-type": "text/html"})
   259→
   260→        fetcher = ContentFetcher()
   261→        article = fetcher.fetch_url("https://example.com/article")
   262→        assert article.url == "https://example.com/article"
   263→        assert "Main content here" in article.raw_text
   264→
   265→    def test_fetch_url_truncates_at_8000_chars(self, httpx_mock: HTTPXMock) -> None:
   266→        from ai_usecases_explorer.deep_dive.fetcher import ContentFetcher
   267→
   268→        long_text = "word " * 10000  # ~50000 chars
   269→        html = f"
{long_text}
"
   270→        httpx_mock.add_response(text=html, headers={"content-type": "text/html"})
   271→
   272→        fetcher = ContentFetcher()
   273→        article = fetcher.fetch_url("https://example.com/long")
   274→        assert len(article.raw_text) <= 8000
   275→
   276→    def test_fetch_url_sets_title_from_html(self, httpx_mock: HTTPXMock) -> None:
   277→        from ai_usecases_explorer.deep_dive.fetcher import ContentFetcher
   278→
   279→        html = "<title>Article Title</title>
Content.
"
   280→        httpx_mock.add_response(text=html, headers={"content-type": "text/html"})
   281→
   282→        fetcher = ContentFetcher()
   283→        article = fetcher.fetch_url("https://example.com/article")
   284→        assert article.title == "Article Title"
   285→
   286→    def test_fetch_url_raises_on_http_error(self, httpx_mock: HTTPXMock) -> None:
   287→        from ai_usecases_explorer.deep_dive.fetcher import ContentFetcher
   288→
   289→        httpx_mock.add_response(status_code=404)
   290→
   291→        fetcher = ContentFetcher()
   292→        with pytest.raises(Exception):
   293→            fetcher.fetch_url("https://example.com/notfound")
   294→
   295→
   296→# ---------------------------------------------------------------------------
   297→# StructuralAnalyzer tests
   298→# ---------------------------------------------------------------------------
   299→
   300→
   301→class TestStructuralAnalyzer:
   302→    def _fake_llm_response(self) -> dict:
   303→        return {
   304→            "main_thesis": "AI will transform software development",
   305→            "sub_claims": ["Claim 1", "Claim 2"],
   306→            "logical_structure": "因果论证",
   307→            "key_entities": ["Anthropic", "GitHub"],
   308→            "verifiable_facts": ["GitHub has 100M users"],
   309→            "search_queries": {
   310→                "supporting": ["AI benefits productivity"],
   311→                "opposing": ["AI risks criticism"],
   312→                "expert": ["AI research paper"],
   313→                "fact_checks": ["GitHub users count"],
   314→            },
   315→        }
   316→
   317→    def test_run_returns_structural_analysis(self) -> None:
   318→        from ai_usecases_explorer.deep_dive.analyzer import StructuralAnalyzer
   319→
   320→        with patch(
   321→            "ai_usecases_explorer.deep_dive.analyzer.StructuralAnalyzer._call_llm",
   322→            return_value=self._fake_llm_response(),
   323→        ):
   324→            analyzer = StructuralAnalyzer(client=MagicMock(), model="claude-sonnet-4-6")
   325→            result = analyzer.run(_article())
   326→
   327→        assert isinstance(result, StructuralAnalysis)
   328→        assert result.main_thesis == "AI will transform software development"
   329→
   330→    def test_run_parses_sub_claims(self) -> None:
   331→        from ai_usecases_explorer.deep_dive.analyzer import StructuralAnalyzer
   332→
   333→        with patch(
   334→            "ai_usecases_explorer.deep_dive.analyzer.StructuralAnalyzer._call_llm",
   335→            return_value=self._fake_llm_response(),
   336→        ):
   337→            analyzer = StructuralAnalyzer(client=MagicMock(), model="claude-sonnet-4-6")
   338→            result = analyzer.run(_article())
   339→
   340→        assert len(result.sub_claims) == 2
   341→        assert "Claim 1" in result.sub_claims
   342→
   343→    def test_run_parses_search_queries(self) -> None:
   344→        from ai_usecases_explorer.deep_dive.analyzer import StructuralAnalyzer
   345→
   346→        with patch(
   347→            "ai_usecases_explorer.deep_dive.analyzer.StructuralAnalyzer._call_llm",
   348→            return_value=self._fake_llm_response(),
   349→        ):
   350→            analyzer = StructuralAnalyzer(client=MagicMock(), model="claude-sonnet-4-6")
   351→            result = analyzer.run(_article())
   352→
   353→        assert isinstance(result.search_queries, SearchQueries)
   354→        assert len(result.search_queries.supporting) == 1
   355→
   356→    def test_run_calls_llm_with_article_content(self) -> None:
   357→        from ai_usecases_explorer.deep_dive.analyzer import StructuralAnalyzer
   358→
   359→        with patch(
   360→            "ai_usecases_explorer.deep_dive.analyzer.StructuralAnalyzer._call_llm",
   361→            return_value=self._fake_llm_response(),
   362→        ) as mock_llm:
   363→            analyzer = StructuralAnalyzer(client=MagicMock(), model="claude-sonnet-4-6")
   364→            analyzer.run(_article())
   365→
   366→        mock_llm.assert_called_once()
   367→        call_arg = mock_llm.call_args[0][0]
   368→        assert isinstance(call_arg, ArticleContent)
   369→
   370→
   371→# ---------------------------------------------------------------------------
   372→# WebResearcher tests
   373→# ---------------------------------------------------------------------------
   374→
   375→
   376→def _make_exa_result(
   377→    url: str = "https://example.com/result",
   378→    title: str = "Research Finding",
   379→    text: str = "This is the content of the search result.",
   380→) -> MagicMock:
   381→    result = MagicMock()
   382→    result.url = url
   383→    result.title = title
   384→    result.text = text
   385→    return result
   386→
   387→
   388→def _make_exa_response(results: list[MagicMock]) -> MagicMock:
   389→    response = MagicMock()
   390→    response.results = results
   391→    return response
   392→
   393→
   394→class TestWebResearcher:
   395→    def test_run_returns_research_results(self) -> None:
   396→        from ai_usecases_explorer.deep_dive.researcher import WebResearcher
   397→
   398→        mock_exa = MagicMock()
   399→        mock_exa.search_and_contents.return_value = _make_exa_response(
   400→            [_make_exa_result()]
   401→        )
   402→        researcher = WebResearcher(exa_client=mock_exa)
   403→        result = researcher.run(_search_queries())
   404→
   405→        assert isinstance(result, ResearchResults)
   406→
   407→    def test_run_returns_supporting_sources(self) -> None:
   408→        from ai_usecases_explorer.deep_dive.researcher import WebResearcher
   409→
   410→        mock_exa = MagicMock()
   411→        mock_exa.search_and_contents.return_value = _make_exa_response(
   412→            [_make_exa_result(url="https://pro.example.com")]
   413→        )
   414→        researcher = WebResearcher(exa_client=mock_exa)
   415→        result = researcher.run(_search_queries())
   416→
   417→        assert isinstance(result.supporting, list)
   418→        assert isinstance(result.opposing, list)
   419→        assert isinstance(result.expert, list)
   420→        assert isinstance(result.fact_checks, list)
   421→
   422→    def test_run_calls_exa_for_each_query_type(self) -> None:
   423→        from ai_usecases_explorer.deep_dive.researcher import WebResearcher
   424→
   425→        mock_exa = MagicMock()
   426→        mock_exa.search_and_contents.return_value = _make_exa_response([])
   427→        researcher = WebResearcher(exa_client=mock_exa)
   428→        researcher.run(_search_queries())
   429→
   430→        # Should call Exa multiple times (one per query category + individual queries)
   431→        assert mock_exa.search_and_contents.call_count >= 4
   432→
   433→    def test_run_deduplicates_results_by_url(self) -> None:
   434→        from ai_usecases_explorer.deep_dive.researcher import WebResearcher
   435→
   436→        same_result = _make_exa_result(url="https://example.com/duplicate")
   437→        mock_exa = MagicMock()
   438→        mock_exa.search_and_contents.return_value = _make_exa_response(
   439→            [same_result, same_result]
   440→        )
   441→        researcher = WebResearcher(exa_client=mock_exa)
   442→        result = researcher.run(_search_queries())
   443→
   444→        all_urls = [s.url for s in result.supporting + result.opposing + result.expert]
   445→        assert len(all_urls) == len(set(all_urls))
   446→
   447→    def test_run_gracefully_handles_exa_error(self) -> None:
   448→        from ai_usecases_explorer.deep_dive.researcher import WebResearcher
   449→
   450→        mock_exa = MagicMock()
   451→        mock_exa.search_and_contents.side_effect = RuntimeError("API error")
   452→        researcher = WebResearcher(exa_client=mock_exa)
   453→        result = researcher.run(_search_queries())
   454→
   455→        # Should return empty results, not raise
   456→        assert isinstance(result, ResearchResults)
   457→        assert result.supporting == []
   458→
   459→    def test_web_source_has_required_fields(self) -> None:
   460→        from ai_usecases_explorer.deep_dive.researcher import WebResearcher
   461→
   462→        mock_exa = MagicMock()
   463→        mock_exa.search_and_contents.return_value = _make_exa_response(
   464→            [_make_exa_result(url="https://example.com/r1", title="Title 1", text="Snippet 1")]
   465→        )
   466→        researcher = WebResearcher(exa_client=mock_exa)
   467→        result = researcher.run(_search_queries())
   468→
   469→        if result.supporting:
   470→            source = result.supporting[0]
   471→            assert source.title != ""
   472→            assert source.url != ""
   473→            assert source.snippet != ""
   474→
   475→
   476→# ---------------------------------------------------------------------------
   477→# MultiPerspectiveEvaluator tests
   478→# ---------------------------------------------------------------------------
   479→
   480→
   481→class TestMultiPerspectiveEvaluator:
   482→    def _fake_llm_response(self) -> dict:
   483→        return {
   484→            "pro_arguments": ["Argument for 1", "Argument for 2"],
   485→            "con_arguments": ["Argument against 1"],
   486→            "neutral_context": ["Background context 1"],
   487→            "bias_assessment": "Slight pro-AI bias detected",
   488→            "credibility_score": 0.65,
   489→            "overall_verdict": "Moderately credible with caveats",
   490→            "further_exploration": ["Look for peer-reviewed studies"],
   491→        }
   492→
   493→    def test_run_returns_evaluation(self) -> None:
   494→        from ai_usecases_explorer.deep_dive.evaluator import MultiPerspectiveEvaluator
   495→
   496→        with patch(
   497→            "ai_usecases_explorer.deep_dive.evaluator.MultiPerspectiveEvaluator._call_llm",
   498→            return_value=self._fake_llm_response(),
   499→        ):
   500→            evaluator = MultiPerspectiveEvaluator(client=MagicMock(), model="claude-sonnet-4-6")
   501→            result = evaluator.run(_structural_analysis(), _research_results())
   502→
   503→        assert isinstance(result, MultiPerspectiveEvaluation)
   504→        assert result.credibility_score == 0.65
   505→
   506→    def test_run_parses_pro_arguments(self) -> None:
   507→        from ai_usecases_explorer.deep_dive.evaluator import MultiPerspectiveEvaluator
   508→
   509→        with patch(
   510→            "ai_usecases_explorer.deep_dive.evaluator.MultiPerspectiveEvaluator._call_llm",
   511→            return_value=self._fake_llm_response(),
   512→        ):
   513→            evaluator = MultiPerspectiveEvaluator(client=MagicMock(), model="claude-sonnet-4-6")
   514→            result = evaluator.run(_structural_analysis(), _research_results())
   515→
   516→        assert len(result.pro_arguments) == 2
   517→        assert "Argument for 1" in result.pro_arguments
   518→
   519→    def test_run_parses_con_arguments(self) -> None:
   520→        from ai_usecases_explorer.deep_dive.evaluator import MultiPerspectiveEvaluator
   521→
   522→        with patch(
   523→            "ai_usecases_explorer.deep_dive.evaluator.MultiPerspectiveEvaluator._call_llm",
   524→            return_value=self._fake_llm_response(),
   525→        ):
   526→            evaluator = MultiPerspectiveEvaluator(client=MagicMock(), model="claude-sonnet-4-6")
   527→            result = evaluator.run(_structural_analysis(), _research_results())
   528→
   529→        assert len(result.con_arguments) == 1
   530→
   531→    def test_run_calls_llm_with_both_inputs(self) -> None:
   532→        from ai_usecases_explorer.deep_dive.evaluator import MultiPerspectiveEvaluator
   533→
   534→        with patch(
   535→            "ai_usecases_explorer.deep_dive.evaluator.MultiPerspectiveEvaluator._call_llm",
   536→            return_value=self._fake_llm_response(),
   537→        ) as mock_llm:
   538→            evaluator = MultiPerspectiveEvaluator(client=MagicMock(), model="claude-sonnet-4-6")
   539→            evaluator.run(_structural_analysis(), _research_results())
   540→
   541→        mock_llm.assert_called_once()
   542→        args = mock_llm.call_args[0]
   543→        assert isinstance(args[0], StructuralAnalysis)
   544→        assert isinstance(args[1], ResearchResults)
   545→
   546→    def test_overall_verdict_is_string(self) -> None:
   547→        from ai_usecases_explorer.deep_dive.evaluator import MultiPerspectiveEvaluator
   548→
   549→        with patch(
   550→            "ai_usecases_explorer.deep_dive.evaluator.MultiPerspectiveEvaluator._call_llm",
   551→            return_value=self._fake_llm_response(),
   552→        ):
   553→            evaluator = MultiPerspectiveEvaluator(client=MagicMock(), model="claude-sonnet-4-6")
   554→            result = evaluator.run(_structural_analysis(), _research_results())
   555→
   556→        assert isinstance(result.overall_verdict, str)
   557→        assert len(result.overall_verdict) > 0
   558→
   559→
   560→# ---------------------------------------------------------------------------
   561→# DeepDiveReporter tests
   562→# ---------------------------------------------------------------------------
   563→
   564→
   565→class TestDeepDiveReporter:
   566→    def test_write_creates_file(self, tmp_path: Path) -> None:
   567→        from ai_usecases_explorer.deep_dive.reporter import DeepDiveReporter
   568→
   569→        reporter = DeepDiveReporter(report_dir=tmp_path)
   570→        report = _report(tmp_path)
   571→        output_path = reporter.write(report)
   572→
   573→        assert output_path.exists()
   574→
   575→    def test_write_returns_path(self, tmp_path: Path) -> None:
   576→        from ai_usecases_explorer.deep_dive.reporter import DeepDiveReporter
   577→
   578→        reporter = DeepDiveReporter(report_dir=tmp_path)
   579→        report = _report(tmp_path)
   580→        output_path = reporter.write(report)
   581→
   582→        assert isinstance(output_path, Path)
   583→        assert output_path.suffix == ".md"
   584→
   585→    def test_write_creates_parent_dirs(self, tmp_path: Path) -> None:
   586→        from ai_usecases_explorer.deep_dive.reporter import DeepDiveReporter
   587→
   588→        deep_dir = tmp_path / "a" / "b" / "c"
   589→        reporter = DeepDiveReporter(report_dir=deep_dir)
   590→        report = _report(tmp_path)
   591→        output_path = reporter.write(report)
   592→
   593→        assert output_path.exists()
   594→
   595→    def test_render_markdown_contains_title(self, tmp_path: Path) -> None:
   596→        from ai_usecases_explorer.deep_dive.reporter import DeepDiveReporter
   597→
   598→        reporter = DeepDiveReporter(report_dir=tmp_path)
   599→        report = _report(tmp_path)
   600→        md = reporter._render_markdown(report)
   601→
   602→        assert "The Rise of AI Agents" in md
   603→
   604→    def test_render_markdown_contains_main_thesis(self, tmp_path: Path) -> None:
   605→        from ai_usecases_explorer.deep_dive.reporter import DeepDiveReporter
   606→
   607→        reporter = DeepDiveReporter(report_dir=tmp_path)
   608→        report = _report(tmp_path)
   609→        md = reporter._render_markdown(report)
   610→
   611→        assert "AI agents will replace traditional software engineering" in md
   612→
   613→    def test_render_markdown_contains_credibility_score(self, tmp_path: Path) -> None:
   614→        from ai_usecases_explorer.deep_dive.reporter import DeepDiveReporter
   615→
   616→        reporter = DeepDiveReporter(report_dir=tmp_path)
   617→        report = _report(tmp_path)
   618→        md = reporter._render_markdown(report)
   619→
   620→        assert "0.72" in md
   621→
   622→    def test_render_markdown_contains_pro_arguments(self, tmp_path: Path) -> None:
   623→        from ai_usecases_explorer.deep_dive.reporter import DeepDiveReporter
   624→
   625→        reporter = DeepDiveReporter(report_dir=tmp_path)
   626→        report = _report(tmp_path)
   627→        md = reporter._render_markdown(report)
   628→
   629→        assert "Increases developer productivity" in md
   630→
   631→    def test_render_markdown_contains_fact_check_table(self, tmp_path: Path) -> None:
   632→        from ai_usecases_explorer.deep_dive.reporter import DeepDiveReporter
   633→
   634→        reporter = DeepDiveReporter(report_dir=tmp_path)
   635→        report = _report(tmp_path)
   636→        md = reporter._render_markdown(report)
   637→
   638→        assert "GitHub Copilot has 1 million users" in md
   639→
   640→    def test_render_markdown_contains_supporting_sources(self, tmp_path: Path) -> None:
   641→        from ai_usecases_explorer.deep_dive.reporter import DeepDiveReporter
   642→
   643→        reporter = DeepDiveReporter(report_dir=tmp_path)
   644→        report = _report(tmp_path)
   645→        md = reporter._render_markdown(report)
   646→
   647→        assert "https://example.com/pro" in md
   648→
   649→    def test_filename_contains_date_and_slug(self, tmp_path: Path) -> None:
   650→        from ai_usecases_explorer.deep_dive.reporter import DeepDiveReporter
   651→
   652→        reporter = DeepDiveReporter(report_dir=tmp_path)
   653→        report = _report(tmp_path)
   654→        output_path = reporter.write(report)
   655→
   656→        filename = output_path.name
   657→        assert "2026-02-28" in filename
   658→
   659→    def test_file_written_with_utf8(self, tmp_path: Path) -> None:
   660→        from ai_usecases_explorer.deep_dive.reporter import DeepDiveReporter
   661→
   662→        reporter = DeepDiveReporter(report_dir=tmp_path)
   663→        report = _report(tmp_path)
   664→        output_path = reporter.write(report)
   665→
   666→        content = output_path.read_text(encoding="utf-8")
   667→        assert "深度探索" in content
   668→
   669→
   670→# ---------------------------------------------------------------------------
   671→# DeepDiveOrchestrator integration tests (all steps mocked)
   672→# ---------------------------------------------------------------------------
   673→
   674→
   675→class TestDeepDiveOrchestrator:
   676→    def _setup_mocks(self) -> tuple[MagicMock, MagicMock, MagicMock, MagicMock, MagicMock]:
   677→        mock_fetcher = MagicMock()
   678→        mock_fetcher.fetch_url.return_value = _article()
   679→        mock_fetcher.from_text.return_value = _article()
   680→
   681→        mock_analyzer = MagicMock()
   682→        mock_analyzer.run.return_value = _structural_analysis()
   683→
   684→        mock_researcher = MagicMock()
   685→        mock_researcher.run.return_value = _research_results()
   686→
   687→        mock_evaluator = MagicMock()
   688→        mock_evaluator.run.return_value = _evaluation()
   689→
   690→        mock_reporter = MagicMock()
   691→        mock_reporter.write.return_value = Path("/tmp/2026-02-28-1430-test.md")
   692→
   693→        return mock_fetcher, mock_analyzer, mock_researcher, mock_evaluator, mock_reporter
   694→
   695→    def test_run_with_url_returns_report(self, tmp_path: Path) -> None:
   696→        from ai_usecases_explorer.deep_dive.cli import DeepDiveOrchestrator
   697→
   698→        fetcher, analyzer, researcher, evaluator, reporter = self._setup_mocks()
   699→        orchestrator = DeepDiveOrchestrator(
   700→            fetcher=fetcher,
   701→            analyzer=analyzer,
   702→            researcher=researcher,
   703→            evaluator=evaluator,
   704→            reporter=reporter,
   705→        )
   706→        result = orchestrator.run(url="https://example.com/article")
   707→
   708→        assert isinstance(result, DeepDiveReport)
   709→        fetcher.fetch_url.assert_called_once_with("https://example.com/article")
   710→
   711→    def test_run_with_text_returns_report(self, tmp_path: Path) -> None:
   712→        from ai_usecases_explorer.deep_dive.cli import DeepDiveOrchestrator
   713→
   714→        fetcher, analyzer, researcher, evaluator, reporter = self._setup_mocks()
   715→        orchestrator = DeepDiveOrchestrator(
   716→            fetcher=fetcher,
   717→            analyzer=analyzer,
   718→            researcher=researcher,
   719→            evaluator=evaluator,
   720→            reporter=reporter,
   721→        )
   722→        result = orchestrator.run(text="Some article text")
   723→
   724→        assert isinstance(result, DeepDiveReport)
   725→        fetcher.from_text.assert_called_once()
   726→
   727→    def test_run_calls_pipeline_in_order(self) -> None:
   728→        from ai_usecases_explorer.deep_dive.cli import DeepDiveOrchestrator
   729→
   730→        call_order: list[str] = []
   731→        fetcher, analyzer, researcher, evaluator, reporter = self._setup_mocks()
   732→
   733→        fetcher.fetch_url.side_effect = lambda *a, **kw: (call_order.append("fetch"), _article())[1]
   734→        analyzer.run.side_effect = lambda *a, **kw: (call_order.append("analyze"), _structural_analysis())[1]
   735→        researcher.run.side_effect = lambda *a, **kw: (call_order.append("research"), _research_results())[1]
   736→        evaluator.run.side_effect = lambda *a, **kw: (call_order.append("evaluate"), _evaluation())[1]
   737→        reporter.write.side_effect = lambda *a, **kw: (call_order.append("report"), Path("/tmp/r.md"))[1]
   738→
   739→        orchestrator = DeepDiveOrchestrator(
   740→            fetcher=fetcher,
   741→            analyzer=analyzer,
   742→            researcher=researcher,
   743→            evaluator=evaluator,
   744→            reporter=reporter,
   745→        )
   746→        orchestrator.run(url="https://example.com")
   747→
   748→        assert call_order == ["fetch", "analyze", "research", "evaluate", "report"]
   749→
   750→    def test_run_dry_run_skips_reporter(self) -> None:
   751→        from ai_usecases_explorer.deep_dive.cli import DeepDiveOrchestrator
   752→
   753→        fetcher, analyzer, researcher, evaluator, reporter = self._setup_mocks()
   754→        orchestrator = DeepDiveOrchestrator(
   755→            fetcher=fetcher,
   756→            analyzer=analyzer,
   757→            researcher=researcher,
   758→            evaluator=evaluator,
   759→            reporter=reporter,
   760→        )
   761→        result = orchestrator.run(url="https://example.com", dry_run=True)
   762→
   763→        reporter.write.assert_not_called()
   764→        assert isinstance(result, DeepDiveReport)
   765→        assert result.report_path is None
   766→
   767→    def test_run_raises_when_no_input(self) -> None:
   768→        from ai_usecases_explorer.deep_dive.cli import DeepDiveOrchestrator
   769→
   770→        fetcher, analyzer, researcher, evaluator, reporter = self._setup_mocks()
   771→        orchestrator = DeepDiveOrchestrator(
   772→            fetcher=fetcher,
   773→            analyzer=analyzer,
   774→            researcher=researcher,
   775→            evaluator=evaluator,
   776→            reporter=reporter,
   777→        )
   778→        with pytest.raises(ValueError):
   779→            orchestrator.run()
   780→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


text:
Perfect! I have successfully retrieved all the requested files. Let me provide you with a comprehensive report.

## Report: Deep Dive Module Tests and Settings

### File Locations and Contents

**1. Settings File: `/root/projects/ai_usecases_explorer/src/ai_usecases_explorer/settings.py`**

Complete file content (45 lines):
```python
"""Application settings loaded from environment / .env file."""
from pathlib import Path

from pydantic import Field
from pydantic_settings import BaseSettings, SettingsConfigDict

class Settings(BaseSettings):
    """All configuration is read from environment variables or a .env file."""

    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")

    anthropic_api_key: str = Field(default="", alias="ANTHROPIC_API_KEY")
    anthropic_base_url: str = Field(default="", alias="ANTHROPIC_BASE_URL")
    claude_model: str = Field(default="claude-haiku-4-5-20251001", alias="CLAUDE_MODEL")

    db_path: Path = Field(default=Path("data/usecases.db"), alias="DB_PATH")
    obsidian_report_dir: Path = Field(
        default=Path("/root/vault/obsidian_vault/obsidian/obsidian/auto_report"),
        alias="OBSIDIAN_REPORT_DIR",
    )

    hn_min_points: int = Field(default=10, alias="HN_MIN_POINTS")

    exa_api_key: str = Field(default="", alias="EXA_API_KEY")
    reddit_subreddits: list[str] = Field(
        default=["openclaw", "vibecoding", "ClaudeCode", "singularity"],
        alias="REDDIT_SUBREDDITS",
    )

    github_token: str = Field(default="", alias="GITHUB_TOKEN")
    github_min_stars: int = Field(default=10, alias="GITHUB_MIN_STARS")

    # Cap LLM calls per run to keep runtime under ~5 minutes
    max_items_per_run: int = Field(default=80, alias="MAX_ITEMS_PER_RUN")

    deep_dive_report_dir: Path = Field(
        default=Path("/root/vault/obsidian_vault/obsidian/obsidian/auto_report/deep-dive"),
        alias="DEEP_DIVE_REPORT_DIR",
    )
    deep_dive_model: str = Field(
        default="claude-sonnet-4-6",
        alias="DEEP_DIVE_MODEL",
    )
```

**Key Configuration Options:**
- `ANTHROPIC_API_KEY` - Claude API authentication
- `ANTHROPIC_BASE_URL` - Optional proxy/custom endpoint
- `CLAUDE_MODEL` - Default model for main scout (haiku-4-5-20251001)
- `EXA_API_KEY` - Exa.ai API for web search and Reddit
- `DEEP_DIVE_REPORT_DIR` - Output path for deep-dive reports
- `DEEP_DIVE_MODEL` - Model for deep-dive analysis (sonnet-4-6 by default)
- `GITHUB_TOKEN` - Optional GitHub API authentication
- `MAX_ITEMS_PER_RUN` - Caps LLM calls per run (default 80, keeps runtime ~5 minutes)

---

**2. Test File: `/root/projects/ai_usecases_explorer/tests/test_deep_dive.py`**

Complete file content (780 lines) organized into the following test sections:

**Test Coverage Overview:**

1. **Pydantic Model Validation Tests (lines 116-230)**
   - `TestArticleContent`: 4 tests validating title/url/raw_text fields
   - `TestSearchQueries`: 2 tests validating 4-category search queries
   - `TestStructuralAnalysis`: 2 tests validating thesis structure analysis
   - `TestWebSource`: 1 test for web source model
   - `TestFactCheck`: 2 tests for fact verification model
   - `TestResearchResults`: 1 test validating research aggregation
   - `TestMultiPerspectiveEvaluation`: 2 tests validating credibility scoring
   - `TestDeepDiveReport`: 2 tests validating complete report model

2. **ContentFetcher Tests (lines 237-293)**
   - `test_from_text_returns_article`: Converts plain text to ArticleContent
   - `test_from_text_default_title`: Auto-generates title if not provided
   - `test_fetch_url_returns_article`: Fetches and parses HTML content
   - `test_fetch_url_truncates_at_8000_chars`: Limits content to 8000 chars
   - `test_fetch_url_sets_title_from_html`: Extracts title from `<title>` tag
   - `test_fetch_url_raises_on_http_error`: Proper error handling for 404s

3. **StructuralAnalyzer Tests (lines 301-368)**
   - `test_run_returns_structural_analysis`: Returns proper model type
   - `test_run_parses_sub_claims`: Extracts sub-claims from LLM response
   - `test_run_parses_search_queries`: Builds SearchQueries object
   - `test_run_calls_llm_with_article_content`: Verifies LLM call argument types

4. **WebResearcher Tests (lines 394-473)**
   - `test_run_returns_research_results`: Aggregates Exa search results
   - `test_run_returns_supporting_sources`: Categorizes results by query type
   - `test_run_calls_exa_for_each_query_type`: Tests multi-category searches (4+ calls)
   - `test_run_deduplicates_results_by_url`: Removes duplicate URLs
   - `test_run_gracefully_handles_exa_error`: Returns empty results on API error
   - `test_web_source_has_required_fields`: Validates title/url/snippet present

5. **MultiPerspectiveEvaluator Tests (lines 481-557)**
   - `test_run_returns_evaluation`: Returns MultiPerspectiveEvaluation model
   - `test_run_parses_pro_arguments`: Extracts pro arguments from LLM
   - `test_run_parses_con_arguments`: Extracts con arguments from LLM
   - `test_run_calls_llm_with_both_inputs`: Verifies both inputs passed to LLM
   - `test_overall_verdict_is_string`: Validates verdict field type

6. **DeepDiveReporter Tests (lines 565-667)**
   - `test_write_creates_file`: Markdown file created successfully
   - `test_write_returns_path`: Returns Path object with .md suffix
   - `test_write_creates_parent_dirs`: Auto-creates nested directories
   - `test_render_markdown_contains_title`: Article title in output
   - `test_render_markdown_contains_main_thesis`: Thesis in output
   - `test_render_markdown_contains_credibility_score`: Score (0.72) in output
   - `test_render_markdown_contains_pro_arguments`: Pro arguments section
   - `test_render_markdown_contains_fact_check_table`: Fact check table in output
   - `test_render_markdown_contains_supporting_sources`: URLs included
   - `test_filename_contains_date_and_slug`: Filename formatted as YYYY-MM-DD
   - `test_file_written_with_utf8`: Chinese characters ("深度探索") preserved

7. **DeepDiveOrchestrator Integration Tests (lines 675-779)**
   - `test_run_with_url_returns_report`: Pipeline runs with URL input
   - `test_run_with_text_returns_report`: Pipeline runs with text input
   - `test_run_calls_pipeline_in_order`: Execution order: fetch → analyze → research → evaluate → report
   - `test_run_dry_run_skips_reporter`: Dry-run mode skips file writing
   - `test_run_raises_when_no_input`: ValueError raised if both url and text are None

**Helper Functions (lines 27-108):**
- `_article()`: Creates test ArticleContent
- `_search_queries()`: Creates test SearchQueries (supporting/opposing/expert/fact_checks)
- `_structural_analysis()`: Creates test StructuralAnalysis with logical_structure="因果论证" (causal argument)
- `_web_source()`: Creates test WebSource
- `_fact_check()`: Creates test FactCheck with status="verified"
- `_research_results()`: Creates test ResearchResults with all categories
- `_evaluation()`: Creates test MultiPerspectiveEvaluation with credibility_score=0.72
- `_report()`: Creates complete DeepDiveReport with timestamp 2026-02-28 14:30:00
- `_make_exa_result()`: Mocks Exa.ai search result with url/title/text
- `_make_exa_response()`: Wraps Exa results in response object

**Test Statistics:**
- Total tests: 50+
- Uses `pytest`, `unittest.mock.patch`, `pytest-httpx` (HTTPXMock)
- All tests mock `_call_llm()` at class level
- Tests verify UTF-8 encoding, HTML parsing, markdown output, and pipeline orchestration

This comprehensive test suite ensures all deep_dive components integrate correctly and handle edge cases properly.