chore: merge executor worktree (worktree-agent-afe278fe2f809d30b)

.planning/phases/07-redo-and-optimize-llm-integration/07-02-SUMMARY.md

@@ -0,0 +1,182 @@
+---
+phase: 07-redo-and-optimize-llm-integration
+plan: "02"
+subsystem: backend/ai-providers
+tags:
+  - ai
+  - provider-refactor
+  - singleton-client
+  - json-mode
+  - smart-truncation
+  - registry-pattern
+  - pydantic
+  - wave-2
+dependency_graph:
+  requires:
+    - "07-01 (system_settings table, HKDF helpers, xfail stubs)"
+  provides:
+    - "ProviderConfig Pydantic model + PROVIDER_DEFAULTS + SUPPORTS_JSON_MODE"
+    - "GenericOpenAIProvider covering all 8 OpenAI-compat vendors"
+    - "OpenAIProvider singleton _client lifecycle (D-07)"
+    - "Smart truncation _truncate() 60/40 (D-13)"
+    - "Registry-based get_provider(config: ProviderConfig) — no if/elif chain"
+    - "MAX_AI_CHARS removed from openai_provider.py and classifier.py"
+    - "anthropic SDK floor bumped to >=0.95.0"
+    - "6 Wave-2 xfail stubs promoted to passing tests"
+  affects:
+    - "07-03 (Anthropic output_config — depends on ProviderConfig and registry)"
+    - "07-04 (Celery retry — depends on classifier.py clean pass-through)"
+    - "07-05 (Admin AI panel — depends on ProviderConfig for form validation)"
+tech_stack:
+  added: []
+  patterns:
+    - "ProviderConfig(BaseModel) with extra=forbid; context_chars=0 sentinel for PROVIDER_DEFAULTS resolution"
+    - "PROVIDER_DEFAULTS dict with 10 entries; SUPPORTS_JSON_MODE dict with gemini=False"
+    - "GenericOpenAIProvider(OpenAIProvider) with conditional response_format kwarg (D-01/D-02)"
+    - "Singleton self._client = AsyncOpenAI(...) in __init__ (D-07)"
+    - "_truncate(): first 60% + last 40% of context_chars (D-13)"
+    - "_REGISTRY dict in ai/__init__.py replaces if/elif chain (O(1) lookup)"
+    - "D-02 invariant: parse_classification/parse_suggestions always imported from ai.utils"
+key_files:
+  created:
+    - backend/ai/provider_config.py
+    - backend/ai/generic_openai_provider.py
+  modified:
+    - backend/ai/openai_provider.py
+    - backend/ai/ollama_provider.py
+    - backend/ai/lmstudio_provider.py
+    - backend/ai/__init__.py
+    - backend/services/classifier.py
+    - backend/requirements.txt
+    - backend/tests/test_ai_providers.py
+decisions:
+  - "context_chars=0 as sentinel in ProviderConfig (not 8000) — enables factory to resolve PROVIDER_DEFAULTS via `config.context_chars or defaults['context_chars']`"
+  - "GenericOpenAIProvider always calls parse_classification() as last-resort regardless of json_mode — D-02 invariant preserved even when json_object is requested"
+  - "ai/__init__.py imports GenericOpenAIProvider at module load time; no lazy import needed since provider_config.py has no side effects"
+  - "anthropic floor bumped to >=0.95.0 to unblock Plan 03 output_config usage (A5 from RESEARCH.md)"
+metrics:
+  duration: "~35 minutes"
+  completed: "2026-06-04"
+  tasks_completed: 4
+  tasks_total: 4
+  files_created: 2
+  files_modified: 7
+---
+
+# Phase 7 Plan 02: Provider Config, GenericOpenAIProvider, and Registry Factory Summary
+
+ProviderConfig Pydantic model with 10-provider PROVIDER_DEFAULTS + SUPPORTS_JSON_MODE; GenericOpenAIProvider subclassing OpenAIProvider with JSON-mode conditional on supports_json_mode; singleton _client lifecycle; smart truncation; registry-based get_provider(); MAX_AI_CHARS removed from two files; 6 Wave-2 xfail tests promoted.
+
+## Tasks Completed
+
+| Task | Description | Commit | Files |
+|------|-------------|--------|-------|
+| 1 | ProviderConfig + PROVIDER_DEFAULTS + SUPPORTS_JSON_MODE | beb5b5e | backend/ai/provider_config.py |
+| 2 | GenericOpenAIProvider + singleton OpenAIProvider + MAX_AI_CHARS removal + ollama/lmstudio context_chars | 02bcbb9 | openai_provider.py, generic_openai_provider.py, ollama_provider.py, lmstudio_provider.py, classifier.py, requirements.txt |
+| 3 | Registry-based get_provider(config: ProviderConfig) | 13eef37 | ai/__init__.py, provider_config.py |
+| 4 | Promote 6 Wave-2 xfail stubs to passing | 209b156 | tests/test_ai_providers.py |
+
+## What Was Built
+
+### Task 1: ProviderConfig + PROVIDER_DEFAULTS + SUPPORTS_JSON_MODE
+
+Created `backend/ai/provider_config.py` as a pure data module (no provider class imports):
+
+- `ProviderConfig(BaseModel)` with `extra="forbid"`: provider_id (str, required), api_key (str, default ""), base_url (Optional[str], default None), model (str, default ""), context_chars (int, default 0 — sentinel meaning "use PROVIDER_DEFAULTS")
+- `PROVIDER_DEFAULTS: dict[str, dict]` with 10 entries from RESEARCH.md Pattern 2: openai, anthropic, gemini, groq, xai, deepseek, openrouter, mistral, ollama, lmstudio — each with base_url, model, context_chars
+- `SUPPORTS_JSON_MODE: dict[str, bool]` with gemini=False (OpenAI compat endpoint does not support `json_object` string form) and True for all others
+
+### Task 2: GenericOpenAIProvider + Singleton OpenAIProvider + Removals
+
+**backend/ai/openai_provider.py** refactored:
+- `__init__` now accepts `context_chars: int` parameter
+- `self._client = AsyncOpenAI(api_key=self._api_key, base_url=self._base_url)` stored as singleton in `__init__` (D-07)
+- `def _client(self)` method deleted entirely
+- `MAX_AI_CHARS = 8_000` constant deleted
+- `def _truncate(self, text: str) -> str` added: returns text unchanged if len <= context_chars, otherwise `text[:head] + "\n[...truncated...]\n" + text[-tail:]` where head = int(context_chars*0.6), tail = context_chars - head (D-13)
+- `classify()`, `suggest_topics()`, `health_check()` updated to use `self._client.chat.completions.create(...)` (no parentheses)
+
+**backend/ai/generic_openai_provider.py** created:
+- `class GenericOpenAIProvider(OpenAIProvider)` with `supports_json_mode` instance attribute
+- `__init__` accepts `supports_json_mode: bool = True` kwarg, calls `super().__init__(...)`
+- `classify()` and `suggest_topics()` conditionally add `response_format={"type":"json_object"}` when `supports_json_mode is True`; omit it for Gemini preset (D-01/D-02)
+- Both methods import and call `parse_classification` / `parse_suggestions` from `ai.utils` — D-02 contract enforced via import line
+- `health_check()` inherited from OpenAIProvider
+
+**backend/ai/ollama_provider.py** and **lmstudio_provider.py**: Added `context_chars: int = 8000` parameter, passed through to `super().__init__()`.
+
+**backend/services/classifier.py**: Removed `MAX_AI_CHARS = 8_000` constant and replaced `text[:MAX_AI_CHARS]` slices with `text` (truncation now inside provider via `_truncate()`).
+
+**backend/requirements.txt**: `anthropic>=0.26` bumped to `anthropic>=0.95.0` (D-03 output_config support gate for Plan 03).
+
+### Task 3: Registry-Based Factory
+
+**backend/ai/__init__.py** rewritten:
+- `_REGISTRY: dict[str, type[AIProvider]]` maps 10 provider_ids to classes
+- `def get_provider(config: ProviderConfig) -> AIProvider` with typed signature (no raw dict)
+- Resolves effective values from PROVIDER_DEFAULTS when config fields are empty/zero
+- AnthropicProvider instantiated without base_url (current ctor contract; Plan 03 widens)
+- GenericOpenAIProvider gets `supports_json_mode=SUPPORTS_JSON_MODE[config.provider_id]`
+- Raises `ValueError(f"Unknown AI provider: {config.provider_id!r}")` for unknown ids
+
+### Task 4: Six Wave-2 Tests Promoted
+
+All 6 tests now pass without `@pytest.mark.xfail`:
+
+1. **test_get_provider_typed**: Registry lookup returns GenericOpenAIProvider for groq/gemini; ValueError for unknown; supports_json_mode correct; _context_chars from PROVIDER_DEFAULTS
+2. **test_client_singleton**: `AsyncOpenAI` class called exactly once per provider instance (mocked via patch)
+3. **test_generic_openai_json_mode**: `response_format` present in kwargs when supports_json_mode=True; absent when False
+4. **test_context_chars_truncation**: Provider with context_chars=100 truncates 500-char input with "[...truncated...]"
+5. **test_smart_truncation**: 1000-char limit on 10000-char input → starts with 600 'H's, ends with 400 'T's
+6. **test_gemini_fallback_to_parse_classification**: D-02 contract — `parse_classification` called with raw content AND `response_format` absent from API call kwargs
+
+`test_anthropic_structured_output` remains xfail (Plan 07-03).
+
+## Verification Results
+
+- `grep -v '^#' backend/ai/openai_provider.py | grep -c 'MAX_AI_CHARS'` → 0
+- `grep -v '^#' backend/services/classifier.py | grep -c 'MAX_AI_CHARS'` → 0
+- `grep "from ai.utils import parse_classification" backend/ai/generic_openai_provider.py` → match found
+- `grep -c "def parse_classification" backend/ai/utils.py` → 1 (file untouched)
+- `backend/ai/__init__.py` contains `_REGISTRY` and `def get_provider(config: ProviderConfig)` and all 10 provider keys
+- `requirements.txt` contains `anthropic>=0.95.0`
+- Full test suite: **1 failed** (pre-existing test_extract_docx ModuleNotFoundError), **363 passed**, **15 xfailed**, **5 skipped** — no new failures; xfailed count down by 6
+
+## Deviations from Plan
+
+### Auto-fixed Issues
+
+**1. [Rule 1 - Bug] ProviderConfig.context_chars default changed from 8000 to 0**
+- **Found during:** Task 3 (test_get_provider_typed assertion failure)
+- **Issue:** Task 1 spec said `context_chars: int = 8000` but Task 3 test asserts `result._context_chars == PROVIDER_DEFAULTS["groq"]["context_chars"]` (128000) when `ProviderConfig(provider_id="groq")` is created without specifying context_chars. With default=8000, `8000 or 128000 = 8000` (truthy short-circuit) — test fails.
+- **Fix:** Changed `context_chars: int = 0` (sentinel meaning "unset — use PROVIDER_DEFAULTS in factory"). The factory's `config.context_chars or defaults["context_chars"]` then correctly resolves: `0 or 128000 = 128000`.
+- **Files modified:** backend/ai/provider_config.py
+- **Commit:** 13eef37
+
+## Known Stubs
+
+None — all plan goals implemented; no placeholders.
+
+## Threat Flags
+
+No new threat surface introduced. Changes are purely internal provider class refactoring and factory logic — no new API endpoints, no new DB access patterns, no new network paths. T-07-04 (empty api_key) mitigated: factory normalizes `api_key or "not-needed"` before passing to AsyncOpenAI constructor.
+
+## Self-Check: PASSED
+
+Files created/modified:
+
+- [x] backend/ai/provider_config.py — FOUND (ProviderConfig + PROVIDER_DEFAULTS + SUPPORTS_JSON_MODE)
+- [x] backend/ai/generic_openai_provider.py — FOUND (GenericOpenAIProvider class present)
+- [x] backend/ai/openai_provider.py — FOUND (singleton _client, _truncate, no MAX_AI_CHARS)
+- [x] backend/ai/ollama_provider.py — FOUND (context_chars param present)
+- [x] backend/ai/lmstudio_provider.py — FOUND (context_chars param present)
+- [x] backend/ai/__init__.py — FOUND (_REGISTRY and get_provider(config: ProviderConfig))
+- [x] backend/services/classifier.py — FOUND (MAX_AI_CHARS removed)
+- [x] backend/requirements.txt — FOUND (anthropic>=0.95.0)
+- [x] backend/tests/test_ai_providers.py — FOUND (6 tests promoted, 1 xfail remaining)
+
+Commits:
+- [x] beb5b5e — feat(07-02): ProviderConfig Pydantic model + PROVIDER_DEFAULTS + SUPPORTS_JSON_MODE
+- [x] 02bcbb9 — feat(07-02): singleton OpenAIProvider + GenericOpenAIProvider + MAX_AI_CHARS removal
+- [x] 13eef37 — feat(07-02): registry-based get_provider(config: ProviderConfig) — D-06
+- [x] 209b156 — test(07-02): promote 6 Wave-2 xfail tests to passing — D-01/D-02/D-07/D-12/D-13

backend/ai/__init__.py

@@ -1,35 +1,89 @@
-from ai.base import AIProvider, ClassificationResult
+"""AI provider factory — registry-based O(1) lookup.
+
+Usage:
+    from ai import get_provider
+    from ai.provider_config import ProviderConfig
+
+    config = ProviderConfig(provider_id="groq", api_key="sk-...")
+    provider = get_provider(config)
+    result = await provider.classify(text, topics, system_prompt)
+"""
+from __future__ import annotations
+
+from ai.base import AIProvider
 from ai.anthropic_provider import AnthropicProvider
 from ai.openai_provider import OpenAIProvider
-from ai.ollama_provider import OllamaProvider
+from ai.generic_openai_provider import GenericOpenAIProvider
-from ai.lmstudio_provider import LMStudioProvider
+from ai.provider_config import ProviderConfig, PROVIDER_DEFAULTS, SUPPORTS_JSON_MODE
 
 
-def get_provider(settings: dict) -> AIProvider:
+# Registry: maps provider_id → provider class
-    active = settings.get("active_provider", "lmstudio")
+# "openai" uses OpenAIProvider (no response_format override needed — plain OpenAI)
-    providers = settings.get("providers", {})
+# "anthropic" uses AnthropicProvider (native output_config, no base_url ctor arg until Plan 03)
-    cfg = providers.get(active, {})
+# All 8 OpenAI-compat vendors use GenericOpenAIProvider (D-16/D-17/D-18)
+_REGISTRY: dict[str, type[AIProvider]] = {
+    "openai": OpenAIProvider,
+    "anthropic": AnthropicProvider,
+    "gemini": GenericOpenAIProvider,
+    "groq": GenericOpenAIProvider,
+    "xai": GenericOpenAIProvider,
+    "deepseek": GenericOpenAIProvider,
+    "openrouter": GenericOpenAIProvider,
+    "mistral": GenericOpenAIProvider,
+    "ollama": GenericOpenAIProvider,
+    "lmstudio": GenericOpenAIProvider,
+}
 
-    if active == "anthropic":
+
-        return AnthropicProvider(
+def get_provider(config: ProviderConfig) -> AIProvider:
-            api_key=cfg.get("api_key", ""),
+    """Instantiate and return an AI provider for the given ProviderConfig.
-            model=cfg.get("model", "claude-sonnet-4-6"),
+
+    Resolves defaults from PROVIDER_DEFAULTS when config fields are absent,
+    normalises an empty api_key to "not-needed" (OpenAI SDK 2.34+ rejects ""),
+    and sets supports_json_mode from the SUPPORTS_JSON_MODE lookup for
+    GenericOpenAIProvider instances.
+
+    Args:
+        config: A ProviderConfig with at minimum provider_id set.
+
+    Returns:
+        A fully-constructed AIProvider instance.
+
+    Raises:
+        ValueError: If config.provider_id is not in the registry.
+    """
+    cls = _REGISTRY.get(config.provider_id)
+    if cls is None:
+        raise ValueError(f"Unknown AI provider: {config.provider_id!r}")
+
+    defaults = PROVIDER_DEFAULTS[config.provider_id]
+
+    # Resolve effective values — config fields take precedence over defaults
+    effective_api_key = config.api_key or "not-needed"
+    effective_model = config.model or defaults["model"]
+    effective_base_url = config.base_url if config.base_url is not None else defaults["base_url"]
+    effective_context_chars = config.context_chars or defaults["context_chars"]
+
+    if config.provider_id == "anthropic":
+        # AnthropicProvider does not accept base_url until Plan 03 refactors it;
+        # pass only the args its current __init__ accepts.
+        return cls(
+            api_key=effective_api_key,
+            model=effective_model,
         )
-    elif active == "openai":
+    elif cls is GenericOpenAIProvider:
-        return OpenAIProvider(
+        return cls(
-            api_key=cfg.get("api_key", ""),
+            api_key=effective_api_key,
-            model=cfg.get("model", "gpt-4o"),
+            model=effective_model,
-            base_url=cfg.get("base_url") or None,
+            base_url=effective_base_url,
-        )
+            context_chars=effective_context_chars,
-    elif active == "ollama":
+            supports_json_mode=SUPPORTS_JSON_MODE[config.provider_id],
-        return OllamaProvider(
-            base_url=cfg.get("base_url", "http://host.docker.internal:11434"),
-            model=cfg.get("model", "llama3.2"),
-        )
-    elif active == "lmstudio":
-        return LMStudioProvider(
-            base_url=cfg.get("base_url", "http://host.docker.internal:1234"),
-            model=cfg.get("model", "gemma-4-e4b-it"),
         )
     else:
-        raise ValueError(f"Unknown AI provider: {active}")
+        # OpenAIProvider
+        return cls(
+            api_key=effective_api_key,
+            model=effective_model,
+            base_url=effective_base_url,
+            context_chars=effective_context_chars,
+        )

backend/ai/generic_openai_provider.py

@@ -0,0 +1,103 @@
+"""GenericOpenAIProvider — unified OpenAI-compatible provider for all 8 compat vendors.
+
+Covers: Groq, xAI/Grok, DeepSeek, OpenRouter, Gemini-compat, Mistral-compat,
+        Ollama, LMStudio (D-16/D-17/D-18).
+
+Key design decisions:
+  - Subclasses OpenAIProvider to inherit singleton _client and _truncate (D-07/D-13).
+  - Conditionally passes response_format={"type":"json_object"} based on
+    supports_json_mode flag (D-01) — Gemini preset sets this to False (D-02).
+  - Always parses the raw response with parse_classification / parse_suggestions
+    imported from ai.utils (D-02 last-resort fallback contract — NEVER redefine
+    locally; CLAUDE.md shared module map rule).
+"""
+from __future__ import annotations
+
+from ai.openai_provider import OpenAIProvider
+from ai.utils import parse_classification, parse_suggestions  # D-02 contract
+
+
+class GenericOpenAIProvider(OpenAIProvider):
+    """OpenAI-compatible provider that enforces JSON mode on every call.
+
+    Named presets (Groq, xAI, DeepSeek, OpenRouter, Gemini-compat, Mistral-compat,
+    Ollama, LMStudio) are factory shortcuts in ai/__init__.py that pass the known
+    base_url default for each vendor.
+
+    supports_json_mode=False routes the provider through the parse_classification()
+    fallback path without sending response_format — used for Gemini (D-02).
+    """
+
+    supports_json_mode: bool = True  # class-level default; overridden per instance
+
+    def __init__(
+        self,
+        api_key: str,
+        model: str,
+        base_url: str | None,
+        context_chars: int = 8000,
+        supports_json_mode: bool = True,
+    ):
+        super().__init__(
+            api_key=api_key,
+            model=model,
+            base_url=base_url,
+            context_chars=context_chars,
+        )
+        # Instance-level flag (may differ from class-level default)
+        self.supports_json_mode = supports_json_mode
+
+    async def classify(
+        self,
+        document_text: str,
+        existing_topics: list[str],
+        system_prompt: str,
+    ):
+        topics_str = ", ".join(existing_topics) if existing_topics else "(none yet)"
+        user_msg = (
+            f"Existing topics: [{topics_str}]\n\n"
+            f"Document text:\n{self._truncate(document_text)}"
+        )
+        create_kwargs = dict(
+            model=self._model,
+            max_tokens=1024,
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_msg},
+            ],
+        )
+        if self.supports_json_mode:
+            # D-01: enforce structured JSON output on all supporting providers
+            create_kwargs["response_format"] = {"type": "json_object"}
+        # else: Gemini preset — omit response_format, fall back to parse_classification()
+        response = await self._client.chat.completions.create(**create_kwargs)
+        raw = response.choices[0].message.content or ""
+        # D-02: parse_classification is the last-resort fallback — always called
+        return parse_classification(raw)
+
+    async def suggest_topics(
+        self,
+        document_text: str,
+        system_prompt: str,
+    ) -> list[str]:
+        user_msg = (
+            "Suggest 3-5 topic names for this document. "
+            "Return ONLY valid JSON: {\"suggested_topics\": [\"topic1\", \"topic2\"]}\n\n"
+            f"Document text:\n{self._truncate(document_text)}"
+        )
+        create_kwargs = dict(
+            model=self._model,
+            max_tokens=256,
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_msg},
+            ],
+        )
+        if self.supports_json_mode:
+            create_kwargs["response_format"] = {"type": "json_object"}
+        response = await self._client.chat.completions.create(**create_kwargs)
+        raw = response.choices[0].message.content or ""
+        # D-02: parse_suggestions is the last-resort fallback — always called
+        return parse_suggestions(raw)
+
+    # health_check is inherited from OpenAIProvider — no override needed

backend/ai/lmstudio_provider.py

@@ -2,9 +2,15 @@ from ai.openai_provider import OpenAIProvider
 
 
 class LMStudioProvider(OpenAIProvider):
-    def __init__(self, base_url: str = "http://host.docker.internal:1234", model: str = "gemma-4-e4b-it"):
+    def __init__(
+        self,
+        base_url: str = "http://host.docker.internal:1234",
+        model: str = "gemma-4-e4b-it",
+        context_chars: int = 8000,
+    ):
         super().__init__(
             api_key="lm-studio",
             model=model,
             base_url=base_url.rstrip("/") + "/v1",
+            context_chars=context_chars,
         )

backend/ai/ollama_provider.py

@@ -2,9 +2,15 @@ from ai.openai_provider import OpenAIProvider
 
 
 class OllamaProvider(OpenAIProvider):
-    def __init__(self, base_url: str = "http://host.docker.internal:11434", model: str = "llama3.2"):
+    def __init__(
+        self,
+        base_url: str = "http://host.docker.internal:11434",
+        model: str = "llama3.2",
+        context_chars: int = 8000,
+    ):
         super().__init__(
             api_key="ollama",
             model=model,
             base_url=base_url.rstrip("/") + "/v1",
+            context_chars=context_chars,
         )

backend/ai/openai_provider.py

@@ -1,18 +1,39 @@
+from __future__ import annotations
+
 from openai import AsyncOpenAI
 from ai.base import AIProvider, ClassificationResult
 from ai.utils import parse_classification, parse_suggestions
 
-MAX_AI_CHARS = 8_000
-
 
 class OpenAIProvider(AIProvider):
-    def __init__(self, api_key: str, model: str = "gpt-4o", base_url=None):  # type: ignore[type-arg]
+    def __init__(
-        self._api_key = api_key
+        self,
+        api_key: str,
+        model: str = "gpt-4o",
+        base_url: str | None = None,
+        context_chars: int = 8000,
+    ):
+        self._api_key = api_key or "not-needed"
         self._model = model
         self._base_url = base_url
+        self._context_chars = context_chars
+        # Singleton: created once in __init__, reused for all calls on this instance.
+        # Do NOT recreate per API call — AsyncOpenAI wraps an httpx.AsyncClient
+        # that maintains a connection pool; recreating per call destroys pool reuse
+        # and forces a new TLS handshake per request (D-07 / RESEARCH.md).
+        self._client = AsyncOpenAI(api_key=self._api_key, base_url=self._base_url)
 
-    def _client(self) -> AsyncOpenAI:
+    def _truncate(self, text: str) -> str:
-        return AsyncOpenAI(api_key=self._api_key or "placeholder", base_url=self._base_url)
+        """D-13 smart truncation: first 60% + last 40% of context window.
+
+        Captures both document introduction and conclusion, which carry the
+        most topic signal for long documents.
+        """
+        if len(text) <= self._context_chars:
+            return text
+        head_len = int(self._context_chars * 0.6)
+        tail_len = self._context_chars - head_len
+        return text[:head_len] + "\n[...truncated...]\n" + text[-tail_len:]
 
     async def classify(
         self,
@@ -23,9 +44,9 @@ class OpenAIProvider(AIProvider):
         topics_str = ", ".join(existing_topics) if existing_topics else "(none yet)"
         user_msg = (
             f"Existing topics: [{topics_str}]\n\n"
-            f"Document text:\n{document_text[:MAX_AI_CHARS]}"
+            f"Document text:\n{self._truncate(document_text)}"
         )
-        response = await self._client().chat.completions.create(
+        response = await self._client.chat.completions.create(
             model=self._model,
             max_tokens=1024,
             messages=[
@@ -44,9 +65,9 @@ class OpenAIProvider(AIProvider):
         user_msg = (
             "Suggest 3-5 topic names for this document. "
             "Return ONLY valid JSON: {\"suggested_topics\": [\"topic1\", \"topic2\"]}\n\n"
-            f"Document text:\n{document_text[:MAX_AI_CHARS]}"
+            f"Document text:\n{self._truncate(document_text)}"
         )
-        response = await self._client().chat.completions.create(
+        response = await self._client.chat.completions.create(
             model=self._model,
             max_tokens=256,
             messages=[
@@ -59,7 +80,7 @@ class OpenAIProvider(AIProvider):
 
     async def health_check(self) -> bool:
         try:
-            await self._client().chat.completions.create(
+            await self._client.chat.completions.create(
                 model=self._model,
                 max_tokens=5,
                 messages=[{"role": "user", "content": "ping"}],
@@ -67,5 +88,3 @@ class OpenAIProvider(AIProvider):
             return True
         except Exception:
             return False
-
-

backend/ai/provider_config.py

@@ -0,0 +1,107 @@
+"""ProviderConfig Pydantic model and per-provider defaults.
+
+Loaded by get_provider() in ai/__init__.py; populated by load_provider_config()
+in services/ai_config.py.
+
+This file is a pure data module — it does NOT import any provider class.
+"""
+from __future__ import annotations
+
+from typing import Optional
+
+from pydantic import BaseModel
+
+
+class ProviderConfig(BaseModel):
+    """Typed configuration for a single AI provider instance.
+
+    Fields:
+        provider_id: One of the keys in PROVIDER_DEFAULTS (e.g. "openai", "groq").
+        api_key: Decrypted API key; empty string for local providers (Ollama, LMStudio).
+        base_url: Override for the provider's base URL; None means use the default.
+        model: Model name; empty string means use the default from PROVIDER_DEFAULTS.
+        context_chars: Character budget for input truncation; 0 means use the default.
+    """
+
+    model_config = {"extra": "forbid"}
+
+    provider_id: str
+    api_key: str = ""
+    base_url: Optional[str] = None
+    model: str = ""
+    context_chars: int = 0  # 0 means "unset — use PROVIDER_DEFAULTS in get_provider()"
+
+
+# Named preset defaults for all 10 supported providers.
+# Values are [ASSUMED] approximations based on well-known context window sizes.
+# Admins can override all fields via the system_settings DB table (D-04).
+PROVIDER_DEFAULTS: dict[str, dict] = {
+    "openai": {
+        "base_url": None,
+        "model": "gpt-4o",
+        "context_chars": 120_000,
+    },
+    "anthropic": {
+        "base_url": None,
+        "model": "claude-sonnet-4-6",
+        "context_chars": 180_000,
+    },
+    "gemini": {
+        "base_url": "https://generativelanguage.googleapis.com/v1beta/openai/",
+        "model": "gemini-2.0-flash",
+        "context_chars": 800_000,
+    },
+    "groq": {
+        "base_url": "https://api.groq.com/openai/v1",
+        "model": "llama-3.3-70b-versatile",
+        "context_chars": 128_000,
+    },
+    "xai": {
+        "base_url": "https://api.x.ai/v1",
+        "model": "grok-3-mini",
+        "context_chars": 128_000,
+    },
+    "deepseek": {
+        "base_url": "https://api.deepseek.com",
+        "model": "deepseek-chat",
+        "context_chars": 60_000,
+    },
+    "openrouter": {
+        "base_url": "https://openrouter.ai/api/v1",
+        "model": "anthropic/claude-3.5-sonnet",
+        "context_chars": 180_000,
+    },
+    "mistral": {
+        "base_url": "https://api.mistral.ai/v1",
+        "model": "mistral-large-latest",
+        "context_chars": 128_000,
+    },
+    "ollama": {
+        "base_url": "http://host.docker.internal:11434/v1",
+        "model": "llama3.2",
+        "context_chars": 8_000,
+    },
+    "lmstudio": {
+        "base_url": "http://host.docker.internal:1234/v1",
+        "model": "gemma-4-e4b-it",
+        "context_chars": 8_000,
+    },
+}
+
+# Whether the provider honours response_format={"type": "json_object"}.
+# Gemini's OpenAI-compat endpoint does NOT support the string form (D-02/D-03).
+# Ollama and LMStudio accept the parameter but some models ignore it — the
+# GenericOpenAIProvider always wraps the raw response with parse_classification()
+# regardless, so they are left as True (the parameter is still sent).
+SUPPORTS_JSON_MODE: dict[str, bool] = {
+    "openai": True,
+    "anthropic": True,
+    "gemini": False,
+    "groq": True,
+    "xai": True,
+    "deepseek": True,
+    "openrouter": True,
+    "mistral": True,
+    "ollama": True,
+    "lmstudio": True,
+}

backend/requirements.txt

@@ -3,7 +3,7 @@ uvicorn[standard]>=0.29
 python-multipart>=0.0.27
 pydantic-settings>=2.2
 pydantic[email]>=2.0
-anthropic>=0.26
+anthropic>=0.95.0
 openai>=1.30
 PyMuPDF>=1.26.7
 python-docx>=1.1

backend/services/classifier.py

@@ -25,8 +25,6 @@ from db.models import Document
 from services import storage
 from ai import get_provider
 
-MAX_AI_CHARS = 8_000
-
 _DEFAULT_SYSTEM_PROMPT = """You are a document classification assistant. When given a document's text content and a list of existing topics, you must:
 1. Assign the document to one or more relevant topics from the list.
 2. If no existing topics fit well, suggest new topic names.
@@ -82,7 +80,7 @@ async def classify_document(
         topic_names = [t["name"] for t in all_topics]
 
     text = meta.get("extracted_text", "")
-    result = await provider.classify(text[:MAX_AI_CHARS], topic_names, system_prompt)
+    result = await provider.classify(text, topic_names, system_prompt)
 
     # Collect all topic names to persist (assigned + suggested)
     all_new_names = set(result.suggested_new_topics) | set(result.topics)
@@ -124,4 +122,4 @@ async def suggest_topics_for_document(
     }
     provider = get_provider(_settings)
     text = meta.get("extracted_text", "")
-    return await provider.suggest_topics(text[:MAX_AI_CHARS], system_prompt)
+    return await provider.suggest_topics(text, system_prompt)

backend/tests/test_ai_providers.py

@@ -1,46 +1,197 @@
 """
-Wave 0 xfail stubs for Phase 7 AI provider tests.
+Tests for Phase 7 AI provider refactor.
 
-Each function is a placeholder for a test that will be promoted to green
+Wave 2 (Plan 07-02) promotes: test_get_provider_typed, test_client_singleton,
-in a later plan wave (per 07-VALIDATION.md per-task-verification map).
+test_generic_openai_json_mode, test_context_chars_truncation, test_smart_truncation,
+test_gemini_fallback_to_parse_classification.
 
-Stub policy (STATE.md decision: xfail(strict=False) for Wave 0):
+Remaining stubs (promoted in Plan 07-03): test_anthropic_structured_output.
-  - Body is a single pytest.xfail() call — no assertion code.
-  - strict=False so unexpected passes (xpass) never break CI.
 """
 import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+
+from ai.generic_openai_provider import GenericOpenAIProvider
+from ai.openai_provider import OpenAIProvider
+from ai.provider_config import ProviderConfig, PROVIDER_DEFAULTS
+from ai.utils import parse_classification
 
 
-@pytest.mark.xfail(strict=False, reason="Wave 0 stub — promoted in Plan 07-02")
+# ---------------------------------------------------------------------------
+# Task 3: Registry-based get_provider(config: ProviderConfig) — D-06
+# ---------------------------------------------------------------------------
+
+def test_get_provider_typed():
+    """get_provider() accepts a ProviderConfig and returns the correct provider class."""
+    from ai import get_provider
+
+    # Groq → GenericOpenAIProvider with supports_json_mode=True
+    config = ProviderConfig(provider_id="groq")
+    result = get_provider(config)
+    assert isinstance(result, GenericOpenAIProvider)
+    assert result.supports_json_mode is True
+    assert result._context_chars == PROVIDER_DEFAULTS["groq"]["context_chars"]
+
+    # Gemini → GenericOpenAIProvider with supports_json_mode=False (D-02)
+    config_gemini = ProviderConfig(provider_id="gemini")
+    result_gemini = get_provider(config_gemini)
+    assert isinstance(result_gemini, GenericOpenAIProvider)
+    assert result_gemini.supports_json_mode is False
+
+    # Unknown provider → ValueError
+    config_bogus = ProviderConfig(provider_id="bogus")
+    with pytest.raises(ValueError, match="Unknown AI provider"):
+        get_provider(config_bogus)
+
+
+# ---------------------------------------------------------------------------
+# Task 4: Singleton client lifecycle — D-07
+# ---------------------------------------------------------------------------
+
+@pytest.mark.asyncio
+async def test_client_singleton():
+    """AsyncOpenAI is instantiated exactly once per OpenAIProvider instance (D-07)."""
+    with patch("ai.openai_provider.AsyncOpenAI") as mock_cls:
+        # mock_cls() returns a mock instance — configure chat.completions.create
+        mock_instance = MagicMock()
+        mock_instance.chat = MagicMock()
+        mock_instance.chat.completions = MagicMock()
+        mock_instance.chat.completions.create = AsyncMock(
+            return_value=MagicMock(
+                choices=[MagicMock(message=MagicMock(content='{"assigned_topics":[],"new_topic_suggestions":[]}'))]
+            )
+        )
+        mock_cls.return_value = mock_instance
+
+        provider = OpenAIProvider(api_key="test-key", model="gpt-4o", base_url=None, context_chars=1000)
+
+        # Call classify twice on the same instance
+        await provider.classify("doc text", [], "sys")
+        await provider.classify("doc text", [], "sys")
+
+        # AsyncOpenAI class should have been called exactly once (in __init__)
+        assert mock_cls.call_count == 1
+
+
+# ---------------------------------------------------------------------------
+# Task 4: JSON-mode conditional — D-01
+# ---------------------------------------------------------------------------
+
+@pytest.mark.asyncio
 async def test_generic_openai_json_mode():
-    pytest.xfail("not implemented yet — Plan 07-02")
+    """GenericOpenAIProvider passes response_format only when supports_json_mode=True."""
+    synthetic_response = MagicMock(
+        choices=[MagicMock(message=MagicMock(
+            content='{"assigned_topics":["finance"],"new_topic_suggestions":[]}'
+        ))]
+    )
 
+    # supports_json_mode=True → response_format present in call kwargs
+    with patch("ai.openai_provider.AsyncOpenAI") as mock_cls:
+        mock_instance = MagicMock()
+        mock_instance.chat.completions.create = AsyncMock(return_value=synthetic_response)
+        mock_cls.return_value = mock_instance
+
+        provider_json = GenericOpenAIProvider(
+            api_key="key", model="gpt-4o", base_url=None,
+            context_chars=1000, supports_json_mode=True
+        )
+        await provider_json.classify("text", [], "sys")
+        call_kwargs = mock_instance.chat.completions.create.call_args.kwargs
+        assert "response_format" in call_kwargs
+        assert call_kwargs["response_format"] == {"type": "json_object"}
+
+    # supports_json_mode=False → response_format absent from call kwargs (Gemini preset path)
+    with patch("ai.openai_provider.AsyncOpenAI") as mock_cls2:
+        mock_instance2 = MagicMock()
+        mock_instance2.chat.completions.create = AsyncMock(return_value=synthetic_response)
+        mock_cls2.return_value = mock_instance2
+
+        provider_no_json = GenericOpenAIProvider(
+            api_key="key", model="gemini-2.0-flash",
+            base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
+            context_chars=1000, supports_json_mode=False
+        )
+        await provider_no_json.classify("text", [], "sys")
+        call_kwargs2 = mock_instance2.chat.completions.create.call_args.kwargs
+        assert "response_format" not in call_kwargs2
+
+
+# ---------------------------------------------------------------------------
+# Task 4: Smart truncation — D-12/D-13
+# ---------------------------------------------------------------------------
+
+def test_context_chars_truncation():
+    """Provider with context_chars=100 truncates a 500-char input."""
+    provider = OpenAIProvider(api_key="", model="gpt-4o", base_url=None, context_chars=100)
+    long_text = "a" * 500
+    result = provider._truncate(long_text)
+    assert len(result) < 500
+    assert "[...truncated...]" in result
+
+
+def test_smart_truncation():
+    """_truncate uses 60% head + 40% tail of context_chars."""
+    provider = OpenAIProvider(api_key="", model="gpt-4o", base_url=None, context_chars=1000)
+    # Build a distinguishable input where head and tail chars differ
+    input_text = "H" * 5000 + "T" * 5000  # 10000 chars total
+    result = provider._truncate(input_text)
+    # head = int(1000 * 0.6) = 600, tail = 1000 - 600 = 400
+    assert result.startswith("H" * 600)
+    assert result.endswith("T" * 400)
+    assert "[...truncated...]" in result
+
+
+# ---------------------------------------------------------------------------
+# Task 4: Gemini fallback to parse_classification (D-02 contract enforcement)
+# ---------------------------------------------------------------------------
+
+@pytest.mark.asyncio
+async def test_gemini_fallback_to_parse_classification():
+    """D-02: GenericOpenAIProvider(supports_json_mode=False) calls parse_classification()
+    and does NOT send response_format to the API.
+    """
+    raw_content = '{"assigned_topics":["x"],"new_topic_suggestions":[],"reasoning":"r"}'
+
+    with patch("ai.openai_provider.AsyncOpenAI") as mock_cls:
+        mock_create = AsyncMock(
+            return_value=MagicMock(
+                choices=[MagicMock(message=MagicMock(content=raw_content))]
+            )
+        )
+        mock_instance = MagicMock()
+        mock_instance.chat.completions.create = mock_create
+        mock_cls.return_value = mock_instance
+
+        # Wrap parse_classification so we can assert it was called
+        with patch(
+            "ai.generic_openai_provider.parse_classification",
+            wraps=parse_classification,
+        ) as mock_parse:
+            provider = GenericOpenAIProvider(
+                api_key="",
+                model="gemini-2.0-flash",
+                base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
+                context_chars=8000,
+                supports_json_mode=False,
+            )
+            result = await provider.classify("doc text", [], "sys")
+
+    # Result must be a valid ClassificationResult
+    assert result.topics == ["x"]
+
+    # parse_classification was called with the raw content (D-02 contract)
+    assert mock_parse.called
+    mock_parse.assert_called_once_with(raw_content)
+
+    # response_format must NOT have been sent to the API (Gemini preset path)
+    call_kwargs = mock_create.call_args.kwargs
+    assert "response_format" not in call_kwargs
+
+
+# ---------------------------------------------------------------------------
+# Stub: promoted in Plan 07-03
+# ---------------------------------------------------------------------------
 
 @pytest.mark.xfail(strict=False, reason="Wave 0 stub — promoted in Plan 07-03")
 async def test_anthropic_structured_output():
     pytest.xfail("not implemented yet — Plan 07-03")
-
-
-@pytest.mark.xfail(strict=False, reason="Wave 0 stub — promoted in Plan 07-02")
-async def test_get_provider_typed():
-    pytest.xfail("not implemented yet — Plan 07-02")
-
-
-@pytest.mark.xfail(strict=False, reason="Wave 0 stub — promoted in Plan 07-02")
-async def test_client_singleton():
-    pytest.xfail("not implemented yet — Plan 07-02")
-
-
-@pytest.mark.xfail(strict=False, reason="Wave 0 stub — promoted in Plan 07-03")
-async def test_context_chars_truncation():
-    pytest.xfail("not implemented yet — Plan 07-03")
-
-
-@pytest.mark.xfail(strict=False, reason="Wave 0 stub — promoted in Plan 07-03")
-async def test_smart_truncation():
-    pytest.xfail("not implemented yet — Plan 07-03")
-
-
-@pytest.mark.xfail(strict=False, reason="Wave 0 stub — promoted in Plan 07-02 Task 4 (D-02 Gemini fallback path)")
-async def test_gemini_fallback_to_parse_classification():
-    pytest.xfail("not implemented yet — Plan 07-02")