feat(07-03): AnthropicProvider singleton + output_config + truncation — D-03/D-07/D-12/D-13
- Delete MAX_AI_CHARS constant and _client() method from anthropic_provider.py
- Add self._client = AsyncAnthropic(...) singleton in __init__ (D-07)
- Add _truncate() with 60/40 split using self._context_chars (D-13)
- Add _CLASSIFICATION_SCHEMA and _SUGGESTIONS_SCHEMA module-level constants
- classify() and suggest_topics() pass output_config with json_schema format (D-03)
- stop_reason != "end_turn" degrades to parse_classification("") (T-07-08)
- Widen __init__ signature to (api_key, model, context_chars, base_url) (uniform ctor)
- Update ai/__init__.py to pass context_chars + base_url to AnthropicProvider
- Promote test_anthropic_structured_output + add test_anthropic_stop_reason_fallback
This commit is contained in:
curo1305
@@ -19,7 +19,7 @@ from ai.provider_config import ProviderConfig, PROVIDER_DEFAULTS, SUPPORTS_JSON_
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# Registry: maps provider_id → provider class
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# "openai" uses OpenAIProvider (no response_format override needed — plain OpenAI)
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# "anthropic" uses AnthropicProvider (native output_config, no base_url ctor arg until Plan 03)
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# "anthropic" uses AnthropicProvider (native output_config, Plan 03 widened ctor accepts context_chars+base_url)
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# All 8 OpenAI-compat vendors use GenericOpenAIProvider (D-16/D-17/D-18)
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_REGISTRY: dict[str, type[AIProvider]] = {
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"openai": OpenAIProvider,
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@@ -65,11 +65,13 @@ def get_provider(config: ProviderConfig) -> AIProvider:
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effective_context_chars = config.context_chars or defaults["context_chars"]
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if config.provider_id == "anthropic":
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# AnthropicProvider does not accept base_url until Plan 03 refactors it;
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# pass only the args its current __init__ accepts.
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# AnthropicProvider accepts context_chars and base_url (Plan 03 widened ctor).
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# base_url is accepted for uniform factory signature but unused by the SDK.
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return cls(
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api_key=effective_api_key,
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model=effective_model,
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context_chars=effective_context_chars,
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base_url=effective_base_url,
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)
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elif cls is GenericOpenAIProvider:
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return cls(
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@@ -1,17 +1,83 @@
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"""Anthropic AI provider — singleton client, output_config structured output, smart truncation.
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D-03: Uses output_config={"format": {"type": "json_schema", "schema": ...}} with constrained
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decoding available in anthropic SDK >=0.95.0 (GA, no beta headers needed).
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D-07: self._client = AsyncAnthropic(...) created once in __init__ and reused — never recreated
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per API call to preserve the httpx connection pool.
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D-12/D-13: Global char constant removed; uses self._context_chars with 60/40 smart truncation.
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Security: api_key is accepted from the caller (loaded from system_settings by ai_config.py
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and decrypted before being passed here). The key is never stored beyond this instance's
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lifetime. T-07-06 mitigated: this class never reads the api_key from env vars directly.
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"""
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from __future__ import annotations
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import anthropic
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from ai.base import AIProvider, ClassificationResult
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from ai.utils import parse_classification, parse_suggestions
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MAX_AI_CHARS = 8_000
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# ── Output schemas for constrained decoding (D-03 / RESEARCH.md) ────────────────────────────
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# additionalProperties=False required by Anthropic output_config grammar.
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# "reasoning" is intentionally absent from "required" so legacy prompts that don't include it
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# still produce valid JSON (Anthropic will emit it because it is declared in properties, but
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# we do not enforce it in the schema to avoid refusal on minimal responses).
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_CLASSIFICATION_SCHEMA: dict = {
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"type": "object",
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"properties": {
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"assigned_topics": {"type": "array", "items": {"type": "string"}},
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"new_topic_suggestions": {"type": "array", "items": {"type": "string"}},
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"reasoning": {"type": "string"},
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},
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"required": ["assigned_topics", "new_topic_suggestions"],
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"additionalProperties": False,
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}
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_SUGGESTIONS_SCHEMA: dict = {
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"type": "object",
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"properties": {
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"suggested_topics": {"type": "array", "items": {"type": "string"}},
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},
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"required": ["suggested_topics"],
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"additionalProperties": False,
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}
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class AnthropicProvider(AIProvider):
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def __init__(self, api_key: str, model: str = "claude-sonnet-4-6"):
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"""Anthropic Claude provider with singleton client and output_config structured output.
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Constructor signature matches the uniform factory contract in ai/__init__.py:
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api_key, model, context_chars, base_url (accepted but unused — Anthropic SDK
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manages the endpoint; widened so get_provider() can call all providers uniformly).
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"""
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def __init__(
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self,
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api_key: str,
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model: str = "claude-sonnet-4-6",
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context_chars: int = 180_000,
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base_url: str | None = None, # accepted for uniform factory signature; unused
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):
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self._api_key = api_key
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self._model = model
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self._context_chars = context_chars
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# Singleton: created once in __init__, reused for all calls on this instance.
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# Do NOT recreate per API call — AsyncAnthropic wraps an httpx.AsyncClient
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# that maintains a connection pool; recreating per call destroys pool reuse
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# and forces a new TLS handshake per request (D-07 / RESEARCH.md).
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self._client = anthropic.AsyncAnthropic(api_key=self._api_key)
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def _client(self):
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return anthropic.AsyncAnthropic(api_key=self._api_key)
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def _truncate(self, text: str) -> str:
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"""D-13 smart truncation: first 60% + last 40% of context window.
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Captures both document introduction and conclusion, which carry the
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most topic signal for long documents.
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"""
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if len(text) <= self._context_chars:
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return text
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head_len = int(self._context_chars * 0.6)
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tail_len = self._context_chars - head_len
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return text[:head_len] + "\n[...truncated...]\n" + text[-tail_len:]
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async def classify(
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self,
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@@ -22,16 +88,23 @@ class AnthropicProvider(AIProvider):
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topics_str = ", ".join(existing_topics) if existing_topics else "(none yet)"
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user_msg = (
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f"Existing topics: [{topics_str}]\n\n"
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f"Document text:\n{document_text[:MAX_AI_CHARS]}"
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f"Document text:\n{self._truncate(document_text)}"
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)
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client = self._client()
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response = await client.messages.create(
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response = await self._client.messages.create(
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model=self._model,
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max_tokens=1024,
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system=system_prompt,
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messages=[{"role": "user", "content": user_msg}],
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output_config={"format": {"type": "json_schema", "schema": _CLASSIFICATION_SCHEMA}},
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)
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raw = response.content[0].text
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# Graceful degradation (T-07-08): when stop_reason is "refusal" or "max_tokens"
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# the constrained decoding did not complete — fall back to parse_classification("")
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# which returns an empty ClassificationResult rather than raising an exception.
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stop_reason = getattr(response, "stop_reason", "end_turn")
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if response.content and stop_reason == "end_turn":
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raw = response.content[0].text
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else:
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raw = ""
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return parse_classification(raw)
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async def suggest_topics(
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@@ -42,28 +115,34 @@ class AnthropicProvider(AIProvider):
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user_msg = (
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"Suggest 3-5 topic names for this document. "
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"Return ONLY valid JSON: {\"suggested_topics\": [\"topic1\", \"topic2\"]}\n\n"
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f"Document text:\n{document_text[:MAX_AI_CHARS]}"
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f"Document text:\n{self._truncate(document_text)}"
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)
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client = self._client()
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response = await client.messages.create(
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response = await self._client.messages.create(
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model=self._model,
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max_tokens=256,
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system=system_prompt,
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messages=[{"role": "user", "content": user_msg}],
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output_config={"format": {"type": "json_schema", "schema": _SUGGESTIONS_SCHEMA}},
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)
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raw = response.content[0].text
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stop_reason = getattr(response, "stop_reason", "end_turn")
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if response.content and stop_reason == "end_turn":
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raw = response.content[0].text
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else:
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raw = ""
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return parse_suggestions(raw)
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async def health_check(self) -> bool:
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"""Verify API key validity and connectivity by sending a minimal message.
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Does NOT pass output_config — the response shape does not matter here;
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this only confirms the api_key and network path are working.
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"""
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try:
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client = self._client()
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await client.messages.create(
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await self._client.messages.create(
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model=self._model,
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max_tokens=5,
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max_tokens=8,
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messages=[{"role": "user", "content": "ping"}],
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)
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return True
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except Exception:
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return False
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