feat(07-02): singleton OpenAIProvider + GenericOpenAIProvider + MAX_AI_CHARS removal
- openai_provider.py: singleton self._client=AsyncOpenAI(...) in __init__ (D-07),
_truncate() 60/40 smart truncation (D-13), removed MAX_AI_CHARS constant,
changed __init__ signature to include context_chars, removed _client() method
- generic_openai_provider.py: new class, subclasses OpenAIProvider, conditional
response_format={"type":"json_object"} on supports_json_mode flag (D-01/D-02),
imports parse_classification + parse_suggestions from ai.utils (D-02 contract)
- ollama_provider.py: added context_chars kwarg with default 8000, passes through
- lmstudio_provider.py: added context_chars kwarg with default 8000, passes through
- classifier.py: removed MAX_AI_CHARS constant and text[:MAX_AI_CHARS] slices;
truncation now handled inside each provider via _truncate()
- requirements.txt: bumped anthropic floor to >=0.95.0 (D-03 output_config support)
This commit is contained in:
curo1305
@@ -0,0 +1,103 @@
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"""GenericOpenAIProvider — unified OpenAI-compatible provider for all 8 compat vendors.
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Covers: Groq, xAI/Grok, DeepSeek, OpenRouter, Gemini-compat, Mistral-compat,
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Ollama, LMStudio (D-16/D-17/D-18).
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Key design decisions:
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- Subclasses OpenAIProvider to inherit singleton _client and _truncate (D-07/D-13).
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- Conditionally passes response_format={"type":"json_object"} based on
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supports_json_mode flag (D-01) — Gemini preset sets this to False (D-02).
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- Always parses the raw response with parse_classification / parse_suggestions
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imported from ai.utils (D-02 last-resort fallback contract — NEVER redefine
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locally; CLAUDE.md shared module map rule).
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"""
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from __future__ import annotations
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from ai.openai_provider import OpenAIProvider
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from ai.utils import parse_classification, parse_suggestions # D-02 contract
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class GenericOpenAIProvider(OpenAIProvider):
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"""OpenAI-compatible provider that enforces JSON mode on every call.
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Named presets (Groq, xAI, DeepSeek, OpenRouter, Gemini-compat, Mistral-compat,
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Ollama, LMStudio) are factory shortcuts in ai/__init__.py that pass the known
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base_url default for each vendor.
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supports_json_mode=False routes the provider through the parse_classification()
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fallback path without sending response_format — used for Gemini (D-02).
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"""
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supports_json_mode: bool = True # class-level default; overridden per instance
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def __init__(
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self,
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api_key: str,
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model: str,
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base_url: str | None,
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context_chars: int = 8000,
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supports_json_mode: bool = True,
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):
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super().__init__(
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api_key=api_key,
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model=model,
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base_url=base_url,
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context_chars=context_chars,
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)
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# Instance-level flag (may differ from class-level default)
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self.supports_json_mode = supports_json_mode
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async def classify(
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self,
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document_text: str,
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existing_topics: list[str],
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system_prompt: str,
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):
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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{self._truncate(document_text)}"
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)
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create_kwargs = dict(
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model=self._model,
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max_tokens=1024,
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_msg},
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],
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)
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if self.supports_json_mode:
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# D-01: enforce structured JSON output on all supporting providers
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create_kwargs["response_format"] = {"type": "json_object"}
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# else: Gemini preset — omit response_format, fall back to parse_classification()
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response = await self._client.chat.completions.create(**create_kwargs)
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raw = response.choices[0].message.content or ""
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# D-02: parse_classification is the last-resort fallback — always called
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return parse_classification(raw)
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async def suggest_topics(
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self,
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document_text: str,
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system_prompt: str,
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) -> list[str]:
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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{self._truncate(document_text)}"
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)
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create_kwargs = dict(
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model=self._model,
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max_tokens=256,
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_msg},
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],
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)
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if self.supports_json_mode:
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create_kwargs["response_format"] = {"type": "json_object"}
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response = await self._client.chat.completions.create(**create_kwargs)
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raw = response.choices[0].message.content or ""
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# D-02: parse_suggestions is the last-resort fallback — always called
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return parse_suggestions(raw)
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# health_check is inherited from OpenAIProvider — no override needed
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@@ -2,9 +2,15 @@ from ai.openai_provider import OpenAIProvider
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class LMStudioProvider(OpenAIProvider):
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def __init__(self, base_url: str = "http://host.docker.internal:1234", model: str = "gemma-4-e4b-it"):
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def __init__(
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self,
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base_url: str = "http://host.docker.internal:1234",
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model: str = "gemma-4-e4b-it",
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context_chars: int = 8000,
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):
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super().__init__(
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api_key="lm-studio",
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model=model,
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base_url=base_url.rstrip("/") + "/v1",
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context_chars=context_chars,
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)
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@@ -2,9 +2,15 @@ from ai.openai_provider import OpenAIProvider
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class OllamaProvider(OpenAIProvider):
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def __init__(self, base_url: str = "http://host.docker.internal:11434", model: str = "llama3.2"):
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def __init__(
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self,
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base_url: str = "http://host.docker.internal:11434",
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model: str = "llama3.2",
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context_chars: int = 8000,
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):
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super().__init__(
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api_key="ollama",
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model=model,
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base_url=base_url.rstrip("/") + "/v1",
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context_chars=context_chars,
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)
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@@ -1,18 +1,39 @@
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from __future__ import annotations
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from openai import AsyncOpenAI
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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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class OpenAIProvider(AIProvider):
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def __init__(self, api_key: str, model: str = "gpt-4o", base_url=None): # type: ignore[type-arg]
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self._api_key = api_key
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def __init__(
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self,
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api_key: str,
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model: str = "gpt-4o",
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base_url: str | None = None,
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context_chars: int = 8000,
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):
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self._api_key = api_key or "not-needed"
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self._model = model
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self._base_url = base_url
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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 — AsyncOpenAI 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 = AsyncOpenAI(api_key=self._api_key, base_url=self._base_url)
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def _client(self) -> AsyncOpenAI:
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return AsyncOpenAI(api_key=self._api_key or "placeholder", base_url=self._base_url)
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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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@@ -23,9 +44,9 @@ class OpenAIProvider(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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response = await self._client().chat.completions.create(
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response = await self._client.chat.completions.create(
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model=self._model,
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max_tokens=1024,
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messages=[
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@@ -44,9 +65,9 @@ class OpenAIProvider(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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response = await self._client().chat.completions.create(
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response = await self._client.chat.completions.create(
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model=self._model,
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max_tokens=256,
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messages=[
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@@ -59,7 +80,7 @@ class OpenAIProvider(AIProvider):
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async def health_check(self) -> bool:
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try:
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await self._client().chat.completions.create(
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await self._client.chat.completions.create(
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model=self._model,
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max_tokens=5,
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messages=[{"role": "user", "content": "ping"}],
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@@ -67,5 +88,3 @@ class OpenAIProvider(AIProvider):
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return True
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except Exception:
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return False
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@@ -3,7 +3,7 @@ uvicorn[standard]>=0.29
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python-multipart>=0.0.27
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pydantic-settings>=2.2
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pydantic[email]>=2.0
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anthropic>=0.26
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anthropic>=0.95.0
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openai>=1.30
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PyMuPDF>=1.26.7
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python-docx>=1.1
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@@ -25,8 +25,6 @@ from db.models import Document
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from services import storage
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from ai import get_provider
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MAX_AI_CHARS = 8_000
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_DEFAULT_SYSTEM_PROMPT = """You are a document classification assistant. When given a document's text content and a list of existing topics, you must:
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1. Assign the document to one or more relevant topics from the list.
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2. If no existing topics fit well, suggest new topic names.
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@@ -82,7 +80,7 @@ async def classify_document(
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topic_names = [t["name"] for t in all_topics]
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text = meta.get("extracted_text", "")
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result = await provider.classify(text[:MAX_AI_CHARS], topic_names, system_prompt)
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result = await provider.classify(text, topic_names, system_prompt)
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# Collect all topic names to persist (assigned + suggested)
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all_new_names = set(result.suggested_new_topics) | set(result.topics)
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@@ -124,4 +122,4 @@ async def suggest_topics_for_document(
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}
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provider = get_provider(_settings)
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text = meta.get("extracted_text", "")
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return await provider.suggest_topics(text[:MAX_AI_CHARS], system_prompt)
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return await provider.suggest_topics(text, system_prompt)
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