curo1305
02bcbb9143
- 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)
126 lines
5.2 KiB
Python
126 lines
5.2 KiB
Python
"""
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Classification orchestrator.
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Loads settings, selects AI provider, classifies document, auto-creates suggested topics.
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Updated in Plan 05: classify_document and suggest_topics_for_document now accept
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an AsyncSession as their first argument so they can be called from the Celery task
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wrapper and from API route handlers that already hold a session.
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Updated in Plan 03-03: classify_document uses load_topics_for_user (D-17) to scope
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topic lookup to the document owner's namespace, and creates AI-suggested topics in
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the user's namespace via create_topic(user_id=doc.user_id) (D-11).
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Updated in Plan 03-04: classify_document and suggest_topics_for_document now accept
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ai_provider and ai_model kwargs. No longer calls storage.load_settings(). Provider
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resolved via get_provider() using per-user settings from DB (D-14, D-15).
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"""
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from __future__ import annotations
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import uuid as _uuid
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from sqlalchemy.ext.asyncio import AsyncSession
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from config import settings as app_settings
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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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_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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Return ONLY valid JSON in this exact format, with no additional text or explanation:
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{"assigned_topics": ["topic1"], "new_topic_suggestions": ["new topic name"]}
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If the document fits no topics and you have no suggestions, return: {"assigned_topics": [], "new_topic_suggestions": []}"""
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async def classify_document(
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session: AsyncSession,
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doc_id: str,
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topic_names: list[str] | None = None,
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ai_provider: str | None = None,
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ai_model: str | None = None,
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) -> list[str]:
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"""
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Classify a document by its ID. Returns the list of assigned topic names.
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If topic_names is provided, restrict classification to those topics.
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Auto-creates any newly suggested topics in the document owner's namespace (D-11).
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ai_provider and ai_model come from the document owner's User record (D-14).
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Falls back to app_settings.default_ai_provider / default_ai_model when None (D-15).
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"""
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meta = await storage.get_metadata(session, doc_id)
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if meta is None:
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raise ValueError(f"Document {doc_id} not found")
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_ai_provider = ai_provider or app_settings.default_ai_provider
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_ai_model = ai_model or app_settings.default_ai_model
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system_prompt = app_settings.system_prompt or _DEFAULT_SYSTEM_PROMPT
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_settings = {
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"active_provider": _ai_provider,
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"providers": {_ai_provider: {"model": _ai_model}},
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}
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provider = get_provider(_settings)
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# Load the Document ORM object to get the owner's user_id (D-11, D-17)
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try:
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uid = _uuid.UUID(doc_id)
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except ValueError:
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uid = None
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doc = await session.get(Document, uid) if uid is not None else None
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doc_user_id = doc.user_id if doc is not None else None
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# Use namespace-scoped topic list if not specified (D-17)
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if topic_names is None:
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if doc_user_id is not None:
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all_topics = await storage.load_topics_for_user(session, user_id=doc_user_id)
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else:
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# Fallback for documents without a user (legacy / test data)
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all_topics = await storage.load_topics(session)
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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, 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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# Auto-create any topic not already in the registry — in the user's namespace (D-11)
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existing_names = {t.lower() for t in topic_names}
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for name in all_new_names:
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if name.strip() and name.lower() not in existing_names:
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await storage.create_topic(session, name.strip(), user_id=doc_user_id)
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# Final list: everything the AI assigned or suggested
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final_topics = [t for t in list(set(result.topics + result.suggested_new_topics)) if t.strip()]
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await storage.update_document_topics(session, doc_id, final_topics)
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return final_topics
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async def suggest_topics_for_document(
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session: AsyncSession,
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doc_id: str,
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ai_provider: str | None = None,
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ai_model: str | None = None,
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) -> list[str]:
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"""Return AI-suggested topic names without modifying the document.
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ai_provider and ai_model come from the document owner's User record (D-14).
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Falls back to app_settings.default_ai_provider / default_ai_model when None (D-15).
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"""
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meta = await storage.get_metadata(session, doc_id)
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if meta is None:
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raise ValueError(f"Document {doc_id} not found")
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_ai_provider = ai_provider or app_settings.default_ai_provider
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_ai_model = ai_model or app_settings.default_ai_model
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system_prompt = app_settings.system_prompt or _DEFAULT_SYSTEM_PROMPT
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_settings = {
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"active_provider": _ai_provider,
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"providers": {_ai_provider: {"model": _ai_model}},
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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, system_prompt)
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