c3218f70c4
Domain layer (zero framework imports): - domain/models.py: pure dataclasses (RuleDocument, RuleSearchResult, Conversation, ChatMessage, LLMResponse, ChatResult) - domain/ports.py: ABC interfaces (RuleRepository, LLMPort, ConversationStore, IssueTracker) - domain/services.py: ChatService orchestrates Q&A flow using only ports Outbound adapters (implement domain ports): - adapters/outbound/openrouter.py: OpenRouterLLM with persistent httpx client, robust JSON parsing, regex citation fallback - adapters/outbound/sqlite_convos.py: SQLiteConversationStore with async_sessionmaker, timezone-aware datetimes, cleanup support - adapters/outbound/gitea_issues.py: GiteaIssueTracker with markdown injection protection (fenced code blocks) - adapters/outbound/chroma_rules.py: ChromaRuleRepository with clamped similarity scores Inbound adapter: - adapters/inbound/api.py: thin FastAPI router with input validation (max_length constraints), proper HTTP status codes (503 for missing LLM) Configuration & wiring: - config/settings.py: Pydantic v2 SettingsConfigDict (no module-level singleton) - config/container.py: create_app() factory with lifespan-managed DI - main.py: minimal entry point Test infrastructure (90 tests, all passing): - tests/fakes/: in-memory implementations of all 4 ports - tests/domain/: 26 tests for models and ChatService - tests/adapters/: 64 tests for all adapters using fakes/mocks - No real API calls, no model downloads, no disk I/O in fast tests Also fixes: aiosqlite version constraint (>=0.19.0), adds hatch build targets for new package layout. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
107 lines
3.4 KiB
Python
107 lines
3.4 KiB
Python
"""Domain services — core business logic with no framework dependencies.
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ChatService orchestrates the Q&A flow using only domain ports.
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"""
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import logging
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from typing import Optional
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from .models import ChatResult
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from .ports import RuleRepository, LLMPort, ConversationStore, IssueTracker
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logger = logging.getLogger(__name__)
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CONFIDENCE_THRESHOLD = 0.4
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class ChatService:
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"""Orchestrates the rules Q&A use case.
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All external dependencies are injected via ports — this class has zero
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knowledge of ChromaDB, OpenRouter, SQLite, or Gitea.
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"""
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def __init__(
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self,
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rules: RuleRepository,
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llm: LLMPort,
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conversations: ConversationStore,
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issues: Optional[IssueTracker] = None,
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top_k_rules: int = 10,
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):
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self.rules = rules
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self.llm = llm
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self.conversations = conversations
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self.issues = issues
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self.top_k_rules = top_k_rules
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async def answer_question(
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self,
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message: str,
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user_id: str,
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channel_id: str,
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conversation_id: Optional[str] = None,
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parent_message_id: Optional[str] = None,
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) -> ChatResult:
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"""Full Q&A flow: search rules → get history → call LLM → persist → maybe create issue."""
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# Get or create conversation
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conv_id = await self.conversations.get_or_create_conversation(
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user_id=user_id,
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channel_id=channel_id,
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conversation_id=conversation_id,
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)
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# Save user message
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user_msg_id = await self.conversations.add_message(
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conversation_id=conv_id,
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content=message,
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is_user=True,
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parent_id=parent_message_id,
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)
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# Search for relevant rules
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search_results = self.rules.search(query=message, top_k=self.top_k_rules)
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# Get conversation history for context
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history = await self.conversations.get_conversation_history(conv_id, limit=10)
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# Generate response from LLM
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llm_response = await self.llm.generate_response(
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question=message,
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rules=search_results,
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conversation_history=history,
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)
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# Save assistant message
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assistant_msg_id = await self.conversations.add_message(
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conversation_id=conv_id,
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content=llm_response.answer,
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is_user=False,
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parent_id=user_msg_id,
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)
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# Create issue if confidence is low or human review needed
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if self.issues and (
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llm_response.needs_human or llm_response.confidence < CONFIDENCE_THRESHOLD
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):
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try:
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await self.issues.create_unanswered_issue(
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question=message,
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user_id=user_id,
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channel_id=channel_id,
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attempted_rules=[r.rule_id for r in search_results],
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conversation_id=conv_id,
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)
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except Exception:
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logger.exception("Failed to create issue for unanswered question")
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return ChatResult(
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response=llm_response.answer,
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conversation_id=conv_id,
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message_id=assistant_msg_id,
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parent_message_id=user_msg_id,
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cited_rules=llm_response.cited_rules,
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confidence=llm_response.confidence,
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needs_human=llm_response.needs_human,
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)
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