Odysseus v1.0

services/research/service.py

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+# services/research/service.py
+"""Research service — deep research with LLM-in-the-loop."""
+
+from dataclasses import dataclass, field
+from typing import List, Optional, Callable
+
+from .research_handler import ResearchHandler
+
+
+@dataclass
+class ResearchSource:
+    """A source found during research."""
+    url: str
+    title: str
+    snippet: str
+    relevance: float = 0.0
+
+
+@dataclass
+class ResearchResult:
+    """Result of a deep research query."""
+    query: str
+    summary: str
+    sources: List[ResearchSource] = field(default_factory=list)
+    sections: List[str] = field(default_factory=list)
+    tokens_used: int = 0
+    duration_seconds: float = 0.0
+
+
+class ResearchService:
+    """
+    Deep research service.
+
+    Usage:
+        service = ResearchService()
+        result = await service.research("quantum computing advances 2024")
+        print(result.summary)
+    """
+
+    def __init__(self):
+        self.handler = ResearchHandler()
+        self._active: dict = {}
+
+    async def research(
+        self,
+        topic: str,
+        llm_endpoint: str,
+        llm_model: str,
+        max_time: int = 300,
+        on_progress: Optional[Callable[[dict], None]] = None,
+    ) -> ResearchResult:
+        """
+        Perform deep research on a topic.
+
+        Args:
+            topic: Research topic/question
+            llm_endpoint: LLM API endpoint
+            llm_model: Model to use
+            max_time: Maximum time in seconds
+            on_progress: Optional progress callback
+
+        Returns:
+            ResearchResult with findings
+        """
+        import time
+        start = time.time()
+
+        result = await self.handler.call_research_service(
+            topic,
+            llm_endpoint,
+            llm_model,
+            max_time=max_time,
+            progress_callback=on_progress,
+        )
+
+        duration = time.time() - start
+
+        # Parse result into structured format
+        sources = [
+            ResearchSource(
+                url=s.get("url", ""),
+                title=s.get("title", ""),
+                snippet=s.get("snippet", ""),
+                relevance=s.get("relevance", 0.0),
+            )
+            for s in result.get("sources", [])
+        ]
+
+        return ResearchResult(
+            query=topic,
+            summary=result.get("summary", result.get("answer", "")),
+            sources=sources,
+            sections=result.get("sections", []),
+            tokens_used=result.get("tokens_used", 0),
+            duration_seconds=duration,
+        )
+
+    def start_background(
+        self,
+        session_id: str,
+        topic: str,
+        llm_endpoint: str,
+        llm_model: str,
+        max_time: int = 300,
+    ) -> dict:
+        """Start research in background. Returns task info."""
+        return self.handler.start_research(
+            session_id, topic, llm_endpoint, llm_model, max_time
+        )
+
+    def get_status(self, session_id: str) -> Optional[dict]:
+        """Get status of background research."""
+        return self.handler.get_status(session_id)
+
+    def cancel(self, session_id: str) -> bool:
+        """Cancel background research."""
+        return self.handler.cancel_research(session_id)