aigenrec/luminabackend

1from typing import Dict, Any, List2from supabase import create_client, Client3from config.settings import settings4from utils.logger import logger5from uuid import uuid46from datetime import datetime7from services.llm_service import llm_service8from services.embedding_service import embedding_service9from services.qdrant_service import qdrant_service10 11class NotesService:12    def __init__(self):13        self.client: Client = create_client(14            settings.SUPABASE_URL,15            settings.SUPABASE_SERVICE_KEY16        )17    18    async def get_notes(self, project_id: str, user_id: str) -> Dict[str, Any]:19        """Get notes for a project"""20        try:21            response = self.client.table("notes").select("*").eq(22                "project_id", project_id23            ).eq("user_id", user_id).execute()24            25            if response.data:26                note = response.data[0]27                return {28                    "id": note["id"],29                    "project_id": note["project_id"],30                    "user_id": note["user_id"],31                    "content": note["content"],32                    "created_at": note["created_at"],33                    "updated_at": note["updated_at"]34                }35            36            return None37            38        except Exception as e:39            logger.error(f"Error getting notes: {str(e)}")40            raise41    42    async def create_or_update_notes(43        self,44        project_id: str,45        user_id: str,46        content: str47    ) -> Dict[str, Any]:48        """Create or update notes for a project"""49        try:50            # Check if notes exist51            existing = await self.get_notes(project_id, user_id)52            53            if existing:54                # Update existing notes55                response = self.client.table("notes").update({56                    "content": content,57                    "updated_at": datetime.utcnow().isoformat()58                }).eq("id", existing["id"]).execute()59                60                logger.info(f"Updated notes for project {project_id}")61            else:62                # Create new notes63                note_id = str(uuid4())64                response = self.client.table("notes").insert({65                    "id": note_id,66                    "project_id": project_id,67                    "user_id": user_id,68                    "content": content69                }).execute()70                71                logger.info(f"Created notes for project {project_id}")72            73            return response.data[0] if response.data else {}74            75        except Exception as e:76            logger.error(f"Error creating/updating notes: {str(e)}")77            raise78 79    async def generate_notes(80        self,81        project_id: str,82        note_type: str,83        topic: str = None,84        selected_documents: List[str] = None85    ) -> str:86        """Generate notes using AI"""87        try:88            logger.info(f"Generating notes ({note_type}) for project {project_id}, topic: {topic}")89            90            # 1. Retrieve Content91            queries = []92            if topic:93                # If topic is provided, prioritize it94                queries = [topic, f"{note_type} of {topic}"]95            elif "Summary" in note_type:96                queries = ["overview of the document", "main concepts and themes", "conclusion and results"]97            elif "Key Points" in note_type:98                queries = ["important definitions", "key takeaways", "critical points"]99            else:100                queries = [note_type]101            102            collection_name = f"project_{project_id}"103            all_hits = []104            seen_texts = set()105            106            for q in queries:107                embedding = await embedding_service.generate_embedding(q)108                results = await qdrant_service.search(109                    collection_name=collection_name,110                    query_vector=embedding,111                    limit=10, # Fetch robust amount112                    filter_conditions={"document_ids": selected_documents} if selected_documents else None113                )114                for hit in results:115                    if hit["text"] not in seen_texts:116                        all_hits.append(hit)117                        seen_texts.add(hit["text"])118            119            if not all_hits:120                return "No content found to generate notes."121                122            # Combine content (limit to reasonable context window)123            context = "\n\n".join([hit["text"] for hit in all_hits[:20]])124            125            # 2. Generate Note126            prompt = f"""Generate a **{note_type}** based on the following content.127            128Content:129{context}130 131Requirements:132- Use clear, professional Markdown formatting.133- Use headers, bullet points, and bold text for readability.134- Be comprehensive but concise.135- Structure it as a study guide or note set.136 137Respond ONLY with the Markdown content."""138 139            messages = [{"role": "user", "content": prompt}]140            response = await llm_service.chat_completion(messages, temperature=0.5, max_tokens=2500)141            142            return response143            144        except Exception as e:145            logger.error(f"Error generating notes: {str(e)}")146            raise147 148notes_service = NotesService()149