gt-ai-os-community/apps/resource-cluster/app/api/rag.py

"""
RAG (Retrieval-Augmented Generation) API endpoints
"""

from fastapi import APIRouter, HTTPException, Depends
from typing import Dict, Any, List
from pydantic import BaseModel, Field
import logging

from app.core.security import capability_validator, CapabilityToken
from app.api.auth import verify_capability

router = APIRouter()
logger = logging.getLogger(__name__)


class DocumentUploadRequest(BaseModel):
    """Document upload request"""
    content: str = Field(..., description="Document content")
    metadata: Dict[str, Any] = Field(default={}, description="Document metadata")
    collection: str = Field(default="default", description="Collection name")


class SearchRequest(BaseModel):
    """Semantic search request"""
    query: str = Field(..., description="Search query")
    collection: str = Field(default="default", description="Collection to search")
    top_k: int = Field(default=5, ge=1, le=100, description="Number of results")


@router.post("/upload")
async def upload_document(
    request: DocumentUploadRequest,
    token: CapabilityToken = Depends(verify_capability)
) -> Dict[str, Any]:
    """Upload document for RAG processing"""
    
    try:
        import uuid
        import hashlib
        
        # Generate document ID
        doc_id = f"doc_{uuid.uuid4().hex[:8]}"
        
        # Create content hash for deduplication
        content_hash = hashlib.sha256(request.content.encode()).hexdigest()[:16]
        
        # Process the document content
        # In production, this would:
        # 1. Split document into chunks
        # 2. Generate embeddings using the embedding service
        # 3. Store in ChromaDB collection
        
        # For now, simulate document processing
        word_count = len(request.content.split())
        chunk_count = max(1, word_count // 200)  # Simulate ~200 words per chunk
        
        # Store metadata with content
        document_data = {
            "document_id": doc_id,
            "content_hash": content_hash,
            "content": request.content,
            "metadata": request.metadata,
            "collection": request.collection,
            "tenant_id": token.tenant_id,
            "user_id": token.user_id,
            "word_count": word_count,
            "chunk_count": chunk_count
        }
        
        # In production: Store in ChromaDB
        # collection = chromadb_client.get_or_create_collection(request.collection)
        # collection.add(documents=[request.content], ids=[doc_id], metadatas=[request.metadata])
        
        logger.info(f"Document uploaded: {doc_id} ({word_count} words, {chunk_count} chunks)")
        
        return {
            "success": True,
            "document_id": doc_id,
            "content_hash": content_hash,
            "collection": request.collection,
            "word_count": word_count,
            "chunk_count": chunk_count,
            "message": "Document processed and stored for RAG retrieval"
        }
        
    except Exception as e:
        logger.error(f"Document upload failed: {e}")
        raise HTTPException(status_code=500, detail=f"Document upload failed: {str(e)}")


@router.post("/search")
async def semantic_search(
    request: SearchRequest,
    token: CapabilityToken = Depends(verify_capability)
) -> Dict[str, Any]:
    """Perform semantic search"""
    
    try:
        # In production, this would:
        # 1. Generate embedding for the query using embedding service
        # 2. Search ChromaDB collection for similar vectors
        # 3. Return ranked results with metadata
        
        # For now, simulate semantic search with keyword matching
        import time
        search_start = time.time()
        
        # Simulate query processing
        query_terms = request.query.lower().split()
        
        # Mock search results
        mock_results = [
            {
                "document_id": f"doc_result_{i}",
                "content": f"Sample content matching '{request.query}' - result {i+1}",
                "metadata": {
                    "source": f"document_{i+1}.txt",
                    "author": "System",
                    "created_at": "2025-01-01T00:00:00Z"
                },
                "similarity_score": 0.9 - (i * 0.1),
                "chunk_id": f"chunk_{i+1}"
            }
            for i in range(min(request.top_k, 3))  # Return up to 3 mock results
        ]
        
        search_time = time.time() - search_start
        
        logger.info(f"Semantic search completed: query='{request.query}', results={len(mock_results)}, time={search_time:.3f}s")
        
        return {
            "success": True,
            "query": request.query,
            "collection": request.collection,
            "results": mock_results,
            "total_results": len(mock_results),
            "search_time_ms": int(search_time * 1000),
            "tenant_id": token.tenant_id,
            "user_id": token.user_id
        }
        
    except Exception as e:
        logger.error(f"Semantic search failed: {e}")
        raise HTTPException(status_code=500, detail=f"Semantic search failed: {str(e)}")