2:I[7012,["4765","static/chunks/4765-f5afdf8061f456f3.js","9856","static/chunks/9856-3b185291364d9bef.js","6687","static/chunks/app/docs/%5B...slug%5D/page-e07536548216bee4.js"],"MarkdownRenderer"] 4:I[9856,["4765","static/chunks/4765-f5afdf8061f456f3.js","9856","static/chunks/9856-3b185291364d9bef.js","6687","static/chunks/app/docs/%5B...slug%5D/page-e07536548216bee4.js"],""] 5:I[4126,[],""] 7:I[9630,[],""] 8:I[4278,["9856","static/chunks/9856-3b185291364d9bef.js","8172","static/chunks/8172-b3a2d6fe4ae10d40.js","3185","static/chunks/app/layout-2814fa5d15b84fe4.js"],"HeadingProvider"] 9:I[1476,["9856","static/chunks/9856-3b185291364d9bef.js","8172","static/chunks/8172-b3a2d6fe4ae10d40.js","3185","static/chunks/app/layout-2814fa5d15b84fe4.js"],"Header"] a:I[3167,["9856","static/chunks/9856-3b185291364d9bef.js","8172","static/chunks/8172-b3a2d6fe4ae10d40.js","3185","static/chunks/app/layout-2814fa5d15b84fe4.js"],"Sidebar"] b:I[7409,["9856","static/chunks/9856-3b185291364d9bef.js","8172","static/chunks/8172-b3a2d6fe4ae10d40.js","3185","static/chunks/app/layout-2814fa5d15b84fe4.js"],"PageFrame"] 3:Tdd92, # Semantic Search & Knowledge Base Design ## Overview VoiceAssist uses a sophisticated semantic search system to retrieve relevant medical knowledge from textbooks, journals, and clinical guidelines. This document describes the complete ingestion and query pipeline. **Note**: For canonical entity definitions (KnowledgeDocument, KBChunk, IndexingJob), see [DATA_MODEL.md](DATA_MODEL.md). This document describes their usage in the search pipeline. ## Architecture Diagram ``` ┌─────────────────────────────────────────────────────────────────┐ │ INGESTION PIPELINE │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ [PDF Upload] → [Text Extraction] → [Chunking] → [Embedding] │ │ ↓ ↓ ↓ │ │ [OCR/Parse] [Metadata] [Vectors] │ │ ↓ ↓ ↓ │ │ [Preprocessing] [Enrichment] [Indexing] │ │ ↓ │ │ [Qdrant Vector DB] │ └─────────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────────┐ │ QUERY PIPELINE │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ [User Query] → [Intent Detection] → [Query Enhancement] │ │ ↓ │ │ [Vector Search (Hybrid)] │ │ - Dense vectors (Qdrant) │ │ - Sparse (BM25) fallback │ │ ↓ │ │ [Post-processing] │ │ - Reranking │ │ - Deduplication │ │ - Filtering │ │ ↓ │ │ [Top-K Results] │ │ ↓ │ │ [RAG: LLM + Citations] │ └─────────────────────────────────────────────────────────────────┘ ``` ## Ingestion Pipeline ### 1. Document Upload & Storage **Process:** 1. User uploads PDF/DOCX via admin panel 2. File saved to `/data/documents/{doc_id}/{filename}` 3. Document record created in PostgreSQL with status `uploaded` 4. Background task triggered for processing **Python Implementation:** ```python # app/services/medical/document_processor.py from pathlib import Path import hashlib from typing import Optional from sqlalchemy.orm import Session from app.models.document import Document from app.core.config import settings class DocumentUploader: def __init__(self, db: Session): self.db = db self.storage_path = Path(settings.DOCUMENT_STORAGE_PATH) async def upload_document( self, file_data: bytes, filename: str, source_type: str, specialty: str, metadata: Optional[dict] = None ) -> Document: """ Upload and store a document, returning Document model. """ # Generate document ID from hash file_hash = hashlib.sha256(file_data).hexdigest() doc_id = file_hash[:16] # Check if already exists existing = self.db.query(Document).filter( Document.file_hash == file_hash ).first() if existing: return existing # Create storage directory doc_dir = self.storage_path / doc_id doc_dir.mkdir(parents=True, exist_ok=True) # Save file file_path = doc_dir / filename with open(file_path, 'wb') as f: f.write(file_data) # Create database record document = Document( id=doc_id, filename=filename, file_path=str(file_path), file_hash=file_hash, file_size=len(file_data), source_type=source_type, specialty=specialty, status='uploaded', metadata=metadata or {} ) self.db.add(document) self.db.commit() self.db.refresh(document) return document ``` ### 2. Text Extraction **Supported Formats:** - PDF (native text extraction + OCR fallback) - DOCX (python-docx) - HTML (BeautifulSoup) - Plain text **Libraries:** - **PyPDF2**: Fast PDF text extraction - **pdfplumber**: Better table/structure handling - **Tesseract OCR**: For scanned documents - **python-docx**: DOCX extraction **Python Implementation:** ```python # app/services/medical/text_extractor.py import io from typing import List, Dict import PyPDF2 import pdfplumber from pdf2image import convert_from_path import pytesseract from PIL import Image class TextExtractor: """ Extract text from various document formats. """ def extract_from_pdf(self, file_path: str) -> List[Dict[str, str]]: """ Extract text from PDF, returning list of pages. Falls back to OCR if native extraction fails. """ pages = [] try: # Try native text extraction first with pdfplumber.open(file_path) as pdf: for page_num, page in enumerate(pdf.pages, start=1): text = page.extract_text() # If page has little text, try OCR if len(text.strip()) < 100: text = self._ocr_page(file_path, page_num) pages.append({ 'page': page_num, 'text': text, 'width': page.width, 'height': page.height }) except Exception as e: print(f"Native extraction failed, falling back to OCR: {e}") pages = self._ocr_entire_pdf(file_path) return pages def _ocr_page(self, file_path: str, page_num: int) -> str: """ OCR a single page using Tesseract. """ images = convert_from_path( file_path, first_page=page_num, last_page=page_num, dpi=300 ) if images: text = pytesseract.image_to_string(images[0]) return text return "" def _ocr_entire_pdf(self, file_path: str) -> List[Dict[str, str]]: """ OCR entire PDF. """ images = convert_from_path(file_path, dpi=300) pages = [] for page_num, image in enumerate(images, start=1): text = pytesseract.image_to_string(image) pages.append({ 'page': page_num, 'text': text }) return pages ``` ### 3. Text Preprocessing & Cleaning **Steps:** 1. Remove headers/footers (page numbers, running headers) 2. Fix encoding issues 3. Normalize whitespace 4. Remove references section (if at end) 5. Preserve medical formatting (units, dosages) **Python Implementation:** ```python # app/services/medical/text_preprocessor.py import re from typing import List, Dict class TextPreprocessor: """ Clean and normalize extracted text. """ def __init__(self): # Common medical textbook footer patterns self.footer_patterns = [ r'^\d+\s*$', # Page numbers r'^Chapter \d+.*$', r'^Copyright \d{4}.*$' ] def preprocess_pages(self, pages: List[Dict[str, str]]) -> List[Dict[str, str]]: """ Preprocess extracted pages. """ cleaned_pages = [] for page in pages: text = page['text'] # Remove footers/headers text = self._remove_footers(text) # Fix common encoding issues text = self._fix_encoding(text) # Normalize whitespace text = self._normalize_whitespace(text) # Preserve medical formatting text = self._preserve_medical_units(text) cleaned_pages.append({ **page, 'text': text, 'original_text': page['text'] # Keep original }) return cleaned_pages def _remove_footers(self, text: str) -> str: """Remove common footer patterns.""" lines = text.split('\n') cleaned_lines = [] for line in lines: is_footer = False for pattern in self.footer_patterns: if re.match(pattern, line.strip()): is_footer = True break if not is_footer: cleaned_lines.append(line) return '\n'.join(cleaned_lines) def _fix_encoding(self, text: str) -> str: """Fix common encoding issues.""" replacements = { '�': '', # Common replacement character '’': "'", '“': '"', 'â€': '"', 'â€"': '—', } for old, new in replacements.items(): text = text.replace(old, new) return text def _normalize_whitespace(self, text: str) -> str: """Normalize whitespace while preserving structure.""" # Replace multiple spaces with single space text = re.sub(r' +', ' ', text) # Remove spaces before punctuation text = re.sub(r' ([,.;:!?])', r'\1', text) # Normalize newlines (max 2 consecutive) text = re.sub(r'\n{3,}', '\n\n', text) return text.strip() def _preserve_medical_units(self, text: str) -> str: """Ensure medical units and dosages are properly formatted.""" # Ensure space before units text = re.sub(r'(\d+)(mg|mcg|g|kg|mL|L)', r'\1 \2', text) # Preserve blood pressure format text = re.sub(r'(\d+) / (\d+)', r'\1/\2', text) return text ``` ### 4. Semantic Chunking **Strategy:** Semantic chunking with overlap **Parameters:** - **Chunk size**: 500 tokens (~400 words) - **Overlap**: 100 tokens (20%) - **Max chunk size**: 750 tokens - **Min chunk size**: 200 tokens **Chunking Methods:** 1. **Sentence-based**: Split on sentence boundaries 2. **Heading-aware**: Keep sections together when possible 3. **Table/figure extraction**: Handle structured content separately **Python Implementation:** ```python # app/services/medical/chunker.py from typing import List, Dict import tiktoken import re class SemanticChunker: """ Create semantic chunks from text with intelligent splitting. """ def __init__(self, chunk_size: int = 500, overlap: int = 100): self.chunk_size = chunk_size self.overlap = overlap self.encoder = tiktoken.get_encoding("cl100k_base") # Medical section heading patterns self.heading_patterns = [ r'^#+\s+', # Markdown headings r'^[A-Z][A-Z\s]+$', # ALL CAPS headings r'^\d+\.\d+', # Numbered sections ] def chunk_document( self, pages: List[Dict[str, str]], metadata: Dict ) -> List[Dict]: """ Chunk document into semantic segments. """ chunks = [] current_chunk = "" current_tokens = 0 chunk_id = 0 for page in pages: sentences = self._split_sentences(page['text']) for sentence in sentences: sentence_tokens = len(self.encoder.encode(sentence)) # Check if adding sentence exceeds chunk size if current_tokens + sentence_tokens > self.chunk_size: # Save current chunk if current_chunk: chunks.append(self._create_chunk( chunk_id, current_chunk, page['page'], metadata )) chunk_id += 1 # Start new chunk with overlap current_chunk = self._get_overlap_text(current_chunk) current_tokens = len(self.encoder.encode(current_chunk)) # Add sentence to current chunk current_chunk += " " + sentence current_tokens += sentence_tokens # Add final chunk if current_chunk: chunks.append(self._create_chunk( chunk_id, current_chunk, pages[-1]['page'], metadata )) return chunks def _split_sentences(self, text: str) -> List[str]: """ Split text into sentences, handling medical abbreviations. """ # Protect common medical abbreviations from sentence splitting protected_abbrevs = [ 'Dr.', 'Mr.', 'Mrs.', 'Ms.', 'e.g.', 'i.e.', 'et al.', 'vs.', 'Fig.', 'Ref.', 'Vol.', 'No.' ] text_protected = text for abbrev in protected_abbrevs: text_protected = text_protected.replace(abbrev, abbrev.replace('.', '')) # Split on sentence boundaries sentences = re.split(r'[.!?]+\s+', text_protected) # Restore abbreviations sentences = [s.replace('', '.') for s in sentences] return [s.strip() for s in sentences if s.strip()] def _get_overlap_text(self, text: str) -> str: """ Get last `overlap` tokens from text for next chunk. """ tokens = self.encoder.encode(text) if len(tokens) <= self.overlap: return text overlap_tokens = tokens[-self.overlap:] return self.encoder.decode(overlap_tokens) def _create_chunk( self, chunk_id: int, text: str, page_num: int, metadata: Dict ) -> Dict: """ Create chunk dictionary with metadata. """ return { 'chunk_id': chunk_id, 'text': text.strip(), 'page': page_num, 'tokens': len(self.encoder.encode(text)), 'metadata': { **metadata, 'page': page_num } } ``` ### 5. Embedding Generation **Embedding Model:** OpenAI `text-embedding-3-large` **Specifications:** - **Dimensions**: 3072 (can be reduced to 1024/512 for efficiency) - **Max input**: 8191 tokens - **Cost**: $0.13 per 1M tokens - **Performance**: MTEB score 64.6 **Alternative Models:** - **Local**: `sentence-transformers/all-MiniLM-L6-v2` (384 dim) - **Local Medical**: `microsoft/BiomedNLP-PubMedBERT-base` (768 dim) **Python Implementation:** ```python # app/services/medical/embeddings.py from typing import List, Dict import openai from tenacity import retry, stop_after_attempt, wait_exponential import numpy as np class EmbeddingGenerator: """ Generate embeddings for text chunks. """ def __init__(self, model: str = "text-embedding-3-large", dimensions: int = 3072): self.model = model self.dimensions = dimensions self.openai_client = openai.OpenAI() @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=2, max=10) ) async def embed_chunks(self, chunks: List[Dict]) -> List[Dict]: """ Generate embeddings for list of chunks. Uses batch processing for efficiency. """ # Extract texts texts = [chunk['text'] for chunk in chunks] # Generate embeddings in batches batch_size = 100 all_embeddings = [] for i in range(0, len(texts), batch_size): batch = texts[i:i + batch_size] response = self.openai_client.embeddings.create( model=self.model, input=batch, dimensions=self.dimensions ) batch_embeddings = [item.embedding for item in response.data] all_embeddings.extend(batch_embeddings) # Add embeddings to chunks for chunk, embedding in zip(chunks, all_embeddings): chunk['embedding'] = embedding chunk['embedding_model'] = self.model chunk['embedding_dimensions'] = self.dimensions return chunks async def embed_query(self, query: str) -> List[float]: """ Generate embedding for a single query. """ response = self.openai_client.embeddings.create( model=self.model, input=query, dimensions=self.dimensions ) return response.data[0].embedding ``` ### Idempotency & Deduplication #### Document Keys Each `KnowledgeDocument` has a **stable `doc_key`** that serves as the idempotency key: - **Format**: `{source_type}-{identifier}` (e.g., `textbook-harrisons-21e-ch252`, `guideline-cdc-heart-failure-2023`) - **Purpose**: Prevent duplicate ingestion of the same document - **Uniqueness**: Enforced at database level with unique constraint **Examples**: - `textbook-harrisons-21e-ch252` - Harrison's 21st edition, Chapter 252 - `guideline-cdc-heart-failure-2023` - CDC heart failure guideline (2023 version) - `journal-nejm-2023-12345` - NEJM article with DOI suffix - `note-user123-clinical-note-456` - User-uploaded clinical note #### Upsert Behavior When a document is re-ingested (same `doc_key`): 1. **Check existing document** by `doc_key` 2. **If exists**: - Compare `content_hash` (SHA-256 of document content) - If hash matches: Skip ingestion, return existing `KnowledgeDocument.id` - If hash differs: Create new version, mark old chunks as superseded 3. **If not exists**: Create new document **Database Schema**: ```sql CREATE TABLE knowledge_documents ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), doc_key VARCHAR(255) UNIQUE NOT NULL, -- Idempotency key content_hash VARCHAR(64) NOT NULL, -- SHA-256 for change detection version INTEGER DEFAULT 1, -- Increment on update superseded_by UUID REFERENCES knowledge_documents(id), -- Points to newer version created_at TIMESTAMP DEFAULT NOW(), ... ); CREATE INDEX idx_doc_key ON knowledge_documents(doc_key); CREATE INDEX idx_superseded ON knowledge_documents(superseded_by) WHERE superseded_by IS NOT NULL; ``` #### Chunk Deduplication Chunks from the same document share `document_id`: ```sql CREATE TABLE kb_chunks ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), document_id UUID REFERENCES knowledge_documents(id) ON DELETE CASCADE, chunk_index INTEGER NOT NULL, -- Position in document (0, 1, 2, ...) content TEXT NOT NULL, embedding VECTOR(768), -- Or dimension of your embedding model superseded BOOLEAN DEFAULT FALSE, -- Mark old chunks when document updated ... ); CREATE INDEX idx_document_chunks ON kb_chunks(document_id, chunk_index); CREATE INDEX idx_superseded_chunks ON kb_chunks(document_id) WHERE superseded = false; ``` **When document is updated:** 1. Set `superseded = true` on old chunks 2. Create new chunks with `superseded = false` 3. Old chunks remain for audit but excluded from search #### API Example ```python from hashlib import sha256 async def ingest_document( file_path: str, doc_key: str, source_type: str, metadata: dict, ) -> KnowledgeDocument: """ Ingest document with idempotency. Returns existing document if content unchanged, or new version if content updated. """ # Read file content content = read_file(file_path) content_hash = sha256(content.encode()).hexdigest() # Check for existing document existing = await db.query( "SELECT * FROM knowledge_documents WHERE doc_key = $1", doc_key, ) if existing: if existing.content_hash == content_hash: # Content unchanged, return existing logger.info(f"Document {doc_key} unchanged, skipping ingestion") return existing # Content changed, create new version logger.info(f"Document {doc_key} updated, creating new version") new_version = existing.version + 1 # Mark old chunks as superseded await db.execute( "UPDATE kb_chunks SET superseded = true WHERE document_id = $1", existing.id, ) else: new_version = 1 # Create new document (or version) new_doc = await db.insert( "knowledge_documents", doc_key=doc_key, content_hash=content_hash, version=new_version, source_type=source_type, metadata=metadata, ) # Process chunks (extract, embed, store) chunks = await process_and_embed(content, new_doc.id) return new_doc ``` ### 6. Vector Database Indexing **Vector DB:** Qdrant **Collection Schema:** ```python # app/services/medical/vector_db.py from qdrant_client import QdrantClient from qdrant_client.models import ( Distance, VectorParams, PointStruct, Filter, FieldCondition, MatchValue ) from typing import List, Dict from uuid import uuid4 class VectorDatabase: """ Qdrant vector database interface. """ def __init__(self, url: str = "http://localhost:6333"): self.client = QdrantClient(url=url) self.collection_name = "medical_knowledge" def create_collection(self, dimensions: int = 3072): """ Create collection with schema. """ self.client.recreate_collection( collection_name=self.collection_name, vectors_config=VectorParams( size=dimensions, distance=Distance.COSINE ) ) # Create payload indexes for filtering self.client.create_payload_index( collection_name=self.collection_name, field_name="source_type", field_schema="keyword" ) self.client.create_payload_index( collection_name=self.collection_name, field_name="specialty", field_schema="keyword" ) self.client.create_payload_index( collection_name=self.collection_name, field_name="document_id", field_schema="keyword" ) async def index_chunks(self, chunks: List[Dict]): """ Index chunks into vector database. """ points = [] for chunk in chunks: point_id = str(uuid4()) point = PointStruct( id=point_id, vector=chunk['embedding'], payload={ # Text content 'text': chunk['text'], 'chunk_id': chunk['chunk_id'], 'page': chunk['page'], 'tokens': chunk['tokens'], # Document metadata 'document_id': chunk['metadata']['document_id'], 'document_title': chunk['metadata']['title'], 'source_type': chunk['metadata']['source_type'], 'specialty': chunk['metadata']['specialty'], # Source details 'authors': chunk['metadata'].get('authors', []), 'publication_year': chunk['metadata'].get('publication_year'), 'publisher': chunk['metadata'].get('publisher'), 'doi': chunk['metadata'].get('doi'), 'pmid': chunk['metadata'].get('pmid'), # Indexing metadata 'embedding_model': chunk['embedding_model'], 'indexed_at': chunk['metadata'].get('indexed_at') } ) points.append(point) # Upsert in batches batch_size = 100 for i in range(0, len(points), batch_size): batch = points[i:i + batch_size] self.client.upsert( collection_name=self.collection_name, points=batch ) ``` **Payload Schema:** ```json { "text": "Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia...", "chunk_id": 42, "page": 234, "tokens": 487, "document_id": "a1b2c3d4e5f6", "document_title": "Harrison's Principles of Internal Medicine - Chapter 45", "source_type": "textbook", "specialty": "cardiology", "authors": ["Dennis Kasper", "Stephen Hauser"], "publication_year": 2022, "publisher": "McGraw-Hill", "doi": null, "pmid": null, "embedding_model": "text-embedding-3-large", "indexed_at": "2024-11-19T10:34:00Z" } ``` ### Indexing Job State Machine Each document ingestion creates an `IndexingJob` that tracks progress through these states: ``` ┌─────────────────────────────────────────────────────────────┐ │ IndexingJob State Machine │ └─────────────────────────────────────────────────────────────┘ ┌──────────┐ ────▶│ PENDING │ (Job created, queued) └─────┬────┘ │ ▼ ┌──────────┐ │ RUNNING │ (Worker processing) └─────┬────┘ │ ┌─────┴─────┐ │ │ ▼ ▼ ┌──────────┐ ┌─────────┐ │COMPLETED │ │ FAILED │ └────┬─────┘ └────┬────┘ │ │ │ │ (Manual retry) │ └──────────┐ │ ▼ │ ┌──────────┐ └────────────────▶│SUPERSEDED│ (Newer version ingested) └──────────┘ ``` #### State Definitions | State | Description | Next States | Can Retry? | | -------------- | --------------------------- | --------------------------- | ---------- | | **PENDING** | Job queued, not yet started | RUNNING, FAILED | N/A | | **RUNNING** | Worker processing document | COMPLETED, FAILED | N/A | | **COMPLETED** | Successfully indexed | SUPERSEDED | No | | **FAILED** | Error during processing | PENDING (retry), SUPERSEDED | Yes | | **SUPERSEDED** | Replaced by newer version | (terminal) | No | #### State Transitions ```python class IndexingJobState(str, Enum): PENDING = "pending" RUNNING = "running" COMPLETED = "completed" FAILED = "failed" SUPERSEDED = "superseded" class IndexingJob(BaseModel): """From DATA_MODEL.md - enhanced with state machine.""" id: str document_id: str doc_key: str state: IndexingJobState created_at: datetime started_at: Optional[datetime] = None completed_at: Optional[datetime] = None failed_at: Optional[datetime] = None error_message: Optional[str] = None error_details: Optional[dict] = None retry_count: int = 0 max_retries: int = 3 # Progress tracking total_chunks: Optional[int] = None processed_chunks: int = 0 # Superseded tracking superseded_by: Optional[str] = None # ID of newer job async def transition_state( job_id: str, new_state: IndexingJobState, error: Optional[Exception] = None, ) -> IndexingJob: """Transition job to new state with validation.""" job = await get_job(job_id) # Validate transition valid_transitions = { IndexingJobState.PENDING: [IndexingJobState.RUNNING, IndexingJobState.FAILED], IndexingJobState.RUNNING: [IndexingJobState.COMPLETED, IndexingJobState.FAILED], IndexingJobState.COMPLETED: [IndexingJobState.SUPERSEDED], IndexingJobState.FAILED: [IndexingJobState.PENDING, IndexingJobState.SUPERSEDED], IndexingJobState.SUPERSEDED: [], # Terminal state } if new_state not in valid_transitions[job.state]: raise ValueError( f"Invalid transition: {job.state} → {new_state}" ) # Update job job.state = new_state if new_state == IndexingJobState.RUNNING: job.started_at = datetime.utcnow() elif new_state == IndexingJobState.COMPLETED: job.completed_at = datetime.utcnow() elif new_state == IndexingJobState.FAILED: job.failed_at = datetime.utcnow() job.error_message = str(error) if error else None job.error_details = {"type": type(error).__name__} if error else None await db.update("indexing_jobs", job) return job ``` #### Retry Logic ```python async def retry_failed_job(job_id: str) -> IndexingJob: """Retry a failed indexing job.""" job = await get_job(job_id) if job.state != IndexingJobState.FAILED: raise ValueError(f"Can only retry FAILED jobs, got {job.state}") if job.retry_count >= job.max_retries: raise ValueError(f"Max retries ({job.max_retries}) exceeded") job.retry_count += 1 job.state = IndexingJobState.PENDING job.error_message = None job.error_details = None await db.update("indexing_jobs", job) # Re-queue job await queue.enqueue(process_indexing_job, job.id) return job ``` #### Admin API Endpoints ```python @router.get("/api/admin/kb/jobs", response_model=APIEnvelope) async def list_indexing_jobs( state: Optional[IndexingJobState] = None, limit: int = 50, ) -> APIEnvelope: """List indexing jobs with optional state filter.""" jobs = await db.query_jobs(state=state, limit=limit) return success_response(data=[job.dict() for job in jobs]) @router.post("/api/admin/kb/jobs/{job_id}/retry", response_model=APIEnvelope) async def retry_indexing_job(job_id: str) -> APIEnvelope: """Retry a failed indexing job.""" try: job = await retry_failed_job(job_id) return success_response(data=job.dict()) except ValueError as e: return error_response( code="VALIDATION_ERROR", message=str(e), status_code=422, ) ``` ## Query Pipeline ### 1. Query Enhancement **Steps:** 1. Detect user intent (quick lookup vs deep analysis) 2. Extract medical entities (drugs, conditions, procedures) 3. Expand abbreviations 4. Add specialty context **Python Implementation:** ```python # app/services/medical/query_enhancer.py from typing import Dict, List import re class QueryEnhancer: """ Enhance user queries for better retrieval. """ def __init__(self): # Common medical abbreviations self.abbreviations = { 'HTN': 'hypertension', 'DM': 'diabetes mellitus', 'CAD': 'coronary artery disease', 'CHF': 'congestive heart failure', 'AF': 'atrial fibrillation', 'MI': 'myocardial infarction', 'CVA': 'cerebrovascular accident', # ... more abbreviations } def enhance_query( self, query: str, context: Dict = None ) -> Dict[str, any]: """ Enhance query with expansions and metadata. """ # Detect intent intent = self._detect_intent(query) # Expand abbreviations expanded_query = self._expand_abbreviations(query) # Extract entities entities = self._extract_entities(expanded_query) # Add context if context and context.get('specialty'): specialty_filter = context['specialty'] else: specialty_filter = self._infer_specialty(expanded_query, entities) return { 'original_query': query, 'enhanced_query': expanded_query, 'intent': intent, 'entities': entities, 'specialty_filter': specialty_filter } def _detect_intent(self, query: str) -> str: """ Detect user intent from query. """ query_lower = query.lower() if any(word in query_lower for word in ['dose', 'dosing', 'how much']): return 'dosing' elif any(word in query_lower for word in ['side effect', 'adverse', 'toxicity']): return 'safety' elif any(word in query_lower for word in ['manage', 'treatment', 'therapy']): return 'management' elif any(word in query_lower for word in ['diagnosis', 'workup', 'test']): return 'diagnosis' else: return 'general' def _expand_abbreviations(self, query: str) -> str: """ Expand medical abbreviations. """ words = query.split() expanded_words = [] for word in words: word_upper = word.strip('.,!?').upper() if word_upper in self.abbreviations: expanded_words.append(f"{word} ({self.abbreviations[word_upper]})") else: expanded_words.append(word) return ' '.join(expanded_words) def _extract_entities(self, query: str) -> Dict[str, List[str]]: """ Extract medical entities from query. Simple pattern-based for now, can use NER later. """ # This is simplified - production should use medical NER entities = { 'conditions': [], 'medications': [], 'procedures': [] } # Simple pattern matching condition_patterns = [ r'\b(hypertension|diabetes|heart failure|pneumonia)\b' ] for pattern in condition_patterns: matches = re.findall(pattern, query, re.IGNORECASE) entities['conditions'].extend(matches) return entities def _infer_specialty(self, query: str, entities: Dict) -> List[str]: """ Infer medical specialty from query. """ query_lower = query.lower() specialties = [] # Keyword-based specialty detection specialty_keywords = { 'cardiology': ['heart', 'cardiac', 'af', 'atrial', 'chf', 'mi'], 'endocrinology': ['diabetes', 'thyroid', 'insulin', 'glucose'], 'infectious_disease': ['infection', 'antibiotic', 'sepsis', 'fever'], 'nephrology': ['kidney', 'renal', 'dialysis', 'ckd'], # ... more specialties } for specialty, keywords in specialty_keywords.items(): if any(keyword in query_lower for keyword in keywords): specialties.append(specialty) return specialties ``` ### 2. Hybrid Search (Dense + Sparse) **Strategy:** - **Dense (Vector)**: Semantic similarity using embeddings - **Sparse (BM25)**: Keyword matching for exact terms - **Fusion**: Combine scores with learned weights **Python Implementation:** ```python # app/services/medical/rag.py from typing import List, Dict from qdrant_client.models import Filter, FieldCondition, MatchAny, SearchRequest import numpy as np class RAGService: """ Retrieval-Augmented Generation service. """ def __init__(self, vector_db: VectorDatabase, embedding_gen: EmbeddingGenerator): self.vector_db = vector_db self.embedding_gen = embedding_gen async def search( self, query: str, filters: Dict = None, limit: int = 10, hybrid: bool = True ) -> List[Dict]: """ Hybrid search combining vector and keyword matching. """ # Enhance query enhancer = QueryEnhancer() enhanced = enhancer.enhance_query(query, filters) # Generate query embedding query_embedding = await self.embedding_gen.embed_query( enhanced['enhanced_query'] ) # Build filters search_filter = self._build_filter(enhanced, filters) # Vector search vector_results = self.vector_db.client.search( collection_name=self.vector_db.collection_name, query_vector=query_embedding, query_filter=search_filter, limit=limit * 2, # Get more for reranking with_payload=True ) # Convert to standardized format results = [] for hit in vector_results: results.append({ 'id': hit.id, 'score': hit.score, 'text': hit.payload['text'], 'document_id': hit.payload['document_id'], 'document_title': hit.payload['document_title'], 'page': hit.payload['page'], 'source_type': hit.payload['source_type'], 'specialty': hit.payload['specialty'], 'metadata': { 'authors': hit.payload.get('authors', []), 'publication_year': hit.payload.get('publication_year'), 'doi': hit.payload.get('doi'), 'pmid': hit.payload.get('pmid') } }) # Rerank results results = self._rerank(results, enhanced['original_query']) # Return top-k return results[:limit] def _build_filter(self, enhanced: Dict, filters: Dict = None) -> Filter: """ Build Qdrant filter from query enhancement and user filters. """ conditions = [] # Add specialty filter if inferred if enhanced.get('specialty_filter'): conditions.append( FieldCondition( key="specialty", match=MatchAny(any=enhanced['specialty_filter']) ) ) # Add user-provided filters if filters: if filters.get('source_type'): conditions.append( FieldCondition( key="source_type", match=MatchAny(any=filters['source_type']) ) ) if filters.get('specialty'): conditions.append( FieldCondition( key="specialty", match=MatchAny(any=filters['specialty']) ) ) if conditions: return Filter(must=conditions) return None def _rerank(self, results: List[Dict], query: str) -> List[Dict]: """ Rerank results using cross-encoder or heuristics. Simple implementation - can use cross-encoder for better results. """ # For now, boost results that contain exact query terms query_terms = set(query.lower().split()) for result in results: text_terms = set(result['text'].lower().split()) overlap = len(query_terms & text_terms) # Boost score based on keyword overlap boost = 1.0 + (overlap * 0.05) result['score'] *= boost # Sort by boosted score results.sort(key=lambda x: x['score'], reverse=True) return results ``` ### 3. Result Post-processing **Steps:** 1. Deduplication (remove near-duplicate chunks) 2. Citation formatting 3. Relevance filtering (threshold) 4. Grouping by document ## Complete Indexing Example ```python # app/tasks/indexing_task.py from app.services.medical.document_processor import DocumentUploader from app.services.medical.text_extractor import TextExtractor from app.services.medical.text_preprocessor import TextPreprocessor from app.services.medical.chunker import SemanticChunker from app.services.medical.embeddings import EmbeddingGenerator from app.services.medical.vector_db import VectorDatabase from sqlalchemy.orm import Session from app.models.document import Document from datetime import datetime async def index_document_task(document_id: str, db: Session): """ Complete document indexing pipeline. """ # 1. Load document document = db.query(Document).filter(Document.id == document_id).first() if not document: raise ValueError(f"Document {document_id} not found") document.status = 'processing' db.commit() try: # 2. Extract text extractor = TextExtractor() pages = extractor.extract_from_pdf(document.file_path) # 3. Preprocess preprocessor = TextPreprocessor() cleaned_pages = preprocessor.preprocess_pages(pages) # 4. Chunk chunker = SemanticChunker(chunk_size=500, overlap=100) chunks = chunker.chunk_document( cleaned_pages, metadata={ 'document_id': document.id, 'title': document.filename, 'source_type': document.source_type, 'specialty': document.specialty, 'authors': document.metadata.get('authors', []), 'publication_year': document.metadata.get('publication_year'), 'indexed_at': datetime.utcnow().isoformat() } ) # 5. Generate embeddings embedding_gen = EmbeddingGenerator() chunks_with_embeddings = await embedding_gen.embed_chunks(chunks) # 6. Index in vector DB vector_db = VectorDatabase() await vector_db.index_chunks(chunks_with_embeddings) # 7. Update document status document.status = 'indexed' document.chunk_count = len(chunks) document.indexed_at = datetime.utcnow() db.commit() except Exception as e: document.status = 'failed' document.error_message = str(e) db.commit() raise ``` ## Complete Query Example ```python # Example usage in API endpoint from app.services.medical.rag import RAGService from app.services.ai.orchestrator import AIOrchestrator @router.post("/api/chat/message") async def handle_query(query: str, clinical_context: dict = None): """ Handle user query with RAG. """ # 1. Search knowledge base rag_service = RAGService(vector_db, embedding_gen) search_results = await rag_service.search( query=query, filters={ 'specialty': clinical_context.get('specialty') if clinical_context else None }, limit=10 ) # 2. Format context for LLM context_text = "\n\n".join([ f"[Source {i+1}] {result['document_title']} (Page {result['page']})\n{result['text']}" for i, result in enumerate(search_results) ]) # 3. Generate response with LLM orchestrator = AIOrchestrator() prompt = f"""You are a medical AI assistant. Use the following sources to answer the question. Sources: {context_text} Question: {query} Provide a clear answer with citations to the sources above.""" response = await orchestrator.generate(prompt) # 4. Format citations citations = [ { 'id': result['id'], 'title': result['document_title'], 'source_type': result['source_type'], 'page': result['page'], 'excerpt': result['text'][:200] + '...', 'relevance_score': result['score'] } for result in search_results[:5] # Top 5 citations ] return { 'answer': response, 'citations': citations, 'sources_count': len(search_results) } ``` ## Performance Optimization ### Caching ```python from functools import lru_cache from typing import Tuple import hashlib class CachedEmbeddingGenerator(EmbeddingGenerator): """ Embedding generator with Redis cache. """ def __init__(self, redis_client, *args, **kwargs): super().__init__(*args, **kwargs) self.redis = redis_client async def embed_query(self, query: str) -> List[float]: """ Generate embedding with caching. """ # Generate cache key cache_key = f"embedding:{self.model}:{hashlib.md5(query.encode()).hexdigest()}" # Check cache cached = self.redis.get(cache_key) if cached: return json.loads(cached) # Generate embedding embedding = await super().embed_query(query) # Cache for 24 hours self.redis.setex(cache_key, 86400, json.dumps(embedding)) return embedding ``` ### Batch Processing For large document uploads, process in parallel: ```python import asyncio from concurrent.futures import ThreadPoolExecutor async def batch_index_documents(document_ids: List[str], db: Session): """ Index multiple documents in parallel. """ tasks = [ index_document_task(doc_id, db) for doc_id in document_ids ] # Run with concurrency limit semaphore = asyncio.Semaphore(5) # Max 5 concurrent async def bounded_task(task): async with semaphore: return await task results = await asyncio.gather(*[bounded_task(task) for task in tasks]) return results ``` ## Monitoring & Analytics ### Query Performance Tracking ```python import time from app.models.analytics import QueryLog async def search_with_logging(query: str, user_id: str, **kwargs): """ Search with performance logging. """ start_time = time.time() try: results = await rag_service.search(query, **kwargs) latency = time.time() - start_time # Log query query_log = QueryLog( user_id=user_id, query=query, results_count=len(results), latency=latency, success=True ) db.add(query_log) db.commit() return results except Exception as e: latency = time.time() - start_time query_log = QueryLog( user_id=user_id, query=query, latency=latency, success=False, error=str(e) ) db.add(query_log) db.commit() raise ``` ### Example Queries & Conductor Usage These examples demonstrate how the semantic search system and conductor work together to process real clinical queries. #### Example 1: Heart Failure Management Query **User Query**: _"What are the current guidelines for managing acute decompensated heart failure in the emergency department?"_ **Step 1: Intent Classification** ```json { "intent": "guideline", "confidence": 0.92, "sub_intent": "treatment", "clinical_domain": "cardiology" } ``` **Step 2: Source Selection** (based on intent) - Internal KB: Filter `source_type = 'guideline'` - External: UpToDate, PubMed (recent RCTs) - Priority: Official guidelines (AHA, ACC, ESC) **Step 3: KB Search with Filters** ```python kb_results = await kb_engine.search( query="acute decompensated heart failure emergency management", filters={ "source_type": ["guideline", "textbook"], "specialty": ["cardiology", "emergency_medicine"], "date_published": {"gte": "2020-01-01"}, # Recent guidelines }, limit=10, min_score=0.7, ) ``` **KB Search Results** (vector similarity + BM25 hybrid): ```json [ { "chunk_id": "chunk_abc123", "document_id": "doc_xyz789", "doc_key": "guideline-aha-heart-failure-2023", "title": "2023 AHA/ACC/HFSA Guideline for the Management of Heart Failure", "excerpt": "In acute decompensated heart failure (ADHF), initial management in the ED should focus on...", "score": 0.89, "source_type": "guideline", "metadata": { "organization": "AHA/ACC/HFSA", "year": 2023, "section": "Emergency Management", "recommendation_class": "Class I", "evidence_level": "A" } }, { "chunk_id": "chunk_def456", "document_id": "doc_uvw012", "doc_key": "textbook-harrisons-21e-ch252", "title": "Harrison's Principles of Internal Medicine, 21e - Chapter 252: Heart Failure", "excerpt": "Acute decompensated HF requires prompt diuresis with IV loop diuretics...", "score": 0.82, "source_type": "textbook", "metadata": { "edition": "21", "chapter": "252", "page": "1763" } } ] ``` **Step 4: External Tool Results** ```json { "uptodate": [ { "title": "Treatment of acute decompensated heart failure: General principles", "url": "https://uptodate.com/...", "last_updated": "2024-01-15" } ], "pubmed": [ { "title": "GALACTIC-HF trial: omecamtiv mecarbil in acute heart failure", "doi": "10.1056/NEJMoa2025797", "publication_date": "2023-09-01", "journal": "New England Journal of Medicine" } ] } ``` **Step 5: Conductor Merges & Ranks Sources** - Priority 1: Official guidelines (AHA 2023) - Priority 2: Textbooks (Harrison's) - Priority 3: UpToDate (for practical management) - Priority 4: Recent RCTs (GALACTIC-HF) **Step 6: LLM Answer Generation** ```json { "answer": "For acute decompensated heart failure in the emergency department, current guidelines recommend the following approach:\n\n1. **Initial Assessment** (Class I, Level A): Rapid evaluation of volume status, respiratory distress, and end-organ perfusion.\n\n2. **Immediate Management**:\n - IV loop diuretics (furosemide 40-80mg bolus initially) for volume overload\n - Supplemental oxygen to maintain SpO2 >90%\n - Upright positioning to reduce preload\n\n3. **Hemodynamic Monitoring**: Consider invasive monitoring if severe hypotension or cardiogenic shock.\n\n4. **Vasodilators**: Nitroglycerin for elevated blood pressure and pulmonary congestion (avoid if SBP <90 mmHg).\n\n5. **Inotropic Support**: Only if evidence of hypoperfusion despite adequate filling pressures.\n\nRecent data from the GALACTIC-HF trial suggests omecamtiv mecarbil may reduce cardiovascular death in chronic HF, though not specifically studied in acute settings.", "citations": [ { "id": "1", "source_type": "guideline", "source_id": "doc_xyz789", "title": "2023 AHA/ACC/HFSA Guideline for the Management of Heart Failure", "location": "Section 4.2: Emergency Management", "url": "https://www.ahajournals.org/doi/10.1161/CIR.0000000000001063" }, { "id": "2", "source_type": "textbook", "source_id": "doc_uvw012", "title": "Harrison's Principles of Internal Medicine, 21e", "subtitle": "Chapter 252: Heart Failure", "location": "p. 1763" }, { "id": "3", "source_type": "journal", "source_id": "external_pubmed", "title": "GALACTIC-HF trial", "doi": "10.1056/NEJMoa2025797" } ] } ``` --- #### Example 2: Drug Dosing Query **User Query**: _"What's the appropriate dose of metformin for a patient with CKD stage 3?"_ **Step 1: Intent Classification** ```json { "intent": "drug_reference", "confidence": 0.95, "sub_intent": "dosing", "drug_name": "metformin", "clinical_context": "CKD stage 3" } ``` **Step 2: Source Selection** - Internal KB: Filter `source_type = 'drug_formulary'` or `'guideline'` - External: Drug interaction databases, FDA prescribing information - Priority: Official prescribing info, renal dosing guidelines **Step 3: KB Search with Drug-Specific Filters** ```python kb_results = await kb_engine.search( query="metformin dosing chronic kidney disease stage 3 renal impairment", filters={ "source_type": ["drug_formulary", "guideline"], "drug_name": "metformin", # Exact match on drug field "specialty": ["nephrology", "endocrinology"], }, limit=5, min_score=0.75, ) ``` **KB Search Results**: ```json [ { "chunk_id": "chunk_ghi789", "document_id": "doc_rst345", "doc_key": "formulary-lexicomp-metformin", "title": "Lexicomp Drug Information: Metformin", "excerpt": "Renal Impairment Dosing:\n- eGFR 45-59 mL/min/1.73m² (Stage 3a): Max 2000 mg/day, monitor eGFR every 3-6 months\n- eGFR 30-44 mL/min/1.73m² (Stage 3b): Max 1000 mg/day, monitor eGFR every 3 months\n- eGFR <30 mL/min/1.73m² (Stage 4-5): Contraindicated", "score": 0.94, "source_type": "drug_formulary", "metadata": { "drug_class": "biguanide", "indication": "type 2 diabetes", "last_updated": "2024-01-10" } } ] ``` **Step 4: External Tool Results** (FDA prescribing info, drug databases) **Step 5: LLM Answer with Safety Warning** ```json { "answer": "For a patient with CKD Stage 3, metformin dosing depends on the specific eGFR:\n\n**Stage 3a (eGFR 45-59 mL/min/1.73m²)**:\n- Maximum dose: 2000 mg/day (divided doses)\n- Monitoring: Check eGFR every 3-6 months\n- Continue metformin if eGFR remains stable\n\n**Stage 3b (eGFR 30-44 mL/min/1.73m²)**:\n- Maximum dose: 1000 mg/day\n- Monitoring: Check eGFR every 3 months\n- Consider discontinuation if eGFR declining\n\n**Important**: Metformin is contraindicated if eGFR falls below 30 mL/min/1.73m² due to risk of lactic acidosis. Always verify current eGFR before prescribing.", "warnings": [ "Contraindicated in eGFR <30", "Risk of lactic acidosis in renal impairment", "Monitor renal function regularly" ], "citations": [...] } ``` --- #### How Conductor Uses Search Results ```python # Conductor processes search results async def process_query(request: ChatRequest, trace_id: str): # ... (steps 1-5 from ORCHESTRATION_DESIGN.md) # After receiving KB search results: kb_results = await kb_engine.search(...) # Conductor applies intent-specific logic: if intent.type == "guideline": # Prioritize official guidelines with high recommendation class kb_results = prioritize_by_metadata( kb_results, priority_fields=["recommendation_class", "evidence_level"], ) elif intent.type == "drug_reference": # Prioritize exact drug name matches, recent updates kb_results = prioritize_by_metadata( kb_results, priority_fields=["drug_name_match", "last_updated"], ) # Extract safety warnings from results warnings = extract_warnings(kb_results) # Merge with external results combined_sources = merge_sources(kb_results, external_results) # Generate answer with appropriate context answer = await llm_router.generate_answer( query=request.query, sources=combined_sources, intent=intent, include_warnings=(intent.type == "drug_reference"), ) return answer ``` --- ## Summary This semantic search system 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