query_bm25.py

"""

BM25 keyword search with cross-encoder re-ranking and hybrid search support.

"""

import numpy as np
import faiss
from typing import Tuple, List, Optional
from openai import OpenAI
from sentence_transformers import CrossEncoder

import config
import utils

# Initialize models
client = OpenAI(api_key=config.OPENAI_API_KEY)
cross_encoder = CrossEncoder(config.CROSS_ENCODER_MODEL)

# Global variables for lazy loading
_bm25_index = None
_texts = None
_metadata = None
_semantic_index = None

def _load_bm25_index():
    """Lazy load BM25 index and metadata."""
    global _bm25_index, _texts, _metadata, _semantic_index
    
    if _bm25_index is None:
        # Initialize defaults
        _texts = []
        _metadata = []
        _semantic_index = None
        try:
            import pickle
            
            if config.BM25_INDEX.exists():
                with open(config.BM25_INDEX, 'rb') as f:
                    bm25_data = pickle.load(f)
                
                if isinstance(bm25_data, dict):
                    _bm25_index = bm25_data.get('index') or bm25_data.get('bm25')
                    chunks = bm25_data.get('texts', [])
                    if chunks:
                        _texts = [chunk.text for chunk in chunks if hasattr(chunk, 'text')]
                        _metadata = [chunk.metadata for chunk in chunks if hasattr(chunk, 'metadata')]
                    else:
                        _texts = []
                        _metadata = []
                    
                    # Load semantic embeddings if available for hybrid search
                    if 'embeddings' in bm25_data:
                        semantic_embeddings = bm25_data['embeddings']
                        # Build FAISS index
                        import faiss
                        dimension = semantic_embeddings.shape[1]
                        _semantic_index = faiss.IndexFlatIP(dimension)
                        faiss.normalize_L2(semantic_embeddings)
                        _semantic_index.add(semantic_embeddings)
                else:
                    _bm25_index = bm25_data
                    _texts = []
                    _metadata = []
                
                print(f"Loaded BM25 index with {len(_texts)} documents")
            else:
                print("BM25 index not found. Run preprocess.py first.")
                
        except Exception as e:
            print(f"Error loading BM25 index: {e}")
            _bm25_index = None
            _texts = []
            _metadata = []


def query(question: str, image_path: Optional[str] = None, top_k: int = None) -> Tuple[str, List[dict]]:
    """

    Query using BM25 keyword search with re-ranking.

    

    Args:

        question: User's question

        image_path: Optional path to an image

        top_k: Number of relevant chunks to retrieve

    

    Returns:

        Tuple of (answer, citations)

    """
    if top_k is None:
        top_k = config.DEFAULT_TOP_K
    
    # Load index if not already loaded
    _load_bm25_index()
    
    if _bm25_index is None or len(_texts) == 0:
        return "BM25 index not loaded. Please run preprocess.py first.", []
    
    # Tokenize query for BM25
    tokenized_query = question.lower().split()
    
    # Get BM25 scores
    bm25_scores = _bm25_index.get_scores(tokenized_query)
    
    # Get top candidates (retrieve more for re-ranking)
    top_indices = np.argsort(bm25_scores)[::-1][:top_k * config.RERANK_MULTIPLIER]
    
    # Prepare candidates for re-ranking
    candidates = []
    for idx in top_indices:
        if idx < len(_texts) and bm25_scores[idx] > 0:
            candidates.append({
                'text': _texts[idx],
                'bm25_score': bm25_scores[idx],
                'metadata': _metadata[idx],
                'idx': idx
            })
    
    # Re-rank with cross-encoder
    if candidates:
        pairs = [[question, cand['text']] for cand in candidates]
        cross_scores = cross_encoder.predict(pairs)
        
        # Add cross-encoder scores and sort
        for i, score in enumerate(cross_scores):
            candidates[i]['cross_score'] = score
        
        candidates = sorted(candidates, key=lambda x: x['cross_score'], reverse=True)[:top_k]
    
    # Collect citations
    citations = []
    sources_seen = set()
    
    for chunk in candidates:
        chunk_meta = chunk['metadata']
        
        if chunk_meta['source'] not in sources_seen:
            citation = {
                'source': chunk_meta['source'],
                'type': chunk_meta['type'],
                'bm25_score': round(chunk['bm25_score'], 3),
                'rerank_score': round(chunk['cross_score'], 3)
            }
            
            if chunk_meta['type'] == 'pdf':
                citation['path'] = chunk_meta['path']
            else:
                citation['url'] = chunk_meta.get('url', '')
            
            citations.append(citation)
            sources_seen.add(chunk_meta['source'])
    
    # Handle image if provided
    image_context = ""
    if image_path:
        try:
            classification = utils.classify_image(image_path)
            # classification is a string, not a dict
            image_context = f"\n\n[Image Analysis: The image appears to show a {classification}.]"
        except Exception as e:
            print(f"Error processing image: {e}")
    
    # Build context from retrieved chunks
    context = "\n\n---\n\n".join([chunk['text'] for chunk in candidates])
    
    if not context:
        return "No relevant documents found for your query.", []
    
    # Generate answer
    prompt = f"""Answer the following question using the retrieved documents:



Retrieved Documents:

{context}{image_context}



Question: {question}



Instructions:

1. Provide a comprehensive answer based on the retrieved documents

2. Mention specific details from the sources

3. If the documents don't fully answer the question, indicate what information is missing"""
    
    # For GPT-5, temperature must be default (1.0)
    response = client.chat.completions.create(
        model=config.OPENAI_CHAT_MODEL,
        messages=[
            {"role": "system", "content": "You are a technical expert on manufacturing safety and regulations. Provide accurate, detailed answers based on the retrieved documents."},
            {"role": "user", "content": prompt}
        ],
        max_completion_tokens=config.DEFAULT_MAX_TOKENS
    )
    
    answer = response.choices[0].message.content
    
    return answer, citations


def query_hybrid(question: str, top_k: int = None, alpha: float = None) -> Tuple[str, List[dict]]:
    """

    Hybrid search combining BM25 and semantic search.

    

    Args:

        question: User's question

        top_k: Number of relevant chunks to retrieve

        alpha: Weight for BM25 scores (1-alpha for semantic)

    

    Returns:

        Tuple of (answer, citations)

    """
    if top_k is None:
        top_k = config.DEFAULT_TOP_K
    if alpha is None:
        alpha = config.DEFAULT_HYBRID_ALPHA
    
    # Load index if not already loaded
    _load_bm25_index()
    
    if _bm25_index is None or _semantic_index is None:
        return "Hybrid search requires both BM25 and semantic indices. Please run preprocess.py with semantic embeddings.", []
    
    # Get BM25 scores
    tokenized_query = question.lower().split()
    bm25_scores = _bm25_index.get_scores(tokenized_query)
    
    # Normalize BM25 scores
    if bm25_scores.max() > 0:
        bm25_scores = bm25_scores / bm25_scores.max()
    
    # Get semantic scores using FAISS
    embedding_generator = utils.EmbeddingGenerator()
    query_embedding = embedding_generator.embed_text_openai([question]).astype(np.float32)
    faiss.normalize_L2(query_embedding)
    
    # Search semantic index for all documents
    k_search = min(len(_texts), top_k * config.RERANK_MULTIPLIER)
    distances, indices = _semantic_index.search(query_embedding.reshape(1, -1), k_search)
    
    # Create semantic scores array
    semantic_scores = np.zeros(len(_texts))
    for idx, dist in zip(indices[0], distances[0]):
        if idx < len(_texts):
            semantic_scores[idx] = dist
    
    # Combine scores
    hybrid_scores = alpha * bm25_scores + (1 - alpha) * semantic_scores
    
    # Get top candidates
    top_indices = np.argsort(hybrid_scores)[::-1][:top_k * config.RERANK_MULTIPLIER]
    
    # Prepare candidates
    candidates = []
    for idx in top_indices:
        if idx < len(_texts) and hybrid_scores[idx] > 0:
            candidates.append({
                'text': _texts[idx],
                'hybrid_score': hybrid_scores[idx],
                'bm25_score': bm25_scores[idx],
                'semantic_score': semantic_scores[idx],
                'metadata': _metadata[idx],
                'idx': idx
            })
    
    # Re-rank with cross-encoder
    if candidates:
        pairs = [[question, cand['text']] for cand in candidates]
        cross_scores = cross_encoder.predict(pairs)
        
        for i, score in enumerate(cross_scores):
            candidates[i]['cross_score'] = score
        
        # Final ranking using cross-encoder scores
        candidates = sorted(candidates, key=lambda x: x['cross_score'], reverse=True)[:top_k]
    
    # Collect citations
    citations = []
    sources_seen = set()
    
    for chunk in candidates:
        chunk_meta = chunk['metadata']
        
        if chunk_meta['source'] not in sources_seen:
            citation = {
                'source': chunk_meta['source'],
                'type': chunk_meta['type'],
                'hybrid_score': round(chunk['hybrid_score'], 3),
                'rerank_score': round(chunk.get('cross_score', 0), 3)
            }
            
            if chunk_meta['type'] == 'pdf':
                citation['path'] = chunk_meta['path']
            else:
                citation['url'] = chunk_meta.get('url', '')
            
            citations.append(citation)
            sources_seen.add(chunk_meta['source'])
    
    # Build context
    context = "\n\n---\n\n".join([chunk['text'] for chunk in candidates])
    
    if not context:
        return "No relevant documents found for your query.", []
    
    # Generate answer
    prompt = f"""Using the following retrieved passages, answer the question:



{context}



Question: {question}



Provide a clear, detailed answer based on the information in the passages."""
    
    # For GPT-5, temperature must be default (1.0)
    response = client.chat.completions.create(
        model=config.OPENAI_CHAT_MODEL,
        messages=[
            {"role": "system", "content": "You are a safety expert. Answer questions accurately using the provided passages."},
            {"role": "user", "content": prompt}
        ],
        max_completion_tokens=config.DEFAULT_MAX_TOKENS
    )
    
    answer = response.choices[0].message.content
    
    return answer, citations


if __name__ == "__main__":
    # Test BM25 query
    test_questions = [
        "lockout tagout procedures",
        "machine guard requirements OSHA",
        "robot safety collaborative workspace"
    ]
    
    for q in test_questions:
        print(f"\nQuestion: {q}")
        answer, citations = query(q)
        print(f"Answer: {answer[:200]}...")
        print(f"Citations: {citations}")
        print("-" * 50)