query_graph.py

"""

Graph-based RAG using NetworkX.

Updated to match the common query signature used by other methods.

"""

import numpy as np
import logging
from typing import Tuple, List, Optional
from openai import OpenAI
import networkx as nx
from sklearn.metrics.pairwise import cosine_similarity

from config import *
from utils import classify_image

logger = logging.getLogger(__name__)

# Initialize OpenAI client
client = OpenAI(api_key=OPENAI_API_KEY)

# Global variables for lazy loading
_graph = None
_enodes = None
_embeddings = None

def _load_graph():
    """Lazy load graph database."""
    global _graph, _enodes, _embeddings
    
    if _graph is None:
        try:
            if GRAPH_FILE.exists():
                logger.info("Loading graph database...")
                _graph = nx.read_gml(str(GRAPH_FILE))
                _enodes = list(_graph.nodes)
                # Convert embeddings from lists back to numpy arrays
                embeddings_list = []
                for n in _enodes:
                    embedding = _graph.nodes[n]['embedding']
                    if isinstance(embedding, list):
                        embeddings_list.append(np.array(embedding))
                    else:
                        embeddings_list.append(embedding)
                _embeddings = np.array(embeddings_list)
                logger.info(f"✓ Loaded graph with {len(_enodes)} nodes")
            else:
                logger.warning("Graph database not found. Run preprocess.py first.")
                _graph = nx.Graph()
                _enodes = []
                _embeddings = np.array([])
        except Exception as e:
            logger.error(f"Error loading graph: {e}")
            _graph = nx.Graph()
            _enodes = []
            _embeddings = np.array([])


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

    Query using graph-based retrieval.

    

    Args:

        question: User's question

        image_path: Optional path to an image (for multimodal queries)

        top_k: Number of relevant chunks to retrieve

    

    Returns:

        Tuple of (answer, citations)

    """
    
    # Load graph if not already loaded
    _load_graph()
    
    if len(_enodes) == 0:
        return "Graph database is empty. Please run preprocess.py first.", []
    
    # Embed question using OpenAI
    emb_resp = client.embeddings.create(
        model=OPENAI_EMBEDDING_MODEL,
        input=question
    )
    q_vec = np.array(emb_resp.data[0].embedding)
    
    # Compute cosine similarities
    sims = cosine_similarity([q_vec], _embeddings)[0]
    idxs = sims.argsort()[::-1][:top_k]
    
    # Collect chunk-level info
    chunks = []
    citations = []
    sources_seen = set()
    
    for rank, i in enumerate(idxs, start=1):
        node = _enodes[i]
        node_data = _graph.nodes[node]
        text = node_data['text']
        
        # Extract header from text
        header = text.split('\n', 1)[0].lstrip('#').strip()
        score = sims[i]
        
        # Extract citation format - get source from metadata or node_data
        metadata = node_data.get('metadata', {})
        source = metadata.get('source') or node_data.get('source')
        
        if not source:
            continue
        
        if 'url' in metadata:  # HTML source
            citation_ref = metadata['url']
            cite_type = 'html'
        elif 'path' in metadata:  # PDF source
            citation_ref = metadata['path']
            cite_type = 'pdf'
        elif 'url' in node_data:  # Legacy format
            citation_ref = node_data['url']
            cite_type = 'html'
        elif 'path' in node_data:  # Legacy format
            citation_ref = node_data['path']
            cite_type = 'pdf'
        else:
            citation_ref = source
            cite_type = 'unknown'
        
        chunks.append({
            'header': header,
            'score': score,
            'text': text,
            'citation': citation_ref
        })
        
        # Add unique citation
        if source not in sources_seen:
            citation_entry = {
                'source': source,
                'type': cite_type,
                'relevance_score': round(float(score), 3)
            }
            
            if cite_type == 'html':
                citation_entry['url'] = citation_ref
            elif cite_type == 'pdf':
                citation_entry['path'] = citation_ref
            
            citations.append(citation_entry)
            sources_seen.add(source)
    
    # Handle image if provided
    image_context = ""
    if image_path:
        try:
            # Classify the image
            classification = classify_image(image_path)
            image_context = f"\n\n[Image Context: The provided image appears to be a {classification}.]"
            
            # Optionally, find related nodes in graph based on image classification
            # This would require storing image-related metadata in the graph
            
        except Exception as e:
            print(f"Error processing image: {e}")
    
    # Assemble context for prompt
    context = "\n\n---\n\n".join([c['text'] for c in chunks])
    
    prompt = f"""Use the following context to answer the question:



{context}{image_context}



Question: {question}



Please provide a comprehensive answer based on the context provided. Cite specific sources when providing information."""
    
    # For GPT-5, temperature must be default (1.0)
    chat_resp = client.chat.completions.create(
        model=OPENAI_CHAT_MODEL,
        messages=[
            {"role": "system", "content": "You are a helpful assistant for manufacturing equipment safety. Always provide accurate information based on the given context."},
            {"role": "user", "content": prompt}
        ],
        max_completion_tokens=DEFAULT_MAX_TOKENS
    )
    
    answer = chat_resp.choices[0].message.content
    
    return answer, citations


def query_with_graph_traversal(question: str, top_k: int = 5, max_hops: int = 2) -> Tuple[str, List[dict]]:
    """

    Enhanced graph query that can traverse edges to find related information.

    

    Args:

        question: User's question

        top_k: Number of initial nodes to retrieve

        max_hops: Maximum graph traversal depth

    

    Returns:

        Tuple of (answer, citations)

    """
    
    # Load graph if not already loaded
    _load_graph()
    
    if len(_enodes) == 0:
        return "Graph database is empty. Please run preprocess.py first.", []
    
    # Get initial nodes using standard query
    initial_answer, initial_citations = query(question, top_k=top_k)
    
    # For a more sophisticated implementation, you would:
    # 1. Add edges between related nodes during preprocessing
    # 2. Traverse from initial nodes to find related content
    # 3. Score the related nodes based on path distance and relevance
    
    # For now, return the standard query results
    return initial_answer, initial_citations


def query_subgraph(question: str, source_filter: str = None, top_k: int = 5) -> Tuple[str, List[dict]]:
    """

    Query a specific subgraph filtered by source.

    

    Args:

        question: User's question

        source_filter: Filter nodes by source (e.g., specific PDF name)

        top_k: Number of relevant chunks to retrieve

    

    Returns:

        Tuple of (answer, citations)

    """
    
    # Load graph if not already loaded
    _load_graph()
    
    # Filter nodes if source specified
    if source_filter:
        filtered_nodes = []
        for n in _enodes:
            node_data = _graph.nodes[n]
            metadata = node_data.get('metadata', {})
            source = metadata.get('source') or node_data.get('source', '')
            source_from_meta = metadata.get('source', '')
            
            # Check both direct source and metadata source
            if (source_filter.lower() in source.lower() or 
                source_filter.lower() in source_from_meta.lower()):
                filtered_nodes.append(n)
                
        if not filtered_nodes:
            return f"No nodes found for source: {source_filter}", []
    else:
        filtered_nodes = _enodes
    
    # Get embeddings for filtered nodes
    filtered_embeddings = np.array([_graph.nodes[n]['embedding'] for n in filtered_nodes])
    
    # Embed question
    emb_resp = client.embeddings.create(
        model=OPENAI_EMBEDDING_MODEL,
        input=question
    )
    q_vec = np.array(emb_resp.data[0].embedding)
    
    # Compute similarities
    sims = cosine_similarity([q_vec], filtered_embeddings)[0]
    idxs = sims.argsort()[::-1][:top_k]
    
    # Collect results
    chunks = []
    citations = []
    sources_seen = set()
    
    for i in idxs:
        if i < len(filtered_nodes):
            node = filtered_nodes[i]
            node_data = _graph.nodes[node]
            
            chunks.append(node_data['text'])
            
            # Skip if source information missing
            metadata = node_data.get('metadata', {})
            source = metadata.get('source') or node_data.get('source')
            
            if not source:
                continue
            
            if source not in sources_seen:
                citation = {
                    'source': source,
                    'type': 'pdf' if ('path' in metadata or 'path' in node_data) else 'html',
                    'relevance_score': round(float(sims[i]), 3)
                }
                
                # Check metadata first, then node_data for legacy support
                if 'url' in metadata:
                    citation['url'] = metadata['url']
                elif 'path' in metadata:
                    citation['path'] = metadata['path']
                elif 'url' in node_data:
                    citation['url'] = node_data['url']
                elif 'path' in node_data:
                    citation['path'] = node_data['path']
                
                citations.append(citation)
                sources_seen.add(source)
    
    # Build context and generate answer
    context = "\n\n---\n\n".join(chunks)
    
    prompt = f"""Answer the following question using the provided context:



Context from {source_filter if source_filter else 'all sources'}:

{context}



Question: {question}



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


# Maintain backward compatibility with original function signature
def query_graph(question: str, top_k: int = 5) -> Tuple[str, List[str], List[tuple]]:
    """

    Original query_graph function signature for backward compatibility.

    

    Args:

        question: User's question

        top_k: Number of relevant chunks to retrieve

    

    Returns:

        Tuple of (answer, sources, chunks)

    """
    
    # Call the new query function
    answer, citations = query(question, top_k=top_k)
    
    # Convert citations to old format
    sources = [c['source'] for c in citations]
    
    # Get chunks in old format (header, score, text, citation)
    _load_graph()
    
    if len(_enodes) == 0:
        return answer, sources, []
    
    # Regenerate chunks for backward compatibility
    emb_resp = client.embeddings.create(
        model=OPENAI_EMBEDDING_MODEL,
        input=question
    )
    q_vec = np.array(emb_resp.data[0].embedding)
    
    sims = cosine_similarity([q_vec], _embeddings)[0]
    idxs = sims.argsort()[::-1][:top_k]
    
    chunks = []
    for i in idxs:
        node = _enodes[i]
        node_data = _graph.nodes[node]
        text = node_data['text']
        header = text.split('\n', 1)[0].lstrip('#').strip()
        score = sims[i]
        
        # Skip if source information missing
        metadata = node_data.get('metadata', {})
        source = metadata.get('source') or node_data.get('source')
        
        if not source:
            continue
        
        if 'url' in metadata:
            citation = metadata['url']
        elif 'path' in metadata:
            citation = metadata['path']
        elif 'url' in node_data:
            citation = node_data['url']
        elif 'path' in node_data:
            citation = node_data['path']
        else:
            citation = source
        
        chunks.append((header, score, text, citation))
    
    return answer, sources, chunks


if __name__ == "__main__":
    # Test the updated graph query
    test_questions = [
        "What are general machine guarding requirements?",
        "How do I perform lockout/tagout procedures?",
        "What safety measures are needed for robotic systems?"
    ]
    
    for q in test_questions:
        print(f"\nQuestion: {q}")
        answer, citations = query(q)
        print(f"Answer: {answer[:200]}...")
        print(f"Citations: {[c['source'] for c in citations]}")
        print("-" * 50)