modules/rag_indexer.py

import chromadb
import json

COLLECTION_NAME = 'video_analysis_data' 
# ... (rest of imports/constants)

def generate_text_summary(record):
    """
    Converts a structured detection record into a natural language text description
    by summarizing all detected objects clearly.
    """
    video_id = record['video_id']
    timestamp = record['timestamp_sec']
    detections = record['detections']
    
    if not detections:
        return f"Analysis of video '{video_id}' at {timestamp} seconds: No objects were detected in this frame."

    # Group detections by label for a complete object count summary
    object_counts = {}
    for det in detections:
        label = det['label']
        object_counts[label] = object_counts.get(label, 0) + 1

    summary_parts = []
    
    if object_counts:
        # Format: N instances of 'label', M instances of 'other_label', etc.
        object_descriptions = [
            f"{count} instances of '{label}'"
            for label, count in object_counts.items()
        ]
        summary_parts.append("Detected objects include: " + ", ".join(object_descriptions) + ".")

    summary_doc = f"Analysis of video '{video_id}' at {timestamp} seconds: {' '.join(summary_parts)}"
    return summary_doc


def index_analysis_data(json_file='raw_analysis.json', collection_name='video_analysis_data'):
    """
    Loads raw analysis, generates documents, and indexes them in ChromaDB.
    """
    try:
        with open(json_file, 'r') as f:
            raw_data = json.load(f)
    except FileNotFoundError:
        print(f"Error: {json_file} not found. Run 'video_analyzer.py' first.")
        return

    # Initialize ChromaDB client
    client = chromadb.PersistentClient(path="./chroma_db") # Stores data locally
    # Changed collection name to be more generic
    collection = client.get_or_create_collection(name=collection_name)
    
    documents = []
    metadatas = []
    ids = []
    
    print(f"Indexing {len(raw_data)} analysis records...")

    for i, record in enumerate(raw_data):
        doc_text = generate_text_summary(record)
        if doc_text:
            documents.append(doc_text)
            # Metadata is crucial for filtering and context
            metadatas.append({
                'video_id': record['video_id'],
                'timestamp_sec': record['timestamp_sec'],
                'frame_id': record['frame_id']
            })
            ids.append(f"doc_{i}")

    # ChromaDB automatically handles embedding and storage
    if documents:
        collection.add(
            documents=documents,
            metadatas=metadatas,
            ids=ids
        )
        print(f"Successfully indexed {len(documents)} documents into ChromaDB collection '{collection_name}'.")
    else:
        print("No valid documents generated for indexing.")


if __name__ == '__main__':
    index_analysis_data()