import chromadb
from chromadb.utils import embedding_functions
import csv
default_ef = embedding_functions.DefaultEmbeddingFunction()

def createEmbeddings():
    try:
        client = chromadb.PersistentClient("./chromaDB")
        collection = client.get_or_create_collection("ICC_Rules")
        
        # Use with statements for proper file handling
        with open("notes.txt", "r") as f:
            data = [line.strip() for line in f.readlines()]  # Strip whitespace/newlines
        
        with open("metaData.csv", "r") as f:
            metaData = list(csv.reader(f))
        
        # Validate data lengths
        if len(data) != len(metaData):
            print(f"Warning: Mismatch in data lengths. notes.txt has {len(data)} lines but metaData.csv has {len(metaData)} rows.")
        
        # Process in batches if possible for better efficiency
        for i in range(len(data)):
            if i < len(metaData):
                collection.add(
                    ids=[str(i)],
                    embeddings=default_ef([data[i]]),
                    metadatas=[{"ruleDescription": metaData[i][2]}],
                    documents=[data[i]]  # Also store the original text
                )
            else:
                # Handle case where metaData is shorter than data
                collection.add(
                    ids=[str(i)],
                    embeddings=default_ef([data[i]]),
                    metadatas=[{"ruleDescription": "No metadata available"}],
                    documents=[data[i]]
                )
        
        print(f"Successfully added {len(data)} documents to the collection.")
    except Exception as e:
        print(f"Error creating embeddings: {e}")


def retrieveInfo(query, n_results=3):
    try:
        client = chromadb.PersistentClient("./chromaDB")
        collection = client.get_or_create_collection("ICC_Rules")
        results = collection.query(
            query_embeddings=default_ef([query]),  # Pass as a list
            n_results=n_results,
        )
        
        if not results or not results['documents'] or len(results['documents'][0]) == 0:
            return "No relevant information found."
            
        # Format the results for easier consumption
        formatted_results = []
        for i in range(len(results['documents'][0])):
            formatted_results.append({
                "document": results['documents'][0][i],
                "metadata": results['metadatas'][0][i] if 'metadatas' in results else {},
                "distance": results['distances'][0][i] if 'distances' in results else None
            })
        
        print("Retrieved information successfully.")
        return formatted_results
    except Exception as e:
        print(f"Error retrieving information: {e}")
        return f"An error occurred: {e}"

if __name__ == "__main__":
    #createEmbeddings()
    print(retrieveInfo("What happens when there are less than 11 players fit?"))