app/database/chromadb_client.py

"""
ChromaDB Client Module.

Provides vector storage and semantic search for:
- Conversation embeddings
- Similar scam pattern detection
- Knowledge base queries
"""

from typing import Dict, List, Optional, Any


# Placeholder for ChromaDB client
chroma_client = None
collection = None


def get_chromadb_client():
    """
    Get ChromaDB client.
    
    Returns:
        ChromaDB client object
    """
    # TODO: Implement ChromaDB client initialization
    # import chromadb
    # return chromadb.Client()
    
    return None


def init_collection(collection_name: str = "conversations") -> None:
    """
    Initialize or get ChromaDB collection.
    
    Args:
        collection_name: Name of the collection
    """
    # TODO: Implement collection initialization
    # global collection
    # client = get_chromadb_client()
    # collection = client.get_or_create_collection(
    #     name=collection_name,
    #     metadata={"hnsw:space": "cosine"}
    # )
    pass


def store_embedding(
    document_id: str,
    text: str,
    embedding: Optional[List[float]] = None,
    metadata: Optional[Dict[str, Any]] = None,
) -> bool:
    """
    Store document embedding in ChromaDB.
    
    Args:
        document_id: Unique document identifier
        text: Text content
        embedding: Pre-computed embedding (optional, auto-generated if None)
        metadata: Additional metadata
        
    Returns:
        True if successful, False otherwise
    """
    # TODO: Implement embedding storage
    # collection.add(
    #     ids=[document_id],
    #     documents=[text],
    #     embeddings=[embedding] if embedding else None,
    #     metadatas=[metadata] if metadata else None,
    # )
    
    return False


def search_similar(
    query_text: str,
    n_results: int = 5,
    filter_metadata: Optional[Dict[str, Any]] = None,
) -> List[Dict[str, Any]]:
    """
    Search for similar documents.
    
    Args:
        query_text: Query text to find similar documents
        n_results: Number of results to return
        filter_metadata: Metadata filters
        
    Returns:
        List of matching documents with scores
    """
    # TODO: Implement semantic search
    # results = collection.query(
    #     query_texts=[query_text],
    #     n_results=n_results,
    #     where=filter_metadata,
    # )
    # return results
    
    return []


def delete_embedding(document_id: str) -> bool:
    """
    Delete document embedding.
    
    Args:
        document_id: Document identifier to delete
        
    Returns:
        True if deleted, False if not found
    """
    # TODO: Implement embedding deletion
    return False


def update_embedding(
    document_id: str,
    text: Optional[str] = None,
    metadata: Optional[Dict[str, Any]] = None,
) -> bool:
    """
    Update existing embedding.
    
    Args:
        document_id: Document identifier
        text: New text content (optional)
        metadata: New metadata (optional)
        
    Returns:
        True if successful, False otherwise
    """
    # TODO: Implement embedding update
    return False


def get_collection_stats() -> Dict[str, Any]:
    """
    Get collection statistics.
    
    Returns:
        Dict with collection count and metadata
    """
    # TODO: Implement stats retrieval
    return {
        "count": 0,
        "collection_name": "conversations",
    }


def health_check() -> bool:
    """
    Check ChromaDB health.
    
    Returns:
        True if ChromaDB is operational, False otherwise
    """
    # TODO: Implement health check
    return False