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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
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