Delete createVectorDB.py

createVectorDB.py

@@ -1,49 +0,0 @@
-import chromadb
-from chromadb.utils import embedding_functions
-from typing import Optional, Dict
-
-def createVectorDB(
-    collection_name: Optional[str],
-    chroma_data_path: Optional[str] = None,
-    embed_model: Optional[str] = "all-MiniLM-L6-v2",
-    metadata: Optional[Dict[str, str]] = None
-) -> chromadb.Collection:
-    """Creates the vector database to store embeddings.
-
-    Args:
-        collection_name (str): The name of the collection.
-        chroma_data_path (Optional[str]): Path for chroma embeddings.
-        embed_model (Optional[str]): Model name for embeddings.
-        metadata (Optional[Dict[str, str]]): Metadata for the collection.
-
-    Returns:
-        chromadb.Collection: The created collection object.
-    """
-    if chroma_data_path is None:
-        chroma_data_path = r"CHROMA_EMBEDDINGS_PATH"  # Default path if not provided
-
-    client = chromadb.PersistentClient(path=chroma_data_path)
-
-    embedding_func = embedding_functions.SentenceTransformerEmbeddingFunction(
-        model_name=embed_model
-    )
-
-    # Use provided metadata or default to empty dictionary
-    if metadata is None:
-        metadata = {"hnsw:space": "cosine"}
-
-    collection = client.create_collection(
-        collection_name=collection_name,
-        embedding_function=embedding_func,
-        metadata=metadata,
-    )
-    
-    return collection
-
-#unsure how to create unittest
-
-#collection = createVectorDB(
-    #COLLECTION_NAME="123456789",
-    #C#HROMA_DATA_PATH=r"C:\Users\navan\Downloads\BioModelsRAG\CHROMA_EMBEDDINGS_PATH",
-    #EMBED_MODEL="all-MiniLM-L6-v2",
-    #metadata={"hnsw:space": "cosine"}