This gist illustrates how to store vectors of your documents in chroma without providing your actual text documents:

chroma_no_docs.py

import uuid

from chromadb.utils import embedding_functions
import chromadb
ef = embedding_functions.DefaultEmbeddingFunction()

docs = ["Article by john", "Article by Jack", "Article by Jill"]
client = chromadb.Client()
embeddings = ef(docs)
collection = client.get_or_create_collection("test-where-list")
collection.upsert(documents=["" for _ in range(len(docs))], embeddings=embeddings, metadatas=[{"source": "blogger.com","author":"John"}, {"source": "medium","author":"Jack"}, {"source": "notion","author":"Jill"}],ids=[str(uuid.uuid4()) for _ in range(len(docs))])

#collection.get(include=["embeddings","metadatas"])

qr = collection.query(query_texts=["All articles by John"], include=["metadatas","distances"])
print(qr)