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collection.add(
    ids=["id1", "id2", "id3", ...],
    documents=["lorem ipsum...", "doc2", "doc3", ...],
    metadatas=[{"chapter": 3, "verse": 16}, {"chapter": 3, "verse": 5}, {"chapter": 29, "verse": 11}, ...],
)
If you add a record with an ID that already exists in the collection, it will be ignored and no exception will be raised. This means that if a batch add operation fails, you can safely run it again. Alternatively, you can supply a list of document-associated embeddings directly, and Chroma will store the associated documents without embedding them itself. Note that in this case there will be no guarantee that the embedding is mapped to the document associated with it. 
collection.add(
    ids=["id1", "id2", "id3", ...],
    embeddings=[[1.1, 2.3, 3.2], [4.5, 6.9, 4.4], [1.1, 2.3, 3.2], ...],
    documents=["doc1", "doc2", "doc3", ...],
    metadatas=[{"chapter": 3, "verse": 16}, {"chapter": 3, "verse": 5}, {"chapter": 29, "verse": 11}, ...],

)
If the supplied embeddings are not the same dimension as the embeddings already indexed in the collection, an exception will be raised. You can also store documents elsewhere, and just supply a list of embeddings and metadata to Chroma. You can use the ids to associate the embeddings with your documents stored elsewhere. 
collection.add(
    embeddings=[[1.1, 2.3, 3.2], [4.5, 6.9, 4.4], [1.1, 2.3, 3.2], ...],
    metadatas=[{"chapter": 3, "verse": 16}, {"chapter": 3, "verse": 5}, {"chapter": 29, "verse": 11}, ...],
    ids=["id1", "id2", "id3", ...]
)