Collection Objects#

count#

The total number of embeddings added to the database

Returns:

• int - The total number of embeddings added to the database

add#

Add embeddings to the data store.

Arguments:

• ids - The ids of the embeddings you wish to add
• embeddings - The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional.
• metadatas - The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional.
• documents - The documents to associate with the embeddings. Optional.

Returns:

None

Raises:

• ValueError - If you don't provide either embeddings or documents
• ValueError - If the length of ids, embeddings, metadatas, or documents don't match
• ValueError - If you don't provide an embedding function and don't provide embeddings
• DuplicateIDError - If you provide an id that already exists

get#

Get embeddings and their associate data from the data store. If no ids or where filter is provided returns all embeddings up to limit starting at offset.

Arguments:

• ids - The ids of the embeddings to get. Optional.
• where - A Where type dict used to filter results by. E.g. {"color" : "red", "price": 4.20}. Optional.
• limit - The number of documents to return. Optional.
• offset - The offset to start returning results from. Useful for paging results with limit. Optional.
• where_document - A WhereDocument type dict used to filter by the documents. E.g. {$contains: {"text": "hello"}}. Optional.
• include - A list of what to include in the results. Can contain "embeddings", "metadatas", "documents". Ids are always included. Defaults to ["metadatas", "documents"]. Optional.

Returns:

• GetResult - A GetResult object containing the results.

peek#

Get the first few results in the database up to limit

Arguments:

• limit - The number of results to return.

Returns:

• GetResult - A GetResult object containing the results.

query#

Get the n_results nearest neighbor embeddings for provided query_embeddings or query_texts.

Arguments:

• query_embeddings - The embeddings to get the closes neighbors of. Optional.
• query_texts - The document texts to get the closes neighbors of. Optional.
• n_results - The number of neighbors to return for each query_embedding or query_texts. Optional.
• where - A Where type dict used to filter results by. E.g. {"color" : "red", "price": 4.20}. Optional.
• where_document - A WhereDocument type dict used to filter by the documents. E.g. {$contains: {"text": "hello"}}. Optional.
• include - A list of what to include in the results. Can contain "embeddings", "metadatas", "documents", "distances". Ids are always included. Defaults to ["metadatas", "documents", "distances"]. Optional.

Returns:

• QueryResult - A QueryResult object containing the results.

Raises:

• ValueError - If you don't provide either query_embeddings or query_texts
• ValueError - If you provide both query_embeddings and query_texts

modify#

Modify the collection name or metadata

Arguments:

• name - The updated name for the collection. Optional.
• metadata - The updated metadata for the collection. Optional.

Returns:

None

update#

Update the embeddings, metadatas or documents for provided ids.

Arguments:

• ids - The ids of the embeddings to update
• embeddings - The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional.
• metadatas - The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional.
• documents - The documents to associate with the embeddings. Optional.

Returns:

None

upsert#

Update the embeddings, metadatas or documents for provided ids, or create them if they don't exist.

Arguments:

• ids - The ids of the embeddings to update
• embeddings - The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional.
• metadatas - The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional.
• documents - The documents to associate with the embeddings. Optional.

Returns:

None

delete#

Delete the embeddings based on ids and/or a where filter

Arguments:

• ids - The ids of the embeddings to delete
• where - A Where type dict used to filter the delection by. E.g. {"color" : "red", "price": 4.20}. Optional.
• where_document - A WhereDocument type dict used to filter the deletion by the document content. E.g. {$contains: {"text": "hello"}}. Optional.

Returns:

None