Collection Objects
class Collection(BaseModel)
count
def count() -> int
The total number of embeddings added to the database
Returns:
int
- The total number of embeddings added to the database
add
def add(ids: OneOrMany[ID],
embeddings: Optional[OneOrMany[Embedding]] = None,
metadatas: Optional[OneOrMany[Metadata]] = None,
documents: Optional[OneOrMany[Document]] = None) -> None
Add embeddings to the data store.
Arguments:
ids
- The ids of the embeddings you wish to addembeddings
- 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 documentsValueError
- If the length of ids, embeddings, metadatas, or documents don't matchValueError
- If you don't provide an embedding function and don't provide embeddingsDuplicateIDError
- If you provide an id that already exists
get
def get(ids: Optional[OneOrMany[ID]] = None,
where: Optional[Where] = None,
limit: Optional[int] = None,
offset: Optional[int] = None,
where_document: Optional[WhereDocument] = None,
include: Include = ["metadatas", "documents"]) -> GetResult
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.{$contains: {"text": "hello"}}
. Optional.include
- A list of what to include in the results. Can contain"embeddings"
,"metadatas"
,"documents"
. Ids are always included. Defaults to["metadatas", "documents"]
. Optional.
Returns:
GetResult
- A GetResult object containing the results.
peek
def peek(limit: int = 10) -> GetResult
Get the first few results in the database up to limit
Arguments:
limit
- The number of results to return.
Returns:
GetResult
- A GetResult object containing the results.
query
def query(
query_embeddings: Optional[OneOrMany[Embedding]] = None,
query_texts: Optional[OneOrMany[Document]] = None,
n_results: int = 10,
where: Optional[Where] = None,
where_document: Optional[WhereDocument] = None,
include: Include = ["metadatas", "documents",
"distances"]) -> QueryResult
Get the n_results nearest neighbor embeddings for provided query_embeddings or query_texts.
Arguments:
query_embeddings
- The embeddings to get the closes neighbors of. Optional.query_texts
- The document texts to get the closes neighbors of. Optional.n_results
- The number of neighbors to return for each query_embedding or query_texts. Optional.where
- A Where type dict used to filter results by. E.g.{"color" : "red", "price": 4.20}
. Optional.where_document
- A WhereDocument type dict used to filter by the documents. E.g.{$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_textsValueError
- If you provide both query_embeddings and query_texts
modify
def modify(name: Optional[str] = None,
metadata: Optional[CollectionMetadata] = None) -> None
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
def update(ids: OneOrMany[ID],
embeddings: Optional[OneOrMany[Embedding]] = None,
metadatas: Optional[OneOrMany[Metadata]] = None,
documents: Optional[OneOrMany[Document]] = None) -> None
Update the embeddings, metadatas or documents for provided ids.
Arguments:
ids
- The ids of the embeddings to updateembeddings
- 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
def upsert(ids: OneOrMany[ID],
embeddings: Optional[OneOrMany[Embedding]] = None,
metadatas: Optional[OneOrMany[Metadata]] = None,
documents: Optional[OneOrMany[Document]] = None) -> None
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 updateembeddings
- 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
def delete(ids: Optional[IDs] = None,
where: Optional[Where] = None,
where_document: Optional[WhereDocument] = None) -> None
Delete the embeddings based on ids and/or a where filter
Arguments:
ids
- The ids of the embeddings to deletewhere
- 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