Collection ↗

Documentation Index#

Fetch the complete documentation index at: https://docs.trychroma.com/llms.txt Use this file to discover all available pages before exploring further.

Collection Methods#

count#

Return the number of records in the collection.

add#

Add records to the collection.

Raises:

• ValueError: If embeddings and documents are both missing.
• ValueError: If embeddings and documents are both provided.
• ValueError: If lengths of provided fields do not match.
• ValueError: If an ID already exists.

get#

Retrieve records from the collection.

If no filters are provided, returns records up to limit starting at offset.

Returns: Retrieved records and requested fields as a GetResult object.

peek#

Return the first limit records from the collection.

Returns: Retrieved records and requested fields.

query#

Query for the K nearest neighbor records in the collection.

This is a batch query API. Multiple queries can be performed at once by providing multiple embeddings, texts, or images.

query_1 = [0.1, 0.2, 0.3]
query_2 = [0.4, 0.5, 0.6]
results = collection.query(
    query_embeddings=[query_1, query_2],
    n_results=10,
)

If query_texts, query_images, or query_uris are provided, the collection’s embedding function will be used to create embeddings before querying the API.

The ids, where, where_document, and include parameters are applied to all queries.

Returns: Nearest neighbor results.

Raises:

• ValueError: If no query input is provided.
• ValueError: If multiple query input types are provided.

modify#

Update collection name, metadata, or configuration.

update#

Update existing records by ID.

Records are provided in columnar format. If provided, the embeddings, metadatas, documents, and uris lists must be the same length. Entries in each list correspond to the same record.

ids = ["id1", "id2", "id3"]
embeddings = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]]
metadatas = [{"key": "value"}, {"key": "value"}, {"key": "value"}]
documents = ["document1", "document2", "document3"]
uris = ["uri1", "uri2", "uri3"]
collection.update(ids, embeddings, metadatas, documents, uris)

If embeddings are not provided, the embeddings will be computed based on documents using the collection’s embedding function.

upsert#

Create or update records by ID.

delete#

Delete records by ID or filters.

All documents that match the ids or where and where_document filters will be deleted.

Raises:

• ValueError: If no IDs or filters are provided.

Types#

GetResult#

Result payload for collection.get() operations.

The returned records are in columnar form. Corresponding entries in each list correspond to the same record.

results = collection.get(ids=["id1", "id2", "id3"])
records = zip(results["ids"], results["documents"], results["metadatas"])
for id, document, metadata in records:
    print(id, document, metadata)

GetResult will only include ids and the fields specified in the include param when making the get() operation.

Properties

results = collection.query(query_embeddings=[batch_1, batch_2, ...])
batches = zip(results["ids"], results["documents"], results["metadatas"])

for batch in batches:
    records = zip(batch["ids"], batch["documents"], batch["metadatas"])
    for id, document, metadata in records:
        print(id, document, metadata)