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from typing import TYPE_CHECKING, Optional, Tuple, cast, List
from pydantic import BaseModel, PrivateAttr
from uuid import UUID
import chromadb.utils.embedding_functions as ef
from chromadb.api.types import (
CollectionMetadata,
Embedding,
Include,
Metadata,
Document,
Where,
IDs,
EmbeddingFunction,
GetResult,
QueryResult,
ID,
OneOrMany,
WhereDocument,
maybe_cast_one_to_many,
validate_ids,
validate_include,
validate_metadatas,
validate_where,
validate_where_document,
validate_n_results,
validate_embeddings,
)
import logging
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from chromadb.api import API
class Collection(BaseModel):
name: str
id: UUID
metadata: Optional[CollectionMetadata] = None
_client: "API" = PrivateAttr()
_embedding_function: Optional[EmbeddingFunction] = PrivateAttr()
def __init__(
self,
client: "API",
name: str,
id: UUID,
embedding_function: Optional[EmbeddingFunction] = ef.DefaultEmbeddingFunction(),
metadata: Optional[CollectionMetadata] = None,
):
self._client = client
self._embedding_function = embedding_function
super().__init__(name=name, metadata=metadata, id=id)
def __repr__(self) -> str:
return f"Collection(name={self.name})"
def count(self) -> int:
"""The total number of embeddings added to the database
Returns:
int: The total number of embeddings added to the database
"""
return self._client._count(collection_id=self.id)
def add(
self,
ids: OneOrMany[ID],
embeddings: Optional[OneOrMany[Embedding]] = None,
metadatas: Optional[OneOrMany[Metadata]] = None,
documents: Optional[OneOrMany[Document]] = None,
increment_index: bool = True,
) -> None:
"""Add embeddings to the data store.
Args:
ids: The ids of the embeddings you wish to add
embedding: The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional.
metadata: 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.
ids: The ids 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
ValueError: If you provide both embeddings and documents
ValueError: If you provide an id that already exists
"""
ids, embeddings, metadatas, documents = self._validate_embedding_set(
ids, embeddings, metadatas, documents
)
self._client._add(
ids, self.id, embeddings, metadatas, documents, increment_index
)
def get(
self,
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.
Args:
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. `{$contains: {"text": "hello"}}`. Optional.
include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`. Ids are always included. Defaults to `["metadatas", "documents"]`. Optional.
Returns:
GetResult: A GetResult object containing the results.
"""
where = validate_where(where) if where else None
where_document = (
validate_where_document(where_document) if where_document else None
)
ids = validate_ids(maybe_cast_one_to_many(ids)) if ids else None
include = validate_include(include, allow_distances=False)
return self._client._get(
self.id,
ids,
where,
None,
limit,
offset,
where_document=where_document,
include=include,
)
def peek(self, limit: int = 10) -> GetResult:
"""Get the first few results in the database up to limit
Args:
limit: The number of results to return.
Returns:
GetResult: A GetResult object containing the results.
"""
return self._client._peek(self.id, limit)
def query(
self,
query_embeddings: Optional[OneOrMany[Embedding]] = None,
query_texts: Optional[OneOrMany[Document]] = None,
n_results: int = 10,
where: Optional[Where] = None,
where_document: Optional[WhereDocument] = None,
include: Include = ["metadatas", "documents", "distances"],
) -> QueryResult:
"""Get the n_results nearest neighbor embeddings for provided query_embeddings or query_texts.
Args:
query_embeddings: The embeddings to get the closes neighbors of. Optional.
query_texts: The document texts to get the closes neighbors of. Optional.
n_results: The number of neighbors to return for each query_embedding or query_texts. Optional.
where: A Where type dict used to filter results by. E.g. `{"color" : "red", "price": 4.20}`. Optional.
where_document: A WhereDocument type dict used to filter by the documents. E.g. `{$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
"""
where = validate_where(where) if where else None
where_document = (
validate_where_document(where_document) if where_document else None
)
query_embeddings = (
validate_embeddings(maybe_cast_one_to_many(query_embeddings))
if query_embeddings is not None
else None
)
query_texts = (
maybe_cast_one_to_many(query_texts) if query_texts is not None else None
)
include = validate_include(include, allow_distances=True)
n_results = validate_n_results(n_results)
# If neither query_embeddings nor query_texts are provided, or both are provided, raise an error
if (query_embeddings is None and query_texts is None) or (
query_embeddings is not None and query_texts is not None
):
raise ValueError(
"You must provide either query embeddings or query texts, but not both"
)
# If query_embeddings are not provided, we need to compute them from the query_texts
if query_embeddings is None:
if self._embedding_function is None:
raise ValueError(
"You must provide embeddings or a function to compute them"
)
# We know query texts is not None at this point, cast for the typechecker
query_embeddings = self._embedding_function(
cast(List[Document], query_texts)
)
if where is None:
where = {}
if where_document is None:
where_document = {}
return self._client._query(
collection_id=self.id,
query_embeddings=query_embeddings,
n_results=n_results,
where=where,
where_document=where_document,
include=include,
)
def modify(
self, name: Optional[str] = None, metadata: Optional[CollectionMetadata] = None
) -> None:
"""Modify the collection name or metadata
Args:
name: The updated name for the collection. Optional.
metadata: The updated metadata for the collection. Optional.
Returns:
None
"""
self._client._modify(id=self.id, new_name=name, new_metadata=metadata)
if name:
self.name = name
if metadata:
self.metadata = metadata
def update(
self,
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.
Args:
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
"""
ids, embeddings, metadatas, documents = self._validate_embedding_set(
ids, embeddings, metadatas, documents, require_embeddings_or_documents=False
)
self._client._update(self.id, ids, embeddings, metadatas, documents)
def upsert(
self,
ids: OneOrMany[ID],
embeddings: Optional[OneOrMany[Embedding]] = None,
metadatas: Optional[OneOrMany[Metadata]] = None,
documents: Optional[OneOrMany[Document]] = None,
increment_index: bool = True,
) -> None:
"""Update the embeddings, metadatas or documents for provided ids, or create them if they don't exist.
Args:
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
"""
ids, embeddings, metadatas, documents = self._validate_embedding_set(
ids, embeddings, metadatas, documents
)
self._client._upsert(
collection_id=self.id,
ids=ids,
embeddings=embeddings,
metadatas=metadatas,
documents=documents,
increment_index=increment_index,
)
def delete(
self,
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
Args:
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
"""
ids = validate_ids(maybe_cast_one_to_many(ids)) if ids else None
where = validate_where(where) if where else None
where_document = (
validate_where_document(where_document) if where_document else None
)
self._client._delete(self.id, ids, where, where_document)
def create_index(self) -> None:
self._client.create_index(self.name)
def _validate_embedding_set(
self,
ids: OneOrMany[ID],
embeddings: Optional[OneOrMany[Embedding]],
metadatas: Optional[OneOrMany[Metadata]],
documents: Optional[OneOrMany[Document]],
require_embeddings_or_documents: bool = True,
) -> Tuple[
IDs,
List[Embedding],
Optional[List[Metadata]],
Optional[List[Document]],
]:
ids = validate_ids(maybe_cast_one_to_many(ids))
embeddings = (
validate_embeddings(maybe_cast_one_to_many(embeddings))
if embeddings is not None
else None
)
metadatas = (
validate_metadatas(maybe_cast_one_to_many(metadatas))
if metadatas is not None
else None
)
documents = maybe_cast_one_to_many(documents) if documents is not None else None
# Check that one of embeddings or documents is provided
if require_embeddings_or_documents:
if embeddings is None and documents is None:
raise ValueError(
"You must provide either embeddings or documents, or both"
)
# Check that, if they're provided, the lengths of the arrays match the length of ids
if embeddings is not None and len(embeddings) != len(ids):
raise ValueError(
f"Number of embeddings {len(embeddings)} must match number of ids {len(ids)}"
)
if metadatas is not None and len(metadatas) != len(ids):
raise ValueError(
f"Number of metadatas {len(metadatas)} must match number of ids {len(ids)}"
)
if documents is not None and len(documents) != len(ids):
raise ValueError(
f"Number of documents {len(documents)} must match number of ids {len(ids)}"
)
# If document embeddings are not provided, we need to compute them
if embeddings is None and documents is not None:
if self._embedding_function is None:
raise ValueError(
"You must provide embeddings or a function to compute them"
)
embeddings = self._embedding_function(documents)
# if embeddings is None:
# raise ValueError(
# "Something went wrong. Embeddings should be computed at this point"
# )
return ids, embeddings, metadatas, documents # type: ignore
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