o
    g6                     @   s   d dl mZmZmZ d dlmZmZmZmZm	Z	m
Z
mZmZmZmZmZmZmZmZmZmZ d dlmZ er<d dlmZ G dd ded ZdS )	    )TYPE_CHECKINGOptionalUnion)URICollectionMetadata	EmbeddingIncludeEnumPyEmbeddingIncludeMetadataDocumentImageWhereIDs	GetResultQueryResultID	OneOrManyWhereDocument)CollectionCommon)AsyncServerAPIc                   @   s  e Zd Z					d(dee deeee ee f  deee	  deee
  deee  deee  ddfd	d
ZdefddZdddddejejgfdeee  dee dee dee dee dedefddZd)dedefddZdddddddejejejgfdeeee ee f  deee
  deee  deee  dedee dee dedefddZ	d*dee dee ddfd d!Z					d(dee deeee ee f  deee	  deee
  deee  deee  ddfd"d#Z					d(dee deeee ee f  deee	  deee
  deee  deee  ddfd$d%Z 			d+dee! dee dee ddfd&d'Z"dS ),AsyncCollectionNids
embeddings	metadatas	documentsimagesurisreturnc              
      X   | j ||||||d}| jj| j|d |d |d |d |d | j| jdI dH  dS )	a]  Add embeddings to the data store.
        Args:
            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 or images 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.
            images: The images to associate with the embeddings. Optional.
            uris: The uris of the images 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

        r   r   r   r   r   r   r   r   r   r   r   collection_idr   r   r   r   r   tenantdatabaseN)!_validate_and_prepare_add_request_client_addidr#   r$   )selfr   r   r   r   r   r   add_request r+   Z/var/www/visachat/venv/lib/python3.10/site-packages/chromadb/api/models/AsyncCollection.pyadd   s&   "	zAsyncCollection.addc                    s    | j j| j| j| jdI dH S )zThe total number of embeddings added to the database

        Returns:
            int: The total number of embeddings added to the database

        )r"   r#   r$   N)r&   _countr(   r#   r$   )r)   r+   r+   r,   countS   s   zAsyncCollection.countwherelimitoffsetwhere_documentincludec           	         sb   | j ||||d}| jj| j|d |d |d |d d||| j| jd
I dH }| j||d dS )	a  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. `{"$and": ["color" : "red", "price": {"$gte": 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.

        )r   r0   r3   r4   r   r0   r3   r4   N)
r"   r   r0   r3   r4   sortr1   r2   r#   r$   responser4   )!_validate_and_prepare_get_requestr&   _getr(   r#   r$   _transform_get_response)	r)   r   r0   r1   r2   r3   r4   get_requestget_resultsr+   r+   r,   get`   s,   zAsyncCollection.get
   c                    s(   |  | jj| j|| j| jdI dH S )zGet 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.
        )r"   nr#   r$   N)_transform_peek_responser&   _peekr(   r#   r$   )r)   r1   r+   r+   r,   peek   s   	
zAsyncCollection.peekquery_embeddingsquery_textsquery_images
query_uris	n_resultsc	              
      sj   | j ||||||||d}	| jj| j|	d |	d |	d |	d |	d | j| jdI dH }
| j|
|	d d	S )
a}  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.
            query_images: The images 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. `{"$and": ["color" : "red", "price": {"$gte": 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, query_texts, or query_images
            ValueError: If you provide both query_embeddings and query_texts
            ValueError: If you provide both query_embeddings and query_images
            ValueError: If you provide both query_texts and query_images

        )rC   rD   rE   rF   rG   r0   r3   r4   r   rG   r0   r3   r4   )r"   rC   rG   r0   r3   r4   r#   r$   Nr6   )#_validate_and_prepare_query_requestr&   _queryr(   r#   r$   _transform_query_response)r)   rC   rD   rE   rF   rG   r0   r3   r4   query_requestquery_resultsr+   r+   r,   query   s0   *zAsyncCollection.querynamemetadatac                    s6   |  | | jj| j||dI dH  | || dS )zModify the collection name or metadata

        Args:
            name: The updated name for the collection. Optional.
            metadata: The updated metadata for the collection. Optional.

        Returns:
            None
        )r(   new_namenew_metadataN)_validate_modify_requestr&   _modifyr(   "_update_model_after_modify_success)r)   rN   rO   r+   r+   r,   modify   s   
zAsyncCollection.modifyc              
      r   )	a  Update the embeddings, metadatas or documents for provided ids.

        Args:
            ids: The ids of the embeddings to update
            embeddings: The embeddings to update. If None, embeddings will be computed based on the documents or images 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.
            images: The images to associate with the embeddings. Optional.
        Returns:
            None
        r    r   r   r   r   r   r!   N)$_validate_and_prepare_update_requestr&   _updater(   r#   r$   )r)   r   r   r   r   r   r   update_requestr+   r+   r,   update   &   	zAsyncCollection.updatec              
      r   )	aO  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
        r    r   r   r   r   r   r!   N)$_validate_and_prepare_upsert_requestr&   _upsertr(   r#   r$   )r)   r   r   r   r   r   r   upsert_requestr+   r+   r,   upsert)  rZ   zAsyncCollection.upsertc                    sD   |  |||}| jj| j|d |d |d | j| jdI dH  dS )a4  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. `{"$and": ["color" : "red", "price": {"$gte": 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

        Raises:
            ValueError: If you don't provide either ids, where, or where_document
        r   r0   r3   )r"   r   r0   r3   r#   r$   N)$_validate_and_prepare_delete_requestr&   _deleter(   r#   r$   )r)   r   r0   r3   delete_requestr+   r+   r,   deleteV  s   zAsyncCollection.delete)NNNNN)r>   )NN)NNN)#__name__
__module____qualname__r   r   r   r   r   r	   r   r   r   r   r-   intr/   r   r   r   r   r   r
   r   r=   rB   	distancesr   rM   strr   rU   rY   r^   r   rb   r+   r+   r+   r,   r      s<   	
	




6


0

	


E

	




5
	




/r   r   N)typingr   r   r   chromadb.api.typesr   r   r   r   r	   r
   r   r   r   r   r   r   r   r   r   r   $chromadb.api.models.CollectionCommonr   chromadb.apir   r   r+   r+   r+   r,   <module>   s    H