U h%@sfddlmZmZmZmZddlmZddlmZm Z ddl m Z GdddeeZ Gddde Z d S) )AnyDictListOptional) Embeddings) BaseModelroot_validator)get_from_dict_or_envc@seZdZUdZeed<dZeed<dZe e ed<dZ e ed<dZ e e ed <d Ze ed <dZe eed <d Zeed<dZe eed<dZe ed<dZe ed<dZe ed<ed deedddZeeeeedddZeeedddZdS)%AlephAlphaAsymmetricSemanticEmbeddinga6Aleph Alpha's asymmetric semantic embedding. AA provides you with an endpoint to embed a document and a query. The models were optimized to make the embeddings of documents and the query for a document as similar as possible. To learn more, check out: https://docs.aleph-alpha.com/docs/tasks/semantic_embed/ Example: .. code-block:: python from aleph_alpha import AlephAlphaAsymmetricSemanticEmbedding embeddings = AlephAlphaAsymmetricSemanticEmbedding( normalize=True, compress_to_size=128 ) document = "This is a content of the document" query = "What is the content of the document?" doc_result = embeddings.embed_documents([document]) query_result = embeddings.embed_query(query) clientz luminous-basemodelNcompress_to_sizeF normalizecontextual_control_thresholdTcontrol_log_additivealeph_alpha_api_keyzhttps://api.aleph-alpha.comhosthostingi1request_timeout_seconds total_retriesnice)pre)valuesreturncCsjt|dd}z|S)zCall out to Aleph Alpha's asymmetric Document endpoint. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. rPromptSemanticEmbeddingRequestSemanticRepresentationrpromptZrepresentationr rrrrequestr )rr'r(r)r from_textDocumentr rrrr semantic_embedr append embedding) selfr%r'r(r)document_embeddingstextZdocument_paramsZdocument_requestZdocument_responser!r!r"embed_documentsls,   z5AlephAlphaAsymmetricSemanticEmbedding.embed_documentsr5rcCszzddlm}m}m}Wntk r4tdYnX|||j|j|j|j |j d}|f|}|j j ||j d}|jS)Call out to Aleph Alpha's asymmetric, query embedding endpoint Args: text: The text to embed. Returns: Embeddings for the text. rr&rr*r,)rr'r(r)rr.ZQueryr rrrr r0r r2)r3r5r'r(r)Zsymmetric_paramsZsymmetric_requestZsymmetric_responser!r!r" embed_querys&  z1AlephAlphaAsymmetricSemanticEmbedding.embed_query)__name__ __module__ __qualname____doc__r__annotations__r strr rintrboolrrrrrrrrrrr#rfloatr6r9r!r!r!r"r s"       )r c@sTeZdZdZeeedddZeeeeedddZeeeddd Z d S) $AlephAlphaSymmetricSemanticEmbeddingaWSymmetric version of the Aleph Alpha's semantic embeddings. The main difference is that here, both the documents and queries are embedded with a SemanticRepresentation.Symmetric Example: .. code-block:: python from aleph_alpha import AlephAlphaSymmetricSemanticEmbedding embeddings = AlephAlphaAsymmetricSemanticEmbedding( normalize=True, compress_to_size=128 ) text = "This is a test text" doc_result = embeddings.embed_documents([text]) query_result = embeddings.embed_query(text) r7cCszzddlm}m}m}Wntk r4tdYnX|||j|j|j|j |j d}|f|}|j j ||j d}|jS)Nrr&rr*r,)rr'r(r)rr.Z Symmetricr rrrr r0r r2)r3r5r'r(r)Z query_paramsZ query_requestZquery_responser!r!r"_embeds&  z+AlephAlphaSymmetricSemanticEmbedding._embedr$cCs"g}|D]}|||q|S)zCall out to Aleph Alpha's Document endpoint. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. )r1rD)r3r%r4r5r!r!r"r6s z4AlephAlphaSymmetricSemanticEmbedding.embed_documentscCs ||S)r8)rD)r3r5r!r!r"r9sz0AlephAlphaSymmetricSemanticEmbedding.embed_queryN) r:r;r<r=r?rrBrDr6r9r!r!r!r"rCsrCN)typingrrrrZlangchain_core.embeddingsrZlangchain_core.pydantic_v1rrZlangchain_core.utilsr r rCr!r!r!r"s   2