o Zh%@sfddlmZmZmZmZddlmZddlmZddl m Z m Z Gddde eZ Gddde Z d S) )AnyDictListOptional) Embeddings)get_from_dict_or_env) BaseModelmodel_validatorc@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< eddededefddZdeedeeefddZdedeefdd ZdS)!%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_retriesnicebefore)modevaluesreturncCsbt|dd}zddlm}|||d|d|d|d|d d |d <W|Sty0td w) z?Validate that api key and python package exists in environment.rZALEPH_ALPHA_API_KEYr)Clientrrrrr)tokenrrrrrr lCould not import aleph_alpha_client python package. Please install it with `pip install aleph_alpha_client`.)raleph_alpha_clientr ImportError)clsrrrr"a/var/www/html/lang_env/lib/python3.10/site-packages/langchain_community/embeddings/aleph_alpha.pyvalidate_environmentSs&   z:AlephAlphaAsymmetricSemanticEmbedding.validate_environmenttextsc Csz ddlm}m}m}Wn tytdwg}|D])}|||j|j|j|j |j d}|di|}|j j ||j d} || jq|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 Nr")rr'r(r)r from_textZDocumentr rrrr semantic_embedr append embedding) selfr%r'r(r)document_embeddingstextZdocument_paramsZdocument_requestZdocument_responser"r"r#embed_documentsms,   z5AlephAlphaAsymmetricSemanticEmbedding.embed_documentsr4cCsxz ddlm}m}m}Wn tytdw|||j|j|j|j |j d}|di|}|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,Nr")rr'r(r)r r.ZQueryr rrrr r/r r1)r2r4r'r(r)Zsymmetric_paramsZsymmetric_requestZsymmetric_responser"r"r# embed_querys&  z1AlephAlphaAsymmetricSemanticEmbedding.embed_query)__name__ __module__ __qualname____doc__r__annotations__r strr rintrboolrrrrrrrrr classmethodrr$rfloatr5r7r"r"r"r#r s<        )r c@sZeZdZdZdedeefddZdeedeeefddZdedeefd d 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) r4rcCsxz ddlm}m}m}Wn tytdw|||j|j|j|j |j d}|di|}|j j ||j d}|jS)Nrr&rr*r,r")rr'r(r)r r.Z Symmetricr rrrr r/r r1)r2r4r'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. )r0rC)r2r%r3r4r"r"r#r5s z4AlephAlphaSymmetricSemanticEmbedding.embed_documentscCs ||S)r6)rC)r2r4r"r"r#r7s z0AlephAlphaSymmetricSemanticEmbedding.embed_queryN) r8r9r:r;r=rrArCr5r7r"r"r"r#rBs rBN)typingrrrrZlangchain_core.embeddingsrZlangchain_core.utilsrZpydanticrr r rBr"r"r"r#s  3