U DAf- @sddlmZddlmZmZmZmZmZddlm Z m Z m Z m Z m Z ddlmZddlmZmZmZmZddlmZmZmZe eZedd&Zdd lZdd lZdd lm Z m!Z!W5QRXe Gd d d Z"d S) )Path)AnyDictListOptionalUnion)Document componentdefault_from_dictdefault_to_dictlogging) LazyImport)ComponentDevice DeviceMapSecretdeserialize_secrets_inplace)deserialize_hf_model_kwargsresolve_hf_device_mapserialize_hf_model_kwargsz3Run 'pip install transformers[torch,sentencepiece]')messageN)"AutoModelForSequenceClassification AutoTokenizerc@seZdZdZddejddgdddd d dd d d ddf eeefe e e ee eee e eee e ee ee eeefd ddZeeefdddZddZeeefdddZeeeefddddZeje eddee ee e e e e ee edddZdS)TransformersSimilarityRankeraY Ranks Documents based on their similarity to the query. It uses a pre-trained cross-encoder model (from the Hugging Face Hub) to embed the query and the Documents. Usage example: ```python from haystack import Document from haystack.components.rankers import TransformersSimilarityRanker ranker = TransformersSimilarityRanker() docs = [Document(content="Paris"), Document(content="Berlin")] query = "City in Germany" ranker.warm_up() result = ranker.run(query=query, documents=docs) docs = result["documents"] print(docs[0].content) ``` z$cross-encoder/ms-marco-MiniLM-L-6-v2NZ HF_API_TOKENZHF_TOKENF)strict  Tg?) modeldevicetokentop_k query_prefixdocument_prefixmeta_fields_to_embedembedding_separator scale_scorecalibration_factorscore_threshold model_kwargsc Cstt||_d|_||_||_d|_d|_||_ ||_ |pBg|_ ||_ | |_ | |_| |_t|| d} | |_|j r|jdkrtd| |j dkrtd|dS)ac Creates an instance of TransformersSimilarityRanker. :param model: The name or path of a pre-trained cross-encoder model from the Hugging Face Hub. :param device: The device on which the model is loaded. If `None`, the default device is automatically selected. :param token: The API token used to download private models from Hugging Face. :param top_k: The maximum number of Documents to return per query. :param query_prefix: A string to add to the beginning of the query text before ranking. Can be used to prepend the text with an instruction, as required by some reranking models, such as bge. :param document_prefix: A string to add to the beginning of each Document text before ranking. Can be used to prepend the text with an instruction, as required by some embedding models, such as bge. :param meta_fields_to_embed: List of meta fields that should be embedded along with the Document content. :param embedding_separator: Separator used to concatenate the meta fields to the Document content. :param scale_score: Whether the raw logit predictions will be scaled using a Sigmoid activation function. Set this to False if you do not want any scaling of the raw logit predictions. :param calibration_factor: Factor used for calibrating probabilities calculated by `sigmoid(logits * calibration_factor)`. This is only used if `scale_score` is set to True. :param score_threshold: If provided only returns documents with a score above this threshold. :param model_kwargs: Additional keyword arguments passed to `AutoModelForSequenceClassification.from_pretrained` when loading the model specified in `model`. For details on what kwargs you can pass, see the model's documentation. :raises ValueError: If `top_k` is not > 0. If `scale_score` is True and `calibration_factor` is not provided. N)rr(Dscale_score is True so calibration_factor must be provided, but got rtop_k must be > 0, but got )torch_and_transformers_importcheckstrmodel_name_or_pathrr!r" tokenizerrr rr#r$r%r&r'rr( ValueError) selfrrrr r!r"r#r$r%r&r'r(r2W/tmp/pip-unpacked-wheel-z752163x/haystack/components/rankers/transformers_similarity.py__init__,s,4    z%TransformersSimilarityRanker.__init__)returncCs d|jiS)zC Data that is sent to Posthog for usage analytics. r)r.r1r2r2r3_get_telemetry_data|sz0TransformersSimilarityRanker._get_telemetry_datacCst|jdkrptj|jfd|jr&|jndi|j|_tj|j|jrN|jndd|_t j t |jj d|_dS)z, Initializes the component. Nr)r)Z device_map)rrZfrom_pretrainedr.rZ resolve_valuer(rr/rZ from_multiplerZfrom_hfZ hf_device_maprr6r2r2r3warm_ups z$TransformersSimilarityRanker.warm_upcCsZt|d|j|jr|jnd|j|j|j|j|j|j |j |j |j d }t |dd|S)z{ Serializes the component to a dictionary. :returns: Dictionary with serialized data. N) rrrr r!r"r#r$r%r&r'r(init_parametersr()r r.rto_dictr r!r"r#r$r%r&r'r(r)r1Zserialization_dictr2r2r3r:s"z$TransformersSimilarityRanker.to_dict)datar5cCsZ|d}|ddk r(t|d|d<|ddk rBt|dt|dgdt||S)z Deserializes the component from a dictionary. :param data: Dictionary to deserialize from. :returns: Deserialized component. r9rNr(r)keys)getr from_dictrrr )clsr;Z init_paramsr2r2r3r>s  z&TransformersSimilarityRanker.from_dict) documents)queryr@r r%r&r'c s|jdkrtd|sdgiS|p&|j}|p0|j}|p:|j}pD|j|dkr\td||rv|dkrvtd|g}|D]Jfdd|jD}|j |j pd g} | |j ||j | gq~|j|d d d d |jj} t|jf| jjd d} W5QRX|r(t| |} tj| d d\} } | } | } g}| D]&}|}| |||_| ||qZdk rfdd|D}d|d|iS)a Returns a list of Documents ranked by their similarity to the given query. :param query: Query string. :param documents: List of Documents. :param top_k: The maximum number of Documents you want the Ranker to return. :param scale_score: Whether the raw logit predictions will be scaled using a Sigmoid activation function. Set this to False if you do not want any scaling of the raw logit predictions. :param calibration_factor: Factor used for calibrating probabilities calculated by `sigmoid(logits * calibration_factor)`. This is only used if `scale_score` is set to True. :param score_threshold: If provided only returns documents with a score above this threshold. :returns: A dictionary with the following keys: - `documents`: List of Documents most similar to the given query in descending order of similarity. :raises ValueError: If `top_k` is not > 0. If `scale_score` is True and `calibration_factor` is not provided. :raises RuntimeError: If the model is not loaded because `warm_up()` was not called before. NzdThe component TransformersSimilarityRanker wasn't warmed up. Run 'warm_up()' before calling 'run()'.r@rr*r)cs.g|]&}|jkrj|rtj|qSr2)metar-).0key)docr2r3 s z4TransformersSimilarityRanker.run..rTpt)paddingZ truncationZreturn_tensors)Zdim)Z descendingcsg|]}|jkr|qSr2)score)rCrE)r'r2r3rFs )r RuntimeErrorr r%r&r'r0r#r$joincontentappendr!r"r/torZ first_deviceZto_torchtorchZinference_modeZlogitsZsqueezeZsigmoidsortcputolistrJ)r1rAr@r r%r&r'Zquery_doc_pairsZmeta_values_to_embedZ text_to_embedfeaturesZsimilarity_scores_Zsorted_indicesZ ranked_docsZ sorted_indexir2)rEr'r3runsR&             z TransformersSimilarityRanker.run)NNNN)__name__ __module__ __qualname____doc__rZ from_env_varrr-rrrintrboolfloatrrr4r7r8r: classmethodr>r Z output_typesrrWr2r2r2r3rsZ   P r)#pathlibrtypingrrrrrZhaystackrr r r r Zhaystack.lazy_importsr Zhaystack.utilsrrrrZhaystack.utils.hfrrr getLoggerrXloggerr+Z acceleraterPZ transformersrrrr2r2r2r3s