o Zh@sddlmZddlZddlmZddlmZmZmZm Z m Z m Z ddl m Z er,ddlZeeZe eeeeeefZdd d ZdddZGddde ddZGdddZdS)) annotationsN)Sequence) TYPE_CHECKINGAnyCallableLiteralOptionalUnion) TypedDictXMatrixYreturn np.ndarrayc Csnddl}t|dkst|dkr|gS||}||}|jd|jdkr6td|jd|jdz0ddl}|j||jd}|j||jd}d|j||dd }t|t ra||gWS||WSt yt d |j j|dd }|j j|dd }|jd d d |||j|||}Wdn1swYd|||||B<|YSw)z       r,4Callable[[Sequence[str]], Sequence[Sequence[float]]]cs:zddlmWn tytdwd fdd }|S) zGet the OpenAI GPT-3 encoder.r)ClientzTHe default encoder for the EmbeddingDistance class uses the OpenAI API. Please either install the openai library with `pip install openai` or provide a custom encoder function (Callable[[str], Sequence[float]]).texts Sequence[str]rSequence[Sequence[float]]cs*}|jjt|dd}dd|jDS)Nztext-embedding-3-small)inputmodelcSsg|]}|jqSr*)Z embedding).0dr*r*r+ Nsz<_get_openai_encoder..encode_text..) embeddingscreatelistdata)r/clientresponseZ OpenAIClientr*r+ encode_textIs z(_get_openai_encoder..encode_textN)r/r0rr1)Zopenair.r)r>r*r=r+_get_openai_encoder>s r?c@seZdZUded<ded<dS)EmbeddingConfigz0Callable[[list[str]], Sequence[Sequence[float]]]encoderzCLiteral['cosine', 'euclidean', 'manhattan', 'chebyshev', 'hamming']rN)__name__ __module__ __qualname____annotations__r*r*r*r+r@Ss  r@F)totalc@steZdZ ddddZdd d Zd ddZed!ddZed ddZed ddZ ed ddZ ed ddZ dS)"EmbeddingDistanceNconfigOptional[EmbeddingConfig]cCs.|pi}|dp d|_|dpt|_dS)NrrrA)getdistancer?rA)selfrHr*r*r+__init__YszEmbeddingDistance.__init__ predictionstr referencerrcCsTzddl}Wn tytdw|||g}||}||d|dS)NrzWThe EmbeddingDistance class requires NumPy. Please install it with `pip install numpy`.r)rrrAr_compute_distanceitem)rLrNrPr(r7Zvectorr*r*r+evaluateas   zEmbeddingDistance.evaluatearb np.floatingcCs~|jdkr |||S|jdkr|||S|jdkr!|||S|jdkr,|||S|jdkr7|||Std|j)NrZ euclideanZ manhattanZ chebyshevZhammingzInvalid distance metric: )rK_cosine_distance_euclidean_distance_manhattan_distance_chebyshev_distance_hamming_distancer)rLrTrUr*r*r+rQqs          z#EmbeddingDistance._compute_distancecCsdt|g|gS)zCompute the cosine distance between two vectors. Args: a (np.ndarray): The first vector. b (np.ndarray): The second vector. Returns: np.ndarray: The cosine distance. g?)r,rTrUr*r*r+rWs z"EmbeddingDistance._cosine_distancecCstj||S)zCompute the Euclidean distance between two vectors. Args: a (np.ndarray): The first vector. b (np.ndarray): The second vector. Returns: np.floating: The Euclidean distance. )r(r!r"r\r*r*r+rXs z%EmbeddingDistance._euclidean_distancecCtt||S)zCompute the Manhattan distance between two vectors. Args: a (np.ndarray): The first vector. b (np.ndarray): The second vector. Returns: np.floating: The Manhattan distance. )r(sumabsr\r*r*r+rY z%EmbeddingDistance._manhattan_distancecCr])zCompute the Chebyshev distance between two vectors. Args: a (np.ndarray): The first vector. b (np.ndarray): The second vector. Returns: np.floating: The Chebyshev distance. )r(maxr_r\r*r*r+rZr`z%EmbeddingDistance._chebyshev_distancecCst||kS)zCompute the Hamming distance between two vectors. Args: a (np.ndarray): The first vector. b (np.ndarray): The second vector. Returns: np.floating: The Hamming distance. )r(meanr\r*r*r+r[s z#EmbeddingDistance._hamming_distance)N)rHrI)rNrOrPrOrr)rTrrUrrrV)rTrrUrrr) rBrCrDrMrSrQ staticmethodrWrXrYrZr[r*r*r*r+rGXs       rG)r r r r rr)rr-) __future__rloggingcollections.abcrtypingrrrrrr typing_extensionsr rr( getLoggerrBrr9rr r,r?r@rGr*r*r*r+s       %