Ng PddlmZddlZddlZddlmZmZddlZddl m Z ej e Z erddlZddlZ d'd(dZ d'd)d!Z d*d+d&ZdS),) annotationsN) TYPE_CHECKINGLiteral)Tensorfloat32 TFquery_embeddings np.ndarraycorpus_embeddingsnp.ndarray | None corpus_indexfaiss.Index | Nonecorpus_precision&Literal['float32', 'uint8', 'ubinary']top_kintrangescalibration_embeddingsrescoreboolrescore_multiplierexact output_indexreturn=tuple[list[list[dict[str, int | float]]], float, faiss.Index]c ddl} |td|td|dvr;| r| j|jdnc| j|jddnF|dkr@| r| j|jdd zn| j|jdd zd|d} |} |jtj tj fvr>|r(|d kr|} | |z} nt d t|||| }n|rt d tj}|| \|  tjfdD}|dkr/tj|dt(}n|t(}tjd| |}tj| ddd|f}tjt1|dddf|f|tjt1|dddf|ftj|z }fdt3t1|D|f}| rg|R}|S)a" Performs semantic search using the FAISS library. Rescoring will be performed if: 1. `rescore` is True 2. The query embeddings are not quantized 3. The corpus is quantized, i.e. the corpus precision is not float32 Only if these conditions are true, will we search for `top_k * rescore_multiplier` samples and then rescore to only keep `top_k`. Args: query_embeddings: Embeddings of the query sentences. Ideally not quantized to allow for rescoring. corpus_embeddings: Embeddings of the corpus sentences. Either `corpus_embeddings` or `corpus_index` should be used, not both. The embeddings can be quantized to "int8" or "binary" for more efficient search. corpus_index: FAISS index for the corpus sentences. Either `corpus_embeddings` or `corpus_index` should be used, not both. corpus_precision: Precision of the corpus embeddings. The options are "float32", "int8", or "binary". Default is "float32". top_k: Number of top results to retrieve. Default is 10. ranges: Ranges for quantization of embeddings. This is only used for int8 quantization, where the ranges refers to the minimum and maximum values for each dimension. So, it's a 2D array with shape (2, embedding_dim). Default is None, which means that the ranges will be calculated from the calibration embeddings. calibration_embeddings: Embeddings used for calibration during quantization. This is only used for int8 quantization, where the calibration embeddings can be used to compute ranges, i.e. the minimum and maximum values for each dimension. Default is None, which means that the ranges will be calculated from the query embeddings. This is not recommended. rescore: Whether to perform rescoring. Note that rescoring still will only be used if the query embeddings are not quantized and the corpus is quantized, i.e. the corpus precision is not "float32". Default is True. rescore_multiplier: Oversampling factor for rescoring. The code will now search `top_k * rescore_multiplier` samples and then rescore to only keep `top_k`. Default is 2. exact: Whether to use exact search or approximate search. Default is True. output_index: Whether to output the FAISS index used for the search. Default is False. Returns: A tuple containing a list of search results and the time taken for the search. If `output_index` is True, the tuple will also contain the FAISS index used for the search. Raises: ValueError: If both `corpus_embeddings` and `corpus_index` are provided or if neither is provided. The list of search results is in the format: [[{"corpus_id": int, "score": float}, ...], ...] The time taken for the search is a float value. rN@Only corpus_embeddings or corpus_index should be used, not both.8Either corpus_embeddings or corpus_index should be used.)ruint8ubinaryrzRescoring is enabled but the corpus is not quantized. Either pass `rescore=False` or quantize the corpus embeddings with `quantize_embeddings(embeddings, precision="...") `and pass `corpus_precision="..."` to `semantic_search_faiss`. precisionrrRescoring is enabled but the query embeddings are quantized. Either pass `rescore=False` or don't quantize the query embeddings.c,g|]}fd|DS)c^g|])}|*S) reconstructitem).0idxrs ^/var/www/html/ai-engine/env/lib/python3.11/site-packages/sentence_transformers/quantization.py z4semantic_search_faiss...s/ L L Lsl&&sxxzz22 L L Lr*r- query_indicesrs r/r0z)semantic_search_faiss..s/ j j jQ^ L L L Lm L L L j j jr1axis ij,ikj->ikc\g|](}dt||D)S)cRg|]$\}}t|t|d%S) corpus_idscorerfloatr-r<neighbors r/r0z4semantic_search_faiss...@   #E8"(mmeEllCC   r1zipr-query_idindicesscoress r/r0z)semantic_search_faiss..V      '*6(+;WX=N'O'O      r1)faiss ValueError IndexFlatIPshape IndexHNSWFlatIndexBinaryFlatIndexBinaryHNSWadddtypenpr int8loggerwarningquantize_embeddingstimesearcharray unpackbitsastypereinsumargsortarangelenrange)r r rrrrrrrrrrIrescore_embeddingskstart_ttop_k_embeddingsrescored_scoresrescored_indicesdelta_toutputsrFrGs ` @@r/semantic_search_faissriswTLLL$)A[\\\ \%9STTT 3 3 3 S0u01B1H1KLL 2u23D3J13MrRR  * * Y4u45F5LQ5ORS5STT 4u45F5LQ5ORS5SUWXX *+++  Abh%888  9,,%5"''T / &#9       O   ikkG"))*:A>>OFG%8 j j j jbi j j j   y ( (!}-=BGGGNNsSS  /66s;; )L2DFVWW:&6776E6 B")C(8$9$9::111d7CEUUV 3/?+@+@!A!A!!!T'!JL\!\]ikkG#G     "#&6"7"788      G+*G*\** Nr1usearch.index.Index | None$Literal['float32', 'int8', 'binary']Etuple[list[list[dict[str, int | float]]], float, usearch.index.Index]c ddlm} ddlm} |t d|t d|dvrt d|d kr| |jd d d nb|dkr| |jd dd nB|dkr| |jd dd n"|dkr| |jd dzdd tjt||d} |}|j tj tj fvrH|r2j | j kr|} ||z}ntdt!||||}n|rtdt#j}||| }|j|jjdkrtjjdkrtj| tjfdD}|dvr3tj|tj d}|t4}tjd| |}tj| ddd|f}tjt|dddf|f|tjt|dddf|ft#j|z }fd t;t|D|f}| rg|R}|S)!a4 Performs semantic search using the usearch library. Rescoring will be performed if: 1. `rescore` is True 2. The query embeddings are not quantized 3. The corpus is quantized, i.e. the corpus precision is not float32 Only if these conditions are true, will we search for `top_k * rescore_multiplier` samples and then rescore to only keep `top_k`. Args: query_embeddings: Embeddings of the query sentences. Ideally not quantized to allow for rescoring. corpus_embeddings: Embeddings of the corpus sentences. Either `corpus_embeddings` or `corpus_index` should be used, not both. The embeddings can be quantized to "int8" or "binary" for more efficient search. corpus_index: usearch index for the corpus sentences. Either `corpus_embeddings` or `corpus_index` should be used, not both. corpus_precision: Precision of the corpus embeddings. The options are "float32", "int8", "ubinary" or "binary". Default is "float32". top_k: Number of top results to retrieve. Default is 10. ranges: Ranges for quantization of embeddings. This is only used for int8 quantization, where the ranges refers to the minimum and maximum values for each dimension. So, it's a 2D array with shape (2, embedding_dim). Default is None, which means that the ranges will be calculated from the calibration embeddings. calibration_embeddings: Embeddings used for calibration during quantization. This is only used for int8 quantization, where the calibration embeddings can be used to compute ranges, i.e. the minimum and maximum values for each dimension. Default is None, which means that the ranges will be calculated from the query embeddings. This is not recommended. rescore: Whether to perform rescoring. Note that rescoring still will only be used if the query embeddings are not quantized and the corpus is quantized, i.e. the corpus precision is not "float32". Default is True. rescore_multiplier: Oversampling factor for rescoring. The code will now search `top_k * rescore_multiplier` samples and then rescore to only keep `top_k`. Default is 2. exact: Whether to use exact search or approximate search. Default is True. output_index: Whether to output the usearch index used for the search. Default is False. Returns: A tuple containing a list of search results and the time taken for the search. If `output_index` is True, the tuple will also contain the usearch index used for the search. Raises: ValueError: If both `corpus_embeddings` and `corpus_index` are provided or if neither is provided. The list of search results is in the format: [[{"corpus_id": int, "score": float}, ...], ...] The time taken for the search is a float value. r) ScalarKind)IndexNrr)rrSr#binaryzKcorpus_precision must be "float32", "int8", "ubinary", "binary" for usearchrr!cosf32)ndimmetricrQrSipi8rphammingr#r$b1zRescoring is enabled but the corpus is not quantized. Either pass `rescore=False` or quantize the corpus embeddings with `quantize_embeddings(embeddings, precision="...") `and pass `corpus_precision="..."` to `semantic_search_usearch`.r%r')countrr c:g|]}|Sr*)getr2s r/r0z+semantic_search_usearch..Rs'$b$b$b\%5%5m%D%D$b$b$br1)r#rpr4r5r7c\g|](}dt||D)S)cRg|]$\}}t|t|d%Sr:r=r?s r/r0z6semantic_search_usearch...frAr1rBrDs r/r0z+semantic_search_usearch..erHr1)usearch.compiledrn usearch.indexrorJrLrPrRr^r_rQr rSF32rTrUrVrWrX distanceskeysrs atleast_2drYrZr[rr\r]r`)r r rrrrrrrrrrnrorarbrcmatchesrdrerfrgrhrFrGs ` @@r/semantic_search_usearchrs7T,+++++######$)A[\\\ \%9STTTGGGfggg y ( ( 5&,Q/LL  ' ' 5&,Q/LL  ) ) 5&,Q/ LL  * * 5&,Q/!3 L 3'8#9#9::.s,NNNi)--////11NNNr1z?Embeddings to quantize must be float rather than int8 or uint8.rrSNr5dz Computing z quantization buckets based on z embeddingr!sz. z quantization is more stable with `ranges` calculated from more embeddings or a `calibration_embeddings` that can be used to calculate the buckets.r rpr4r#z Precision z is not supported) isinstancerrrlistrRrYrQr rS Exceptionr[rendswithvstackminmaxrLrTrUr_packbitsreshaperJ)rr&rrstartsstepss r/rVrVss/@*f%%*^^%%++-- J % %* jmV , , ONN:NNNJXj)) BHbg...YZZZI  ,,,&!!I >%1BF+A$J$J$JBFSipqLrLrLr#stt#A&,,NNcYccsS]ccqturArAEFrFrFjmjmLNcc%ccc BF:A$>$>$>zXY@Z@Z@Z#[\\111111q!!!t ,3   &(E199"(CC C & &(E1C7??HH HH JN++33J4DQ4GLLsRZZ[][bcccI{:>**22:3CA3FKKK >)>>> ? ??r1) NNrrNNTr TF)r r r r rrrrrrrr rr rrrrrrrrrr)r r r r rrjrrkrrrr rr rrrrrrrrrrl)NN) rrr&rrr rr rr ) __future__rloggingrWtypingrrrrRtorchr getLogger__name__rTrIusearchrirrVr*r1r/rsD"""""" ))))))))  8 $ $LLLNNN ,0'+?H $04dddddR,0/3=F $04wwwwwz!%04 G@G@G@G@G@G@G@r1