hXSSKJr SSKrSSKrSSKJrJr SSKrSSK r SSK J r \R"\ 5r \(aSSKJr SSKJr SSKJr SSKJr S SS jjrS SS jjrSSS jjrS SS jjrg!\a Nf=f!\a Nf=f!\a Nf=f!\a Nf=f)) annotationsN) TYPE_CHECKINGAny)tqdm) Elasticsearch QdrantClient) SeismicIndex) OpenSearchc  SSKJn SSKJn UR (aUR [R:wa [S5eUGc=Uc [S5eUR (aUR [R:wa [S5eU"SSS 0UD6nS [[R"553n URU 0S URURS S 9S90S9 UR5n U R!5R#5R%5n U R'5R#5R%5n UR)S5n Sn/nSnU Sn[*R,"U[*R."U 5SS9n[*R,"U[*R."U 5SS9n[1[3U 5SS9HnUUnUUnU SUUR55nU UUR55nS UR7UUS90nUR9U5 [;U5U:d UU S- :XdMsUR=U U[3UU[;U5-5S9 U[;U5- n/nM X4nUup/n[R"5nR)S55GHnUR?5S:XaUR5R!5SR#5R%5R55nUR5R'5R#5R%5R55nOUR5R!5SR#5R%5U:HnUR5R!5SUR#5R%5R55nUR5R'5UR#5R%5R55nURAU UR7UUS9US S9RBn U V!s/sHn!U!RDU!RFS.PM n"n!UR9U"5 GM [R"5U- n#U(aUU#U4$UU#4$![a [ S5ef=fs sn!f)a Performs semantic search using sparse embeddings with Qdrant. Args: query_embeddings: PyTorch COO sparse tensor containing query embeddings corpus_embeddings: PyTorch COO sparse tensor containing corpus embeddings Only used if corpus_index is None corpus_index: Tuple of (QdrantClient, collection_name) If provided, uses this existing index for search top_k: Number of top results to retrieve output_index: Whether to return the Qdrant client and collection name Returns: A tuple containing: - List of search results in format [[{"corpus_id": int, "score": float}, ...], ...] - Time taken for search - (Optional) Tuple of (QdrantClient, collection_name) if output_index is True rr)modelszWPlease install the Qdrant client with `pip install qdrant-client` to use this function.z,Query embeddings must be a sparse COO tensorz9Either corpus_embeddings or corpus_index must be providedz-Corpus embeddings must be a sparse COO tensorurlzhttp://localhost:6333sparse_collection_textF)on_diskindex)collection_namevectors_configsparse_vectors_configi'left)siderightz#Processing and Upserting embeddingsdesc)indicesvalues)rvectorsids)rquerylimitusing corpus_idscore)$ qdrant_clientr qdrant_client.httpr ImportError is_sparselayouttorch sparse_coo ValueErrorinttimecreate_collectionSparseVectorParamsSparseIndexParamscoalescercpunumpyrsizenp searchsortedarangerrangetolist SparseVectorappendlenupload_collection sparse_dim query_pointspointsidr&)$query_embeddingscorpus_embeddings corpus_indextop_k output_indexkwargsr r clientrcorpus indices_arr values_arr num_vectors batch_size vectors_batch insert_idxrow_idsstartsendsistartend vec_indices vec_values vector_data all_resultssearch_start_timeq_idx q_indicesq_valuesmasksearch_resultshitformatted_results search_times$ m/home/james-whalen/.local/lib/python3.13/site-packages/sentence_transformers/sparse_encoder/search_engines.pysemantic_search_qdrantrh s:u.-  % %)9)@)@EDTDT)TGHH  $XY Y **.?.F.F%JZJZ.ZLM MD"9DVD.s499;/?.@A  ./#)6+D+D6KcKclqKcKr+D+s"t ! #++-nn&**,224 ]]_((*002 ',,Q/    a."))K*@vNw +(>WMeK(/TUA1IEq'C%a.s3:::semantic_search_elasticsearch...*&ZUYPQz!U';'KA! 'KUY24Nrplistallrsitems rgru0semantic_search_elasticsearch..3A,D 4Z3&ZUY&Z#ZZ,68UQuery embeddings must be a list of lists in the format [[('token', value), ...], ...]AEither corpus_embeddings_decoded or corpus_index must be providedc3t# UH.n[U[5=(a [SU55v M0 g7f)c3l# UH*n[U[5=(a [U5S:Hv M, g7frmrorrs rgrurv**^Y]TU:a+?+OCFaK+OY]rxNryr|s rgrur~3F 1 tT " ^s*^Y]*^'^ ^1rVCorpus embeddings must be a list of lists in the format [[('token', value), ...], ...] sparse_index_rmappings propertiestype rank_featureskeywordtokensrErbodyUpserting embeddingsr._rEr_index_id_source rank_featureztokens.)field saturationboostboolr)shouldminimum_should_matchr8r!hitsr_scorer$zhttp://localhost:9200) elasticsearchrrjr*rprzr{r/r0r1rexistsdeletecreater@rr<mindictitemsstrreplacer?bulkrefreshsearch)query_embeddings_decodedcorpus_embeddings_decodedrHrIrJrKrrjes index_namenum_docsrQ start_idxend_idxactionsrWrkvr]r^r_ query_tokensshould_clausestokenweightr!resultrd formattedrfs rgsemantic_search_elasticsearchrsH 8 . 5 5SA,A>>pqq $ ,`a a3T::#F 1F C C uv v  =f =$S%5$67 ::  :  . JJ  J  /  "O# &y1 #   01 eAxwug=N^`kq3r"s t2&^ef2g)hi%8X^^dWeflWmnWmPS3s5z?S]KWm n9%6"))+ 11KK55K''E   l   f6 osL21)M )MM2Mc SSKJnJnJn Uc0nUc0nU "5n [ U[ 5(a[SU55(d [S5eUGcUc [S5e[ U[ 5(a[SU55(d [S5eU"5n [U5n [[U 5S S 9Hn [X5nU R[U 5[R "[ UR#55U S 9[R "[ UR%55[R&S 95 M UR("U 40UD6n[*R*"5n[U5n/n/n[U5Hn[UU5nUR-[R "[ UR#55U S 95 UR-[R "[ UR%55[R&S 95 M S U;aS US 'SU;aSUS'UR."S[R "[U5U S 9UUUS.UD6n[1USS9nUVVVVs/sH*nUVVVs/sHunnn[3U5US.PM snnnPM, nnnnn[*R*"5U- nU(aUUU4$UU4$![a [ S5ef=fs snnnfs snnnnf)a8 Performs semantic search using sparse embeddings with Seismic. Args: query_embeddings_decoded: List of query embeddings in format [[("token": value), ...], ...] Example: To get this format from a SparseEncoder model:: model = SparseEncoder('my-sparse-model') query_texts = ["your query text"] query_embeddings = model.encode(query_texts) query_embeddings_decoded = model.decode(query_embeddings) corpus_embeddings_decoded: List of corpus embeddings in format [[("token": value), ...], ...] Only used if corpus_index is None Can be obtained using the same decode method as query embeddings corpus_index: Tuple of (SeismicIndex, collection_name) If provided, uses this existing index for search top_k: Number of top results to retrieve output_index: Whether to return the SeismicIndex client and collection name index_kwargs: Additional arguments for SeismicIndex passed to build_from_dataset, such as centroid_fraction, min_cluster_size, summary_energy, nknn, knn_path, batched_indexing, or num_threads. search_kwargs: Additional arguments for SeismicIndex passed to batch_search, such as query_cut, heap_factor, n_knn, sorted, or num_threads. Note: query_cut and heap_factor are set to default values if not provided. Returns: A tuple containing: - List of search results in format [[{"corpus_id": int, "score": float}, ...], ...] - Time taken for search - (Optional) Tuple of (SeismicIndex, collection_name) if output_index is True r)SeismicDatasetr get_seismic_stringzMPlease install Seismic with `pip install pyseismic-lsr` to use this function.c3t# UH.n[U[5=(a [SU55v M0 g7f)c3l# UH*n[U[5=(a [U5S:Hv M, g7frmrorrs rgru4semantic_search_seismic...crwrxNryr|s rgru*semantic_search_seismic..brrrrc3t# UH.n[U[5=(a [SU55v M0 g7f)c3l# UH*n[U[5=(a [U5S:Hv M, g7frmrorrs rgrurmrrxNryr|s rgrurlrrrzAdding documents to Seismicr)dtype query_cut heap_factorgffffff?) queries_idsquery_components query_valuesrc$[USS5$)Nr)r0)xs rg)semantic_search_seismic..sC!QL)keyr$r')seismicrr rr*rprzr{r/r@rr<r add_documentrr9arraykeysrfloat32build_from_datasetr1r? batch_searchsortedr0)rrrHrIrJ index_kwargs search_kwargsrr r string_typedatasetrPidxrr^ num_queriesrrr_rresults query_result query_idxr&r%r]rfs rgsemantic_search_seismicr+sTkLL  $&K . 5 5SA,A>>pqq $ ,`a a3T::#F 1F C C uv v!"34 k*1NOC389F  Cfkkm,K@fmmo.bjjA P$66wO,O  ./KL{#4U;< l.?.?.A)B+ VWBHHT,*=*=*?%@ ST$ -'%' k"M)'* m$''HHU;/{C)!    GW"89G $#L[ggZf;V9eYs9~ 6Zfg# ))+ 11KK55K''e kijjkR hs# K( L L2 L (K>L c :SSKJnJn [ U[ 5(a[ SU55(d [S5eUGcxUc [S5e[ U[ 5(a[ SU55(d [S5eU"S0UD6nS [[R"553n URRU S 9(aURRU S 9 URRU S S S S0S S0S.00S9 [U5n Sn [[!SX5SS9Hmn [#X-U 5n /n[!X5H:n[%X5nUR'U [)U5[)U5US.S.5 M< UR+X5 Mo URR-U S 9 X4nUup/n[R"5n[![U55Hgn[%UU5nUSSSU000S.nUR/U US9nUSSVs/sHn[US5USS.PM nnUR'U5 Mi [R"5U- nU(aUUU4$UU4$![a [S5ef=fs snf) a Performs semantic search using sparse embeddings with OpenSearch. Args: query_embeddings_decoded: List of query embeddings in format [[("token": value), ...], ...] Example: To get this format from a SparseEncoder model:: model = SparseEncoder('my-sparse-model') query_texts = ["your query text"] query_embeddings = model.encode(query_texts) query_embeddings_decoded = model.decode(query_embeddings) corpus_embeddings_decoded: List of corpus embeddings in format [[("token": value), ...], ...] Only used if corpus_index is None Can be obtained using the same decode method as query embeddings corpus_index: Tuple of (OpenSearch, collection_name) If provided, uses this existing index for search top_k: Number of top results to retrieve output_index: Whether to return the OpenSearch client and collection name vocab: The dict to transform tokens into token ids Returns: A tuple containing: - List of search results in format [[{"corpus_id": int, "score": float}, ...], ...] - Time taken for search - (Optional) Tuple of (OpenSearch, collection_name) if output_index is True r)r rjz[Please install the OpenSearch client with `pip install opensearch-py` to use this function.c3t# UH.n[U[5=(a [SU55v M0 g7f)c3l# UH*n[U[5=(a [U5S:Hv M, g7frmrorrs rgru7semantic_search_opensearch...rwrxNryr|s rgru-semantic_search_opensearch..rrrrc3t# UH.n[U[5=(a [SU55v M0 g7f)c3l# UH*n[U[5=(a [U5S:Hv M, g7frmrorrs rgrurrrxNryr|s rgrurrrrrrrrrrrrrrrrrr neural_sparserrrrrrr$r) opensearchpyr rjr*rprzr{r/r0r1rrrrr@rr<rrr?rrrr)rrrHrIrJrKr rj os_clientrrrQrrrrWrr]r^r_rr!rrdrrfs rgsemantic_search_opensearchrsJ 4 . 5 5SA,A>>pqq $ ,`a a3T::#F 1F C C uv vA&A $S%5$67    # #* # 5    $ $: $ 6   "O# &y1 #  ! 01 eAxrs-" %    8 $ / . ( +.248 }("}(*}(2}(  }(  }(  }(Q}(DGK59 H(;H(CH(3H(  H(  H(  H(RH(ZGK48*.+/~(;~(C~(2~(  ~(  ~( ( ~()~(Q~(FGK26 (;(C(0(  (  (  (O(y                sGB?C CC#?CC CCC C #C,+C,