150 lines
4.4 KiB
SQL
150 lines
4.4 KiB
SQL
set long 999999
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set pagesize 1000
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set linesize 200
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/* --------------------------------
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HNSW INDEX
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-------------------------------- */
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-- crear indice HNSW para busquedas vectoriales aproximadas
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CREATE VECTOR INDEX markdown_chunks_hnsw_idx
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ON markdown_chunks (embedding)
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ORGANIZATION INMEMORY NEIGHBOR GRAPH
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DISTANCE COSINE
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WITH TARGET ACCURACY 90
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PARAMETERS (
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TYPE HNSW,
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NEIGHBORS 32,
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EFCONSTRUCTION 200
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);
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DROP INDEX IF EXISTS markdown_chunks_hnsw_idx;
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/* --------------------------------
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IVF INDEX
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-------------------------------- */
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-- crear indice IVF con DOC_ID incluido para filtros por documento
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CREATE VECTOR INDEX markdown_chunks_ivf_idx
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ON markdown_chunks (embedding)
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INCLUDE (doc_id)
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ORGANIZATION NEIGHBOR PARTITIONS
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DISTANCE COSINE
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WITH TARGET ACCURACY 90
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PARAMETERS (
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TYPE IVF,
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NEIGHBOR PARTITIONS 4,
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MIN_VECTORS_PER_PARTITION 1
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);
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DROP INDEX IF EXISTS markdown_chunks_ivf_idx;
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/* --------------------------------
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TEXT INDEX + VECTOR SEARCH
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-------------------------------- */
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-- hybrid indexes / text indexes para busquedas textuales y combinadas
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DROP INDEX IF EXISTS markdown_chunks_text_idx;
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CREATE SEARCH INDEX markdown_chunks_text_idx
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ON markdown_chunks (chunk_text)
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FOR TEXT;
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select
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doc_id,
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chunk_id,
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SCORE(1) as text_score,
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VECTOR_DISTANCE(embedding,
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VECTOR_EMBEDDING(MULTILINGUAL_E5_BASE
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USING 'Sucursales adheridas' as DATA
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)
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) as vector_distance,
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chunk_text
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from markdown_chunks
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where contains(chunk_text, '(Mastercard ACCUM NEAR((Dúo,Clásica),1))', 1) > 0
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order by vector_distance
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fetch first 3 rows only;
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-- Promoción
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-- FUZZY(Promción)
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-- ABOUT(Promoción)
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-- NEAR((tarjetas, físicas, billeteras, electrónicas), 5)
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-- Black OR Albirroja
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-- BENDITA ACCUM STYLE
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-- (Mastercard ACCUM NEAR((Dúo,Clásica),1))
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/* --------------------------------
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HYBRID VECTOR INDEX
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-------------------------------- */
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-- crear indice hibrido para combinar busqueda textual y semantica
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DROP INDEX IF EXISTS markdown_chunks_hybrid_idx;
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CREATE HYBRID VECTOR INDEX markdown_chunks_hybrid_idx
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ON markdown_chunks (chunk_text)
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PARAMETERS ('MODEL MULTILINGUAL_E5_BASE
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VECTOR_IDXTYPE HNSW
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MEMORY 128M')
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PARALLEL 2;
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-- ejemplo de consulta usando el indice hibrido
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SELECT JSON_SERIALIZE(
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DBMS_HYBRID_VECTOR.SEARCH(
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JSON('{
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"hybrid_index_name" : "markdown_chunks_hybrid_idx",
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"search_fusion" : "UNION",
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"search_scorer" : "rsf",
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"vector" : {
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"search_text" : "Vigencia",
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"search_mode" : "CHUNK"
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},
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"text" : {
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"contains" : "Shopping NEAR San NEAR Lorenzo"
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},
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"return" : {
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"values" : [
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"chunk_id",
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"chunk_text",
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"score",
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"vector_score",
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"text_score"
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],
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"topN" : 5
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}
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}')
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) RETURNING CLOB PRETTY
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) AS hybrid_results;
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SELECT jt.*
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FROM
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JSON_TABLE(
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dbms_hybrid_vector.search(
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json_object(
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'hybrid_index_name' VALUE 'markdown_chunks_hybrid_idx',
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'search_fusion' VALUE 'INTERSECT',
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'search_scorer' VALUE 'rsf',
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'vector' VALUE json_object('search_text' VALUE 'COMERCIOS ADHERIDOS'),
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'text' VALUE json_object('contains' VALUE 'BENDITA ACCUM STYLE'),
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'return' VALUE json_object(
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'values' VALUE json_array('rowid', 'score', 'vector_score', 'vector_rank', 'text_score', 'text_rank', 'chunk_text'),
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'topN' VALUE 3
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)
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RETURNING JSON
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)
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),
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'$[*]' COLUMNS idx for ORDINALITY,
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score NUMBER PATH '$.score',
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vector_score NUMBER PATH '$.vector_score',
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vector_rank NUMBER PATH '$.vector_rank',
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text_score NUMBER PATH '$.text_score',
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text_rank NUMBER PATH '$.text_rank',
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chunk_text VARCHAR2(4000) PATH '$.chunk_text'
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) jt; |