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Conector Git
extractors = text / markdown : textExtractor , text / yaml : textExtractor...extractors = text / markdown : textExtractor , text / plain : textExtractor...fess.codelibs.org/es/15.5/config/datastore/ds-git.html -
Slack连接器
text digest=message.text.length() > 100 ? message.text.substring(0,...digest = message . text content = message . text + " \n 附件: " + message...fess.codelibs.org/zh-cn/15.5/config/datastore/ds-slack.html -
API de Suggest
Campo Requerido Descripcion text Si Texto de sugerencia reading No...[ { "id" : "suggest_id_1" , "text" : "fess" , "reading" : "fess"...fess.codelibs.org/es/15.5/api/admin/api-admin-suggest.html -
What is vector search? Better search with ML | ...
including text and images, transforming it...the unstructured data beyond text, such as videos, images, and...www.elastic.co/what-is/vector-search -
Exploring vision language models (VLMs) and how...
that take texts and images as input and generate new text as outputs....to provide alternative text (alt-text) for digitized images on...www.elastic.co/search-labs/blog/visual-language-model-jina-vlm -
kNN search in Elasticsearch | Elastic Docs
"text": { "type": "text", "index": false },..."nested", "properties": { "text": { "type": "text" }, "vector": { "type":...www.elastic.co/docs/solutions/search/vector/knn -
Transform and enrich data | Elastic Docs
to customize how text is analyzed and how text fields are defined...field mappings. Text analysis Like index mapping, text analysis is...www.elastic.co/docs/manage-data/ingest/transform-enrich -
Store Embeddings in Elasticsearch - Elasticsear...
Field Types In the Full-Text Search chapter of this tutorial...that were created in the Full-Text Search portion of this tutorial...www.elastic.co/search-labs/tutorials/search-tutorial/vector-search/store-embeddings -
Vector Database | #1 Most Downloaded | Elastics...
{ "type" : "text" }, "category": { "type": "text" } } } } # Index...vectors: Fast and efficient Sparse text expansion and dense meaning...www.elastic.co/elasticsearch/vector-database -
f1_score — scikit-learn 1.8.0 documentation
\[\text{F1} = \frac{2 * \text{TP}}{2 * \text{TP} + \text{FP}...\text{FP} + \text{FN}}\] Where \(\text{TP}\) is the number of true positives,...scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html