home.social

#semanticsearch — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #semanticsearch, aggregated by home.social.

  1. Exciting to see LA Referencia do semantic search:

    "Search in your language. Discover research across languages."

    (with a friendly reminder that semantic search leverages AI techniques including NLP, ML, and embeddings...)

    la-referencia-ioi.github.io/la

    #OpenAccess #SemanticSearch

  2. Exciting to see LA Referencia do semantic search:

    "Search in your language. Discover research across languages."

    (with a friendly reminder that semantic search leverages AI techniques including NLP, ML, and embeddings...)

    la-referencia-ioi.github.io/la

    #OpenAccess #SemanticSearch

  3. Exciting to see LA Referencia do semantic search:

    "Search in your language. Discover research across languages."

    (with a friendly reminder that semantic search leverages AI techniques including NLP, ML, and embeddings...)

    la-referencia-ioi.github.io/la

    #OpenAccess #SemanticSearch

  4. Exciting to see LA Referencia do semantic search:

    "Search in your language. Discover research across languages."

    (with a friendly reminder that semantic search leverages AI techniques including NLP, ML, and embeddings...)

    la-referencia-ioi.github.io/la

    #OpenAccess #SemanticSearch

  5. Exciting to see LA Referencia do semantic search:

    "Search in your language. Discover research across languages."

    (with a friendly reminder that semantic search leverages AI techniques including NLP, ML, and embeddings...)

    la-referencia-ioi.github.io/la

    #OpenAccess #SemanticSearch

  6. I spent some time trying to make search behavior visible in one small Quarkus app.

    Full-text is good at exact terms. Vector search helps when user language and catalog language drift apart. Hybrid is usually the one I’d trust first in a real product search.

    This article walks through all three with Quarkus, PostgreSQL, Elasticsearch, Hibernate Search, and local embeddings.

    the-main-thread.com/p/full-tex

    #Java #Quarkus #PostgreSQL #Elasticsearch #SemanticSearch #HibernateSearch #VectorSearch

  7. I spent some time trying to make search behavior visible in one small Quarkus app.

    Full-text is good at exact terms. Vector search helps when user language and catalog language drift apart. Hybrid is usually the one I’d trust first in a real product search.

    This article walks through all three with Quarkus, PostgreSQL, Elasticsearch, Hibernate Search, and local embeddings.

    the-main-thread.com/p/full-tex

    #Java #Quarkus #PostgreSQL #Elasticsearch #SemanticSearch #HibernateSearch #VectorSearch

  8. I spent some time trying to make search behavior visible in one small Quarkus app.

    Full-text is good at exact terms. Vector search helps when user language and catalog language drift apart. Hybrid is usually the one I’d trust first in a real product search.

    This article walks through all three with Quarkus, PostgreSQL, Elasticsearch, Hibernate Search, and local embeddings.

    the-main-thread.com/p/full-tex

    #Java #Quarkus #PostgreSQL #Elasticsearch #SemanticSearch #HibernateSearch #VectorSearch

  9. I spent some time trying to make search behavior visible in one small Quarkus app.

    Full-text is good at exact terms. Vector search helps when user language and catalog language drift apart. Hybrid is usually the one I’d trust first in a real product search.

    This article walks through all three with Quarkus, PostgreSQL, Elasticsearch, Hibernate Search, and local embeddings.

    the-main-thread.com/p/full-tex

    #Java #Quarkus #PostgreSQL #Elasticsearch #SemanticSearch #HibernateSearch #VectorSearch

  10. I spent some time trying to make search behavior visible in one small Quarkus app.

    Full-text is good at exact terms. Vector search helps when user language and catalog language drift apart. Hybrid is usually the one I’d trust first in a real product search.

    This article walks through all three with Quarkus, PostgreSQL, Elasticsearch, Hibernate Search, and local embeddings.

    the-main-thread.com/p/full-tex

    #Java #Quarkus #PostgreSQL #Elasticsearch #SemanticSearch #HibernateSearch #VectorSearch

  11. pg_semantic_cache: an open-source extension that enables semantic query result caching in #PostgreSQL. Traditional caching requires exact query matches; this extension uses vector embeddings to find and retrieve cached results for semantically similar queries.

    ✨ Give the project a try on GitHub (and don't forget to star the project while you're there): github.com/pgEdge/pg_semantic_

    ➡️ Read more: pgedge.com/blog/pg_semantic_ca

    #postgres #data #llm #semanticsearch #ai #aiengineering #opensourceai #opensource

  12. pg_semantic_cache: an open-source extension that enables semantic query result caching in #PostgreSQL. Traditional caching requires exact query matches; this extension uses vector embeddings to find and retrieve cached results for semantically similar queries.

    ✨ Give the project a try on GitHub (and don't forget to star the project while you're there): github.com/pgEdge/pg_semantic_

    ➡️ Read more: pgedge.com/blog/pg_semantic_ca

    #postgres #data #llm #semanticsearch #ai #aiengineering #opensourceai #opensource

  13. pg_semantic_cache: an open-source extension that enables semantic query result caching in #PostgreSQL. Traditional caching requires exact query matches; this extension uses vector embeddings to find and retrieve cached results for semantically similar queries.

    ✨ Give the project a try on GitHub (and don't forget to star the project while you're there): github.com/pgEdge/pg_semantic_

    ➡️ Read more: pgedge.com/blog/pg_semantic_ca

    #postgres #data #llm #semanticsearch #ai #aiengineering #opensourceai #opensource

  14. pg_semantic_cache: an open-source extension that enables semantic query result caching in #PostgreSQL. Traditional caching requires exact query matches; this extension uses vector embeddings to find and retrieve cached results for semantically similar queries.

    ✨ Give the project a try on GitHub (and don't forget to star the project while you're there): github.com/pgEdge/pg_semantic_

    ➡️ Read more: pgedge.com/blog/pg_semantic_ca

    #postgres #data #llm #semanticsearch #ai #aiengineering #opensourceai #opensource

  15. pg_semantic_cache: an open-source extension that enables semantic query result caching in #PostgreSQL. Traditional caching requires exact query matches; this extension uses vector embeddings to find and retrieve cached results for semantically similar queries.

    ✨ Give the project a try on GitHub (and don't forget to star the project while you're there): github.com/pgEdge/pg_semantic_

    ➡️ Read more: pgedge.com/blog/pg_semantic_ca

    #postgres #data #llm #semanticsearch #ai #aiengineering #opensourceai #opensource