“vector search and databases”

Elasticsearch as a vector database, combining vector search with hybrid lexical search, filters and reranking.

Vector and hybrid search built into MongoDB, running on the same operational data as the database.

Implement efficient similarity search with vector databases.

Build vector databases on Redis, with vector search combined with exact-match and full-text search.

Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors).

Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns

Open-source vector similarity search engine and database written in Rust, with a managed cloud.

Expert in vector databases, embedding strategies, and semantic search implementation.

Connect to Supabase for database operations, vector search, and storage.

Patterns for Databricks Vector Search: create endpoints and indexes, query with filters, manage embeddings.

Apache Cassandra-based NoSQL database with vector search for generative AI workloads.

Open-source, cloud-native vector database for semantic search, RAG and hybrid queries.

Encrypted-first embedded database with vector search and agent memory, exposed as MCP tools

High-performance vector similarity search engine for RAG and semantic search.

Find the questions buyers ask right before they buy — from Search Console, Bing, sales calls, and what ChatGPT, Claude, Gemini and Perplexity answer about…

Triggers on keywords RAG, GraphRAG, vector database, agentic RAG, semantic search

Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin).

Production vector database design, HNSW index optimization, similarity metrics, metadata filtering, and high-performance querying (pgvector, Qdrant, Pinecone).

Vector search best practices for Azure DocumentDB using `cosmosSearch` — choosing between DiskANN / HNSW / IVF, creating indexes, tuning `lBuild` / `lSearch`…

Search database for vector and full-text queries, built on object storage and available as a Python client.

Open-source vector database inside the OpenSearch search suite, for semantic search and RAG.

Add semantic search with native vector queries (Laravel 13+); pgvector similarity clauses, embedding workflows, and hybrid search patterns

Serverless vector database with dense, sparse and hybrid indexes for semantic and keyword search.

Fuzzy or regex search databases in Archery cached catalog with instance info.