Results for “vector”

Local search/indexing CLI (BM25 + vectors + rerank) with MCP mode.

Guides embedding model migration in Qdrant without downtime, covering alias swap, side-by-side, and hybrid search strategies.

Designs persistent memory architectures for AI agents, covering cross-session knowledge retention, entity tracking, temporal validity, graph/vector retrieval, and memory consolidation.

Store and query embeddings with metadata filtering, vector search, and full-text search using an open-source database that scales from notebooks to production.

Search local knowledge bases, notes, docs, and meeting transcripts with hybrid retrieval combining BM25, vector search, and LLM reranking, all running on-device.

Index local folders and search documents with BM25, vector, or hybrid queries, plus AI answers with citations and a web UI.

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.

Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.

Build search applications with full-text, vector, semantic, and hybrid search using the Azure AI Search SDK for .NET.

Guides scaling Qdrant vector search with time-based data rotation using shard rotation, collection rotation, or filter-and-delete strategies.

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.

Index folders of PDFs and other documents into LanceDB for vector search, then query them with semantic search, page filters, verbatim quotes, and cross-document aggregation.

Expert guidance for architecting, developing, and operating SurrealDB 3, covering SurrealQL, multi-model data modeling, vector search, security, deployment, performance tuning, SDK integration, and ecosystem tools.

Provides a standardized interface for ingesting raw data across domains such as genomics, network analysis, document review, and spatial mapping, converting it into semantic vectors for agent use.

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Expert en illustration (Procreate, vector art, character design, flat design, infographies)

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Manages vector embeddings with Pinecone for semantic search, recommendation, and RAG pipelines.

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Vector-powered CLI for semantic file search with a Claude/Codex skill

Search Azure AI Search indexes using the Python SDK for full-text, vector, hybrid, and semantic search with AI enrichment.

Guides scaling decisions for Qdrant vector databases when data volume exceeds single-node capacity, covering tenant scaling, time window rotation, vertical scaling, and horizontal sharding.

Retrieval-Augmented Generation pipelines — ingestion, chunking, embedding, vector stores, retrieval, evaluation. Use when building a RAG pipeline, choosing chunking strategies or embedding models, debugging retrieval quality or hallucinations, evaluating an existing RAG system, or scaling/migrating vector stores.

Vector graphics and diagram creation, format conversion (ODG/SVG/PDF) with LibreOffice Draw.