Results for “vector”

Manage vector database operations across Pinecone, Weaviate, Qdrant, and ChromaDB, including embedding generation, index creation, metadata filtering, hybrid search, and production deployment for RAG and similarity search.

Enables semantic file search using vector embeddings from the command line, integrated with Claude/Codex.

Indexes local files and searches them with BM25, vector, and hybrid queries, plus MCP mode.

Optimize Qdrant vector search performance through indexing strategies, query tuning, memory management, and hardware considerations.

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.

Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.

Guides scaling decisions for Qdrant vector databases based on data volume, query throughput, latency, or query volume.

Integrate Qdrant vector search into applications using officially supported client SDKs for Python, JavaScript, Rust, Go, .NET, and Java.

Provides guidance on using the ZVec in-process vector database for efficient similarity search and embedding storage in agent memory systems.

Ingests EPUBs and ebooks into a local vector index, then answers questions and searches passages via a command-line interface.

Search, query, inspect, create, and import data into Weaviate vector database collections using official scripts and references.

Guides monitoring and observability setup for Qdrant vector search deployments, including Prometheus scraping, health checks, and metric-based debugging of production issues.

Ingests EPUB and ebook files into a local vector index and provides a command-line interface for asking questions about the books.

Connects LLMs with user data for RAG applications, document Q&A, and knowledge retrieval using 300+ data connectors and vector indices.

Provides code examples and best practices for using Pinecone, a managed vector database for production RAG, recommendation, and semantic search applications.

Enables fast similarity search and clustering of dense vectors using FAISS, covering index types, GPU acceleration, and integrations with LangChain and LlamaIndex.

Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.

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

Build search applications with vector, hybrid, and semantic search using the Azure AI Search SDK for TypeScript.

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

Store and query embeddings with metadata, vector and full-text search, and filtering. Integrates with LangChain and LlamaIndex for RAG and semantic search applications.

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

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.

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

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

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

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.