Results for “search-index”

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

Ingest, index, search, edit, and generate video and audio content from files, URLs, live streams, or desktop capture.

Index and search code repositories, documentation, research papers, HuggingFace datasets, local folders, and packages via the Nia API, with AI-powered research and code advisor capabilities.

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

Optimize PostgreSQL databases with advanced features, performance tuning, and best practices for JSONB, full-text search, window functions, and indexing.

Ingests video and audio from files, URLs, and live streams, builds searchable visual and spoken indexes, edits timelines with subtitles and overlays, and generates real-time alerts.

Diagnoses and resolves slow Qdrant search performance issues including high latency, low throughput, and slow filtered searches.

Guides database index selection, creation, and maintenance with SQL examples for PostgreSQL and MySQL.

Builds production RAG and semantic search systems with Qdrant, covering collection setup, vector indexing, filtered and hybrid search, and integration with LangChain and LlamaIndex.

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

Guides selection of Qdrant search strategies including hybrid search, reranking, relevance feedback, MMR, and discovery APIs to improve retrieval quality.

Search and retrieve past work from claude-mem's persistent cross-session memory database using a three-layer workflow: search, timeline, and fetch.

Enables fast similarity search and clustering of dense vectors using FAISS, supporting billions of vectors, GPU acceleration, and various index types.

Extracts structured data from Google Search results pages, including organic listings, paid ads, related searches, People Also Ask, and AI Overview.

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

Ingest, index, search, and edit video and audio content from files, URLs, live streams, or desktop sessions. Build visual and spoken indexes with timestamps, generate clips, subtitles, and overlays, and set up real-time monitoring alerts.

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.

Monitors and manages Coveo enterprise search platform: sources, indexes, query pipelines, ML models, usage analytics, and security providers, with health checks and license utilization.

Retrieves and answers questions from a local knowledge base directory by navigating hierarchical index files, then progressively searching Markdown, PDF, and Excel files using grep, Read, pdfplumber, and pandas.

Ingests video and audio from files, URLs, RTSP feeds, or desktop capture; indexes and searches moments with timestamps; transcodes, edits timelines, generates media assets, and creates real-time alerts for live streams.

Ingest, index, search, and edit video and audio from files, URLs, live streams, or desktop sessions with timestamps, subtitles, overlays, and real-time alerts.

Provides best practices for using MariaDB's built-in vector support for AI workloads, including SQL syntax for vector columns, indexes, distance functions, and RAG patterns.

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.

Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.