Results for “search”

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

Enables semantic file search across a codebase using a vector-powered CLI, with integration for Claude and Codex agents.

Build production RAG and semantic search systems with a high-performance vector database written in Rust, supporting hybrid search, filtering, and horizontal scaling.

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

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.

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.

Search archived video using natural language, ingest video files or RTSP streams, and manage ingested sources.

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

Automate Semantic Scholar operations such as searching papers, fetching citations, and managing references through the Rube MCP interface.

Builds or modifies a RAG pipeline with intent-based routing, vector store search, citation rendering, and book fidelity enforcement.

Search the web and extract content from URLs using Tavily and Exa APIs via the inference.sh CLI.

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

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

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

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

Designs and implements RAG pipelines, covering document chunking, embedding strategies, hybrid search, answer synthesis with source attribution, and evaluation using RAGAS metrics.

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.

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

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.

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.

Adds a persistent memory layer that stores and retrieves user preferences and context across conversations using semantic search.

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

Reference for building Tavily-powered search, extraction, crawling, and research into agentic workflows and RAG systems.

Augments nearest-neighbour search with graph Laplacian features to retrieve items based on both semantic similarity and structural role.

Build or modify a RAG retrieval pipeline with vector store search, corpus routing, citation rendering, and book fidelity enforcement.

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

Build production-ready LLM applications, advanced RAG systems, and intelligent agents with vector search, multimodal AI, and enterprise integrations.

Explains dense vector embeddings, their key concepts, common use cases, and best practices for semantic search and RAG applications.