Results for vectorizer-ai

Python SDK for vectorizer.ai

Official Ruby SDK for the Vectorizer.AI image vectorization API.

Rails AI Kit provides AI building blocks for Rails apps: embeddings (OpenAI, Cohere), vector-based classification, similarity search, and generators. Start with the Classifier feature; more capabilities as the gem grows.

Manage AI vector embeddings in C with Ruby integration

Provides Rails-native integration with TimescaleDB's pgai extension, making AI-powered vectorization and semantic search simple and intuitive in Rails applications.

Add semantic search and AI-powered chat to any ActiveRecord model. Uses pgvector for vector storage, OpenAI for embeddings, and your existing PostgreSQL database.

A Rails Engine wrapping SmarterCSV for CSV importing with three-tier AI header matching (lookup + vector similarity + LLM fallback), batch processing, and import tracking. Only headers are sent to AI — row data never leaves your application.

The app provides a command-line interface (CLI) to an Ollama AI model, allowing users to engage in text-based conversations and generate human-like responses. Users can import data from local files or web pages, which are then processed through three different modes: fully importing the content into the conversation context, summarizing the information for concise reference, or storing it in an embedding vector database for later retrieval based on the conversation.

Seamlessly integrate Cloudflare AutoRag into your Ruby on Rails applications with this gem. It provides a simple and idiomatic Ruby interface for leveraging AutoRag’s automatic retrieval-augmented generation (RAG) pipeline—making it easy to connect your app’s data with powerful AI models using Cloudflare Workers and Vectorize.

llm.rb is Ruby's most capable AI runtime. It runs on Ruby's standard library by default. loads optional pieces only when needed, and offers a single runtime for providers, agents, tools, skills, MCP, A2A (Agent2Agent), RAG (vector stores & embeddings), streaming, files, and persisted state. As a bonus, llm.rb is also available for mruby. It supports OpenAI, OpenAI-compatible endpoints, Anthropic, Google Gemini, DeepSeek, xAI, Z.ai, AWS Bedrock, Ollama, and llama.cpp. It also includes built-in ActiveRecord and Sequel support, plus concurrent tool execution through threads, tasks (via async gem), fibers, ractors, and fork (via xchan.rb gem).

Bring the power of AI to your Rails app without the boilerplate. VectorRecord seamlessly synchronizes your Active Record models with vector databases (like pgvector), providing an idiomatic, convention-driven API for generating LLM embeddings and powering Retrieval-Augmented Generation (RAG).

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