VATES

txtai is an all-in-one open source embeddings database designed for semantic search, large language model orchestration, and language model workflows. It unifies vector indexes (both sparse and dense), graph networks, and relational databases, providing a robust foundation for vector search and serving as a powerful knowledge source for LLM applications. With txtai, users can build autonomous agents, implement retrieval augmented generation processes, and develop multi-modal workflows. Key features include vector search with SQL support, object storage integration, topic modeling, graph analysis, and multimodal indexing capabilities. It supports the creation of embeddings for various data types, including text, documents, audio, images, and video. Additionally, txtai offers pipelines powered by language models that handle tasks such as LLM prompting, question-answering, labeling, transcription, translation, and summarization.

FAQ Ally is a trust-first knowledge platform that transforms your business documents, policies, and data into intelligent AI assistants capable of delivering accurate, evidence-backed answers. Rather than relying solely on AI generation, FAQ Ally prepares and verifies information before the language model responds, helping reduce unsupported answers and increasing consistency across your organization. Upload documents in formats such as PDF, Word, text, CSV, JSON, XML, and HTML to create a secure, searchable knowledge base that employees, customers, and teams can access through natural conversation. AI assistants are available through an embeddable chat widget or a RESTful Chat API, making it easy to integrate trusted knowledge into websites, portals, and custom applications. Built with advanced retrieval, vector search, deterministic reasoning, role-based access control, and encrypted data handling, FAQ Ally helps organizations deliver answers they can understand, verify, and trust.