A self-learning vector database integrating GNN-driven search optimization, local LLM inference, Cypher graph queries, and a PostgreSQL vector extension, deployable from WASM embeddings to Raft-distributed clusters.
Official code repository for the O'Reilly book "Hands-On Large Language Models". Features 12 core chapters and bonus content covering Tokens, Transformers, RAG, and Fine-tuning. Includes 300+ illustrations and runnable Jupyter Notebooks optimized for Colab and local environments.
A comprehensive AI engineering hub featuring 93+ production-ready projects with in-depth tutorials and implementations for LLMs, RAGs, AI Agents, and MCP, covering beginner to advanced skill levels.
An AI agent framework built with Rust, powered by ICP blockchain and TEEs, designed to create a highly composable, autonomous, and perpetually memorizing network of AI agents.
PageIndex is a vectorless, reasoning-based RAG system that builds hierarchical tree indexes from long documents and uses LLM reasoning for human-like retrieval, delivering superior performance in professional document analysis.
An LLM-powered retrieval engine designed to process extensive sources to collect comprehensive entity information, generating enriched tabular results rather than traditional research reports or answers.
A curated collection of resources that bridge the gap between Retrieval-Augmented Generation (RAG) and Reasoning in Large Language Models and Agents, featuring papers, tools, and implementations.
A collection of Jupyter notebooks accompanying the "Building LLMs for Production" book by Towards AI, providing practical tutorials on RAG, LLMs, prompt engineering, fine-tuning, and more.
A knowledge base question-answering system built on large language models that supports document import and private model integration to quickly build knowledge base applications.
An NLP framework that enables building applications with LLMs and transformer models for semantic search, question answering, and document retrieval.
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