Official code companion to the O'Reilly book by Jay Alammar and Maarten Grootendorst: 12 chapters of runnable notebooks on tokens, embeddings, Transformers, text classification, clustering, prompt engineering, semantic search, RAG, and fine-tuning.
Multi-tenant agent harness that makes enterprise knowledge retrievable, graph-reasonable, and deliverable by LLM-powered agents. Integrates RAG + a Milvus-based knowledge graph, LangGraph orchestration, and document parsing for citation-backed answers and graph reasoning; deployable via Docker (requires a compatible LLM API).
Connects any LLM to internal knowledge sources and lets teams chat with cited, RAG-style answers. Notable for broad connectors (Drive, Notion, GitHub, YouTube), universal LLM/embedding support, and self-hostable Docker deployment — aimed at teams that need private, searchable LLM-backed knowledge.
Stores agent memory as human- and agent-readable Markdown files with wikilinks instead of an opaque vector DB. Auto Memory/Resource/Dream jobs distill conversations into long-term notes, and hybrid wikilink + BM25 + embedding search retrieves them.
Provides pre-parsed Parquet snapshots of English and French Wikipedia articles with structured fields (sections, infoboxes, tables, references, images) and credibility signals — optimized for large-scale analysis, retrieval-augmented generation, and model development.
Gives LLM agents self-editing memory that persists across sessions, so they keep learning about a user instead of resetting each chat. Model-agnostic: bring your own LLM while it handles the memory and agent state, run via API or open source.
Converts PDFs, Office files, HTML, images and audio into one structured DoclingDocument, with deep PDF layout, reading order, table-structure and formula recognition, OCR, and native LangChain/LlamaIndex/Haystack integrations for RAG pipelines.
Self-hostable alternative to Google NotebookLM: organize PDFs, videos, audio, web pages, and Office docs, then chat over them, take AI-assisted notes, and search via full-text and vector. Routes to 18+ model providers and generates 1-4 speaker podcasts.
Reference architectures and microservices for building GPU-accelerated vision agents that enable natural-language video search, long-video summarization, visual Q&A, and alert verification. Integrates NVIDIA NIM models, embeddings, VLMs/LLMs, and agent workflows for deployable video-analytics stacks.
Provides a local-first Markdown knowledge graph that LLMs and humans can both read and write via the Model Context Protocol (MCP). Features two-way, editable notes, semantic search (embeddings + hybrid ranking), and optional cloud sync and team workspaces.
Connects AI agents to 50+ apps and databases — Notion, Slack, Salesforce, GitHub, Jira — then continuously syncs and indexes their data behind one search API, with auth, ingestion, and retrieval exposed via MCP, REST, and SDKs.
Runs penetration tests autonomously: a multi-agent system (researcher, developer, executor) plans attacks, writes and runs exploit code, and chains 20+ tools like nmap, metasploit and sqlmap in isolated Docker containers — for authorized testing only.