microsoft/typeagent-py
Structured RAG: ingest, index, query
What it solves
TypeAgent is designed to implement structured RAG (Retrieval-Augmented Generation), providing a Pythonic translation of the TypeAgent KnowPro system to help users build more organized and structured retrieval systems.
How it works
It acts as a Python package that interfaces with third-party LLMs to process and index information for structured retrieval, translating the logic and functionality of the original TypeScript-based TypeAgent KnowPro project into Python.
Who it’s for
Developers who want to implement structured RAG patterns in Python and those interested in the experimental prototype of the TypeAgent ecosystem.
Highlights
- Experimental prototype for structured RAG MVP.
- Python translation of the TypeScript TypeAgent KnowPro project.
- Easy installation via pip.
Related
- Project
microsoft/TypeAgentTypeAgent is a Microsoft open‑source sample that shows how to build a single personal AI assistant using large language models, structured prompting, and a novel “Structured RAG” memory system. It includes an Electron shell, a dispatcher that routes natural‑language requests to typed agents, a memory layer that stores conversation facts as logical entities, and a cache to reduce LLM calls. The repo provides many example agents (calendar, email, browser, etc.) and an SDK for adding custom agents. It is early‑stage sample code, tested with Azure OpenAI in English, and not intended for production without further validation.
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weiwill88/Local_Pdf_Chat_RAGA modular Python implementation of a RAG pipeline designed for learning and experimentation, featuring hybrid retrieval and support for multiple LLM backends.
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awslabs/graphrag-toolkitA collection of Python tools for building graph-enhanced Generative AI applications using hierarchical lexical graphs and structured knowledge graphs.
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NirDiamant/Controllable-RAG-AgentA controllable RAG agent that uses a deterministic graph and adaptive planning to solve complex, multi-step questions from custom PDF data while preventing hallucinations.
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GiovanniPasq/agentic-rag-for-dummiesThe repository provides a concrete, modular example of an Agentic Retrieval‑Augmented Generation (RAG) system built with LangGraph. It covers hierarchical document indexing, multi‑agent query processing, human‑in‑the‑loop clarification, and supports multiple LLM providers (default Ollama). Includes notebooks for setup, evaluation, and observability, making it a solid learning and prototyping resource.