PIKE-RAG is a Python project from Microsoft implementing specialized knowledge extraction and rationale augmented generation with a domain-focused RAG pipeline. Latest release is pikerag-0.0.1 on 2025-01-24.
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What it is
PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation. The repository describes a Retrieval Augmented Generation framework focused on extracting domain-specific knowledge and building reasoning for LLMs. The README presents a pipeline with modules for document parsing, knowledge extraction, knowledge storage, retrieval, organization, knowledge-centric reasoning, and task decomposition.
How it works
The project outlines a modular pipeline for knowledge extraction and reasoning within industrial domains. It emphasizes context-aware segmentation, term label alignment, multi-granularity knowledge extraction, and multi-agent planning ideas to guide LLMs toward accurate responses. The README includes figures illustrating pipeline components and different pipeline configurations for factual information retrieval and more complex tasks.
Getting started
Suggested steps in the README Quick Start section:
- Clone this repo and set up the Python environment, refer to this document;
- Create a
.envfile to save your endpoint information (and some other environment variables if needed), refer to this document; - Modify the yaml config files and try the scripts under examples/, refer to this document;
- Build up your own pipeline and/or add your own components!
Recent releases
- pikerag-0.0.1 pikerag-0.0.1🚀 (2025-01-24): Initial release
Traction
2480 stars, 236 forks, 10 open issues.
Behind the repo
None provided in the supplied facts.
Caveats
License: MIT. Created: 2024-09-26. Last push: 2025-09-10. Language: Python.