Traditional Retrieval-Augmented Generation (RAG) systems rely on a two-step process: first, semantic search retrieves documents based on surface-level similarities; then, a language model generates answers from those documents. While this method works, it often misses deeper contextual insights and can pull in irrelevant information. ReAG – Reasoning Augmented Generation – offers a robust alternative by feeding raw documents directly to the language model, allowing it to assess and integrate the full context. This unified approach leads to more accurate, nuanced, and context-aware responses.
ReAG transforms document querying with a streamlined process:
- Raw Document Ingestion: Documents are processed in full, without prior chunking or indexing.
- Holistic Evaluation: The language model reads entire texts to determine their relevance and extract key information.
- Dynamic Synthesis: Relevant details are combined into comprehensive answers, mirroring human research methods.
This method eliminates the pitfalls of over-simplified semantic matches and delivers insights that truly address the query's intent.
- Multi-language Support: Available for both Python and Typescript.
- Document Ingestion: Ingest markdown formatted documents with associated metadata.
- Intelligent Querying: Retrieve sources and insights based on contextual queries.
- Language Model Agnostic: Works with any preferred language model.
- Production Ready: Robust, scalable, and designed for real-world applications.
We welcome contributions from the community. Please refer to the CONTRIBUTING.md file for guidelines on reporting issues, suggesting improvements, and submitting pull requests.
This project is licensed under the MIT License.
- ReAG Blog Post - A deep dive into ReAG.
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