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Current RAG Landscape Retrieval-Augmented Generation has evolved far beyond simple vector search. The latest survey "Retrieval-Augmented Generation for Large Language Models" highlights three active areas of innovation: Query-side augmentation (query transformation) Agentic orchestration of retrieval Post-retrieval refinement Self-R ...

When working with extensive text documents, it's essential to divide them into manageable pieces. While this might appear straightforward, numerous complexities arise. Ideally, we want to maintain semantically related text segments together, though what constitutes "semantic relevance" can vary based on the document type. This article ...

Retrieval-Augmented Generation Architecture Retrieval-Augmented Generation (RAG) enhances generative models by dynamically fetching relevant context from external knowledge stores before synthesizing a resposne. This methodology addresses core limitations of standalone large language models, including factual hallucination, temporal knowledge d ...

Auto-GPT: Autonomous AI Agent Architecture Auto-GPT represents a experimental open-source application that demonstrates the capabilities of GPT-4 as an autonomous agent. The project gained significant traction, accumulating stars at a remarkable rate. It serves as one of the first examples of GPT-4 operating with full autonomy to achieve user-d ...

Prerequisites and Environment Setup Establishing a self-hosted foundation for large language models is essential for leveraging LangChain without reliance on external paid APIs. This procedure outlines the initialization process using the Baichuan2-13B-Chat model optimized for 4-bit quantization. Ensure the server environment supports specific ...