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14 lines
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14 lines
1.0 KiB
Plaintext
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Retrieval-Augmented Generation (RAG) is a technique that combines retrieval-based and generation-based approaches
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for natural language processing tasks. It involves retrieving relevant information from a knowledge base and then
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using that information to generate more accurate and informed responses.
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RAG models first retrieve documents that are relevant to a given query, then use these documents as additional context
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for language generation. This approach helps to ground the model's responses in factual information and reduces hallucinations.
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The llama.cpp library is a C/C++ implementation of Meta's LLaMA model, optimized for CPU usage. It allows running LLaMA models
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on consumer hardware without requiring high-end GPUs.
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LocalAI is a framework that enables running AI models locally without relying on cloud services. It provides APIs compatible
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with OpenAI's interfaces, allowing developers to use their own models with the same code they would use for OpenAI services.
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