Better Llama 2 with Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) allows us to keep our Large Language Models (LLMs) up to date with the latest information, reduce hallucinations, and allow us to cite the original source of information being used by the LLM.
We build the RAG pipeline using a Pinecone vector database, a Llama 2 13B chat model, and wrap everything in Hugging Face and LangChain code.
📌 Code:
https://github.com/pinecone-io/exampl...
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👋🏼 AI Consulting:
https://aurelio.ai
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Twitter: / jamescalam
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00:00 Retrieval Augmented Generation with Llama 2
00:29 Python Prerequisites and Llama 2 Access
01:39 Retrieval Augmented Generation 101
03:53 Creating Embeddings with Open Source
06:23 Building Pinecone Vector DB
08:38 Creating Embedding Dataset
11:45 Initializing Llama 2
14:38 Creating the RAG RetrievalQA Component
15:43 Comparing Llama 2 vs RAG Llama 2
#artificialintelligence #nlp #opensource #llama2