Enhancing Language Generation and Natural Language Search with Better Llama 2 and Retrieval Augmented Generation (RAG)

🦙 Retrieval augmented generation using the llama 2 model and a single T4 GPU.

💡 Lamas have limited knowledge and access to the outside world.

🔍 Retrieval augmented generation gives lamas access to a subset of the outside world through natural language searching.

🔸 Using natural language for search and retrieval allows for accessing relevant information based on semantic meaning.

🔸 The embedding model translates human-readable text into machine-readable vectors for performing semantic-based searches.

🔸 The use of an open-source model, specifically the Sentence Transformers Library, enables efficient and accessible embedding creation.

📌 Performance of opening eye embeddings depends on the use case.

🔑 A Pinecone API key is needed to create a Vector database and index.

🗄️ Initializing the index to store vectors with the specified dimensionality and metric.

🔍 Populating the Vector database to enable retrieval of stored items.

📑 The video's transcription discusses the creation of a small dataset containing chunks of text from the Llama 2 paper and related papers.

📊 The dataset is converted into a pandas data frame and uploaded to Pinecone in batches of 32, with the option to increase the batch size.

🔎 The LM model, Llama2, is added to the database using the text generation pipeline from Hugging Face.

📚 Loading the model and getting the home face authentication token.

💻 Switching the model to evaluation mode and checking GPU usage.

💡 Initializing the retrieval QA chain for LMS and confirming its functionality.

🔍 The retrieval augmented generation (RAG) pipeline improves document retrieval.

💡 Llama 2 is a collection of pre-trained large language models optimized for dialogue.

🔒 Safety measures in the development of Llama 2 include pre-training, fine-tuning, and model safety approaches.

🔍 Retrieval augmented pipeline improves performance and safety of Llama 2

🚀 Llama 2 outperforms other local language models in helpfulness and safety benchmarks

💡 Retrieval augmentation allows LM to answer questions on up-to-date topics and internal documents