Advantages of BGE embeddings for Retrieval Augmented Generation

🔑 BGE embeddings are a new development in the embedding space that fit into the retrieval augmented generation space.

⚙️ BG embeddings are used to create vector stores for retrieval augmented generation, where large llm models are used to produce contextual answers.

🏆 BG embeddings perform well in the Massive Text Embedding Benchmark, ranking highly in tasks like clustering, re-ranking, and semantic textual similarity.

🔍 BGE embeddings outperform open ai's text embedding ada002.

⚙️ Flag embedding is used to train the models.

🔗 BG embeddings have connectivity with other libraries like Lang chain.

📚 The speaker created a dataset from IPO documents for analysis.

💼 The dataset contains OCR text from 500-page IPO prospectus documents.

💡 The dataset can be used to train a model for various industries.

💻 The video discusses the process of fetching data using Hugging Face and installing necessary libraries.

📊 The data set, focused on IPO prospectus, is split into train and test sets, with the test set being used for analysis.

🔍 The OCR text and content pages of the prospectus are retrieved and split into smaller chunks for analysis.

📚 The video discusses the process of extracting and organizing data sets for retrieval augmented generation.

💻 Json line format is introduced as a way to store data sets in separate lines in a Json format.

⚙️ The pre-training process involves specifying configurable parameters and monitoring the loss, which decreases over time.

📚 The video discusses the use of state-of-the-art BGE embeddings for retrieval augmented generation.

💻 The speaker saves pre-trained embeddings and compares the similarity between two sentences using the BGE base embeddings.

✅ The results show that the embeddings indicate a high level of similarity between the sentences.

💡 Creating custom embeddings and comparing them to the base model.

⚠️ Use a machine with sufficient GPU memory for training the model.

🔎 Tips for training the model: use smaller models and batch sizes to pre-train faster.