Build an efficient Python-based Retrieval-Augmented Generation (RAG) system for contextual query answering over personal data, all with natural language using ChatGoogleGenerativeAI (gemini-pro).
This Python-based system uses advanced text processing and cutting-edge AI to provide insightful answers based on this classic book. Key highlights include:
👉 Generative AI: Utilized ChatGoogleGenerativeAI for natural, context-aware responses.
👉 Embedding Function: Powered by AWS BedrockEmbeddings.
👉 Vector Database: Integrated with Chroma for fast similarity searches.
👉 Text Splitting: Managed with RecursiveCharacterTextSplitter for optimal context retention.
👉 Document Loading: Efficiently parsed with BSHTMLLoader.
The image showcases the database undergoing an update process, wherein a dataset comprising 661 chunks is being integrated. Additionally, the image depicts the inclusion of two sample queries within the system.
