Build a RAG system using gemini LLM, the chatbot able to understand text or speech
To build a Retrieval-Augmented Generation (RAG) system using the Gemini LLM, we create a chatbot that understands both text and speech queries and provides responses based on specific data. The system would use the following components:
Chroma Database: This will serve as the main storage and retrieval system, housing the specific data that the chatbot will reference in its responses.
HuggingFace Embeddings: These embeddings will process the data and user queries, enabling more precise retrieval by capturing the semantic meaning of the text.
Gemini LLM as Generator: Gemini will act as the generative model, using the data retrieved from Chroma and the query context to craft specific and relevant responses.
Speech Capabilities: transcribes the user's spoken question into text. The response can then be converted back to speech using a Text-to-Speech engine or text.
Solution Architecture:
