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RAG AI Voice Assistant

Block Diagram

Overview

The RAG AI Voice Assistant is a comprehensive system designed to interact with users through spoken commands and questions. It leverages various components to provide a seamless user experience:

  • User: Interacts with the voice assistant by providing spoken instructions or questions.
  • Voice to Text Conversion: Converts the user's speech into text for computer processing.
  • Faster Whisper Model: Recognizes speech using the Faster Whisper model.
  • OLLAMA & MISTRAL: Large language models (LLMs) used for processing user queries and generating responses.
  • RAG Retrieval Augmented Generation: Combines information from conversation history and the internet to generate responses.
  • Chat Memory: Stores past conversations with the user.
  • Text to Voice Conversion: Converts generated text responses back into speech.
  • Voice Assistant Bot: Integrates all components to enable voice-based interaction.

Components

Memory Buffer

Provides fast retrieval of data for quick access.

# Example code for memory buffer usage
memory_buffer = MemoryBuffer()
data = memory_buffer.retrieve_data(key)

Huge Data Storage

Utilizes a vector database for efficient storage and retrieval of large datasets.

# Example code for vector database usage
vector_db = VectorDatabase()
vector_db.store_vectors(vectors)
results = vector_db.search(query_vector)

Models

Faster Whisper

An implementation of a model for speech recognition.

# Example code for Faster Whisper model usage
faster_whisper = FasterWhisperModel()
transcription = faster_whisper.transcribe(audio)

Mistral

An implementation of a model for natural language understanding and generation.

# Example code for Mistral model usage
mistral = MistralModel()
response = mistral.generate_response(query)

Qdrant

Qdrant is a vector search engine designed for similarity search, nearest neighbor search, and clustering of high-dimensional data.

Docker Installation

To use Qdrant, pull the Docker image and run it as a container:

docker pull qdrant/qdrant
docker run -p 6333:6333 qdrant/qdrant

For detailed instructions, visit the Qdrant website.

Usage

  1. Memory Buffer: Utilize the memory buffer for fast data retrieval.
  2. Huge Data Storage: Store and retrieve large datasets efficiently using the vector database.
  3. Models: Use Faster Whisper for speech recognition and Mistral for natural language processing.

Contributing

Contributions are welcome! Feel free to submit pull requests or open issues for any improvements or suggestions.