EchoAi

EchoAi is an innovative tool designed to revolutionize customer support by enabling seamless, real-time voice interactions. It utilizes cutting-edge technologies to provide a smooth and efficient way for users to interact with automated support systems through natural conversation.

EchoAi GitHub Repository

Key Features

• Speech-to-Text Conversion: EchoAi accurately transcribes user voice input into text using advanced speech recognition algorithms.
• Natural Language Processing: The transcribed text is processed by a Large Language Model (LLM) with Retrieval-Augmented Generation (RAG), allowing for intelligent and context-aware responses based on your specific product documentation or knowledge base.
• Text-to-Speech Response: The system converts the generated response back into a natural, human-like voice, enabling a fully conversational experience.
• Custom Knowledge Integration: Users can input custom documents, FAQs, or datasets, allowing EchoAi to deliver precise and tailored support responses.
• Scalable and Modular Design: Built with scalability in mind, EchoAi can easily adapt to various use cases, from small businesses to enterprise-level customer support.
• Dockerized for Ease of Deployment: The entire application is containerized using Docker, ensuring a simple, portable, and reproducible deployment process.

How It Works

1. User Interaction: The user speaks into the system, initiating the support interaction.
2. Voice-to-Text Conversion: The spoken input is converted into text using Speech-to-Text (STT) technology.
3. LLM with RAG: The transcribed text is processed by an LLM that uses Retrieval-Augmented Generation to fetch relevant information from the provided knowledge base.
4. Text-to-Voice Conversion: The system converts the LLM’s response into spoken output using Text-to-Speech (TTS) technology, completing the interaction loop.

Use Cases

• Customer service helplines
• Product inquiry systems
• Automated support for FAQs
• Voice-enabled chatbots
• AI-powered IVR (Interactive Voice Response) systems

Technologies Used

• Speech-to-Text (STT): Converts user speech to text.
• Large Language Model (LLM): Processes user queries intelligently.
• Retrieval-Augmented Generation (RAG): Enhances response quality by integrating relevant external knowledge.
• Text-to-Speech (TTS): Delivers responses in a natural voice.
• Docker: Ensures a seamless and portable deployment process.

Vision

EchoAi aims to redefine customer support by providing a natural, efficient, and scalable solution that reduces the workload on human support teams while improving customer satisfaction.

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Building and Deploying EchoAi with Docker

Follow these instructions to build and deploy the EchoAi application using Docker.

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Prerequisites

1. Ensure you have Docker installed on your system. You can download and install Docker from here.
2. Clone this repository to your local machine:

   git clone https://github.com/abhayprabhakar/EchoAi.git
   cd EchoAi
   

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API Endpoints

EchoAi provides RESTful API endpoints for key functionalities:

• /llm: Processes text input using the LLM API, with Retrieval-Augmented Generation (RAG) if documentation is available.
• /tts: Converts a given text input into speech and returns the audio file.
• /stt: Converts speech input into text.

Using the Endpoints

• LLM API (/llm):
  • Input: JSON containing user query text.
  • Output: JSON with the processed response from the LLM.

  Example:

  curl -X POST http://localhost:5000/llm -H "Content-Type: application/json" -d '{"query": "What is EchoAi?"}'
  

• Text-to-Speech (TTS) (/tts):
  • Input: JSON containing the text to be converted.
  • Output: Audio file.

  Example:

  curl -X POST http://localhost:5000/tts -H "Content-Type: application/json" -d '{"text": "Hello, welcome to EchoAi!"}' --output output.mp3
  

• Speech-to-Text (STT) (/stt):
  • Input: Audio file.
  • Output: Transcribed text.

  Example:

  curl -X POST http://localhost:5000/stt -H "Content-Type: multipart/form-data" -F "file=@audio_sample.wav"
  

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Documentation for RAG

EchoAi utilizes Retrieval-Augmented Generation (RAG) to provide accurate and context-aware responses. All PDF and CSV files placed in the documentation folder will be considered as the knowledge base for RAG.

To include your own documentation:

1. Add your .pdf and .csv files to the documentation folder.
2. The application will automatically index these files and use them for generating responses.

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Steps to Build and Run the Docker Container

1. Build the Docker Image

Build the Docker image using the provided Dockerfile:

docker build -t echoai:latest .

• This command creates a Docker image named echoai with the latest tag.

2. Run the Docker Container

Run a container using the built image:

docker run -d -p 5000:5000 --name echoai-container echoai:latest

• -d: Runs the container in detached mode (in the background).
• -p 5000:5000: Maps port 5000 of the container to port 5000 on your host machine.
• --name echoai-container: Assigns a name to the container.

After this step, the EchoAi application will be accessible at http://localhost:5000 in your web browser or via API endpoints.

3. Environment Variables (Optional)

If the application requires environment variables (e.g., API keys for LLM or STT), you can pass them during container creation using the -e flag:

docker run -d -p 5000:5000 --name echoai-container -e GROQ_API_KEY=<your_api_key> -e AZURE_API_KEY=<your_api_key> -e AZURE_REGION=<azure_region> echoai:latest

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Using Custom Documentation

To customize the RAG responses:

1. Place your .pdf and .csv files in the documentation folder.
2. Restart the container to re-index the documents:

   docker restart echoai-container
   

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Stopping and Restarting the Container

Stop the Container

To stop the running container:

docker stop echoai-container

Start the Container Again

To restart the container:

docker start echoai-container

Remove the Container

If you need to remove the container completely:

docker rm -f echoai-container

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Updating the Application

If you update the code, Dockerfile, or documentation folder, you’ll need to rebuild the Docker image:

1. Stop and remove the existing container:

   docker stop echoai-container
   docker rm echoai-container
   

2. Rebuild the image:

   docker build -t echoai:latest .
   

3. Run the container again:

   docker run -d -p 5000:5000 --name echoai-container echoai:latest
   

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Logs and Debugging

To view the logs of the running container:

docker logs echoai-container

For interactive debugging inside the container:

docker exec -it echoai-container /bin/bash

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Deploying in Production

For production deployment:

1. Use a reverse proxy like Nginx to handle HTTPS and routing.
2. Optionally, set up Docker Compose for easier multi-container orchestration.