mirror of
https://github.com/Shubhamsaboo/awesome-llm-apps.git
synced 2026-05-02 10:07:35 -05:00
68 lines
2.4 KiB
Markdown
68 lines
2.4 KiB
Markdown
## 🎙️ Voice RAG with OpenAI SDK
|
|
|
|
This script demonstrates how to build a voice-enabled Retrieval-Augmented Generation (RAG) system using OpenAI's SDK and Streamlit. The application allows users to upload PDF documents, ask questions, and receive both text and voice responses using OpenAI's text-to-speech capabilities.
|
|
|
|
### Features
|
|
|
|
- Creates a voice-enabled RAG system using OpenAI's SDK
|
|
- Supports PDF document processing and chunking
|
|
- Uses Qdrant as the vector database for efficient similarity search
|
|
- Implements real-time text-to-speech with multiple voice options
|
|
- Provides a user-friendly Streamlit interface
|
|
- Allows downloading of generated audio responses
|
|
- Supports multiple document uploads and tracking
|
|
|
|
### How to get Started?
|
|
|
|
1. Clone the GitHub repository
|
|
```bash
|
|
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
|
|
cd awesome-llm-apps/rag_tutorials/voice_rag_openaisdk
|
|
```
|
|
|
|
2. Install the required dependencies:
|
|
```bash
|
|
pip install -r requirements.txt
|
|
```
|
|
|
|
3. Set up your API keys:
|
|
- Get your [OpenAI API key](https://platform.openai.com/)
|
|
- Set up a [Qdrant Cloud](https://cloud.qdrant.io/) account and get your API key and URL
|
|
- Create a `.env` file with your credentials:
|
|
```bash
|
|
OPENAI_API_KEY='your-openai-api-key'
|
|
QDRANT_URL='your-qdrant-url'
|
|
QDRANT_API_KEY='your-qdrant-api-key'
|
|
```
|
|
|
|
4. Run the Voice RAG application:
|
|
```bash
|
|
streamlit run rag_voice.py
|
|
```
|
|
|
|
5. Open your web browser and navigate to the URL provided in the console output to interact with the Voice RAG system.
|
|
|
|
### How it works?
|
|
|
|
1. **Document Processing:**
|
|
- Upload PDF documents through the Streamlit interface
|
|
- Documents are split into chunks using LangChain's RecursiveCharacterTextSplitter
|
|
- Each chunk is embedded using FastEmbed and stored in Qdrant
|
|
|
|
2. **Query Processing:**
|
|
- User questions are converted to embeddings
|
|
- Similar documents are retrieved from Qdrant
|
|
- A processing agent generates a clear, spoken-word friendly response
|
|
- A TTS agent optimizes the response for speech synthesis
|
|
|
|
3. **Voice Generation:**
|
|
- Text responses are converted to speech using OpenAI's TTS
|
|
- Users can choose from multiple voice options
|
|
- Audio can be played directly or downloaded as MP3
|
|
|
|
4. **Features:**
|
|
- Real-time audio streaming
|
|
- Multiple voice personality options
|
|
- Document source tracking
|
|
- Download capability for audio responses
|
|
- Progress tracking for document processing |