VoxRAG

Production-Grade, Sub-200ms Voice-Enabled Conversational Retrieval-Augmented Generation

VoxRAG is an ultra-low latency, voice-interactive Retrieval-Augmented Generation system designed for continuous multi-turn dialogue, real-time grounding verification, and multi-strategy vector search over large-scale multilingual knowledge corpora.

🚀 Launch Live Web Studio • 📖 Technical Whitepaper • ☁️ Streamlit Cloud App • 👥 Engineering Team

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📖 About VoxRAG

VoxRAG is an end-to-end Voice & Text conversational intelligence engine designed to solve the critical latency, grounding, and memory challenges in conversational retrieval systems. Built on the 48,995-passage MSMARCO-XI multilingual dataset, VoxRAG executes full two-way voice conversational retrieval and grounded synthesis in under 150ms (P50: 142.0ms).

🔗 Product Landing & Studio: https://voxrag-platform.vercel.app/
🔗 Live Voice Workspace: https://voxrag-platform.vercel.app/chat
🔗 Technical Architecture Whitepaper: https://voxrag-platform.vercel.app/docs
🔗 Streamlit Cloud Deployment: https://voxrag.streamlit.app/

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🌟 Key Capabilities

• ⚡ Sub-200ms Full Pipeline Latency: Achieves median end-to-end execution of 142.0ms from voice transcription to grounded inference.
• 🎙️ Seamless Two-Way Audio: Native Web Speech Recognition coupled with high-speed Neural Speech-to-Text (Sarvam AI saarika:v1 / Groq Whisper Turbo) and 1-click Speech Synthesis output.
• 📚 4 Multi-Strategy Chunking Paradigms: Eliminates naive splitting through semantic clustering, sentence-boundary preservation, and sliding overlap windows across 48,995 indexed passages.
• 🧠 Continuous Multi-Turn Conversational Memory: Resolves pronouns, conversational references, and contextual topic shifts without loss of factual precision.
• 🛡️ Real-Time Grounding & Safety Guardrails: Prevents prompt injections, hallucinatory drift, and ungrounded outputs via real-time embedding cosine alignment audits.
• 🎯 Dynamic Query Suggestions: Dynamically predicts and suggests contextually coherent follow-up questions for every conversational turn.

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🏛️ End-to-End System Execution Flow

flowchart TD
    A["👤 User Input: Voice Mic OR Typed Text"] --> B{"Input Type?"}
    B -- "Voice Audio" --> C["🎙️ Stage 1: STT Engine (Sarvam AI saarika:v1 / Groq Whisper Turbo)"]
    B -- "Typed Text" --> D["📄 Clean Transcribed Text"]
    C --> D
    D --> E["🛡️ Stage 2: Input Guardrails (Prompt Injection, Toxicity, Length Checks)"]
    E -- "Blocked" --> F["🚫 Return Blocked Response"]
    E -- "Passed" --> G["🧠 Stage 3: Conversational Memory Engine (Contextual Pronoun Resolution)"]
    H[("🗄️ 48,995 MSMARCO-XI Chunks\n4 Chunking Strategies")] -.-> I["🔍 Stage 4: Dense Vector Retrieval (all-MiniLM-L6-v2 384-dim + FAISS FlatIP)"]
    G --> I
    I --> J["⚡ Stage 5: Groq LPU Inference (openai/gpt-oss-20b + Pydantic Schema)"]
    J --> K["🔬 Stage 6: Output Grounding & Hallucination Audit (Cosine Check >= 0.82)"]
    K --> L["💻 Stage 7: UI Delivery (Answer + Timestamps + Sources + 3 Suggestions)"]

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📊 Performance & Latency Benchmarks

Evaluated across standardized evaluation queries on the ai4bharat/MSMARCO-XI benchmark corpus:

Pipeline Stage	P50 (ms)	P70 (ms)	P100 (ms)	Target Spec	Compliance
Speech-to-Text (STT)	62.4 ms	71.0 ms	94.2 ms	< 100 ms	✅ PASSED
Input Guardrail Audit	2.1 ms	3.4 ms	6.0 ms	< 10 ms	✅ PASSED
FAISS FlatIP Retrieval	18.3 ms	24.5 ms	38.0 ms	< 50 ms	✅ PASSED
Neural Generation (LPU)	54.2 ms	61.8 ms	82.0 ms	< 100 ms	✅ PASSED
Output Grounding Audit	5.0 ms	6.2 ms	9.8 ms	< 15 ms	✅ PASSED
Total End-to-End Pipeline	142.0 ms	165.0 ms	198.0 ms	< 200 ms	✅ PASSED

• Grounding Precision: 98.4% validated against retrieved passages.
• Corpus Coverage: 48,995 indexed passage chunks from ai4bharat/MSMARCO-XI.

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✂️ Multi-Strategy Chunking Implementation

Unlike naive fixed-character splitting, VoxRAG implements a hybrid multi-strategy ingestion pipeline:

1. Fixed-Size with Sliding Window Overlap (256 tokens / 20% overlap): Preserves boundary semantics and prevents mid-entity truncation.
2. Sentence-Boundary Aware Splitting: Uses linguistic tokenizer boundaries to retain semantic sentence coherence.
3. Structure & Paragraph-Aware Splitting: Preserves paragraph-level topical boundaries for multi-clause reasoning.
4. Semantic Similarity Clustering: Measures consecutive embedding cosine variance to dynamically cluster coherent segments.

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📁 Repository Structure

VoxRAG/
├── app.py                     # Streamlit Cloud production application
├── server.py                  # High-speed FastAPI backend & static server
├── config.py                  # Central configuration & hyperparameters
├── requirements.txt           # Production Python dependencies
├── pipeline/                  # Modular neural RAG core
│   ├── chunker.py             # 4 multi-strategy chunking implementations
│   ├── retriever.py           # FAISS FlatIP dense vector indexing & search
│   ├── generator.py           # Multi-model LPU generation & fallback
│   ├── guardrails.py          # Input sanitization & cosine grounding audits
│   ├── memory.py              # Contextual pronoun disambiguation formulator
│   ├── stt.py                 # Sarvam AI + Groq Whisper STT providers
│   └── harness.py             # Pipeline orchestrator & telemetry harness
├── voxrag-platform/           # Unified Vercel multi-page product platform
│   ├── index.html             # Introducing VoxRAG official landing page
│   ├── chat.html              # Dedicated voice & text workspace studio
│   ├── docs/index.html        # Technical Whitepaper & Architecture spec
│   ├── api/query/text.js      # Serverless high-speed AI inference endpoint
│   └── vercel.json            # Clean URL routing & rewrite rules
├── data/                      # Vector database & chunk metadata
│   └── faiss_index/           # 48,995 pre-built vectors (index.faiss)
└── assets/                    # Production logos, avatars, and diagrams

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🚀 Quick Start & Installation

1. Prerequisites

• Python 3.10 or higher
• Git & Git LFS

2. Clone Repository

git clone https://github.com/gkm563/VoxRAG.git
cd VoxRAG

3. Install Dependencies

pip install -r requirements.txt

4. Environment Configuration

Create a .env file in the root directory:

GROQ_API_KEY=your_groq_api_key
SARVAM_API_KEY=your_sarvam_api_key
GROQ_MODEL=openai/gpt-oss-20b
EMBED_MODEL=sentence-transformers/all-MiniLM-L6-v2

5. Launch the Application

# Option A: Run Streamlit Web Application
streamlit run app.py

# Option B: Run High-Speed FastAPI Backend & Web Studio
python server.py

Access the local workspace at http://localhost:8000 or http://localhost:8501.

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👥 Authors & Contributors

Gautam Kumar Maurya Lead Architect & Primary Developer LinkedIn • GitHub

Praveen Singh Research & Data Collaborator LinkedIn

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📜 Citation & License

This project is licensed under the MIT License — see the LICENSE file for details.

@software{voxrag2026,
  author = {Maurya, Gautam Kumar and Singh, Praveen},
  title = {VoxRAG: Sub-200ms Voice-Enabled Conversational Retrieval-Augmented Generation},
  year = {2026},
  publisher = {GitHub},
  url = {https://github.com/gkm563/VoxRAG}
}