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ARIA - AI-Powered Citizen Support Assistant

A Voice-First Service Assistant that enables citizens to ask questions (via voice or text) about government services and receive accurate, context-aware guidance powered by RAG (Retrieval-Augmented Generation).

Branches

Branch Description Status
main Streamlit chat interface using the ARIA API Deployed
aria-agent Advanced features: LiveKit integration with automatic voice detection for real-time conversations Local only

Note: The aria-agent branch includes a more advanced implementation with LiveKit-powered live conversations featuring automatic voice activity detection (VAD) and turn-taking. Due to infrastructure limitations, this branch is not deployed online but works locally. Feel free to test it!

Live Demo

Service URL
Chat Interface aria.lunaroot.rw
API Documentation aria-api.lunaroot.rw/docs

System Architecture

═══════════════════════════════════════════════════════════════════════════════
                            ARIA SYSTEM ARCHITECTURE
═══════════════════════════════════════════════════════════════════════════════

                              ┌─────────────────┐
                              │    CITIZEN      │
                              │                 │
                              │  Voice / Text   │
                              └────────┬────────┘
                                       │
                                       ▼
        ┌──────────────────────────────────────────────────────────────────┐
        │                    FRONTEND (Streamlit)                          │
        │                                                                  │
        │   Chat Interface (app.py)  │     Stats Dashboard (pages/stats.py)│
        │   ─────────────────────    │     ────────────────────────────    │
        │   • Text input             │     • Response time metrics         │
        │   • Audio recording        │     • Cache hit rates               │
        │   • Message history        │     • Performance visualization     │
        │   • Source citations       │     • System health monitoring      │
        │   • Audio playback (TTS)   │                                     │
        └──────────────────────────────────────────────────────────────────┘
                                       │
                                       │ HTTP / SSE
                                       ▼
        ┌──────────────────────────────────────────────────────────────────┐
        │                    BACKEND API (FastAPI + Gunicorn)              │
        │                         api/server.py                            │
        │                                                                  │
        │   ┌─────────────────────────────────────────────────────────┐    │
        │   │                    API ROUTES                           │    │
        │   │   routes/query.py      routes/documents.py              │    │
        │   │   ─────────────────    ────────────────────             │    │
        │   │   POST /api/query/     POST /api/documents/upload       │    │
        │   │     text               POST /api/documents/refresh      │    │
        │   │   POST /api/query/     GET  /api/documents              │    │
        │   │     text/stream        DELETE /api/documents/{id}       │    │
        │   │   POST /api/query/                                      │    │
        │   │     audio              routes/health.py  routes/stats.py│    │
        │   │                        GET /health       GET /api/stats │    │
        │   └─────────────────────────────────────────────────────────┘    │
        │                              │                                   │
        │                              ▼                                   │
        │   ┌─────────────────────────────────────────────────────────┐    │
        │   │              AGENT LAYER (agent/)                       │    │
        │   │                                                         │    │
        │   │   ┌─────────────────────────────────────────────────┐   │    │
        │   │   │  Domain Classifier (rag/domain_classifier.py)   │   │    │
        │   │   │  • Pydantic AI + GPT-4o-mini                    │   │    │
        │   │   │  • Routes: irembo_service → RAG pipeline        │   │    │
        │   │   │           greeting/small_talk → Direct response │   │    │
        │   │   │           off_topic → Polite decline            │   │    │
        │   │   └─────────────────────────────────────────────────┘   │    │
        │   │                         │                               │    │
        │   │                         ▼                               │    │
        │   │   ┌─────────────────────────────────────────────────┐   │    │
        │   │   │        RAG PIPELINE (LlamaIndex)                │   │    │
        │   │   │                                                 │   │    │
        │   │   │   rag/loaders.py ─► rag/indexer.py              │   │    │
        │   │   │   (JSON/MD/PDF/     (Vector Store +             │   │    │
        │   │   │    DOCX/TXT)         OpenAI Embeddings)         │   │    │
        │   │   │         │                  │                    │   │    │
        │   │   │         └────────┬─────────┘                    │   │    │
        │   │   │                  ▼                              │   │    │
        │   │   │   rag/query_engine.py ◄── rag/prompts.py        │   │    │
        │   │   │   (GPT-4o-mini, top_k=5,  (QA & Refine          │   │    │
        │   │   │    response_mode=refine)   templates)           │   │    │
        │   │   └─────────────────────────────────────────────────┘   │    │
        │   │                                                         │    │
        │   │   session/redis_store.py    utils/config.py             │    │
        │   │   (Session management)      (Environment config)        │    │
        │   └─────────────────────────────────────────────────────────┘    │
        └──────────────────────────────────────────────────────────────────┘
                       │                               │
                       ▼                               ▼
        ┌─────────────────────────┐     ┌─────────────────────────┐
        │   EXTERNAL AI SERVICES  │     │   SESSION & CACHE       │
        │                         │     │       (Redis)           │
        │   ┌───────────────────┐ │     │                         │
        │   │ OpenAI API        │ │     │   Session Store         │
        │   │ • GPT-4o-mini     │ │     │   ─────────────         │
        │   │   (LLM + Class.)  │ │     │   Chat history (30m)    │
        │   │ • Embeddings      │ │     │                         │
        │   │   (text-embed-3)  │ │     │   Query Cache           │
        │   │ • gpt-4o-mini-tts │ │     │   ───────────           │
        │   │   (streaming TTS) │ │     │   Responses (1hr)       │
        │   └───────────────────┘ │     │                         │
        │                         │     │   Embedding Cache       │
        │   ┌───────────────────┐ │     │   ───────────────       │
        │   │ Groq API          │ │     │   Vectors (24hr)        │
        │   │ • Whisper STT     │ │     │                         │
        │   │   (large-v3-turbo)│ │     │   Audio Cache           │
        │   │ • Orpheus TTS     │ │     │   ───────────           │
        │   │   (fallback only) │ │     │   TTS output (1hr)      │
        │   └───────────────────┘ │     │                         │
        └─────────────────────────┘     └─────────────────────────┘
                       │
                       ▼
        ┌─────────────────────────┐
        │   KNOWLEDGE BASE        │
        │   data/knowledge/       │
        │                         │
        │   • IremboGov JSON docs │
        │   • Immigration services│
        │   • Passport/Visa info  │
        │                         │
        │   storage/index/        │
        │   • Persisted vectors   │
        └─────────────────────────┘

═══════════════════════════════════════════════════════════════════════════════

Request Flow

═══════════════════════════════════════════════════════════════════════════════
                              REQUEST FLOW DIAGRAM
═══════════════════════════════════════════════════════════════════════════════


  TEXT QUERY FLOW
  ───────────────

                    ┌──────────────┐
                    │  User Input  │
                    └──────┬───────┘
                           │
                           ▼
                    ┌──────────────┐         ┌─────────────────────┐
                    │ Cache Check  │───Yes──►│ Return Cached       │
                    └──────┬───────┘         │ Response + TTS      │
                           │ No              └─────────────────────┘
                           ▼
                    ┌──────────────┐
                    │Vector Search │
                    │  (top_k=5)   │
                    └──────┬───────┘
                           │
                           ▼
                    ┌──────────────┐
                    │LLM Generate  │
                    │(GPT-4o-mini) │
                    └──────┬───────┘
                           │
                           ▼
                    ┌──────────────┐
                    │Cache Response│
                    └──────┬───────┘
                           │
                           ▼
                    ┌──────────────┐
                    │TTS Generation│
                    │(Groq/OpenAI) │
                    └──────┬───────┘
                           │
                           ▼
                    ┌──────────────┐
                    │Return to User│
                    └──────────────┘


  AUDIO QUERY FLOW
  ────────────────

    ┌────────────┐      ┌─────────────┐      ┌───────────────┐      ┌──────────────┐
    │ Audio File │─────►│Groq Whisper │─────►│ Transcribed   │─────►│ Text Query   │
    └────────────┘      │    STT      │      │    Text       │      │    Flow      │
                        └─────────────┘      └───────────────┘      └──────┬───────┘
                                                                          │
                                                                          ▼
                                                                   ┌──────────────┐
                                                                   │Response +    │
                                                                   │Transcript +  │
                                                                   │Audio Output  │
                                                                   └──────────────┘


  STREAMING RESPONSE (SSE) - WITH STREAMING AUDIO
  ─────────────────────────────────────────────────

    Query ──► Session ──► Tokens ──► Done ──► Sources ──► Audio Stream
                │            │          │         │            │
                ▼            ▼          ▼         ▼            ▼
           session_id   "tok","en"  full_resp  sources[]   audio_start
                                                            audio_chunk (×N)
                                                            audio_end

    Event Order:
    1. session      → Session ID for conversation tracking
    2. tokens       → Streaming text response (multiple events)
    3. done         → Text complete with full_response
    4. sources      → Source documents with relevance scores
    5. audio_start  → TTS streaming begins (sample_rate, channels, bits)
    6. audio_chunk  → PCM audio chunks (base64, streamed as generated)
    7. audio_end    → Audio streaming complete


═══════════════════════════════════════════════════════════════════════════════

Tech Stack

Component Technology Purpose
Frontend Streamlit 1.40+ Chat UI with voice input
Backend FastAPI + Gunicorn RESTful API server
RAG Framework LlamaIndex Document indexing & retrieval
Domain Classifier Pydantic AI + GPT-4o-mini Query classification
LLM OpenAI GPT-4o-mini Response generation
Embeddings OpenAI text-embedding-3-small Vector embeddings
STT Groq Whisper large-v3-turbo Speech-to-text
TTS OpenAI gpt-4o-mini-tts (streaming) Text-to-speech with streaming audio
TTS Fallback Groq Orpheus Fallback when OpenAI unavailable
Cache/Sessions Redis 7 Caching & session management
Container Docker + Docker Compose Deployment

Quick Start

Prerequisites

  • Docker and Docker Compose
  • OpenAI API Key
  • Groq API Key

1. Clone and Configure

git clone https://github.com/yourusername/aria-assistant.git
cd aria-assistant

# Create environment file
cp .env.example .env

Edit .env with your API keys:

OPENAI_API_KEY=sk-...
GROQ_API_KEY=gsk_...
REDIS_PASSWORD=your-secure-password

2. Add Knowledge Documents

Place your knowledge documents in backend/data/knowledge/:

# Supported formats: .json (IremboGov), .md, .txt, .pdf, .docx
cp your-documents/* backend/data/knowledge/

Included Knowledge Base: IremboGov Immigration and Emigration services documentation (passport applications, visas, travel documents, etc.)

3. Run with Docker

# Development
docker compose up --build

# Production
docker compose -f docker-compose.prod.yml up --build -d

4. Index Knowledge Documents

After adding documents, trigger indexing:

# Via curl
curl -X POST http://localhost:8635/api/documents/refresh

# Or use the API docs UI at http://localhost:8635/docs

5. Access the Application

API Reference

Query Endpoints

Text Query

POST /api/query/text
Content-Type: application/json

{
  "query": "How do I apply for a passport?",
  "session_id": "optional-session-id",
  "include_audio": true
}

Response:

{
  "query": "How do I apply for a passport?",
  "answer": "To apply for a passport, you need to...",
  "sources": [
    {"title": "Passport Application", "url": "...", "score": 0.95}
  ],
  "session_id": "abc123",
  "audio_base64": "UklGRi..."
}

Text Query with Streaming

POST /api/query/text/stream
Content-Type: application/json
Accept: text/event-stream

{
  "query": "What documents do I need?",
  "session_id": "abc123"
}

SSE Events (with streaming audio):

event: session
data: {"type": "session", "session_id": "abc123"}

event: token
data: {"type": "token", "token": "You"}

event: done
data: {"type": "done", "full_response": "You need..."}

event: sources
data: {"type": "sources", "sources": [...], "confidence": 0.85}

event: audio_start
data: {"type": "audio_start", "sample_rate": 24000, "channels": 1, "bits_per_sample": 16}

event: audio_chunk
data: {"type": "audio_chunk", "chunk": "<base64 PCM data>", "index": 0}

event: audio_chunk
data: {"type": "audio_chunk", "chunk": "<base64 PCM data>", "index": 1}
... (multiple chunks)

event: audio_end
data: {"type": "audio_end", "total_chunks": 42}

Note: Audio is streamed as PCM chunks (24kHz, 16-bit, mono) for low-latency playback. The frontend combines chunks and adds a WAV header for playback.

Audio Query

POST /api/query/audio
Content-Type: multipart/form-data

file: <audio-file.wav>
session_id: optional-session-id
include_audio: true

Response:

{
  "transcript": "How do I apply for a birth certificate?",
  "answer": "To apply for a birth certificate...",
  "sources": [...],
  "session_id": "abc123",
  "audio_base64": "..."
}

Document Management

# Upload document
POST /api/documents/upload
Content-Type: multipart/form-data
file: <document.pdf>

# List documents
GET /api/documents

# Delete document
DELETE /api/documents/{doc_id}

# Refresh index
POST /api/documents/refresh

Health & Stats

# Health check
GET /health

# Performance stats
GET /api/stats

Key Concerns Addressed

1. Architecture for Inference (Embedded vs. API Calls)

Decision: API-based inference for all AI models

Model Provider Rationale
STT Groq API (Whisper) 10x faster than local, no GPU required
LLM OpenAI API (GPT-4o-mini) Consistent quality, managed scaling
TTS OpenAI API (gpt-4o-mini-tts) Streaming audio support, lower latency
TTS Fallback Groq API (Orpheus) Fallback when OpenAI unavailable (rate-limited on free tier)
Embeddings OpenAI API Cached in Redis to minimize API calls

Why not embedded models?

  • Eliminates GPU infrastructure costs
  • Faster cold starts (no model loading)
  • Automatic scaling by provider
  • Focus on application logic, not ML ops

2. Handling Ambiguity

Vague queries:

  • Confidence scoring (0.0-1.0) based on document similarity
  • Users see High/Medium/Low confidence badges
  • Sources displayed with relevance percentages

Out-of-scope queries:

  • System prompt explicitly instructs: "If the question is unrelated to government services, politely explain you can only help with Irembo services"
  • RAG retrieval returns low scores for irrelevant queries
  • No hallucination: answers grounded strictly in retrieved documents

Vague audio:

  • Whisper provides transcription confidence
  • If transcription unclear, user sees transcript to verify
  • Follow-up question detection uses conversation history

3. Latency Optimization

Strategy Impact Implementation
Response Streaming 2-3s TTFT vs 5-8s wait SSE events stream tokens
Streaming TTS Faster time-to-first-audio OpenAI gpt-4o-mini-tts streams PCM chunks
Multi-layer Caching 10-35x speedup Redis: queries (1hr), embeddings (24hr), audio (1hr)
Connection Pooling -50ms per request Reused Redis/HTTP connections
Async Everything Higher throughput FastAPI async, async Redis, async LLM

4. Scalability (1,000+ Concurrent Users)

Current architecture (prototype scale):

  • Single Redis instance (sufficient for demo/prototype)
  • Gunicorn with 4 workers = ~100 concurrent per container
  • Stateless API = horizontal scaling ready

Future scaling with Kubernetes HPA:

                    ┌─────────────────────┐
                    │   Load Balancer     │
                    │  (Traefik/Ingress)  │
                    └─────────┬───────────┘
                              │
          ┌───────────────────┼───────────────────┐
          │                   │                   │
          ▼                   ▼                   ▼
    ┌───────────┐       ┌───────────┐       ┌───────────┐
    │ API Pod 1 │       │ API Pod 2 │       │ API Pod N │
    │(4 workers)│       │(4 workers)│       │(4 workers)│
    └─────┬─────┘       └─────┬─────┘       └─────┬─────┘
          │                   │                   │
          └───────────────────┼───────────────────┘
                              │
                              ▼
                    ┌─────────────────────┐
                    │   Redis Cluster     │
                    │   (3+ nodes)        │
                    └─────────────────────┘

    * Kubernetes HPA auto-scales pods based on CPU/memory
    * Currently using Gunicorn workers for concurrency

Scaling steps (future):

  1. Deploy 10+ API containers behind load balancer
  2. Migrate Redis to cluster mode (3+ nodes)
  3. Add Pinecone/Qdrant for distributed vector search
  4. Implement rate limiting (10 req/min per session)
  5. Add CDN for static assets

5. Accuracy & Hallucination Prevention

✅ Production Tested: The Pydantic AI domain classifier successfully blocks off-topic queries (e.g., "how to cook rice", "what is 2+2") while allowing Irembo government service questions through to the RAG pipeline.

Preventing hallucination is critical for a government services assistant. ARIA implements a multi-layer defense:

═══════════════════════════════════════════════════════════════════════════════
                        HALLUCINATION PREVENTION PIPELINE
═══════════════════════════════════════════════════════════════════════════════

  User Query: "How to cook rice?"
        │
        ▼
  ┌─────────────────────────────────────────────────────────────────────────┐
  │  LAYER 0: Pydantic AI Domain Classifier (gpt-4o-mini)                    │
  │  ─────────────────────────────────────────────────────────              │
  │  • Uses OpenAI gpt-4o-mini for fast structured classification           │
  │  • Structured output: QueryCategory (irembo_service/greeting/off_topic) │
  │  • Handles greetings & small talk with direct responses (no RAG)        │
  │  • Off-topic → Polite decline; Irembo queries → Continue to RAG         │
  │  • Fallback to pattern matching if classifier fails                     │
  └─────────────────────────────────────────────────────────────────────────┘
        │
        ▼
  ┌─────────────────────────────────────────────────────────────────────────┐
  │  LAYER 1: Retrieval Filtering (SimilarityPostprocessor)                 │
  │  ─────────────────────────────────────────────────────────              │
  │  • Retrieve top 5 documents from vector store                           │
  │  • Filter out documents with similarity < 40%                           │
  │  • If all filtered → LLM receives EMPTY context                         │
  └─────────────────────────────────────────────────────────────────────────┘
        │
        ▼
  ┌─────────────────────────────────────────────────────────────────────────┐
  │  LAYER 2: Strict Prompt Engineering (QA_PROMPT_TEMPLATE)                │
  │  ─────────────────────────────────────────────────────────              │
  │  • 2-step evaluation: Is it about Irembo? Does context answer it?       │
  │  • Automatic decline triggers for cooking, math, coding, etc.           │
  │  • "If in doubt, DECLINE" instruction                                   │
  │  • FORBIDDEN: Using training knowledge, guessing, being "helpful"       │
  └─────────────────────────────────────────────────────────────────────────┘
        │
        ▼
  ┌─────────────────────────────────────────────────────────────────────────┐
  │  LAYER 3: Refine Response Mode (REFINE_PROMPT_TEMPLATE)                 │
  │  ─────────────────────────────────────────────────────────              │
  │  • Process each context chunk sequentially                              │
  │  • If existing answer DECLINES → Keep the decline                       │
  │  • Only refine with DIRECTLY relevant Irembo information                │
  └─────────────────────────────────────────────────────────────────────────┘
        │
        ▼
  ┌─────────────────────────────────────────────────────────────────────────┐
  │  LAYER 4: Post-Generation Validation                                    │
  │  ─────────────────────────────────────────────────────────              │
  │  • Check max source score after LLM response                            │
  │  • If max_score < 40% → Replace with NO_INFORMATION_RESPONSE            │
  │  • If max_score < 60% → Add LOW_CONFIDENCE_PREFIX                       │
  │  • Don't return sources if query rejected (prevents confusion)          │
  └─────────────────────────────────────────────────────────────────────────┘
        │
        ▼
  Result: "I can only help with questions about Irembo government services."

═══════════════════════════════════════════════════════════════════════════════

Current Implementation:

Layer Technique Purpose
Layer 0 Pydantic AI + gpt-4o-mini Intelligent domain classification before RAG
Layer 1 SimilarityPostprocessor(cutoff=0.6) Filter irrelevant documents before LLM
Layer 2 Strict QA template with decline triggers Force LLM to refuse off-topic questions
Layer 3 response_mode="refine" Careful multi-step response generation
Layer 4 Max score threshold check Reject low-confidence answers post-generation

Pydantic AI Classifier Details (✅ Production Tested):

# domain_classifier.py - Uses OpenAI gpt-4o-mini for fast structured classification
# Tested: Successfully blocks "how to cook rice", "what is 2+2", etc.
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIResponsesModel

model = OpenAIResponsesModel("gpt-4o-mini")

class QueryCategory(Enum):
    IREMBO_SERVICE = "irembo_service"  # → Continue to RAG pipeline
    GREETING = "greeting"              # → Direct friendly response
    SMALL_TALK = "small_talk"          # → Direct response (thank you, bye, etc.)
    OFF_TOPIC = "off_topic"            # → Polite decline with redirect

6. Future: Agentic RAG Architecture

Current Limitations of Simple RAG:

  • No reasoning step before answering ✅ Pydantic AI classifier (Layer 0) classifies queries before RAG
  • Off-topic queries bypass domain filtering ✅ Classifier integrated in both streaming & non-streaming endpoints
  • Cannot decompose complex multi-part questions
  • Limited ability to handle follow-up questions with context
  • Single-shot retrieval without query refinement

Reference Implementation: RWAKA.rw - An advanced Agentic RAG system for Rwandan legal precedents that demonstrates reasoning capabilities:

┌─────────────────────────────────────────────────────────────────────────────┐
│  RWAKA EXAMPLE: Agentic RAG with Reasoning                                  │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  User: "how to cook rice"                                                   │
│                                                                             │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │  Reasoning                                                          │   │
│  │  ──────────                                                         │   │
│  │  STEP 1                                                             │   │
│  │                                                                     │   │
│  │  I'm specialized in Rwandan legal precedents and can help retrieve  │   │
│  │  and analyze relevant caselaws. For cooking or culinary questions,  │   │
│  │  you may want to refer to cooking guides or online recipes. If you  │   │
│  │  have any legal-related queries, especially concerning Rwandan      │   │
│  │  law, feel free to ask!                                             │   │
│  └─────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  Key Features:                                                              │
│  • Explicit reasoning step visible to user                                  │
│  • Domain classification before retrieval                                   │
│  • Graceful decline with redirection                                        │
│  • Maintains professional, helpful tone                                     │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Future Agentic RAG Improvements for ARIA:

═══════════════════════════════════════════════════════════════════════════════
                        AGENTIC RAG ARCHITECTURE (FUTURE)
═══════════════════════════════════════════════════════════════════════════════

  User Query: "What documents do I need for a passport, and how much does it cost?"
        │
        ▼
  ┌─────────────────────────────────────────────────────────────────────────┐
  │  QUERY ANALYSIS AGENT                                                   │
  │  ─────────────────────                                                  │
  │  • Decompose into sub-questions:                                        │
  │    1. "What documents are required for passport application?"           │
  │    2. "How much does a passport cost in Rwanda?"                        │
  │  • Classify query type: factual, procedural, comparison                 │
  │  • Detect if follow-up question (use conversation context)              │
  └─────────────────────────────────────────────────────────────────────────┘
        │
        ├──────────────────────┬──────────────────────┐
        ▼                      ▼                      ▼
  ┌────────────┐        ┌────────────┐        ┌────────────┐
  │ Sub-Query  │        │ Sub-Query  │        │ Metadata   │
  │ Retriever  │        │ Retriever  │        │ Filter     │
  │ (docs)     │        │ (fees)     │        │ Agent      │
  └─────┬──────┘        └─────┬──────┘        └─────┬──────┘
        │                      │                      │
        └──────────────────────┴──────────────────────┘
                               │
                               ▼
  ┌─────────────────────────────────────────────────────────────────────────┐
  │  RESPONSE SYNTHESIS AGENT                                               │
  │  ─────────────────────────                                              │
  │  • Combine sub-answers coherently                                       │
  │  • Cross-reference for consistency                                      │
  │  • Add source citations per claim                                       │
  └─────────────────────────────────────────────────────────────────────────┘
        │
        ▼
  ┌─────────────────────────────────────────────────────────────────────────┐
  │  FAITHFULNESS EVALUATOR                                                 │
  │  ─────────────────────────                                              │
  │  • Check each claim against source documents                            │
  │  • Score: faithfulness (0-1), relevancy (0-1)                           │
  │  • If faithfulness < 0.8 → Regenerate or decline                        │
  └─────────────────────────────────────────────────────────────────────────┘

═══════════════════════════════════════════════════════════════════════════════

Recommended Future Enhancements:

Enhancement Technology Benefit
Agentic Reasoning Claude/GPT-4 with CoT prompting Visible reasoning steps like RWAKA (domain check → retrieval decision → response)
Sub-Question Decomposition LlamaIndex SubQuestionQueryEngine Handle complex multi-part questions
Metadata Filtering AutoRetriever with filters Route queries to relevant document types
Reranking Cohere Rerank / BGE Reranker Improve retrieval precision
Faithfulness Evaluation LlamaIndex FaithfulnessEvaluator Verify claims against sources
Hybrid Search BM25 + Vector (Pinecone/Qdrant) Better keyword + semantic matching
Query Routing RouterQueryEngine Different strategies for different query types
Conversation Memory ChatMemoryBuffer Better follow-up question handling
Self-Correction Agent loop with reflection Retry with refined query if confidence low

Implementation Priority:

  1. High: Agentic Reasoning + Reranking (transparency + accuracy)
  2. Medium: Sub-question decomposition + Faithfulness Evaluation
  3. Lower: Full agentic loop with self-correction (requires more compute)

7. Data Privacy (Public Sector Context)

Data Privacy:

  • Session IDs are auto-generated UUIDs (not tied to personal identity)
  • Chat history accessible only via session ID (no user accounts)
  • Redis protected by password authentication
  • Sessions expire after 30 minutes, chat history after 7 days
  • Audio files processed in-memory, not persisted to disk
  • API keys stored in environment variables, never in code
  • Future improvements: encryption at rest, audit logging

Design Choices

Why These Technologies?

Decision Choice Rationale
STT Model Groq Whisper 10x faster than local Whisper, near real-time transcription
LLM GPT-4o-mini Best cost/performance ratio, fast inference
RAG Framework LlamaIndex Better RAG abstractions than LangChain for document Q&A
Embeddings text-embedding-3-small Good accuracy, lower cost, 1536 dimensions
TTS OpenAI gpt-4o-mini-tts Streaming audio support, faster time-to-first-audio
TTS Fallback Groq Orpheus Fallback only (heavily rate-limited on free tier: 3600 TPD)
Vector Store LlamaIndex In-Memory Sufficient for prototype scale, persistent to disk
Cache Redis Fast, supports TTL, production-proven

Latency Optimization Strategies

  1. Response Streaming (SSE): Tokens stream as generated, reducing perceived latency
  2. Streaming TTS: Audio chunks stream as generated using OpenAI gpt-4o-mini-tts, faster time-to-first-audio
  3. Multi-Layer Caching:
    • Embedding cache (24hr TTL) - avoid redundant API calls
    • Query cache (1hr TTL) - instant responses for repeated queries
    • Audio cache (1hr TTL) - skip TTS for cached responses
  4. Connection Pooling: Redis and HTTP clients maintain connection pools
  5. Async Everything: FastAPI async handlers, async Redis, async LLM calls

Performance Benchmarks

Scenario First Response Cached Response Speedup
Text only 2-5s 50-200ms 10-25x
Text + Audio 7-15s 100-500ms 15-30x
Audio input + Audio output 10-35s 500ms-2s 15-35x
Streaming text 2-3s TTFT <100ms TTFT 20-30x

TTFT = Time to First Token

Handling Ambiguity

  1. Confidence Scoring: Each response includes a confidence score (0.0-1.0)
    • Formula: 0.7 * max_score + 0.3 * avg_top_3_scores
    • Displayed to users with High/Medium/Low badges
  2. Source Citations: All answers include source documents with relevance scores
  3. Follow-up Detection: System detects pronouns ("it", "that", "this") and uses conversation history
  4. Out-of-Scope Handling: System prompt instructs to politely decline unrelated queries

Scalability Considerations

For 1,000+ concurrent users:

  1. Horizontal Scaling: Gunicorn with 4 workers per container, scale containers
  2. Redis Cluster: Replace single Redis with Redis Cluster for session distribution
  3. Vector Store: Migrate to Pinecone/Qdrant for distributed vector search
  4. Load Balancer: Traefik/nginx in front of API containers
  5. Rate Limiting: Add per-session rate limits to prevent abuse

Assignment Requirements Checklist

Requirement Status Implementation
Multi-Modal Input Done /api/query/text and /api/query/audio endpoints
Speech Processing (STT) Done Groq Whisper large-v3-turbo
Knowledge Ingestion Done LlamaIndex document loaders (JSON, MD, PDF, DOCX, TXT)
RAG Pattern Done LlamaIndex VectorIndexRetriever + GPT-4o-mini
Text Response Done All endpoints return text answers
Audio Response (TTS) Done Groq Orpheus with OpenAI fallback
Session Management Done Redis-backed sessions with 30min TTL
Low Latency Done Streaming, caching, connection pooling
No Hallucination Done Pydantic AI classifier (Layer 0) + RAG grounding + strict prompts
Production Quality Done Docker, health checks, logging, error handling
Architecture Diagram Done ASCII diagrams in this README
Dockerized Setup Done docker-compose.yml and docker-compose.prod.yml

Project Structure

aria-assistant/
│
├─── backend/
│    │
│    ├─── agent/                          # Core AI Agent Layer
│    │    │
│    │    ├─── rag/                       # RAG Pipeline Components
│    │    │    ├── __init__.py
│    │    │    ├── domain_classifier.py   # Pydantic AI query classifier (GPT-4o-mini)
│    │    │    ├── indexer.py             # Vector index management & persistence
│    │    │    ├── loaders.py             # Document loaders (JSON/MD/PDF/DOCX/TXT)
│    │    │    ├── query_engine.py        # RAG query engine with streaming
│    │    │    └── prompts.py             # QA & Refine prompt templates
│    │    │
│    │    ├─── session/                   # Session Management
│    │    │    ├── __init__.py
│    │    │    └── redis_store.py         # Redis-backed session & cache store
│    │    │
│    │    └─── utils/                     # Utilities
│    │         ├── __init__.py
│    │         └── config.py              # Environment configuration
│    │
│    ├─── api/                            # FastAPI Application
│    │    ├── __init__.py
│    │    ├── server.py                   # FastAPI app entry point
│    │    │
│    │    └─── routes/                    # API Endpoints
│    │         ├── __init__.py
│    │         ├── query.py               # /api/query/* (text, audio, stream)
│    │         ├── documents.py           # /api/documents/* (upload, list, delete)
│    │         ├── health.py              # /health endpoint
│    │         ├── stats.py               # /api/stats endpoint
│    │         └── token.py               # Token validation
│    │
│    ├─── data/
│    │    └─── knowledge/                 # Knowledge base documents (43 JSON files)
│    │
│    ├─── storage/
│    │    └─── index/                     # Persisted vector index
│    │
│    ├── Dockerfile.api                   # Development container
│    ├── Dockerfile.api.prod              # Production container
│    ├── gunicorn.conf.py                 # Gunicorn server config
│    └── pyproject.toml                   # Python dependencies
│
├─── frontend-streamlit/
│    ├── app.py                           # Main chat interface
│    │
│    ├─── pages/
│    │    └── stats.py                    # Performance stats dashboard
│    │
│    ├─── .streamlit/
│    │    └── config.toml                 # Streamlit configuration
│    │
│    ├── requirements.txt                 # Frontend dependencies
│    ├── Dockerfile                       # Development container
│    └── Dockerfile.prod                  # Production container
│
├── docker-compose.yml                    # Development environment
├── docker-compose.prod.yml               # Production environment
├── .env.example.local                    # Local env template
├── .env.example.prod                     # Production env template
├── .gitignore
├── LICENSE                               # MIT License
└── README.md                             # This file

Environment Variables

Variable Required Default Description
OPENAI_API_KEY Yes - OpenAI API key for LLM and embeddings
GROQ_API_KEY Yes - Groq API key for STT and TTS
REDIS_PASSWORD Yes - Redis authentication password
REDIS_URL No redis://localhost:6379 Redis connection URL
API_URL No http://localhost:8000 Backend API URL (for frontend)
LOG_LEVEL No info Logging level
CORS_ORIGINS No * Allowed CORS origins

Development

Local Development (without Docker)

# Backend
cd backend
pip install -e .
uvicorn api.server:app --reload --port 8000

# Frontend
cd frontend-streamlit
pip install -r requirements.txt
streamlit run app.py

Latency Highlights

The caching system delivers exceptional performance gains:

                    CACHE SPEEDUP VISUALIZATION

    First Request:     |████████████████████████████████████| 5-15s
    Cached Request:    |██|                                   50-500ms

    Improvement:       10-35x FASTER
  • Text queries: From 2-5s down to 50-200ms (25x faster)
  • Voice + TTS: From 10-35s down to 500ms-2s (35x faster)
  • Streaming TTFT: From 2-3s down to <100ms (30x faster)

This means returning users get near-instant responses for previously asked questions.

Future Considerations

Short-term Improvements

  • Add Kinyarwanda language support (STT/TTS)
  • Implement conversation memory summarization for longer contexts
  • Add rate limiting per session to prevent abuse
  • WebSocket support for real-time bidirectional audio

Medium-term Enhancements

  • Multi-tenant support (multiple government departments)
  • Admin dashboard for knowledge base management
  • Analytics dashboard for citizen query patterns
  • A/B testing framework for response quality

Long-term Vision

  • On-premise deployment option for sensitive data
  • Fine-tuned model for Rwandan government terminology
  • Integration with actual Irembo service APIs
  • Mobile app with offline-first architecture
  • Multi-language support (French, Swahili)

Security Hardening

  • Encryption at rest for Redis data
  • Audit logging for all queries
  • PII detection and redaction
  • SOC 2 compliance preparation

License

MIT License - See LICENSE file for details.

Acknowledgments

  • Built by @Cedric0852
  • AI-Powered Citizen Support Assistant take-home assignment
  • Designed for Rwanda's Irembo e-government platform context

About

AI-Powered Citizen Support Assistant

aria.lunaroot.rw

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ai-assistant

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Jan 9, 2026

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