VKRA Protocol

Open Source LLMA Framework for Contextual Product Matching in LLM Applications

Vision

Revolutionizing accessible advertisements in LLMs. VKRA Protocol provides a modular, open-source framework for contextual product matching that enables developers to build monetization into their LLM applications while maintaining user trust and conversation quality.

What is VKRA Protocol?

VKRA Protocol is a modular LLMA (Large Language Model Advertising) framework that implements the "Polite Ad Network" approach. It provides pluggable modules for:

• Presentation: Creating product cards without modifying LLM responses
• Commission: Extracting affiliate commission rates from product data
• Prediction: Calculating conversion probability using personalization
• Ranking: Balancing revenue (commission × conversion) with user satisfaction

The framework is database-agnostic and infrastructure-independent, making it easy to integrate into any application.

Architecture

Module Pipeline

┌─────────────────────────────────────────────────────────┐
│              LLMAOrchestrator                           │
│  Coordinates the complete matching pipeline             │
└─────────────────────────────────────────────────────────┘
                        │
        ┌───────────────┼───────────────┐
        │               │               │
        ▼               ▼               ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ Presentation │ │  Commission  │ │  Prediction  │
│   Module     │ │   Module     │ │   Module     │
└──────────────┘ └──────────────┘ └──────────────┘
        │               │               │
        └───────────────┼───────────────┘
                        │
                        ▼
                ┌──────────────┐
                │   Ranking    │
                │   Module     │
                └──────────────┘

Core Components

1. LLMAOrchestrator: Coordinates all modules in the pipeline
2. VectorDatabase Interface: Abstract interface for vector search
3. UserProfileStore Interface: Abstract interface for user profile management
4. Module Base Classes: Pluggable interfaces for all modules

Key Features

• 🎯 Contextual Matching: Intent-based product recommendations using embeddings
• 🔄 Modular Design: Pluggable modules for easy customization
• 🛡️ Privacy-First: GDPR-compliant with pseudonymization and differential privacy
• ⚡ High Performance: Optimized for sub-200ms latency
• 🔌 Database Agnostic: Works with any vector database implementation
• 📊 Polite Ad Network: Balances revenue with user trust (SR_query metric)

Installation

pip install vkra-protocol

Or install from source:

git clone https://github.com/vkra/vkra-protocol.git
cd vkra-protocol
pip install -e .

Quick Start

1. Implement Database Interfaces

First, implement the abstract interfaces for your database:

from vkra_protocol import VectorDatabase, UserProfileStore
from typing import Any
from uuid import UUID

class MyVectorDatabase(VectorDatabase):
    async def search(
        self,
        query_embedding: list[float],
        limit: int = 10,
        filters: dict[str, Any] | None = None,
        user_profile_embedding: list[float] | None = None,
        query_weight: float = 0.7,
        profile_weight: float = 0.3,
    ) -> tuple[list[dict[str, Any]], int]:
        # Your vector search implementation
        # Returns (products list, total_matches count)
        pass

class MyUserProfileStore(UserProfileStore):
    async def get_user_profile(
        self,
        user_id: UUID,
        privacy_level: str = "standard",
    ) -> dict[str, Any] | None:
        # Your user profile retrieval
        # Returns profile dict or None
        pass
    
    async def update_profile_from_context(
        self,
        user_id: UUID,
        active_context: str,
        background: bool = True,
    ) -> None:
        # Your profile update logic
        pass

2. Set Up Embedding and LLM Services

Default: Local Ollama (Zero-Friction Setup)

# Install Ollama (if not already installed)
# Visit https://ollama.ai for installation instructions

# Pull the Qwen3 embedding model
ollama pull qwen3-embedding

from vkra_protocol import LLMAOrchestrator, EmbeddingService, LLMService

# Initialize services (defaults to local Ollama)
embedding_service = EmbeddingService()  # Uses local Ollama by default
llm_service = LLMService()  # Uses local Ollama by default

# Or use OpenAI (optional)
# Set environment variables:
# EMBED_PROVIDER=openai
# LLM_PROVIDER=openai
# OPENAI_API_KEY=your-key

3. Initialize Orchestrator

from vkra_protocol import LLMAOrchestrator, EmbeddingService, LLMService

# Initialize your implementations
vector_db = MyVectorDatabase()
profile_store = MyUserProfileStore()
embedding_service = EmbeddingService()  # Default: local Ollama
llm_service = LLMService()  # Default: local Ollama

# Create orchestrator
orchestrator = LLMAOrchestrator(
    vector_db=vector_db,
    profile_store=profile_store,
    embedding_service=embedding_service,
    llm_service=llm_service,
)

4. Execute Search

from vkra_protocol.schemas import SearchRequest, UserContext
from uuid import uuid4

# Create search request
request = SearchRequest(
    query="wireless headphones for running",
    context=UserContext(
        user_id=uuid4(),  # Optional: for personalization
        session_id=uuid4(),
        privacy_level="standard",
    ),
    limit=3,
)

# Execute search
response, embedding_time, search_time, cache_hit = await orchestrator.execute_search(request)

# Access results
for result in response.results:
    print(f"{result['title']} - {result['price']}")
    print(f"Relevance: {result['relevance_score']:.2f}")
    print(f"Commission: {result.get('commission_rate', 0):.1%}")

Environment Variables

Default (Local Ollama):

• EMBED_PROVIDER=local (default)
• LOCAL_EMBED_URL=http://localhost:11434/api/embeddings (default)
• EMBED_MODEL=qwen3-embedding (default)
• LLM_PROVIDER=local (default)
• LOCAL_LLM_URL=http://localhost:11434/api/chat (default)
• LLM_MODEL=qwen3:8b (default)

Optional (OpenAI):

• EMBED_PROVIDER=openai (to use OpenAI)
• OPENAI_API_KEY: Required if using OpenAI
• OPENAI_EMBED_MODEL=text-embedding-3-small (default)
• OPENAI_LLM_MODEL=gpt-4o-mini (default)

Module Configuration

Customize module behavior per request:

from vkra_protocol.schemas import ModuleConfig, SearchRequest

module_config = ModuleConfig(
    presentation_module="product_card",
    commission_module="extract",
    prediction_module="conversion",
    ranking_module="affiliate",
    module_params={
        "prediction_sr_query_threshold": 0.7,  # Polite Ad filter
        "prediction_sr_query_weight": 0.3,
        "prediction_sr_history_weight": 0.5,
        "ranking_sr_query_weight": 0.6,
        "ranking_sr_history_weight": 0.4,
    },
)

request = SearchRequest(
    query="laptop for programming",
    module_config=module_config,
    limit=5,
)

Module Documentation

Presentation Module

Creates product cards from database results. Does NOT modify LLM response text - preserves agent's voice.

Interface: PresentationModule.create_product_cards()

Implementation: ProductCardPresentationModule

Commission Module

Extracts commission rates from product data. Rates are stored in product metadata (no external lookup).

Interface: CommissionModule.extract_commission_rates()

Implementation: CommissionExtractionModule

Prediction Module

Calculates:

• SR_query: Query-based satisfaction rate (Polite Ad metric - measures interruption)
• SR_history: History-based satisfaction rate (personalization)
• Conversion probability: Combined prediction

Interface: PredictionModule.predict_conversion()

Implementation: ConversionPredictionModule

Polite Ad Filter: Products with SR_query < threshold (default 0.7) are filtered out to maintain conversation quality.

Ranking Module

Ranks products using:

Score = Commission × Conversion × (SR_query × w1 + SR_history × w2)

This balances revenue with user trust.

Interface: RankingModule.rank_products()

Implementation: AffiliateRankingModule

User Preference Module

Handles user profile management with GDPR compliance:

• Profile retrieval and summarization
• Embedding generation
• Privacy safeguards (pseudonymization, differential privacy)

Interface: UserPreferenceModule.get_user_profile(), update_profile_from_context()

Implementation: UserPreferenceMaintenanceModule

Privacy & GDPR Compliance

VKRA Protocol supports three privacy levels:

• minimal: Query-only, no user profile
• standard: Pseudonymized user IDs, profile embeddings
• full: Differential privacy noise added to embeddings

context = UserContext(
    user_id=user_id,
    privacy_level="standard",  # or "minimal", "full"
    include_history=True,
)

Examples

See the examples/ directory for complete working examples:

• Basic search
• Personalized search with user profiles
• Custom module configuration
• Integration with FastAPI

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

Architecture Principles

1. Interface First: All modules use abstract base classes
2. Database Agnostic: No hardcoded database dependencies
3. Async Everything: All I/O operations are async
4. Privacy by Design: GDPR compliance built-in
5. Performance First: Optimized for low latency

Performance Targets

• End-to-end latency: < 200ms
• Profile loading: < 50ms (cached)
• Vector search: < 100ms
• Module pipeline: < 50ms

License

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

Cloud Offering

For a managed service with:

• Pre-configured databases (LanceDB Cloud, Supabase)
• API key management
• Analytics dashboard
• MCP server integration

Visit: https://www.vkra.org

Support

• Documentation: https://docs.vkra.org
• Issues: GitHub Issues
• Email: hello@vkra.org

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Get started today: https://www.vkra.org