OMNIMIND

OMNIMIND is a decentralized AI knowledge infrastructure that transforms unstructured data into intelligent, queryable knowledge graphs for Web3 AI agents. We solve the critical knowledge gap in the rapidly growing Web3 AI ecosystem by providing the foundational layer that makes AI agents truly intelligent without expensive fine-tuning.

The Problem: With 1K+ Web3 AI agents projected by 2025, current solutions focus on compute but ignore the knowledge layer. Agents can execute transactions but lack domain-specific intelligence about DeFi protocols, governance proposals, or market dynamics. Existing solutions require expensive fine-tuning ($50K-$500K per model) or rely on centralized knowledge silos.

Our Solution: OMNIMIND acts as "The Chainlink of AI Knowledge" - a universal, chain-agnostic infrastructure layer that enables AI agents to access domain-specific intelligence through a sustainable pay-per-query model.

Key Features:

Multi-modal Processing: Upload PDFs, images, videos, and audio files Automatic Knowledge Extraction: AI-powered knowledge graph generation using Mistral 7B Decentralized Storage: Distributed across Walrus, Akave O3, Filecoin, and Lighthouse Smart Retrieval: RAG (Retrieval-Augmented Generation) with semantic search and caching Universal Data ID System: Content-addressed storage enabling verifiable, cross-platform access Pay-Per-Query Economics: Sustainable model benefiting both data providers and consumers Use Cases:

DeFi Agents: Real-time protocol intelligence and risk assessment Governance Agents: Comprehensive proposal analysis and voting recommendations Trading Agents: Multi-modal market signals from news, charts, and social sentiment Enterprise Solutions: Private knowledge networks with client-side encryption Market Opportunity: The AI agent market is projected to grow from $4.2B to $47B by 2030, with Web3 representing a significant untapped segment. OMNIMIND addresses the foundational infrastructure need that enables this growth.

OMNIMIND leverages cutting-edge Web3 infrastructure and AI technologies to create a robust, decentralized knowledge processing pipeline:

Core Architecture:

Storage Layer (Multi-Protocol Approach):

Walrus: Erasure-coded blob storage for raw data files with built-in redundancy Akave O3: S3-compatible decentralized storage for fast knowledge graph access Lighthouse SDK: IPFS-based storage with client-side encryption for embeddings Filecoin (via Synapse SDK): Warm storage for knowledge graph metadata and indices MongoDB: Fallback document store ensuring 99.9% availability AI/ML Processing Pipeline:

Mistral 7B (GGUF): Local language model for knowledge graph extraction with 30-core optimization Sentence Transformers: Dual embedding models (384D/1024D) for semantic search BLIP-2: Image captioning and visual content analysis Pixtral: Advanced multi-modal image understanding via HTTP API Smart Model Selection: Automatic embedding model matching for query consistency Backend Infrastructure:

FastAPI: High-performance Python server with async processing Thread Optimization: 30 cores for Mistral/image processing, 16 cores for embeddings RAG Caching: 30-second TTL cache system for performance optimization Batch Processing: Configurable batch sizes for efficient embedding generation Notable Technical Innovations:

1. Universal Data ID System: We created a content-addressed identification system (CID::storage_type:location) that enables verifiable, cross-platform data access. This allows the same knowledge to be accessed regardless of the underlying storage backend.

2. Smart Model Selection: Our system automatically detects which embedding model was used during storage (384D vs 1024D) and matches it during query time, ensuring dimensional consistency and optimal retrieval accuracy.

3. Reliable Fallback Architecture: When Mistral 7B fails to generate valid JSON knowledge graphs, our system automatically falls back to a deterministic extraction algorithm that analyzes text patterns and creates structured knowledge representations.

4. Multi-Modal Knowledge Fusion: We process images through multiple AI models (BLIP-2 for basic captioning, Pixtral for detailed analysis) and fuse the results into comprehensive knowledge graphs that capture both visual and textual information.

Partner Technology Integration:

Walrus Integration: Provides erasure-coded storage ensuring data availability even with node failures. This gives us enterprise-grade reliability for raw data storage.

Akave O3: S3-compatible API allows seamless integration with existing cloud workflows while maintaining decentralization. Critical for enterprise adoption.

Lighthouse SDK: Enables client-side encryption and IPFS storage, ensuring data privacy while maintaining content-addressable benefits.

Synapse SDK: Provides warm storage on Filecoin with built-in retrieval guarantees, essential for knowledge graph metadata that needs frequent access.

Particularly Hacky/Notable Elements:

1. Thread Management Optimization: We dynamically adjust PyTorch thread counts based on operation type - 30 cores for CPU-intensive Mistral inference, 16 cores for embedding generation to prevent resource contention.

2. JSON Extraction from LLM Output: Implemented a robust JSON parser that handles malformed LLM output by tracking brace counts and attempting multiple extraction strategies before falling back to deterministic methods.

3. Embedding Payload Packing: Created a custom serialization format that efficiently stores embeddings, texts, and metadata together while maintaining compatibility with different storage backends.

4. Cross-Storage Retrieval: Built a unified query interface that can seamlessly retrieve knowledge graphs from either O3 or MongoDB based on the data ID format, abstracting storage complexity from users.

The result is a production-ready system that processes multi-modal data, generates intelligent knowledge representations, and serves them through a decentralized infrastructure - all while maintaining sub-second query performance and 99.9% uptime.