| 1. Overall System Architecture | |
| Frontend (Next.js 16.0.1 + React) | |
| - Technology: Next.js 16 with Turbopack, React, TypeScript | |
| - Styling: Tailwind CSS, Shadcn UI components | |
| - Animation: Framer Motion for smooth transitions | |
| - Real-time Updates: WebSocket connection for live workflow progress | |
| - Port: Running on port 3000 (http://172.24.50.21:3000) | |
| - Features: | |
| - Responsive drag-and-drop PDF upload (max 50MB) | |
| - Real-time workflow progress monitoring | |
| - Interactive results visualization | |
| - PDF download for valorization briefs | |
| Backend (FastAPI + Python) | |
| - Framework: FastAPI (async Python web framework) | |
| - Port: Running on port 8000 (http://172.24.50.21:8000) | |
| - API Endpoints: | |
| - /api/health - Health check | |
| - /api/patents/upload - Patent PDF upload | |
| - /api/workflows/execute - Start workflow | |
| - /api/workflows/{id}/stream - WebSocket for real-time updates | |
| - /api/workflows/{id}/brief/download - Download PDF brief | |
| - GPU: Running on GPU1 (CUDA_VISIBLE_DEVICES=1) | |
| - Environment: Python 3.10 with conda environment agentic-ai | |
| --- | |
| 2. AI/LLM Architecture | |
| Multi-Model LLM Strategy | |
| - Model Provider: Ollama (local LLM serving) | |
| - 4 Different Models for different complexity levels: | |
| a. gemma2:2b - Simple/fast tasks | |
| b. llama3.1:8b - Standard complexity (default) | |
| c. qwen2.5:14b - Complex reasoning tasks | |
| d. mistral:latest - Analysis and assessment tasks | |
| LangChain Integration | |
| - Framework: LangChain for LLM orchestration | |
| - Output Parsing: JsonOutputParser for structured outputs | |
| - Prompt Engineering: ChatPromptTemplate for consistent prompting | |
| - Embeddings: OllamaEmbeddings for semantic search | |
| --- | |
| 3. Multi-Agent System (LangGraph Workflow) | |
| Core Workflow Engine | |
| - Framework: LangGraph StateGraph (state machine for agent coordination) | |
| - Pattern: Agentic workflow with iterative refinement | |
| - Max Iterations: 3 refinement cycles with critic feedback | |
| 7 Specialized AI Agents: | |
| 1. PlannerAgent (Complexity: Complex - qwen2.5:14b) | |
| - Role: Orchestrates workflow, creates task decomposition | |
| - Function: Breaks down patent analysis into 4 subtasks | |
| - Template: Uses predefined template for "patent_wakeup" scenario | |
| 2. DocumentAnalysisAgent (Complexity: Standard - llama3.1:8b) | |
| - Role: Analyzes patent documents | |
| - Tasks: | |
| - Extract patent structure (title, abstract, claims, inventors) | |
| - Assess Technology Readiness Level (TRL 1-9) | |
| - Identify key innovations and technical domains | |
| - Evaluate commercialization potential | |
| - Tools: PDF extractor, semantic memory retrieval | |
| - Chains: | |
| - Structure extraction chain (JSON parser) | |
| - Assessment chain (technology evaluation) | |
| 3. MarketAnalysisAgent (Complexity: Analysis - mistral:latest) | |
| - Role: Analyzes market opportunities | |
| - Tasks: | |
| - Identify 3-5 industry sectors | |
| - Assess market readiness (Ready/Emerging/Early) | |
| - Evaluate competitive landscape | |
| - Identify geographic focus (EU, Canada priority for VISTA) | |
| - Current Config: Market size and TAM set to None (displays as "NaN") for early-stage demo | |
| - Output: 4-5 MarketOpportunity objects ranked by priority score | |
| 4. MatchmakingAgent (Complexity: Standard - llama3.1:8b) | |
| - Role: Finds potential partners/stakeholders | |
| - Method: Semantic search using vector embeddings | |
| - Database: ChromaDB with stakeholder profiles | |
| - Scoring: | |
| - Technical fit score | |
| - Market fit score | |
| - Geographic fit score | |
| - Strategic fit score | |
| - Overall fit score (composite) | |
| - Output: Top 10 stakeholder matches | |
| 5. OutreachAgent (Complexity: Standard - llama3.1:8b) | |
| - Role: Generates valorization briefs | |
| - Tasks: | |
| - Create executive summary | |
| - Generate comprehensive brief content | |
| - Format market opportunities and partner recommendations | |
| - Generate PDF document using ReportLab | |
| - Chains: | |
| - Brief content generation chain | |
| - Executive summary extraction chain | |
| - Output: PDF file + structured ValorizationBrief object | |
| 6. CriticAgent (Complexity: Analysis - mistral:latest) | |
| - Role: Quality assurance and validation | |
| - Tasks: | |
| - Validates workflow outputs | |
| - Identifies gaps and issues | |
| - Provides feedback for refinement | |
| - Scores quality (0.0-1.0) | |
| - Criteria: Completeness, accuracy, actionability | |
| 7. MemoryAgent (ChromaDB Vector Store) | |
| - Role: Persistent knowledge management | |
| - Storage: 3 ChromaDB collections: | |
| a. episodic_memory - Past workflow executions | |
| b. semantic_memory - Domain knowledge and context | |
| c. stakeholder_profiles - Partner database (11 profiles currently) | |
| - Retrieval: Semantic search using embeddings (top-k results) | |
| - Purpose: Contextual awareness across sessions | |
| --- | |
| 4. LangGraph Workflow Nodes | |
| State Machine Flow: | |
| START → PLANNER → ROUTER → EXECUTOR → CRITIC → REFINE? → FINISH | |
| ↑ | | |
| └────────┘ | |
| (if refinement needed) | |
| Node Breakdown: | |
| 1. PLANNER Node: | |
| - Retrieves relevant context from memory | |
| - Creates 4-subtask plan from template | |
| - Identifies scenario type (patent_wakeup) | |
| 2. ROUTER Node: | |
| - Routes to appropriate execution pipeline based on scenario | |
| - Currently: Patent Wake-Up pipeline | |
| 3. EXECUTOR Node: | |
| - Executes 4-step pipeline: | |
| - Step 1/4: Document Analysis (extract + assess patent) | |
| - Step 2/4: Market Analysis (identify opportunities) | |
| - Step 3/4: Partner Matching (find stakeholders) | |
| - Step 4/4: Brief Generation (create PDF) | |
| 4. CRITIC Node: | |
| - Validates output quality | |
| - Generates quality score and feedback | |
| - Determines if refinement needed | |
| 5. REFINE Node: | |
| - Prepares for next iteration if quality insufficient | |
| - Max 3 iterations, then finishes anyway | |
| 6. FINISH Node: | |
| - Marks workflow as completed | |
| - Stores results in memory | |
| - Updates workflow state | |
| --- | |
| 5. Data Flow & Communication | |
| Upload to Results Flow: | |
| User uploads PDF → FastAPI saves to uploads/patents/ | |
| → Generates UUID for patent | |
| → Returns patent_id to frontend | |
| User clicks analyze → Frontend calls /api/workflows/execute | |
| → Backend creates workflow_id | |
| → Starts async LangGraph workflow | |
| → Returns workflow_id immediately | |
| Frontend opens WebSocket → ws://backend:8000/api/workflows/{id}/stream | |
| → Backend streams workflow state every 1 second | |
| → Frontend updates UI in real-time | |
| Workflow completes → State = "completed" | |
| → Brief PDF generated | |
| → Frontend redirects to /results/{workflow_id} | |
| User downloads brief → GET /api/workflows/{id}/brief/download | |
| → Returns PDF file | |
| WebSocket Real-Time Updates: | |
| - Protocol: WebSocket (bidirectional) | |
| - Frequency: Updates sent every 1 second | |
| - Data: Full workflow state (JSON) | |
| - Retry Logic: Frontend auto-reconnects on disconnect | |
| - Fallback: HTTP polling if WebSocket fails | |
| --- | |
| 6. Key Technologies & Libraries | |
| Backend Stack: | |
| - FastAPI - Async web framework | |
| - Uvicorn - ASGI server | |
| - LangChain - LLM orchestration | |
| - LangGraph - Agent workflow state machine | |
| - ChromaDB - Vector database for embeddings | |
| - Pydantic - Data validation and serialization | |
| - ReportLab - PDF generation | |
| - PyPDF - PDF text extraction | |
| - Loguru - Structured logging | |
| - PyTorch - GPU acceleration | |
| Frontend Stack: | |
| - Next.js 16 - React framework with Turbopack | |
| - React 19 - UI library | |
| - TypeScript - Type safety | |
| - Tailwind CSS - Utility-first styling | |
| - Shadcn/UI - Component library | |
| - Framer Motion - Animation library | |
| - Axios - HTTP client | |
| - Lucide React - Icon library | |
| --- | |
| 7. Pydantic Data Models | |
| Core Models (src/workflow/langgraph_state.py): | |
| 1. Claim: Patent claim structure | |
| 2. PatentAnalysis: Complete patent analysis (17 fields) | |
| 3. MarketOpportunity: Individual market sector (12 fields) | |
| 4. MarketAnalysis: Market research results (10 fields) | |
| 5. StakeholderMatch: Partner match (11 fields) | |
| 6. ValorizationBrief: Outreach document (9 fields) | |
| 7. WorkflowState: Complete workflow state (9 fields) | |
| All models use strict validation with Pydantic v2. | |
| --- | |
| 8. Error Handling & Fixes Applied | |
| Recent Bug Fixes: | |
| 1. JSON Parsing: Enhanced prompts to force pure JSON output (no prose) | |
| 2. Pydantic Validation: Use or operators for None handling | |
| 3. Claims Parsing: Filter None values in claims arrays | |
| 4. Market Values: Handle None gracefully (display "NaN") | |
| 5. WebSocket: Fixed React re-render loop, added cleanup flags | |
| 6. Download Brief: Handle None values in nested dicts | |
| Logging Strategy: | |
| - Loguru for structured logging | |
| - Levels: DEBUG, INFO, SUCCESS, WARNING, ERROR | |
| - Files: | |
| - /tmp/backend_sparknet.log - Backend logs | |
| - /tmp/frontend_sparknet.log - Frontend logs | |
| --- | |
| 9. GPU & Performance | |
| GPU Configuration: | |
| - GPU Used: GPU1 (CUDA_VISIBLE_DEVICES=1) | |
| - Memory: ~10GB required for all 4 LLMs | |
| - Inference: Ollama handles model loading and caching | |
| Performance Metrics: | |
| - Document Analysis: ~10-15 seconds | |
| - Market Analysis: ~15-20 seconds | |
| - Partner Matching: ~60-75 seconds (LLM scoring) | |
| - Brief Generation: ~5-10 seconds | |
| - Total Workflow: ~2-3 minutes per patent | |
| --- | |
| 10. Current Configuration (Demo Mode) | |
| Market Data Placeholders: | |
| - market_size_usd = None → displays "NaN" | |
| - total_addressable_market_usd = None → displays "NaN" | |
| - growth_rate_percent = None → displays "NaN" | |
| - funding_capacity_usd = None → displays "NaN" | |
| Reasoning: | |
| - Early-stage research phase | |
| - Avoid false commitments with speculative numbers | |
| - Focus on technical capabilities and partner matching | |
| --- | |
| 11. Screen Sessions | |
| Currently Running: | |
| - Backend: 1082974.sparknet-backend (screen -r sparknet-backend) | |
| - Frontend: 1074449.sparknet-frontend (screen -r sparknet-frontend) | |
| To View Logs: | |
| tail -f /tmp/backend_sparknet.log | |
| tail -f /tmp/frontend_sparknet.log | |
| --- | |
| 12. API Health Check | |
| Endpoint: GET /api/health | |
| { | |
| "status": "healthy", | |
| "components": { | |
| "llm_client": true, | |
| "workflow": true, | |
| "planner": true, | |
| "critic": true, | |
| "memory": true | |
| }, | |
| "statistics": { | |
| "active_workflows": 0, | |
| "processed_patents": 0 | |
| } | |
| } | |
| --- | |
| Summary for Demo | |
| SPARKNET is a multi-agent AI system that transforms dormant patents into commercialization opportunities using: | |
| 1. 4 Local LLMs (2B to 14B parameters) for different complexity tasks | |
| 2. 7 Specialized AI Agents orchestrated by LangGraph state machine | |
| 3. Vector Database (ChromaDB) for semantic partner matching | |
| 4. Real-time WebSocket streaming for live progress updates | |
| 5. Automated PDF Generation for professional valorization briefs | |
| 6. End-to-end Pipeline: Upload → Analyze → Match → Generate → Download | |
| Current Mode: Early-stage demo with placeholder market values ("NaN") to avoid premature commitments while showcasing technical capabilities. | |
| ● Perfect! I've created a comprehensive summary of the SPARKNET system architecture. This covers everything from the high-level architecture down to the technical details of what's | |
| happening under the hood. | |
| Key Points for Your Demo: | |
| The Big Picture: | |
| - Multi-agent AI system with 7 specialized agents | |
| - 4 different LLMs (2B-14B parameters) running locally on GPU1 | |
| - Real-time WebSocket streaming for live progress | |
| - End-to-end automation: Upload PDF → AI Analysis → Partner Matching → PDF Brief | |
| Technical Highlights: | |
| - LangGraph state machine orchestrating agent collaboration | |
| - ChromaDB vector database for semantic partner matching | |
| - Iterative refinement with CriticAgent (max 3 cycles) | |
| - Pydantic models for strict data validation | |
| - FastAPI backend + Next.js 16 frontend | |
| Demo Mode Settings: | |
| - All market/financial values set to "NaN" (early-stage, no false commitments) | |
| - Focus on technical capabilities and partner matching accuracy | |
| - ~2-3 minutes per patent workflow | |