| # SPARKNET: Technical Report | |
| ## AI-Powered Multi-Agent System for Research Valorization | |
| --- | |
| ## Table of Contents | |
| 1. [Executive Summary](#1-executive-summary) | |
| 2. [Introduction](#2-introduction) | |
| 3. [System Architecture](#3-system-architecture) | |
| 4. [Theoretical Foundations](#4-theoretical-foundations) | |
| 5. [Core Components](#5-core-components) | |
| 6. [Workflow Engine](#6-workflow-engine) | |
| 7. [Implementation Details](#7-implementation-details) | |
| 8. [Use Case: Patent Wake-Up](#8-use-case-patent-wake-up) | |
| 9. [Performance Considerations](#9-performance-considerations) | |
| 10. [Conclusion](#10-conclusion) | |
| --- | |
| ## 1. Executive Summary | |
| SPARKNET is an autonomous multi-agent AI system designed for research valorization and technology transfer. Built on modern agentic AI principles, it leverages LangGraph for workflow orchestration, LangChain for LLM integration, and ChromaDB for vector-based memory. The system transforms dormant intellectual property into commercialization opportunities throughs a coordinated pipeline of specialized agents. | |
| **Key Capabilities:** | |
| - Multi-agent orchestration with cyclic refinement | |
| - Local LLM deployment via Ollama (privacy-preserving) | |
| - Vector-based episodic and semantic memory | |
| - Automated patent analysis and Technology Readiness Level (TRL) assessment | |
| - Market opportunity identification and stakeholder matching | |
| - Professional valorization brief generation | |
| --- | |
| ## 2. Introduction | |
| ### 2.1 Problem Statement | |
| University technology transfer offices face significant challenges: | |
| - **Volume**: Thousands of patents remain dormant in institutional portfolios | |
| - **Complexity**: Manual analysis requires deep domain expertise | |
| - **Time**: Traditional evaluation takes days to weeks per patent | |
| - **Resources**: Limited staff cannot process the backlog efficiently | |
| ### 2.2 Solution Approach | |
| SPARKNET addresses these challenges through an **agentic AI architecture** that: | |
| 1. Automates document analysis and information extraction | |
| 2. Applies domain expertise through specialized agents | |
| 3. Provides structured, actionable outputs | |
| 4. Learns from past experiences to improve future performance | |
| ### 2.3 Design Principles | |
| | Principle | Implementation | | |
| |-----------|----------------| | |
| | **Autonomy** | Agents operate independently with defined goals | | |
| | **Specialization** | Each agent focuses on specific tasks | | |
| | **Collaboration** | Agents share information through structured state | | |
| | **Iteration** | Quality-driven refinement cycles | | |
| | **Memory** | Vector stores for contextual learning | | |
| | **Privacy** | Local LLM deployment via Ollama | | |
| --- | |
| ## 3. System Architecture | |
| ### 3.1 High-Level Architecture | |
| ``` | |
| ┌──────────────────────────────────────────────────────────────────────┐ | |
| │ SPARKNET SYSTEM │ | |
| ├──────────────────────────────────────────────────────────────────────┤ | |
| │ │ | |
| │ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────┐ │ | |
| │ │ Frontend │ │ Backend │ │ LLM Layer │ │ | |
| │ │ Next.js │◄──►│ FastAPI │◄──►│ Ollama (4 Models) │ │ | |
| │ │ Port 3000 │ │ Port 8000 │ │ - llama3.1:8b │ │ | |
| │ └─────────────┘ └──────┬──────┘ │ - mistral:latest │ │ | |
| │ │ │ - qwen2.5:14b │ │ | |
| │ ▼ │ - gemma2:2b │ │ | |
| │ ┌────────────────┐ └─────────────────────────┘ │ | |
| │ │ LangGraph │ │ | |
| │ │ Workflow │◄──► ChromaDB (Vector Store) │ | |
| │ │ (StateGraph) │ │ | |
| │ └───────┬────────┘ │ | |
| │ │ │ | |
| │ ┌──────────────────┼──────────────────┐ │ | |
| │ ▼ ▼ ▼ │ | |
| │ ┌───────────┐ ┌─────────────┐ ┌───────────┐ │ | |
| │ │ Planner │ │ Executor │ │ Critic │ │ | |
| │ │ Agent │ │ Agents │ │ Agent │ │ | |
| │ └───────────┘ └─────────────┘ └───────────┘ │ | |
| │ │ | |
| │ ┌───────────┐ ┌─────────────┐ ┌───────────┐ │ | |
| │ │ Memory │ │ VisionOCR │ │ Tools │ │ | |
| │ │ Agent │ │ Agent │ │ Registry │ │ | |
| │ └───────────┘ └─────────────┘ └───────────┘ │ | |
| │ │ | |
| └──────────────────────────────────────────────────────────────────────┘ | |
| ``` | |
| ### 3.2 Layer Description | |
| | Layer | Technology | Purpose | | |
| |-------|------------|---------| | |
| | **Presentation** | Next.js, React, TypeScript | User interface, file upload, results display | | |
| | **API** | FastAPI, Python 3.10+ | RESTful endpoints, async processing | | |
| | **Orchestration** | LangGraph (StateGraph) | Workflow execution, conditional routing | | |
| | **Agent** | LangChain, Custom Agents | Task-specific processing | | |
| | **LLM** | Ollama (Local) | Natural language understanding and generation | | |
| | **Memory** | ChromaDB | Vector storage, semantic search | | |
| --- | |
| ## 4. Theoretical Foundations | |
| ### 4.1 Agentic AI Paradigm | |
| SPARKNET implements the modern **agentic AI** paradigm characterized by: | |
| #### 4.1.1 Agent Definition | |
| An agent in SPARKNET is defined as a tuple: | |
| ``` | |
| Agent = (S, A, T, R, π) | |
| ``` | |
| Where: | |
| - **S** = State space (AgentState in LangGraph) | |
| - **A** = Action space (tool calls, LLM invocations) | |
| - **T** = Transition function (workflow edges) | |
| - **R** = Reward signal (validation score) | |
| - **π** = Policy (LLM-based decision making) | |
| #### 4.1.2 Multi-Agent Coordination | |
| The system employs **hierarchical coordination**: | |
| ``` | |
| Coordinator (Workflow) | |
| │ | |
| ┌─────────────────┼─────────────────┐ | |
| ▼ ▼ ▼ | |
| Planner Executors Critic | |
| (Strategic) (Tactical) (Evaluative) | |
| │ │ │ | |
| └────────────────┴─────────────────┘ | |
| ▼ | |
| Shared State (AgentState) | |
| ``` | |
| ### 4.2 State Machine Formalism | |
| The LangGraph workflow is formally a **Finite State Machine with Memory**: | |
| ``` | |
| FSM-M = (Q, Σ, δ, q₀, F, M) | |
| ``` | |
| Where: | |
| - **Q** = {PLANNER, ROUTER, EXECUTOR, CRITIC, REFINE, FINISH} | |
| - **Σ** = Input alphabet (task descriptions, documents) | |
| - **δ** = Transition function (conditional edges) | |
| - **q₀** = PLANNER (initial state) | |
| - **F** = {FINISH} (accepting states) | |
| - **M** = AgentState (memory/context) | |
| ### 4.3 Quality-Driven Refinement | |
| The system implements a **feedback control loop**: | |
| ``` | |
| ┌─────────────────────────────┐ | |
| │ │ | |
| ▼ │ | |
| Input → PLAN → EXECUTE → VALIDATE ──YES──→ OUTPUT | |
| │ | |
| NO (score < threshold) | |
| │ | |
| ▼ | |
| REFINE | |
| │ | |
| └─────────────────→ (back to PLAN) | |
| ``` | |
| **Convergence Condition:** | |
| ``` | |
| terminate iff (validation_score ≥ quality_threshold) OR (iterations ≥ max_iterations) | |
| ``` | |
| ### 4.4 Vector Memory Architecture | |
| The memory system uses **dense vector embeddings** for semantic retrieval: | |
| ``` | |
| Memory Types: | |
| ├── Episodic Memory → Past workflow executions, outcomes | |
| ├── Semantic Memory → Domain knowledge, legal frameworks | |
| └── Stakeholder Memory → Partner profiles, capabilities | |
| ``` | |
| **Retrieval Function:** | |
| ```python | |
| retrieve(query, top_k) = argmax_k(cosine_similarity(embed(query), embed(documents))) | |
| ``` | |
| --- | |
| ## 5. Core Components | |
| ### 5.1 BaseAgent Abstract Class | |
| All agents inherit from `BaseAgent`, providing: | |
| ```python | |
| class BaseAgent(ABC): | |
| """Core agent interface""" | |
| # Attributes | |
| name: str # Agent identifier | |
| description: str # Agent purpose | |
| llm_client: OllamaClient # LLM interface | |
| model: str # Model to use | |
| system_prompt: str # Agent persona | |
| tools: Dict[str, BaseTool] # Available tools | |
| messages: List[Message] # Conversation history | |
| # Core Methods | |
| async def call_llm(prompt, messages, temperature) -> str | |
| async def execute_tool(tool_name, **kwargs) -> ToolResult | |
| async def process_task(task: Task) -> Task # Abstract | |
| async def send_message(recipient: Agent, content: str) -> str | |
| ``` | |
| ### 5.2 Specialized Agents | |
| | Agent | Purpose | Model | Complexity | | |
| |-------|---------|-------|------------| | |
| | **PlannerAgent** | Task decomposition, dependency analysis | qwen2.5:14b | Complex | | |
| | **CriticAgent** | Output validation, quality scoring | mistral:latest | Analysis | | |
| | **MemoryAgent** | Context retrieval, episode storage | nomic-embed-text | Embeddings | | |
| | **VisionOCRAgent** | Image/PDF text extraction | llava:7b | Vision | | |
| | **DocumentAnalysisAgent** | Patent structure extraction | llama3.1:8b | Standard | | |
| | **MarketAnalysisAgent** | Market opportunity identification | mistral:latest | Analysis | | |
| | **MatchmakingAgent** | Stakeholder matching | qwen2.5:14b | Complex | | |
| | **OutreachAgent** | Brief generation | llama3.1:8b | Standard | | |
| ### 5.3 Tool System | |
| Tools extend agent capabilities: | |
| ```python | |
| class BaseTool(ABC): | |
| name: str | |
| description: str | |
| parameters: Dict[str, ToolParameter] | |
| async def execute(**kwargs) -> ToolResult | |
| async def safe_execute(**kwargs) -> ToolResult # With error handling | |
| ``` | |
| **Built-in Tools:** | |
| - `file_reader`, `file_writer`, `file_search`, `directory_list` | |
| - `python_executor`, `bash_executor` | |
| - `gpu_monitor`, `gpu_select` | |
| - `document_generator_tool` (PDF creation) | |
| --- | |
| ## 6. Workflow Engine | |
| ### 6.1 LangGraph StateGraph | |
| The workflow is defined as a directed graph: | |
| ```python | |
| class SparknetWorkflow: | |
| def _build_graph(self) -> StateGraph: | |
| workflow = StateGraph(AgentState) | |
| # Define nodes (processing functions) | |
| workflow.add_node("planner", self._planner_node) | |
| workflow.add_node("router", self._router_node) | |
| workflow.add_node("executor", self._executor_node) | |
| workflow.add_node("critic", self._critic_node) | |
| workflow.add_node("refine", self._refine_node) | |
| workflow.add_node("finish", self._finish_node) | |
| # Define edges (transitions) | |
| workflow.set_entry_point("planner") | |
| workflow.add_edge("planner", "router") | |
| workflow.add_edge("router", "executor") | |
| workflow.add_edge("executor", "critic") | |
| # Conditional routing based on validation | |
| workflow.add_conditional_edges( | |
| "critic", | |
| self._should_refine, | |
| {"refine": "refine", "finish": "finish"} | |
| ) | |
| workflow.add_edge("refine", "planner") # Cyclic refinement | |
| workflow.add_edge("finish", END) | |
| return workflow | |
| ``` | |
| ### 6.2 AgentState Schema | |
| The shared state passed between nodes: | |
| ```python | |
| class AgentState(TypedDict): | |
| # Message History (auto-managed by LangGraph) | |
| messages: Annotated[Sequence[BaseMessage], add_messages] | |
| # Task Information | |
| task_id: str | |
| task_description: str | |
| scenario: ScenarioType # PATENT_WAKEUP, AGREEMENT_SAFETY, etc. | |
| status: TaskStatus # PENDING → PLANNING → EXECUTING → VALIDATING → COMPLETED | |
| # Workflow Execution | |
| current_agent: Optional[str] | |
| iteration_count: int | |
| max_iterations: int | |
| # Planning Outputs | |
| subtasks: Optional[List[Dict]] | |
| execution_order: Optional[List[List[str]]] | |
| # Execution Outputs | |
| agent_outputs: Dict[str, Any] | |
| intermediate_results: List[Dict] | |
| # Validation | |
| validation_score: Optional[float] | |
| validation_feedback: Optional[str] | |
| validation_issues: List[str] | |
| validation_suggestions: List[str] | |
| # Memory Context | |
| retrieved_context: List[Dict] | |
| document_metadata: Dict[str, Any] | |
| input_data: Dict[str, Any] | |
| # Final Output | |
| final_output: Optional[Any] | |
| success: bool | |
| error: Optional[str] | |
| # Timing | |
| start_time: datetime | |
| end_time: Optional[datetime] | |
| execution_time_seconds: Optional[float] | |
| ``` | |
| ### 6.3 Workflow Execution Flow | |
| ``` | |
| ┌─────────────────────────────────────────────────────────────────────┐ | |
| │ WORKFLOW EXECUTION FLOW │ | |
| ├─────────────────────────────────────────────────────────────────────┤ | |
| │ │ | |
| │ 1. PLANNER NODE │ | |
| │ ├─ Retrieve context from MemoryAgent │ | |
| │ ├─ Decompose task into subtasks │ | |
| │ ├─ Determine execution order (dependency resolution) │ | |
| │ └─ Output: subtasks[], execution_order[] │ | |
| │ │ │ | |
| │ ▼ │ | |
| │ 2. ROUTER NODE │ | |
| │ ├─ Identify scenario type (PATENT_WAKEUP, etc.) │ | |
| │ ├─ Select appropriate executor agents │ | |
| │ └─ Output: agents_to_use[] │ | |
| │ │ │ | |
| │ ▼ │ | |
| │ 3. EXECUTOR NODE │ | |
| │ ├─ Route to scenario-specific pipeline │ | |
| │ │ └─ Patent Wake-Up: Doc → Market → Match → Outreach │ | |
| │ ├─ Execute each specialized agent sequentially │ | |
| │ └─ Output: agent_outputs{}, final_output │ | |
| │ │ │ | |
| │ ▼ │ | |
| │ 4. CRITIC NODE │ | |
| │ ├─ Validate output quality (0.0-1.0 score) │ | |
| │ ├─ Identify issues and suggestions │ | |
| │ └─ Output: validation_score, validation_feedback │ | |
| │ │ │ | |
| │ ▼ │ | |
| │ 5. CONDITIONAL ROUTING │ | |
| │ ├─ IF score ≥ threshold (0.85) → FINISH │ | |
| │ ├─ IF iterations ≥ max → FINISH (with warning) │ | |
| │ └─ ELSE → REFINE → back to PLANNER │ | |
| │ │ │ | |
| │ ▼ │ | |
| │ 6. FINISH NODE │ | |
| │ ├─ Store episode in MemoryAgent (if quality ≥ 0.75) │ | |
| │ ├─ Calculate execution statistics │ | |
| │ └─ Return WorkflowOutput │ | |
| │ │ | |
| └─────────────────────────────────────────────────────────────────────┘ | |
| ``` | |
| --- | |
| ## 7. Implementation Details | |
| ### 7.1 LLM Integration (Ollama) | |
| SPARKNET uses **Ollama** for local LLM deployment: | |
| ```python | |
| class LangChainOllamaClient: | |
| """LangChain-compatible Ollama client with model routing""" | |
| COMPLEXITY_MODELS = { | |
| "simple": "gemma2:2b", # Classification, routing | |
| "standard": "llama3.1:8b", # General tasks | |
| "analysis": "mistral:latest", # Analysis, reasoning | |
| "complex": "qwen2.5:14b", # Complex multi-step | |
| } | |
| def get_llm(self, complexity: str) -> ChatOllama: | |
| """Get LLM instance for specified complexity level""" | |
| model = self.COMPLEXITY_MODELS.get(complexity, "llama3.1:8b") | |
| return ChatOllama(model=model, base_url=self.base_url) | |
| def get_embeddings(self) -> OllamaEmbeddings: | |
| """Get embeddings model for vector operations""" | |
| return OllamaEmbeddings(model="nomic-embed-text:latest") | |
| ``` | |
| ### 7.2 Memory System (ChromaDB) | |
| Three specialized collections: | |
| ```python | |
| class MemoryAgent: | |
| def _initialize_collections(self): | |
| # Episodic: Past workflow executions | |
| self.episodic_memory = Chroma( | |
| collection_name="episodic_memory", | |
| embedding_function=self.embeddings, | |
| persist_directory="data/vector_store/episodic" | |
| ) | |
| # Semantic: Domain knowledge | |
| self.semantic_memory = Chroma( | |
| collection_name="semantic_memory", | |
| embedding_function=self.embeddings, | |
| persist_directory="data/vector_store/semantic" | |
| ) | |
| # Stakeholders: Partner profiles | |
| self.stakeholder_profiles = Chroma( | |
| collection_name="stakeholder_profiles", | |
| embedding_function=self.embeddings, | |
| persist_directory="data/vector_store/stakeholders" | |
| ) | |
| ``` | |
| ### 7.3 Pydantic Data Models | |
| Structured outputs ensure type safety: | |
| ```python | |
| class PatentAnalysis(BaseModel): | |
| patent_id: str | |
| title: str | |
| abstract: str | |
| independent_claims: List[Claim] | |
| dependent_claims: List[Claim] | |
| ipc_classification: List[str] | |
| technical_domains: List[str] | |
| key_innovations: List[str] | |
| trl_level: int = Field(ge=1, le=9) | |
| trl_justification: str | |
| commercialization_potential: str # High/Medium/Low | |
| potential_applications: List[str] | |
| confidence_score: float = Field(ge=0.0, le=1.0) | |
| class MarketOpportunity(BaseModel): | |
| sector: str | |
| market_size_usd: Optional[float] | |
| growth_rate_percent: Optional[float] | |
| technology_fit: str # Excellent/Good/Fair | |
| priority_score: float = Field(ge=0.0, le=1.0) | |
| class StakeholderMatch(BaseModel): | |
| stakeholder_name: str | |
| stakeholder_type: str # Investor/Company/University | |
| overall_fit_score: float | |
| technical_fit: float | |
| market_fit: float | |
| geographic_fit: float | |
| match_rationale: str | |
| recommended_approach: str | |
| ``` | |
| --- | |
| ## 8. Use Case: Patent Wake-Up | |
| ### 8.1 Scenario Overview | |
| The **Patent Wake-Up** workflow transforms dormant patents into commercialization opportunities: | |
| ``` | |
| Patent Document → Analysis → Market Opportunities → Partner Matching → Valorization Brief | |
| ``` | |
| ### 8.2 Pipeline Execution | |
| ```python | |
| async def _execute_patent_wakeup(self, state: AgentState) -> AgentState: | |
| """Four-stage Patent Wake-Up pipeline""" | |
| # Stage 1: Document Analysis | |
| doc_agent = DocumentAnalysisAgent(llm_client, memory_agent, vision_ocr_agent) | |
| patent_analysis = await doc_agent.analyze_patent(patent_path) | |
| # Output: PatentAnalysis (title, claims, TRL, innovations) | |
| # Stage 2: Market Analysis | |
| market_agent = MarketAnalysisAgent(llm_client, memory_agent) | |
| market_analysis = await market_agent.analyze_market(patent_analysis) | |
| # Output: MarketAnalysis (opportunities, sectors, strategy) | |
| # Stage 3: Stakeholder Matching | |
| matching_agent = MatchmakingAgent(llm_client, memory_agent) | |
| matches = await matching_agent.find_matches(patent_analysis, market_analysis) | |
| # Output: List[StakeholderMatch] (scored partners) | |
| # Stage 4: Brief Generation | |
| outreach_agent = OutreachAgent(llm_client, memory_agent) | |
| brief = await outreach_agent.create_valorization_brief( | |
| patent_analysis, market_analysis, matches | |
| ) | |
| # Output: ValorizationBrief (markdown + PDF) | |
| return state | |
| ``` | |
| ### 8.3 Example Output | |
| ```yaml | |
| Patent: AI-Powered Drug Discovery Platform | |
| ───────────────────────────────────────────── | |
| Technology Assessment: | |
| TRL Level: 7/9 (System Demonstration) | |
| Key Innovations: | |
| • Novel neural network for molecular interaction prediction | |
| • Transfer learning from existing drug databases | |
| • Automated screening pipeline (60% time reduction) | |
| Market Opportunities (Top 3): | |
| 1. Pharmaceutical R&D Automation ($150B market, 12% CAGR) | |
| 2. Biotechnology Platform Services ($45B market, 15% CAGR) | |
| 3. Clinical Trial Optimization ($8B market, 18% CAGR) | |
| Top Partner Matches: | |
| 1. PharmaTech Solutions Inc. (Basel) - 92% fit score | |
| 2. BioVentures Capital (Toronto) - 88% fit score | |
| 3. European Patent Office Services (Munich) - 85% fit score | |
| Output: outputs/valorization_brief_patent_20251204.pdf | |
| ``` | |
| --- | |
| ## 9. Performance Considerations | |
| ### 9.1 Model Selection Strategy | |
| | Task Complexity | Model | VRAM | Latency | | |
| |-----------------|-------|------|---------| | |
| | Simple (routing, classification) | gemma2:2b | 1.6 GB | ~1s | | |
| | Standard (extraction, generation) | llama3.1:8b | 4.9 GB | ~3s | | |
| | Analysis (reasoning, evaluation) | mistral:latest | 4.4 GB | ~4s | | |
| | Complex (planning, multi-step) | qwen2.5:14b | 9.0 GB | ~8s | | |
| ### 9.2 GPU Resource Management | |
| ```python | |
| class GPUManager: | |
| """Multi-GPU resource allocation""" | |
| def select_best_gpu(self, min_memory_gb: float = 4.0) -> int: | |
| """Select GPU with most available memory""" | |
| gpus = self.get_gpu_status() | |
| available = [g for g in gpus if g.free_memory_gb >= min_memory_gb] | |
| return max(available, key=lambda g: g.free_memory_gb).id | |
| @contextmanager | |
| def gpu_context(self, min_memory_gb: float): | |
| """Context manager for GPU allocation""" | |
| gpu_id = self.select_best_gpu(min_memory_gb) | |
| os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id) | |
| yield gpu_id | |
| ``` | |
| ### 9.3 Workflow Timing | |
| | Stage | Typical Duration | Notes | | |
| |-------|------------------|-------| | |
| | Planning | 5-10s | Depends on task complexity | | |
| | Document Analysis | 15-30s | OCR adds ~10s for scanned PDFs | | |
| | Market Analysis | 10-20s | Context retrieval included | | |
| | Stakeholder Matching | 20-40s | Semantic search + scoring | | |
| | Brief Generation | 15-25s | Includes PDF rendering | | |
| | Validation | 5-10s | Per iteration | | |
| | **Total** | **2-5 minutes** | Single patent, no refinement | | |
| ### 9.4 Scalability | |
| - **Batch Processing**: Process multiple patents in parallel | |
| - **ChromaDB Capacity**: Supports 10,000+ stakeholder profiles | |
| - **Checkpointing**: Resume failed workflows from last checkpoint | |
| - **Memory Persistence**: Vector stores persist across sessions | |
| --- | |
| ## 10. Conclusion | |
| ### 10.1 Summary | |
| SPARKNET demonstrates a practical implementation of **agentic AI** for research valorization: | |
| 1. **Multi-Agent Architecture**: Specialized agents collaborate through shared state | |
| 2. **LangGraph Orchestration**: Cyclic workflows with quality-driven refinement | |
| 3. **Local LLM Deployment**: Privacy-preserving inference via Ollama | |
| 4. **Vector Memory**: Contextual learning from past experiences | |
| 5. **Structured Outputs**: Pydantic models ensure data integrity | |
| ### 10.2 Key Contributions | |
| | Aspect | Innovation | | |
| |--------|------------| | |
| | **Architecture** | Hierarchical multi-agent system with conditional routing | | |
| | **Workflow** | State machine with memory and iterative refinement | | |
| | **Memory** | Tri-partite vector store (episodic, semantic, stakeholder) | | |
| | **Privacy** | Full local deployment without cloud dependencies | | |
| | **Output** | Professional PDF briefs with actionable recommendations | | |
| ### 10.3 Future Directions | |
| 1. **LangSmith Integration**: Observability and debugging | |
| 2. **Real Stakeholder Database**: CRM integration for live partner data | |
| 3. **Scenario Expansion**: Agreement Safety, Partner Matching workflows | |
| 4. **Multi-Language Support**: International patent processing | |
| 5. **Advanced Learning**: Reinforcement learning from user feedback | |
| --- | |
| ## Appendix A: Technology Stack | |
| | Component | Technology | Version | | |
| |-----------|------------|---------| | |
| | Runtime | Python | 3.10+ | | |
| | Orchestration | LangGraph | 0.2+ | | |
| | LLM Framework | LangChain | 1.0+ | | |
| | Local LLM | Ollama | Latest | | |
| | Vector Store | ChromaDB | 1.3+ | | |
| | API | FastAPI | 0.100+ | | |
| | Frontend | Next.js | 16+ | | |
| | Validation | Pydantic | 2.0+ | | |
| ## Appendix B: Model Requirements | |
| ```bash | |
| # Required models (download via Ollama) | |
| ollama pull llama3.1:8b # Standard tasks (4.9 GB) | |
| ollama pull mistral:latest # Analysis tasks (4.4 GB) | |
| ollama pull qwen2.5:14b # Complex reasoning (9.0 GB) | |
| ollama pull gemma2:2b # Simple routing (1.6 GB) | |
| ollama pull nomic-embed-text # Embeddings (274 MB) | |
| ollama pull llava:7b # Vision/OCR (optional, 4.7 GB) | |
| ``` | |
| ## Appendix C: Running SPARKNET | |
| ```bash | |
| # 1. Start Ollama server | |
| ollama serve | |
| # 2. Activate environment | |
| conda activate sparknet | |
| # 3. Start backend | |
| cd /home/mhamdan/SPARKNET | |
| python -m uvicorn api.main:app --reload --port 8000 | |
| # 4. Start frontend (separate terminal) | |
| cd frontend && npm run dev | |
| # 5. Access application | |
| # Frontend: http://localhost:3000 | |
| # API Docs: http://localhost:8000/api/docs | |
| ``` | |
| --- | |
| **Document Generated:** December 2025 | |
| **SPARKNET Version:** 1.0 (Production Ready) | |