| # 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) |
|
|
