From 847a1b446695105ee75b32a6e191e7bedd8297c4 Mon Sep 17 00:00:00 2001 From: jravenel Date: Tue, 5 Aug 2025 13:14:55 +0200 Subject: [PATCH] research: DPROD implementation strategy and documentation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit COMPLETE DPROD (Data Product Ontology) RESEARCH: 📋 STRATEGIC DOCUMENTATION: - README.md: Research overview and navigation - strategy.md: Business case and positioning - implementation-scope.md: 5-phase implementation plan - technical-architecture.md: Technical design patterns - integration-roadmap.md: 20-week timeline with milestones - api-specifications.md: Enterprise API specifications 📊 PRACTICAL EXAMPLES: - examples/qwen-agent-dprod.json: DPROD-compliant agent metadata - examples/observability-example.json: Lineage and metrics tracking - examples/sparql-queries.md: 15 SPARQL query examples 🎯 VALUE PROPOSITION: Position ABI as first DPROD-compliant AI agent platform enabling: - Semantic agent discovery via SPARQL - Enterprise data catalog integration - W3C standards compliance for governance - Automated observability and lineage tracking This research establishes foundation for future DPROD implementation. --- docs/research/DPROD/README.md | 84 +++ docs/research/DPROD/api-specifications.md | 488 +++++++++++++ .../DPROD/examples/observability-example.json | 166 +++++ .../DPROD/examples/qwen-agent-dprod.json | 151 ++++ .../research/DPROD/examples/sparql-queries.md | 416 +++++++++++ docs/research/DPROD/implementation-scope.md | 366 ++++++++++ docs/research/DPROD/integration-roadmap.md | 485 +++++++++++++ docs/research/DPROD/strategy.md | 179 +++++ docs/research/DPROD/technical-architecture.md | 663 ++++++++++++++++++ 9 files changed, 2998 insertions(+) create mode 100644 docs/research/DPROD/README.md create mode 100644 docs/research/DPROD/api-specifications.md create mode 100644 docs/research/DPROD/examples/observability-example.json create mode 100644 docs/research/DPROD/examples/qwen-agent-dprod.json create mode 100644 docs/research/DPROD/examples/sparql-queries.md create mode 100644 docs/research/DPROD/implementation-scope.md create mode 100644 docs/research/DPROD/integration-roadmap.md create mode 100644 docs/research/DPROD/strategy.md create mode 100644 docs/research/DPROD/technical-architecture.md diff --git a/docs/research/DPROD/README.md b/docs/research/DPROD/README.md new file mode 100644 index 000000000..e8029cfaf --- /dev/null +++ b/docs/research/DPROD/README.md @@ -0,0 +1,84 @@ +# DPROD Implementation Strategy for ABI + +**Data Product Ontology (DPROD) Integration Research** + +This directory contains the strategic planning and implementation guidance for integrating the [W3C Data Product Ontology (DPROD)](https://ekgf.github.io/dprod/) standard into the ABI (Agentic Brain Infrastructure) system. + +## Overview + +DPROD is a W3C standard for describing data products using Linked Data principles. By implementing DPROD compliance in ABI, we can: + +- **Standardize AI agent metadata** using industry standards +- **Enable agent discoverability** through semantic queries +- **Track conversation lineage** across multi-agent flows +- **Provide observability** into agent performance and usage +- **Ensure interoperability** with enterprise data governance systems + +## Documentation Structure + +### Strategic Planning +- **[Strategy](./strategy.md)** - High-level strategic approach and business rationale +- **[Implementation Scope](./implementation-scope.md)** - Detailed scope, phases, and deliverables +- **[Technical Architecture](./technical-architecture.md)** - Technical implementation details and design patterns + +### Implementation Guidance +- **[Integration Roadmap](./integration-roadmap.md)** - Timeline, milestones, and dependencies +- **[Examples](./examples/)** - Concrete implementation examples and use cases +- **[API Specifications](./api-specifications.md)** - DPROD-compliant API designs + +### Research & Analysis +- **[DPROD Analysis](./dprod-analysis.md)** - Analysis of DPROD specification relevance to ABI +- **[Competitive Analysis](./competitive-analysis.md)** - How DPROD implementation differentiates ABI +- **[Standards Compliance](./standards-compliance.md)** - Alignment with W3C and enterprise standards + +## Quick Start + +1. **Read the [Strategy](./strategy.md)** to understand the business case +2. **Review [Implementation Scope](./implementation-scope.md)** for technical details +3. **Explore [Examples](./examples/)** for concrete use cases +4. **Check [Integration Roadmap](./integration-roadmap.md)** for timeline + +## Key Benefits for ABI + +### For Organizations +- **Data Governance Compliance**: Meet enterprise data management requirements +- **Agent Discoverability**: Find the right AI agent for specific tasks +- **Performance Analytics**: Data-driven insights into agent effectiveness +- **Lineage Tracking**: Understand conversation flows and decision paths + +### For Developers +- **Standard Metadata**: Consistent agent descriptions across platforms +- **Semantic Queries**: SPARQL-based agent discovery and analytics +- **Observability APIs**: Built-in monitoring and metrics collection +- **Enterprise Integration**: Native compatibility with data catalog systems + +### For Users +- **Better Agent Selection**: Intelligent routing based on capabilities +- **Transparency**: Clear understanding of which models are being used +- **Quality Assurance**: Metrics-driven agent performance visibility +- **Consistent Experience**: Standardized interfaces across all agents + +## Implementation Status + +| Component | Status | Priority | Notes | +|-----------|--------|----------|-------| +| Strategy Definition | ✅ Complete | High | Business case established | +| Technical Architecture | 🔄 In Progress | High | Core patterns defined | +| Ontology Module Enhancement | 📋 Planned | High | Extend existing module | +| Agent Registration System | 📋 Planned | Medium | DPROD-compliant metadata | +| Observability Framework | 📋 Planned | Medium | Metrics collection | +| Query Interface | 📋 Planned | Low | SPARQL endpoint | + +## Next Steps + +1. **Finalize technical architecture** based on existing ABI patterns +2. **Create proof-of-concept** implementation for one agent module +3. **Develop integration strategy** with existing ontology module +4. **Plan observability framework** for agent metrics collection +5. **Design DPROD-compliant APIs** for external system integration + +--- + +**Research Lead**: ABI Development Team +**Last Updated**: January 2025 +**Status**: Strategic Planning Phase \ No newline at end of file diff --git a/docs/research/DPROD/api-specifications.md b/docs/research/DPROD/api-specifications.md new file mode 100644 index 000000000..858c06caf --- /dev/null +++ b/docs/research/DPROD/api-specifications.md @@ -0,0 +1,488 @@ +# DPROD API Specifications + +## Overview + +This document defines the API specifications for interacting with DPROD-compliant data in the ABI system. These APIs enable enterprise systems to discover agents, query metadata, track lineage, and monitor performance using standard DPROD formats. + +## Base Configuration + +**Base URL**: `https://api.abi.naas.ai/v1/dprod` +**Authentication**: Bearer token (OAuth 2.0 / API Key) +**Content-Type**: `application/json` or `application/ld+json` + +## Agent Discovery APIs + +### List All Data Products (Agents) + +**Endpoint**: `GET /data-products` + +**Description**: Retrieve all AI agents as DPROD-compliant data products. + +**Query Parameters**: +- `category` (optional): Filter by category (`cloud`, `local`, `utility`) +- `capability` (optional): Filter by capability (e.g., `coding`, `reasoning`) +- `privacy_level` (optional): Filter by privacy level (`local`, `cloud`) +- `performance_tier` (optional): Filter by performance (`high`, `medium`, `standard`) +- `format` (optional): Response format (`json`, `jsonld`, `turtle`, `rdf-xml`) + +**Example Request**: +```http +GET /v1/dprod/data-products?capability=coding&privacy_level=local&format=jsonld +Authorization: Bearer your-api-token +``` + +**Example Response**: +```json +{ + "@context": "https://ekgf.github.io/dprod/dprod.jsonld", + "dataProducts": [ + { + "id": "https://abi.naas.ai/data-product/qwen", + "type": "DataProduct", + "label": "Qwen Local AI Agent", + "description": "Privacy-focused multilingual AI agent via Ollama", + "abi:capabilities": ["coding", "multilingual", "reasoning"], + "abi:privacyLevel": "local", + "abi:performanceTier": "standard", + "inputPort": { + "id": "https://abi.naas.ai/data-product/qwen/input", + "type": "DataService", + "endpointURL": "https://api.naas.ai/agents/qwen/chat" + }, + "outputPort": [ + { + "id": "https://abi.naas.ai/data-product/qwen/output", + "type": "DataService" + } + ] + } + ], + "totalCount": 1, + "page": 1, + "pageSize": 50 +} +``` + +### Get Specific Data Product + +**Endpoint**: `GET /data-products/{agent-name}` + +**Description**: Retrieve detailed DPROD metadata for a specific agent. + +**Path Parameters**: +- `agent-name`: Name of the agent (e.g., `qwen`, `chatgpt`, `claude`) + +**Example Request**: +```http +GET /v1/dprod/data-products/qwen +Authorization: Bearer your-api-token +``` + +## Agent Discovery & Selection APIs + +### Find Agents by Capability + +**Endpoint**: `POST /agents/discover` + +**Description**: Discover agents using semantic queries based on capabilities and requirements. + +**Request Body**: +```json +{ + "requirements": { + "capabilities": ["coding", "reasoning"], + "privacyLevel": "local", + "maxResponseTime": 3000, + "languages": ["en", "zh"] + }, + "ranking": { + "criteria": ["performance", "privacy", "cost"], + "weights": [0.4, 0.4, 0.2] + }, + "limit": 5 +} +``` + +**Example Response**: +```json +{ + "recommendations": [ + { + "agent": "https://abi.naas.ai/data-product/qwen", + "label": "Qwen Local AI Agent", + "score": 0.92, + "reasoning": "High privacy (local), supports coding and reasoning, multilingual", + "matchedCapabilities": ["coding", "reasoning"], + "estimatedResponseTime": 1200 + }, + { + "agent": "https://abi.naas.ai/data-product/deepseek", + "label": "DeepSeek R1 Agent", + "score": 0.87, + "reasoning": "Local deployment, excellent reasoning capabilities", + "matchedCapabilities": ["reasoning"], + "estimatedResponseTime": 1800 + } + ], + "totalFound": 2 +} +``` + +## SPARQL Query API + +### Execute SPARQL Query + +**Endpoint**: `POST /sparql` + +**Description**: Execute SPARQL queries against the DPROD knowledge graph. + +**Request Body**: +```json +{ + "query": "PREFIX dprod: PREFIX abi: SELECT ?agent ?label ?capabilities WHERE { ?agent a dprod:DataProduct . ?agent rdfs:label ?label . ?agent abi:capabilities ?capabilities . FILTER(CONTAINS(?capabilities, \"coding\")) }", + "format": "json", + "reasoning": true +} +``` + +**Example Response**: +```json +{ + "head": { + "vars": ["agent", "label", "capabilities"] + }, + "results": { + "bindings": [ + { + "agent": { + "type": "uri", + "value": "https://abi.naas.ai/data-product/qwen" + }, + "label": { + "type": "literal", + "value": "Qwen Local AI Agent" + }, + "capabilities": { + "type": "literal", + "value": "coding,multilingual,reasoning" + } + } + ] + }, + "executionTime": 47 +} +``` + +### Predefined Query Templates + +**Endpoint**: `GET /sparql/templates` + +**Description**: Get predefined SPARQL query templates for common use cases. + +**Example Response**: +```json +{ + "templates": [ + { + "id": "find-by-capability", + "name": "Find Agents by Capability", + "description": "Find agents with specific capabilities", + "query": "PREFIX dprod: SELECT ?agent ?label WHERE { ?agent a dprod:DataProduct . ?agent rdfs:label ?label . ?agent abi:capabilities ?cap . FILTER(CONTAINS(?cap, \"{{capability}}\")) }", + "parameters": [ + { + "name": "capability", + "type": "string", + "description": "Required capability (e.g., coding, reasoning)" + } + ] + } + ] +} +``` + +## Observability APIs + +### Get Agent Metrics + +**Endpoint**: `GET /observability/{agent-name}` + +**Description**: Retrieve observability data for a specific agent. + +**Path Parameters**: +- `agent-name`: Name of the agent + +**Query Parameters**: +- `start_time`: Start time (ISO 8601) +- `end_time`: End time (ISO 8601) +- `metrics`: Comma-separated list of metrics (`response_time`, `token_usage`, `success_rate`) +- `aggregation`: Aggregation level (`raw`, `hourly`, `daily`) + +**Example Request**: +```http +GET /v1/dprod/observability/qwen?start_time=2025-01-04T00:00:00Z&end_time=2025-01-04T23:59:59Z&metrics=response_time,success_rate&aggregation=hourly +``` + +**Example Response**: +```json +{ + "@context": "https://ekgf.github.io/dprod/dprod.jsonld", + "agent": "https://abi.naas.ai/data-product/qwen", + "timeWindow": { + "start": "2025-01-04T00:00:00Z", + "end": "2025-01-04T23:59:59Z", + "aggregation": "hourly" + }, + "metrics": [ + { + "timestamp": "2025-01-04T15:00:00Z", + "responseTimeMs": { + "avg": 1247.5, + "min": 456.2, + "max": 3421.8, + "p95": 2156.7 + }, + "successRate": 0.984, + "requestCount": 127 + } + ], + "conformsTo": "https://abi.naas.ai/schema/AgentObservability" +} +``` + +### System Performance Dashboard + +**Endpoint**: `GET /observability/dashboard` + +**Description**: Get system-wide performance metrics and analytics. + +**Example Response**: +```json +{ + "systemMetrics": { + "totalAgents": 13, + "activeAgents": 11, + "totalRequests24h": 1247, + "avgSystemResponseTime": 1756.3, + "overallSuccessRate": 0.987 + }, + "topAgents": [ + { + "agent": "chatgpt", + "requests": 203, + "avgResponseTime": 1789.2, + "successRate": 0.995 + }, + { + "agent": "qwen", + "requests": 127, + "avgResponseTime": 1156.7, + "successRate": 0.984 + } + ], + "capabilityUsage": { + "coding": 34.2, + "analysis": 23.1, + "general": 19.4, + "creative": 12.8, + "research": 10.5 + } +} +``` + +## Lineage Tracking APIs + +### Get Conversation Lineage + +**Endpoint**: `GET /lineage/conversations/{conversation-id}` + +**Description**: Retrieve the complete lineage for a conversation showing agent handoffs and data flow. + +**Path Parameters**: +- `conversation-id`: Unique conversation identifier + +**Example Response**: +```json +{ + "@context": [ + "https://ekgf.github.io/dprod/dprod.jsonld", + {"prov": "http://www.w3.org/ns/prov#"} + ], + "conversationId": "conv-12345", + "startTime": "2025-01-04T15:30:00Z", + "endTime": "2025-01-04T15:32:15Z", + "steps": [ + { + "stepNumber": 1, + "activity": "initial_routing", + "from": "user", + "to": "abi", + "timestamp": "2025-01-04T15:30:00Z", + "duration": "PT2S" + }, + { + "stepNumber": 2, + "activity": "agent_execution", + "from": "abi", + "to": "qwen", + "timestamp": "2025-01-04T15:30:02Z", + "duration": "PT43S", + "reason": "local_privacy_preferred" + } + ], + "summary": { + "totalSteps": 4, + "agentsUsed": ["abi", "qwen", "claude"], + "totalDuration": "PT2M15S" + } +} +``` + +### Agent Transition Analytics + +**Endpoint**: `GET /lineage/transitions` + +**Description**: Analyze common agent transition patterns across conversations. + +**Query Parameters**: +- `start_date`: Start date for analysis +- `end_date`: End date for analysis +- `min_occurrences`: Minimum occurrences to include + +**Example Response**: +```json +{ + "transitions": [ + { + "from": "abi", + "to": "chatgpt", + "count": 78, + "percentage": 31.2, + "avgDuration": "PT45S", + "successRate": 0.995 + }, + { + "from": "abi", + "to": "qwen", + "count": 45, + "percentage": 18.0, + "avgDuration": "PT38S", + "successRate": 0.984 + } + ], + "totalTransitions": 250, + "analysisWindow": "P7D" +} +``` + +## Data Catalog Integration APIs + +### Export for Enterprise Catalogs + +**Endpoint**: `GET /export/{format}` + +**Description**: Export agent metadata in enterprise data catalog formats. + +**Path Parameters**: +- `format`: Export format (`datahub`, `purview`, `collibra`, `atlas`) + +**Query Parameters**: +- `agents`: Comma-separated list of agents to export (optional, defaults to all) +- `include_observability`: Include observability metadata (default: false) + +**Example Request**: +```http +GET /v1/dprod/export/datahub?agents=qwen,chatgpt&include_observability=true +``` + +### Webhook Registration + +**Endpoint**: `POST /webhooks` + +**Description**: Register webhooks for real-time notifications of metadata changes. + +**Request Body**: +```json +{ + "url": "https://your-system.com/webhooks/abi-metadata", + "events": ["agent_registered", "metadata_updated", "performance_alert"], + "filters": { + "agents": ["qwen", "chatgpt"], + "metrics": ["response_time", "success_rate"] + }, + "auth": { + "type": "bearer", + "token": "your-webhook-token" + } +} +``` + +## Authentication & Security + +### API Key Management + +**Endpoint**: `POST /auth/keys` + +**Description**: Generate API keys with specific permissions. + +**Request Body**: +```json +{ + "name": "Enterprise Data Catalog Integration", + "permissions": [ + "dprod:read", + "sparql:execute", + "observability:read", + "lineage:read" + ], + "expiresAt": "2026-01-04T00:00:00Z", + "ipWhitelist": ["10.0.0.0/8", "192.168.1.100"] +} +``` + +### Rate Limiting + +All APIs are subject to rate limiting: +- **Standard**: 1000 requests/hour +- **Premium**: 10000 requests/hour +- **Enterprise**: Custom limits + +Rate limit headers: +``` +X-RateLimit-Limit: 1000 +X-RateLimit-Remaining: 999 +X-RateLimit-Reset: 1641024000 +``` + +## Error Handling + +### Standard Error Response + +```json +{ + "error": { + "code": "INVALID_QUERY", + "message": "SPARQL query syntax error at line 2", + "details": { + "line": 2, + "column": 15, + "suggestion": "Missing closing quote" + }, + "timestamp": "2025-01-04T15:30:45Z", + "requestId": "req-12345" + } +} +``` + +### Error Codes + +| Code | HTTP Status | Description | +|------|-------------|-------------| +| `INVALID_QUERY` | 400 | Malformed SPARQL query | +| `AGENT_NOT_FOUND` | 404 | Requested agent does not exist | +| `UNAUTHORIZED` | 401 | Invalid or missing authentication | +| `RATE_LIMITED` | 429 | Too many requests | +| `INTERNAL_ERROR` | 500 | Server error | + +--- + +These APIs provide comprehensive access to DPROD-compliant data while maintaining enterprise-grade security and performance standards. \ No newline at end of file diff --git a/docs/research/DPROD/examples/observability-example.json b/docs/research/DPROD/examples/observability-example.json new file mode 100644 index 000000000..4c00bd3b8 --- /dev/null +++ b/docs/research/DPROD/examples/observability-example.json @@ -0,0 +1,166 @@ +{ + "@context": { + "dprod": "https://ekgf.github.io/dprod/", + "prov": "http://www.w3.org/ns/prov#", + "dcat": "http://www.w3.org/ns/dcat#", + "abi": "https://naas.ai/abi/ontology/", + "xsd": "http://www.w3.org/2001/XMLSchema#" + }, + "@type": "dprod:ObservabilityReport", + "@id": "abi:observability/2025-01-15", + "dprod:reportDate": "2025-01-15T10:30:00Z", + "dprod:dataProduct": "abi:agents/qwen", + "dprod:metrics": { + "performance": { + "avgResponseTime": { + "value": 1250, + "unit": "milliseconds", + "timestamp": "2025-01-15T10:30:00Z" + }, + "throughput": { + "value": 45.2, + "unit": "requests_per_minute", + "timestamp": "2025-01-15T10:30:00Z" + }, + "errorRate": { + "value": 0.02, + "unit": "percentage", + "timestamp": "2025-01-15T10:30:00Z" + } + }, + "quality": { + "accuracyScore": { + "value": 0.89, + "unit": "percentage", + "measurement": "user_satisfaction_rating" + }, + "completenessScore": { + "value": 0.94, + "unit": "percentage", + "measurement": "response_completeness" + }, + "relevanceScore": { + "value": 0.91, + "unit": "percentage", + "measurement": "contextual_relevance" + } + }, + "usage": { + "activeUsers": { + "value": 127, + "unit": "count", + "period": "last_24_hours" + }, + "totalConversations": { + "value": 1842, + "unit": "count", + "period": "last_24_hours" + }, + "avgConversationLength": { + "value": 8.3, + "unit": "exchanges", + "period": "last_24_hours" + } + } + }, + "dprod:dataLineage": { + "@type": "prov:Activity", + "@id": "abi:conversation/conv-123456", + "prov:startedAtTime": "2025-01-15T10:15:00Z", + "prov:endedAtTime": "2025-01-15T10:18:00Z", + "prov:used": [ + { + "@id": "abi:input/user-query-123456", + "@type": "prov:Entity", + "prov:value": "Help me write a Python function for data validation", + "prov:generatedAtTime": "2025-01-15T10:15:00Z" + }, + { + "@id": "abi:models/qwen3:8b", + "@type": "prov:Entity", + "prov:label": "Qwen3 8B Model", + "dprod:modelVersion": "v2.5.0" + } + ], + "prov:generated": [ + { + "@id": "abi:output/response-123456", + "@type": "prov:Entity", + "prov:value": "Here's a comprehensive Python function for data validation...", + "prov:generatedAtTime": "2025-01-15T10:17:45Z", + "dprod:responseLength": 1247, + "dprod:codeSnippets": 1 + } + ], + "prov:wasAssociatedWith": { + "@id": "abi:agents/qwen", + "@type": "prov:Agent", + "prov:label": "Qwen AI Agent" + } + }, + "dprod:alertsAndIncidents": [ + { + "@type": "dprod:Alert", + "@id": "abi:alert/response-time-high", + "dprod:severity": "warning", + "dprod:description": "Response time exceeded 2s threshold", + "dprod:timestamp": "2025-01-15T10:25:00Z", + "dprod:affectedMetric": "avgResponseTime", + "dprod:currentValue": 2350, + "dprod:threshold": 2000, + "dprod:status": "resolved", + "dprod:resolvedAt": "2025-01-15T10:28:00Z" + } + ], + "dprod:dataQualityIssues": [ + { + "@type": "dprod:DataQualityIssue", + "@id": "abi:quality-issue/incomplete-response", + "dprod:issueType": "completeness", + "dprod:description": "Some responses were truncated due to context window limits", + "dprod:affectedRecords": 23, + "dprod:detectionTime": "2025-01-15T09:45:00Z", + "dprod:severity": "medium", + "dprod:remediation": "Implement context window management for long conversations" + } + ], + "dprod:complianceStatus": { + "privacyCompliance": { + "status": "compliant", + "lastAudit": "2025-01-10T00:00:00Z", + "details": "Local processing ensures no data leaves user environment" + }, + "dataRetention": { + "status": "compliant", + "policy": "conversations_deleted_after_session", + "implementation": "automatic" + }, + "accessControl": { + "status": "compliant", + "mechanism": "local_user_access_only", + "lastReview": "2025-01-10T00:00:00Z" + } + }, + "dprod:recommendations": [ + { + "@type": "dprod:Recommendation", + "dprod:category": "performance", + "dprod:priority": "medium", + "dprod:description": "Consider implementing response caching for frequently asked questions", + "dprod:expectedImpact": "20% reduction in response time" + }, + { + "@type": "dprod:Recommendation", + "dprod:category": "quality", + "dprod:priority": "high", + "dprod:description": "Implement context window management to prevent response truncation", + "dprod:expectedImpact": "Improved response completeness" + } + ], + "dprod:generatedBy": { + "@id": "abi:monitoring/observability-engine", + "@type": "prov:SoftwareAgent", + "prov:label": "ABI Observability Engine", + "dprod:version": "1.0.0" + } +} \ No newline at end of file diff --git a/docs/research/DPROD/examples/qwen-agent-dprod.json b/docs/research/DPROD/examples/qwen-agent-dprod.json new file mode 100644 index 000000000..0f2d2de60 --- /dev/null +++ b/docs/research/DPROD/examples/qwen-agent-dprod.json @@ -0,0 +1,151 @@ +{ + "@context": { + "dprod": "https://ekgf.github.io/dprod/", + "dcat": "http://www.w3.org/ns/dcat#", + "foaf": "http://xmlns.com/foaf/0.1/", + "schema": "https://schema.org/", + "abi": "https://naas.ai/abi/ontology/", + "xsd": "http://www.w3.org/2001/XMLSchema#" + }, + "@type": "dprod:DataProduct", + "@id": "abi:agents/qwen", + "dcat:title": "Qwen AI Agent", + "dcat:description": "Local privacy-focused AI agent powered by Qwen3 8B model via Ollama, specialized in multilingual conversations, code generation, and general problem-solving.", + "schema:version": "1.0.0", + "dprod:domain": "artificial-intelligence", + "dprod:dataProductOwner": { + "@type": "foaf:Agent", + "foaf:name": "ABI System", + "foaf:mbox": "support@naas.ai" + }, + "dprod:lifecycle": "production", + "dprod:maturityLevel": "beta", + "dcat:keyword": [ + "ai-agent", + "multilingual", + "local-deployment", + "privacy-focused", + "code-generation", + "ollama", + "qwen3" + ], + "dprod:purpose": [ + "privacy-focused conversational AI", + "multilingual communication", + "code generation and assistance", + "local AI deployment" + ], + "dprod:informationSensitivityClassification": "internal", + "dprod:personalDataHandling": "none", + "dcat:distribution": [ + { + "@type": "dcat:Distribution", + "@id": "abi:agents/qwen/api", + "dcat:title": "Qwen Agent API", + "dcat:format": "application/json", + "dcat:accessService": { + "@type": "dcat:DataService", + "dcat:endpointURL": "https://api.naas.ai/agents/qwen", + "dcat:servesDataset": "abi:agents/qwen" + } + }, + { + "@type": "dcat:Distribution", + "@id": "abi:agents/qwen/local", + "dcat:title": "Local Ollama Deployment", + "dcat:format": "ollama/model", + "dprod:deploymentMode": "local", + "dprod:computeRequirements": { + "memory": "8GB", + "storage": "5GB", + "gpu": "optional" + } + } + ], + "dprod:inputDatasets": [ + { + "@type": "dprod:InputDataset", + "@id": "abi:conversations/qwen", + "dcat:title": "User Conversations", + "dcat:description": "Conversational inputs for processing", + "dprod:dataFormat": "text/plain" + } + ], + "dprod:outputDatasets": [ + { + "@type": "dprod:OutputDataset", + "@id": "abi:responses/qwen", + "dcat:title": "AI Agent Responses", + "dcat:description": "Generated responses and assistance", + "dprod:dataFormat": "text/plain" + }, + { + "@type": "dprod:OutputDataset", + "@id": "abi:code/qwen", + "dcat:title": "Generated Code", + "dcat:description": "Code snippets and programming assistance", + "dprod:dataFormat": "text/code" + } + ], + "dprod:qualityMetrics": [ + { + "@type": "dprod:QualityMetric", + "dprod:metricType": "response_time", + "dprod:targetValue": "<2000ms", + "dprod:description": "Local processing response time" + }, + { + "@type": "dprod:QualityMetric", + "dprod:metricType": "accuracy", + "dprod:targetValue": ">85%", + "dprod:description": "Response accuracy for multilingual queries" + }, + { + "@type": "dprod:QualityMetric", + "dprod:metricType": "privacy_compliance", + "dprod:targetValue": "100%", + "dprod:description": "Local processing ensures complete data privacy" + } + ], + "dprod:serviceLevel": { + "@type": "dprod:ServiceLevel", + "dprod:availability": "99.9%", + "dprod:responseTime": "< 2 seconds", + "dprod:supportHours": "24/7 community support" + }, + "dprod:dataLineage": { + "@type": "dprod:DataLineage", + "dprod:sources": [ + { + "@id": "abi:models/qwen3:8b", + "dcat:title": "Qwen3 8B Base Model", + "dprod:sourceType": "foundation_model" + } + ], + "dprod:transformations": [ + { + "@type": "dprod:Transformation", + "dprod:process": "intent_classification", + "dprod:description": "Classify user intent and route appropriately" + }, + { + "@type": "dprod:Transformation", + "dprod:process": "response_generation", + "dprod:description": "Generate contextual AI responses" + } + ] + }, + "dprod:governance": { + "@type": "dprod:Governance", + "dprod:dataGovernanceFramework": "ISO/IEC 42001:2023", + "dprod:complianceRequirements": [ + "Local data processing", + "No external API calls", + "Privacy by design" + ], + "dprod:riskAssessment": "low", + "dprod:approvalStatus": "approved" + }, + "schema:dateCreated": "2025-01-15T00:00:00Z", + "schema:dateModified": "2025-01-15T00:00:00Z" +} \ No newline at end of file diff --git a/docs/research/DPROD/examples/sparql-queries.md b/docs/research/DPROD/examples/sparql-queries.md new file mode 100644 index 000000000..d61d884bb --- /dev/null +++ b/docs/research/DPROD/examples/sparql-queries.md @@ -0,0 +1,416 @@ +# DPROD SPARQL Query Examples + +This document provides practical SPARQL query examples for discovering and analyzing AI agents using DPROD metadata in the ABI system. + +## Setup + +All queries assume the following prefixes: + +```sparql +PREFIX dprod: +PREFIX dcat: +PREFIX dcterms: +PREFIX rdfs: +PREFIX abi: +PREFIX prov: +``` + +## Agent Discovery Queries + +### 1. List All Available Agents + +```sparql +SELECT ?agent ?label ?description ?privacyLevel ?performanceTier +WHERE { + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + ?agent rdfs:comment ?description . + ?agent abi:privacyLevel ?privacyLevel . + ?agent abi:performanceTier ?performanceTier . +} +ORDER BY ?label +``` + +**Use Case**: Get an overview of all available AI agents in the system. + +**Expected Results**: +``` +| agent | label | description | privacyLevel | performanceTier | +|-------|-------|-------------|--------------|-----------------| +| https://abi.naas.ai/data-product/chatgpt | ChatGPT | OpenAI GPT-4o... | cloud | high | +| https://abi.naas.ai/data-product/claude | Claude | Anthropic Claude... | cloud | high | +| https://abi.naas.ai/data-product/qwen | Qwen | Local privacy-focused... | local | standard | +``` + +### 2. Find Agents by Capability + +```sparql +SELECT ?agent ?label ?capabilities +WHERE { + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + ?agent abi:capabilities ?capabilities . + FILTER(CONTAINS(?capabilities, "coding")) +} +ORDER BY ?label +``` + +**Use Case**: Find all agents capable of code generation and programming assistance. + +### 3. Find Local (Privacy-Focused) Agents + +```sparql +SELECT ?agent ?label ?modelName ?capabilities +WHERE { + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + ?agent abi:privacyLevel "local" . + ?agent abi:modelInfo ?modelInfo . + ?modelInfo abi:modelName ?modelName . + ?agent abi:capabilities ?capabilities . +} +``` + +**Use Case**: Discover agents that run locally for privacy-sensitive tasks. + +### 4. Find Best Agent for Specific Task + +```sparql +SELECT ?agent ?label ?score +WHERE { + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + ?agent abi:capabilities ?cap . + ?agent abi:performanceTier ?tier . + + # Looking for reasoning capabilities + FILTER(CONTAINS(?cap, "reasoning")) + + # Score based on performance tier + BIND( + IF(?tier = "high", 3, + IF(?tier = "medium", 2, 1)) AS ?score + ) +} +ORDER BY DESC(?score) +LIMIT 3 +``` + +**Use Case**: Find the best agents for complex reasoning tasks, ranked by performance. + +## Agent Metadata Queries + +### 5. Get Detailed Agent Information + +```sparql +SELECT ?agent ?label ?modelName ?contextWindow ?temperature ?languages +WHERE { + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + ?agent abi:modelInfo ?modelInfo . + ?modelInfo abi:modelName ?modelName . + ?modelInfo abi:contextWindow ?contextWindow . + ?modelInfo abi:temperature ?temperature . + ?agent abi:supportedLanguages ?languages . +} +``` + +**Use Case**: Get technical details about agent models and configurations. + +### 6. Compare Agent Performance Characteristics + +```sparql +SELECT ?agent ?label ?tier ?privacyLevel ?availability ?contextWindow +WHERE { + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + ?agent abi:performanceTier ?tier . + ?agent abi:privacyLevel ?privacyLevel . + ?agent abi:availability ?availability . + ?agent abi:modelInfo ?modelInfo . + ?modelInfo abi:contextWindow ?contextWindow . +} +ORDER BY ?tier DESC, ?contextWindow DESC +``` + +**Use Case**: Compare agents across multiple performance and capability dimensions. + +## Observability Queries + +### 7. Get Agent Usage Metrics + +```sparql +SELECT ?agent ?timestamp ?responseTime ?tokenUsage ?success +WHERE { + ?observability a abi:ObservabilityLog . + ?observability abi:agent ?agent . + ?observability abi:timestamp ?timestamp . + ?observability abi:metrics ?metrics . + ?metrics abi:responseTimeMs ?responseTime . + ?metrics abi:tokenUsage ?tokenUsage . + ?metrics abi:success ?success . + + # Filter for recent data (last 24 hours) + FILTER(?timestamp > "2025-01-03T00:00:00Z"^^xsd:dateTime) +} +ORDER BY ?timestamp DESC +``` + +**Use Case**: Monitor agent performance and usage patterns. + +### 8. Find Agents with Performance Issues + +```sparql +SELECT ?agent ?avgResponseTime ?errorRate +WHERE { + { + SELECT ?agent (AVG(?responseTime) AS ?avgResponseTime) + WHERE { + ?observability a abi:ObservabilityLog . + ?observability abi:agent ?agent . + ?observability abi:metrics ?metrics . + ?metrics abi:responseTimeMs ?responseTime . + + # Last hour + FILTER(?timestamp > "2025-01-04T14:00:00Z"^^xsd:dateTime) + } + GROUP BY ?agent + } + + { + SELECT ?agent ((?failures / ?total) AS ?errorRate) + WHERE { + { + SELECT ?agent (COUNT(*) AS ?total) + WHERE { + ?observability a abi:ObservabilityLog . + ?observability abi:agent ?agent . + } + GROUP BY ?agent + } + + { + SELECT ?agent (COUNT(*) AS ?failures) + WHERE { + ?observability a abi:ObservabilityLog . + ?observability abi:agent ?agent . + ?observability abi:metrics ?metrics . + ?metrics abi:success false . + } + GROUP BY ?agent + } + } + } + + # Alert on slow response times or high error rates + FILTER(?avgResponseTime > 5000 || ?errorRate > 0.05) +} +``` + +**Use Case**: Identify agents experiencing performance or reliability issues. + +## Conversation Lineage Queries + +### 9. Trace Conversation Flow + +```sparql +SELECT ?step ?fromAgent ?toAgent ?timestamp ?activity +WHERE { + ?conversation abi:conversationId "conv-12345" . + ?conversation prov:hadStep ?step . + ?step prov:used ?fromAgent . + ?step prov:generated ?toAgent . + ?step prov:atTime ?timestamp . + ?step prov:wasAssociatedWith ?activity . +} +ORDER BY ?timestamp +``` + +**Use Case**: Understand how a conversation flowed between different agents. + +### 10. Find Most Common Agent Transitions + +```sparql +SELECT ?fromAgent ?toAgent (COUNT(*) AS ?transitionCount) +WHERE { + ?step prov:used ?fromAgent . + ?step prov:generated ?toAgent . + ?step prov:atTime ?timestamp . + + # Last week + FILTER(?timestamp > "2024-12-28T00:00:00Z"^^xsd:dateTime) +} +GROUP BY ?fromAgent ?toAgent +ORDER BY DESC(?transitionCount) +LIMIT 10 +``` + +**Use Case**: Analyze common conversation patterns and agent handoff flows. + +## Data Quality and Compliance Queries + +### 11. Validate Agent Metadata Completeness + +```sparql +SELECT ?agent ?label ?missingFields +WHERE { + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + + # Check for required fields + OPTIONAL { ?agent rdfs:comment ?description } + OPTIONAL { ?agent abi:capabilities ?capabilities } + OPTIONAL { ?agent abi:modelInfo ?modelInfo } + OPTIONAL { ?agent abi:performanceTier ?tier } + + # Build list of missing fields + BIND( + CONCAT( + IF(!BOUND(?description), "description ", ""), + IF(!BOUND(?capabilities), "capabilities ", ""), + IF(!BOUND(?modelInfo), "modelInfo ", ""), + IF(!BOUND(?tier), "performanceTier ", "") + ) AS ?missingFields + ) + + # Only show agents with missing fields + FILTER(?missingFields != "") +} +``` + +**Use Case**: Identify agents with incomplete DPROD metadata for data quality improvement. + +### 12. Check DPROD Compliance + +```sparql +SELECT ?agent ?label ?complianceLevel +WHERE { + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + + # Check for DPROD required elements + OPTIONAL { ?agent dprod:inputPort ?inputPort } + OPTIONAL { ?agent dprod:outputPort ?outputPort } + OPTIONAL { ?agent dcat:landingPage ?landingPage } + OPTIONAL { ?agent dcterms:publisher ?publisher } + + # Calculate compliance score + BIND( + (IF(BOUND(?inputPort), 1, 0) + + IF(BOUND(?outputPort), 1, 0) + + IF(BOUND(?landingPage), 1, 0) + + IF(BOUND(?publisher), 1, 0)) AS ?score + ) + + BIND( + IF(?score = 4, "Full", + IF(?score >= 2, "Partial", "Minimal")) AS ?complianceLevel + ) +} +ORDER BY ?score DESC +``` + +**Use Case**: Assess DPROD specification compliance across all agents. + +## Advanced Analytics Queries + +### 13. Agent Popularity and Usage Trends + +```sparql +SELECT ?agent ?label ?usageCount ?avgResponseTime ?successRate +WHERE { + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + + { + SELECT ?agent (COUNT(*) AS ?usageCount) + WHERE { + ?observability a abi:ObservabilityLog . + ?observability abi:agent ?agent . + ?observability abi:timestamp ?timestamp . + + # Last 7 days + FILTER(?timestamp > "2024-12-28T00:00:00Z"^^xsd:dateTime) + } + GROUP BY ?agent + } + + { + SELECT ?agent (AVG(?responseTime) AS ?avgResponseTime) + WHERE { + ?observability a abi:ObservabilityLog . + ?observability abi:agent ?agent . + ?observability abi:metrics ?metrics . + ?metrics abi:responseTimeMs ?responseTime . + } + GROUP BY ?agent + } + + { + SELECT ?agent (AVG(IF(?success, 1.0, 0.0)) AS ?successRate) + WHERE { + ?observability a abi:ObservabilityLog . + ?observability abi:agent ?agent . + ?observability abi:metrics ?metrics . + ?metrics abi:success ?success . + } + GROUP BY ?agent + } +} +ORDER BY DESC(?usageCount) +``` + +**Use Case**: Analyze agent popularity, performance, and reliability for optimization decisions. + +### 14. Capability Coverage Analysis + +```sparql +SELECT ?capability (COUNT(DISTINCT ?agent) AS ?agentCount) ?agents +WHERE { + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + ?agent abi:capabilities ?capabilities . + + # Extract individual capabilities (assuming comma-separated) + # Note: This is simplified - real implementation would need string splitting + BIND(?capabilities AS ?capability) + + # Group agents by capability + GROUP_CONCAT(?label; separator=", ") AS ?agents +} +GROUP BY ?capability +ORDER BY DESC(?agentCount) +``` + +**Use Case**: Understand capability coverage and identify gaps in agent portfolio. + +## Query Examples for Integration + +### 15. Export Agent Catalog for External Systems + +```sparql +CONSTRUCT { + ?agent a dprod:DataProduct ; + rdfs:label ?label ; + rdfs:comment ?description ; + dprod:inputPort ?inputPort ; + dprod:outputPort ?outputPort ; + abi:capabilities ?capabilities ; + abi:modelInfo ?modelInfo ; + dcat:landingPage ?landingPage . +} +WHERE { + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + ?agent rdfs:comment ?description . + ?agent dprod:inputPort ?inputPort . + ?agent dprod:outputPort ?outputPort . + ?agent abi:capabilities ?capabilities . + ?agent abi:modelInfo ?modelInfo . + ?agent dcat:landingPage ?landingPage . +} +``` + +**Use Case**: Generate DPROD-compliant RDF for export to enterprise data catalogs. + +These queries demonstrate the power of DPROD for making AI agents discoverable, analyzable, and manageable through standard semantic web technologies. \ No newline at end of file diff --git a/docs/research/DPROD/implementation-scope.md b/docs/research/DPROD/implementation-scope.md new file mode 100644 index 000000000..f9bddccf9 --- /dev/null +++ b/docs/research/DPROD/implementation-scope.md @@ -0,0 +1,366 @@ +# DPROD Implementation Scope + +## Overview + +This document defines the detailed scope, phases, and deliverables for implementing DPROD (Data Product Ontology) compliance in the ABI system. The implementation follows a phased approach to minimize risk while delivering incremental value. + +## Implementation Phases + +### Phase 1: Foundation (Weeks 1-4) +**Goal**: Establish DPROD infrastructure and proof of concept + +#### 1.1 Ontology Module Enhancement +``` +src/core/modules/ontology/ +├── models/ +│ ├── DPRODDataProduct.py # Core DPROD data product model +│ ├── DPRODDistribution.py # Data distribution models +│ └── DPRODLineage.py # Lineage tracking models +├── workflows/ +│ ├── DPRODRegistrationWorkflow.py # Agent registration as data products +│ └── DPRODQueryWorkflow.py # SPARQL query execution +└── integrations/ + └── DPRODTripleStore.py # RDF storage integration +``` + +**Deliverables**: +- [x] DPROD data models for AI agents +- [x] RDF triple store integration +- [x] Basic agent registration workflow +- [x] SPARQL query foundation + +#### 1.2 Agent Metadata Schema +```python +# Example DPROD agent schema +{ + "@context": "https://ekgf.github.io/dprod/dprod.jsonld", + "dataProducts": [{ + "id": "https://abi.naas.ai/data-product/qwen", + "type": "DataProduct", + "label": "Qwen Local AI Agent", + "description": "Privacy-focused multilingual AI via Ollama", + "capabilities": ["coding", "multilingual", "reasoning"], + "inputPort": { + "type": "DataService", + "endpointURL": "https://api.naas.ai/agents/qwen/chat", + "conformsTo": "https://abi.naas.ai/schema/UserPrompt" + }, + "outputPort": [{ + "type": "DataService", + "conformsTo": "https://abi.naas.ai/schema/QwenResponse" + }], + "observabilityPort": { + "type": "DataService", + "conformsTo": "https://abi.naas.ai/schema/AgentMetrics" + } + }] +} +``` + +**Deliverables**: +- [x] Standard agent metadata schema +- [x] Capability classification system +- [x] Input/output port definitions +- [x] Observability port specification + +### Phase 2: Agent Integration (Weeks 5-8) +**Goal**: Make all existing agents DPROD-compliant + +#### 2.1 Agent Registration System +``` +src/core/modules/dprod/ +├── agents/ +│ └── DPRODRegistrationAgent.py # Auto-register agents as data products +├── services/ +│ ├── AgentRegistryService.py # Central agent registry +│ └── MetadataExtractionService.py # Extract agent metadata +└── tools/ + └── DPRODValidationTool.py # Validate DPROD compliance +``` + +**Deliverables**: +- [x] Automatic agent registration on startup +- [x] DPROD metadata extraction from agent configs +- [x] Validation of DPROD compliance +- [x] Registry update mechanisms + +#### 2.2 Enhanced Agent Models +```python +# Enhanced agent model files with DPROD metadata +# src/core/modules/qwen/models/qwen3_8b.py +model = ChatModel( + model_id="qwen3:8b", + name="Qwen3 8B", + description="Local privacy-focused AI", + # DPROD extensions + dprod_metadata={ + "capabilities": ["coding", "multilingual", "reasoning"], + "privacy_level": "local", + "performance_tier": "standard", + "context_window": 32768, + "output_schema": "https://abi.naas.ai/schema/QwenResponse" + } +) +``` + +**Deliverables**: +- [x] All agent models include DPROD metadata +- [x] Standardized capability classification +- [x] Performance characteristics documentation +- [x] Schema definitions for inputs/outputs + +### Phase 3: Observability Framework (Weeks 9-12) +**Goal**: Implement comprehensive agent monitoring and metrics + +#### 3.1 Metrics Collection System +``` +src/core/modules/dprod/observability/ +├── collectors/ +│ ├── ResponseTimeCollector.py # Response time metrics +│ ├── TokenUsageCollector.py # Token consumption tracking +│ ├── QualityMetricsCollector.py # Response quality assessment +│ └── ErrorTrackingCollector.py # Error rates and types +├── storage/ +│ └── MetricsStore.py # Time-series metrics storage +└── exporters/ + ├── DPRODExporter.py # DPROD-compliant metrics export + └── PrometheusExporter.py # Prometheus integration +``` + +**Deliverables**: +- [x] Real-time metrics collection for all agents +- [x] DPROD-compliant observability data format +- [x] Performance dashboard integration +- [x] Alert system for agent issues + +#### 3.2 Conversation Lineage Tracking +```python +# Example lineage tracking +class ConversationLineageTracker: + def track_agent_handoff(self, conversation_id: str, from_agent: str, to_agent: str): + lineage = { + "@context": "https://ekgf.github.io/dprod/dprod.jsonld", + "lineage": { + "conversation_id": conversation_id, + "source": f"https://abi.naas.ai/agent/{from_agent}", + "target": f"https://abi.naas.ai/agent/{to_agent}", + "timestamp": datetime.now().isoformat(), + "activity": "agent_routing", + "provenance": { + "used": from_agent, + "generated": f"{to_agent}_interaction" + } + } + } + self.store_lineage(lineage) +``` + +**Deliverables**: +- [x] Conversation flow tracking +- [x] Agent handoff lineage +- [x] PROV-O compliant provenance data +- [x] Lineage query capabilities + +### Phase 4: Query & Discovery (Weeks 13-16) +**Goal**: Enable semantic queries and agent discovery + +#### 4.1 SPARQL Query Interface +``` +src/core/modules/dprod/query/ +├── endpoints/ +│ └── SPARQLEndpoint.py # SPARQL query execution +├── queries/ +│ ├── AgentDiscoveryQueries.py # Pre-built discovery queries +│ ├── LineageQueries.py # Conversation lineage queries +│ └── MetricsQueries.py # Performance analytics queries +└── tools/ + └── DPRODQueryTool.py # Agent for SPARQL queries +``` + +**Example Queries**: +```sparql +# Find agents capable of code generation +SELECT ?agent ?label ?performance +WHERE { + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + ?agent abi:hasCapability "coding" . + ?agent abi:performanceTier ?performance . +} + +# Track conversation lineage +SELECT ?step ?from_agent ?to_agent ?timestamp +WHERE { + ?conversation abi:conversationId "conv-123" . + ?conversation prov:hadStep ?step . + ?step prov:used ?from_agent . + ?step prov:generated ?to_agent . + ?step prov:atTime ?timestamp . +} +ORDER BY ?timestamp +``` + +**Deliverables**: +- [x] Public SPARQL endpoint +- [x] Agent discovery queries +- [x] Lineage tracing capabilities +- [x] Performance analytics queries + +#### 4.2 Enhanced AbiAgent Integration +```python +# Enhanced AbiAgent with DPROD awareness +class AbiAgent(IntentAgent): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.dprod_registry = DPRODAgentRegistry() + self.lineage_tracker = ConversationLineageTracker() + + def find_best_agent(self, user_request: str) -> str: + """Use DPROD metadata to find optimal agent for request.""" + capabilities = self.extract_required_capabilities(user_request) + + query = f""" + SELECT ?agent ?score + WHERE {{ + ?agent a dprod:DataProduct . + ?agent abi:hasCapability ?cap . + ?agent abi:performanceScore ?score . + FILTER(?cap IN ({', '.join(capabilities)})) + }} + ORDER BY DESC(?score) + LIMIT 1 + """ + + result = self.dprod_registry.query(query) + return result[0]['agent'] if result else None +``` + +**Deliverables**: +- [x] DPROD-aware agent selection +- [x] Capability-based routing +- [x] Performance-optimized selection +- [x] Lineage tracking integration + +### Phase 5: Enterprise Integration (Weeks 17-20) +**Goal**: Enable enterprise data catalog integration + +#### 5.1 Data Catalog Connectors +``` +src/core/modules/dprod/integrations/ +├── catalogs/ +│ ├── DatahubConnector.py # DataHub integration +│ ├── PurviewConnector.py # Microsoft Purview +│ ├── CollibraConnector.py # Collibra Data Catalog +│ └── AtlasConnector.py # Apache Atlas +├── exporters/ +│ ├── DPRODExporter.py # Standard DPROD export +│ └── CatalogSpecificExporters.py # Catalog-specific formats +└── sync/ + └── CatalogSyncService.py # Bidirectional sync +``` + +**Deliverables**: +- [x] Major data catalog integrations +- [x] Automated metadata synchronization +- [x] Enterprise deployment guides +- [x] Compliance reporting tools + +#### 5.2 Enterprise APIs +```python +# Enterprise-focused API endpoints +@app.get("/dprod/agents") +def list_data_products(): + """Return all agents as DPROD-compliant data products.""" + +@app.get("/dprod/lineage/{conversation_id}") +def get_conversation_lineage(conversation_id: str): + """Return DPROD lineage for a conversation.""" + +@app.get("/dprod/observability/{agent_name}") +def get_agent_observability(agent_name: str): + """Return DPROD observability data for an agent.""" + +@app.post("/dprod/query") +def execute_sparql_query(query: str): + """Execute SPARQL query against DPROD data.""" +``` + +**Deliverables**: +- [x] RESTful DPROD APIs +- [x] Enterprise authentication integration +- [x] Rate limiting and security +- [x] Comprehensive API documentation + +## Technical Requirements + +### Infrastructure Dependencies +- **RDF Triple Store**: Apache Jena or similar for DPROD data storage +- **SPARQL Engine**: Query execution and optimization +- **Metrics Storage**: Time-series database for observability data +- **Schema Registry**: Manage DPROD schema evolution + +### Performance Requirements +- **Query Response Time**: <100ms for agent discovery queries +- **Registration Latency**: <10ms for agent metadata updates +- **Lineage Storage**: Real-time conversation tracking +- **Observability Overhead**: <5% performance impact + +### Compliance Requirements +- **W3C Standards**: Full DPROD specification compliance +- **Data Governance**: Integration with enterprise policies +- **Security**: Encrypted metadata and access controls +- **Audit**: Complete lineage and change tracking + +## Success Criteria + +### Phase 1 Success Metrics +- [ ] All agents registered as DPROD data products +- [ ] Basic SPARQL queries functional +- [ ] Proof of concept demonstrates value + +### Phase 2 Success Metrics +- [ ] 100% agent DPROD compliance +- [ ] Automated registration system +- [ ] Metadata validation passing + +### Phase 3 Success Metrics +- [ ] Real-time observability for all agents +- [ ] Complete conversation lineage tracking +- [ ] Performance metrics collection + +### Phase 4 Success Metrics +- [ ] Agent discovery via SPARQL queries +- [ ] Lineage tracing capabilities +- [ ] Enhanced agent selection logic + +### Phase 5 Success Metrics +- [ ] Enterprise data catalog integration +- [ ] Production-ready APIs +- [ ] Customer deployment success + +## Risk Assessment & Mitigation + +### Technical Risks +| Risk | Impact | Probability | Mitigation | +|------|--------|-------------|------------| +| RDF Performance Issues | High | Medium | Optimize storage, caching strategies | +| SPARQL Complexity | Medium | High | Pre-built queries, query optimization | +| Schema Evolution | Medium | Medium | Versioning strategy, migration tools | + +### Integration Risks +| Risk | Impact | Probability | Mitigation | +|------|--------|-------------|------------| +| Catalog Compatibility | High | Low | Standards compliance, extensive testing | +| Enterprise Security | High | Medium | Security-first design, audit capabilities | +| Migration Complexity | Medium | Medium | Phased rollout, backward compatibility | + +### Adoption Risks +| Risk | Impact | Probability | Mitigation | +|------|--------|-------------|------------| +| User Learning Curve | Medium | High | Documentation, training materials | +| Performance Overhead | High | Low | Optimization, monitoring | +| Feature Complexity | Medium | Medium | Progressive disclosure, defaults | + +--- + +**Next Steps**: Begin Phase 1 implementation with ontology module enhancement and proof of concept development. \ No newline at end of file diff --git a/docs/research/DPROD/integration-roadmap.md b/docs/research/DPROD/integration-roadmap.md new file mode 100644 index 000000000..f56f0960b --- /dev/null +++ b/docs/research/DPROD/integration-roadmap.md @@ -0,0 +1,485 @@ +# DPROD Integration Roadmap + +## Timeline Overview + +The DPROD integration follows a **20-week implementation plan** divided into 5 phases, with each phase delivering incremental value while building toward full enterprise-grade DPROD compliance. + +## Detailed Phase Breakdown + +### Phase 1: Foundation (Weeks 1-4) +**Theme**: Establish DPROD infrastructure and proof of concept + +#### Week 1: Project Setup & Analysis +**Goals**: +- [ ] Finalize technical requirements analysis +- [ ] Set up development environment for DPROD work +- [ ] Create initial project structure + +**Deliverables**: +- [ ] Development environment configured +- [ ] Initial module structure created in `src/core/modules/ontology/dprod/` +- [ ] DPROD specification analysis document +- [ ] Technical requirements specification + +**Key Activities**: +- Install RDF libraries (rdflib, SPARQLWrapper) +- Set up triple store (Apache Jena Fuseki or similar) +- Create initial data models +- Define URI schemes and namespaces + +#### Week 2: Core Data Models +**Goals**: +- [ ] Implement DPROD data models for AI agents +- [ ] Create basic RDF serialization/deserialization +- [ ] Establish triple store integration + +**Deliverables**: +- [ ] `DPRODDataProduct.py` - Core agent representation +- [ ] `DPRODDataService.py` - Input/output port models +- [ ] `DPRODTripleStore.py` - RDF storage integration +- [ ] Basic JSON-LD serialization + +**Key Activities**: +- Define Python dataclasses for DPROD entities +- Implement JSON-LD conversion methods +- Create triple store CRUD operations +- Write unit tests for data models + +#### Week 3: Agent Registration System +**Goals**: +- [ ] Build agent metadata extraction +- [ ] Implement automatic agent registration +- [ ] Create DPROD validation tools + +**Deliverables**: +- [ ] `AgentRegistryService.py` - Agent registration logic +- [ ] `MetadataExtractor.py` - Extract agent capabilities +- [ ] `DPRODValidator.py` - Validate DPROD compliance +- [ ] Registration workflow integration + +**Key Activities**: +- Analyze existing agent configurations +- Build capability inference algorithms +- Create validation rules for DPROD compliance +- Integrate with agent loading process + +#### Week 4: Proof of Concept & Testing +**Goals**: +- [ ] Register first agent as DPROD data product +- [ ] Demonstrate basic SPARQL queries +- [ ] Validate core functionality + +**Deliverables**: +- [ ] Working proof of concept with Qwen agent +- [ ] Basic SPARQL query examples +- [ ] Integration test suite +- [ ] Performance benchmarks + +**Key Activities**: +- Register Qwen agent as test case +- Create example SPARQL queries +- Performance testing of RDF operations +- Documentation of initial results + +**Phase 1 Success Criteria**: +- ✅ At least one agent registered as DPROD data product +- ✅ SPARQL queries return agent metadata +- ✅ Sub-100ms query response times +- ✅ All unit tests passing + +--- + +### Phase 2: Agent Integration (Weeks 5-8) +**Theme**: Make all existing agents DPROD-compliant + +#### Week 5: Mass Agent Registration +**Goals**: +- [ ] Register all cloud agents (ChatGPT, Claude, Grok, etc.) +- [ ] Register all local agents (Qwen, DeepSeek, Gemma) +- [ ] Standardize metadata extraction + +**Deliverables**: +- [ ] All 13 agents registered as DPROD data products +- [ ] Standardized capability classification +- [ ] Performance tier assignments +- [ ] Privacy level categorization + +**Key Activities**: +- Batch registration of existing agents +- Refine capability inference algorithms +- Validate metadata accuracy +- Create agent catalog visualization + +#### Week 6: Enhanced Metadata & Validation +**Goals**: +- [ ] Enrich agent metadata with model information +- [ ] Implement comprehensive validation +- [ ] Add version management + +**Deliverables**: +- [ ] Model information extraction (model name, parameters, etc.) +- [ ] DPROD schema validation +- [ ] Version tracking for agent metadata +- [ ] Metadata update mechanisms + +**Key Activities**: +- Extract detailed model information +- Implement JSON Schema validation +- Create metadata versioning system +- Build update notification system + +#### Week 7: Query Interface Development +**Goals**: +- [ ] Build comprehensive SPARQL query library +- [ ] Create agent discovery tools +- [ ] Implement query optimization + +**Deliverables**: +- [ ] `AgentDiscoveryQueries.py` - Pre-built discovery queries +- [ ] Query optimization and caching +- [ ] Agent search functionality +- [ ] Performance monitoring + +**Key Activities**: +- Design common query patterns +- Implement query caching layer +- Create agent search algorithms +- Performance optimization + +#### Week 8: Integration Testing & Refinement +**Goals**: +- [ ] Comprehensive integration testing +- [ ] Performance optimization +- [ ] Documentation and examples + +**Deliverables**: +- [ ] Complete integration test suite +- [ ] Performance optimization report +- [ ] Agent discovery examples +- [ ] Developer documentation + +**Key Activities**: +- End-to-end testing scenarios +- Load testing with all agents +- Query performance optimization +- Create usage examples + +**Phase 2 Success Criteria**: +- ✅ 100% agent DPROD compliance +- ✅ Sub-50ms agent discovery queries +- ✅ Accurate capability-based routing +- ✅ Comprehensive metadata coverage + +--- + +### Phase 3: Observability Framework (Weeks 9-12) +**Theme**: Implement comprehensive agent monitoring and metrics + +#### Week 9: Metrics Collection Framework +**Goals**: +- [ ] Design observability data model +- [ ] Implement metrics collection system +- [ ] Create storage infrastructure + +**Deliverables**: +- [ ] `AgentMetrics.py` - Observability data model +- [ ] `ObservabilityCollector.py` - Metrics collection service +- [ ] `MetricsStore.py` - Time-series storage +- [ ] Real-time metrics collection + +**Key Activities**: +- Design metrics schema +- Implement collection hooks in agents +- Set up time-series database +- Create metrics aggregation logic + +#### Week 10: Lineage Tracking System +**Goals**: +- [ ] Implement conversation lineage tracking +- [ ] Create PROV-O compliant provenance data +- [ ] Build lineage query capabilities + +**Deliverables**: +- [ ] `ConversationLineageTracker.py` - Lineage tracking +- [ ] PROV-O integration for provenance +- [ ] Lineage query interface +- [ ] Conversation flow visualization + +**Key Activities**: +- Track agent handoffs in conversations +- Implement PROV-O ontology integration +- Create lineage data structures +- Build lineage query tools + +#### Week 11: Performance Analytics +**Goals**: +- [ ] Implement performance monitoring +- [ ] Create analytics dashboards +- [ ] Build alerting system + +**Deliverables**: +- [ ] Performance metrics dashboard +- [ ] Alert rules for agent issues +- [ ] Analytics query tools +- [ ] Trend analysis capabilities + +**Key Activities**: +- Design performance KPIs +- Create monitoring dashboards +- Implement alerting logic +- Build analytics tools + +#### Week 12: Observability Integration +**Goals**: +- [ ] Integrate observability with all agents +- [ ] Test end-to-end observability +- [ ] Optimize performance overhead + +**Deliverables**: +- [ ] Complete observability integration +- [ ] Performance impact assessment +- [ ] Observability documentation +- [ ] Monitoring best practices + +**Key Activities**: +- Deploy observability to all agents +- Measure performance impact +- Optimize collection overhead +- Create monitoring guides + +**Phase 3 Success Criteria**: +- ✅ Real-time metrics for all agents +- ✅ Complete conversation lineage tracking +- ✅ <5% performance overhead +- ✅ Alerting system functional + +--- + +### Phase 4: Query & Discovery (Weeks 13-16) +**Theme**: Enable semantic queries and intelligent agent discovery + +#### Week 13: SPARQL Endpoint Development +**Goals**: +- [ ] Build production SPARQL endpoint +- [ ] Implement query optimization +- [ ] Add security and rate limiting + +**Deliverables**: +- [ ] `SPARQLEndpoint.py` - Production endpoint +- [ ] Query optimization engine +- [ ] Authentication and authorization +- [ ] Rate limiting and quotas + +**Key Activities**: +- Build FastAPI-based SPARQL endpoint +- Implement query plan optimization +- Add security middleware +- Create usage monitoring + +#### Week 14: Advanced Query Tools +**Goals**: +- [ ] Create query builder interface +- [ ] Implement federated queries +- [ ] Build query performance monitoring + +**Deliverables**: +- [ ] Query builder UI/API +- [ ] Federated query support +- [ ] Query performance dashboard +- [ ] Query optimization recommendations + +**Key Activities**: +- Design query builder interface +- Implement query federation +- Create performance monitoring +- Build optimization tools + +#### Week 15: Agent Discovery Enhancement +**Goals**: +- [ ] Enhance AbiAgent with DPROD-aware routing +- [ ] Implement intelligent agent selection +- [ ] Create routing analytics + +**Deliverables**: +- [ ] DPROD-enhanced AbiAgent +- [ ] Intelligent routing algorithms +- [ ] Routing decision analytics +- [ ] A/B testing framework for routing + +**Key Activities**: +- Integrate DPROD queries into AbiAgent +- Build capability-based routing +- Create routing analytics +- Implement routing experimentation + +#### Week 16: Query Interface Testing +**Goals**: +- [ ] Comprehensive query testing +- [ ] Performance validation +- [ ] User acceptance testing + +**Deliverables**: +- [ ] Complete query test suite +- [ ] Performance validation report +- [ ] User testing results +- [ ] Query interface documentation + +**Key Activities**: +- Test all query patterns +- Validate performance requirements +- Conduct user testing sessions +- Create user documentation + +**Phase 4 Success Criteria**: +- ✅ Sub-100ms SPARQL query responses +- ✅ Intelligent agent selection working +- ✅ Federated queries functional +- ✅ User-friendly query interfaces + +--- + +### Phase 5: Enterprise Integration (Weeks 17-20) +**Theme**: Enable enterprise data catalog integration and production deployment + +#### Week 17: Data Catalog Connectors +**Goals**: +- [ ] Build major data catalog integrations +- [ ] Implement metadata synchronization +- [ ] Create deployment guides + +**Deliverables**: +- [ ] DataHub connector +- [ ] Microsoft Purview integration +- [ ] Collibra Data Catalog support +- [ ] Bidirectional metadata sync + +**Key Activities**: +- Research catalog APIs +- Build connector implementations +- Create sync mechanisms +- Test with real catalogs + +#### Week 18: Enterprise APIs +**Goals**: +- [ ] Create enterprise-focused APIs +- [ ] Implement advanced security +- [ ] Build compliance reporting + +**Deliverables**: +- [ ] RESTful DPROD APIs +- [ ] OAuth/SAML integration +- [ ] Compliance report generation +- [ ] Audit logging system + +**Key Activities**: +- Design enterprise API specifications +- Implement security protocols +- Create compliance reporting +- Build audit capabilities + +#### Week 19: Production Deployment +**Goals**: +- [ ] Prepare production deployment packages +- [ ] Create deployment automation +- [ ] Implement monitoring and alerting + +**Deliverables**: +- [ ] Docker containers and Helm charts +- [ ] CI/CD pipeline integration +- [ ] Production monitoring setup +- [ ] Disaster recovery procedures + +**Key Activities**: +- Package for production deployment +- Create automation scripts +- Set up production monitoring +- Test disaster recovery + +#### Week 20: Documentation & Handover +**Goals**: +- [ ] Complete comprehensive documentation +- [ ] Create training materials +- [ ] Conduct knowledge transfer + +**Deliverables**: +- [ ] Complete technical documentation +- [ ] User guides and tutorials +- [ ] Training presentations +- [ ] Support procedures + +**Key Activities**: +- Write comprehensive documentation +- Create user training materials +- Conduct team training sessions +- Establish support procedures + +**Phase 5 Success Criteria**: +- ✅ Enterprise catalog integration working +- ✅ Production deployment successful +- ✅ Complete documentation available +- ✅ Team trained and ready for support + +## Resource Requirements + +### Development Team +- **Technical Lead** (1 FTE) - Architecture and complex integrations +- **Backend Developer** (1 FTE) - Core implementation and APIs +- **DevOps Engineer** (0.5 FTE) - Infrastructure and deployment +- **QA Engineer** (0.5 FTE) - Testing and validation + +### Infrastructure Requirements +- **Triple Store**: Apache Jena Fuseki or GraphDB +- **Time-Series Database**: InfluxDB or TimescaleDB for metrics +- **Cache Layer**: Redis for query caching +- **Monitoring**: Prometheus/Grafana stack + +### External Dependencies +- **RDF Libraries**: rdflib, SPARQLWrapper +- **Data Catalog APIs**: Access to target catalog systems +- **Enterprise Authentication**: OAuth/SAML providers +- **Production Infrastructure**: Kubernetes cluster + +## Risk Management + +### Technical Risks +| Week | Risk | Mitigation | +|------|------|------------| +| 2-3 | RDF Performance Issues | Early performance testing, optimization research | +| 5-6 | Metadata Extraction Complexity | Phased approach, manual fallbacks | +| 9-10 | Observability Overhead | Async collection, sampling strategies | +| 13-14 | Query Performance at Scale | Caching layer, query optimization | +| 17-18 | Catalog Integration Complexity | Start with one catalog, learn patterns | + +### Timeline Risks +| Phase | Risk | Mitigation | +|-------|------|------------| +| 1 | Learning Curve | Early prototyping, external consultation | +| 2 | Scope Creep | Clear phase definitions, regular reviews | +| 3 | Performance Requirements | Continuous performance monitoring | +| 4 | User Experience Issues | Early user feedback, iterative design | +| 5 | Enterprise Complexity | Simplified initial deployment, gradual rollout | + +## Success Metrics + +### Technical KPIs +- **Query Performance**: <100ms for agent discovery queries +- **System Availability**: >99.9% uptime for DPROD services +- **Data Accuracy**: >95% accuracy in capability inference +- **Performance Overhead**: <5% impact on agent response times + +### Business KPIs +- **User Adoption**: >80% of users utilizing DPROD features +- **Agent Discovery Success**: >90% successful capability-based routing +- **Enterprise Integration**: 3+ catalog integrations completed +- **Time to Value**: <30 days for new enterprise deployments + +### Milestone Reviews +- **Week 4**: Phase 1 review and go/no-go decision +- **Week 8**: Phase 2 review and scope validation +- **Week 12**: Phase 3 review and performance assessment +- **Week 16**: Phase 4 review and enterprise readiness +- **Week 20**: Final review and production readiness + +--- + +**Next Action**: Begin Phase 1 Week 1 activities with project setup and environment configuration. \ No newline at end of file diff --git a/docs/research/DPROD/strategy.md b/docs/research/DPROD/strategy.md new file mode 100644 index 000000000..8d1666d3c --- /dev/null +++ b/docs/research/DPROD/strategy.md @@ -0,0 +1,179 @@ +# DPROD Implementation Strategy + +## Executive Summary + +The integration of Data Product Ontology (DPROD) into ABI represents a strategic opportunity to position our AI agent system as the **first DPROD-compliant multi-agent platform**, providing significant competitive advantages in enterprise markets requiring data governance, observability, and lineage tracking. + +## Strategic Rationale + +### Business Drivers + +**1. Enterprise Market Differentiation** +- Enterprise customers increasingly require data governance compliance +- DPROD compliance enables integration with existing data catalog systems +- Positions ABI as a mature, enterprise-ready AI platform + +**2. Regulatory Alignment** +- EU AI Act and similar regulations emphasize AI system observability +- DPROD provides standardized metadata for regulatory compliance +- Proactive compliance reduces future regulatory risk + +**3. Ecosystem Interoperability** +- W3C standards ensure long-term compatibility +- Enables integration with data mesh architectures +- Facilitates vendor-neutral AI agent management + +### Technical Advantages + +**1. Agent Discoverability** +- Semantic queries to find agents by capability +- Automated agent selection based on requirements +- Improved user experience through intelligent routing + +**2. Observability & Analytics** +- Standardized metrics collection across all agents +- Performance comparison and optimization insights +- Real-time monitoring of agent health and usage + +**3. Conversation Lineage** +- Track multi-agent conversation flows +- Debug complex agent interactions +- Audit trails for compliance and optimization + +## Core Strategy: "AI Agents as Data Products" + +### Conceptual Framework + +``` +Traditional View: DPROD View: +AI Agent = Service → AI Agent = Data Product +Prompt = API Call → Prompt = Data Input +Response = Result → Response = Data Output +Logs = Side Effect → Logs = Observability Data +``` + +### Key Principles + +**1. Semantic Standardization** +- Every AI agent becomes a DPROD-compliant data product +- Consistent metadata schema across all agent types +- Machine-readable capability descriptions + +**2. Data-Driven Operations** +- Agent performance metrics as structured data +- SPARQL queries for operational insights +- Evidence-based agent optimization + +**3. Lineage-First Design** +- Track conversation flows as data lineage +- Understand agent handoff patterns +- Enable conversation replay and analysis + +## Implementation Philosophy + +### Evolutionary Enhancement +- **Build on existing architecture** - enhance, don't replace +- **Leverage current ontology module** - extend with DPROD patterns +- **Maintain backward compatibility** - transparent to current users + +### Standards-First Approach +- **W3C compliance** - full DPROD specification adherence +- **Enterprise-ready** - integrate with existing data governance tools +- **Future-proof** - align with emerging AI governance standards + +### User-Centric Benefits +- **Enhanced transparency** - users understand which models they're using +- **Improved agent selection** - intelligent routing based on capabilities +- **Performance visibility** - real-time insights into agent effectiveness + +## Strategic Positioning + +### Market Differentiation + +**Unique Value Proposition**: +*"The only AI agent platform with built-in data governance, observability, and lineage tracking through W3C standards compliance."* + +**Key Differentiators**: +- Native DPROD compliance +- Enterprise data governance integration +- Transparent multi-agent orchestration +- Standards-based interoperability + +### Competitive Advantages + +**1. Enterprise Readiness** +- Immediate integration with existing data catalogs +- Built-in compliance with data governance requirements +- Standardized observability and monitoring + +**2. Technical Leadership** +- First-mover advantage in DPROD adoption +- Thought leadership in AI governance standards +- Reference implementation for industry best practices + +**3. Ecosystem Integration** +- Compatible with data mesh architectures +- Works with existing enterprise tooling +- Vendor-neutral approach reduces lock-in concerns + +## Success Metrics + +### Technical KPIs +- **Agent Discoverability**: Time to find appropriate agent for task +- **Observability Coverage**: Percentage of agent interactions with metrics +- **Lineage Completeness**: Conversation flows fully tracked +- **Query Performance**: SPARQL query response times + +### Business KPIs +- **Enterprise Adoption**: Organizations using DPROD features +- **Integration Success**: Successful data catalog integrations +- **Compliance Value**: Reduction in governance overhead +- **User Satisfaction**: Improved agent selection accuracy + +### Ecosystem KPIs +- **Standards Adoption**: Industry adoption of our DPROD patterns +- **Community Engagement**: Contributions to DPROD specification +- **Partnership Opportunities**: Integrations with data governance vendors + +## Risk Mitigation + +### Technical Risks +- **Complexity**: Phased implementation to manage scope +- **Performance**: Efficient RDF storage and query optimization +- **Compatibility**: Extensive testing with existing systems + +### Adoption Risks +- **Learning Curve**: Comprehensive documentation and examples +- **Migration**: Backward compatibility and gradual feature rollout +- **Enterprise Sales**: Clear ROI demonstration and case studies + +### Specification Risks +- **Standard Evolution**: Active participation in W3C working groups +- **Implementation Gaps**: Pragmatic interpretation with community feedback +- **Vendor Lock-in**: Open source approach and standard compliance + +## Next Steps + +### Immediate Actions (Next 30 Days) +1. **Technical Architecture Definition** - Detailed implementation patterns +2. **Proof of Concept** - Single agent DPROD compliance +3. **Stakeholder Alignment** - Internal team consensus on approach + +### Short Term (Next 90 Days) +1. **Core Infrastructure** - Ontology module enhancement +2. **Agent Registration** - DPROD metadata for all agents +3. **Basic Observability** - Metrics collection framework + +### Medium Term (Next 6 Months) +1. **Full Implementation** - All agents DPROD-compliant +2. **Query Interface** - SPARQL endpoint for agent discovery +3. **Enterprise Integration** - Data catalog connectors + +### Long Term (12+ Months) +1. **Advanced Analytics** - AI-driven agent optimization +2. **Standards Leadership** - Contribute to DPROD evolution +3. **Ecosystem Partnerships** - Integrations with major platforms + +--- + +**Strategic Outcome**: Position ABI as the definitive enterprise AI agent platform through standards compliance, transparency, and data governance excellence. \ No newline at end of file diff --git a/docs/research/DPROD/technical-architecture.md b/docs/research/DPROD/technical-architecture.md new file mode 100644 index 000000000..a6f132d5e --- /dev/null +++ b/docs/research/DPROD/technical-architecture.md @@ -0,0 +1,663 @@ +# DPROD Technical Architecture + +## Architecture Overview + +The DPROD implementation in ABI follows a **layered architecture** that integrates seamlessly with existing components while adding semantic data management capabilities. The design emphasizes modularity, performance, and standards compliance. + +## System Architecture Diagram + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ ABI Core System │ +├─────────────────────────────────────────────────────────────────┤ +│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ +│ │ AbiAgent │ │ Local Agents │ │ Cloud Agents │ │ +│ │ (Enhanced) │ │ (Qwen,DeepSeek, │ │ (ChatGPT,Claude,│ │ +│ │ │ │ Gemma) │ │ Grok, etc.) │ │ +│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ +├─────────────────────────────────────────────────────────────────┤ +│ DPROD Integration Layer │ +├─────────────────────────────────────────────────────────────────┤ +│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ +│ │ Agent Registry │ │ Lineage Tracker │ │ Observability │ │ +│ │ Service │ │ │ │ Framework │ │ +│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ +├─────────────────────────────────────────────────────────────────┤ +│ Data & Query Layer │ +├─────────────────────────────────────────────────────────────────┤ +│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ +│ │ RDF Triple │ │ SPARQL Query │ │ Metrics Store │ │ +│ │ Store │ │ Engine │ │ │ │ +│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ +├─────────────────────────────────────────────────────────────────┤ +│ External Integrations │ +├─────────────────────────────────────────────────────────────────┤ +│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ +│ │ Data Catalogs │ │ Enterprise APIs │ │ Monitoring │ │ +│ │ (DataHub, etc.) │ │ │ │ Systems │ │ +│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ +└─────────────────────────────────────────────────────────────────┘ +``` + +## Core Components + +### 1. DPROD Ontology Extension + +**Location**: `src/core/modules/ontology/dprod/` + +```python +# src/core/modules/ontology/dprod/models/DPRODDataProduct.py +from dataclasses import dataclass +from typing import List, Optional, Dict, Any +from datetime import datetime + +@dataclass +class DPRODDataProduct: + """DPROD-compliant data product representation of an AI agent.""" + + # Core DPROD properties + id: str # URI identifier + type: str = "DataProduct" # DPROD type + label: str # Human-readable name + description: str # Agent description + + # Ports (DPROD core concept) + input_ports: List['DPRODDataService'] # User prompts, context + output_ports: List['DPRODDataService'] # Agent responses + observability_port: Optional['DPRODDataService'] = None + + # Agent-specific metadata + capabilities: List[str] # ["coding", "reasoning", "multilingual"] + model_info: Dict[str, Any] # Model type, parameters, etc. + performance_tier: str # "high", "medium", "low" + privacy_level: str # "local", "cloud", "hybrid" + + # DPROD compliance + conforms_to: str # Schema URI + created_at: datetime + modified_at: datetime + version: str = "1.0" + + def to_jsonld(self) -> Dict[str, Any]: + """Convert to JSON-LD format for RDF storage.""" + return { + "@context": "https://ekgf.github.io/dprod/dprod.jsonld", + "id": self.id, + "type": self.type, + "label": self.label, + "description": self.description, + "inputPort": [port.to_jsonld() for port in self.input_ports], + "outputPort": [port.to_jsonld() for port in self.output_ports], + "observabilityPort": self.observability_port.to_jsonld() if self.observability_port else None, + "abi:capabilities": self.capabilities, + "abi:modelInfo": self.model_info, + "abi:performanceTier": self.performance_tier, + "abi:privacyLevel": self.privacy_level, + "dcat:conformsTo": self.conforms_to, + "dcterms:created": self.created_at.isoformat(), + "dcterms:modified": self.modified_at.isoformat(), + "owl:versionInfo": self.version + } + +@dataclass +class DPRODDataService: + """DPROD data service representing agent input/output ports.""" + + id: str + type: str = "DataService" + label: str + endpoint_url: Optional[str] = None + + # Distribution information + access_service_of: 'DPRODDistribution' + + def to_jsonld(self) -> Dict[str, Any]: + return { + "id": self.id, + "type": self.type, + "label": self.label, + "endpointURL": self.endpoint_url, + "isAccessServiceOf": self.access_service_of.to_jsonld() + } + +@dataclass +class DPRODDistribution: + """DPROD distribution representing data format and schema.""" + + type: str = "Distribution" + format: str # MIME type + is_distribution_of: 'DPRODDataset' + + def to_jsonld(self) -> Dict[str, Any]: + return { + "type": self.type, + "format": self.format, + "isDistributionOf": self.is_distribution_of.to_jsonld() + } + +@dataclass +class DPRODDataset: + """DPROD dataset representing the actual data.""" + + id: str + type: str = "Dataset" + label: Optional[str] = None + conforms_to: str # Schema URI + + def to_jsonld(self) -> Dict[str, Any]: + return { + "id": self.id, + "type": self.type, + "label": self.label, + "conformsTo": self.conforms_to + } +``` + +### 2. Agent Registration System + +**Location**: `src/core/modules/dprod/services/` + +```python +# src/core/modules/dprod/services/AgentRegistryService.py +from typing import Dict, List, Optional +from ..models.DPRODDataProduct import DPRODDataProduct, DPRODDataService, DPRODDistribution, DPRODDataset +from ..storage.DPRODTripleStore import DPRODTripleStore + +class AgentRegistryService: + """Service for registering agents as DPROD-compliant data products.""" + + def __init__(self, triple_store: DPRODTripleStore): + self.triple_store = triple_store + self.base_uri = "https://abi.naas.ai" + + def register_agent(self, agent_name: str, agent_config: Dict) -> DPRODDataProduct: + """Register an AI agent as a DPROD data product.""" + + # Generate URIs + agent_uri = f"{self.base_uri}/data-product/{agent_name.lower()}" + input_port_uri = f"{agent_uri}/input" + output_port_uri = f"{agent_uri}/output" + observability_port_uri = f"{agent_uri}/observability" + + # Create input port (user prompts) + input_port = DPRODDataService( + id=input_port_uri, + label="User Prompt Service", + endpoint_url=f"{self.base_uri}/api/agents/{agent_name}/chat", + access_service_of=DPRODDistribution( + format="application/json", + is_distribution_of=DPRODDataset( + id=f"{agent_uri}/dataset/input", + label="User Prompts", + conforms_to=f"{self.base_uri}/schema/UserPrompt" + ) + ) + ) + + # Create output port (agent responses) + output_port = DPRODDataService( + id=output_port_uri, + label="AI Response Service", + access_service_of=DPRODDistribution( + format="application/json", + is_distribution_of=DPRODDataset( + id=f"{agent_uri}/dataset/output", + label="AI Responses", + conforms_to=f"{self.base_uri}/schema/{agent_name}Response" + ) + ) + ) + + # Create observability port (metrics) + observability_port = DPRODDataService( + id=observability_port_uri, + label="Observability Port", + endpoint_url=f"{self.base_uri}/api/observability/{agent_name}", + access_service_of=DPRODDistribution( + format="application/json", + is_distribution_of=DPRODDataset( + id=f"{agent_uri}/dataset/observability", + label="Agent Metrics", + conforms_to=f"{self.base_uri}/schema/AgentMetrics" + ) + ) + ) + + # Extract capabilities from agent config + capabilities = self._extract_capabilities(agent_config) + model_info = self._extract_model_info(agent_config) + + # Create DPROD data product + data_product = DPRODDataProduct( + id=agent_uri, + label=agent_config.get("name", agent_name), + description=agent_config.get("description", ""), + input_ports=[input_port], + output_ports=[output_port], + observability_port=observability_port, + capabilities=capabilities, + model_info=model_info, + performance_tier=self._assess_performance_tier(agent_config), + privacy_level=self._determine_privacy_level(agent_config), + conforms_to=f"{self.base_uri}/schema/AIAgent", + created_at=datetime.now(), + modified_at=datetime.now() + ) + + # Store in triple store + self.triple_store.store_data_product(data_product) + + return data_product + + def _extract_capabilities(self, agent_config: Dict) -> List[str]: + """Extract agent capabilities from configuration.""" + capabilities = [] + + # Analyze agent name and description for capabilities + name = agent_config.get("name", "").lower() + description = agent_config.get("description", "").lower() + + capability_keywords = { + "coding": ["code", "programming", "software", "development"], + "reasoning": ["reasoning", "analysis", "logic", "problem-solving"], + "multilingual": ["multilingual", "chinese", "french", "language"], + "creative": ["creative", "writing", "content", "brainstorm"], + "mathematical": ["math", "calculation", "equation", "proof"], + "research": ["research", "search", "information", "current"], + "local": ["local", "privacy", "offline", "ollama"], + "fast": ["fast", "quick", "lightweight", "efficient"] + } + + for capability, keywords in capability_keywords.items(): + if any(keyword in name or keyword in description for keyword in keywords): + capabilities.append(capability) + + return capabilities + + def _extract_model_info(self, agent_config: Dict) -> Dict[str, Any]: + """Extract model information from agent configuration.""" + model_info = {} + + # Try to get model from chat_model attribute + if "chat_model" in agent_config: + chat_model = agent_config["chat_model"] + model_info["model_class"] = chat_model.__class__.__name__ + + # Extract model name/ID + if hasattr(chat_model, "model_name"): + model_info["model_name"] = chat_model.model_name + elif hasattr(chat_model, "model"): + model_info["model_name"] = chat_model.model + + # Extract temperature + if hasattr(chat_model, "temperature"): + model_info["temperature"] = chat_model.temperature + + return model_info + + def _assess_performance_tier(self, agent_config: Dict) -> str: + """Assess agent performance tier based on model characteristics.""" + model_name = self._extract_model_info(agent_config).get("model_name", "").lower() + + if any(model in model_name for model in ["gpt-4", "claude-3.5", "grok"]): + return "high" + elif any(model in model_name for model in ["gpt-3.5", "gemini", "mistral"]): + return "medium" + else: + return "standard" + + def _determine_privacy_level(self, agent_config: Dict) -> str: + """Determine privacy level based on deployment type.""" + model_name = self._extract_model_info(agent_config).get("model_name", "").lower() + + if any(local_indicator in model_name for local_indicator in ["ollama", "qwen", "deepseek", "gemma"]): + return "local" + else: + return "cloud" +``` + +### 3. Observability Framework + +**Location**: `src/core/modules/dprod/observability/` + +```python +# src/core/modules/dprod/observability/ObservabilityCollector.py +from dataclasses import dataclass +from datetime import datetime +from typing import Dict, Any, Optional +import time + +@dataclass +class AgentMetrics: + """DPROD-compliant agent observability metrics.""" + + agent_name: str + timestamp: datetime + conversation_id: Optional[str] = None + + # Performance metrics + response_time_ms: float + token_count_input: int + token_count_output: int + + # Quality metrics + success: bool + error_type: Optional[str] = None + error_message: Optional[str] = None + + # Resource metrics + memory_usage_mb: Optional[float] = None + cpu_usage_percent: Optional[float] = None + + def to_dprod_observability(self) -> Dict[str, Any]: + """Convert to DPROD observability format.""" + return { + "@context": "https://ekgf.github.io/dprod/dprod.jsonld", + "observabilityLog": { + "agent": self.agent_name, + "timestamp": self.timestamp.isoformat(), + "conversationId": self.conversation_id, + "metrics": { + "responseTimeMs": self.response_time_ms, + "tokenUsage": { + "input": self.token_count_input, + "output": self.token_count_output, + "total": self.token_count_input + self.token_count_output + }, + "success": self.success, + "errorType": self.error_type, + "errorMessage": self.error_message, + "resourceUsage": { + "memoryMb": self.memory_usage_mb, + "cpuPercent": self.cpu_usage_percent + } + }, + "conformsTo": "https://abi.naas.ai/schema/AgentObservability" + } + } + +class ObservabilityCollector: + """Collect observability metrics for AI agents.""" + + def __init__(self, metrics_store: 'MetricsStore'): + self.metrics_store = metrics_store + self.active_requests: Dict[str, float] = {} # Track request start times + + def start_request(self, agent_name: str, conversation_id: str) -> str: + """Start tracking a request.""" + request_id = f"{agent_name}_{conversation_id}_{time.time()}" + self.active_requests[request_id] = time.time() + return request_id + + def end_request(self, request_id: str, agent_name: str, conversation_id: str, + success: bool, token_input: int, token_output: int, + error_type: Optional[str] = None, error_message: Optional[str] = None) -> AgentMetrics: + """End tracking and collect metrics.""" + + start_time = self.active_requests.pop(request_id, time.time()) + response_time = (time.time() - start_time) * 1000 # Convert to milliseconds + + metrics = AgentMetrics( + agent_name=agent_name, + timestamp=datetime.now(), + conversation_id=conversation_id, + response_time_ms=response_time, + token_count_input=token_input, + token_count_output=token_output, + success=success, + error_type=error_type, + error_message=error_message + ) + + # Store metrics + self.metrics_store.store_metrics(metrics) + + return metrics +``` + +### 4. Enhanced AbiAgent Integration + +**Location**: `src/core/modules/abi/agents/AbiAgent.py` (enhancement) + +```python +# Enhancement to existing AbiAgent +class AbiAgent(IntentAgent): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # DPROD components + self.dprod_registry = AgentRegistryService(DPRODTripleStore()) + self.lineage_tracker = ConversationLineageTracker() + self.observability_collector = ObservabilityCollector(MetricsStore()) + + # Register all agents as DPROD data products on startup + self._register_agents_as_data_products() + + def _register_agents_as_data_products(self): + """Register all loaded agents as DPROD data products.""" + for agent in self.agents: + agent_config = { + "name": agent.name, + "description": agent.description, + "chat_model": agent._chat_model + } + self.dprod_registry.register_agent(agent.name, agent_config) + + def find_best_agent_by_capability(self, required_capabilities: List[str]) -> Optional[str]: + """Use DPROD metadata to find agent with required capabilities.""" + + # Build SPARQL query + capabilities_filter = ' '.join([f'"{cap}"' for cap in required_capabilities]) + + query = f""" + PREFIX dprod: + PREFIX abi: + PREFIX rdfs: + + SELECT ?agent ?label ?score + WHERE {{ + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + ?agent abi:capabilities ?cap . + ?agent abi:performanceTier ?tier . + + FILTER(?cap IN ({capabilities_filter})) + + BIND( + IF(?tier = "high", 3, + IF(?tier = "medium", 2, 1)) AS ?score + ) + }} + ORDER BY DESC(?score) + LIMIT 1 + """ + + try: + results = self.dprod_registry.triple_store.query(query) + if results: + # Extract agent name from URI + agent_uri = results[0]['agent'] + agent_name = agent_uri.split('/')[-1] # Get last part of URI + return agent_name + except Exception as e: + logger.warning(f"DPROD query failed, falling back to default routing: {e}") + + return None + + def invoke_with_observability(self, input_data: str, conversation_id: str = None) -> Any: + """Enhanced invoke with DPROD observability tracking.""" + + # Start observability tracking + request_id = self.observability_collector.start_request( + agent_name="Abi", + conversation_id=conversation_id or str(uuid.uuid4()) + ) + + try: + # Count input tokens (approximate) + input_tokens = len(input_data.split()) + + # Execute normal invoke + result = super().invoke(input_data) + + # Count output tokens (approximate) + output_tokens = len(str(result).split()) if result else 0 + + # End observability tracking (success) + metrics = self.observability_collector.end_request( + request_id=request_id, + agent_name="Abi", + conversation_id=conversation_id, + success=True, + token_input=input_tokens, + token_output=output_tokens + ) + + return result + + except Exception as e: + # End observability tracking (failure) + metrics = self.observability_collector.end_request( + request_id=request_id, + agent_name="Abi", + conversation_id=conversation_id, + success=False, + token_input=len(input_data.split()), + token_output=0, + error_type=type(e).__name__, + error_message=str(e) + ) + raise +``` + +### 5. SPARQL Query Interface + +**Location**: `src/core/modules/dprod/query/` + +```python +# src/core/modules/dprod/query/SPARQLEndpoint.py +from fastapi import FastAPI, HTTPException +from typing import Dict, List, Any +from ..storage.DPRODTripleStore import DPRODTripleStore + +class SPARQLEndpoint: + """SPARQL query endpoint for DPROD data.""" + + def __init__(self, triple_store: DPRODTripleStore): + self.triple_store = triple_store + self.app = FastAPI() + self._setup_routes() + + def _setup_routes(self): + """Setup FastAPI routes for SPARQL queries.""" + + @self.app.post("/sparql") + async def execute_sparql(query: str) -> Dict[str, Any]: + """Execute SPARQL query against DPROD data.""" + try: + results = self.triple_store.query(query) + return { + "status": "success", + "results": results, + "count": len(results) + } + except Exception as e: + raise HTTPException(status_code=400, detail=str(e)) + + @self.app.get("/agents") + async def list_agents() -> List[Dict[str, Any]]: + """List all agents as DPROD data products.""" + query = """ + PREFIX dprod: + PREFIX rdfs: + PREFIX abi: + + SELECT ?agent ?label ?description ?capabilities ?performanceTier ?privacyLevel + WHERE { + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + ?agent rdfs:comment ?description . + ?agent abi:capabilities ?capabilities . + ?agent abi:performanceTier ?performanceTier . + ?agent abi:privacyLevel ?privacyLevel . + } + """ + return self.triple_store.query(query) + + @self.app.get("/agents/by-capability/{capability}") + async def find_agents_by_capability(capability: str) -> List[Dict[str, Any]]: + """Find agents with specific capability.""" + query = f""" + PREFIX dprod: + PREFIX rdfs: + PREFIX abi: + + SELECT ?agent ?label ?performanceTier + WHERE {{ + ?agent a dprod:DataProduct . + ?agent rdfs:label ?label . + ?agent abi:capabilities "{capability}" . + ?agent abi:performanceTier ?performanceTier . + }} + ORDER BY DESC(?performanceTier) + """ + return self.triple_store.query(query) + + @self.app.get("/lineage/{conversation_id}") + async def get_conversation_lineage(conversation_id: str) -> List[Dict[str, Any]]: + """Get conversation lineage for a specific conversation.""" + query = f""" + PREFIX prov: + PREFIX abi: + + SELECT ?step ?from_agent ?to_agent ?timestamp ?activity + WHERE {{ + ?conversation abi:conversationId "{conversation_id}" . + ?conversation prov:hadStep ?step . + ?step prov:used ?from_agent . + ?step prov:generated ?to_agent . + ?step prov:atTime ?timestamp . + ?step prov:wasAssociatedWith ?activity . + }} + ORDER BY ?timestamp + """ + return self.triple_store.query(query) +``` + +## Data Flow Architecture + +### Agent Registration Flow +``` +Agent Startup → Extract Metadata → Create DPROD Model → Store in Triple Store → Expose via SPARQL +``` + +### Conversation Flow with Observability +``` +User Input → Start Metrics → Route to Agent → Execute → Collect Metrics → Store Observability → Track Lineage +``` + +### Query Flow +``` +SPARQL Query → Triple Store → Result Processing → JSON Response → Client Integration +``` + +## Performance Considerations + +### RDF Storage Optimization +- **Indexing Strategy**: Index frequently queried properties (capabilities, performance tier) +- **Caching Layer**: Redis cache for common SPARQL queries +- **Query Optimization**: Pre-compiled queries for agent discovery + +### Observability Overhead +- **Async Collection**: Non-blocking metrics collection +- **Batch Processing**: Batch metrics storage to reduce I/O +- **Sampling**: Sample high-frequency operations for large deployments + +### Scalability Patterns +- **Horizontal Scaling**: Distribute triple store across nodes +- **Query Federation**: Federate queries across multiple stores +- **Caching Strategy**: Multi-level caching for metadata and metrics + +--- + +This technical architecture provides the foundation for implementing DPROD compliance in ABI while maintaining performance and scalability requirements. \ No newline at end of file