From 21fc32086d6114cc84f2fb1d9e29c37ae15593f0 Mon Sep 17 00:00:00 2001 From: Cyber MacGeddon Date: Mon, 4 Aug 2025 21:04:25 +0100 Subject: [PATCH] Architecture principles --- docs/tech-specs/ARCHITECTURE_PRINCIPLES.md | 106 +++++++++++++++++++++ 1 file changed, 106 insertions(+) create mode 100644 docs/tech-specs/ARCHITECTURE_PRINCIPLES.md diff --git a/docs/tech-specs/ARCHITECTURE_PRINCIPLES.md b/docs/tech-specs/ARCHITECTURE_PRINCIPLES.md new file mode 100644 index 00000000..319859ce --- /dev/null +++ b/docs/tech-specs/ARCHITECTURE_PRINCIPLES.md @@ -0,0 +1,106 @@ +# Knowledge Graph Architecture Foundations + +## Foundation 1: Subject-Predicate-Object (SPO) Graph Model +**Decision**: Adopt SPO/RDF as the core knowledge representation model + +**Rationale**: +- Provides maximum flexibility and interoperability with existing graph technologies +- Enables seamless translation to other graph query languages (e.g., SPO → Cypher, but not vice versa) +- Creates a foundation that "unlocks a lot" of downstream capabilities +- Supports both node-to-node relationships (SPO) and node-to-literal relationships (RDF) + +**Implementation**: +- Core data structure: `node → edge → {node | literal}` +- Maintain compatibility with RDF standards while supporting extended SPO operations + +## Foundation 2: LLM-Native Knowledge Graph Integration +**Decision**: Optimize knowledge graph structure and operations for LLM interaction + +**Rationale**: +- Primary use case involves LLMs interfacing with knowledge graphs +- Graph technology choices must prioritize LLM compatibility over other considerations +- Enables natural language processing workflows that leverage structured knowledge + +**Implementation**: +- Design graph schemas that LLMs can effectively reason about +- Optimize for common LLM interaction patterns + +## Foundation 3: Embedding-Based Graph Navigation +**Decision**: Implement direct mapping from natural language queries to graph nodes via embeddings + +**Rationale**: +- Enables the simplest possible path from NLP query to graph navigation +- Avoids complex intermediate query generation steps +- Provides efficient semantic search capabilities within the graph structure + +**Implementation**: +- `NLP Query → Graph Embeddings → Graph Nodes` +- Maintain embedding representations for all graph entities +- Support direct semantic similarity matching for query resolution + +## Foundation 4: Distributed Entity Resolution with Deterministic Identifiers +**Decision**: Support parallel knowledge extraction with deterministic entity identification (80% rule) + +**Rationale**: +- **Ideal**: Single-process extraction with complete state visibility enables perfect entity resolution +- **Reality**: Scalability requirements demand parallel processing capabilities +- **Compromise**: Design for deterministic entity identification across distributed processes + +**Implementation**: +- Develop mechanisms for generating consistent, unique identifiers across different knowledge extractors +- Same entity mentioned in different processes must resolve to the same identifier +- Acknowledge that ~20% of edge cases may require alternative processing models +- Design fallback mechanisms for complex entity resolution scenarios + +## Foundation 5: Event-Driven Architecture with Publish-Subscribe +**Decision**: Implement pub-sub messaging system for system coordination + +**Rationale**: +- Enables loose coupling between knowledge extraction, storage, and query components +- Supports real-time updates and notifications across the system +- Facilitates scalable, distributed processing workflows + +**Implementation**: +- Message-driven coordination between system components +- Event streams for knowledge updates, extraction completion, and query results + +## Foundation 6: Reentrant Agent Communication +**Decision**: Support reentrant pub-sub operations for agent-based processing + +**Rationale**: +- Enables sophisticated agent workflows where agents can trigger and respond to each other +- Supports complex, multi-step knowledge processing pipelines +- Allows for recursive and iterative processing patterns + +**Implementation**: +- Pub-sub system must handle reentrant calls safely +- Agent coordination mechanisms that prevent infinite loops +- Support for agent workflow orchestration + +## Foundation 7: Columnar Data Store Integration +**Decision**: Ensure query compatibility with columnar storage systems + +**Rationale**: +- Enables efficient analytical queries over large knowledge datasets +- Supports business intelligence and reporting use cases +- Bridges graph-based knowledge representation with traditional analytical workflows + +**Implementation**: +- Query translation layer: Graph queries → Columnar queries +- Hybrid storage strategy supporting both graph operations and analytical workloads +- Maintain query performance across both paradigms + +--- + +## Architecture Principles Summary + +1. **Flexibility First**: SPO/RDF model provides maximum adaptability +2. **LLM Optimization**: All design decisions consider LLM interaction requirements +3. **Semantic Efficiency**: Direct embedding-to-node mapping for optimal query performance +4. **Pragmatic Scalability**: Balance perfect accuracy with practical distributed processing +5. **Event-Driven Coordination**: Pub-sub enables loose coupling and scalability +6. **Agent-Friendly**: Support complex, multi-agent processing workflows +7. **Analytical Compatibility**: Bridge graph and columnar paradigms for comprehensive querying + +These foundations establish a knowledge graph architecture that balances theoretical rigor with practical scalability requirements, optimized for LLM integration and distributed processing. +