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Native CLI i18n: The TrustGraph CLI has built-in translation support that dynamically loads language strings. You can test and use different languages by simply passing the --lang flag (e.g., --lang es for Spanish, --lang ru for Russian) or by configuring your environment's LANG variable. Automated Docs Translations: This PR introduces autonomously translated Markdown documentation into several target languages, including Spanish, Swahili, Portuguese, Turkish, Hindi, Hebrew, Arabic, Simplified Chinese, and Russian.
97 lines
4.2 KiB
Markdown
97 lines
4.2 KiB
Markdown
---
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layout: default
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title: "Schema Directory Refactoring Proposal"
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parent: "Tech Specs"
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---
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# Schema Directory Refactoring Proposal
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## Current Issues
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1. **Flat structure** - All schemas in one directory makes it hard to understand relationships
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2. **Mixed concerns** - Core types, domain objects, and API contracts all mixed together
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3. **Unclear naming** - Files like "object.py", "types.py", "topic.py" don't clearly indicate their purpose
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4. **No clear layering** - Can't easily see what depends on what
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## Proposed Structure
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```
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trustgraph-base/trustgraph/schema/
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├── __init__.py
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├── core/ # Core primitive types used everywhere
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│ ├── __init__.py
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│ ├── primitives.py # Error, Value, Triple, Field, RowSchema
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│ ├── metadata.py # Metadata record
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│ └── topic.py # Topic utilities
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│
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├── knowledge/ # Knowledge domain models and extraction
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│ ├── __init__.py
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│ ├── graph.py # EntityContext, EntityEmbeddings, Triples
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│ ├── document.py # Document, TextDocument, Chunk
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│ ├── knowledge.py # Knowledge extraction types
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│ ├── embeddings.py # All embedding-related types (moved from multiple files)
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│ └── nlp.py # Definition, Topic, Relationship, Fact types
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│
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└── services/ # Service request/response contracts
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├── __init__.py
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├── llm.py # TextCompletion, Embeddings, Tool requests/responses
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├── retrieval.py # GraphRAG, DocumentRAG queries/responses
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├── query.py # GraphEmbeddingsRequest/Response, DocumentEmbeddingsRequest/Response
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├── agent.py # Agent requests/responses
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├── flow.py # Flow requests/responses
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├── prompt.py # Prompt service requests/responses
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├── config.py # Configuration service
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├── library.py # Librarian service
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└── lookup.py # Lookup service
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```
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## Key Changes
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1. **Hierarchical organization** - Clear separation between core types, knowledge models, and service contracts
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2. **Better naming**:
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- `types.py` → `core/primitives.py` (clearer purpose)
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- `object.py` → Split between appropriate files based on actual content
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- `documents.py` → `knowledge/document.py` (singular, consistent)
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- `models.py` → `services/llm.py` (clearer what kind of models)
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- `prompt.py` → Split: service parts to `services/prompt.py`, data types to `knowledge/nlp.py`
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3. **Logical grouping**:
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- All embedding types consolidated in `knowledge/embeddings.py`
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- All LLM-related service contracts in `services/llm.py`
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- Clear separation of request/response pairs in services directory
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- Knowledge extraction types grouped with other knowledge domain models
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4. **Dependency clarity**:
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- Core types have no dependencies
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- Knowledge models depend only on core
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- Service contracts can depend on both core and knowledge models
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## Migration Benefits
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1. **Easier navigation** - Developers can quickly find what they need
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2. **Better modularity** - Clear boundaries between different concerns
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3. **Simpler imports** - More intuitive import paths
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4. **Future-proof** - Easy to add new knowledge types or services without cluttering
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## Example Import Changes
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```python
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# Before
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from trustgraph.schema import Error, Triple, GraphEmbeddings, TextCompletionRequest
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# After
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from trustgraph.schema.core import Error, Triple
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from trustgraph.schema.knowledge import GraphEmbeddings
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from trustgraph.schema.services import TextCompletionRequest
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```
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## Implementation Notes
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1. Keep backward compatibility by maintaining imports in root `__init__.py`
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2. Move files gradually, updating imports as needed
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3. Consider adding a `legacy.py` that imports everything for transition period
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4. Update documentation to reflect new structure
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<function_calls>
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<invoke name="TodoWrite">
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<parameter name="todos">[{"id": "1", "content": "Examine current schema directory structure", "status": "completed", "priority": "high"}, {"id": "2", "content": "Analyze schema files and their purposes", "status": "completed", "priority": "high"}, {"id": "3", "content": "Propose improved naming and structure", "status": "completed", "priority": "high"}]
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