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Phase 01-model-interface: Foundation systems - 3 plan(s) in 2 wave(s) - 2 parallel, 1 sequential - Ready for execution
126 lines
4.8 KiB
Markdown
126 lines
4.8 KiB
Markdown
---
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phase: 01-model-interface
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plan: 02
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type: execute
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wave: 1
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depends_on: []
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files_modified: ["src/models/context_manager.py", "src/models/conversation.py"]
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autonomous: true
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must_haves:
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truths:
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- "Conversation history is stored and retrieved correctly"
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- "Context window is managed to prevent overflow"
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- "Old messages are compressed when approaching limits"
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artifacts:
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- path: "src/models/context_manager.py"
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provides: "Conversation context and memory management"
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min_lines: 60
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- path: "src/models/conversation.py"
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provides: "Message data structures and types"
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min_lines: 30
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key_links:
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- from: "src/models/context_manager.py"
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to: "src/models/conversation.py"
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via: "import conversation types"
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pattern: "from.*conversation import"
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- from: "src/models/context_manager.py"
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to: "future model manager"
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via: "context passing interface"
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pattern: "def get_context_for_model"
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---
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<objective>
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Implement conversation context management and memory system.
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Purpose: Create the foundation for managing conversation history, context windows, and memory compression before model switching logic is added.
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Output: Working context manager with message storage, compression, and token budget management.
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</objective>
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<execution_context>
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@~/.opencode/get-shit-done/workflows/execute-plan.md
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@~/.opencode/get-shit-done/templates/summary.md
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</execution_context>
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<context>
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@.planning/PROJECT.md
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@.planning/ROADMAP.md
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@.planning/phases/01-model-interface/01-RESEARCH.md
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@.planning/phases/01-model-interface/01-CONTEXT.md
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@.planning/codebase/ARCHITECTURE.md
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@.planning/codebase/STRUCTURE.md
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</context>
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<tasks>
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<task type="auto">
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<name>Task 1: Create conversation data structures</name>
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<files>src/models/conversation.py</files>
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<action>
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Create conversation data models following research architecture:
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1. Define Message class with role, content, timestamp, metadata
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2. Define Conversation class to manage message sequence
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3. Define ContextWindow class for token budget tracking
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4. Include message importance scoring for compression decisions
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5. Add Pydantic models for validation and serialization
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6. Support message types: user, assistant, system, tool_call
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Key classes:
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- Message: role, content, timestamp, token_count, importance_score
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- Conversation: messages list, metadata, total_tokens
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- ContextBudget: max_tokens, used_tokens, available_tokens
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- MessageMetadata: source, context, priority flags
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Use dataclasses or Pydantic BaseModel for type safety and validation. Include proper type hints throughout.
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</action>
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<verify>python -c "from src.models.conversation import Message, Conversation; msg = Message(role='user', content='test'); print(msg.role)"</verify>
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<done>Conversation data structures support message creation and management</done>
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</task>
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<task type="auto">
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<name>Task 2: Implement context manager with compression</name>
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<files>src/models/context_manager.py</files>
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<action>
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Create ContextManager class following research patterns:
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1. Implement sliding window context management
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2. Add hybrid compression: summarize old messages, preserve recent ones
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3. Trigger compression at 70% of context window (from CONTEXT.md)
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4. Prioritize user instructions and explicit requests during compression
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5. Implement semantic importance scoring for message retention
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6. Support different model context sizes (adaptive based on model)
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Key methods:
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- add_message(message): Add message to conversation, check compression need
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- get_context_for_model(model_key): Return context within model's token limit
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- compress_conversation(target_ratio): Apply hybrid compression strategy
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- estimate_tokens(text): Estimate token count for text (approximate)
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- get_conversation_summary(): Generate summary of compressed messages
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Follow research anti-patterns: Don't ignore context window overflow, use proven compression algorithms.
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</action>
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<verify>python -c "from src.models.context_manager import ContextManager; cm = ContextManager(); print(cm.add_message) and hasattr(cm, 'compress_conversation')"</verify>
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<done>Context manager handles conversation history with intelligent compression</done>
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</task>
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</tasks>
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<verification>
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Verify conversation management:
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1. Messages can be added and retrieved from conversation
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2. Context compression triggers at correct thresholds
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3. Important messages are preserved during compression
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4. Token estimation works reasonably well
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5. Context adapts to different model window sizes
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</verification>
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<success_criteria>
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Conversation context system operational:
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- Message storage and retrieval works correctly
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- Context window management prevents overflow
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- Intelligent compression preserves important information
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- System ready for integration with model switching
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</success_criteria>
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<output>
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After completion, create `.planning/phases/01-model-interface/01-02-SUMMARY.md`
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</output> |