Mai/.planning/phases/04-memory-context-management/04-02-PLAN.md

phase, plan, type, wave, depends_on, files_modified, autonomous, must_haves

phase	plan	type	wave	depends_on	files_modified	autonomous	must_haves
04-memory-context-management	02	execute	2	04-01	src/memory/retrieval/__init__.py src/memory/retrieval/semantic_search.py src/memory/retrieval/context_aware.py src/memory/retrieval/timeline_search.py src/memory/__init__.py	true	truths artifacts key_links User can search conversations by semantic meaning Search results are ranked by relevance to query Context-aware search prioritizes current topic discussions Timeline search allows filtering by date ranges Hybrid search combines semantic and keyword matching path provides min_lines src/memory/retrieval/semantic_search.py Semantic search with embedding-based similarity 70 path provides min_lines src/memory/retrieval/context_aware.py Topic-based search prioritization 50 path provides min_lines src/memory/retrieval/timeline_search.py Date-range filtering and temporal search 40 path provides exports src/memory/__init__.py Updated MemoryManager with search capabilities MemoryManager SemanticSearch from to via pattern src/memory/retrieval/semantic_search.py src/memory/storage/vector_store.py vector similarity search operations vector_store.search_similar from to via pattern src/memory/retrieval/context_aware.py src/memory/storage/sqlite_manager.py conversation metadata for topic analysis sqlite_manager.get_conversation_metadata from to via pattern src/memory/__init__.py src/memory/retrieval/ search method delegation semantic_search.find

Implement the memory retrieval system with semantic search, context-aware prioritization, and timeline filtering. This enables intelligent recall of past conversations using multiple search strategies.

Purpose: Allow users and the system to find relevant conversations quickly using semantic meaning, context awareness, and temporal filters Output: Working search system that can retrieve conversations by meaning, topic, and time range

<execution_context> @/.opencode/get-shit-done/workflows/execute-plan.md @/.opencode/get-shit-done/templates/summary.md </execution_context>

@.planning/phases/04-memory-context-management/04-CONTEXT.md @.planning/phases/04-memory-context-management/04-RESEARCH.md @.planning/PROJECT.md @.planning/ROADMAP.md @.planning/STATE.md

Reference storage foundation

@.planning/phases/04-memory-context-management/04-01-SUMMARY.md

Reference existing conversation handling

@src/models/conversation.py @src/models/context_manager.py

Task 1: Create semantic search with embedding-based retrieval src/memory/retrieval/__init__.py, src/memory/retrieval/semantic_search.py Create src/memory/retrieval/semantic_search.py with SemanticSearch class:

1. Add sentence-transformers to requirements.txt (use all-MiniLM-L6-v2 for efficiency)
2. Implement SemanticSearch with:
   • Embedding model loading (lazy loading for performance)
   • Query embedding generation
   • Vector similarity search using VectorStore from plan 04-01
   • Hybrid search combining semantic and keyword matching
   • Result ranking and relevance scoring
   • Conversation snippet generation for context

Follow research pattern for hybrid search:

• Generate query embedding
• Search vector store for similar conversations
• Fallback to keyword search if no semantic results
• Combine and rank results with weighted scoring

Include methods to:

• search(query: str, limit: int = 5) -> List[SearchResult]
• search_by_embedding(embedding: np.ndarray, limit: int = 5) -> List[SearchResult]
• keyword_search(query: str, limit: int = 5) -> List[SearchResult]

Use existing error handling patterns and type hints from src/models/ modules. python -c "from src.memory.retrieval.semantic_search import SemanticSearch; search = SemanticSearch(':memory:'); print('Semantic search created successfully')" Semantic search can generate embeddings and perform basic search operations

Task 2: Implement context-aware and timeline search capabilities src/memory/retrieval/context_aware.py, src/memory/retrieval/timeline_search.py, src/memory/__init__.py Create context-aware and timeline search components:

1. Create src/memory/retrieval/context_aware.py with ContextAwareSearch:

   • Topic extraction from current conversation context
   • Conversation topic classification using simple heuristics
   • Topic-based result prioritization
   • Current conversation context tracking
   • Methods: prioritize_by_topic(results: List[SearchResult], current_topic: str) -> List[SearchResult]

2. Create src/memory/retrieval/timeline_search.py with TimelineSearch:

   • Date range filtering for conversations
   • Temporal proximity search (find conversations near specific dates)
   • Recency-based result weighting
   • Conversation age calculation and compression level awareness
   • Methods: search_by_date_range(start: datetime, end: datetime, limit: int = 5) -> List[SearchResult]

3. Update src/memory/init.py to integrate search capabilities:

   • Import all search classes
   • Add search methods to MemoryManager
   • Provide unified search interface combining semantic, context-aware, and timeline search
   • Add search result dataclasses with relevance scores and conversation snippets

Follow existing patterns from src/models/ for data structures and error handling. Ensure search results include conversation metadata for context. python -c "from src.memory import MemoryManager; mm = MemoryManager(':memory:'); print('Memory manager with search created successfully')" Memory manager provides unified search interface with all search modes

After completion, verify: 1. Semantic search can find conversations by meaning 2. Context-aware search prioritizes relevant topics 3. Timeline search filters by date ranges correctly 4. Hybrid search combines semantic and keyword results 5. Search results include proper relevance scoring and conversation snippets 6. Integration with storage layer works correctly

<success_criteria>

• Semantic search uses sentence-transformers for embedding generation
• Context-aware search prioritizes topics relevant to current discussion
• Timeline search enables date-range filtering and temporal search
• Hybrid search combines multiple search strategies with proper ranking
• Memory manager provides unified search interface
• Search results include conversation context and relevance scoring </success_criteria>

After completion, create `.planning/phases/04-memory-context-management/04-02-SUMMARY.md`