docs(04): create phase plan

Phase 04: Memory & Context Management
- 4 plan(s) in 3 wave(s)
- 2 parallel, 2 sequential
- Ready for execution

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

@@ -0,0 +1,161 @@
+---
+phase: 04-memory-context-management
+plan: 02
+type: execute
+wave: 2
+depends_on: ["04-01"]
+files_modified: ["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"]
+autonomous: true
+
+must_haves:
+  truths:
+    - "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"
+  artifacts:
+    - path: "src/memory/retrieval/semantic_search.py"
+      provides: "Semantic search with embedding-based similarity"
+      min_lines: 70
+    - path: "src/memory/retrieval/context_aware.py"
+      provides: "Topic-based search prioritization"
+      min_lines: 50
+    - path: "src/memory/retrieval/timeline_search.py"
+      provides: "Date-range filtering and temporal search"
+      min_lines: 40
+    - path: "src/memory/__init__.py"
+      provides: "Updated MemoryManager with search capabilities"
+      exports: ["MemoryManager", "SemanticSearch"]
+  key_links:
+    - from: "src/memory/retrieval/semantic_search.py"
+      to: "src/memory/storage/vector_store.py"
+      via: "vector similarity search operations"
+      pattern: "vector_store\\.search_similar"
+    - from: "src/memory/retrieval/context_aware.py"
+      to: "src/memory/storage/sqlite_manager.py"
+      via: "conversation metadata for topic analysis"
+      pattern: "sqlite_manager\\.get_conversation_metadata"
+    - from: "src/memory/__init__.py"
+      to: "src/memory/retrieval/"
+      via: "search method delegation"
+      pattern: "semantic_search\\.find"
+---
+
+<objective>
+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
+</objective>
+
+<execution_context>
+@~/.opencode/get-shit-done/workflows/execute-plan.md
+@~/.opencode/get-shit-done/templates/summary.md
+</execution_context>
+
+<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
+</context>
+
+<tasks>
+
+<task type="auto">
+  <name>Task 1: Create semantic search with embedding-based retrieval</name>
+  <files>src/memory/retrieval/__init__.py, src/memory/retrieval/semantic_search.py</files>
+  <action>
+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.
+  </action>
+  <verify>python -c "from src.memory.retrieval.semantic_search import SemanticSearch; search = SemanticSearch(':memory:'); print('Semantic search created successfully')"</verify>
+  <done>Semantic search can generate embeddings and perform basic search operations</done>
+</task>
+
+<task type="auto">
+  <name>Task 2: Implement context-aware and timeline search capabilities</name>
+  <files>src/memory/retrieval/context_aware.py, src/memory/retrieval/timeline_search.py, src/memory/__init__.py</files>
+  <action>
+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.
+  </action>
+  <verify>python -c "from src.memory import MemoryManager; mm = MemoryManager(':memory:'); print('Memory manager with search created successfully')"</verify>
+  <done>Memory manager provides unified search interface with all search modes</done>
+</task>
+
+</tasks>
+
+<verification>
+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
+</verification>
+
+<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>
+
+<output>
+After completion, create `.planning/phases/04-memory-context-management/04-02-SUMMARY.md`
+</output>