35 lines
1.4 KiB
Markdown
35 lines
1.4 KiB
Markdown
# Feature Plan: Agent Knowledge Memory Foundation (Pre-11/12)
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## Goal
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Establish a scalable, queryable memory substrate so wiki and MCP features can rely on fast retrieval instead of markdown-file scans.
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## Why This Comes Before 11/12
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- Plan 11 (personal memory) needs performant retrieval and indexing guarantees.
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- Plan 12 (MCP wiki/tools) needs a stable backend abstraction independent of UI and tool transport.
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## Scope
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- Pluggable memory search backend interface.
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- Default Django backend for zero-infra operation.
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- Optional Manticore backend for scalable full-text/vector-ready indexing.
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- Reindex + query operational commands.
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- System diagnostics endpoints for backend status and query inspection.
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## Implementation Slice
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1. Add `core/memory/search_backend.py` abstraction and backends.
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2. Add `memory_search_reindex` and `memory_search_query` management commands.
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3. Add system APIs:
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- backend status
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- memory query
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4. Add lightweight Podman utility script for Manticore runtime.
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5. Add tests for diagnostics and query behavior.
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## Acceptance Criteria
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- Memory retrieval works with `MEMORY_SEARCH_BACKEND=django` out of the box.
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- Switching to `MEMORY_SEARCH_BACKEND=manticore` requires only env/config + container startup.
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- Operators can verify backend health and query output from system settings.
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## Out of Scope
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- Full wiki article model/UI.
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- Full MCP server process/tooling.
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- Embedding generation pipeline (next slice after backend foundation).
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