roboomp
66b6597829
The beam backend never invoked the embedding pipeline during normal operation: `remember()`/`rememberBatch()`/`updateWorking()` skipped `embed()` entirely and `recall()`/`recallEnhanced()` never called `embedQuery()` on the query text. As a result `memory_embeddings` stayed empty in every deployment and recall silently degraded to FTS-only regardless of the configured provider (fastembed, OpenAI-compatible API, custom). - Added `scheduleEmbedding` on `beam.pendingExtractions` (mirroring `scheduleFactExtraction`) and wired it from `remember`, `rememberBatch`, `updateWorking`, and `consolidateToEpisodic`. Writes `INSERT OR REPLACE INTO memory_embeddings(memory_id, embedding_json, model)` with the active runtime-options model, captured before the AsyncLocalStorage scope exits and re-entered inside the task. - Auto-derived `queryEmbedding` inside `recall()` via `embedQuery(query)` when the caller did not pass one. `queryEmbedding: null` is preserved as the explicit FTS-only opt-out; `undefined` triggers auto-derive. - Propagated `queryEmbedding` through `Mnemopi`'s `toRecallOptions` so the facade no longer strips the override on the way to the beam layer. - Made `Mnemopi.recall`/`recallEnhanced`/`search`/`query`, the module-level exports, `BeamMemory.recall`/`recallEnhanced`, the free `recall`/`recallEnhanced`, and `orchestrateRecall` async. MCP `handleToolCall`/`callToolJson`/`handleJsonRpc` follow suit so the recall handler can await. - Fixed `withBeam`/`withSharedBeam` to defer `beam.close()` until the async handler resolves; otherwise the new async recall hit `RangeError: Cannot use a closed database`. - Updated CLI, MCP entrypoints, coding-agent `MnemopiSessionState`, and every affected test to await the new shapes. Verified with a new regression suite (`test/issue-1832-embedding-population.test.ts`) exercising both ends of the bug: empty `memory_embeddings` and zero `dense_score`. Fixes #1832
@oh-my-pi/pi-coding-agent
Core implementation package for the omp coding agent in the oh-my-pi monorepo.
For installation, setup, provider configuration, model roles, slash commands, and full CLI reference, see:
Package-specific references:
- CHANGELOG
- MCP configuration guide
- MCP runtime lifecycle
- MCP server/tool authoring
- DEVELOPMENT
- RenderMermaid guide
Memory backends
The agent supports three mutually-exclusive memory backends, selected via the memory.backend setting (Settings → Memory tab, or ~/.omp/config.yml):
off(default) — no memory subsystem runs.local— existing rollout-summarisation pipeline; writesmemory_summary.mdand consolidated artifacts under the agent dir.hindsight— talks to a Hindsight server (Cloud or self-hosted Docker), retains transcripts every Nth user turn, recalls memories on the first turn of a session, and exposesretain,recall, andreflect.
Hindsight quickstart
- Run a Hindsight server (Cloud or
docker run -p 8888:8888 ghcr.io/vectorize-io/hindsight:latest). - Set
memory.backend = "hindsight"andhindsight.apiUrl = "http://localhost:8888"(or your Cloud URL). - Optional environment overrides (env wins over settings):
HINDSIGHT_API_URL,HINDSIGHT_API_TOKEN— connectionHINDSIGHT_BANK_ID,HINDSIGHT_DYNAMIC_BANK_ID,HINDSIGHT_AGENT_NAME— bank addressingHINDSIGHT_AUTO_RECALL,HINDSIGHT_AUTO_RETAIN,HINDSIGHT_RETAIN_MODE— lifecycleHINDSIGHT_RECALL_BUDGET,HINDSIGHT_RECALL_MAX_TOKENS— recall sizingHINDSIGHT_BANK_MISSION,HINDSIGHT_DEBUG
Switching backends mid-session is honoured on the next system-prompt rebuild and the next /memory slash command. Existing users with memories.enabled = true|false are migrated to memory.backend = "local"|"off" exactly once on first launch.