dsh-recall

🌏 中文 · English

AI never forgets what you told it.

A native DeepSeek Harness (DSH) plugin that gives the agent a memory maze — corridors and rooms built from every conversation you have had together. Every decision, setting, discussion, or casually mentioned requirement is remembered. Ask "where were we?" and it walks the maze, brings back the conversation verbatim, and answers as naturally as if it had never forgotten — you won't even notice it thought for a moment.

Conversation history recall · Three-layer retrieval (literal / fuzzy / semantic) · Fully local & offline · Compaction-proof

• While searching, a quiet sweeping light appears in the corner:

  

• When done, no trace:

  

Who is it for

• Heavy users of long sessions — conversations spanning days and hundreds of turns, too long to scroll back through
• Writers / RP / tavern players — settings, foreshadowing, and character relationships scattered across months of chat
• Code & doc maintainers — the reasoning behind past decisions and pitfalls, now reduced to a one-line summary
• Anyone who has said "didn't we discuss this before?" — it brings back the original words instead of making you retell them

Conversely, if your sessions are short and easy to scroll, you probably don't need it — it is built for "history too long, memory compacted" scenarios.

Quick start

dsh plugin --profile web add dsh-recall

One command: the package ships its own composition patch (bundle layer), so the plugin and the search index it needs are wired up automatically. Restart dsh web. Nothing else to do — the model ships with the package (~37MB full install), the index builds on first search, and semantic warm-up finishes quietly in the background (a few minutes, imperceptible to you).

You can also install / disable / uninstall dsh-recall from the Add-ons block of the Plugin Management tab in dsh-extension-hub.

Optional configuration

- id: recall
  name: dsh-recall
  config:
    semantic: false   # disable the semantic layer (literal + fuzzy only, smaller package)
    warmup: gentle    # slower warm-up, lower background CPU (only during warm-up; zero afterwards)

What it is not

• ❌ Not context engineering — it does not cram history into the model window
• ❌ Not prompt engineering — it does not rely on prompts to make the model "pretend to remember"
• ❌ Not a memory-document system — no MEMORY.md or manual notes to maintain
• ✅ It is actual recall: on-demand retrieval of the original records — including history already compacted away (compaction only summarizes; the original text stays searchable forever)

Three-layer retrieval

Layer	Technique	Covers
Literal	Official FTS5 full-text index	Exact keyword matches
Fuzzy	Self-built trigram + char-bigram index (zero dependencies)	Rough wording, remembered fragments, typos / missing chars
Semantic	Local bge-small-zh model (int8, 24MB, bundled)	Paraphrase, word substitution, "roughly what it was about"

Every recall merges the three layers automatically, ranks by relevance, and groups by session. Everything runs locally and offline — no external model APIs.

Feature matrix

Capability	Description
Three-layer hybrid retrieval	Literal / fuzzy / semantic merged automatically; silent degradation chain (any failure falls back to the layer below)
Scope control	Current session only by default; workspace / all only on explicit user request
Context window	±N original messages around every hit (configurable, default 3)
Compaction-proof	Index covers the full history, including shadowed (compacted) events
Incremental indexing	Live sessions via ctx.sessions, persisted via sessionPersistence, append-only deltas
Background warm-up	Worker-thread embedding (~10 texts/sec), host event loop never blocked
Invisible UI	"Recalling…" sweep → one quiet "Recall complete" line; results never enter the UI, the agent presents them naturally

Recent updates

Recent updates (click to expand)

The npm package first published as 0.1.0; the 0.0.x entries below are development milestones.

• 2026-08 — v0.1.0: first release — one-command install (dsh.bundle.patch wires the plugin row and enables full-text session search automatically), optional dsh-recall-models package for the 23.9MB embedding model (--omit=optional for a lightweight build), bilingual README + locale-aware UI.
• 2026-08 — v0.0.6: semantic layer — local bge-small-zh (int8, bundled, fully offline) running in a worker thread; three-layer hybrid retrieval (literal / fuzzy / semantic) with a coverage gate (≥90%) and silent degradation; background warm-up (~10 texts/sec, host event loop never blocked).
• 2026-08 — v0.0.4: fuzzy retrieval — self-built trigram + char-bigram index (zero npm dependencies): find conversations when you remember only fragments, rough wording, typos or missing characters.
• 2026-08 — v0.0.2: the recall tool — official FTS5 full-text search over every past session (including compacted history), grouped by session with a bounded context window; scope control (current session by default); invisible UI (Recalling… / Recall complete).

How it works

recall tool (defineTool)
├─ Semantic: bge-small-zh int8 ONNX (23MB bundled) → worker-thread WASM inference
│            → 512-dim cosine search, joins the mix only after ≥90% coverage
├─ Fuzzy:    self-built SQLite trigram FTS + bigram LIKE + containment rerank (primary)
├─ Literal:  official ctx.sessionQuery (fallback)
└─ Mix:      semantic ∪ fuzzy, best score per doc → group by session → title + context window

• Data comes from official services (ctx.sessions / ctx.sessionPersistence) — no .zstd parsing, no private formats
• Index & model: ~/.dsh/storages/recall-index.db, bundled models/
• Inference runs in a worker thread — WASM on the main thread would block the host event loop (measured: ~9.6 texts/sec with zero main-thread impact)

Roadmap

v1 · Now — Three-layer hybrid retrieval: official FTS5 literal / self-built trigram+bigram fuzzy / local bge embedding semantic; coverage gate, background warm-up, silent degradation chain.

v2 · Retrieval control

• browse mode: read a full session forward from a hit event (paginated) — turn "recall" into "scrolling the chat log"
• Two-stage recall: lightweight coarse recall by default (title + snippet, ~600 tokens); the agent picks the relevant sessions and requests full context on demand — irrelevant content never enters the context
• Time-range filters: constrain retrieval by message time windows
• Result aggregation: merge repeated mentions of one topic into a complete "episode" instead of scattered hits
• Compaction anchors: subscribe to compaction/summary log events, register summaries as searchable anchors, restore originals via shadowedSeqs

v3 · Memory organization

• Topic clustering: embed similarity clustering, present results grouped by topic
• Memory distillation: extract settings & decisions across sessions into durable long-term memory
• Longer term: evaluate topic-based / layered compaction mechanisms — evaluation only, no changes to DSH core

Known boundaries

• Semantic bridging for very short queries (≤4 chars) is weak (bge short-text cosine has limited separation); the fuzzy layer's LIKE fallback covers it
• Semantic ranking is not fully reliable for queries with zero literal overlap — the fuzzy layer is always the primary path, and the agent makes the final call
• The model is int8-quantized: semantic quality is "good enough" by design; swap in an fp32 model (~4× size) for maximum quality

Development & testing

node .smoke-recall.mjs      # unit + integration (mocked, no model needed) — 60+ assertions
node .smoke-semantic.mjs    # real-model integration (requires models/ present)

Covers: tokenizer alignment (token-for-token against transformers.js), index increments, scoping, hybrid ranking, degradation, warm-up.

Modules

File	Responsibility
lib/index.js	Tool registration, scope resolution, hybrid ranking, session aggregation, warm-up scheduling
lib/fuzzy-index.js	Self-built SQLite index (trigram FTS + bigram + vector table), zero npm dependencies
lib/tokenizer.js	BERT WordPiece tokenizer (pure JS, token-for-token aligned with the reference)
lib/semantic.js	Embedder: worker thread, batched embedding, lazy loading
lib/embed-worker.js	WASM inference + mask-aware mean pooling + L2 normalization inside the worker
lib/vendor/	Vendored onnxruntime-web (0.8MB entry + 12MB wasm) + tokenizer.json
models/	Merged single-file int8 model (23MB; split into an optional package at publish)
lib/client.js	Minimal ToolView ("Recalling…" / "Recall complete"), locale-aware zh/en

Publish structure

• dsh-recall — main package (code + vendored runtime + tokenizer)
• dsh-recall-models — optional dependency (23MB model); npm installs it by default; --omit=optional yields the lightweight build, which degrades silently when the model is absent

References & acknowledgments

• Official: @deepseek-ai/dsh-session-query(-sqlite), dsh-tools, dsh-session-persistence
• Model: BAAI/bge-small-zh-v1.5 (MIT) · onnx-community int8 export · onnxruntime-web (MIT)
• Ecosystem: dsh-plugin-recall (official-FTS recall tool), dsh-mneme (local semantic memory, hybrid-recall degradation ideas)

License

MIT