JianFeeeee e985151da9 feat(memory): 千问字节级 BPE 分词器 + 与上游逐条对齐的回归测试
为「千问嵌入模型导出 ONNX 并内嵌」的 Go 侧准备。CLIP 那套 tokenizer 不能复用:
CLIP 是「小写化 + 空白规整 + 词表 BPE」,千问是 **GPT-2 式字节级 BPE**
(先按字节映射到安全 unicode,再对映射结果做合并),中文与空白输入的切分
完全不同。

实现中撞到两个与上游对齐的坑,都由测试暴露:

1. **`\s+(?!\S)` 的语义依赖正则回溯**,不是「等价于 `\s+`」。
   `\s+` 先贪婪吃完整段空白,发现后面是非空白导致 `(?!\S)` 失败,于是回退
   一个字符,**正好留下末尾一个空白**给前面以 ` ?` / `[^…]?` 开头的分支合并。
   这直接决定切分点:`"   leading"` 会切成 `"  "` + `" leading"`,
   而不是 `"   "` + `"leading"`。RE2 不支持 lookaround,近似改写必然对不上,
   所以改成按分支顺序**显式实现**(含 `\s*[\r\n]+` 的回溯语义)。
   实测:近似改写时 26 条里错 3 条,全部是空白串用例。

2. **Go 的 `\s` 只有 ASCII,且 regexp 不支持二进制属性 `\p{White_Space}`**
   (只支持 script/category,直接写会报 invalid character class range)。
   而上游 Rust regex 的 `\s` 正是 White_Space。改用 `unicode.IsSpace` 作为
   唯一判据,避免全角空格/NBSP/行分隔符的切分点漂移。

另外特殊 token(`<|im_start|>` 等 24 个 AddedToken)必须**整体优先匹配**并
按长度降序,否则会被 BPE 拆成子 token,模型收到的输入就变了——且不会报任何错。

验证:`testdata/qwen_tokenizer_reference.json` 由 HuggingFace 真实 tokenizer
生成(26 条用例,覆盖中/英/中英混排/数字/各类空白形态/标点/emoji/特殊 token/
长文本/空串/单字符边界),Go 实现逐条精确对齐。字节↔unicode 映射另测双射性
(有碰撞会让不同字节编成同一 token,静默产生错误输入)。
2026-09-11 00:11:58 +08:00
2026-07-29 15:45:24 +08:00

⚠️ AI-Assisted Programming Notice: Parts of this project's code, documentation, and commit history were generated or modified with AI assistance. Key changes have been human-reviewed, but please evaluate and verify before use.

HomeAgent

中文: README.md

An Agent framework designed around separation of core domain and application domain. The kernel enforces a zero-IO policy — all external interaction (WebUI, QQ, CLI, file operations, web search, memos, etc.) is handled by the plugin layer; the kernel performs no direct IO operations.

Combined with a three-layer memory architecture (Context → Document → Graph), it maintains contextual coherence across long-running single-conversation sessions through tiered storage and automated archival.

homed (kernel, zero IO)  PluginSDK  plugins (all IO capabilities)

Since v1.1.1 media reaches the plugin boundary: plugins and the model can both read and write images/audio in memory (InsertWithMedia, InjectInputMedia). Media lives in plain-text memory as a [<mime> <short digest>] <description> marker — the description is the searchable semantic memory, the digest is the key back to the bytes.

Since v1.0.0 external plugins are independent subprocesses, communicating with the kernel over stdio JSON-RPC (control plane) + a shared memory segment (data plane) + an event ring (notification plane). A plugin crash cannot take down the kernel and it restarts automatically; swapping plugin.bin gives true hot-reload.

Design Principles

Separation of Core Domain and Application Domain — The kernel's responsibilities are limited to LLM orchestration, memory management, and knowledge retrieval; all IO capabilities (message send/receive, file read/write, network requests, hardware interaction, etc.) are implemented by plugins. This separation defines domain boundaries at the Agent framework level, with distinct responsibility scopes for the kernel and plugins.

Three-Layer Memory Architecture — Manages information retention in long-running agents through a tiered storage strategy:

  • Context Layer: Pretrained word embedding / TF-IDF fallback relevance-scored event window, protects last 10 entries, maintains topK context entries
  • Document Layer: Temporary memory with automatic cold data sinking, also supports user-initiated submissions
  • Graph Layer: SQLite graph database, persists entity relationships and semantic memory, supports distillation pipelines to extract triples from conversations

Architecture Diagrams

1. Message Processing Sequence

sequenceDiagram
    participant U as User/Plugin
    participant IO as IOManager
    participant EV as eventLoop
    participant CTX as RelevanceContext
    participant LLM as LLM+Tool Loop
    participant ST as StageHost
    participant MEM as Three-Layer Memory

    U->>IO: InjectInput(type, payload)
    IO->>EV: inputCh
    rect lavender
        Note over EV: processTextInput
        EV->>ST: StageOnInput  Plugin can rewrite/short-circuit
        EV->>CTX: Prune(input,topK)  StaticEmbedder/TF-IDF cosine pruning
        CTX->>MEM: Low-score events archived to Document (original timestamp)
        EV->>CTX: Append(input)  CleanTemplateText→three-branch vector→5s write
    end
    rect lightgreen
        Note over EV,LLM: process()
        EV->>MEM: buildMemoryContext  Indexer recalls from Graph (vector+jieba→BFS depth=2)
        EV->>MEM: buildSystemPrompt  DocQuery summary+Graph memory index+Persona+Skills
        EV->>ST: StagePreAction  Plugin can pre-intercept
        loop Tool loop
            LLM->>LLM: drainInterrupts
            LLM->>LLM: LLM Chat
            LLM->>ST: StagePostAction  Plugin can modify/short-circuit
            alt No tool call
                LLM-->>EV: Returns response
            else
                loop Each tool
                    ST->>ST: StageBeforeToolcall  Plugin can reject
                    LLM->>LLM: executeToolCall
                    ST->>ST: StageAfterToolcall
                end
            end
        end
    end
    rect lightpink
        Note over EV: emitResponse
        CTX->>CTX: Append(response)
        ST->>ST: StageBeforeOutput  Plugin can rewrite
        EV-->>U: ResponseCh CLI sync
        EV-->>EV: Event bus WebUI SSE
        ST->>ST: StageAfterOutput  Read-only
        EV->>MEM: emitMemoryCandidate
    end

2. Stage Pipeline

flowchart LR
    S1[① on_input] --> S2[② pre_action]
    S2 --> S3[③ post_action]
    S3 --> Q{Has tool?}
    Q -->|Yes| S4[④ before_toolcall]
    S4 --> T[executeToolCall]
    T --> S5[⑤ after_toolcall]
    S5 --> S3
    Q -->|No| S6[⑥ before_output]
    S6 --> S7[⑦ after_output]
    style S1 fill:#e1f5fe
    style S3 fill:#fff3e0
    style S6 fill:#e8f5e9

3. Three-Layer Memory

flowchart TB
    subgraph C[① Context Working Window]
        RC[RelevanceContext]
        A[Append] -->|CleanTemplateText→three-branch vector| RC
        P[Prune StaticEmbedder/TF-IDF Cosine] -->|Low score original timestamp| D
        P -->|Keep| TL[timeline→chronological→system prompt]
    end
    subgraph D[② Document File Memory]
        DS[DocStore JSON+TF-IDF]
        Q1[Query summary auto-inject] -->|[Related Memory Docs]| SP
        Q2[doc_query LLM active recall] -->|Consume+delete source| DS
        Q2 -->|Original timestamp write to context| RC
        CD[FindColdDocs 72h] -->|docToTriples| G
    end
    subgraph G[③ Graph Database]
        DB[(SQLite)]
        IDX[Indexer vector+jieba→BFS depth=2] -->|[Memory Index]| SP
        MEM[memory_recall/commit/merge/purge/edit]
        SOC[person_query/set_trait]
    end
    subgraph H[④ Heartbeat Distillation]
        REORG -->|Step3 Cold docs| CD
        REORG -->|Step4 Bigram Jaccard| CONS[consolidation]
        PIPE[Pipeline regex] -->|Name/Address/Likes/Age/Job| DB
    end
    SP[System Prompt] -->|Sequential assembly| LLM
    LLM[LLM] -->|doc_query| Q2
    LLM -->|memory_recall| MEM

See assets/docs/en/ARCHITECTURE.md for details.

Web Mascot

HomeAgent Web Mascot Xiaozhai

Xiaozhai — HomeAgent Web Mascot

Quick Start

make build build-cli
./build/homed -data /tmp/ha
# Interactive mode
./build/waiter

# Or single message
echo "Hello, remember that I like coffee" | ./build/waiter

API keys are configured via WebUI http://localhost:8080 settings page, persisted in SQLite.

Code Structure

cmd/homed/          Daemon entry, assembles all subsystems
cmd/waiter/         CLI client (Unix socket)
internal/
├── agent/core/     Agent core: event loop, LLM tool loop, 7-stage pipeline
├── agent/api/      LLM Provider + 8 Lua adapters
├── memory/         Three-layer memory: Graph(SQLite) / Document(JSON+TF-IDF) / Text(JSONL) + StaticEmbedder(pretrained word embedding/TF-IDF fallback) + CleanTemplateText(de-template)
├── knowledge/      Knowledge base (filesystem + TF-IDF)
├── plugin/         Plugin registry + subprocess loader (stdio RPC + shared memory segment + event ring)
├── plugins/        11 built-in plugins (webui/cli/timer/cmd/mcp/clawhubadapter/agentcli/healthcheck/pluginmgr/files/cfgmgr)
├── sdk/            PluginSDK (Tool/Stage/Event three channels)
├── config/         SQLite config center
├── events/         Event bus
└── internal/lua/adapters/   8 LLM protocol adapter scripts
External plugin development: see [homeagent-sdk](https://gitcode.com/JianFeeeee/homeagent-sdk) repo, use `plugindev` toolchain, refer to Go and Lua examples in `example/`

Project Status

v1.1.1 — Multimodal reaches the plugin boundary. v1.1.0 gave the memory system binary multimedia nodes, but that path was open only to the kernel itself; this release opens it to plugins and the model. The public SDK gains media fields and three media injection methods (paired with SDK v1.1.0, shared by the whole 1.1.x line), and the kernel implements the four matching RPCs. The bridge layer had been silently dropping fields: Confidence/types/SentenceText handed in by a plugin were discarded, Doc kept only three fields, and Remove never released references (media stayed "referenced" forever, so GC could never reclaim it). processTextInput and processMediaInput were unified into a single processInput, which finally gives the media path the dedup, no_memory, channel Cleaner, interrupt semantics and correct EventRawInput it had always lacked. Three real defects fixed: user-sent images never appeared in the WebUI chat log (the media path published a map while the subscriber asserted a string), memory_commit's sentence_text had never been exposed to the model (though it is the mandatory link in the media binding chain), and two data races in PluginSDK (11 reported by -race; in production this showed up as sporadic nil-dereference crashes during plugin reload).

v1.1.0 — Memory system supports binary multimedia nodes. Content-addressed media store (CAS + SQLite metadata + on-disk blobs, Get always re-verifies the digest) wired through L0 (context events) / L2 (documents) / L3 (graph sentences), with reference-counted GC (referenced items are never deleted). The description text produced by the vision model is the durable semantic memory; the blob is only a cache that capacity GC may evict.

v1.0.0 — External plugins moved from C ABI shared libraries to subprocess + shared memory. The first release that no longer loads .so/.dll, and it is incompatible with 0.9.x (existing plugins must be rebuilt into plugin.bin with the new plugindev, though business code needs zero changes). Eliminates 6 classes of defects that had caused production incidents: hot-reload silently failing (DF_1_NODELETE making dlclose a no-op), no crash isolation (a plugin panic took down homed), stage lost updates (35.8~36.8% loss under the copy model), uncancellable cgo timeouts (linear OS-thread leaks), output_send reporting false success (the model was told "sent" while the message never went out), and Windows capability degradation (only 3 stage fields visible, no write-back). Three communication planes: stdio JSON-RPC (control) + shared memory segment (data) + event ring (notification); the privilege gradient is now enforced by three explicit gates. RPC round-trip p50 24.1µs; crash-to-recovery under 1s.

v0.9.0 — C ABI v2: external plugin Stage callbacks can now write back (invoke_stage gained a result out-param; plugins may mutate RawMessage/LLMText/ToolResults etc. in OnInput/AfterToolcall/PostAction and have them synced to the core). ABI version now tracks core minor releases (v0.9.x → ABIVersion=2, version_min=1 keeps old plugins loadable). Also fixes the tool-loop zen-compat placeholder that wrongly fired on first-turn system context tail. The SDK ships an enhanced sanitizer example (bad-UTF-8 / U+FFFD / ANSI-escape scrub across the whole pipeline). This ABI retired with v1.0.0.

v0.8.0 — Core is functional, plugin system enhanced. 20+ built-in plugins. External plugin development via homeagent-sdk repo. Added input channel NoMemory/Cleaner, ChannelDef, plugin disable/enable system (CLI + WebUI), plugindev toolchain C ABI ChannelDef support.

Documentation

Downloads

Releases ship three variants:

Variant Contents For
full homed + waiter + desktop GUI + systemd unit Single-machine, everything
server homed + waiter + systemd unit Servers (no desktop environment)
client waiter + desktop GUI Connecting to a remote HomeAgent
  • Linux: .deb (amd64/arm64), .rpm (x86_64), .tar.gz
  • Windows: HomeAgent_v1.1.1_{Full,Server,Client}_win64.exe (NSIS installer)
  • Portable: homeagent-bin-<os>_<arch>.tar.gz (homed/waiter/initconfig)
  • Verification: SHA256SUMS

The macOS homed requires a native macOS build (CGO + sqlite3), so release packages ship only waiter/initconfig.

Build

make build build-cli    # Build daemon + CLI
make test               # go test ./...
make install            # Install to system

Dependencies: Go 1.25+, CGo (go-sqlite3), Linux/Windows.

Description
No description provided
Readme AGPL-3.0 83 MiB
Languages
Go 74.6%
JavaScript 12.8%
HTML 3.9%
CSS 3.1%
Python 2.4%
Other 3.1%