JianFeeeee bfdb395731 feat(memory): 新增 chineseclip provider —— text+image 的小体积可商用向量空间
## 为什么

用户决定「本轮不覆盖 video,先支持 text+image」。这一刀正好解锁了此前
「小 + 可商用 + 覆盖视频」三者不可兼得的僵局:不要求视频后,唯一同时满足
**小、可商用、中文原生** 的选项是 Chinese-CLIP ViT-B/16。

实测对比(同机、真实跑出来的数字):

| | Chinese-CLIP | jina-v5-omni-nano | Qwen3-VL-Emb-2B |
|---|---|---|---|
| 参数量 | 188M | 1.04B | 2B |
| 产物 / 常驻内存 | 754MB / **1.15GB** | ~2GB / 2.23GB | 8GB / 9.4GB |
| 维度 | 512 | 768 | 2048 |
| 许可 | **Apache-2.0** | CC BY-NC(不可商用) | Apache-2.0 |
| 视频 | 无 | 有 | 有 |

本机可用内存只有 5.3GB,Qwen 的 9.4GB 无法进程内使用;而 ORT format + mmap
那条路被证实当前不通(转换器对三段图段错误;走通还需同时升 ORT 运行时与
Go 绑定,v1.36 要求 API 29 而本机只有 28)。1.15GB 则可以直接进程内跑。

**代价已写进包注释与文档**:CLIP 是双塔对比学习,text↔image 是强项,但纯文本
语义明显弱于 MLLM 型嵌入器;文本检索仍由既有词向量/TF-IDF 路径兜底。
需要更强文本语义或视频时切回 qwen3vl。

## 内容

- `providers/chineseclip/`:按公共 SPI 实现的 provider(注册名 `chineseclip`),
  含 BERT WordPiece 分词器、图像预处理、ONNX 双塔推理、无标签 stub。
- `scripts/export_chineseclip_onnx.py`:从官方权重导出规范产物 + 冻结参考,
  自带逐用例 PyTorch 对比与覆盖度断言(计划集合≠执行集合即非零退出)。
- `cmd/homed/main.go`:空白导入两个 provider,由配置选其一。
- `go.mod`:`golang.org/x/text` 由间接依赖转为直接依赖(删音标需要 NFD)。

## 实现要点

- **分词器逐 token 对齐官方**。第一版探针自己拼 BertTokenizer(只给 vocab.txt、
  没删音标、中文没逐字切),中文被整体切成 [UNK],三个不同句子产出几乎相同的
  向量(余弦 0.98)——差点把「模型坏了」当成结论。官方配置是 do_lower_case=true
  + 删音标生效 + 中文逐字切分;`TestTokenizerMatchesOfficialReference` 钉住
  逐 token 一致。
- **图像缩放自写 bicubic**(复刻 PIL 的 precompute_coeffs + a=-0.5 核),不引
  golang.org/x/image:它未进本机模块缓存,且最新版要求把整个工具链升到 Go 1.26,
  为一个缩放函数动工具链不划算。
- **归一化在 provider 侧**(两个塔的图里都没归一化),检索按余弦。
- **指纹覆盖全部影响语义的产物**:两个 ONNX 图 + vocab.txt + embed_config.json,
  读不到就写 MISSING(跳过等于对缺件不敏感)。
- 会话 Run 用 runMu 串行化(ORT 会话不保证并发安全),创建/销毁用 mu。

## 模态范围

只声明 `text` 与 `image`;`audio`/`video` 明确返回 `ErrUnsupportedModality`,
绝不用别的模型向量冒充(这是「音频明确 unsupported」纪律的落地)。

## 验证(实测)

导出侧:10 个用例(5 文本 + 5 图像)ONNX vs 官方 PyTorch 全部
`cos = 1.000000000`,覆盖度断言 10/10 通过。

Go 侧(`CHINESECLIP_MODEL_DIR=... go test -tags onnxruntime ./providers/chineseclip/ -v`):
11/11 通过,其中
- 文本 5 用例 `cos = 1.000000000000`(逐位一致)
- 图像 4 纯色用例 `cos = 1.000000`(与官方预处理在 6 位小数内一致)
- 跨模态判别:红图对「红色」文本高于「蓝色」文本
- 模态拒绝 / 空输入 / 指纹稳定 / 产物缺失报错

顺带修掉测试自身的一个假通过:参考向量是**未归一化**的原始输出(模长 10~36),
原先「点积当余弦 + 单侧下界」会让 13.6 也判过,已改为真余弦 + 双侧容差。

构建矩阵:`go build/vet ./...` 与 `-tags onnxruntime` 两种都过;
`providers/... pkg/... internal/config/... internal/memory/vector/...` 回归通过
(qwen3vl 的 TestVideoModelInputMRope 需要 QWEN_ONNX_MODEL_DIR 指向含视频档的
v3 目录,缺该环境变量时用的是只有文本+图像的目录,与本改动无关)。

## 未做(明确记录)

- 发行版默认 provider 与构建标签变更:留下一提交(涉及打包与模型分发策略)。
- 模型产物(754MB)不进仓库,由导出脚本生成。
2026-09-11 23:58:53 +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.

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