JianFeeeee 5836c2ce5c refactor(memory): 拆除描述式媒体索引,媒体成为一等块并按原生向量融合
背景:此前媒体是靠「生成的描述文本」将就进记忆的——写 marker 进正文、
再由正则反解成 media_refs 与图库里的 type=Media 实体。这条链路有三个
致命缺陷:描述由异步模型生成(未生成前媒体等于不存在)、语义检索实质上
只搜描述文字、图库里的「媒体节点」是描述文本的投影而不是媒体本身。

本提交把这条链路整体拆除,媒体改为按自己的原生向量参与记忆:

一、描述链彻底删除(无残留、无兼容分支)
- media.Item 去掉 Description/DescribedBy 与对应列;
- 删除 Store.Describe / Store.Search / Store.Pending;
- 删除 Agent.mediaDescribeLoop / describePendingMedia 与配置项
  core.memory.media.describe_on_ingest;
- SDK 侧 MediaAttachment 去掉 Description(见 SDK 仓独立提交)。

二、marker 机制删除,媒体归属改为结构化块边
- 删除 mediaMarkerLine/parseMediaMarkers/mediaEntityName/mediaTriplesFromText/
  extractMediaDigests/sentenceWithMediaMarkers/docMediaContext;
- memory.Triple 新增 MediaDigests 结构化字段;句子文本保持原样,
  不再被 marker 污染;
- 块以 sentence --contains--> block / document --contains--> block 结构边
  挂到承载节点(新增 documents 表与 document 节点种类);
- 模型未给原句时用「主谓宾。」拼一句自然语言作落点,不造 marker 文本。

三、旧数据迁移(幂等)
- 新增 GraphDB.MigrateLegacyMediaEntities:把 type=Media 的旧实体按短 digest
  还原成原生块、挂回原句子、删除旧实体与描述关系;Agent 启动时执行;
- CleanupOrphanedSentences 同时看关系引用与块边,避免把只靠块存活的句子
  连同块边一起删掉。

四、向量融合:媒体按图本身被召回
- 新增 vector.FuseVectors(逐维求和 + L2 归一化);
- Doc.DenseVec = 文本向量 ⊕ 文档块的媒体向量(同 fingerprint 才融合),
  新增 Doc.DenseFP,指纹变化触发重算;
- ContextEvent.DenseVec 同理融合事件块;事件新增 DenseFP,Prune 只在
  同一统一空间内比稠密余弦;
- 跨模态视觉路只召回「仍被某层记忆块持有」的媒体,CAS 全库字节不再
  直接充当记忆检索结果。

五、同时纳入本分支既有的嵌入基础改造(此前工作区未提交,缺它 HEAD 不可构建)
- internal/tfidf 懒回退包、千问三段式多模态 ONNX 空间的 Go 侧
  (qwen/embedder.go、image.go、model_input.go)、CLIP 移除、
  sdk.NewStore 分词器签名与调用点、embed 侧车 systemd 单元。

验证:go build ./... 、go vet ./...(含 -tags medialive)均通过;
在 HEAD 的独立 worktree 上重放本次暂存集后 go test -short ./internal/...
全部通过(端口冲突类用例在隔离环境中亦通过)。未提交工作区中与本改造
无关的改动(HarmonyOS、waiter、devicebridge、plan.md 等)。
2026-09-11 11:45:24 +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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