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12 changed files with 252 additions and 816 deletions
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@ -1,378 +0,0 @@
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app_name: reme-expert
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enable_logo: false
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log_to_console: true
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log_to_file: false
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# Expert-exposure profile: every memory_* read + write primitive plus
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# `sync` (event log) are surfaced as MCP tools. The agent picks whatever
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# it needs — finest granularity, no opinion enforced beyond the
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# wikilink-uniqueness gate.
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#
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# **No `ingest` tool.** Cold-path R-M-W in expert mode is owned by the
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# host agent (Claude Code, with the `reme-expert` SKILL loaded), not by
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# reme2's internal ReActAgent. The reme-expert plugin spawns a
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# `reme-distiller` subagent at SessionEnd to handle distillation in its
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# own context window, using the same memory_* primitives.
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#
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# Use when you want the agent to manage memory directly with full
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# control. For an opinionated minimal surface (where reme2's internal
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# Ingestor owns R-M-W), see ./service.yaml.
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service:
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backend: mcp
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transport: stdio
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sidecar_http: true
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sidecar_http_host: "127.0.0.1"
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sidecar_http_port: 8765
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sidecar_info_path: "./vault/.reme/sidecar.json"
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jobs:
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# -- Write entry points ------------------------------------------------
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- backend: base
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name: sync
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description: |
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Hot-path event sync: idempotent upsert of an event FOLDER under
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`events/{date}/{name}/`. The folder contains the index `{name}.md`
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(Event schema, frontmatter + narrative + Materials footer) plus
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any raw materials you pass — conversation snippets, tool outputs,
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data dumps. The watcher indexes everything inside.
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CONTINUITY MODEL: pick a stable `name` per logical thread and
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call `sync` repeatedly through the task. Each call extends the
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same folder:
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* new `content` → appended under a `## Update — {iso}` section
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* new `materials` → siblings (auto-suffix on filename collision)
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* `topics` + `tags` merged (union) into frontmatter
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* Materials footer regenerated to list every artifact in the folder
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First call (folder doesn't exist) → CREATE; subsequent calls with
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the same `name` while the event is `status: active` → APPEND.
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If the event is `status: distilled` / `archived`, `sync` REFUSES
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and returns a `suggested_name` so you start a fresh thread
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instead of mutating prior cognition.
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Zero LLM cost. Call CONTINUOUSLY through a task as facts land,
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and especially at PreCompact to dump verbose raw text into
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`materials` before context truncation loses it. The folder is
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the unit `ingest` later reads from.
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parameters:
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type: object
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required: [name]
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properties:
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name:
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type: string
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description: "kebab-case event identifier (folder + index stem). Reuse the same name across calls in one thread to keep extending the same folder."
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description: { type: string, description: "one-line summary for index frontmatter (set on initial create only)" }
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content: { type: string, description: "markdown body. Initial create: the body. Subsequent calls: appended as a `## Update — {iso}` section." }
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topics:
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type: array
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items: { type: string }
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description: "related topic wikilinks ('[[X]]' or '[[topics/X/X]]'). Unioned into frontmatter on append."
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tags: { type: array, items: { type: string }, description: "free-form tags; unioned on append." }
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materials:
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type: array
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description: "Raw artifacts written as siblings of the index inside the event folder. Filenames must be safe (letters/digits/dot/underscore/dash). On filename collision with an existing artifact, auto-suffixes (foo.txt → foo-2.txt)."
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items:
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type: object
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required: [filename, content]
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properties:
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filename: { type: string, description: "e.g. 'raw-prompt.md', 'tool-output.txt', 'snapshot.json'" }
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content: { type: string }
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on_date: { type: string, description: "ISO date for events/{date}/ bucket; defaults to today" }
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origin_session_id: { type: string, description: "set on initial create only" }
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steps:
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- backend: sync
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# -- Read tools --------------------------------------------------------
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- backend: base
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name: memory_search
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description: "Hybrid (vector + keyword) search over chunks."
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parameters:
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type: object
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required: [query]
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properties:
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query: { type: string }
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max_results: { type: integer, default: 5 }
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min_score: { type: number, default: 0.1 }
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paths: { type: array, items: { type: string } }
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tags: { type: array, items: { type: string } }
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exclude_paths: { type: array, items: { type: string } }
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steps:
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- backend: memory_search
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- backend: base
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name: memory_graph_search
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description: |
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Three-way fusion search: vector + keyword + graph (BFS over wikilinks).
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Pulls in chunks reachable through linked topics/events that pure
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relevance search would miss.
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parameters:
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type: object
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properties:
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query: { type: string }
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max_results: { type: integer, default: 5 }
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min_score: { type: number, default: 0.0 }
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graph_depth: { type: integer, default: 1 }
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seeds: { type: array, items: { type: string } }
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paths: { type: array, items: { type: string } }
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tags: { type: array, items: { type: string } }
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exclude_paths: { type: array, items: { type: string } }
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steps:
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- backend: memory_graph_search
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- backend: base
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name: memory_get
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description: "Read frontmatter + body of a single file."
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parameters:
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type: object
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required: [path]
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properties:
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path: { type: string }
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include_chunks: { type: boolean, default: false }
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steps:
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- backend: memory_get
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- backend: base
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name: memory_list
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description: |
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List indexed files filtered by frontmatter exact-match, tags, or
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path prefix. Returns {items: [{path, metadata}], count}.
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parameters:
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type: object
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properties:
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metadata: { type: object }
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tags: { type: array, items: { type: string } }
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path_prefix: { type: string }
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limit: { type: integer, default: 100 }
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steps:
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- backend: memory_list
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- backend: base
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name: memory_backlinks
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description: "Files linking TO the given path (with edge predicates)."
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parameters:
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type: object
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required: [path]
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properties:
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path: { type: string }
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steps:
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- backend: memory_backlinks
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- backend: base
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name: memory_links
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description: "Files the given path links to (resolved, with edge predicates)."
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parameters:
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type: object
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required: [path]
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properties:
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path: { type: string }
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steps:
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- backend: memory_links
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- backend: base
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name: memory_resolve_wikilink
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description: |
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Resolve a `[[target]]` wikilink to a vault path. Stem-form (`X`)
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consults the file_store's stem index; path-form (`a/b` or `a/b.md`)
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is anchored at the vault root.
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parameters:
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type: object
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required: [wikilink]
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properties:
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wikilink: { type: string }
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steps:
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- backend: memory_resolve_wikilink
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- backend: base
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name: memory_count_tokens
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description: "Estimate token count for a file body or raw text."
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parameters:
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type: object
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properties:
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path: { type: string }
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text: { type: string }
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steps:
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- backend: memory_count_tokens
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- backend: base
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name: memory_lint
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description: |
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Read-only projection of the Maintainer's lint findings. Walks the
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indexed files (optionally restricted to a path prefix) and returns
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structural issues — broken wikilinks, schema violations, stem
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collisions. Never mutates. The agent decides what to do with each
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finding using existing memory_* primitives (rename, update,
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property_update, archive, delete).
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parameters:
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type: object
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properties:
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target_prefix:
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type: string
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description: "restrict scan to relpaths starting with this prefix (e.g. 'events/2026-05-09/'). Empty string scans the whole vault."
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steps:
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- backend: memory_lint
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# -- Write primitives (raw building blocks) ----------------------------
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- backend: base
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name: memory_create
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description: |
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Create a new file (raw primitive — no LLM reasoning). Prefer
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`ingest` when you want an LLM curator. Two gates run unless
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`force=true`: (1) path template — must be `topics/{folder}/{name}.md`,
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`events/{date}/{name}/...`, or `Archive/...`; (2) wikilink uniqueness
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— `[[stem]]` must resolve to ≤1 path post-create.
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parameters:
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type: object
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required: [path]
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properties:
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path: { type: string }
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metadata: { type: object }
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content: { type: string }
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overwrite: { type: boolean, default: false }
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force:
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type: boolean
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default: false
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description: "bypass BOTH the path-template gate and the wikilink-uniqueness gate. Use only when you intentionally need a non-template path or accept the ambiguity."
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steps:
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- backend: memory_create
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- backend: base
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name: memory_update
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description: |
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Edit-style content update: replace `old_string` with `new_string`
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in the file body. For frontmatter changes use `memory_property_update`.
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parameters:
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type: object
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required: [path, old_string, new_string]
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properties:
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path: { type: string }
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old_string: { type: string }
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new_string: { type: string }
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replace_all: { type: boolean, default: false }
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steps:
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- backend: memory_update
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- backend: base
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name: memory_property_update
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description: |
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Update a single YAML frontmatter key. value=null deletes the key.
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When key='status', enforces the active → distilled → archived
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single-direction state machine (skip / reverse refused). Pass
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`force=true` to bypass.
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parameters:
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type: object
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required: [path, key]
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properties:
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path: { type: string }
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key: { type: string }
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value: {}
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force:
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type: boolean
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default: false
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description: "bypass the status state machine (only meaningful when key='status')."
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steps:
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- backend: memory_property_update
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- backend: base
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name: memory_rename
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description: |
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Rename a file and rewrite all incoming `[[wikilink]]` references
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across the vault. Refuses on destination conflict or stem ambiguity.
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parameters:
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type: object
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required: [old_path, new_path]
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properties:
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old_path: { type: string }
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new_path: { type: string }
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steps:
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- backend: memory_rename
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- backend: base
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name: memory_delete
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description: "Delete a file."
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parameters:
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type: object
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required: [path]
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properties:
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path: { type: string }
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steps:
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- backend: memory_delete
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- backend: base
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name: memory_archive
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description: |
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Archive a file: flip `status: archived` then move under
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`<vault>/<archive_dir>/<original_relative_path>`.
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parameters:
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type: object
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required: [path]
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properties:
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path: { type: string }
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archive_dir: { type: string, default: "Archive" }
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steps:
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- backend: memory_archive
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components:
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# Ingestor LLM (opt-in). Without this the Ingestor degrades to a
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# direct create from explicit `target_path`; edits/renames/deletes
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# require the LLM. Uncomment + provide LLM_API_KEY to enable.
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#
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# as_llm:
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# default:
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# backend: openai
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# model_name: ${LLM_MODEL_NAME:-gpt-4o-mini}
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# api_key: ${LLM_API_KEY}
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# client_kwargs:
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# base_url: ${LLM_BASE_URL:-https://api.openai.com/v1}
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# stream: false
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#
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# as_llm_formatter:
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# default:
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# backend: openai
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as_token_counter:
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default:
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backend: estimated
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# Embedding is opt-in: leave embedding_model="" on file_store to run
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# keyword-only; uncomment + flip to "default" to enable hybrid search.
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#
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# embedding_model:
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# default:
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# backend: openai
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# model_name: ${EMBEDDING_MODEL_NAME:-text-embedding-3-small}
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# dimensions: 1536
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# pass_dimensions: false
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# enable_cache: true
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# max_batch_size: 10
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# max_cache_size: 2000
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# max_input_length: 8192
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file_parser:
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md:
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backend: md
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default:
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backend: text
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file_store:
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default:
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backend: local
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embedding_model: ""
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store_name: "reme"
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db_path: "./vault/.reme"
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working_dir: "./vault"
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file_watcher:
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default:
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backend: full
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file_store: default
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default_parser: md
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recursive: true
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# Retriever (`hybrid`) is a Step, not a pre-instantiated component —
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# the `memory_search` / `memory_graph_search` shells build it on
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# demand. To tune defaults, pass knobs (`vector_weight`, `graph_*`,
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# …) on the step config under each job below.
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@ -48,4 +48,70 @@
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2. file_parser
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a. 抽象基类 parse: @jinli
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ⅰ. 输入是path：相对路径
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ⅱ. 输出是FileMetadata & list[FileChunks] & list[FileEdge]
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b. default parser 兼容老方案 @jinli
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ⅰ. 带overlap的chunking策略 ，不输出FileEdge
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c. markdown parser @sen
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ⅰ. 根据markdown ast做chunk，不需要overlap
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ⅱ. 增加一个索引的chunk chunk_type @锦鲤 file_chunk_type content/index
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ⅲ. 增加link的正则解析：predicate:: [[path#anchor]]
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3. file_store @sen
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a. 抽象存储：
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ⅰ. filenode = file + path + st_mtime + metadata + list[FileEdge]
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ⅱ. graph=dict[str, filenode] 内存+json
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ⅲ. list[FileChunk] 存db
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b. 抽象基类
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ⅰ. graph：fellow dict的操作 update/get/set
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ⅱ. chunks dict[str, list[chunk]]
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1. delete_chunks_by_path
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2. update_chunks_by_path
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3. list_chunks_by_path
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4. vector_search/keyword_search
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ⅲ. 手写一个bm25检索
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ⅳ. 【核心】检索机制 vector bm25 graph 如何进行融合
|
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4. file_watcher @jinli
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a. 抽象基类
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||||
ⅰ. on_start:
|
||||
1. file_store 的start 在前，加载graph，file_watcher在后，递归扫描目录
|
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a. 通过ms_time对比graph，on_change 进行改动
|
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ⅱ. on_change:
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1. 更新/增加:
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a. delete_chunks_by_path 更新数据库
|
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b. upate_chunks_by_path 更新数据库
|
||||
c. 更新graph
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2. 删除
|
||||
a. delete_chunks_by_path 更新数据库
|
||||
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MemorySchema
|
||||
1. markdown文件结构 @sen
|
||||
a. formatter：
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||||
ⅰ. title
|
||||
ⅱ. desc
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||||
ⅲ. tags
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||||
ⅳ.
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||||
2. memory文件结构目录
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a. MEMORY.md
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b. msg/files -> daily/YYYYMMDD/YYYYMMDD.md + xxxx.md
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ⅰ. YYYYMMDD.md
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1. xxx -> xxxx.md
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||||
2. xxx -> xxxd.md
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||||
ⅱ.
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||||
c. daily -> topic/topic_l1/topic_l1.md + xxx.md + topic_l2
|
||||
d. proactive
|
||||
|
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steps:
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1. 治理（算法+LLM）：
|
||||
a. 节点关联P0：现有的链接做补充，挖掘新的LLM的link
|
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ⅰ. /Users/yuli/workspace/ReMe/reme2/component/edge_extractor/llm_edge_extractor.py
|
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ⅱ. 移动到steps
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||||
b. 节点整合/节点拆分/节点归档
|
||||
c. 健康度检查
|
||||
2. retrieve 调用store的检索
|
||||
3. 原子steps：reme edit
|
||||
4. 组合steps：总结：
|
||||
a. - freq (every_n_turn、compact) -> daily_summarizer
|
||||
b. topic (/dream ) -> topic_summarizer(daily_xx -> topic_xx)
|
||||
c. proactive -> proactive_summarizer(personal_xxx -> proactive_query - pre_query
|
||||
|
|
@ -1,86 +0,0 @@
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ReMe重构
|
||||
1. 根目录
|
||||
a. ✅ vault_root → working_dir @sen
|
||||
2. file_parser
|
||||
a. 抽象基类 parse: @jinli
|
||||
ⅰ. 输入是path：相对路径
|
||||
ⅱ. 输出是FileMetadata & list[FileChunks] & list[FileEdge]
|
||||
b. default parser 兼容老方案 @jinli
|
||||
ⅰ. 带overlap的chunking策略 ，不输出FileEdge
|
||||
c. markdown parser @sen
|
||||
ⅰ. 根据markdown ast做chunk，不需要overlap
|
||||
ⅱ. 增加一个索引的chunk chunk_type @锦鲤 file_chunk_type content/index
|
||||
ⅲ. 增加link的正则解析：predicate:: [[path#anchor]]
|
||||
3. file_store @sen
|
||||
a. 抽象存储：
|
||||
ⅰ. filenode = file + path + st_mtime + metadata + list[FileEdge]
|
||||
ⅱ. graph=dict[str, filenode] 内存+json
|
||||
ⅲ. list[FileChunk] 存db
|
||||
b. 抽象基类
|
||||
ⅰ. graph：fellow dict的操作 update/get/set
|
||||
ⅱ. chunks dict[str, list[chunk]]
|
||||
1. delete_chunks_by_path
|
||||
2. update_chunks_by_path
|
||||
3. list_chunks_by_path
|
||||
4. vector_search/keyword_search
|
||||
ⅲ. 手写一个bm25检索
|
||||
ⅳ. 【核心】检索机制 vector bm25 graph 如何进行融合
|
||||
4. file_watcher @jinli
|
||||
a. 抽象基类
|
||||
ⅰ. on_start:
|
||||
1. file_store 的start 在前，加载graph，file_watcher在后，递归扫描目录
|
||||
a. 通过ms_time对比graph，on_change 进行改动
|
||||
ⅱ. on_change:
|
||||
1. 更新/增加:
|
||||
a. delete_chunks_by_path 更新数据库
|
||||
b. upate_chunks_by_path 更新数据库
|
||||
c. 更新graph
|
||||
2. 删除
|
||||
a. delete_chunks_by_path 更新数据库
|
||||
|
||||
MemorySchema
|
||||
1. markdown文件结构 @sen
|
||||
a. formatter：
|
||||
ⅰ. title
|
||||
ⅱ. desc
|
||||
ⅲ. tags
|
||||
ⅳ.
|
||||
2. memory文件结构目录
|
||||
a. MEMORY.md
|
||||
b. msg/files -> daily/YYYYMMDD/YYYYMMDD.md + xxxx.md
|
||||
ⅰ. YYYYMMDD.md
|
||||
1. xxx -> xxxx.md
|
||||
2. xxx -> xxxd.md
|
||||
ⅱ.
|
||||
c. daily -> topic/topic_l1/topic_l1.md + xxx.md + topic_l2
|
||||
d. proactive
|
||||
|
||||
steps:
|
||||
1. 治理（算法+LLM）：
|
||||
a. 节点关联P0：现有的链接做补充，挖掘新的LLM的link
|
||||
ⅰ. /Users/yuli/workspace/ReMe/reme2/component/edge_extractor/llm_edge_extractor.py
|
||||
ⅱ. 移动到steps
|
||||
b. 节点整合/节点拆分/节点归档
|
||||
c. 健康度检查
|
||||
2. retrieve 调用store的检索
|
||||
3. 原子steps：reme edit
|
||||
4. 组合steps：总结：
|
||||
a. - freq (every_n_turn、compact) -> daily_summarizer
|
||||
b. topic (/dream ) -> topic_summarizer(daily_xx -> topic_xx)
|
||||
c. proactive -> proactive_summarizer(personal_xxx -> proactive_query - pre_query
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Qwenpaw
|
||||
|
|
@ -1,270 +0,0 @@
|
|||
app_name: reme-service
|
||||
enable_logo: false
|
||||
log_to_console: true
|
||||
log_to_file: false
|
||||
|
||||
# Service profile: service-aligned MCP surface — three tools, one per
|
||||
# memory service:
|
||||
# `retrieve` — graph-aware hybrid retrieval (Retriever projection)
|
||||
# `remember` — single write entry point (Ingestor projection)
|
||||
# * mode=log → zero-LLM event-folder upsert
|
||||
# * mode=distill → LLM R-M-W into topic graph
|
||||
# `maintain` — vault hygiene sweep (Maintainer projection)
|
||||
# runs lint + decay; merge/split require an LLM and
|
||||
# are off by default.
|
||||
#
|
||||
# The agent calls `remember(mode=log, name=…, content=…, materials=…)`
|
||||
# continuously through the task — picking a stable `name` per logical
|
||||
# thread so each call extends the same event folder rather than
|
||||
# fragmenting. At task completion (or PreCompact / SessionEnd) it
|
||||
# calls `remember(mode=distill, content=…, related_paths=…)` once to
|
||||
# distill the active events into topic-level cognition. `maintain` is
|
||||
# typically cron-driven, but the agent can invoke it explicitly when
|
||||
# it suspects vault drift.
|
||||
#
|
||||
# For full direct control over every memory_* primitive (raw writes,
|
||||
# fine-grained reads, separate sync/ingest tools, memory_lint) see
|
||||
# ./expert.yaml.
|
||||
|
||||
service:
|
||||
backend: mcp
|
||||
transport: stdio
|
||||
sidecar_http: true
|
||||
sidecar_http_host: "127.0.0.1"
|
||||
sidecar_http_port: 8765
|
||||
sidecar_info_path: "./vault/.reme/sidecar.json"
|
||||
|
||||
jobs:
|
||||
- backend: base
|
||||
name: retrieve
|
||||
description: |
|
||||
Graph-aware hybrid retrieval: vector + keyword + 1-hop wikilink
|
||||
BFS fusion. Use for "what do I know about X" / "did I work on Y" /
|
||||
"what's connected to [[Z]]". Anchor mode: include `[[Target]]` in
|
||||
the query to seed BFS at that file. Topic-rooted mode: pass `seeds`
|
||||
explicitly. Returns chunks ranked by combined relevance + graph
|
||||
proximity, each tagged with `graph_hop`.
|
||||
parameters:
|
||||
type: object
|
||||
properties:
|
||||
query: { type: string }
|
||||
max_results: { type: integer, default: 5 }
|
||||
min_score: { type: number, default: 0.0 }
|
||||
graph_depth:
|
||||
type: integer
|
||||
default: 1
|
||||
description: "BFS hops from seeds. 1 covers immediate neighbors."
|
||||
seeds:
|
||||
type: array
|
||||
items: { type: string }
|
||||
description: "Explicit seed paths (topic-rooted mode)."
|
||||
paths: { type: array, items: { type: string } }
|
||||
tags: { type: array, items: { type: string } }
|
||||
exclude_paths: { type: array, items: { type: string } }
|
||||
steps:
|
||||
- backend: memory_graph_search
|
||||
|
||||
- backend: base
|
||||
name: remember
|
||||
description: |
|
||||
Single write entry point — projects the Ingestor service. Two
|
||||
modes via the `mode` parameter:
|
||||
|
||||
* `mode: log` (zero LLM, hot path) — idempotent upsert of an
|
||||
event FOLDER under `events/{date}/{name}/`. The folder
|
||||
contains the index `{name}.md` (Event schema, frontmatter +
|
||||
narrative + Materials footer) plus any raw materials you
|
||||
pass — conversation snippets, tool outputs, data dumps. The
|
||||
watcher indexes everything inside.
|
||||
|
||||
CONTINUITY MODEL: pick a stable `name` per logical thread
|
||||
and call `remember(mode=log, ...)` repeatedly through the
|
||||
task. Each call extends the same folder:
|
||||
- new `content` → appended under a `## Update — {iso}`
|
||||
section
|
||||
- new `materials` → siblings (auto-suffix on filename
|
||||
collision)
|
||||
- `topics` + `tags` merged (union) into frontmatter
|
||||
- Materials footer regenerated to list every artifact
|
||||
|
||||
First call (folder doesn't exist) → CREATE; subsequent calls
|
||||
with the same `name` while the event is `status: active` →
|
||||
APPEND. If `status: distilled` / `archived`, REFUSES and
|
||||
returns `suggested_name` so you start a fresh thread instead
|
||||
of mutating prior cognition. Call CONTINUOUSLY through a task
|
||||
as facts land, especially at PreCompact to dump verbose raw
|
||||
text into `materials` before context truncation.
|
||||
|
||||
* `mode: distill` (LLM R-M-W loop, cold path) — DEFAULT. Run
|
||||
on EXPLICIT HANDOFF only: task completion / SessionEnd / when
|
||||
you decide the working set is ready. Not a per-turn tool.
|
||||
|
||||
Hand off the working set in two interchangeable forms:
|
||||
- `content` — inline material to distill (hint, summary, or
|
||||
raw text)
|
||||
- `related_paths` — pointers (event folder indexes; the
|
||||
Ingestor follows `## Materials` to read each artifact,
|
||||
individual material files, candidate topics).
|
||||
|
||||
The Ingestor reads the working set + linked topics, decides
|
||||
which existing topics to update / create, and flips each
|
||||
distilled event's status to "distilled". Returns an audit
|
||||
trail.
|
||||
parameters:
|
||||
type: object
|
||||
required: [content]
|
||||
properties:
|
||||
mode:
|
||||
type: string
|
||||
enum: [log, distill]
|
||||
default: distill
|
||||
description: "log = zero-LLM event-folder upsert (requires `name`); distill = LLM R-M-W into topic graph (default)."
|
||||
# mode=log params
|
||||
name:
|
||||
type: string
|
||||
description: "(mode=log) kebab-case event identifier (folder + index stem). Reuse the same name across calls in one thread to keep extending the same folder."
|
||||
description:
|
||||
type: string
|
||||
description: "(mode=log) one-line summary for index frontmatter (set on initial create only)."
|
||||
topics:
|
||||
type: array
|
||||
items: { type: string }
|
||||
description: "(mode=log) related topic wikilinks. Unioned into frontmatter on append."
|
||||
tags:
|
||||
type: array
|
||||
items: { type: string }
|
||||
description: "(mode=log) free-form tags; unioned on append."
|
||||
materials:
|
||||
type: array
|
||||
description: "(mode=log) raw artifacts written as siblings of the index. Filenames must be safe (letters/digits/dot/underscore/dash). Filename collision auto-suffixes (foo.txt → foo-2.txt)."
|
||||
items:
|
||||
type: object
|
||||
required: [filename, content]
|
||||
properties:
|
||||
filename: { type: string, description: "e.g. 'raw-prompt.md', 'tool-output.txt'" }
|
||||
content: { type: string }
|
||||
on_date:
|
||||
type: string
|
||||
description: "(mode=log) ISO date for events/{date}/ bucket; defaults to today."
|
||||
origin_session_id:
|
||||
type: string
|
||||
description: "(mode=log) optional source session identifier (set on initial create only)."
|
||||
# shared / mode=distill params
|
||||
content:
|
||||
type: string
|
||||
description: "Required. mode=log: markdown body for the index (initial create) or appended `## Update — {iso}` section. mode=distill: inline material the Ingestor distills — hint, summary, or raw text."
|
||||
hint:
|
||||
type: string
|
||||
description: "(mode=distill) caller guidance about target / intent."
|
||||
target_path:
|
||||
type: string
|
||||
description: "(mode=distill) optional suggested path; required for the no-LLM degraded path."
|
||||
metadata:
|
||||
type: object
|
||||
description: "(mode=distill) suggested frontmatter for any new topic."
|
||||
related_paths:
|
||||
type: array
|
||||
items: { type: string }
|
||||
description: "(mode=distill) pointers — event folder indexes (Ingestor reads materials from `## Materials`), individual material files, or candidate topics."
|
||||
steps:
|
||||
- backend: ingestor
|
||||
|
||||
- backend: base
|
||||
name: maintain
|
||||
description: |
|
||||
Vault hygiene sweep — projects the Maintainer service. One pass:
|
||||
scan signals → propose ops → resolve conflicts → apply. Returns
|
||||
an audit trail of what ran and what changed.
|
||||
|
||||
Default behavior runs `lint` (broken wikilinks, schema violations,
|
||||
stem collisions — read-only diagnostics) and `decay` (move stale
|
||||
events past their freshness window under `<vault>/Archive/`).
|
||||
Merge / split require an LLM and are off unless explicitly opted
|
||||
into via `ops`.
|
||||
|
||||
Typically cron-driven; the agent invokes it on demand when it
|
||||
suspects vault drift (after a heavy session of edits, after a
|
||||
bulk rename, etc.). Dry-run by default — flip `dry_run=false` to
|
||||
apply changes.
|
||||
parameters:
|
||||
type: object
|
||||
properties:
|
||||
target_prefix:
|
||||
type: string
|
||||
description: "restrict scan to relpaths starting with this prefix (e.g. 'events/2026-05-09/'). Empty string scans the whole vault."
|
||||
dry_run:
|
||||
type: boolean
|
||||
default: true
|
||||
description: "if true (default) returns the plan without mutating; flip to false to actually apply lint+decay (and merge/split if opted in)."
|
||||
ops:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
enum: [lint, decay, merge, split]
|
||||
description: "subset of ops to run. Defaults to ['lint','decay']. Merge/split are LLM-driven and currently scaffolded — enabling them without an LLM is a no-op."
|
||||
decay_days:
|
||||
type: integer
|
||||
description: "freshness window for the decay proposer (default = Maintainer constructor `decay_days`, typically 90)."
|
||||
steps:
|
||||
- backend: maintainer
|
||||
|
||||
components:
|
||||
# Ingestor LLM (opt-in). Without this the Ingestor degrades to a
|
||||
# direct create from explicit `target_path`; edits/renames/deletes
|
||||
# require the LLM. Uncomment + provide LLM_API_KEY to enable.
|
||||
#
|
||||
# as_llm:
|
||||
# default:
|
||||
# backend: openai
|
||||
# model_name: ${LLM_MODEL_NAME:-gpt-4o-mini}
|
||||
# api_key: ${LLM_API_KEY}
|
||||
# client_kwargs:
|
||||
# base_url: ${LLM_BASE_URL:-https://api.openai.com/v1}
|
||||
# stream: false
|
||||
#
|
||||
# as_llm_formatter:
|
||||
# default:
|
||||
# backend: openai
|
||||
|
||||
as_token_counter:
|
||||
default:
|
||||
backend: estimated
|
||||
|
||||
# Embedding is opt-in: leave embedding_model="" on file_store to run
|
||||
# keyword-only; uncomment + flip to "default" to enable hybrid search.
|
||||
#
|
||||
# embedding_model:
|
||||
# default:
|
||||
# backend: openai
|
||||
# model_name: ${EMBEDDING_MODEL_NAME:-text-embedding-3-small}
|
||||
# dimensions: 1536
|
||||
# pass_dimensions: false
|
||||
# enable_cache: true
|
||||
# max_batch_size: 10
|
||||
# max_cache_size: 2000
|
||||
# max_input_length: 8192
|
||||
|
||||
file_parser:
|
||||
md:
|
||||
backend: md
|
||||
default:
|
||||
backend: text
|
||||
|
||||
file_store:
|
||||
default:
|
||||
backend: local
|
||||
embedding_model: ""
|
||||
store_name: "reme"
|
||||
db_path: "./vault/.reme"
|
||||
working_dir: "./vault"
|
||||
|
||||
file_watcher:
|
||||
default:
|
||||
backend: full
|
||||
file_store: default
|
||||
default_parser: md
|
||||
recursive: true
|
||||
|
||||
# Retriever (`hybrid`) is a Step, not a pre-instantiated component —
|
||||
# the `query` shell builds it on demand. Tune defaults by attaching
|
||||
# knobs to the `memory_graph_search` step under the `query` job above.
|
||||
2
docs4/todo.md
Normal file
2
docs4/todo.md
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
1. 完善mcp_servers config
|
||||
2. 完善mcp/http的服务测试
|
||||
|
|
@ -69,18 +69,29 @@ class BaseFileWatcher(BaseComponent):
|
|||
return True
|
||||
return any(path.endswith("." + s.strip(".")) for s in self.suffix_filters)
|
||||
|
||||
async def scan_existing_files(self) -> list[Path]:
|
||||
"""Collect all watchable files under watch_paths."""
|
||||
files: list[Path] = []
|
||||
def _get_relative_path(self, path: str | Path) -> str:
|
||||
"""Return path relative to working_dir, or absolute path if outside."""
|
||||
file_path = Path(path).absolute()
|
||||
try:
|
||||
return str(file_path.relative_to(self.working_path.absolute()))
|
||||
except ValueError:
|
||||
return str(file_path)
|
||||
|
||||
def _get_absolute_path(self, path: str | Path) -> Path:
|
||||
"""Return absolute path; relative paths are resolved against working_dir."""
|
||||
p = Path(path)
|
||||
return p if p.is_absolute() else self.working_path / p
|
||||
|
||||
async def scan_existing_files(self) -> dict[str, Path]:
|
||||
"""Collect watchable files under watch_paths as {relative_path: absolute_path}."""
|
||||
files: dict[str, Path] = {}
|
||||
for path in self.watch_paths:
|
||||
if not path.exists():
|
||||
continue
|
||||
if path.is_file():
|
||||
if self.watch_filter(Change.added, str(path)):
|
||||
files.append(path)
|
||||
else:
|
||||
items = path.rglob("*") if self.recursive else path.iterdir()
|
||||
files.extend(p for p in items if p.is_file() and self.watch_filter(Change.added, str(p)))
|
||||
candidates = [path] if path.is_file() else (path.rglob("*") if self.recursive else path.iterdir())
|
||||
for p in candidates:
|
||||
if p.is_file() and self.watch_filter(Change.added, str(p)):
|
||||
files[self._get_relative_path(p)] = p.absolute()
|
||||
return files
|
||||
|
||||
async def clear_store(self):
|
||||
|
|
@ -94,7 +105,7 @@ class BaseFileWatcher(BaseComponent):
|
|||
if self.file_store is None:
|
||||
raise ValueError("file_store is not initialized!")
|
||||
await self.file_store.clear()
|
||||
await self.on_added(await self.scan_existing_files())
|
||||
await self.on_added(list((await self.scan_existing_files()).keys()))
|
||||
|
||||
@abstractmethod
|
||||
async def watch_loop(self):
|
||||
|
|
@ -105,13 +116,13 @@ class BaseFileWatcher(BaseComponent):
|
|||
"""Sync the store with the current state of watch_paths."""
|
||||
|
||||
@abstractmethod
|
||||
async def on_added(self, path: Path | list[Path]):
|
||||
"""Handle file added event."""
|
||||
async def on_added(self, path: str | list[str]):
|
||||
"""Handle file added event (relative paths)."""
|
||||
|
||||
@abstractmethod
|
||||
async def on_modified(self, path: Path | list[Path]):
|
||||
"""Handle file modified event."""
|
||||
async def on_modified(self, path: str | list[str]):
|
||||
"""Handle file modified event (relative paths)."""
|
||||
|
||||
@abstractmethod
|
||||
async def on_deleted(self, path: Path | list[Path]):
|
||||
"""Handle file deleted event."""
|
||||
async def on_deleted(self, path: str | list[str]):
|
||||
"""Handle file deleted event (relative paths)."""
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
"""Polling-based file watcher using watchfiles."""
|
||||
|
||||
import asyncio
|
||||
from pathlib import Path
|
||||
|
||||
from watchfiles import Change, awatch
|
||||
|
||||
|
|
@ -58,70 +57,68 @@ class LiteFileWatcher(BaseFileWatcher):
|
|||
|
||||
async def _dispatch_changes(self, changes: set[tuple[Change, str]]):
|
||||
"""Classify raw changes and dispatch to event handlers."""
|
||||
added = [Path(p) for c, p in changes if c == Change.added]
|
||||
modified = [Path(p) for c, p in changes if c == Change.modified]
|
||||
deleted = [Path(p) for c, p in changes if c == Change.deleted]
|
||||
if added:
|
||||
self.logger.info(f"Detected {len(added)} added file(s)")
|
||||
await self.on_added(added)
|
||||
if modified:
|
||||
self.logger.info(f"Detected {len(modified)} modified file(s)")
|
||||
await self.on_modified(modified)
|
||||
if deleted:
|
||||
self.logger.info(f"Detected {len(deleted)} deleted file(s)")
|
||||
await self.on_deleted(deleted)
|
||||
buckets: dict[Change, list[str]] = {Change.added: [], Change.modified: [], Change.deleted: []}
|
||||
for c, p in changes:
|
||||
if c in buckets:
|
||||
buckets[c].append(self._get_relative_path(p))
|
||||
for change, handler, label in (
|
||||
(Change.added, self.on_added, "added"),
|
||||
(Change.modified, self.on_modified, "modified"),
|
||||
(Change.deleted, self.on_deleted, "deleted"),
|
||||
):
|
||||
if buckets[change]:
|
||||
self.logger.info(f"Detected {len(buckets[change])} {label} file(s)")
|
||||
await handler(buckets[change])
|
||||
|
||||
async def update_store(self):
|
||||
if self.file_store is None:
|
||||
raise ValueError("file_store is not initialized!")
|
||||
|
||||
# Diff existing files against indexed entries by path and mtime.
|
||||
existing = {str(p): p.stat().st_mtime for p in await self.scan_existing_files()}
|
||||
indexed = {p: n.st_mtime for p, n in self.file_store.file_nodes.items()}
|
||||
existing_keys, indexed_keys = set(existing), set(indexed)
|
||||
existing: dict[str, float] = {
|
||||
rel: abs_p.stat().st_mtime for rel, abs_p in (await self.scan_existing_files()).items()
|
||||
}
|
||||
indexed: dict[str, float] = {n.path: n.st_mtime for n in await self.file_store.file_graph.get_nodes()}
|
||||
|
||||
to_delete = indexed_keys - existing_keys
|
||||
to_add = existing_keys - indexed_keys
|
||||
to_modify = [p for p in existing_keys & indexed_keys if existing[p] != indexed[p]]
|
||||
to_delete = list(indexed.keys() - existing.keys())
|
||||
to_add = list(existing.keys() - indexed.keys())
|
||||
to_modify = [p for p in existing.keys() & indexed.keys() if existing[p] != indexed[p]]
|
||||
|
||||
if to_modify:
|
||||
self.logger.info(f"Updating {len(to_modify)} modified file(s)")
|
||||
await self.on_modified([Path(p) for p in to_modify])
|
||||
await self.on_modified(to_modify)
|
||||
if to_delete:
|
||||
self.logger.info(f"Removing {len(to_delete)} deleted file(s)")
|
||||
await self.on_deleted([Path(p) for p in to_delete])
|
||||
await self.on_deleted(to_delete)
|
||||
if to_add:
|
||||
self.logger.info(f"Indexing {len(to_add)} new file(s)")
|
||||
await self.on_added([Path(p) for p in to_add])
|
||||
await self.on_added(to_add)
|
||||
if not to_modify and not to_delete and not to_add:
|
||||
self.logger.info("Store is up to date")
|
||||
|
||||
async def _parse_and_upsert(self, paths: list[Path], action: str):
|
||||
async def _parse_and_upsert(self, paths: list[str], action: str):
|
||||
"""Parse files and upsert into store. Shared by on_added / on_modified."""
|
||||
if self.file_parser is None or self.file_store is None:
|
||||
raise RuntimeError("file_parser or file_store is not initialized!")
|
||||
|
||||
parsed: list[tuple[FileNode, list[FileChunk]]] = []
|
||||
for p in paths:
|
||||
if p.is_file():
|
||||
self.logger.info(f"{action} file: {p}")
|
||||
parsed.append(await self.file_parser.parse(p))
|
||||
for rel in paths:
|
||||
abs_path = self._get_absolute_path(rel)
|
||||
if abs_path.is_file():
|
||||
self.logger.info(f"{action} file: {rel}")
|
||||
parsed.append(await self.file_parser.parse(abs_path))
|
||||
if parsed:
|
||||
file_paths = [str(p) for p in paths if p.is_file()]
|
||||
await self.file_store.delete_by_path(file_paths)
|
||||
await self.file_store.delete_by_path([n.path for n, _ in parsed])
|
||||
await self.file_store.upsert_file(parsed)
|
||||
|
||||
async def on_added(self, path: Path | list[Path]):
|
||||
paths = [path] if isinstance(path, Path) else path
|
||||
await self._parse_and_upsert(paths, "Adding")
|
||||
async def on_added(self, path: str | list[str]):
|
||||
await self._parse_and_upsert([path] if isinstance(path, str) else path, "Adding")
|
||||
|
||||
async def on_modified(self, path: Path | list[Path]):
|
||||
paths = [path] if isinstance(path, Path) else path
|
||||
await self._parse_and_upsert(paths, "Updating")
|
||||
async def on_modified(self, path: str | list[str]):
|
||||
await self._parse_and_upsert([path] if isinstance(path, str) else path, "Updating")
|
||||
|
||||
async def on_deleted(self, path: Path | list[Path]):
|
||||
async def on_deleted(self, path: str | list[str]):
|
||||
if self.file_store is None:
|
||||
raise RuntimeError("file_store is not initialized!")
|
||||
paths = [path] if isinstance(path, Path) else path
|
||||
paths = [path] if isinstance(path, str) else path
|
||||
self.logger.info(f"Deleting {len(paths)} file(s)")
|
||||
await self.file_store.delete_by_path([str(p) for p in paths])
|
||||
await self.file_store.delete_by_path(paths)
|
||||
|
|
|
|||
|
|
@ -10,6 +10,7 @@ from collections import Counter
|
|||
from typing import TypedDict
|
||||
|
||||
from .base_keyword_index import BaseKeywordIndex
|
||||
from ..component_registry import R
|
||||
|
||||
|
||||
class DocMeta(TypedDict):
|
||||
|
|
@ -19,6 +20,7 @@ class DocMeta(TypedDict):
|
|||
token_ids: set[int]
|
||||
|
||||
|
||||
@R.register("bm25")
|
||||
class BM25Index(BaseKeywordIndex):
|
||||
"""BM25 search engine with file-based persistence.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,69 @@
|
|||
service:
|
||||
backend: http
|
||||
host: 127.0.0.1
|
||||
port: 2333
|
||||
# backend: mcp
|
||||
|
||||
jobs:
|
||||
- name: demo
|
||||
backend: base
|
||||
description: "Echo back the incoming query (smoke test)."
|
||||
- backend: base
|
||||
name: demo_job
|
||||
description: "demo job description"
|
||||
parameters:
|
||||
type: object
|
||||
properties:
|
||||
query:
|
||||
type: string
|
||||
description: "query"
|
||||
min_score:
|
||||
type: number
|
||||
description: "min score"
|
||||
default: 0.5
|
||||
required:
|
||||
- query
|
||||
steps:
|
||||
- backend: demo_echo
|
||||
- backend: demo_echo_step1
|
||||
- backend: demo_echo_step2
|
||||
|
||||
components:
|
||||
# 1. tokenizer — 无依赖
|
||||
tokenizer:
|
||||
default:
|
||||
backend: regex
|
||||
|
||||
# 2. embedding_model — 无依赖
|
||||
embedding_model:
|
||||
default:
|
||||
backend: openai
|
||||
model_name: text-embedding-3-small
|
||||
dimensions: 1536
|
||||
|
||||
# 3. file_graph — 无依赖
|
||||
file_graph:
|
||||
default:
|
||||
backend: local
|
||||
|
||||
# 4. file_parser — 无依赖
|
||||
file_parser:
|
||||
default:
|
||||
backend: default
|
||||
|
||||
# 5. keyword_index — 依赖 tokenizer
|
||||
keyword_index:
|
||||
default:
|
||||
backend: bm25
|
||||
tokenizer: default
|
||||
|
||||
# 6. file_store — 依赖 embedding_model / keyword_index / file_graph
|
||||
file_store:
|
||||
default:
|
||||
backend: local
|
||||
store_name: default
|
||||
embedding_model: default
|
||||
keyword_index: default
|
||||
file_graph: default
|
||||
|
||||
# 7. file_watcher — 依赖 file_store / file_parser
|
||||
file_watcher:
|
||||
default:
|
||||
backend: lite
|
||||
watch_paths: "."
|
||||
file_store: default
|
||||
file_parser: default
|
||||
|
|
@ -1,9 +1,10 @@
|
|||
"""steps"""
|
||||
|
||||
from . import demo
|
||||
from .base_step import BaseStep
|
||||
from .demo import DemoEchoStep1, DemoEchoStep2
|
||||
|
||||
__all__ = [
|
||||
"BaseStep",
|
||||
"demo",
|
||||
"DemoEchoStep1",
|
||||
"DemoEchoStep2",
|
||||
]
|
||||
|
|
|
|||
53
reme4/steps/demo.py
Normal file
53
reme4/steps/demo.py
Normal file
|
|
@ -0,0 +1,53 @@
|
|||
"""Demo steps for smoke-testing the application stack."""
|
||||
|
||||
from .base_step import BaseStep
|
||||
from ..components import R
|
||||
|
||||
|
||||
@R.register("demo_echo_step1")
|
||||
class DemoEchoStep1(BaseStep):
|
||||
"""Read query/min_score from context, normalize, and write back for Step2."""
|
||||
|
||||
async def execute(self):
|
||||
assert self.context is not None
|
||||
query = self.context.get("query", "")
|
||||
min_score = self.context.get("min_score", 0.5)
|
||||
|
||||
self.logger.info(f"[{self.name}] query={query!r}, min_score={min_score}")
|
||||
|
||||
processed_query = query.strip().lower()
|
||||
adjusted_min_score = float(min_score) * 0.9
|
||||
|
||||
self.context["processed_query"] = processed_query
|
||||
self.context["adjusted_min_score"] = adjusted_min_score
|
||||
|
||||
return self.context.response
|
||||
|
||||
|
||||
@R.register("demo_echo_step2")
|
||||
class DemoEchoStep2(BaseStep):
|
||||
"""Consume Step1's outputs from context and emit the final response."""
|
||||
|
||||
async def execute(self):
|
||||
assert self.context is not None
|
||||
query = self.context.get("query", "")
|
||||
min_score = self.context.get("min_score", 0.5)
|
||||
processed_query = self.context.get("processed_query", "")
|
||||
adjusted_min_score = self.context.get("adjusted_min_score", min_score)
|
||||
|
||||
self.logger.info(
|
||||
f"[{self.name}] query={query!r}, min_score={min_score}, "
|
||||
f"processed_query={processed_query!r}, adjusted_min_score={adjusted_min_score}",
|
||||
)
|
||||
|
||||
self.context.response.answer = f"echo: {processed_query} (min_score={adjusted_min_score})"
|
||||
self.context.response.metadata.update(
|
||||
{
|
||||
"step": self.name,
|
||||
"query": query,
|
||||
"min_score": min_score,
|
||||
"processed_query": processed_query,
|
||||
"adjusted_min_score": adjusted_min_score,
|
||||
},
|
||||
)
|
||||
return self.context.response
|
||||
|
|
@ -1,19 +0,0 @@
|
|||
"""Demo steps for smoke-testing the application stack."""
|
||||
|
||||
from ..base_step import BaseStep
|
||||
from ...components.component_registry import R
|
||||
|
||||
|
||||
@R.register("demo_echo")
|
||||
class DemoEchoStep(BaseStep):
|
||||
"""Echo back the incoming `query` field into `response.answer`."""
|
||||
|
||||
async def execute(self):
|
||||
assert self.context is not None
|
||||
query = self.context.get("query", "")
|
||||
self.context.response.answer = f"echo: {query}"
|
||||
self.context.response.metadata["step"] = self.name
|
||||
return self.context.response
|
||||
|
||||
|
||||
__all__ = ["DemoEchoStep"]
|
||||
Loading…
Add table
Reference in a new issue