L'électron rare
1098 lines
39 KiB
Python
1098 lines
39 KiB
Python
from __future__ import annotations
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import hashlib
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import json
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import math
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import os
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import re
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import sqlite3
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import urllib.error
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import urllib.request
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from collections import Counter
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from pathlib import Path
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from typing import Any
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from .paths import memory_store_path, persona_path
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from .state import (
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append_event,
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load_memory_config,
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load_mission,
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now_utc,
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save_memory_config,
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)
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TEXT_SUFFIXES = {
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".c",
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".cc",
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".cfg",
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".conf",
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".cpp",
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".css",
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".go",
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".h",
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".hpp",
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".html",
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".java",
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".js",
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".json",
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".jsx",
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".md",
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".prompt",
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".py",
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".rb",
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".rs",
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".sh",
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".sql",
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".swift",
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".toml",
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".ts",
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".tsx",
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".txt",
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".yaml",
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".yml",
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".zsh",
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}
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IGNORED_DIR_NAMES = {
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".git",
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".hg",
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".idea",
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".next",
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".venv",
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"__pycache__",
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"build",
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"cache",
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"dist",
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"ide",
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"logs",
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"node_modules",
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"output",
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"session-state",
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"target",
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"tmp",
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"venv",
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}
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INSTRUCTION_DIRS = (".claude", ".copilot", ".agents", ".agent")
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INSTRUCTION_FILES = ("CLAUDE.md", "AGENTS.md")
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INSTRUCTION_SUFFIXES = {".json", ".md", ".prompt", ".toml", ".txt", ".yaml", ".yml"}
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MAX_TEXT_BYTES = 256 * 1024
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MAX_INSTRUCTION_BYTES = 128 * 1024
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def _connect() -> sqlite3.Connection:
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path = memory_store_path()
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path.parent.mkdir(parents=True, exist_ok=True)
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connection = sqlite3.connect(path)
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connection.row_factory = sqlite3.Row
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connection.execute("pragma journal_mode = wal")
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connection.execute("pragma foreign_keys = on")
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_ensure_schema(connection)
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return connection
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def _ensure_schema(connection: sqlite3.Connection) -> None:
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connection.executescript(
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"""
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create table if not exists workspaces (
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path text primary key,
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repo_root text not null,
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enrolled_at text not null,
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last_indexed_at text,
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last_summary_at text
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);
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create table if not exists documents (
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id integer primary key,
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workspace text not null,
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path text not null unique,
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rel_path text not null,
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kind text not null,
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language text,
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source_type text not null,
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content_hash text not null,
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mtime real not null,
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size integer not null,
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weight real not null default 0,
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updated_at text not null
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);
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create table if not exists chunks (
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id integer primary key,
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document_id integer not null references documents(id) on delete cascade,
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workspace text not null,
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path text not null,
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chunk_index integer not null,
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content text not null,
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content_hash text not null,
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token_count integer not null,
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updated_at text not null,
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unique(document_id, chunk_index)
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);
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create virtual table if not exists chunk_fts using fts5(
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chunk_id unindexed,
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workspace,
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path,
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content
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);
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create table if not exists embeddings (
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chunk_id integer primary key references chunks(id) on delete cascade,
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dims integer not null,
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vector_json text not null
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);
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create table if not exists instruction_sources (
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id integer primary key,
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workspace text,
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path text not null unique,
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scope text not null,
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source_kind text not null,
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weight real not null,
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content text not null,
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content_hash text not null,
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updated_at text not null
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);
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create virtual table if not exists instruction_fts using fts5(
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source_id unindexed,
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path,
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content
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);
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create table if not exists persona_facts (
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id integer primary key,
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fact text not null unique,
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weight real not null,
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source_path text,
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updated_at text not null
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);
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create table if not exists mission_summaries (
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mission_id text primary key,
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workspace text not null,
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title text not null,
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status text not null,
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summary text not null,
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keywords_json text not null,
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updated_at text not null
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);
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create virtual table if not exists mission_fts using fts5(
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mission_id unindexed,
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workspace,
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title,
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summary
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);
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create table if not exists decisions (
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decision_id text primary key,
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mission_id text not null,
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step_id text,
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workspace text not null,
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title text not null,
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summary text not null,
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status text not null,
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weight real not null,
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updated_at text not null
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);
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create virtual table if not exists decision_fts using fts5(
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decision_id unindexed,
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workspace,
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title,
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summary
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);
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create table if not exists decision_edges (
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id integer primary key,
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from_decision_id text not null,
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to_decision_id text not null,
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edge_type text not null,
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created_at text not null,
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unique(from_decision_id, to_decision_id, edge_type)
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);
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create table if not exists entities (
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id integer primary key,
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name text not null,
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entity_type text not null,
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source text,
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updated_at text not null,
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unique(name, entity_type)
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);
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"""
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)
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connection.commit()
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def _normalize_workspace(path: str | Path) -> Path:
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return Path(path).expanduser().resolve()
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def _detect_repo_root(workspace: Path) -> Path:
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for candidate in [workspace, *workspace.parents]:
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if (candidate / ".git").exists():
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return candidate
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return workspace
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def _sanitize_fts_query(query: str) -> str:
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tokens = re.findall(r"[A-Za-z0-9_.:/-]+", query.lower())
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return " ".join(tokens[:12]) or "constant"
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def _tokenize(text: str) -> list[str]:
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return re.findall(r"[A-Za-z0-9_./:-]{2,}", text.lower())
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def _sha256_text(text: str) -> str:
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return hashlib.sha256(text.encode("utf-8")).hexdigest()
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def _embed_text(text: str, dims: int) -> list[float]:
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vector = [0.0] * dims
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if dims <= 0:
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return vector
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for token, count in Counter(_tokenize(text)).items():
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digest = hashlib.sha256(token.encode("utf-8")).digest()
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index = int.from_bytes(digest[:4], "big") % dims
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sign = 1.0 if digest[4] % 2 == 0 else -1.0
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vector[index] += sign * (1.0 + math.log1p(count))
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norm = math.sqrt(sum(value * value for value in vector))
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if norm == 0:
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return vector
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return [round(value / norm, 6) for value in vector]
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def _cosine_similarity(vec_a: list[float], vec_b: list[float]) -> float:
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if not vec_a or not vec_b or len(vec_a) != len(vec_b):
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return 0.0
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return sum(a * b for a, b in zip(vec_a, vec_b))
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def _relative_path(path: Path, root: Path) -> str:
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try:
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return str(path.relative_to(root))
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except ValueError:
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return str(path)
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def _language_for(path: Path) -> str:
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suffix = path.suffix.lower()
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return {
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".py": "python",
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".sh": "shell",
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".zsh": "shell",
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".js": "javascript",
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".jsx": "javascript",
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".ts": "typescript",
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".tsx": "typescript",
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".json": "json",
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".yaml": "yaml",
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".yml": "yaml",
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".md": "markdown",
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".toml": "toml",
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".rs": "rust",
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".go": "go",
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".swift": "swift",
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".html": "html",
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".css": "css",
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}.get(suffix, suffix.lstrip(".") or "text")
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def _is_probably_text(path: Path, max_bytes: int) -> bool:
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if not path.is_file():
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return False
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try:
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if path.stat().st_size > max_bytes:
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return False
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with path.open("rb") as handle:
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sample = handle.read(2048)
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return b"\x00" not in sample
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except OSError:
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return False
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def _read_text(path: Path, max_bytes: int) -> str | None:
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if not _is_probably_text(path, max_bytes):
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return None
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try:
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return path.read_text(encoding="utf-8")
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except UnicodeDecodeError:
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try:
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return path.read_text(encoding="utf-8", errors="ignore")
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except OSError:
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return None
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except OSError:
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return None
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def _should_index_repo_file(path: Path) -> bool:
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if path.name.startswith(".") and path.name not in {".gitignore", ".env.example"}:
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return path.name in INSTRUCTION_FILES
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if path.suffix.lower() in TEXT_SUFFIXES:
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return True
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return path.name in {"Dockerfile", "Makefile", "justfile"}
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def _walk_repo_files(workspace: Path) -> list[Path]:
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files: list[Path] = []
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for root, dirnames, filenames in os.walk(workspace):
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dirnames[:] = sorted(name for name in dirnames if name not in IGNORED_DIR_NAMES)
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for filename in sorted(filenames):
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path = Path(root) / filename
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if _should_index_repo_file(path):
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files.append(path)
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return files
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def _iter_instruction_candidates(base: Path) -> list[Path]:
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candidates: list[Path] = []
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for name in INSTRUCTION_FILES:
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candidate = base / name
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if candidate.exists():
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candidates.append(candidate)
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for dirname in INSTRUCTION_DIRS:
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directory = base / dirname
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if not directory.exists() or not directory.is_dir():
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continue
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for root, dirnames, filenames in os.walk(directory):
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dirnames[:] = sorted(name for name in dirnames if name not in IGNORED_DIR_NAMES)
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depth = len(Path(root).relative_to(directory).parts)
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if depth > 3:
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dirnames[:] = []
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continue
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for filename in sorted(filenames):
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path = Path(root) / filename
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if path.suffix.lower() in INSTRUCTION_SUFFIXES or filename in {"config.json", "settings.json"}:
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candidates.append(path)
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return candidates
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def _instruction_scope(path: Path, workspace: Path, repo_root: Path, config: dict[str, Any]) -> tuple[str, float]:
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weights = config["instruction_weights"]
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home = Path.home().resolve()
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if path == workspace or workspace in path.parents:
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return "workspace", float(weights["workspace"])
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if path == repo_root or repo_root in path.parents:
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return "repo", float(weights["repo"])
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if home == path.parent or home in path.parents:
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return "user", float(weights["user"])
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return "ancestor", float(weights["ancestor"])
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def _discover_instruction_files(workspace: Path) -> list[dict[str, Any]]:
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config = load_memory_config()
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repo_root = _detect_repo_root(workspace)
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seen: set[str] = set()
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entries: list[dict[str, Any]] = []
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bases = [workspace, repo_root, *repo_root.parents]
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home = Path.home().resolve()
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if home not in bases:
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bases.append(home)
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for base in bases:
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if not str(base).startswith(str(home)) and base != workspace and base != repo_root:
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continue
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for candidate in _iter_instruction_candidates(base):
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key = str(candidate.resolve())
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if key in seen:
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continue
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seen.add(key)
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content = _read_text(candidate, MAX_INSTRUCTION_BYTES)
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if not content or not content.strip():
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continue
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scope, weight = _instruction_scope(candidate.resolve(), workspace, repo_root, config)
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entries.append(
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{
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"path": candidate.resolve(),
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"scope": scope,
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"weight": weight,
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"kind": candidate.parent.name if candidate.parent != candidate.parent.parent else candidate.name,
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"content": content,
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}
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)
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return sorted(entries, key=lambda item: (-item["weight"], str(item["path"])))
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def _chunk_text(text: str, target_chars: int = 1200, overlap_lines: int = 3) -> list[str]:
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lines = text.splitlines()
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if not lines:
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return []
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chunks: list[str] = []
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start = 0
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while start < len(lines):
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total = 0
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end = start
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while end < len(lines) and total < target_chars:
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total += len(lines[end]) + 1
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end += 1
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chunk = "\n".join(lines[start:end]).strip()
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if chunk:
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chunks.append(chunk)
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if end >= len(lines):
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break
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start = max(end - overlap_lines, start + 1)
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return chunks
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def _upsert_workspace(connection: sqlite3.Connection, workspace: Path) -> None:
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repo_root = _detect_repo_root(workspace)
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connection.execute(
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"""
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insert into workspaces(path, repo_root, enrolled_at, last_indexed_at, last_summary_at)
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values (?, ?, ?, null, null)
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on conflict(path) do update set repo_root=excluded.repo_root
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""",
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(str(workspace), str(repo_root), now_utc()),
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)
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def _prune_missing_documents(connection: sqlite3.Connection, workspace: Path, keep_paths: set[str]) -> int:
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removed = 0
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rows = connection.execute("select id, path from documents where workspace = ?", (str(workspace),)).fetchall()
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for row in rows:
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if row["path"] not in keep_paths:
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connection.execute("delete from chunk_fts where chunk_id in (select id from chunks where document_id = ?)", (row["id"],))
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connection.execute("delete from documents where id = ?", (row["id"],))
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removed += 1
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return removed
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def _index_repo_documents(connection: sqlite3.Connection, workspace: Path, dims: int) -> dict[str, int]:
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indexed = 0
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skipped = 0
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chunks_written = 0
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files = _walk_repo_files(workspace)
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keep_paths = {str(path.resolve()) for path in files}
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pruned = _prune_missing_documents(connection, workspace, keep_paths)
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for path in files:
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resolved = path.resolve()
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stat = resolved.stat()
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existing = connection.execute(
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"select id, mtime, size from documents where path = ?",
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(str(resolved),),
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).fetchone()
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if existing and float(existing["mtime"]) == stat.st_mtime and int(existing["size"]) == stat.st_size:
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skipped += 1
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continue
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|
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content = _read_text(resolved, MAX_TEXT_BYTES)
|
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if not content or not content.strip():
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skipped += 1
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continue
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|
|
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digest = _sha256_text(content)
|
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if existing:
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connection.execute("delete from chunk_fts where chunk_id in (select id from chunks where document_id = ?)", (existing["id"],))
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connection.execute("delete from documents where id = ?", (existing["id"],))
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|
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cursor = connection.execute(
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"""
|
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insert into documents(workspace, path, rel_path, kind, language, source_type, content_hash, mtime, size, weight, updated_at)
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values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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|
""",
|
|
(
|
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str(workspace),
|
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str(resolved),
|
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_relative_path(resolved, workspace),
|
|
"repo",
|
|
_language_for(resolved),
|
|
"repo",
|
|
digest,
|
|
stat.st_mtime,
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stat.st_size,
|
|
1.0,
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|
now_utc(),
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),
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)
|
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document_id = int(cursor.lastrowid)
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for chunk_index, chunk in enumerate(_chunk_text(content), start=1):
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chunk_hash = _sha256_text(chunk)
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chunk_cursor = connection.execute(
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"""
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insert into chunks(document_id, workspace, path, chunk_index, content, content_hash, token_count, updated_at)
|
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values (?, ?, ?, ?, ?, ?, ?, ?)
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""",
|
|
(
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document_id,
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str(workspace),
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str(resolved),
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|
chunk_index,
|
|
chunk,
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|
chunk_hash,
|
|
len(_tokenize(chunk)),
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now_utc(),
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),
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)
|
|
chunk_id = int(chunk_cursor.lastrowid)
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connection.execute(
|
|
"insert into chunk_fts(chunk_id, workspace, path, content) values (?, ?, ?, ?)",
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(chunk_id, str(workspace), str(resolved), chunk),
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)
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connection.execute(
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"insert into embeddings(chunk_id, dims, vector_json) values (?, ?, ?)",
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(chunk_id, dims, json.dumps(_embed_text(chunk, dims))),
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)
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chunks_written += 1
|
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indexed += 1
|
|
|
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connection.execute(
|
|
"update workspaces set last_indexed_at = ? where path = ?",
|
|
(now_utc(), str(workspace)),
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)
|
|
return {"indexed": indexed, "skipped": skipped, "pruned": pruned, "chunks": chunks_written}
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|
|
|
|
def _extract_persona_facts(text: str) -> list[str]:
|
|
facts: list[str] = []
|
|
for raw_line in text.splitlines():
|
|
line = raw_line.strip()
|
|
if not line:
|
|
continue
|
|
if line.startswith(("#", "```", "{", "}", "[", "]")):
|
|
continue
|
|
if len(line) < 12 or len(line) > 220:
|
|
continue
|
|
cleaned = re.sub(r"^[*-]\s*", "", line)
|
|
if cleaned and cleaned not in facts:
|
|
facts.append(cleaned)
|
|
if len(facts) >= 24:
|
|
break
|
|
return facts
|
|
|
|
|
|
def _refresh_instruction_sources(connection: sqlite3.Connection, workspace: Path) -> dict[str, int]:
|
|
discovered = _discover_instruction_files(workspace)
|
|
keep_paths = {str(entry["path"]) for entry in discovered}
|
|
current = connection.execute("select path from instruction_sources").fetchall()
|
|
removed = 0
|
|
for row in current:
|
|
if row["path"] not in keep_paths:
|
|
connection.execute("delete from instruction_fts where path = ?", (row["path"],))
|
|
connection.execute("delete from instruction_sources where path = ?", (row["path"],))
|
|
removed += 1
|
|
|
|
refreshed = 0
|
|
facts = 0
|
|
for entry in discovered:
|
|
path = entry["path"]
|
|
content = entry["content"]
|
|
digest = _sha256_text(content)
|
|
existing = connection.execute(
|
|
"select content_hash from instruction_sources where path = ?",
|
|
(str(path),),
|
|
).fetchone()
|
|
if existing and existing["content_hash"] == digest:
|
|
continue
|
|
connection.execute("delete from instruction_fts where path = ?", (str(path),))
|
|
connection.execute(
|
|
"""
|
|
insert into instruction_sources(workspace, path, scope, source_kind, weight, content, content_hash, updated_at)
|
|
values (?, ?, ?, ?, ?, ?, ?, ?)
|
|
on conflict(path) do update set
|
|
workspace=excluded.workspace,
|
|
scope=excluded.scope,
|
|
source_kind=excluded.source_kind,
|
|
weight=excluded.weight,
|
|
content=excluded.content,
|
|
content_hash=excluded.content_hash,
|
|
updated_at=excluded.updated_at
|
|
""",
|
|
(
|
|
str(workspace),
|
|
str(path),
|
|
entry["scope"],
|
|
entry["kind"],
|
|
float(entry["weight"]),
|
|
content,
|
|
digest,
|
|
now_utc(),
|
|
),
|
|
)
|
|
connection.execute(
|
|
"insert into instruction_fts(source_id, path, content) values ((select id from instruction_sources where path = ?), ?, ?)",
|
|
(str(path), str(path), content),
|
|
)
|
|
for fact in _extract_persona_facts(content):
|
|
connection.execute(
|
|
"""
|
|
insert into persona_facts(fact, weight, source_path, updated_at)
|
|
values (?, ?, ?, ?)
|
|
on conflict(fact) do update set
|
|
weight=max(weight, excluded.weight),
|
|
source_path=excluded.source_path,
|
|
updated_at=excluded.updated_at
|
|
""",
|
|
(fact, float(entry["weight"]), str(path), now_utc()),
|
|
)
|
|
facts += 1
|
|
refreshed += 1
|
|
|
|
return {"sources": len(discovered), "refreshed": refreshed, "removed": removed, "persona_facts": facts}
|
|
|
|
|
|
def _render_persona(connection: sqlite3.Connection) -> str:
|
|
rows = connection.execute(
|
|
"select fact, weight, source_path from persona_facts order by weight desc, fact asc limit 32"
|
|
).fetchall()
|
|
mission_rows = connection.execute(
|
|
"select mission_id, title, summary from mission_summaries order by updated_at desc limit 6"
|
|
).fetchall()
|
|
decision_rows = connection.execute(
|
|
"select decision_id, title, summary from decisions order by updated_at desc limit 8"
|
|
).fetchall()
|
|
|
|
lines = [
|
|
"# Constant Persona",
|
|
"",
|
|
"## Durable Rules",
|
|
]
|
|
if rows:
|
|
for row in rows:
|
|
lines.append(f"- {row['fact']} ({Path(row['source_path']).name})")
|
|
else:
|
|
lines.append("- No durable rules extracted yet.")
|
|
|
|
lines.extend(["", "## Recent Mission Summaries"])
|
|
if mission_rows:
|
|
for row in mission_rows:
|
|
lines.append(f"- `{row['mission_id']}` {row['title']}: {row['summary']}")
|
|
else:
|
|
lines.append("- No mission summaries yet.")
|
|
|
|
lines.extend(["", "## Decision Graph Snapshot"])
|
|
if decision_rows:
|
|
for row in decision_rows:
|
|
lines.append(f"- `{row['decision_id']}` {row['title']}: {row['summary']}")
|
|
else:
|
|
lines.append("- No decisions captured yet.")
|
|
return "\n".join(lines).strip() + "\n"
|
|
|
|
|
|
def rebuild_workspace_memory(workspace: str | Path, enroll: bool = True) -> dict[str, Any]:
|
|
workspace_path = _normalize_workspace(workspace)
|
|
config = load_memory_config()
|
|
if enroll and str(workspace_path) not in config["workspace_enrollments"]:
|
|
config["workspace_enrollments"].append(str(workspace_path))
|
|
config["workspace_enrollments"] = sorted(set(config["workspace_enrollments"]))
|
|
save_memory_config(config)
|
|
|
|
connection = _connect()
|
|
try:
|
|
_upsert_workspace(connection, workspace_path)
|
|
dims = int(config.get("vector_dimensions", 96))
|
|
repo_stats = _index_repo_documents(connection, workspace_path, dims)
|
|
instruction_stats = _refresh_instruction_sources(connection, workspace_path)
|
|
persona_markdown = _render_persona(connection)
|
|
persona_path().parent.mkdir(parents=True, exist_ok=True)
|
|
persona_path().write_text(persona_markdown, encoding="utf-8")
|
|
connection.commit()
|
|
return {
|
|
"workspace": str(workspace_path),
|
|
"repo_root": str(_detect_repo_root(workspace_path)),
|
|
"repo": repo_stats,
|
|
"instructions": instruction_stats,
|
|
"persona_path": str(persona_path()),
|
|
"store_path": str(memory_store_path()),
|
|
}
|
|
finally:
|
|
connection.close()
|
|
|
|
|
|
def memory_status(workspace: str | None = None) -> dict[str, Any]:
|
|
connection = _connect()
|
|
try:
|
|
payload = {
|
|
"store_path": str(memory_store_path()),
|
|
"persona_path": str(persona_path()),
|
|
"enrollments": load_memory_config()["workspace_enrollments"],
|
|
"counts": {
|
|
"workspaces": connection.execute("select count(*) from workspaces").fetchone()[0],
|
|
"documents": connection.execute("select count(*) from documents").fetchone()[0],
|
|
"chunks": connection.execute("select count(*) from chunks").fetchone()[0],
|
|
"instruction_sources": connection.execute("select count(*) from instruction_sources").fetchone()[0],
|
|
"persona_facts": connection.execute("select count(*) from persona_facts").fetchone()[0],
|
|
"mission_summaries": connection.execute("select count(*) from mission_summaries").fetchone()[0],
|
|
"decisions": connection.execute("select count(*) from decisions").fetchone()[0],
|
|
"decision_edges": connection.execute("select count(*) from decision_edges").fetchone()[0],
|
|
},
|
|
}
|
|
if workspace:
|
|
payload["workspace"] = str(_normalize_workspace(workspace))
|
|
payload["workspace_counts"] = {
|
|
"documents": connection.execute("select count(*) from documents where workspace = ?", (payload["workspace"],)).fetchone()[0],
|
|
"mission_summaries": connection.execute("select count(*) from mission_summaries where workspace = ?", (payload["workspace"],)).fetchone()[0],
|
|
"decisions": connection.execute("select count(*) from decisions where workspace = ?", (payload["workspace"],)).fetchone()[0],
|
|
}
|
|
return payload
|
|
finally:
|
|
connection.close()
|
|
|
|
|
|
def enroll_workspace(workspace: str | Path) -> dict[str, Any]:
|
|
workspace_path = str(_normalize_workspace(workspace))
|
|
config = load_memory_config()
|
|
config["workspace_enrollments"] = sorted(set(config["workspace_enrollments"] + [workspace_path]))
|
|
save_memory_config(config)
|
|
return rebuild_workspace_memory(workspace_path, enroll=False)
|
|
|
|
|
|
def _snippet(text: str, limit: int = 220) -> str:
|
|
compact = " ".join(text.split())
|
|
return compact[:limit] + ("..." if len(compact) > limit else "")
|
|
|
|
|
|
def search_memory(query: str, workspace: str | None = None, limit: int | None = None) -> dict[str, Any]:
|
|
config = load_memory_config()
|
|
max_hits = limit or int(config.get("max_chunks_per_query", 8))
|
|
workspace_path = str(_normalize_workspace(workspace)) if workspace else None
|
|
connection = _connect()
|
|
try:
|
|
hits: list[dict[str, Any]] = []
|
|
fts_query = _sanitize_fts_query(query)
|
|
params: list[Any] = [fts_query]
|
|
chunk_sql = (
|
|
"select chunk_id, path, content, bm25(chunk_fts) as rank from chunk_fts where chunk_fts match ?"
|
|
)
|
|
if workspace_path:
|
|
chunk_sql += " and workspace = ?"
|
|
params.append(workspace_path)
|
|
chunk_sql += " order by rank limit ?"
|
|
params.append(max_hits * 3)
|
|
for row in connection.execute(chunk_sql, tuple(params)).fetchall():
|
|
hits.append(
|
|
{
|
|
"kind": "repo",
|
|
"path": row["path"],
|
|
"score": round(2.0 - float(row["rank"]), 4),
|
|
"snippet": _snippet(row["content"]),
|
|
}
|
|
)
|
|
|
|
query_vector = _embed_text(query, int(config.get("vector_dimensions", 96)))
|
|
vector_rows = connection.execute(
|
|
"select chunks.path, chunks.content, embeddings.vector_json from chunks join embeddings on embeddings.chunk_id = chunks.id"
|
|
+ (" where chunks.workspace = ?" if workspace_path else ""),
|
|
((workspace_path,) if workspace_path else ()),
|
|
).fetchall()
|
|
for row in vector_rows:
|
|
score = _cosine_similarity(query_vector, json.loads(row["vector_json"]))
|
|
if score <= 0:
|
|
continue
|
|
hits.append(
|
|
{
|
|
"kind": "repo-vector",
|
|
"path": row["path"],
|
|
"score": round(score, 4),
|
|
"snippet": _snippet(row["content"]),
|
|
}
|
|
)
|
|
|
|
source_rows = connection.execute(
|
|
"select path, scope, weight, content from instruction_sources order by weight desc"
|
|
).fetchall()
|
|
for row in source_rows:
|
|
haystack = row["content"].lower()
|
|
if query.lower() in haystack or any(token in haystack for token in _tokenize(query)[:4]):
|
|
hits.append(
|
|
{
|
|
"kind": "instruction",
|
|
"path": row["path"],
|
|
"score": round(float(row["weight"]) + 1.0, 4),
|
|
"snippet": _snippet(row["content"]),
|
|
"scope": row["scope"],
|
|
}
|
|
)
|
|
|
|
summary_rows = connection.execute(
|
|
"select mission_id, title, summary from mission_summaries order by updated_at desc"
|
|
+ (" limit 64" if not workspace_path else ""),
|
|
).fetchall()
|
|
for row in summary_rows:
|
|
text = f"{row['title']} {row['summary']}".lower()
|
|
if query.lower() in text or any(token in text for token in _tokenize(query)[:4]):
|
|
hits.append(
|
|
{
|
|
"kind": "mission",
|
|
"path": row["mission_id"],
|
|
"score": 1.25,
|
|
"snippet": _snippet(row["summary"]),
|
|
}
|
|
)
|
|
|
|
decision_rows = connection.execute(
|
|
"select decision_id, title, summary, status from decisions order by updated_at desc limit 128"
|
|
).fetchall()
|
|
for row in decision_rows:
|
|
text = f"{row['title']} {row['summary']}".lower()
|
|
if query.lower() in text or any(token in text for token in _tokenize(query)[:4]):
|
|
hits.append(
|
|
{
|
|
"kind": "decision",
|
|
"path": row["decision_id"],
|
|
"score": 1.1,
|
|
"snippet": _snippet(row["summary"]),
|
|
"status": row["status"],
|
|
}
|
|
)
|
|
|
|
ranked = sorted(hits, key=lambda item: item["score"], reverse=True)
|
|
deduped: list[dict[str, Any]] = []
|
|
seen: set[tuple[str, str]] = set()
|
|
for hit in ranked:
|
|
key = (hit["kind"], hit["path"])
|
|
if key in seen:
|
|
continue
|
|
seen.add(key)
|
|
deduped.append(hit)
|
|
if len(deduped) >= max_hits:
|
|
break
|
|
|
|
return {
|
|
"query": query,
|
|
"workspace": workspace_path,
|
|
"hits": deduped,
|
|
}
|
|
finally:
|
|
connection.close()
|
|
|
|
|
|
def persona_markdown() -> str:
|
|
if persona_path().exists():
|
|
return persona_path().read_text(encoding="utf-8")
|
|
rebuild_workspace_memory(Path.cwd(), enroll=False)
|
|
return persona_path().read_text(encoding="utf-8") if persona_path().exists() else "# Constant Persona\n"
|
|
|
|
|
|
def list_decisions(workspace: str | None = None, mission_id: str | None = None) -> dict[str, Any]:
|
|
connection = _connect()
|
|
try:
|
|
clauses = []
|
|
params: list[Any] = []
|
|
if workspace:
|
|
clauses.append("workspace = ?")
|
|
params.append(str(_normalize_workspace(workspace)))
|
|
if mission_id:
|
|
clauses.append("mission_id = ?")
|
|
params.append(mission_id)
|
|
sql = "select decision_id, mission_id, step_id, workspace, title, summary, status, weight, updated_at from decisions"
|
|
if clauses:
|
|
sql += " where " + " and ".join(clauses)
|
|
sql += " order by updated_at desc, decision_id asc"
|
|
rows = connection.execute(sql, tuple(params)).fetchall()
|
|
return {"decisions": [dict(row) for row in rows]}
|
|
finally:
|
|
connection.close()
|
|
|
|
|
|
def _mission_keywords(mission: dict[str, Any]) -> list[str]:
|
|
tokens = set(_tokenize(mission["title"] + " " + mission["goal"]))
|
|
for step in mission["steps"]:
|
|
tokens.update([step.get("machine", ""), step.get("cli", ""), step.get("backend", ""), step.get("status", "")])
|
|
return sorted(token for token in tokens if token)[:24]
|
|
|
|
|
|
def summarize_mission(mission_id: str) -> dict[str, Any]:
|
|
mission = load_mission(mission_id)
|
|
workspace = str(_normalize_workspace(mission["workspace"]))
|
|
connection = _connect()
|
|
try:
|
|
_upsert_workspace(connection, Path(workspace))
|
|
status_counts = Counter(step.get("status", "unknown") for step in mission["steps"])
|
|
route_bits = []
|
|
for step in mission["steps"]:
|
|
route_bits.append(f"{step['step_id']}={step['machine']}/{step['cli']}/{step['backend']}:{step['status']}")
|
|
summary = (
|
|
f"Mission {mission['title']} ended as {mission['status']}. "
|
|
f"Steps={len(mission['steps'])}. "
|
|
f"Status mix={dict(status_counts)}. "
|
|
f"Routes: {'; '.join(route_bits[:6]) or 'none'}."
|
|
)
|
|
keywords = _mission_keywords(mission)
|
|
connection.execute(
|
|
"""
|
|
insert into mission_summaries(mission_id, workspace, title, status, summary, keywords_json, updated_at)
|
|
values (?, ?, ?, ?, ?, ?, ?)
|
|
on conflict(mission_id) do update set
|
|
workspace=excluded.workspace,
|
|
title=excluded.title,
|
|
status=excluded.status,
|
|
summary=excluded.summary,
|
|
keywords_json=excluded.keywords_json,
|
|
updated_at=excluded.updated_at
|
|
""",
|
|
(
|
|
mission_id,
|
|
workspace,
|
|
mission["title"],
|
|
mission["status"],
|
|
summary,
|
|
json.dumps(keywords),
|
|
now_utc(),
|
|
),
|
|
)
|
|
connection.execute(
|
|
"delete from mission_fts where mission_id = ?",
|
|
(mission_id,),
|
|
)
|
|
connection.execute(
|
|
"insert into mission_fts(mission_id, workspace, title, summary) values (?, ?, ?, ?)",
|
|
(mission_id, workspace, mission["title"], summary),
|
|
)
|
|
|
|
previous_decision_id: str | None = None
|
|
decision_count = 0
|
|
for step in mission["steps"]:
|
|
decision_id = f"{mission_id}:{step['step_id']}"
|
|
decision_summary = (
|
|
f"Route {step['machine']}/{step['cli']}/{step['backend']} ended as {step['status']}. "
|
|
f"{step.get('result_summary', '')}".strip()
|
|
)
|
|
connection.execute(
|
|
"""
|
|
insert into decisions(decision_id, mission_id, step_id, workspace, title, summary, status, weight, updated_at)
|
|
values (?, ?, ?, ?, ?, ?, ?, ?, ?)
|
|
on conflict(decision_id) do update set
|
|
mission_id=excluded.mission_id,
|
|
step_id=excluded.step_id,
|
|
workspace=excluded.workspace,
|
|
title=excluded.title,
|
|
summary=excluded.summary,
|
|
status=excluded.status,
|
|
weight=excluded.weight,
|
|
updated_at=excluded.updated_at
|
|
""",
|
|
(
|
|
decision_id,
|
|
mission_id,
|
|
step["step_id"],
|
|
workspace,
|
|
step["title"],
|
|
decision_summary,
|
|
step["status"],
|
|
1.0 if step["status"] == "done" else 0.7,
|
|
now_utc(),
|
|
),
|
|
)
|
|
connection.execute("delete from decision_fts where decision_id = ?", (decision_id,))
|
|
connection.execute(
|
|
"insert into decision_fts(decision_id, workspace, title, summary) values (?, ?, ?, ?)",
|
|
(decision_id, workspace, step["title"], decision_summary),
|
|
)
|
|
if previous_decision_id:
|
|
connection.execute(
|
|
"""
|
|
insert or ignore into decision_edges(from_decision_id, to_decision_id, edge_type, created_at)
|
|
values (?, ?, ?, ?)
|
|
""",
|
|
(previous_decision_id, decision_id, "depends_on", now_utc()),
|
|
)
|
|
previous_decision_id = decision_id
|
|
decision_count += 1
|
|
|
|
connection.execute(
|
|
"update workspaces set last_summary_at = ? where path = ?",
|
|
(now_utc(), workspace),
|
|
)
|
|
persona_markdown_value = _render_persona(connection)
|
|
persona_path().parent.mkdir(parents=True, exist_ok=True)
|
|
persona_path().write_text(persona_markdown_value, encoding="utf-8")
|
|
connection.commit()
|
|
append_event(mission_id, "memory.summary_written", {"summary": summary, "decisions": decision_count})
|
|
return {
|
|
"mission_id": mission_id,
|
|
"summary": summary,
|
|
"keywords": keywords,
|
|
"decisions": decision_count,
|
|
"persona_path": str(persona_path()),
|
|
}
|
|
finally:
|
|
connection.close()
|
|
|
|
|
|
def planner_context(workspace: str | Path, query: str) -> dict[str, Any]:
|
|
workspace_path = str(_normalize_workspace(workspace))
|
|
rebuild_workspace_memory(workspace_path, enroll=True)
|
|
search = search_memory(query, workspace_path)
|
|
connection = _connect()
|
|
try:
|
|
instruction_rows = connection.execute(
|
|
"""
|
|
select path, scope, weight, content
|
|
from instruction_sources
|
|
where workspace = ? or scope = 'user'
|
|
order by weight desc, updated_at desc
|
|
limit 8
|
|
""",
|
|
(workspace_path,),
|
|
).fetchall()
|
|
decision_rows = connection.execute(
|
|
"""
|
|
select decision_id, title, summary, status
|
|
from decisions
|
|
where workspace = ?
|
|
order by updated_at desc
|
|
limit 6
|
|
""",
|
|
(workspace_path,),
|
|
).fetchall()
|
|
persona_rows = connection.execute(
|
|
"select fact from persona_facts order by weight desc, updated_at desc limit 12"
|
|
).fetchall()
|
|
return {
|
|
"workspace": workspace_path,
|
|
"instruction_excerpt": [
|
|
{
|
|
"path": row["path"],
|
|
"scope": row["scope"],
|
|
"weight": row["weight"],
|
|
"snippet": _snippet(row["content"], 180),
|
|
}
|
|
for row in instruction_rows
|
|
],
|
|
"repo_hits": search["hits"],
|
|
"recent_decisions": [dict(row) for row in decision_rows],
|
|
"persona_facts": [row["fact"] for row in persona_rows],
|
|
}
|
|
finally:
|
|
connection.close()
|
|
|
|
|
|
def sync_qdrant(workspace: str | None = None) -> dict[str, Any]:
|
|
config = load_memory_config()
|
|
url = str(config.get("qdrant_url", "")).strip()
|
|
if not url:
|
|
return {"ok": False, "skipped": True, "reason": "qdrant_url is not configured"}
|
|
|
|
search_payload = search_memory("*", workspace, limit=32)
|
|
collection = config.get("qdrant_collection", "constant_memory")
|
|
request_body = {
|
|
"points": [
|
|
{
|
|
"id": abs(hash((hit["kind"], hit["path"]))) % 2_147_483_647,
|
|
"vector": _embed_text(hit["snippet"], int(config.get("vector_dimensions", 96))),
|
|
"payload": hit,
|
|
}
|
|
for hit in search_payload["hits"]
|
|
]
|
|
}
|
|
req = urllib.request.Request(
|
|
url.rstrip("/") + f"/collections/{collection}/points?wait=true",
|
|
data=json.dumps(request_body).encode("utf-8"),
|
|
headers={"Content-Type": "application/json"},
|
|
method="PUT",
|
|
)
|
|
try:
|
|
with urllib.request.urlopen(req, timeout=5) as response:
|
|
payload = json.loads(response.read().decode("utf-8"))
|
|
return {"ok": True, "response": payload, "points": len(request_body["points"])}
|
|
except urllib.error.URLError as exc:
|
|
return {"ok": False, "skipped": False, "reason": str(exc), "points": len(request_body["points"])}
|