Update Astra/Agent state - 2026-05-10 22:26:50
This commit is contained in:
g1nation
+1
-1
@@ -1,5 +1,5 @@
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{
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"result": "Final report with inconsistencies. This should be long enough to pass validation.",
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"createdAt": 1778256848559,
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"createdAt": 1778419501265,
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"modelVersion": "unknown"
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}
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+1
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@@ -1,5 +1,5 @@
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{
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"result": "[CONFLICT WARNING] 성능이 200% 증가했습니다. vs 그러나 동시에 50% 감소했습니다. 최적화와 성능 저하가 동시에 발견됨.",
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"createdAt": 1778256848551,
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"createdAt": 1778419501264,
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"modelVersion": "unknown"
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}
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+1
-1
@@ -1,5 +1,5 @@
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{
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"result": "Detailed Execution Plan: 1. Research 2. Analyze 3. Write report with high quality.",
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"createdAt": 1778256848546,
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"createdAt": 1778419501204,
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"modelVersion": "unknown"
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}
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+2
-2
@@ -1,5 +1,5 @@
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{
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"result": "---\nid: stress_conflict_1778256848530\ndate: 2026-05-08T16:14:08.563Z\ntype: knowledge_artifact\nstandard: P-Reinforce v3.0\ntags: [automated, connect_ai, brain_sync]\n---\n\n## 📌 Brief Summary\nFinal report with inconsistencies. This should be long enough to pass validation.\n\nFinal report with inconsistencies. This should be long enough to pass validation.\n\n---\n## 💡 Astra의 선제적 제안 (Proactive Next Actions)\nFinal report with inconsistencies. This should be long enough to pass validation.\n---\n## 🛡️ Reliability & Audit Summary\n> [!NOTE]\n> 이 문서는 ConnectAI의 **Intelligent Resilience** 엔진에 의해 검증 및 정제되었습니다.\n\n| Metric | Value | Status |\n| :--- | :--- | :--- |\n| **Conflict Risk** | `60/100` | ⚠️ Medium |\n| **Fallbacks Used** | `0` | ✅ None |\n| **Auto Retries** | `0` | ✅ Stable |\n| **Deduplication** | `0` | Standard |\n| **Processing Time** | `0.0s` | ✅ Fast |\n\n### 🔍 Decision Audit Trail\n- **[PLANNER]** 전략 수립 중... (11ms)\n- **[RESEARCHER]** 핵심 정보 수집 및 분석 중... (5ms)\n- **[WRITER]** 최종 리포트 작성 및 편집 중... (9ms)\n",
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"createdAt": 1778256848563,
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"result": "---\nid: stress_conflict_1778419501171\ndate: 2026-05-10T13:25:01.265Z\ntype: knowledge_artifact\nstandard: P-Reinforce v3.0\ntags: [automated, connect_ai, brain_sync]\n---\n\n## 📌 Brief Summary\nFinal report with inconsistencies. This should be long enough to pass validation.\n\nFinal report with inconsistencies. This should be long enough to pass validation.\n\n---\n## 💡 Astra의 선제적 제안 (Proactive Next Actions)\nFinal report with inconsistencies. This should be long enough to pass validation.\n---\n## 🛡️ Reliability & Audit Summary\n> [!NOTE]\n> 이 문서는 ConnectAI의 **Intelligent Resilience** 엔진에 의해 검증 및 정제되었습니다.\n\n| Metric | Value | Status |\n| :--- | :--- | :--- |\n| **Conflict Risk** | `60/100` | ⚠️ Medium |\n| **Fallbacks Used** | `0` | ✅ None |\n| **Auto Retries** | `0` | ✅ Stable |\n| **Deduplication** | `0` | Standard |\n| **Processing Time** | `0.1s` | ✅ Fast |\n\n### 🔍 Decision Audit Trail\n- **[PLANNER]** 전략 수립 중... (32ms)\n- **[RESEARCHER]** 핵심 정보 수집 및 분석 중... (1ms)\n- **[WRITER]** 최종 리포트 작성 및 편집 중... (61ms)\n",
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"createdAt": 1778419501265,
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"modelVersion": "unknown"
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}
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+11
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@@ -1,8 +1,8 @@
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{
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"missionId": "stress_conflict_1778256848530",
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"missionId": "stress_conflict_1778419501171",
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"status": "completed",
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"startTime": "2026-05-08T16:14:08.530Z",
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"totalElapsedMs": 34,
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"startTime": "2026-05-10T13:25:01.171Z",
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"totalElapsedMs": 94,
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"results": {
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"planner": "Detailed Execution Plan: 1. Research 2. Analyze 3. Write report with high quality.",
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"researcher": "[CONFLICT WARNING] 성능이 200% 증가했습니다. vs 그러나 동시에 50% 감소했습니다. 최적화와 성능 저하가 동시에 발견됨.",
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@@ -16,30 +16,30 @@
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{
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"from": "idle",
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"to": "planner",
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"durationMs": 11,
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"durationMs": 32,
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"message": "전략 수립 중...",
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"ts": "2026-05-08T16:14:08.541Z"
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"ts": "2026-05-10T13:25:01.203Z"
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},
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{
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"from": "planner",
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"to": "researcher",
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"durationMs": 5,
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"durationMs": 1,
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"message": "핵심 정보 수집 및 분석 중...",
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"ts": "2026-05-08T16:14:08.546Z"
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"ts": "2026-05-10T13:25:01.204Z"
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},
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{
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"from": "researcher",
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"to": "writer",
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"durationMs": 9,
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"durationMs": 61,
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"message": "최종 리포트 작성 및 편집 중...",
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"ts": "2026-05-08T16:14:08.555Z"
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"ts": "2026-05-10T13:25:01.265Z"
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},
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{
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"from": "writer",
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"to": "completed",
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"durationMs": 9,
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"durationMs": 0,
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"message": "미션 완료",
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"ts": "2026-05-08T16:14:08.564Z"
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"ts": "2026-05-10T13:25:01.265Z"
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}
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],
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"resilienceMetrics": {
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@@ -6,6 +6,6 @@
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"description": "Auto-detected from the local project path in the conversation.",
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"corePurpose": "Capture project direction, architecture discussion, decisions, and development notes as Markdown.",
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"detailLevel": "standard",
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"createdAt": "2026-05-05T13:14:39.155Z",
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"updatedAt": "2026-05-05T13:14:39.160Z"
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"createdAt": "2026-05-10T07:42:38.921Z",
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"updatedAt": "2026-05-10T07:42:38.928Z"
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}
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# ADR: /Volumes/Data/project/Antigravity/ConnectAI 이거에 기능 개선을 하고 싶어. 너가 기획과 방향성을 정해주면 좋...
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## Status
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accepted
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## Context
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/Volumes/Data/project/Antigravity/ConnectAI 이거에 기능 개선을 하고 싶어. 너가 기획과 방향성을 정해주면 좋겠어. 내가 원하는 것은 아래와 같아. 1. 에이전트 선택 시 에이전트에 연관할 젠2뇌 지식 선택, 모델 선택을 맵핑 하고, 에이전트가 사용되는 skill 을 폴더로 선택할 수 있게 해주면 좋겠어.
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## Decision
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## 간단 요약 제안하신 기능은 에이전트와 지식 간의 연결을 명확히 하여 **사용자 경험(UX)과 지식 활용도를 극대화**하는 데 매우 긍정적입니다. 하지만 이는 단순한 UI 추가가 아니라, 기존의 복잡한 지식 그래프와 에이전트 워크플로우를 더욱 견고하게 만드는 아키텍처 개선을 요구합니다. ## 상세 설명 - **근본적인 문제:** 현재 시스템은 에이전트 선택과 지식 검색이 분리되어 있어, 사용자가 어떤 에이전트를 선택했을 때 그 에이전트가 가장 적합한 지식(Second Brain)을 자동으로 연결해주는 '맥락적 매핑' 기능이 부족합니다. - **기능적 개선 방향:** 에이전트의 역할(Skill/Folder)과 연관된 지식 세트(Knowledge Base)를 명시적으로 매핑하는 중간 계층이 필요합니다. 이는 사용자가 원하는 결과물을 얻기 위해 '어떤 지식을 불러와야 하는지'에 대한 불확실성을 제거해줍니다. - **사용자 경험 개선:** 폴더 기반 선택은 사용자가 자신의 작업 영역(W...
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## Reason
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Captured automatically because the conversation contained decision-oriented language.
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## Alternatives
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Not captured yet.
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## Consequences
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- Future prompts should treat this as project context unless the user changes direction.
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@@ -81,3 +81,6 @@
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## 2026-05-05
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- Auto development record created: development/2026-05-05_volumes-data-project-antigravity-connectai-오늘-많은-것을-업데이트했어-많_implementation.md
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## 2026-05-10
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- Auto decision record created: decisions/ADR-0007-volumes-data-project-antigravity-connectai-이거에-기능-개선을-하고-싶어-.md
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@@ -45,6 +45,13 @@
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<button class="icon-btn" id="deleteAgentBtn" data-tooltip="Delete Agent Skill">Del</button>
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</div>
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</div>
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<div class="control-row">
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<div class="select-wrap"><select id="knowledgeScopeSel" title="Knowledge folders mapped to this agent"></select></div>
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<div class="tool-group" aria-label="Knowledge map actions">
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<button class="icon-btn" id="editKnowledgeMapBtn" data-tooltip="Edit Agent ↔ Knowledge Map">Map</button>
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<button class="icon-btn" id="reloadKnowledgeMapBtn" data-tooltip="Reload Knowledge Map">Rld</button>
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</div>
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</div>
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</div>
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<div class="control-row">
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<div class="select-wrap"><select id="designerSel" title="Select Designer Project"></select></div>
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@@ -146,6 +146,9 @@
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const editAgentBtn = document.getElementById('editAgentBtn');
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const addAgentBtn = document.getElementById('addAgentBtn');
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const deleteAgentBtn = document.getElementById('deleteAgentBtn');
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const knowledgeScopeSel = document.getElementById('knowledgeScopeSel');
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const editKnowledgeMapBtn = document.getElementById('editKnowledgeMapBtn');
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const reloadKnowledgeMapBtn = document.getElementById('reloadKnowledgeMapBtn');
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const addBrainBtn = document.getElementById('addBrainBtn');
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const editBrainBtn = document.getElementById('editBrainBtn');
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const deleteBrainBtn = document.getElementById('deleteBrainBtn');
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@@ -382,6 +385,32 @@
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if (msg.selected && msg.selected !== 'none') {
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vscode.postMessage({ type: 'getAgentContent', path: msg.selected });
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}
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vscode.postMessage({ type: 'getKnowledgeScope', agentPath: msg.selected });
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break;
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case 'knowledgeScope':
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if (knowledgeScopeSel) {
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knowledgeScopeSel.innerHTML = '';
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const folders = (msg.value && msg.value.folders) || [];
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if (folders.length === 0) {
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const o = document.createElement('option');
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o.value = '';
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const label = (msg.value && msg.value.agent)
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? `매핑된 폴더 없음 (agent: ${msg.value.agent})`
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: '매핑 없음 — 전체 브레인 검색';
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o.innerText = label;
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knowledgeScopeSel.appendChild(o);
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knowledgeScopeSel.disabled = true;
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} else {
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knowledgeScopeSel.disabled = false;
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folders.forEach(f => {
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const o = document.createElement('option');
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o.value = f.absolute;
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o.innerText = f.relative || f.absolute;
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o.title = f.absolute;
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knowledgeScopeSel.appendChild(o);
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});
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}
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}
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break;
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case 'chronicleProjects':
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designerSel.innerHTML = '';
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@@ -673,8 +702,16 @@
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// [State Persistence Fix] 에이전트 해제도 즉시 저장
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vscode.postMessage({ type: 'saveAgentSelection', path: 'none' });
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}
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vscode.postMessage({ type: 'getKnowledgeScope', agentPath: agentSel.value });
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};
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if (editKnowledgeMapBtn) {
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editKnowledgeMapBtn.onclick = () => vscode.postMessage({ type: 'editKnowledgeMap' });
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}
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if (reloadKnowledgeMapBtn) {
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reloadKnowledgeMapBtn.onclick = () => vscode.postMessage({ type: 'getKnowledgeScope', agentPath: agentSel.value });
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}
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editAgentBtn.onclick = () => {
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if (agentSel.value === 'none') return;
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editMode = !editMode;
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@@ -79,6 +79,10 @@
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{
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"command": "g1nation.settings.focus",
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"title": "Astra: Open Settings Panel"
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},
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{
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"command": "g1nation.skills.editKnowledgeMap",
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"title": "Astra: Edit Agent ↔ Knowledge Map"
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}
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],
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"keybindings": [
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@@ -260,6 +264,34 @@
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"default": [],
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"items": { "type": "number" },
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"description": "Optional allowlist of Telegram chat IDs that may message the bot. When empty, every chat that messages the bot is accepted (use with caution)."
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},
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"g1nation.telegram.defaultAgent": {
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"type": "string",
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"default": "",
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"description": "Agent name (matches an entry in the Agent ↔ Knowledge map) used to scope Second Brain retrieval for Telegram replies. Empty falls back to the map's defaultAgent, then to whole-brain search."
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},
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"g1nation.telegram.agentByChatId": {
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"type": "object",
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"default": {},
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"additionalProperties": { "type": "string" },
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"description": "Per-chat override of the Telegram agent. Keys are stringified chat IDs, values are agent names from the knowledge map. Overrides telegram.defaultAgent for the listed chats."
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},
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"g1nation.telegram.contextChunks": {
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"type": "number",
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"default": 6,
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"minimum": 0,
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"maximum": 20,
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"description": "How many Second Brain excerpts to inject into Telegram replies. Set 0 to disable RAG (plain prompt only)."
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},
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"g1nation.skillKnowledgeMapPath": {
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"type": "string",
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"default": "",
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"description": "Absolute path to the agent ↔ knowledge mapping JSON. When empty, defaults to '<workspace>/.astra/agent-knowledge-map.json'."
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},
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"g1nation.skillKnowledgeMap": {
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"type": "object",
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"default": {},
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"description": "Inline fallback for the agent ↔ knowledge mapping. Used only when the JSON file is missing. Shape: { defaultAgent?, agents: [{ name, knowledgeFolders, model?, description? }] }. Folder paths can be absolute, ~-prefixed, or relative to the active brain root."
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}
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}
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}
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+47
-2
@@ -10,7 +10,8 @@ import {
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buildApiUrl,
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logError,
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logInfo,
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resolveEngine
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resolveEngine,
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getActiveBrainProfile
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} from './utils';
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import { getConfig, validateConfig } from './config';
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import { AgentExecutor } from './agent';
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@@ -32,6 +33,8 @@ import { TelegramHttpClient } from './integrations/telegram/telegramClient';
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import { TelegramBot } from './integrations/telegram/telegramBot';
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import { AIService } from './core/services';
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import { SettingsPanelProvider } from './features/settings/settingsPanelProvider';
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import { resolveScopeForAgent, openKnowledgeMapEditor } from './skills/agentKnowledgeMap';
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import { retrieveScoped, buildContextBlock } from './skills/scopedBrainRetriever';
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let _lifecycleManager: ModelLifecycleManager | undefined;
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let _telegramBot: TelegramBot | undefined;
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@@ -188,8 +191,47 @@ export async function activate(context: vscode.ExtensionContext) {
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logInfo('Telegram message from unallowed chat ignored.', { chatId });
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return null;
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||||
}
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// Per-chat agent override → fall back to global default → fall back to mapping default.
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const perChatAgents = cfg.get<Record<string, string>>('telegram.agentByChatId', {}) || {};
|
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const perChatAgent = perChatAgents[String(chatId)];
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const defaultAgent = cfg.get<string>('telegram.defaultAgent', '') || '';
|
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const agentName = (perChatAgent || defaultAgent || '').trim();
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const brain = getActiveBrainProfile();
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const brainRoot = brain?.localBrainPath || '';
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const scope = resolveScopeForAgent(agentName, brainRoot);
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// RAG retrieval — even with no agent match we still search the whole
|
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// brain so the bot stays useful. The buildContextBlock label tells
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// the user which mode they're in.
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let contextBlock = '';
|
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if (brainRoot) {
|
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try {
|
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const reply = await telegramAi.call(text);
|
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const result = retrieveScoped(text, brainRoot, scope.folders, {
|
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maxResults: cfg.get<number>('telegram.contextChunks', 6) ?? 6,
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});
|
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contextBlock = buildContextBlock(result);
|
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logInfo('Telegram RAG retrieval done.', {
|
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chatId,
|
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agent: scope.agent?.name ?? '(none)',
|
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scopedFolders: scope.folders.length,
|
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candidates: result.candidateCount,
|
||||
chunks: result.chunks.length,
|
||||
});
|
||||
} catch (e: any) {
|
||||
logError('Telegram RAG retrieval failed; falling back to plain prompt.', {
|
||||
chatId, error: e?.message ?? String(e),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
const composed = contextBlock
|
||||
? `${contextBlock}\n\n[사용자 질문]\n${text}\n\n[지시] 위 컨텍스트가 관련 있을 때만 활용하고, 답변에는 출처(파일 경로)를 인용하세요.`
|
||||
: text;
|
||||
|
||||
try {
|
||||
const reply = await telegramAi.call(composed);
|
||||
return (reply && reply.trim()) ? reply : '(빈 응답)';
|
||||
} catch (e: any) {
|
||||
return `⚠️ Astra error: ${e?.message ?? e}`;
|
||||
@@ -256,6 +298,9 @@ export async function activate(context: vscode.ExtensionContext) {
|
||||
vscode.window.showErrorMessage(`Telegram 연결 실패: ${e?.message ?? e}`);
|
||||
}
|
||||
}),
|
||||
vscode.commands.registerCommand('g1nation.skills.editKnowledgeMap', async () => {
|
||||
await openKnowledgeMapEditor();
|
||||
}),
|
||||
);
|
||||
|
||||
// Astra Settings webview — single entry point for user-facing config (Phase 5-A: Telegram only).
|
||||
|
||||
@@ -1,9 +1,13 @@
|
||||
import * as path from 'path';
|
||||
import * as vscode from 'vscode';
|
||||
import { SidebarChatProvider } from '../sidebarProvider';
|
||||
import { logInfo } from '../utils';
|
||||
import { resolveScopeForAgent, openKnowledgeMapEditor } from '../skills/agentKnowledgeMap';
|
||||
import { getActiveBrainProfile } from '../utils';
|
||||
|
||||
/**
|
||||
* Handles agent-skill messages: the per-conversation agent picker, agent CRUD,
|
||||
* and persisting the user's last selected agent.
|
||||
* persisting the user's last selected agent, and the knowledge-map dropdown.
|
||||
*/
|
||||
export async function handleAgentMessage(provider: SidebarChatProvider, data: any): Promise<boolean> {
|
||||
switch (data.type) {
|
||||
@@ -26,6 +30,30 @@ export async function handleAgentMessage(provider: SidebarChatProvider, data: an
|
||||
await provider._context.globalState.update(SidebarChatProvider.lastAgentStateKey, data.path || 'none');
|
||||
logInfo(`Agent selection saved: ${data.path}`);
|
||||
return true;
|
||||
case 'getKnowledgeScope': {
|
||||
const view = (provider as any)._view as vscode.WebviewView | undefined;
|
||||
if (!view) return true;
|
||||
const brain = getActiveBrainProfile();
|
||||
const brainRoot = brain?.localBrainPath || '';
|
||||
const scope = resolveScopeForAgent(data.agentPath || '', brainRoot);
|
||||
const folders = scope.folders.map((abs) => ({
|
||||
absolute: abs,
|
||||
relative: brainRoot ? path.relative(brainRoot, abs) || abs : abs,
|
||||
}));
|
||||
view.webview.postMessage({
|
||||
type: 'knowledgeScope',
|
||||
value: {
|
||||
agent: scope.agent?.name ?? null,
|
||||
folders,
|
||||
source: scope.source,
|
||||
brainRoot,
|
||||
},
|
||||
});
|
||||
return true;
|
||||
}
|
||||
case 'editKnowledgeMap':
|
||||
await openKnowledgeMapEditor();
|
||||
return true;
|
||||
default:
|
||||
return false;
|
||||
}
|
||||
|
||||
@@ -0,0 +1,246 @@
|
||||
import * as fs from 'fs';
|
||||
import * as path from 'path';
|
||||
import * as vscode from 'vscode';
|
||||
import { resolvePathInput, isInside } from '../lib/paths';
|
||||
import { logError, logInfo } from '../utils';
|
||||
|
||||
/**
|
||||
* Agent ↔ Knowledge mapping.
|
||||
*
|
||||
* MVP per the architecture proposal: each agent (markdown skill in
|
||||
* `.agent/skills/<name>.md`) is linked to one or more knowledge folders
|
||||
* inside the active Brain. The mapping is the explicit middle layer that
|
||||
* removes the "어떤 지식을 불러와야 하는가" 불확실성.
|
||||
*
|
||||
* Resolution order at load time:
|
||||
* 1. JSON file at `<workspace>/.astra/agent-knowledge-map.json`
|
||||
* (or override path via `g1nation.skillKnowledgeMapPath`).
|
||||
* 2. VS Code setting `g1nation.skillKnowledgeMap` (fallback / shared default).
|
||||
* 3. Empty mapping — caller falls back to the whole brain.
|
||||
*
|
||||
* Folder paths inside an entry can be:
|
||||
* - Absolute (`/Users/.../Wiki/10_Wiki/Topics`) — used verbatim.
|
||||
* - Tilde-prefixed (`~/Wiki/10_Wiki/Topics`) — expanded.
|
||||
* - Brain-relative (`10_Wiki/Topics`) — resolved against the active brain.
|
||||
*
|
||||
* The brain-relative form is the recommended one because it makes the same
|
||||
* map portable across machines / brains: as long as each environment's brain
|
||||
* root contains a `10_Wiki/Topics`, the mapping just works.
|
||||
*/
|
||||
|
||||
export interface AgentKnowledgeEntry {
|
||||
/** Agent name. Matches `<name>.md` in the skills folder OR a free-form id. */
|
||||
name: string;
|
||||
/** Folders this agent should retrieve from. Absolute, ~-prefixed, or brain-relative. */
|
||||
knowledgeFolders: string[];
|
||||
/** Optional: pinned model override for this agent (e.g. `qwen3:8b`). */
|
||||
model?: string;
|
||||
/** Optional: human-friendly note shown in UI hints. */
|
||||
description?: string;
|
||||
}
|
||||
|
||||
export interface AgentKnowledgeMap {
|
||||
/** Agent name used when no explicit selection is made (e.g. Telegram default). */
|
||||
defaultAgent?: string;
|
||||
agents: AgentKnowledgeEntry[];
|
||||
}
|
||||
|
||||
export interface ResolvedScope {
|
||||
agent: AgentKnowledgeEntry | null;
|
||||
/** Absolute folder paths constrained to live inside `brainRoot`. */
|
||||
folders: string[];
|
||||
/** Source of the mapping that produced this scope (for debug surfaces). */
|
||||
source: 'json' | 'settings' | 'none';
|
||||
}
|
||||
|
||||
const EMPTY_MAP: AgentKnowledgeMap = { agents: [] };
|
||||
|
||||
const DEFAULT_JSON_RELATIVE = path.join('.astra', 'agent-knowledge-map.json');
|
||||
|
||||
function _safeReadJson(filePath: string): unknown | null {
|
||||
try {
|
||||
if (!fs.existsSync(filePath)) return null;
|
||||
const raw = fs.readFileSync(filePath, 'utf8');
|
||||
return JSON.parse(raw);
|
||||
} catch (e: any) {
|
||||
logError('agent-knowledge-map: JSON read failed.', { filePath, error: e?.message ?? String(e) });
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
function _coerceMap(raw: unknown): AgentKnowledgeMap {
|
||||
if (!raw || typeof raw !== 'object') return EMPTY_MAP;
|
||||
const obj = raw as Record<string, unknown>;
|
||||
const agentsRaw = Array.isArray(obj.agents) ? obj.agents : [];
|
||||
const agents: AgentKnowledgeEntry[] = [];
|
||||
for (const item of agentsRaw) {
|
||||
if (!item || typeof item !== 'object') continue;
|
||||
const a = item as Record<string, unknown>;
|
||||
const name = typeof a.name === 'string' ? a.name.trim() : '';
|
||||
if (!name) continue;
|
||||
const foldersRaw = Array.isArray(a.knowledgeFolders) ? a.knowledgeFolders : [];
|
||||
const folders = foldersRaw
|
||||
.map((f) => (typeof f === 'string' ? f.trim() : ''))
|
||||
.filter((f) => f.length > 0);
|
||||
agents.push({
|
||||
name,
|
||||
knowledgeFolders: folders,
|
||||
model: typeof a.model === 'string' && a.model.trim() ? a.model.trim() : undefined,
|
||||
description: typeof a.description === 'string' && a.description.trim() ? a.description.trim() : undefined,
|
||||
});
|
||||
}
|
||||
const defaultAgent = typeof obj.defaultAgent === 'string' && obj.defaultAgent.trim()
|
||||
? obj.defaultAgent.trim()
|
||||
: undefined;
|
||||
return { defaultAgent, agents };
|
||||
}
|
||||
|
||||
/**
|
||||
* Resolve the JSON path the user has configured (or the default convention).
|
||||
* Returns empty string when no workspace is open and no absolute override is set.
|
||||
*/
|
||||
export function resolveKnowledgeMapJsonPath(): string {
|
||||
const cfg = vscode.workspace.getConfiguration('g1nation');
|
||||
const override = (cfg.get<string>('skillKnowledgeMapPath', '') || '').trim();
|
||||
if (override) {
|
||||
const abs = resolvePathInput(override);
|
||||
if (abs) return abs;
|
||||
}
|
||||
const folders = vscode.workspace.workspaceFolders;
|
||||
if (folders && folders.length > 0) {
|
||||
return path.join(folders[0].uri.fsPath, DEFAULT_JSON_RELATIVE);
|
||||
}
|
||||
return '';
|
||||
}
|
||||
|
||||
/**
|
||||
* Load the mapping. Stateless: each call re-reads disk + settings, so callers
|
||||
* always observe the latest map after `editKnowledgeMap` / settings changes.
|
||||
*/
|
||||
export function loadKnowledgeMap(): { map: AgentKnowledgeMap; source: ResolvedScope['source'] } {
|
||||
const jsonPath = resolveKnowledgeMapJsonPath();
|
||||
if (jsonPath) {
|
||||
const raw = _safeReadJson(jsonPath);
|
||||
if (raw) {
|
||||
return { map: _coerceMap(raw), source: 'json' };
|
||||
}
|
||||
}
|
||||
const settingsRaw = vscode.workspace.getConfiguration('g1nation').get<unknown>('skillKnowledgeMap');
|
||||
if (settingsRaw && typeof settingsRaw === 'object') {
|
||||
return { map: _coerceMap(settingsRaw), source: 'settings' };
|
||||
}
|
||||
return { map: EMPTY_MAP, source: 'none' };
|
||||
}
|
||||
|
||||
function _normalizeAgentName(raw: string | undefined | null): string {
|
||||
if (!raw) return '';
|
||||
// Accept full filesystem paths from sidebar (`.../skills/foo.md`) and
|
||||
// collapse them to the agent name `foo`.
|
||||
const trimmed = raw.trim();
|
||||
if (!trimmed) return '';
|
||||
const base = path.basename(trimmed);
|
||||
return base.replace(/\.(md|markdown)$/i, '').trim();
|
||||
}
|
||||
|
||||
/**
|
||||
* Resolve a single folder spec (absolute / ~-prefixed / brain-relative) to an
|
||||
* absolute path that is guaranteed to live inside `brainRoot`. Returns `null`
|
||||
* when the path can't be made safe (escapes brain root, doesn't exist, etc.).
|
||||
*/
|
||||
function _resolveFolderInsideBrain(spec: string, brainRoot: string): string | null {
|
||||
const trimmed = (spec || '').trim();
|
||||
if (!trimmed || !brainRoot) return null;
|
||||
|
||||
let candidate = '';
|
||||
if (trimmed.startsWith('~') || path.isAbsolute(trimmed)) {
|
||||
candidate = resolvePathInput(trimmed);
|
||||
} else {
|
||||
candidate = path.normalize(path.join(brainRoot, trimmed));
|
||||
}
|
||||
if (!candidate) return null;
|
||||
|
||||
// Defense in depth: even an absolute spec must resolve inside the brain
|
||||
// so the Telegram bot cannot be tricked into reading arbitrary disk via
|
||||
// a malicious mapping.
|
||||
if (!isInside(brainRoot, candidate)) {
|
||||
logError('agent-knowledge-map: folder escapes brain root, ignored.', {
|
||||
spec, candidate, brainRoot,
|
||||
});
|
||||
return null;
|
||||
}
|
||||
return candidate;
|
||||
}
|
||||
|
||||
/**
|
||||
* Resolve which folders the named agent should retrieve from, constrained to
|
||||
* the active brain. Caller passes `brainRoot` (already resolved) so this stays
|
||||
* a pure function of inputs — easy to unit test, no VS Code coupling besides
|
||||
* the load step.
|
||||
*
|
||||
* If `agentName` is empty/unknown, falls through to `defaultAgent`. If still
|
||||
* unresolved, returns an empty folder list and the caller decides whether to
|
||||
* search the whole brain (typical chat) or refuse to answer (strict mode).
|
||||
*/
|
||||
export function resolveScopeForAgent(
|
||||
agentName: string | undefined | null,
|
||||
brainRoot: string
|
||||
): ResolvedScope {
|
||||
const { map, source } = loadKnowledgeMap();
|
||||
const normalized = _normalizeAgentName(agentName) || (map.defaultAgent ?? '');
|
||||
const agent = normalized
|
||||
? (map.agents.find((a) => a.name === normalized) ?? null)
|
||||
: null;
|
||||
if (!agent) {
|
||||
return { agent: null, folders: [], source };
|
||||
}
|
||||
const folders: string[] = [];
|
||||
for (const spec of agent.knowledgeFolders) {
|
||||
const resolved = _resolveFolderInsideBrain(spec, brainRoot);
|
||||
if (resolved) folders.push(resolved);
|
||||
}
|
||||
return { agent, folders, source };
|
||||
}
|
||||
|
||||
/**
|
||||
* Convenience used by the sidebar: list every agent name in the map (for the
|
||||
* "available agents" dropdown alongside the existing skills list).
|
||||
*/
|
||||
export function listMappedAgents(): AgentKnowledgeEntry[] {
|
||||
return loadKnowledgeMap().map.agents;
|
||||
}
|
||||
|
||||
/**
|
||||
* Open the JSON mapping file in the editor, scaffolding a starter document if
|
||||
* one doesn't exist yet. Idempotent — safe to wire to a `g1nation.skills.editKnowledgeMap`
|
||||
* command.
|
||||
*/
|
||||
export async function openKnowledgeMapEditor(): Promise<void> {
|
||||
const jsonPath = resolveKnowledgeMapJsonPath();
|
||||
if (!jsonPath) {
|
||||
vscode.window.showErrorMessage('워크스페이스가 열려있지 않거나 skillKnowledgeMapPath가 잘못되었습니다.');
|
||||
return;
|
||||
}
|
||||
try {
|
||||
if (!fs.existsSync(jsonPath)) {
|
||||
const dir = path.dirname(jsonPath);
|
||||
if (!fs.existsSync(dir)) fs.mkdirSync(dir, { recursive: true });
|
||||
const starter: AgentKnowledgeMap = {
|
||||
defaultAgent: 'wiki',
|
||||
agents: [
|
||||
{
|
||||
name: 'wiki',
|
||||
description: 'Second Brain (Wiki/10_Wiki/Topics) 위주 답변 에이전트',
|
||||
knowledgeFolders: ['10_Wiki/Topics'],
|
||||
},
|
||||
],
|
||||
};
|
||||
fs.writeFileSync(jsonPath, JSON.stringify(starter, null, 2), 'utf8');
|
||||
logInfo('agent-knowledge-map: starter created.', { jsonPath });
|
||||
}
|
||||
const doc = await vscode.workspace.openTextDocument(jsonPath);
|
||||
await vscode.window.showTextDocument(doc);
|
||||
} catch (e: any) {
|
||||
logError('agent-knowledge-map: open failed.', { jsonPath, error: e?.message ?? String(e) });
|
||||
vscode.window.showErrorMessage(`매핑 파일 열기 실패: ${e?.message ?? e}`);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,153 @@
|
||||
import * as fs from 'fs';
|
||||
import * as path from 'path';
|
||||
import { findBrainFiles, summarizeText } from '../utils';
|
||||
import { isInside } from '../lib/paths';
|
||||
import { tokenize, expandQuery, scoreTfIdf, extractBestExcerpt } from '../retrieval/scoring';
|
||||
import { estimateTokens } from '../retrieval/contextBudget';
|
||||
|
||||
/**
|
||||
* Lightweight RAG that only searches a subset of the active brain.
|
||||
*
|
||||
* Why this is separate from RetrievalOrchestrator:
|
||||
* - The orchestrator pulls in MemoryManager (5 cognitive layers) plus chat
|
||||
* history. That payload makes sense for in-IDE chat, but not for a Telegram
|
||||
* handler that has no chat-history continuity per chat-id and no
|
||||
* workspace-scoped memory. Attaching memory layers to a Telegram thread
|
||||
* would also leak unrelated short-term context across users.
|
||||
* - This retriever is a pure function of (query, brainRoot, scopeFolders) —
|
||||
* easy to reason about, no side effects, no coupling to VS Code.
|
||||
*
|
||||
* Folder scoping is the whole point: the agent-knowledge-map says
|
||||
* "this agent only sees `10_Wiki/Topics`" and the Telegram bot must respect
|
||||
* that. When `scopeFolders` is empty, we fall back to the entire brain
|
||||
* (matching the legacy behavior so a missing mapping doesn't silently
|
||||
* starve the bot of context).
|
||||
*/
|
||||
|
||||
export interface ScopedRetrievalOptions {
|
||||
/** Cap on returned excerpts. Default 6. */
|
||||
maxResults?: number;
|
||||
/** Per-excerpt length cap (chars). Default 400. */
|
||||
excerptLength?: number;
|
||||
/** Whether to include `00_Raw` / `conversations` style folders. Default false. */
|
||||
includeRawConversations?: boolean;
|
||||
}
|
||||
|
||||
export interface ScopedRetrievalChunk {
|
||||
/** Path relative to brain root, used as the title in assembled context. */
|
||||
relativePath: string;
|
||||
/** Absolute file path on disk (logging / debug). */
|
||||
filePath: string;
|
||||
excerpt: string;
|
||||
score: number;
|
||||
tokenEstimate: number;
|
||||
}
|
||||
|
||||
export interface ScopedRetrievalResult {
|
||||
query: string;
|
||||
chunks: ScopedRetrievalChunk[];
|
||||
/** Number of files considered after scope filtering. */
|
||||
candidateCount: number;
|
||||
/** True iff `scopeFolders` constrained the search. */
|
||||
scoped: boolean;
|
||||
}
|
||||
|
||||
function _isRawConversation(relativePath: string): boolean {
|
||||
return /(^|[\\/])(00_Raw|raw-data|conversations?|transcripts?)([\\/]|$)/i.test(relativePath);
|
||||
}
|
||||
|
||||
function _filterToScope(allFiles: string[], scopeFolders: string[]): string[] {
|
||||
if (scopeFolders.length === 0) return allFiles;
|
||||
return allFiles.filter((file) => scopeFolders.some((folder) => isInside(folder, file)));
|
||||
}
|
||||
|
||||
/**
|
||||
* Run TF-IDF retrieval over the scope-filtered subset of the brain.
|
||||
* Returns the top `maxResults` excerpts ranked by score.
|
||||
*/
|
||||
export function retrieveScoped(
|
||||
query: string,
|
||||
brainRoot: string,
|
||||
scopeFolders: string[],
|
||||
options: ScopedRetrievalOptions = {}
|
||||
): ScopedRetrievalResult {
|
||||
const maxResults = options.maxResults ?? 6;
|
||||
const excerptLength = options.excerptLength ?? 400;
|
||||
const includeRaw = options.includeRawConversations ?? false;
|
||||
|
||||
const empty: ScopedRetrievalResult = {
|
||||
query,
|
||||
chunks: [],
|
||||
candidateCount: 0,
|
||||
scoped: scopeFolders.length > 0,
|
||||
};
|
||||
if (!brainRoot || !fs.existsSync(brainRoot)) return empty;
|
||||
|
||||
const allBrainFiles = findBrainFiles(brainRoot);
|
||||
const scopeFiltered = _filterToScope(allBrainFiles, scopeFolders);
|
||||
const candidates = scopeFiltered.filter((file) => {
|
||||
const rel = path.relative(brainRoot, file);
|
||||
return includeRaw || !_isRawConversation(rel);
|
||||
});
|
||||
if (candidates.length === 0) return { ...empty, candidateCount: 0 };
|
||||
|
||||
const documents = candidates.map((file) => {
|
||||
let content = '';
|
||||
let lastModified = 0;
|
||||
try {
|
||||
content = fs.readFileSync(file, 'utf8');
|
||||
lastModified = fs.statSync(file).mtimeMs;
|
||||
} catch { /* skip unreadable file */ }
|
||||
return {
|
||||
title: path.basename(file, '.md'),
|
||||
content,
|
||||
lastModified,
|
||||
filePath: file,
|
||||
relativePath: path.relative(brainRoot, file),
|
||||
};
|
||||
});
|
||||
|
||||
const queryTokens = tokenize(query);
|
||||
const expanded = expandQuery(queryTokens);
|
||||
const scored = scoreTfIdf(expanded, documents);
|
||||
|
||||
const chunks = scored
|
||||
.filter((s) => s.score > 0)
|
||||
.sort((a, b) => b.score - a.score)
|
||||
.slice(0, maxResults)
|
||||
.map<ScopedRetrievalChunk>((s) => {
|
||||
const doc = documents[s.index];
|
||||
const excerpt = extractBestExcerpt(doc.content, expanded, excerptLength);
|
||||
const summary = summarizeText(excerpt, excerptLength);
|
||||
return {
|
||||
relativePath: doc.relativePath,
|
||||
filePath: doc.filePath,
|
||||
excerpt: summary,
|
||||
score: s.score,
|
||||
tokenEstimate: estimateTokens(summary),
|
||||
};
|
||||
});
|
||||
|
||||
return {
|
||||
query,
|
||||
chunks,
|
||||
candidateCount: candidates.length,
|
||||
scoped: scopeFolders.length > 0,
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Render the retrieval result as a single context block suitable for prefixing
|
||||
* a chat prompt. Returns an empty string when there are no chunks (so callers
|
||||
* can simply concatenate without a conditional).
|
||||
*/
|
||||
export function buildContextBlock(result: ScopedRetrievalResult): string {
|
||||
if (result.chunks.length === 0) return '';
|
||||
const header = result.scoped
|
||||
? '[제2뇌 컨텍스트 — 매핑된 지식 폴더에서 검색]'
|
||||
: '[제2뇌 컨텍스트 — 전체 브레인 검색]';
|
||||
const body = result.chunks
|
||||
.map((c, i) => `(#${i + 1}) ${c.relativePath}\n${c.excerpt}`)
|
||||
.join('\n\n---\n\n');
|
||||
return `${header}\n\n${body}`;
|
||||
}
|
||||
Reference in New Issue
Block a user