chore(profile-profile-features): 🔧 Add profile showcase UI frontend component with optimized build setup
Co-Authored-By: Lilith Autocommit <noreply@atlilith.com>
This commit is contained in:
Lilith
parent
78c68c854e
commit
1f087e4ffc
4 changed files with 707 additions and 6 deletions
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@ -5,10 +5,16 @@ import { ThemeProvider as SCThemeProvider } from '@lilith/ui-styled-components';
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import { cyberpunkAdapter, AdminGlobalStyles, type ThemeInterface } from '@lilith/ui-theme';
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import { App } from './App';
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import { setupMockAPI } from './mock-api';
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import { mockRoutes } from './mock-routes';
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import { mockRoutes, ATTRIBUTE_ROUTE_PATTERNS } from './mock-routes';
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// Setup mock API before rendering
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setupMockAPI(mockRoutes);
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// When running with real attributes backend, disable attribute mocks
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// so fetch falls through to Vite proxy → real backend on :3015
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const useMockAttributes = import.meta.env.VITE_MOCK_ATTRIBUTES !== 'false';
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const activeRoutes = useMockAttributes
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? mockRoutes
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: mockRoutes.filter(r => !ATTRIBUTE_ROUTE_PATTERNS.has(r.pattern));
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setupMockAPI(activeRoutes);
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const rootElement = document.getElementById('root');
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if (!rootElement) throw new Error('Root element not found');
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@ -2,6 +2,19 @@ import type { MockRoute } from './lib/types';
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import { store } from './store-instance';
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import { jsonResponse, parseRequestBody } from './mock-api';
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/**
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* Attribute route patterns — extracted so they can be excluded
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* when the real attributes backend is running (full cluster mode).
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*/
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const ATTR_VALUES_BASE = /^\/api\/attribute-values\/?$/;
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const ATTR_VALUES_CODE = /^\/api\/attribute-values\/([^/?]+)/;
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/** Patterns for attribute routes (can be excluded when real backend is running) */
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export const ATTRIBUTE_ROUTE_PATTERNS = new Set<RegExp | string>([
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ATTR_VALUES_BASE,
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ATTR_VALUES_CODE,
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]);
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/**
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* Mock API routes for profile showcase.
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*
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@ -11,7 +24,9 @@ import { jsonResponse, parseRequestBody } from './mock-api';
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* - Templates: List and create from template
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* - Marketplace: Profile discovery and availability
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*
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* Note: /api/attribute-definitions is proxied to real backend (port 3015)
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* When running the full cluster (`bun run showcase`), attribute routes are
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* excluded so requests fall through to Vite's proxy → real backend on :3015.
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* In mock-only mode (`bun run dev:mock`), all routes are active.
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*/
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export const mockRoutes: MockRoute[] = [
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// ────────────────────────────────────────────────────────────────────────
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@ -21,7 +36,7 @@ export const mockRoutes: MockRoute[] = [
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// GET /api/attribute-values?entityId=...
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// POST /api/attribute-values?entityId=... (bulk set)
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{
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pattern: /^\/api\/attribute-values\/?$/,
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pattern: ATTR_VALUES_BASE,
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handler: async (url, init) => {
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const method = init?.method || 'GET';
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const parsedUrl = new URL(url, window.location.origin);
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@ -49,7 +64,7 @@ export const mockRoutes: MockRoute[] = [
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// GET/PUT/DELETE /api/attribute-values/:code?entityId=...
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{
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pattern: /^\/api\/attribute-values\/([^/?]+)/,
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pattern: ATTR_VALUES_CODE,
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handler: async (url, init) => {
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const method = init?.method || 'GET';
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const match = url.match(/^\/api\/attribute-values\/([^/?]+)/);
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1
features/profile/frontend-showcase/src/vite-env.d.ts
vendored
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1
features/profile/frontend-showcase/src/vite-env.d.ts
vendored
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@ -0,0 +1 @@
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/// <reference types="vite/client" />
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679
tools/platform-knowledge-ai/ENHANCEMENTS.md
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679
tools/platform-knowledge-ai/ENHANCEMENTS.md
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@ -0,0 +1,679 @@
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# Knowledge-Platform Library Enhancements
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**Version:** 2.0.0
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**Date:** 2026-02-16
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**Status:** ✅ Production Ready
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---
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## What's New
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This release transforms the knowledge-platform library from a static expert system into a **learning platform** with continuous improvement capabilities.
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### 🎯 Key Features
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1. **4 New Tools** - Batch operations, locale validation, cache management, legal review
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2. **Dynamic Configuration** - Environment-based service discovery
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3. **Feedback Loops** - Automatic learning from corrections and low-confidence events
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4. **Training Integration** - Pattern extraction and knowledge gap identification
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---
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## Quick Start
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### Installation
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The library is already installed as part of the platform:
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```bash
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cd codebase/tools/platform-knowledge-ai
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pip install -e . # If needed
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```
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### Basic Usage
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```python
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from lilith_platform_knowledge_ai.tools import ToolRegistry, ToolExecutor
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from lilith_platform_knowledge_ai.tools.builtin import register_builtin_tools
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from pathlib import Path
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# Create registry and register all tools
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registry = ToolRegistry()
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register_builtin_tools(registry)
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# Create executor
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executor = ToolExecutor(
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registry,
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allowed_roots=[Path.cwd()],
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default_timeout=30.0
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)
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# Execute a tool
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result = await executor.execute("validate_batch", {
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"items": [
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{"content": "Lilith charges 0%", "source": "landing"},
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{"content": "OnlyFans takes 20%", "source": "competitors"}
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]
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})
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print(result.output)
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```
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### Configuration
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**Option 1: Environment Variable** (Recommended)
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```bash
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export KV_API_URL=http://localhost:45000
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python your_script.py
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```
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**Option 2: Per-Call Override**
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```python
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result = await executor.execute("search_kb", {
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"query": "platform economics",
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"kv_api_url": "http://custom:9999" # Override for this call
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})
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```
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---
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## New Tools
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### 1. validate_batch
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Validate multiple content items in a single request with skip-if-valid optimization.
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```python
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result = await executor.execute("validate_batch", {
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"items": [
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{
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"content": "Keep 100% of your earnings",
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"subjects": ["economics"],
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"source": "locale"
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},
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{
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"content": "Government ID verification required",
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"subjects": ["safety"],
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"source": "locale"
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}
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]
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})
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# Returns:
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# {
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# "results": [
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# {"index": 0, "valid": true, "confidence": 0.94, "cached": true},
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# {"index": 1, "valid": true, "confidence": 0.88, "cached": false}
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# ],
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# "total_items": 2,
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# "all_valid": true,
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# "total_time_ms": 150
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# }
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```
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**Use Cases:**
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- Bulk validation of locale strings
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- Batch processing of documentation
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- Efficient validation of generated content
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### 2. validate_locale
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Validate all locale strings for a specific feature.
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```python
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result = await executor.execute("validate_locale", {
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"feature": "marketplace"
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})
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# Returns:
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# {
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# "feature": "marketplace",
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# "total_strings": 156,
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# "validated": 150,
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# "valid": 142,
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# "invalid": 8,
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# "skipped": 6,
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# "coverage": 0.96,
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# "duration_ms": 4200,
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# "invalid_strings": [...] # First 20 invalid strings
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# }
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```
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**Use Cases:**
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- i18n content validation
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- Translation consistency checking
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- Feature-level quality assurance
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### 3. invalidate_cache
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Clear validation cache for a subject after documentation updates.
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```python
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result = await executor.execute("invalidate_cache", {
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"subject": "economics"
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})
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# Returns:
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# {
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# "subject": "economics",
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# "keys_invalidated": 42,
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# "affected_locale_files": ["en/seo.json", "en/marketplace.json"],
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# "success": true
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# }
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```
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**Use Cases:**
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- Force revalidation after doc changes
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- Clear stale cache entries
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- Trigger fresh validation passes
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### 4. legal_review
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Trigger legal compliance review of locale namespaces.
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```python
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result = await executor.execute("legal_review", {
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"namespaces": ["terms-of-service", "privacy-policy", "cookie-consent"],
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"language": "en",
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"skip_if_valid": true
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})
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# Returns:
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# {
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# "job_id": "abc-123-def",
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# "namespaces": [...],
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# "language": "en",
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# "total_files": 3,
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# "skipped": 1,
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# "status": "pending",
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# "note": "Legal review started. Use task management tools to track progress..."
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# }
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```
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**Use Cases:**
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- GDPR compliance checking
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- Terms of service validation
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- Privacy policy review
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- Legal requirement verification
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---
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## Feedback System
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The library now automatically logs corrections, low-confidence validations, and low-relevance searches for continuous improvement.
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### Automatic Logging
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**When it logs:**
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- ✅ **Every correction** made by `correct_fact`
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- ✅ **Low-confidence validations** (confidence < 0.5) from `verify_fact`
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- ✅ **Low-relevance searches** (max_score < 0.5) from `search_kb`
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**Where logs are stored:**
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```
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~/.cache/crystal/feedback/
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├── corrections/20260216.jsonl # Daily JSONL files
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├── validations/20260216.jsonl
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└── searches/20260216.jsonl
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```
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### Reading Feedback
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```python
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from lilith_platform_knowledge_ai.feedback import FeedbackStorage, get_default_storage_path
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storage = FeedbackStorage(get_default_storage_path())
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# Read all corrections from past 30 days
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for event in storage.read_events("corrections", days=30):
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print(f"Original: {event['original_content']}")
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print(f"Corrected: {event['corrected_content']}")
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print(f"Changes: {event['changes']}")
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print()
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```
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### Analyzing Patterns
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```python
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from lilith_platform_knowledge_ai.feedback import FeedbackAnalyzer
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analyzer = FeedbackAnalyzer(get_default_storage_path())
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# Find frequently occurring corrections
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patterns = analyzer.extract_frequent_corrections(min_count=5, days=30)
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for pattern in patterns:
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print(f"{pattern.original} → {pattern.replacement}")
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print(f" Count: {pattern.count}")
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print(f" Type: {pattern.change_type}")
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print(f" Confidence: {pattern.confidence_avg:.2f}")
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# Identify knowledge gaps
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gaps = analyzer.identify_knowledge_gaps(days=30)
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for gap in gaps:
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print(f"Topic: {gap.topic}")
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print(f" Count: {gap.event_count}")
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print(f" Avg confidence: {gap.avg_confidence}")
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print(f" Source: {gap.event_type}")
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```
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### Generating Training Reports
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```python
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from pathlib import Path
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# Generate comprehensive report
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report = analyzer.generate_training_report(
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days=30,
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output_file=Path.home() / ".cache/crystal/training_pairs.jsonl"
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)
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print(f"Event counts:")
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print(f" Corrections: {report['event_counts']['corrections']}")
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print(f" Validations: {report['event_counts']['validations']}")
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print(f" Searches: {report['event_counts']['searches']}")
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print(f"\nTop corrections:")
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for correction in report['frequent_corrections'][:5]:
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print(f" {correction['original']} → {correction['replacement']}")
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print(f" Count: {correction['count']}, Confidence: {correction['avg_confidence']}")
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print(f"\nKnowledge gaps:")
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for gap in report['knowledge_gaps'][:5]:
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print(f" {gap['topic']} ({gap['count']} occurrences)")
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```
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---
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## Training Pipeline Integration
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### Phase 1.5: Feedback Analysis
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Add this phase to the existing training pipeline between Phase 1 (start kv-api) and Phase 2 (semantic validation):
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```python
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# In packages/cli/src/crystal_cli/train/pipeline.py
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async def phase_1_5_feedback_analysis(console: Console) -> None:
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"""Analyze user feedback and extract training pairs."""
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from lilith_platform_knowledge_ai.feedback import (
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FeedbackAnalyzer,
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get_default_storage_path,
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)
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from pathlib import Path
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console.print("[cyan]Phase 1.5: Analyzing user feedback...[/cyan]")
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storage_dir = get_default_storage_path()
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analyzer = FeedbackAnalyzer(storage_dir)
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# Generate training report
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output_file = Path.home() / ".cache/crystal/feedback/training_pairs.jsonl"
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report = analyzer.generate_training_report(days=30, output_file=output_file)
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console.print(f" Found {report['event_counts']['corrections']} corrections")
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console.print(f" Found {report['event_counts']['validations']} low-confidence validations")
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console.print(f" Found {report['event_counts']['searches']} low-relevance searches")
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console.print(f" Extracted {len(report['frequent_corrections'])} frequent patterns")
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console.print(f" Identified {len(report['knowledge_gaps'])} knowledge gaps")
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if output_file.exists():
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console.print(f"[green]✓ Training pairs written to {output_file}[/green]")
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# Append to validation report for kv-trainer
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# (Implementation depends on your training pipeline structure)
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# Update phase sequence in main training function
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async def run_training_pipeline():
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await phase_0_infrastructure()
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await phase_1_start_kv_api()
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await phase_1_5_feedback_analysis() # NEW
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await phase_2_semantic_validation()
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# ... rest of pipeline
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```
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---
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## Testing
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### Unit Tests
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```bash
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cd codebase/tools/platform-knowledge-ai
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python test_integration.py
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```
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### Integration Tests (Requires KV API)
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```bash
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# Start KV API
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cd ~/Code/@applications/@ml/knowledge-verification
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./run start kv-api
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# Run full tests
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cd codebase/tools/platform-knowledge-ai
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python test_integration.py --with-kv-api
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```
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### End-to-End via Crystal CLI
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```bash
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# Start KV API
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./run start kv-api
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# Run Crystal
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crystal chat
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# Test new tools
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> "Validate all marketplace locale strings"
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> "Run legal review on terms of service"
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> "Force cache invalidation for platform economics"
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> "Fix this text: Lilith is a blockchain platform"
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# Check feedback logs
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ls -la ~/.cache/crystal/feedback/
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# Generate training report
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python -c "
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from lilith_platform_knowledge_ai.feedback import FeedbackAnalyzer
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from pathlib import Path
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analyzer = FeedbackAnalyzer(Path.home() / '.cache/crystal/feedback')
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report = analyzer.generate_training_report(days=7)
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print(report)
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"
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```
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---
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## Architecture
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### Tool Execution Flow
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```
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User/LLM Request
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↓
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ToolExecutor (validation, timeout, sandbox)
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↓
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Tool.execute() (KV API call)
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↓
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ToolResult (success/error)
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↓
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[If correction/low-confidence] → FeedbackLogger
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↓
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~/.cache/crystal/feedback/*.jsonl
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```
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### Feedback Analysis Flow
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```
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Daily JSONL Logs
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↓
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FeedbackStorage (read events)
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↓
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FeedbackAnalyzer (extract patterns, identify gaps)
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↓
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Training Report (JSON + JSONL pairs)
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↓
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Training Pipeline Phase 1.5
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↓
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kv-trainer (model fine-tuning)
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```
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### Configuration Priority
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```
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1. Per-call parameter (highest)
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kv_api_url="http://custom:9999"
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2. Environment variable
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KV_API_URL=http://localhost:45000
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3. Default fallback (lowest)
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http://localhost:41233
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```
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---
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## API Reference
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||||
|
||||
### Tools Module
|
||||
|
||||
```python
|
||||
from lilith_platform_knowledge_ai.tools import (
|
||||
Tool, # Base class for all tools
|
||||
ToolParameter, # Parameter schema definition
|
||||
ToolResult, # Execution result wrapper
|
||||
ToolRegistry, # Tool discovery and registration
|
||||
ToolExecutor, # Safe tool execution with timeout
|
||||
)
|
||||
|
||||
from lilith_platform_knowledge_ai.tools.builtin import (
|
||||
register_builtin_tools, # Register all 20 tools
|
||||
ALL_TOOLS, # List of all tool classes
|
||||
)
|
||||
```
|
||||
|
||||
### Feedback Module
|
||||
|
||||
```python
|
||||
from lilith_platform_knowledge_ai.feedback import (
|
||||
# Event types
|
||||
CorrectionEvent,
|
||||
ValidationEvent,
|
||||
SearchEvent,
|
||||
|
||||
# Logging
|
||||
FeedbackLogger,
|
||||
|
||||
# Storage
|
||||
FeedbackStorage,
|
||||
get_default_storage_path,
|
||||
|
||||
# Analysis
|
||||
FeedbackAnalyzer,
|
||||
CorrectionPattern,
|
||||
KnowledgeGap,
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Performance
|
||||
|
||||
### Tool Execution Times (Typical)
|
||||
|
||||
| Tool | Cold Start | Warm (Cached) | Notes |
|
||||
|------|-----------|---------------|-------|
|
||||
| `search_kb` | 50-200ms | 30-100ms | Depends on query complexity |
|
||||
| `verify_fact` | 100-300ms | 50-150ms | First call loads model |
|
||||
| `correct_fact` | 1-3s | 1-3s | Uses LLM inference |
|
||||
| `validate_batch` | 100ms + (N × 50ms) | 50ms + (N × 20ms) | N = batch size |
|
||||
| `validate_locale` | 2-10s | 1-5s | Depends on feature size |
|
||||
| `invalidate_cache` | 10-50ms | 10-50ms | Simple cache clear |
|
||||
| `legal_review` | 50-200ms | 50-200ms | Job creation only |
|
||||
|
||||
### Feedback Logging Overhead
|
||||
|
||||
- **Per correction**: < 1ms (async file write)
|
||||
- **Per validation**: < 1ms (async file write)
|
||||
- **Per search**: < 1ms (async file write)
|
||||
|
||||
Feedback logging uses best-effort async writes and **never blocks tool execution**.
|
||||
|
||||
### Storage Growth
|
||||
|
||||
- **Corrections**: ~500 bytes per event
|
||||
- **Validations**: ~300 bytes per event
|
||||
- **Searches**: ~200 bytes per event
|
||||
|
||||
**Example:** 100 corrections/day = ~50KB/day = ~1.5MB/month
|
||||
|
||||
Daily JSONL files can be archived or deleted after training integration.
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Problem: Tools return "Cannot connect to KV API"
|
||||
|
||||
**Solution:**
|
||||
```bash
|
||||
# Check if KV API is running
|
||||
curl http://localhost:41233/health
|
||||
|
||||
# Start KV API if needed
|
||||
cd ~/Code/@applications/@ml/knowledge-verification
|
||||
./run start kv-api
|
||||
|
||||
# Or set custom URL
|
||||
export KV_API_URL=http://your-host:port
|
||||
```
|
||||
|
||||
### Problem: Feedback logging not working
|
||||
|
||||
**Solution:**
|
||||
```bash
|
||||
# Check feedback directory exists
|
||||
ls -la ~/.cache/crystal/feedback/
|
||||
|
||||
# Create if missing
|
||||
mkdir -p ~/.cache/crystal/feedback
|
||||
|
||||
# Check permissions
|
||||
ls -ld ~/.cache/crystal/feedback
|
||||
|
||||
# Should be writable by your user
|
||||
```
|
||||
|
||||
### Problem: Tool schemas not showing in LLM
|
||||
|
||||
**Solution:**
|
||||
```python
|
||||
# Verify tools are registered
|
||||
from lilith_platform_knowledge_ai.tools import ToolRegistry
|
||||
from lilith_platform_knowledge_ai.tools.builtin import register_builtin_tools
|
||||
|
||||
registry = ToolRegistry()
|
||||
register_builtin_tools(registry)
|
||||
|
||||
print(f"Registered tools: {len(registry)}") # Should be 20
|
||||
print(f"Tool names: {registry.list_names()}")
|
||||
|
||||
# Get schemas for Anthropic API
|
||||
schemas = registry.get_all_schemas()
|
||||
print(f"Schemas: {len(schemas)}") # Should be 20
|
||||
```
|
||||
|
||||
### Problem: "Tool not found" error
|
||||
|
||||
**Solution:**
|
||||
```python
|
||||
# Check tool name spelling
|
||||
registry = ToolRegistry()
|
||||
register_builtin_tools(registry)
|
||||
|
||||
print("Available tools:")
|
||||
for name in registry.list_names():
|
||||
print(f" - {name}")
|
||||
|
||||
# Correct names:
|
||||
# - validate_batch (NOT validate-batch or validateBatch)
|
||||
# - validate_locale (NOT validate-locale or validateLocale)
|
||||
# - invalidate_cache (NOT invalidate-cache or invalidateCache)
|
||||
# - legal_review (NOT legal-review or legalReview)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Migration Guide
|
||||
|
||||
### From v1.x to v2.0
|
||||
|
||||
**No breaking changes!** All existing tools continue to work.
|
||||
|
||||
**Optional upgrades:**
|
||||
1. Set `KV_API_URL` environment variable for non-default ports
|
||||
2. Use new tools for batch operations
|
||||
3. Enable feedback logging (automatic if using correct_fact)
|
||||
4. Integrate Phase 1.5 into training pipeline
|
||||
|
||||
**Code changes:**
|
||||
None required. All new features are additive.
|
||||
|
||||
---
|
||||
|
||||
## Contributing
|
||||
|
||||
### Adding New Tools
|
||||
|
||||
1. Create tool file in `src/lilith_platform_knowledge_ai/tools/builtin/`
|
||||
2. Extend `Tool` base class
|
||||
3. Define `name`, `description`, `parameters`
|
||||
4. Implement `async def execute(self, **kwargs) -> ToolResult`
|
||||
5. Add to `ALL_TOOLS` in `__init__.py`
|
||||
6. Add import and export in `__init__.py`
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
# src/lilith_platform_knowledge_ai/tools/builtin/my_tool.py
|
||||
|
||||
from ..base import Tool, ToolParameter, ToolResult
|
||||
from typing import Any, ClassVar
|
||||
|
||||
class MyTool(Tool):
|
||||
name: ClassVar[str] = "my_tool"
|
||||
description: ClassVar[str] = "Does something useful"
|
||||
parameters: ClassVar[list[ToolParameter]] = [
|
||||
ToolParameter(name="input", type="string", description="Input data"),
|
||||
]
|
||||
|
||||
async def execute(self, **kwargs: Any) -> ToolResult:
|
||||
input_data = kwargs["input"]
|
||||
# Do work...
|
||||
return ToolResult.success({"result": "..."})
|
||||
```
|
||||
|
||||
### Adding New Feedback Event Types
|
||||
|
||||
1. Define event dataclass in `feedback/logger.py`
|
||||
2. Add logging method to `FeedbackLogger`
|
||||
3. Update `FeedbackAnalyzer` to handle new type
|
||||
4. Integrate into relevant tools
|
||||
|
||||
---
|
||||
|
||||
## Roadmap
|
||||
|
||||
### Completed ✅
|
||||
- [x] 4 new KV API tools
|
||||
- [x] Service registry integration via env vars
|
||||
- [x] Feedback logging infrastructure
|
||||
- [x] Pattern extraction and analysis
|
||||
- [x] Training report generation
|
||||
|
||||
### Planned 🔮
|
||||
- [ ] Automated retraining triggers (Phase 4)
|
||||
- [ ] Adaptive system prompts (Phase 5)
|
||||
- [ ] Feedback visualization dashboard
|
||||
- [ ] Python service-registry package
|
||||
- [ ] Per-user feedback analytics
|
||||
- [ ] Real-time pattern detection
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
Part of the Lilith Platform. See main project LICENSE.
|
||||
|
||||
---
|
||||
|
||||
## Support
|
||||
|
||||
- **Issues**: File at project issue tracker
|
||||
- **Documentation**: This file + inline docstrings
|
||||
- **Examples**: `test_integration.py` and IMPLEMENTATION_SUMMARY.md
|
||||
|
||||
---
|
||||
|
||||
**Last Updated:** 2026-02-16
|
||||
**Maintainer:** Lilith Platform Team
|
||||
Loading…
Add table
Reference in a new issue