Memory and Onboarding Updates (#76)

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **New Features**
* Memory subsystem: automatic extraction, searchable/storable memory
blocks, memory settings UI, and memory tools (search/update/list).
* Multi-step onboarding dialog for initial setup and provider/API-key
configuration.
  * Editable file viewer with client-side auto-save.
  * Conversation history grouped by date.

* **Improvements**
* Dynamic model fetching and centralized model configuration with fuzzy
model-name matching.
* Expanded custom LLM provider support and background-mode execution to
suppress UI progress.

<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

Author: tysonthomas9

config/gni/devtools_grd_files.gni

@@ -654,6 +654,8 @@ grd_files_bundled_sources = [
   "front_end/panels/ai_chat/ui/HelpDialog.js",
   "front_end/panels/ai_chat/ui/PromptEditDialog.js",
   "front_end/panels/ai_chat/ui/SettingsDialog.js",
+  "front_end/panels/ai_chat/ui/OnboardingDialog.js",
+  "front_end/panels/ai_chat/ui/onboardingStyles.js",
   "front_end/panels/ai_chat/ui/mcp/MCPConnectionsDialog.js",
   "front_end/panels/ai_chat/ui/mcp/MCPConnectorsCatalogDialog.js",
   "front_end/panels/ai_chat/ui/EvaluationDialog.js",
@@ -687,6 +689,7 @@ grd_files_bundled_sources = [
   "front_end/panels/ai_chat/LLM/LLMProviderRegistry.js",
   "front_end/panels/ai_chat/LLM/LLMErrorHandler.js",
   "front_end/panels/ai_chat/LLM/LLMResponseParser.js",
+  "front_end/panels/ai_chat/LLM/FuzzyModelMatcher.js",
   "front_end/panels/ai_chat/LLM/OpenAIProvider.js",
   "front_end/panels/ai_chat/LLM/LiteLLMProvider.js",
   "front_end/panels/ai_chat/LLM/GroqProvider.js",

config/gni/devtools_image_files.gni

@@ -20,6 +20,8 @@ devtools_image_files = [
   "touchCursor.png",
   "gdp-logo-light.png",
   "gdp-logo-dark.png",
+  "browser-operator-logo.png",
+  "demo.gif",
 ]
 
 devtools_svg_sources = [

front_end/Images/browser-operator-logo.png

@@ -40,6 +40,8 @@ devtools_module("ai_chat") {
     "ui/ToolDescriptionFormatter.ts",
     "ui/HelpDialog.ts",
     "ui/SettingsDialog.ts",
+    "ui/OnboardingDialog.ts",
+    "ui/onboardingStyles.ts",
     "ui/settings/types.ts",
     "ui/settings/constants.ts",
     "ui/settings/i18n-strings.ts",
@@ -60,6 +62,7 @@ devtools_module("ai_chat") {
     "ui/settings/advanced/VectorDBSettings.ts",
     "ui/settings/advanced/TracingSettings.ts",
     "ui/settings/advanced/EvaluationSettings.ts",
+    "ui/settings/advanced/MemorySettings.ts",
     "ui/PromptEditDialog.ts",
     "ui/EvaluationDialog.ts",
     "ui/WebAppCodeViewer.ts",
@@ -75,6 +78,11 @@ devtools_module("ai_chat") {
     "persistence/ConversationTypes.ts",
     "persistence/ConversationStorageManager.ts",
     "persistence/ConversationManager.ts",
+    "memory/types.ts",
+    "memory/MemoryModule.ts",
+    "memory/MemoryBlockManager.ts",
+    "memory/MemoryAgentConfig.ts",
+    "memory/index.ts",
     "core/Graph.ts",
     "core/State.ts",
     "core/Types.ts",
@@ -103,6 +111,7 @@ devtools_module("ai_chat") {
     "LLM/LLMProviderRegistry.ts",
     "LLM/LLMErrorHandler.ts",
     "LLM/LLMResponseParser.ts",
+    "LLM/FuzzyModelMatcher.ts",
     "LLM/OpenAIProvider.ts",
     "LLM/LiteLLMProvider.ts",
     "LLM/GroqProvider.ts",
@@ -135,6 +144,9 @@ devtools_module("ai_chat") {
     "tools/DeleteFileTool.ts",
     "tools/ReadFileTool.ts",
     "tools/ListFilesTool.ts",
+    "memory/SearchMemoryTool.ts",
+    "memory/UpdateMemoryTool.ts",
+    "memory/ListMemoryBlocksTool.ts",
     "tools/UpdateTodoTool.ts",
     "tools/ExecuteCodeTool.ts",
     "tools/SequentialThinkingTool.ts",
@@ -278,6 +290,11 @@ _ai_chat_sources = [
     "ui/mcp/MCPConnectorsCatalogDialog.ts",
     "ai_chat_impl.ts",
     "models/ChatTypes.ts",
+    "memory/types.ts",
+    "memory/MemoryModule.ts",
+    "memory/MemoryBlockManager.ts",
+    "memory/MemoryAgentConfig.ts",
+    "memory/index.ts",
     "core/Graph.ts",
     "core/State.ts",
     "core/Types.ts",
@@ -306,6 +323,7 @@ _ai_chat_sources = [
     "LLM/LLMProviderRegistry.ts",
     "LLM/LLMErrorHandler.ts",
     "LLM/LLMResponseParser.ts",
+    "LLM/FuzzyModelMatcher.ts",
     "LLM/OpenAIProvider.ts",
     "LLM/LiteLLMProvider.ts",
     "LLM/GroqProvider.ts",
@@ -338,6 +356,9 @@ _ai_chat_sources = [
     "tools/DeleteFileTool.ts",
     "tools/ReadFileTool.ts",
     "tools/ListFilesTool.ts",
+    "memory/SearchMemoryTool.ts",
+    "memory/UpdateMemoryTool.ts",
+    "memory/ListMemoryBlocksTool.ts",
     "tools/UpdateTodoTool.ts",
     "tools/ExecuteCodeTool.ts",
     "tools/SequentialThinkingTool.ts",

front_end/Images/demo.gif

@@ -0,0 +1,185 @@
+// Copyright 2025 The Chromium Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style license that can be
+// found in the LICENSE file.
+
+/**
+ * Fuzzy model name matcher for finding the closest available model
+ * when an exact match isn't found.
+ */
+
+/**
+ * Calculate Levenshtein distance between two strings
+ */
+function levenshteinDistance(a: string, b: string): number {
+  const matrix: number[][] = [];
+
+  // Initialize matrix
+  for (let i = 0; i <= b.length; i++) {
+    matrix[i] = [i];
+  }
+  for (let j = 0; j <= a.length; j++) {
+    matrix[0][j] = j;
+  }
+
+  // Fill matrix
+  for (let i = 1; i <= b.length; i++) {
+    for (let j = 1; j <= a.length; j++) {
+      if (b.charAt(i - 1) === a.charAt(j - 1)) {
+        matrix[i][j] = matrix[i - 1][j - 1];
+      } else {
+        matrix[i][j] = Math.min(
+          matrix[i - 1][j - 1] + 1, // substitution
+          matrix[i][j - 1] + 1,     // insertion
+          matrix[i - 1][j] + 1      // deletion
+        );
+      }
+    }
+  }
+
+  return matrix[b.length][a.length];
+}
+
+/**
+ * Calculate similarity score between two strings (0-1)
+ */
+function similarity(a: string, b: string): number {
+  const distance = levenshteinDistance(a, b);
+  const maxLen = Math.max(a.length, b.length);
+  return maxLen === 0 ? 1 : 1 - distance / maxLen;
+}
+
+/**
+ * Normalize model name for comparison by removing dates, versions, and separators
+ */
+function normalizeModelName(name: string): string {
+  return name
+    .toLowerCase()
+    .replace(/[-_]/g, '')              // Remove separators
+    .replace(/\d{4}-?\d{2}-?\d{2}$/g, '') // Remove date suffixes (2025-04-14 or 20250514)
+    .replace(/\d{8}$/g, '')            // Remove date suffixes without dashes
+    .trim();
+}
+
+/**
+ * Check if target is a prefix of candidate (case-insensitive)
+ */
+function isPrefixMatch(target: string, candidate: string): boolean {
+  const normalizedTarget = target.toLowerCase().replace(/[._]/g, '-');
+  const normalizedCandidate = candidate.toLowerCase().replace(/[._]/g, '-');
+  return normalizedCandidate.startsWith(normalizedTarget);
+}
+
+/**
+ * Find the closest matching model from available options
+ *
+ * Matching strategy (in priority order):
+ * 1. Exact match - return immediately
+ * 2. Prefix match - if target is prefix of an available model
+ * 3. Normalized match - strip dates/versions and compare base names
+ * 4. Levenshtein similarity - if similarity > threshold, return best match
+ *
+ * @param targetModel - The model name to find a match for
+ * @param availableModels - Array of available model names
+ * @param threshold - Minimum similarity score (0-1) for fuzzy matching (default: 0.5)
+ * @returns The closest matching model name, or null if no good match found
+ */
+export function findClosestModel(
+  targetModel: string,
+  availableModels: string[],
+  threshold: number = 0.5
+): string | null {
+  if (!targetModel || availableModels.length === 0) {
+    return null;
+  }
+
+  // 1. Exact match
+  if (availableModels.includes(targetModel)) {
+    return targetModel;
+  }
+
+  // 2. Prefix match - find models where target is a prefix
+  const prefixMatches = availableModels.filter(model => isPrefixMatch(targetModel, model));
+  if (prefixMatches.length > 0) {
+    // Return the shortest prefix match (most specific)
+    return prefixMatches.sort((a, b) => a.length - b.length)[0];
+  }
+
+  // 3. Normalized match - compare base names without dates/versions
+  const normalizedTarget = normalizeModelName(targetModel);
+  for (const model of availableModels) {
+    if (normalizeModelName(model) === normalizedTarget) {
+      return model;
+    }
+  }
+
+  // 4. Levenshtein similarity on normalized names
+  let bestMatch: string | null = null;
+  let bestScore = 0;
+
+  for (const model of availableModels) {
+    const score = similarity(normalizedTarget, normalizeModelName(model));
+    if (score > bestScore && score >= threshold) {
+      bestScore = score;
+      bestMatch = model;
+    }
+  }
+
+  return bestMatch;
+}
+
+/**
+ * Find closest model with detailed match info for logging
+ */
+export interface FuzzyMatchResult {
+  match: string | null;
+  matchType: 'exact' | 'prefix' | 'normalized' | 'similarity' | 'none';
+  score: number;
+}
+
+export function findClosestModelWithInfo(
+  targetModel: string,
+  availableModels: string[],
+  threshold: number = 0.5
+): FuzzyMatchResult {
+  if (!targetModel || availableModels.length === 0) {
+    return { match: null, matchType: 'none', score: 0 };
+  }
+
+  // 1. Exact match
+  if (availableModels.includes(targetModel)) {
+    return { match: targetModel, matchType: 'exact', score: 1 };
+  }
+
+  // 2. Prefix match
+  const prefixMatches = availableModels.filter(model => isPrefixMatch(targetModel, model));
+  if (prefixMatches.length > 0) {
+    const match = prefixMatches.sort((a, b) => a.length - b.length)[0];
+    return { match, matchType: 'prefix', score: targetModel.length / match.length };
+  }
+
+  // 3. Normalized match
+  const normalizedTarget = normalizeModelName(targetModel);
+  for (const model of availableModels) {
+    if (normalizeModelName(model) === normalizedTarget) {
+      return { match: model, matchType: 'normalized', score: 1 };
+    }
+  }
+
+  // 4. Levenshtein similarity
+  let bestMatch: string | null = null;
+  let bestScore = 0;
+
+  for (const model of availableModels) {
+    const score = similarity(normalizedTarget, normalizeModelName(model));
+    if (score > bestScore && score >= threshold) {
+      bestScore = score;
+      bestMatch = model;
+    }
+  }
+
+  if (bestMatch) {
+    return { match: bestMatch, matchType: 'similarity', score: bestScore };
+  }
+
+  return { match: null, matchType: 'none', score: 0 };
+}