total 51332
drwxrwxrwx  3 root     root         4096 Dec 24 15:58 .
drwxrwxrwx 21 root     root         4096 Feb 20 15:49 ..
-rwxrwxrwx  1 root     root        21088 Dec 17 20:18 ai_analysis.php
-rwxrwxrwx  1 root     root         4224 Dec 17 20:35 ai_chat.php
-rwxrwxrwx  1 root     root        13275 Dec 14 21:06 ai_logs.php
-rwxrwxrwx  1 www-data www-data  2742538 Feb 20 15:58 apiLogs.log
-rwxrwxrwx  1 root     root       173842 Feb 18 22:42 api.php
drwxrwxrwx  2 root     root         4096 Dec 21 17:29 assets
-rwxrwxrwx  1 root     root        20212 Dec 29 09:20 chroma_query.php
-rwxrwxrwx  1 root     root        26733 Dec 29 09:20 client_intelligence.php
-rwxrwxrwx  1 root     root        62813 Dec 18 22:06 contact_dashboard.php
-rwxrwxrwx  1 root     root        27276 Dec 18 00:54 contact_view.php
-rwxrwxrwx  1 root     root        18140 Dec 29 09:20 db_query.php
-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:38 __ddbgSQ: Array
(
)

-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:38 __ddbgSQ: Array
(
    [limit] => 1
    [sort] => Array
        (
            [ea] => -1
        )

)

-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:38 __ddbgSQ: Array
(
    [limit] => 5
    [sort] => Array
        (
            [ta] => -1
        )

)

-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:36 __ddbgSQ: Array
(
    [sort] => Array
        (
            [date] => 1
        )

)

-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:36 __ddbgSQ: Array
(
    [thread_id] => 193464a158d9620a
)

-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:36 __ddbgSQ: Array
(
    [thread_id] => 193464dea4798eec
)

-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:36 __ddbgSQ: Array
(
    [thread_id] => 19350a1acd081161
)

-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:36 __ddbgSQ: Array
(
    [thread_id] => 193545a4809e8ec9
)

-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:36 __ddbgSQ: Array
(
    [thread_id] => 1935a1b0c2eadd69
)

-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:38 __ddbgSQ: Array
(
    [thread_id] => 19a7ed9bc6d36ecd
)

-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:38 __ddbgSQ: Array
(
    [thread_id] => 19a7f0bc2d08f6ee
)

-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:38 __ddbgSQ: Array
(
    [thread_id] => 19ab53ab26868744
)

-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:38 __ddbgSQ: Array
(
    [thread_id] => 19ae5c0cb75e381c
)

-rwxrwxrwx  1 www-data www-data        1 Dec 18 14:38 __ddbgSQ: Array
(
    [thread_id] => 19ae9963e3507fd8
)

-rwxrwxrwx  1 www-data www-data 49210650 Feb 20 15:58 __ddbgSQ.log
-rwxrwxrwx  1 root     root        36369 Feb 18 21:10 index.php
-rwxrwxrwx  1 root     root        19244 Dec 29 09:20 manage_filters.php
-rwxrwxrwx  1 root     root         4125 Dec 29 09:20 oauth_callback.php

Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


Bash

List assets directory

Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


total 152
drwxrwxrwx 2 root root   4096 Dec 21 17:29 .
drwxrwxrwx 3 root root   4096 Dec 24 15:58 ..
-rwxrwxrwx 1 root root 128119 Feb 18 21:13 app.js
-rwxrwxrwx 1 root root  14373 Dec 21 13:06 style.css

Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


2672→const aiChat = {
  2673→    isOpen: false,
  2674→    mode: 'general',
  2675→    threadId: null,
  2676→    contactId: null,
  2677→    emailId: null,
  2678→    threadSubject: null,
  2679→    contactName: null,
  2680→    history: [],
  2681→    isDragging: false,
  2682→    dragOffsetX: 0,
  2683→    dragOffsetY: 0,
  2684→};
  2685→
  2686→const AI_QUICK_PROMPTS = {
  2687→    general: ['What are my most at-risk clients?', 'Which threads need urgent attention?', 'Summarize recent activity'],
  2688→    thread: ['Summarize this thread', 'What are the key risks?', 'What action should I take next?', 'Is this client trustworthy?', 'What are they really asking for?'],
  2689→    contact: ['What is their communication style?', 'Are there any red flags?', 'How should I approach them?', 'What opportunities exist with them?'],
  2690→    quote: ['How much should I quote for this project?', 'What is the scope of work?', 'What are the risks?', 'Suggest a project timeline'],
  2691→    reply: ['Write a professional reply', 'Write a firm but polite response', 'Write a follow-up email', 'Write a brief acknowledgment'],
  2692→};
  2693→
  2694→function toggleAiChat() {
  2695→    const panel = document.getElementById('ai-chat-panel');
  2696→    aiChat.isOpen = !aiChat.isOpen;
  2697→    panel.style.display = aiChat.isOpen ? 'flex' : 'none';
  2698→    if (aiChat.isOpen) { document.getElementById('ai-chat-input').focus(); renderQuickPrompts(); }
  2699→}
  2700→
  2701→function openAiChatForThread(threadId, subject, contactId) {
  2702→    aiChat.threadId = threadId; aiChat.contactId = contactId || null;
  2703→    aiChat.threadSubject = subject || threadId; aiChat.emailId = null;
  2704→    setAiChatMode('thread', document.querySelector('.ai-mode-btn[data-mode="thread"]'));
  2705→    if (!aiChat.isOpen) toggleAiChat(); else document.getElementById('ai-chat-input').focus();
  2706→}
  2707→
  2708→function openAiChatForContact(contactId, contactName) {
  2709→    aiChat.contactId = contactId; aiChat.contactName = contactName || 'Contact';
  2710→    aiChat.threadId = null; aiChat.emailId = null;
  2711→    setAiChatMode('contact', document.querySelector('.ai-mode-btn[data-mode="contact"]'));
  2712→    if (!aiChat.isOpen) toggleAiChat(); else document.getElementById('ai-chat-input').focus();
  2713→}
  2714→
  2715→function openAiChatForReply(threadId, emailId, subject) {
  2716→    aiChat.threadId = threadId; aiChat.emailId = emailId; aiChat.threadSubject = subject || threadId;
  2717→    setAiChatMode('reply', document.querySelector('.ai-mode-btn[data-mode="reply"]'));
  2718→    if (!aiChat.isOpen) toggleAiChat(); else document.getElementById('ai-chat-input').focus();
  2719→}
  2720→
  2721→function setAiChatMode(mode, btn) {
  2722→    aiChat.mode = mode;
  2723→    document.querySelectorAll('.ai-mode-btn').forEach(b => { b.style.background = '#2d2d4e'; b.style.color = '#94a3b8'; });
  2724→    if (btn) { btn.style.background = '#6366f1'; btn.style.color = '#fff'; }
  2725→    updateAiChatContextLabel(); renderQuickPrompts();
  2726→}
  2727→
  2728→function updateAiChatContextLabel() {
  2729→    const label = document.getElementById('ai-chat-context-label');
  2730→    if (!label) return;
  2731→    const modeLabels = { general: 'General Chat', thread: 'Thread Analysis', contact: 'Contact Intelligence', quote: 'Quote Assistant', reply: 'Reply Composer' };
  2732→    let ctx = modeLabels[aiChat.mode] || 'General Chat';
  2733→    if (aiChat.mode === 'thread' && aiChat.threadSubject) ctx += ' · ' + aiChat.threadSubject.substring(0, 30);
  2734→    if (aiChat.mode === 'contact' && aiChat.contactName) ctx += ' · ' + aiChat.contactName;
  2735→    label.textContent = ctx;
  2736→}
  2737→
  2738→function renderQuickPrompts() {
  2739→    const container = document.getElementById('ai-quick-prompts');
  2740→    if (!container) return;
  2741→    const prompts = AI_QUICK_PROMPTS[aiChat.mode] || AI_QUICK_PROMPTS.general;
  2742→    container.innerHTML = prompts.map(p =>
  2743→        `<button onclick="sendAiChatPrompt(${JSON.stringify(p)})" style="background:#2d2d4e;color:#94a3b8;border:1px solid #3d3d5c;border-radius:12px;padding:3px 10px;font-size:0.68em;cursor:pointer;white-space:nowrap;" onmouseover="this.style.background='#6366f1';this.style.color='#fff'" onmouseout="this.style.background='#2d2d4e';this.style.color='#94a3b8'">${escapeHtml(p)}</button>`
  2744→    ).join('');
  2745→}
  2746→
  2747→function sendAiChatPrompt(prompt) {
  2748→    document.getElementById('ai-chat-input').value = prompt;
  2749→    sendAiChat();
  2750→}
  2751→
  2752→function handleAiChatKey(e) {
  2753→    if (e.key === 'Enter' && !e.shiftKey) { e.preventDefault(); sendAiChat(); }
  2754→}
  2755→
  2756→async function sendAiChat() {
  2757→    const input = document.getElementById('ai-chat-input');
  2758→    const message = input.value.trim();
  2759→    if (!message) return;
  2760→    const model = document.getElementById('ai-chat-model').value;
  2761→    const sendBtn = document.getElementById('ai-chat-send-btn');
  2762→
  2763→    appendAiChatMessage('user', message);
  2764→    input.value = '';
  2765→    sendBtn.disabled = true; sendBtn.style.opacity = '0.5';
  2766→    aiChat.history.push({ role: 'user', content: message });
  2767→    const typingId = appendAiChatTyping();
  2768→
  2769→    try {
  2770→        const payload = { message, model, mode: aiChat.mode, history: aiChat.history.slice(-10) };
  2771→        if (aiChat.threadId) payload.thread_id = aiChat.threadId;
  2772→        if (aiChat.contactId) payload.contact_id = aiChat.contactId;
  2773→        if (aiChat.emailId) payload.email_id = aiChat.emailId;
  2774→
  2775→        const resp = await fetch('api.php?action=ai_chat', {
  2776→            method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(payload)
  2777→        });
  2778→        const data = await resp.json();
  2779→        removeAiChatTyping(typingId);
  2780→
  2781→        if (data.success && data.answer) {
  2782→            aiChat.history.push({ role: 'assistant', content: data.answer });
  2783→            appendAiChatMessage('assistant', data.answer, data.model);
  2784→        } else {
  2785→            appendAiChatMessage('error', '⚠ ' + (data.message || data.error || 'Unknown error'));
  2786→        }
  2787→    } catch (err) {
  2788→        removeAiChatTyping(typingId);
  2789→        appendAiChatMessage('error', '⚠ Network error: ' + err.message);
  2790→    } finally {
  2791→        sendBtn.disabled = false; sendBtn.style.opacity = '1';
  2792→    }
  2793→}
  2794→
  2795→function appendAiChatMessage(role, content, model) {
  2796→    const container = document.getElementById('ai-chat-messages');
  2797→    const isUser = role === 'user', isError = role === 'error';
  2798→    const wrapper = document.createElement('div');
  2799→    wrapper.style.cssText = `display:flex;flex-direction:column;align-items:${isUser ? 'flex-end' : 'flex-start'};gap:4px;`;
  2800→    const bubble = document.createElement('div');
  2801→    bubble.style.cssText = `max-width:88%;padding:10px 13px;border-radius:${isUser ? '14px 14px 4px 14px' : '14px 14px 14px 4px'};font-size:0.82em;line-height:1.5;word-break:break-word;background:${isUser ? 'linear-gradient(135deg,#6366f1,#8b5cf6)' : isError ? '#3d1515' : '#252540'};color:${isUser ? '#fff' : isError ? '#fca5a5' : '#e2e8f0'};${isError ? 'border:1px solid #7f1d1d;' : ''}`;
  2802→    if (!isUser && !isError) bubble.innerHTML = renderAiMarkdown(content);
  2803→    else bubble.textContent = content;
  2804→    wrapper.appendChild(bubble);
  2805→
  2806→    if (!isUser && !isError) {
  2807→        const meta = document.createElement('div');
  2808→        meta.style.cssText = 'display:flex;gap:6px;align-items:center;';
  2809→        if (model) {
  2810→            const badge = document.createElement('span');
  2811→            badge.style.cssText = 'font-size:0.65em;color:#4a4a6a;background:#1e1e2e;padding:2px 6px;border-radius:4px;';
  2812→            badge.textContent = model; meta.appendChild(badge);
  2813→        }
  2814→        const copyBtn = document.createElement('button');
  2815→        copyBtn.style.cssText = 'font-size:0.65em;color:#6366f1;background:none;border:none;cursor:pointer;padding:2px 4px;';
  2816→        copyBtn.textContent = '📋 Copy';
  2817→        copyBtn.onclick = () => navigator.clipboard.writeText(content).then(() => { copyBtn.textContent = '✓ Copied!'; setTimeout(() => copyBtn.textContent = '📋 Copy', 2000); });
  2818→        meta.appendChild(copyBtn);
  2819→        wrapper.appendChild(meta);
  2820→    }
  2821→    container.appendChild(wrapper);
  2822→    container.scrollTop = container.scrollHeight;
  2823→    return wrapper;
  2824→}
  2825→
  2826→function appendAiChatTyping() {
  2827→    const container = document.getElementById('ai-chat-messages');
  2828→    const id = 'typing-' + Date.now();
  2829→    const el = document.createElement('div');
  2830→    el.id = id;
  2831→    el.innerHTML = `
●●●
`;
  2832→    if (!document.getElementById('ai-chat-style')) {
  2833→        const s = document.createElement('style'); s.id = 'ai-chat-style';
  2834→        s.textContent = '@keyframes aiBounce{0%,80%,100%{opacity:0.3;transform:translateY(0)}40%{opacity:1;transform:translateY(-4px)}}';
  2835→        document.head.appendChild(s);
  2836→    }
  2837→    container.appendChild(el); container.scrollTop = container.scrollHeight;
  2838→    return id;
  2839→}
  2840→
  2841→function removeAiChatTyping(id) { const el = document.getElementById(id); if (el) el.remove(); }
  2842→
  2843→function renderAiMarkdown(text) {
  2844→    return text
  2845→        .replace(/&/g, '&').replace(/</g, '<').replace(/>/g, '>')
  2846→        .replace(/```([\s\S]*?)```/g, '
$1
')
  2847→        .replace(/`([^`]+)`/g, '$1')
  2848→        .replace(/\*\*([^*]+)\*\*/g, '$1').replace(/\*([^*]+)\*/g, '$1')
  2849→        .replace(/^### (.+)$/gm, '
$1
')
  2850→        .replace(/^## (.+)$/gm, '
$1
')
  2851→        .replace(/^# (.+)$/gm, '
$1
')
  2852→        .replace(/^[-*] (.+)$/gm, '
• $1
')
  2853→        .replace(/\n/g, '
');
  2854→}
  2855→
  2856→function clearAiChat() {
  2857→    aiChat.history = [];
  2858→    document.getElementById('ai-chat-messages').innerHTML = `<div style="text-align:center;color:#4a4a6a;font-size:0.8em;padding:20px 0;"><div style="font-size:2em;margin-bottom:8px;">🤖</div><div>Chat cleared. Ask me anything!</div></div>`;
  2859→}
  2860→
  2861→// Draggable panel
  2862→document.addEventListener('DOMContentLoaded', () => {
  2863→    const header = document.getElementById('ai-chat-header');
  2864→    const panel = document.getElementById('ai-chat-panel');
  2865→    if (!header || !panel) return;
  2866→    header.addEventListener('mousedown', e => {
  2867→        if (e.target.tagName === 'BUTTON') return;
  2868→        aiChat.isDragging = true;
  2869→        const rect = panel.getBoundingClientRect();
  2870→        aiChat.dragOffsetX = e.clientX - rect.left;
  2871→        aiChat.dragOffsetY = e.clientY - rect.top;
  2872→        panel.style.transition = 'none';
  2873→    });
  2874→    document.addEventListener('mousemove', e => {
  2875→        if (!aiChat.isDragging) return;
  2876→        panel.style.right = 'auto'; panel.style.bottom = 'auto';
  2877→        panel.style.left = Math.max(0, Math.min(e.clientX - aiChat.dragOffsetX, window.innerWidth - panel.offsetWidth)) + 'px';
  2878→        panel.style.top = Math.max(0, Math.min(e.clientY - aiChat.dragOffsetY, window.innerHeight - panel.offsetHeight)) + 'px';
  2879→    });
  2880→    document.addEventListener('mouseup', () => { aiChat.isDragging = false; });
  2881→});
  2882→

Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


  2310→        case 'ai_chat':
  2311→            if ($_SERVER['REQUEST_METHOD'] !== 'POST') jsonError('POST required', 405);
  2312→
  2313→            $jsonData = json_decode(file_get_contents('php://input'), true) ?? [];
  2314→            $chatMessage  = trim($jsonData['message'] ?? '');
  2315→            $chatModel    = $jsonData['model'] ?? 'gpt-4o';
  2316→            $chatMode     = $jsonData['mode'] ?? 'general';   // thread|contact|quote|reply|general
  2317→            $chatThreadId = $jsonData['thread_id'] ?? null;
  2318→            $chatContactId= (int)($jsonData['contact_id'] ?? 0);
  2319→            $chatHistory  = $jsonData['history'] ?? [];       // [{role,content}]
  2320→            $chatEmailId  = (int)($jsonData['email_id'] ?? 0);
  2321→
  2322→            if (!$chatMessage) jsonError('message required');
  2323→            if ($chatThreadId) $chatThreadId = resolveThreadId($chatThreadId);
  2324→
  2325→            // --- Build context block ---
  2326→            $ctxParts = [];
  2327→            $ctxAdapter = QueryAdapter::getInstance();
  2328→            $ctxMongo = \EmailIntelligence\MongoDatabase::getInstance();
  2329→            $useMongoDB = $ctxAdapter->getActiveDatabase() === 'mongodb';
  2330→
  2331→            // Thread context - use MongoDB-aware queries
  2332→            if ($chatThreadId) {
  2333→                if ($useMongoDB) {
  2334→                    $ctxEmails = $ctxMongo->findAll('emails', ['thread_id' => $chatThreadId], [
  2335→                        'sort' => ['date' => 1]
  2336→                    ]);
  2337→                } else {
  2338→                    $ctxEmails = $db->fetchAll(
  2339→                        "SELECT direction, date, from_email, to_email, subject, body_text FROM emails WHERE thread_id = ? ORDER BY date ASC",
  2340→                        [$chatThreadId]
  2341→                    );
  2342→                }
  2343→                if ($ctxEmails) {
  2344→                    $ctxParts[] = "=== EMAIL THREAD (" . count($ctxEmails) . " emails) ===";
  2345→                    foreach ($ctxEmails as $i => $em) {
  2346→                        $dir = ($em['direction'] ?? '') === 'inbound' ? 'FROM THEM' : 'FROM US';
  2347→                        $body = substr($em['body_text'] ?? '', 0, 800);
  2348→                        $ctxParts[] = "Email #".($i+1)." {$dir} ({$em['date']})\nSubject: {$em['subject']}\n{$body}";
  2349→                    }
  2350→                    // Include existing analysis if available
  2351→                    if ($useMongoDB) {
  2352→                        $ctxAnalysis = $ctxMongo->findOne('thread_analysis', ['thread_id' => $chatThreadId, 'is_analyzed' => 1]);
  2353→                        if (!$ctxAnalysis) {
  2354→                            // Try without is_analyzed filter (MongoDB may store as bool or int)
  2355→                            $ctxAnalysis = $ctxMongo->findOne('thread_analysis', ['thread_id' => $chatThreadId]);
  2356→                        }
  2357→                    } else {
  2358→                        $ctxAnalysis = $db->fetchOne("SELECT summary, raw_analysis FROM thread_analysis WHERE thread_id = ? AND is_analyzed=1", [$chatThreadId]);
  2359→                    }
  2360→                    if ($ctxAnalysis && !empty($ctxAnalysis['summary'])) {
  2361→                        $ctxParts[] = "=== EXISTING AI ANALYSIS ===\nSummary: " . $ctxAnalysis['summary'];
  2362→                    }
  2363→                }
  2364→            }
  2365→
  2366→            // Contact context - use MongoDB-aware queries
  2367→            if ($chatContactId) {
  2368→                if ($useMongoDB) {
  2369→                    $ctxContact = $ctxMongo->findOne('contacts', ['id' => $chatContactId]);
  2370→                    $ctxProfile = $ctxMongo->findOne('contact_profiles', ['contact_id' => $chatContactId]);
  2371→                } else {
  2372→                    $ctxContact = $db->fetchOne("SELECT name, email, type, company FROM contacts WHERE id = ?", [$chatContactId]);
  2373→                    $ctxProfile = $db->fetchOne("SELECT * FROM contact_profiles WHERE contact_id = ?", [$chatContactId]);
  2374→                }
  2375→                if ($ctxContact) {
  2376→                    $ctxParts[] = "=== CONTACT: {$ctxContact['name']} ({$ctxContact['email']}) ===";
  2377→                    if ($ctxProfile) {
  2378→                        $ctxParts[] = "Trust score: " . ($ctxProfile['overall_trust_score'] ?? 'N/A') .
  2379→                            " | Communication style: " . ($ctxProfile['communication_style'] ?? 'N/A');
  2380→                    }
  2381→                }
  2382→            }
  2383→
  2384→            // Single email context (for reply mode) - use MongoDB-aware queries
  2385→            if ($chatEmailId) {
  2386→                if ($useMongoDB) {
  2387→                    $ctxEmail = $ctxMongo->findOne('emails', ['id' => $chatEmailId]);
  2388→                } else {
  2389→                    $ctxEmail = $db->fetchOne("SELECT * FROM emails WHERE id = ?", [$chatEmailId]);
  2390→                }
  2391→                if ($ctxEmail) {
  2392→                    $ctxParts[] = "=== SPECIFIC EMAIL TO REPLY TO ===\nFrom: {$ctxEmail['from_email']}\nSubject: {$ctxEmail['subject']}\n{$ctxEmail['body_text']}";
  2393→                }
  2394→            }
  2395→
  2396→            // Mode-specific system instructions
  2397→            $systemInstructions = [
  2398→                'thread'  => "You are a business intelligence assistant analyzing an email thread. Provide insights about the conversation, relationships, risks, and opportunities. Be direct and specific.",
  2399→                'contact' => "You are a CRM intelligence assistant analyzing a contact's profile and history. Help understand their behavior, communication patterns, and how best to work with them.",
  2400→                'quote'   => "You are a business consultant helping estimate project costs and create quotes. Based on the email context, suggest realistic pricing, scope, and approach for software/CRM projects.",
  2401→                'reply'   => "You are an email writing assistant. Compose professional, ready-to-send email replies based on the thread context. Write only the reply text with greeting and sign-off.",
  2402→                'general' => "You are a business intelligence assistant with access to email and contact data. Answer questions, provide analysis, and help with business decisions.",
  2403→            ];
  2404→            $sysMsg = $systemInstructions[$chatMode] ?? $systemInstructions['general'];
  2405→            $contextBlock = implode("\n\n", $ctxParts);
  2406→            if ($contextBlock) {
  2407→                $sysMsg .= "\n\nCONTEXT:\n" . $contextBlock;
  2408→            }
  2409→
  2410→            // Build messages array with history
  2411→            $messages = [];
  2412→            foreach ($chatHistory as $h) {
  2413→                if (isset($h['role'], $h['content'])) {
  2414→                    $messages[] = ['role' => $h['role'], 'content' => $h['content']];
  2415→                }
  2416→            }
  2417→            $messages[] = ['role' => 'user', 'content' => $chatMessage];
  2418→
  2419→            // Determine provider from model name
  2420→            $chatProvider = 'openai';
  2421→            if (strpos($chatModel, 'claude') !== false) $chatProvider = 'claude';
  2422→            elseif (strpos($chatModel, 'gemini') !== false) $chatProvider = 'gemini';
  2423→            elseif (strpos($chatModel, 'grok') !== false) $chatProvider = 'grok';
  2424→
  2425→            // Route through proxy for multi-model support
  2426→            $proxyUrl = 'https://mssql.danmarcrm.com/dev1/extension2/aiapi/api_codex51/api/index.php';
  2427→            $proxyPayload = json_encode([
  2428→                'action'       => 'ask',
  2429→                'user_content' => $chatMessage,
  2430→                'system'       => $sysMsg,
  2431→                'models'       => [$chatModel],
  2432→                'providers'    => [$chatProvider],
  2433→                'history'      => array_slice($chatHistory, -10), // last 10 turns
  2434→            ]);
  2435→
  2436→            $ch = curl_init($proxyUrl);
  2437→            curl_setopt_array($ch, [
  2438→                CURLOPT_RETURNTRANSFER => true,
  2439→                CURLOPT_POST => true,
  2440→                CURLOPT_HTTPHEADER => ['Content-Type: application/json'],
  2441→                CURLOPT_POSTFIELDS => $proxyPayload,
  2442→                CURLOPT_TIMEOUT => 120,
  2443→            ]);
  2444→            $proxyResponse = curl_exec($ch);
  2445→            $curlError = curl_error($ch);
  2446→            curl_close($ch);
  2447→
  2448→            if ($curlError) {
  2449→                // Fallback to direct ThreadAnalyzer if proxy fails
  2450→                try {
  2451→                    $analyzer = new ThreadAnalyzer($chatProvider === 'openai' ? 'openai' : 'claude', $chatModel);
  2452→                    $fullPrompt = $sysMsg . "\n\nUser: " . $chatMessage;
  2453→                    $answer = $analyzer->askClaude($fullPrompt);
  2454→                    jsonResponse(['success' => true, 'answer' => $answer, 'model' => $chatModel, 'via' => 'direct']);
  2455→                } catch (Exception $e) {
  2456→                    jsonError('AI request failed: ' . $e->getMessage());
  2457→                }
  2458→                break;
  2459→            }
  2460→
  2461→            $proxyResult = json_decode($proxyResponse, true);
  2462→            if (!$proxyResult || !$proxyResult['success']) {
  2463→                jsonError('AI proxy error: ' . ($proxyResult['error'] ?? 'Unknown error'));
  2464→            }
  2465→
  2466→            // Extract answer from proxy response
  2467→            // Proxy returns: {"success":true,"responses":{"openai_response":{"response":"...","model":"..."}}}
  2468→            $answer = '';
  2469→            $usedModel = $chatModel;
  2470→            $responses = $proxyResult['responses'] ?? [];
  2471→            if (!empty($responses)) {
  2472→                $firstResponse = reset($responses);
  2473→                $answer = $firstResponse['response'] ?? $firstResponse['content'] ?? '';
  2474→                $usedModel = $firstResponse['model'] ?? $chatModel;
  2475→            }
  2476→            if (!$answer) {
  2477→                // Fallback: try top-level content
  2478→                $answer = $proxyResult['content'] ?? $proxyResult['response'] ?? '';
  2479→            }
  2480→            if (!$answer) {
  2481→                jsonError('AI proxy returned empty response. Raw: ' . substr($proxyResponse, 0, 200));
  2482→            }
  2483→
  2484→            jsonResponse([
  2485→                'success' => true,
  2486→                'answer'  => $answer,
  2487→                'model'   => $usedModel,
  2488→                'provider'=> $chatProvider,
  2489→                'via'     => 'proxy'
  2490→            ]);
  2491→            break;
  2492→
  2493→
  2494→
  2495→        // ==========================================
  2496→        case 'generate_reply':
  2497→            if ($_SERVER['REQUEST_METHOD'] !== 'POST') {
  2498→                jsonError('POST required', 405);
  2499→            }
  2500→
  2501→            // Handle JSON or form data
  2502→            $contentType = $_SERVER['CONTENT_TYPE'] ?? '';
  2503→            if (strpos($contentType, 'application/json') !== false) {
  2504→                $jsonData = json_decode(file_get_contents('php://input'), true);
  2505→                $threadId = $jsonData['thread_id'] ?? '';
  2506→                $tone = $jsonData['tone'] ?? 'professional';
  2507→                $intent = $jsonData['intent'] ?? '';
  2508→                $customInstructions = $jsonData['custom_instructions'] ?? '';
  2509→                $aiModel = $jsonData['ai_model'] ?? 'openai';

Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


1→<?php
     2→/**
     3→ * Email Intelligence Dashboard
     4→ *
     5→ * Main web interface for viewing contacts, emails, and analysis results.
     6→ *
     7→ * CALLING MAP:
     8→ * - Entry point: Web browser
     9→ * - Uses: api.php for data (via AJAX)
    10→ * - Uses: oauth_callback.php for Gmail connection
    11→ *
    12→ * @package EmailIntelligence
    13→ */
    14→
    15→// Load configuration
    16→require_once dirname(__DIR__) . '/config/config.php';
    17→require_once dirname(__DIR__) . '/vendor/autoload.php';
    18→
    19→use EmailIntelligence\Database;
    20→use EmailIntelligence\GmailClient;
    21→
    22→// Start session for flash messages
    23→session_start();
    24→
    25→// If contact parameter is present, redirect to contact view
    26→if (isset($_GET['contact']) && is_numeric($_GET['contact'])) {
    27→    header('Location: contact_view.php?contact=' . (int)$_GET['contact']);
    28→    exit;
    29→}
    30→
    31→// Get flash message if any
    32→$flashMessage = $_SESSION['flash_message'] ?? null;
    33→unset($_SESSION['flash_message']);
    34→
    35→// Get initial status
    36→try {
    37→    $db = Database::getInstance();
    38→    $syncStatus = $db->fetchOne("SELECT * FROM sync_status WHERE id = 1");
    39→    $stats = [
    40→        'emails' => $db->fetchColumn("SELECT COUNT(*) FROM emails"),
    41→        'contacts' => $db->fetchColumn("SELECT COUNT(*) FROM contacts"),
    42→        'analyzed' => $db->fetchColumn("SELECT COUNT(*) FROM emails WHERE is_analyzed = 1"),
    43→        'pending' => $db->fetchColumn("SELECT COUNT(*) FROM emails WHERE is_analyzed = 0"),
    44→    ];
    45→
    46→    $gmail = new GmailClient();
    47→    $gmailStatus = $gmail->getAuthStatus();
    48→    $authUrl = $gmail->hasCredentials() ? $gmail->getAuthUrl() : null;
    49→
    50→} catch (Exception $e) {
    51→    $error = $e->getMessage();
    52→    $stats = ['emails' => 0, 'contacts' => 0, 'analyzed' => 0, 'pending' => 0];
    53→    $gmailStatus = ['isAuthenticated' => false];
    54→    $syncStatus = null;
    55→    $authUrl = null;
    56→}
    57→?>
    58→<!DOCTYPE html>
    59→
    60→
    61→    <meta charset="UTF-8">
    62→    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    63→    <title>Email Intelligence Dashboard</title>
    64→    <link rel="stylesheet" href="assets/style.css">
    65→
    66→
    67→

    68→
    69→
    70→
Email Intelligence

    71→
AI-Powered Email Analysis for <?= htmlspecialchars(GMAIL_USER_EMAIL) ?>

    72→
    73→
    74→        <?php if (isset($error)): ?>
    75→

    76→            Error: <?= htmlspecialchars($error) ?>
    77→

    78→        <?php endif; ?>
    79→
    80→        <?php if ($flashMessage): ?>
    81→        <div class="alert alert-<?= $flashMessage['type'] ?>">
    82→            <?= htmlspecialchars($flashMessage['text']) ?>
    83→

    84→        <?php endif; ?>
    85→
    86→
    87→

    88→
    89→            <div class="status-card <?= $gmailStatus['isAuthenticated'] ? 'status-ok' : 'status-warning' ?>">
    90→
Gmail

    91→                <?php if ($gmailStatus['isAuthenticated']): ?>
    92→
Connected

    93→
<?= htmlspecialchars($gmailStatus['userEmail']) ?>

    94→                <?php elseif (!$gmailStatus['hasCredentials']): ?>
    95→
Not Configured

    96→
OAuth credentials missing

    97→                <?php else: ?>
    98→
Not Connected

    99→                    <?php if ($authUrl): ?>
   100→                    <a href="<?= htmlspecialchars($authUrl) ?>" class="btn btn-primary">Connect Gmail

Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


521:        <div id="ai-chat-panel" style="display:none; position:fixed; bottom:80px; right:20px; width:420px; height:580px; background:#1e1e2e; border-radius:16px; box-shadow:0 20px 60px rgba(0,0,0,0.5); z-index:9999; display:none; flex-direction:column; font-family:'Inter',sans-serif; border:1px solid #3d3d5c; overflow:hidden;">

Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


541:                    <select id="ai-chat-model" style="flex:1; background:#1e1e2e; color:#e2e8f0; border:1px solid #3d3d5c; border-radius:6px; padding:4px 8px; font-size:0.78em; cursor:pointer;">

Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


Found 1 file
src/QueryAdapter.php

Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.


Prompt:
Design an implementation plan for adding a "context builder + AI chat" feature to the Email Intelligence web app. Here's all the context:

## What the user wants:
The user wants to replicate the Chrome extension's "Ask AI" functionality in the web app, with ENHANCED capabilities:
1. Search for contacts by email/name
2. Select a single thread, multiple threads, or ALL threads for a contact as AI context
3. Choose an AI model from a dropdown (multi-provider: Claude, GPT, Gemini, Grok, DeepSeek, etc.)
4. Ask questions about the selected context - e.g. "how much should I quote?", "what's the deception level?"

Example use cases:
- "Grab all emails for stephanie.k***@asendia.com and check how much we quoted, her profile, and ask how much to quote for task X"
- "Grab multiple threads of david.zolanz@digitalj2.com and ask the level of deception from david"

## Current web app AI chat (what already exists):
- File: `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` (lines 2672-2882)
- Has modes: general, thread (single), contact, quote, reply
- Sends to `api.php?action=ai_chat` which builds context from ONE thread_id or ONE contact_id
- Has model dropdown but only basic models
- Draggable panel with chat history, quick prompts

## Current backend:
- `api.php?action=ai_chat` (lines 2310-2491) - builds context from single thread or contact
- `api.php?action=threads` - lists threads, filterable by contact_id and search
- `QueryAdapter::getThreads($contactId, $search, $limit, $offset)` - fetches threads
- MongoDB + SQL dual support throughout
- Thread emails stored with thread_id grouping
- Contact profiles with trust scores, communication style

## Chrome extension (what to draw inspiration from):
- Has provider dropdown + model dropdown with ALL providers (Claude, OpenAI, Gemini, Grok, DeepSeek, Qwen, Mistral, Cohere)
- Model list defined in JS (lines 18-56 of contentScript.js)
- `fetchEmailContext()` can fetch by thread ID or by email (returns contact + recent threads)
- Conversation history (last 10 exchanges)
- Quick question buttons

## The enhancement needed:
The current web chat can only load ONE thread or ONE contact as context. We need:

1. A **contact/thread search** UI in the AI chat panel (or a new dedicated panel)
2. Ability to **add multiple threads to context** - like a "shopping cart" for context
3. Ability to **"Add all threads for contact X"** with one click
4. The **multi-provider model dropdown** from the chrome extension
5. A **visible context summary** showing what's loaded (e.g. "3 threads, 15 emails from stephanie@...")
6. Backend API changes to accept multiple thread_ids and build combined context

## Key files to modify:
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/assets/app.js` - Frontend JS
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/api.php` - Backend API
- `/var/www/mssql.danmarcrm.com/dev1/extension2/public/index.php` - HTML for new UI elements

## Constraints:
- Keep using the existing draggable AI chat panel (enhance it, don't replace it)
- Must work with both MongoDB and SQL backends
- Keep the existing modes (thread, contact, quote, reply, general) but enhance them
- Reuse existing QueryAdapter and MongoDatabase classes
- Use the same AI proxy at `aiapi/api_codex51/api/index.php`

Design a detailed, step-by-step implementation plan.