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seawolf2357 commited on 9 days ago

Commit

0b89dc5

verified ·

1 Parent(s): 73eebb3

Update app.py

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Files changed (1) hide show

app.py +486 -37

app.py CHANGED Viewed

@@ -6,7 +6,7 @@ import os
 import numpy as np
 import openai
 from dotenv import load_dotenv
-from fastapi import FastAPI, Request
 from fastapi.responses import HTMLResponse, StreamingResponse
 from fastrtc import (
     AdditionalOutputs,
@@ -27,6 +27,9 @@ import aiosqlite
 from langdetect import detect, LangDetectException
 from datetime import datetime
 import uuid
 load_dotenv()
@@ -41,8 +44,12 @@ else:
 os.makedirs(PERSISTENT_DIR, exist_ok=True)
 DB_PATH = os.path.join(PERSISTENT_DIR, "personal_assistant.db")
 print(f"Using persistent directory: {PERSISTENT_DIR}")
 print(f"Database path: {DB_PATH}")
 # HTML content embedded as a string
 HTML_CONTENT = """<!DOCTYPE html>
@@ -62,6 +69,7 @@ HTML_CONTENT = """<!DOCTYPE html>
             --border-color: #333;
             --hover-color: #8a5cf6;
             --memory-color: #4a9eff;
         }
         body {
             font-family: "SF Pro Display", -apple-system, BlinkMacSystemFont, sans-serif;
@@ -182,6 +190,101 @@ HTML_CONTENT = """<!DOCTYPE html>
         .toggle-switch.active .toggle-slider {
             transform: translateX(24px);
         }
         /* Memory section */
         .memory-section {
             background-color: var(--card-bg);
@@ -198,6 +301,10 @@ HTML_CONTENT = """<!DOCTYPE html>
             border-radius: 6px;
             border-left: 3px solid var(--memory-color);
         }
         .memory-category {
             font-size: 12px;
             color: var(--memory-color);
@@ -336,6 +443,13 @@ HTML_CONTENT = """<!DOCTYPE html>
             margin-bottom: 10px;
             border-left: 3px solid var(--memory-color);
         }
         .language-info {
             font-size: 12px;
             color: #888;
@@ -522,6 +636,9 @@ HTML_CONTENT = """<!DOCTYPE html>
             font-weight: bold;
             color: white;
         }
     </style>
 </head>
@@ -557,6 +674,20 @@ HTML_CONTENT = """<!DOCTYPE html>
                     </div>
                 </div>
                 <div class="memory-section">
                     <h3 style="margin: 0 0 15px 0; color: var(--memory-color);">기억된 정보</h3>
                     <div id="memory-list"></div>
@@ -596,6 +727,8 @@ HTML_CONTENT = """<!DOCTYPE html>
         let currentSessionId = null;
         let userName = localStorage.getItem('userName') || '';
         let userMemories = {};
         const audioOutput = document.getElementById('audio-output');
         const startButton = document.getElementById('start-button');
         const endSessionButton = document.getElementById('end-session-button');
@@ -609,6 +742,10 @@ HTML_CONTENT = """<!DOCTYPE html>
         const memoryList = document.getElementById('memory-list');
         const userNameInput = document.getElementById('user-name');
         const userAvatar = document.getElementById('user-avatar');
         let audioLevel = 0;
         let animationFrame;
         let audioContext, analyser, audioSource;
@@ -631,6 +768,122 @@ HTML_CONTENT = """<!DOCTYPE html>
             }
         });
         // Start new session
         async function startNewSession() {
             const response = await fetch('/session/new', { method: 'POST' });
@@ -657,7 +910,7 @@ HTML_CONTENT = """<!DOCTYPE html>
                     userMemories[memory.category].push(memory.content);
                     const item = document.createElement('div');
-                    item.className = 'memory-item';
                     item.innerHTML = `
                         <div class="memory-category">${memory.category}</div>
                         <div class="memory-content">${memory.content}</div>
@@ -688,6 +941,8 @@ HTML_CONTENT = """<!DOCTYPE html>
                 if (result.status === 'ok') {
                     showToast('기억이 성공적으로 업데이트되었습니다.', 'success');
                     loadMemories();
                     startNewSession();
                 }
             } catch (error) {
@@ -784,7 +1039,8 @@ HTML_CONTENT = """<!DOCTYPE html>
                         web_search_enabled: webSearchEnabled,
                         session_id: currentSessionId,
                         user_name: userName,
-                        memories: userMemories
                     })
                 });
@@ -982,7 +1238,8 @@ HTML_CONTENT = """<!DOCTYPE html>
                         web_search_enabled: webSearchEnabled,
                         session_id: currentSessionId,
                         user_name: userName,
-                        memories: userMemories
                     })
                 });
@@ -1033,7 +1290,7 @@ HTML_CONTENT = """<!DOCTYPE html>
             chatMessages.scrollTop = chatMessages.scrollHeight;
             // Save message to database if save flag is true
-            if (save && currentSessionId && role !== 'memory-update' && role !== 'search-result') {
                 fetch('/message/save', {
                     method: 'POST',
                     headers: { 'Content-Type': 'application/json' },
@@ -1230,6 +1487,8 @@ class PersonalAssistantDB:
                     created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                     updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                     source_session_id TEXT,
                     FOREIGN KEY (source_session_id) REFERENCES conversations(id)
                 )
             """)
@@ -1237,6 +1496,7 @@ class PersonalAssistantDB:
             # Create indexes for better performance
             await db.execute("CREATE INDEX IF NOT EXISTS idx_memories_category ON user_memories(category)")
             await db.execute("CREATE INDEX IF NOT EXISTS idx_messages_session ON messages(session_id)")
             await db.commit()
@@ -1339,33 +1599,44 @@ class PersonalAssistantDB:
             ]
     @staticmethod
-    async def save_memory(category: str, content: str, session_id: str = None, confidence: float = 1.0):
         """Save or update a user memory"""
         async with aiosqlite.connect(DB_PATH) as db:
-            # Check if similar memory exists
-            cursor = await db.execute(
-                """SELECT id, content FROM user_memories
-                   WHERE category = ? AND content LIKE ?
-                   LIMIT 1""",
-                (category, f"%{content[:20]}%")
-            )
-            existing = await cursor.fetchone()
-            if existing:
-                # Update existing memory
-                await db.execute(
-                    """UPDATE user_memories
-                       SET content = ?, confidence = ?, updated_at = CURRENT_TIMESTAMP,
-                           source_session_id = ?
-                       WHERE id = ?""",
-                    (content, confidence, session_id, existing[0])
                 )
             else:
-                # Insert new memory
                 await db.execute(
-                    """INSERT INTO user_memories (category, content, confidence, source_session_id)
-                       VALUES (?, ?, ?, ?)""",
-                    (category, content, confidence, session_id)
                 )
             await db.commit()
@@ -1375,9 +1646,9 @@ class PersonalAssistantDB:
         """Get all user memories"""
         async with aiosqlite.connect(DB_PATH) as db:
             cursor = await db.execute(
-                """SELECT category, content, confidence, updated_at
                    FROM user_memories
-                   ORDER BY category, updated_at DESC"""
             )
             rows = await cursor.fetchall()
             return [
@@ -1385,7 +1656,9 @@ class PersonalAssistantDB:
                     "category": row[0],
                     "content": row[1],
                     "confidence": row[2],
-                    "updated_at": row[3]
                 }
                 for row in rows
             ]
@@ -1505,8 +1778,79 @@ def format_memories_for_prompt(memories: Dict[str, List[str]]) -> str:
     return memory_text
 async def process_text_chat(message: str, web_search_enabled: bool, session_id: str,
-                          user_name: str = "", memories: Dict = None) -> Dict[str, str]:
     """Process text chat using GPT-4o-mini model"""
     try:
         # Check for empty or None message
@@ -1521,7 +1865,7 @@ async def process_text_chat(message: str, web_search_enabled: bool, session_id:
                 "detected_language": "ko"
             }
-        # Build system prompt with memories
         base_prompt = f"""You are a personal AI assistant for {user_name if user_name else 'the user'}.
 You remember all previous conversations and personal information about the user.
 Be friendly, helpful, and personalized in your responses.
@@ -1533,6 +1877,11 @@ IMPORTANT: Give only ONE response. Do not repeat or give multiple answers."""
             memory_text = format_memories_for_prompt(memories)
             base_prompt += memory_text
         messages = [{"role": "system", "content": base_prompt}]
         # Handle web search if enabled
@@ -1592,7 +1941,8 @@ IMPORTANT: Give only ONE response. Do not repeat or give multiple answers."""
 class OpenAIHandler(AsyncStreamHandler):
     def __init__(self, web_search_enabled: bool = False, webrtc_id: str = None,
-                 session_id: str = None, user_name: str = "", memories: Dict = None) -> None:
         super().__init__(
             expected_layout="mono",
             output_sample_rate=SAMPLE_RATE,
@@ -1610,10 +1960,11 @@ class OpenAIHandler(AsyncStreamHandler):
         self.session_id = session_id
         self.user_name = user_name
         self.memories = memories or {}
         self.is_responding = False  # Track if already responding
         self.should_stop = False  # Track if conversation should stop
-        print(f"[INIT] Handler created with web_search={web_search_enabled}, session_id={session_id}, user={user_name}")
     def copy(self):
         if connection_settings:
@@ -1631,7 +1982,8 @@ class OpenAIHandler(AsyncStreamHandler):
                     webrtc_id=recent_id,
                     session_id=settings.get('session_id'),
                     user_name=settings.get('user_name', ''),
-                    memories=settings.get('memories', {})
                 )
         print(f"[COPY] No settings found, creating default handler")
@@ -1678,6 +2030,7 @@ class OpenAIHandler(AsyncStreamHandler):
                 self.session_id = settings.get('session_id')
                 self.user_name = settings.get('user_name', '')
                 self.memories = settings.get('memories', {})
                 print(f"[START_UP] Updated settings from storage for {self.webrtc_id}")
@@ -1685,7 +2038,7 @@ class OpenAIHandler(AsyncStreamHandler):
         print(f"[REALTIME API] Connecting...")
-        # Build system prompt with memories
         base_instructions = f"""You are a personal AI assistant for {self.user_name if self.user_name else 'the user'}.
 You remember all previous conversations and personal information about the user.
 Be friendly, helpful, and personalized in your responses.
@@ -1697,6 +2050,11 @@ IMPORTANT: Give only ONE response per user input. Do not repeat yourself or give
             memory_text = format_memories_for_prompt(self.memories)
             base_instructions += memory_text
         # Define the web search function
         tools = []
         if self.web_search_enabled and self.search_client:
@@ -2000,11 +2358,13 @@ async def custom_offer(request: Request):
     session_id = body.get("session_id")
     user_name = body.get("user_name", "")
     memories = body.get("memories", {})
     print(f"[OFFER] Received offer with webrtc_id: {webrtc_id}")
     print(f"[OFFER] web_search_enabled: {web_search_enabled}")
     print(f"[OFFER] session_id: {session_id}")
     print(f"[OFFER] user_name: {user_name}")
     # Store settings with timestamp
     if webrtc_id:
@@ -2013,6 +2373,7 @@ async def custom_offer(request: Request):
             'session_id': session_id,
             'user_name': user_name,
             'memories': memories,
             'timestamp': asyncio.get_event_loop().time()
         }
@@ -2038,6 +2399,92 @@ async def custom_offer(request: Request):
     return response
 @app.post("/session/new")
 async def create_new_session():
     """Create a new chat session"""
@@ -2107,12 +2554,14 @@ async def chat_text(request: Request):
         session_id = body.get("session_id")
         user_name = body.get("user_name", "")
         memories = body.get("memories", {})
         if not message:
             return {"error": "메시지가 비어있습니다."}
         # Process text chat
-        result = await process_text_chat(message, web_search_enabled, session_id, user_name, memories)
         return result

 import numpy as np
 import openai
 from dotenv import load_dotenv
+from fastapi import FastAPI, Request, UploadFile, File, Form
 from fastapi.responses import HTMLResponse, StreamingResponse
 from fastrtc import (
     AdditionalOutputs,
 from langdetect import detect, LangDetectException
 from datetime import datetime
 import uuid
+import PyPDF2
+import pandas as pd
+import chardet
 load_dotenv()
 os.makedirs(PERSISTENT_DIR, exist_ok=True)
 DB_PATH = os.path.join(PERSISTENT_DIR, "personal_assistant.db")
+UPLOAD_DIR = os.path.join(PERSISTENT_DIR, "uploads")
+os.makedirs(UPLOAD_DIR, exist_ok=True)
 print(f"Using persistent directory: {PERSISTENT_DIR}")
 print(f"Database path: {DB_PATH}")
+print(f"Upload directory: {UPLOAD_DIR}")
 # HTML content embedded as a string
 HTML_CONTENT = """<!DOCTYPE html>
             --border-color: #333;
             --hover-color: #8a5cf6;
             --memory-color: #4a9eff;
+            --doc-color: #28a745;
         }
         body {
             font-family: "SF Pro Display", -apple-system, BlinkMacSystemFont, sans-serif;
         .toggle-switch.active .toggle-slider {
             transform: translateX(24px);
         }
+        /* Document upload section */
+        .document-section {
+            background-color: var(--card-bg);
+            border-radius: 12px;
+            padding: 20px;
+            border: 1px solid var(--border-color);
+            max-height: 300px;
+            overflow-y: auto;
+        }
+        .file-upload-area {
+            border: 2px dashed var(--border-color);
+            border-radius: 8px;
+            padding: 20px;
+            text-align: center;
+            cursor: pointer;
+            transition: all 0.3s;
+            margin-bottom: 15px;
+        }
+        .file-upload-area:hover {
+            border-color: var(--primary-color);
+            background-color: rgba(111, 66, 193, 0.1);
+        }
+        .file-upload-area.drag-over {
+            border-color: var(--primary-color);
+            background-color: rgba(111, 66, 193, 0.2);
+        }
+        .memory-type-selector {
+            display: flex;
+            gap: 10px;
+            margin-bottom: 15px;
+        }
+        .memory-type-btn {
+            flex: 1;
+            padding: 10px;
+            border: 1px solid var(--border-color);
+            background-color: var(--dark-bg);
+            color: var(--text-color);
+            border-radius: 6px;
+            cursor: pointer;
+            transition: all 0.3s;
+        }
+        .memory-type-btn.active {
+            background-color: var(--primary-color);
+            border-color: var(--primary-color);
+        }
+        .document-list {
+            display: flex;
+            flex-direction: column;
+            gap: 10px;
+        }
+        .document-item {
+            padding: 10px;
+            background-color: var(--dark-bg);
+            border-radius: 6px;
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+        }
+        .document-info {
+            display: flex;
+            align-items: center;
+            gap: 10px;
+        }
+        .document-icon {
+            width: 24px;
+            height: 24px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            background-color: var(--doc-color);
+            color: white;
+            border-radius: 4px;
+            font-size: 12px;
+            font-weight: bold;
+        }
+        .document-name {
+            font-size: 14px;
+        }
+        .document-type {
+            font-size: 12px;
+            color: #888;
+            margin-left: 5px;
+        }
+        .remove-doc-btn {
+            background: none;
+            border: none;
+            color: #ff6b6b;
+            cursor: pointer;
+            padding: 5px;
+            border-radius: 4px;
+            transition: background-color 0.2s;
+        }
+        .remove-doc-btn:hover {
+            background-color: rgba(255, 107, 107, 0.2);
+        }
         /* Memory section */
         .memory-section {
             background-color: var(--card-bg);
             border-radius: 6px;
             border-left: 3px solid var(--memory-color);
         }
+        .memory-item.document {
+            background: linear-gradient(135deg, rgba(40, 167, 69, 0.1), rgba(111, 66, 193, 0.1));
+            border-left-color: var(--doc-color);
+        }
         .memory-category {
             font-size: 12px;
             color: var(--memory-color);
             margin-bottom: 10px;
             border-left: 3px solid var(--memory-color);
         }
+        .message.document-loaded {
+            background: linear-gradient(135deg, rgba(40, 167, 69, 0.2), rgba(111, 66, 193, 0.2));
+            font-size: 13px;
+            padding: 8px 12px;
+            margin-bottom: 10px;
+            border-left: 3px solid var(--doc-color);
+        }
         .language-info {
             font-size: 12px;
             color: #888;
             font-weight: bold;
             color: white;
         }
+        input[type="file"] {
+            display: none;
+        }
     </style>
 </head>
                     </div>
                 </div>
+                <div class="document-section">
+                    <h3 style="margin: 0 0 15px 0; color: var(--doc-color);">문서 관리</h3>
+                    <div class="file-upload-area" id="file-upload-area">
+                        <p>📄 파일을 드래그하거나 클릭하여 업로드</p>
+                        <p style="font-size: 12px; color: #888;">PDF, TXT, CSV 지원</p>
+                    </div>
+                    <input type="file" id="file-input" accept=".pdf,.txt,.csv" multiple />
+                    <div class="memory-type-selector">
+                        <button class="memory-type-btn active" data-type="temporary">단기 기억</button>
+                        <button class="memory-type-btn" data-type="permanent">영구 기억</button>
+                    </div>
+                    <div class="document-list" id="document-list"></div>
+                </div>
                 <div class="memory-section">
                     <h3 style="margin: 0 0 15px 0; color: var(--memory-color);">기억된 정보</h3>
                     <div id="memory-list"></div>
         let currentSessionId = null;
         let userName = localStorage.getItem('userName') || '';
         let userMemories = {};
+        let temporaryDocuments = {};  // 단기 기억 문서
+        let selectedMemoryType = 'temporary';
         const audioOutput = document.getElementById('audio-output');
         const startButton = document.getElementById('start-button');
         const endSessionButton = document.getElementById('end-session-button');
         const memoryList = document.getElementById('memory-list');
         const userNameInput = document.getElementById('user-name');
         const userAvatar = document.getElementById('user-avatar');
+        const fileInput = document.getElementById('file-input');
+        const fileUploadArea = document.getElementById('file-upload-area');
+        const documentList = document.getElementById('document-list');
+        const memoryTypeBtns = document.querySelectorAll('.memory-type-btn');
         let audioLevel = 0;
         let animationFrame;
         let audioContext, analyser, audioSource;
             }
         });
+        // Memory type selector
+        memoryTypeBtns.forEach(btn => {
+            btn.addEventListener('click', () => {
+                memoryTypeBtns.forEach(b => b.classList.remove('active'));
+                btn.classList.add('active');
+                selectedMemoryType = btn.dataset.type;
+            });
+        });
+        // File upload handling
+        fileUploadArea.addEventListener('click', () => {
+            fileInput.click();
+        });
+        fileUploadArea.addEventListener('dragover', (e) => {
+            e.preventDefault();
+            fileUploadArea.classList.add('drag-over');
+        });
+        fileUploadArea.addEventListener('dragleave', () => {
+            fileUploadArea.classList.remove('drag-over');
+        });
+        fileUploadArea.addEventListener('drop', (e) => {
+            e.preventDefault();
+            fileUploadArea.classList.remove('drag-over');
+            const files = e.dataTransfer.files;
+            handleFileUpload(files);
+        });
+        fileInput.addEventListener('change', (e) => {
+            handleFileUpload(e.target.files);
+        });
+        async function handleFileUpload(files) {
+            for (let file of files) {
+                const allowedTypes = ['.pdf', '.txt', '.csv'];
+                const fileExt = file.name.substring(file.name.lastIndexOf('.')).toLowerCase();
+                if (!allowedTypes.includes(fileExt)) {
+                    showError(`지원하지 않는 파일 형식: ${file.name}`);
+                    continue;
+                }
+                const formData = new FormData();
+                formData.append('file', file);
+                formData.append('memory_type', selectedMemoryType);
+                formData.append('session_id', currentSessionId);
+                try {
+                    const response = await fetch('/upload/document', {
+                        method: 'POST',
+                        body: formData
+                    });
+                    const result = await response.json();
+                    if (result.error) {
+                        showError(result.error);
+                    } else {
+                        showToast(`${file.name}이(가) ${selectedMemoryType === 'temporary' ? '단기' : '영구'} 기억으로 업로드되었습니다.`, 'success');
+                        if (selectedMemoryType === 'temporary') {
+                            temporaryDocuments[result.document_id] = {
+                                name: file.name,
+                                type: fileExt,
+                                content: result.content
+                            };
+                            updateDocumentList();
+                        } else {
+                            loadMemories();
+                        }
+                        addMessage('document-loaded', `📄 문서 "${file.name}"이(가) ${selectedMemoryType === 'temporary' ? '단기' : '영구'} 기억으로 로드되었습니다.`);
+                    }
+                } catch (error) {
+                    console.error('File upload error:', error);
+                    showError('파일 업로드 중 오류가 발생했습니다.');
+                }
+            }
+        }
+        function updateDocumentList() {
+            documentList.innerHTML = '';
+            for (const [docId, doc] of Object.entries(temporaryDocuments)) {
+                const item = document.createElement('div');
+                item.className = 'document-item';
+                const iconMap = {
+                    '.pdf': 'PDF',
+                    '.txt': 'TXT',
+                    '.csv': 'CSV'
+                };
+                item.innerHTML = `
+                    <div class="document-info">
+                        <div class="document-icon">${iconMap[doc.type]}</div>
+                        <div>
+                            <span class="document-name">${doc.name}</span>
+                            <span class="document-type">단기 기억</span>
+                        </div>
+                    </div>
+                    <button class="remove-doc-btn" onclick="removeDocument('${docId}')">×</button>
+                `;
+                documentList.appendChild(item);
+            }
+        }
+        window.removeDocument = function(docId) {
+            delete temporaryDocuments[docId];
+            updateDocumentList();
+            showToast('문서가 제거되었습니다.', 'success');
+        };
         // Start new session
         async function startNewSession() {
             const response = await fetch('/session/new', { method: 'POST' });
                     userMemories[memory.category].push(memory.content);
                     const item = document.createElement('div');
+                    item.className = memory.is_document ? 'memory-item document' : 'memory-item';
                     item.innerHTML = `
                         <div class="memory-category">${memory.category}</div>
                         <div class="memory-content">${memory.content}</div>
                 if (result.status === 'ok') {
                     showToast('기억이 성공적으로 업데이트되었습니다.', 'success');
                     loadMemories();
+                    temporaryDocuments = {};  // Clear temporary documents
+                    updateDocumentList();
                     startNewSession();
                 }
             } catch (error) {
                         web_search_enabled: webSearchEnabled,
                         session_id: currentSessionId,
                         user_name: userName,
+                        memories: userMemories,
+                        temporary_documents: temporaryDocuments
                     })
                 });
                         web_search_enabled: webSearchEnabled,
                         session_id: currentSessionId,
                         user_name: userName,
+                        memories: userMemories,
+                        temporary_documents: temporaryDocuments
                     })
                 });
             chatMessages.scrollTop = chatMessages.scrollHeight;
             // Save message to database if save flag is true
+            if (save && currentSessionId && role !== 'memory-update' && role !== 'search-result' && role !== 'document-loaded') {
                 fetch('/message/save', {
                     method: 'POST',
                     headers: { 'Content-Type': 'application/json' },
                     created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                     updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                     source_session_id TEXT,
+                    is_document BOOLEAN DEFAULT 0,
+                    document_name TEXT,
                     FOREIGN KEY (source_session_id) REFERENCES conversations(id)
                 )
             """)
             # Create indexes for better performance
             await db.execute("CREATE INDEX IF NOT EXISTS idx_memories_category ON user_memories(category)")
             await db.execute("CREATE INDEX IF NOT EXISTS idx_messages_session ON messages(session_id)")
+            await db.execute("CREATE INDEX IF NOT EXISTS idx_memories_document ON user_memories(is_document)")
             await db.commit()
             ]
     @staticmethod
+    async def save_memory(category: str, content: str, session_id: str = None, confidence: float = 1.0,
+                         is_document: bool = False, document_name: str = None):
         """Save or update a user memory"""
         async with aiosqlite.connect(DB_PATH) as db:
+            # Check if similar memory exists (not for documents)
+            if not is_document:
+                cursor = await db.execute(
+                    """SELECT id, content FROM user_memories
+                       WHERE category = ? AND content LIKE ? AND is_document = 0
+                       LIMIT 1""",
+                    (category, f"%{content[:20]}%")
                 )
+                existing = await cursor.fetchone()
+                if existing:
+                    # Update existing memory
+                    await db.execute(
+                        """UPDATE user_memories
+                           SET content = ?, confidence = ?, updated_at = CURRENT_TIMESTAMP,
+                               source_session_id = ?
+                           WHERE id = ?""",
+                        (content, confidence, session_id, existing[0])
+                    )
+                else:
+                    # Insert new memory
+                    await db.execute(
+                        """INSERT INTO user_memories (category, content, confidence, source_session_id,
+                                                    is_document, document_name)
+                           VALUES (?, ?, ?, ?, ?, ?)""",
+                        (category, content, confidence, session_id, is_document, document_name)
+                    )
             else:
+                # Always insert new document memories
                 await db.execute(
+                    """INSERT INTO user_memories (category, content, confidence, source_session_id,
+                                                is_document, document_name)
+                       VALUES (?, ?, ?, ?, ?, ?)""",
+                    (category, content, confidence, session_id, is_document, document_name)
                 )
             await db.commit()
         """Get all user memories"""
         async with aiosqlite.connect(DB_PATH) as db:
             cursor = await db.execute(
+                """SELECT category, content, confidence, updated_at, is_document, document_name
                    FROM user_memories
+                   ORDER BY is_document DESC, category, updated_at DESC"""
             )
             rows = await cursor.fetchall()
             return [
                     "category": row[0],
                     "content": row[1],
                     "confidence": row[2],
+                    "updated_at": row[3],
+                    "is_document": row[4],
+                    "document_name": row[5]
                 }
                 for row in rows
             ]
     return memory_text
+def format_documents_for_prompt(documents: Dict[str, Dict]) -> str:
+    """Format temporary documents for inclusion in prompt"""
+    if not documents:
+        return ""
+    doc_text = "\n\n=== 참조 문서 ===\n"
+    for doc_id, doc_info in documents.items():
+        if doc_info and doc_info.get('content'):
+            doc_text += f"\n[문서: {doc_info['name']}]\n"
+            doc_text += doc_info['content'][:3000] + "...\n" if len(doc_info['content']) > 3000 else doc_info['content'] + "\n"
+    return doc_text
+async def extract_text_from_pdf(file_content: bytes) -> str:
+    """Extract text from PDF file"""
+    try:
+        pdf_reader = PyPDF2.PdfReader(io.BytesIO(file_content))
+        text = ""
+        for page in pdf_reader.pages:
+            text += page.extract_text() + "\n"
+        return text.strip()
+    except Exception as e:
+        print(f"Error extracting PDF text: {e}")
+        return ""
+async def extract_text_from_csv(file_content: bytes) -> str:
+    """Extract text from CSV file"""
+    try:
+        # Detect encoding
+        detected = chardet.detect(file_content)
+        encoding = detected['encoding'] or 'utf-8'
+        # Read CSV
+        text_content = file_content.decode(encoding)
+        df = pd.read_csv(io.StringIO(text_content))
+        # Convert to readable format
+        text = f"CSV 데이터 (행: {len(df)}, 열: {len(df.columns)})\n\n"
+        text += "컬럼: " + ", ".join(df.columns) + "\n\n"
+        # Add sample data (first 10 rows)
+        text += "데이터 샘플:\n"
+        text += df.head(10).to_string() + "\n"
+        # Add basic statistics for numeric columns
+        numeric_cols = df.select_dtypes(include=[np.number]).columns
+        if len(numeric_cols) > 0:
+            text += "\n통계 정보:\n"
+            text += df[numeric_cols].describe().to_string()
+        return text
+    except Exception as e:
+        print(f"Error extracting CSV text: {e}")
+        return ""
+async def extract_text_from_txt(file_content: bytes) -> str:
+    """Extract text from TXT file"""
+    try:
+        # Detect encoding
+        detected = chardet.detect(file_content)
+        encoding = detected['encoding'] or 'utf-8'
+        return file_content.decode(encoding)
+    except Exception as e:
+        print(f"Error extracting TXT text: {e}")
+        return ""
 async def process_text_chat(message: str, web_search_enabled: bool, session_id: str,
+                          user_name: str = "", memories: Dict = None,
+                          temporary_documents: Dict = None) -> Dict[str, str]:
     """Process text chat using GPT-4o-mini model"""
     try:
         # Check for empty or None message
                 "detected_language": "ko"
             }
+        # Build system prompt with memories and documents
         base_prompt = f"""You are a personal AI assistant for {user_name if user_name else 'the user'}.
 You remember all previous conversations and personal information about the user.
 Be friendly, helpful, and personalized in your responses.
             memory_text = format_memories_for_prompt(memories)
             base_prompt += memory_text
+        # Add temporary documents to prompt
+        if temporary_documents:
+            doc_text = format_documents_for_prompt(temporary_documents)
+            base_prompt += doc_text
         messages = [{"role": "system", "content": base_prompt}]
         # Handle web search if enabled
 class OpenAIHandler(AsyncStreamHandler):
     def __init__(self, web_search_enabled: bool = False, webrtc_id: str = None,
+                 session_id: str = None, user_name: str = "", memories: Dict = None,
+                 temporary_documents: Dict = None) -> None:
         super().__init__(
             expected_layout="mono",
             output_sample_rate=SAMPLE_RATE,
         self.session_id = session_id
         self.user_name = user_name
         self.memories = memories or {}
+        self.temporary_documents = temporary_documents or {}
         self.is_responding = False  # Track if already responding
         self.should_stop = False  # Track if conversation should stop
+        print(f"[INIT] Handler created with web_search={web_search_enabled}, session_id={session_id}, user={user_name}, docs={len(temporary_documents)}")
     def copy(self):
         if connection_settings:
                     webrtc_id=recent_id,
                     session_id=settings.get('session_id'),
                     user_name=settings.get('user_name', ''),
+                    memories=settings.get('memories', {}),
+                    temporary_documents=settings.get('temporary_documents', {})
                 )
         print(f"[COPY] No settings found, creating default handler")
                 self.session_id = settings.get('session_id')
                 self.user_name = settings.get('user_name', '')
                 self.memories = settings.get('memories', {})
+                self.temporary_documents = settings.get('temporary_documents', {})
                 print(f"[START_UP] Updated settings from storage for {self.webrtc_id}")
         print(f"[REALTIME API] Connecting...")
+        # Build system prompt with memories and documents
         base_instructions = f"""You are a personal AI assistant for {self.user_name if self.user_name else 'the user'}.
 You remember all previous conversations and personal information about the user.
 Be friendly, helpful, and personalized in your responses.
             memory_text = format_memories_for_prompt(self.memories)
             base_instructions += memory_text
+        # Add temporary documents to prompt
+        if self.temporary_documents:
+            doc_text = format_documents_for_prompt(self.temporary_documents)
+            base_instructions += doc_text
         # Define the web search function
         tools = []
         if self.web_search_enabled and self.search_client:
     session_id = body.get("session_id")
     user_name = body.get("user_name", "")
     memories = body.get("memories", {})
+    temporary_documents = body.get("temporary_documents", {})
     print(f"[OFFER] Received offer with webrtc_id: {webrtc_id}")
     print(f"[OFFER] web_search_enabled: {web_search_enabled}")
     print(f"[OFFER] session_id: {session_id}")
     print(f"[OFFER] user_name: {user_name}")
+    print(f"[OFFER] temporary_documents: {len(temporary_documents)} documents")
     # Store settings with timestamp
     if webrtc_id:
             'session_id': session_id,
             'user_name': user_name,
             'memories': memories,
+            'temporary_documents': temporary_documents,
             'timestamp': asyncio.get_event_loop().time()
         }
     return response
+@app.post("/upload/document")
+async def upload_document(
+    file: UploadFile = File(...),
+    memory_type: str = Form(...),
+    session_id: str = Form(...)
+):
+    """Upload and process a document"""
+    try:
+        # Read file content
+        file_content = await file.read()
+        file_ext = Path(file.filename).suffix.lower()
+        # Extract text based on file type
+        extracted_text = ""
+        if file_ext == '.pdf':
+            extracted_text = await extract_text_from_pdf(file_content)
+        elif file_ext == '.txt':
+            extracted_text = await extract_text_from_txt(file_content)
+        elif file_ext == '.csv':
+            extracted_text = await extract_text_from_csv(file_content)
+        else:
+            return {"error": "Unsupported file type"}
+        if not extracted_text:
+            return {"error": "Failed to extract text from document"}
+        # Generate document ID
+        document_id = str(uuid.uuid4())
+        # Save to database if permanent
+        if memory_type == 'permanent':
+            # Summarize long documents
+            if len(extracted_text) > 5000:
+                # Use GPT to summarize
+                summary_response = await client.chat.completions.create(
+                    model="gpt-4.1-mini",
+                    messages=[
+                        {
+                            "role": "system",
+                            "content": "Summarize the following document in Korean, preserving key information and structure."
+                        },
+                        {
+                            "role": "user",
+                            "content": extracted_text[:10000]  # Limit to first 10k chars
+                        }
+                    ],
+                    temperature=0.3,
+                    max_tokens=2000
+                )
+                summary = summary_response.choices[0].message.content
+                await PersonalAssistantDB.save_memory(
+                    category="document",
+                    content=f"[문서 요약] {file.filename}\n\n{summary}",
+                    session_id=session_id,
+                    confidence=1.0,
+                    is_document=True,
+                    document_name=file.filename
+                )
+            else:
+                await PersonalAssistantDB.save_memory(
+                    category="document",
+                    content=f"[문서] {file.filename}\n\n{extracted_text}",
+                    session_id=session_id,
+                    confidence=1.0,
+                    is_document=True,
+                    document_name=file.filename
+                )
+        # Save file to uploads directory (optional, for backup)
+        file_path = os.path.join(UPLOAD_DIR, f"{document_id}{file_ext}")
+        with open(file_path, 'wb') as f:
+            f.write(file_content)
+        return {
+            "document_id": document_id,
+            "content": extracted_text,
+            "memory_type": memory_type,
+            "filename": file.filename
+        }
+    except Exception as e:
+        print(f"Error uploading document: {e}")
+        return {"error": str(e)}
 @app.post("/session/new")
 async def create_new_session():
     """Create a new chat session"""
         session_id = body.get("session_id")
         user_name = body.get("user_name", "")
         memories = body.get("memories", {})
+        temporary_documents = body.get("temporary_documents", {})
         if not message:
             return {"error": "메시지가 비어있습니다."}
         # Process text chat
+        result = await process_text_chat(message, web_search_enabled, session_id,
+                                        user_name, memories, temporary_documents)
         return result