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feat: monitor.py, update main.py

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y9938
2025-12-31 06:15:22 +03:00
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main.py
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@@ -1,25 +1,19 @@
#!/usr/bin/env python3 #!/usr/bin/env python3
""" """
RAG Learning System - Simplified Educational Assistant Simplified RAG Learning Assistant
Tracks learning progress across subjects and provides AI tutoring guidance. Tracks learning progress across 17 subjects and provides AI tutoring guidance.
""" """
import os import os
import sys
import json import json
import hashlib import hashlib
import asyncio import asyncio
import re
from pathlib import Path from pathlib import Path
from datetime import datetime from datetime import datetime
from typing import Dict, List, Set, Optional from typing import Dict, List, Set, Optional
from dataclasses import dataclass, asdict from dataclasses import dataclass
from dotenv import load_dotenv
from rich.console import Console from rich.console import Console
from rich.panel import Panel
from rich.prompt import Prompt
from rich.progress import Progress, SpinnerColumn, TextColumn
from prompt_toolkit import PromptSession from prompt_toolkit import PromptSession
from prompt_toolkit.styles import Style from prompt_toolkit.styles import Style
@@ -36,94 +30,10 @@ from langchain_core.output_parsers import StrOutputParser
# ========================= # =========================
console = Console(color_system="standard", force_terminal=True) console = Console(color_system="standard", force_terminal=True)
session = PromptSession() session = PromptSession()
load_dotenv()
style = Style.from_dict({"prompt": "bold #6a0dad"}) style = Style.from_dict({"prompt": "bold #6a0dad"})
# Core Configuration
OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434") OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
ANSWER_COLOR = os.getenv("ANSWER_COLOR", "blue")
# Subject-specific keywords for filtering
SUBJECT_KEYWORDS = {
"english": ["english", "английский", "vocabulary", "grammar", "перевод", "словарь", "грамматика"],
"math": ["math", "математика", "алгебра", "геометрия", "calculus", "дискретная", "logic", "логика"],
"cs": ["computer science", "алгоритмы", "data structures", "oop", "python", "programming", "код"],
"electronics": ["electronics", "электротехника", "circuit", "микроконтроллер", "arduino", "цифровая"],
"linux": ["linux", "kali", "bash", "terminal", "command line", "скрипт", "администрирование"],
"networking": ["network", "сеть", "tcp", "ip", "osi", "маршрутизация", "vlan", "протокол"],
"cybersecurity": ["cybersecurity", "безопасность", "owasp", "уязвимость", "pentest", "hack", "хак"],
"sql": ["sql"]
}
# System Prompt for Educational Assistant
SYSTEM_PROMPT = """Ты — наставник-преподаватель по кибербезопасности. Твоя цель — довести ученика с уровня "пользователь ПК" до уровня junior в кибербезопасности.
КУРСОВАЯ СТРУКТУРА
Модули (6 независимых курсов):
1. Computer Science (фундамент)
2. Математика
3. Основы электротехники
4. Linux + Kali Linux
5. Основы сетей
6. Введение в кибербезопасность
7. Английский язык
СТРУКТУРА КАЖДОГО МОДУЛЯ
• Цель урока
• Темы в хронологическом порядке (от простого к сложному)
• Практические задания
• Прогресс-бар (по нормам Минобрнауки РФ)
• Блок вопросов для самопроверки
• Названия тем для поиска в YouTube/статьях
ОТСЛЕЖИВАНИЕ ПРОГРЕССА
Методология:
• Каждый предмет = числовая прямая от 0 до ∞
• Темы = точки на прямой (например: "цифры" = 0.01, "дроби" = 0.04)
• Без усвоения базы — не переходить дальше
• Адаптация вектора обучения по прогрессу
Критерии Junior-уровня:
• CS: Алгоритмы, структуры данных, ООП
• Математика: Дискретная математика, логика, теория чисел
• Электротехника: Цифровая логика, микроконтроллеры
• Linux: CLI, bash-скрипты, системное администрирование
• Сети: OSI, TCP/IP, маршрутизация, VLAN
• Кибербезопасность: OWASP Top 10, базовые уязвимости, инструменты
• Английский: Технический английский, терминология
РАБОЧИЙ ПРОЦЕСС
Ответ пользователю:
1. Определи стартовую точку по заметкам Obsidian
2. Построй фундамент текущего урока
3. Сверяйся с заметками ученика
4. Комбинируй стиль живого наставника и учебника
Формат ответа:
"В [ПРЕДМЕТ] будем проходить [ТЕМА_1] и [ТЕМА_2].
[Дополнительные инструкции по структуре изучения]"
ПРАВИЛА ПРОГРЕССИИ
• Проверяй усвоение предыдущих тем
• Не суди по одному слову вне контекста
• Учитывай межпредметные связи
• Корректируй траекторию обучения динамически
ПОИСКОВЫЕ ЗАПРОСЫ
Формируй темы для поиска в формате:
"[ПРЕДМЕТ] [УРОВЕНЬ] [ТЕМА] [ЯЗЫК]" Пример: "Computer Science beginner algorithms Russian"
"""
USER_PROMPT_TEMPLATE = """Текущий прогресс обучения:
{progress}
Контекст из заметок:
{context}
Вопрос ученика: {question}"""
# Paths and Models
MD_DIRECTORY = os.getenv("MD_FOLDER", "./notes") MD_DIRECTORY = os.getenv("MD_FOLDER", "./notes")
EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "mxbai-embed-large:latest") EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "mxbai-embed-large:latest")
LLM_MODEL = os.getenv("LLM_MODEL", "qwen2.5:7b-instruct-q8_0") LLM_MODEL = os.getenv("LLM_MODEL", "qwen2.5:7b-instruct-q8_0")
@@ -132,23 +42,110 @@ CHROMA_PATH = "./.cache/chroma_db"
KNOWLEDGE_STATE_PATH = "./.cache/knowledge_state.json" KNOWLEDGE_STATE_PATH = "./.cache/knowledge_state.json"
FILE_HASHES_PATH = "./.cache/file_hashes.json" FILE_HASHES_PATH = "./.cache/file_hashes.json"
# Processing Configuration
CHUNK_SIZE = 400 CHUNK_SIZE = 400
CHUNK_OVERLAP = 50 CHUNK_OVERLAP = 50
TOP_K = 6 TOP_K = 6
COLLECTION_NAME = "learning_rag" COLLECTION_NAME = "learning_rag"
MAX_CONTEXT_CHARS = 8000 MAX_CONTEXT_CHARS = 8000
# =========================
# SUBJECT CONFIGURATION
# =========================
SUBJECTS = {
"computer_science": "Computer Science",
"math": "Математика",
"english": "Английский язык",
"programming": "Основы программирования",
"linux": "Операционные системы Linux",
"windows": "Операционные системы Windows",
"networking": "Сетевые технологии",
"databases": "Базы данных и SQL",
"web": "Веб-технологии",
"cryptography": "Криптография",
"cybersecurity": "Базовые принципы кибербезопасности",
"pentest": "Тестирование на проникновение (Red Team)",
"soc": "SOC и Blue Team",
"devsecops": "DevSecOps",
"tools": "Инструменты и практика",
"certifications": "Сертификации и карьера",
"professional": "Профессиональное развитие"
}
SUBJECT_KEYWORDS = {
"computer_science": ["computer science", "алгоритмы", "data structures", "oop", "структуры данных"],
"math": ["math", "математика", "алгебра", "геометрия", "дискретная", "logic", "логика", "теория чисел"],
"english": ["english", "английский", "vocabulary", "grammar", "перевод", "словарь", "грамматика"],
"programming": ["programming", "python", "код", "code", "разработка", "программирование"],
"linux": ["linux", "kali", "bash", "terminal", "command line", "скрипт", "администрирование"],
"windows": ["windows", "powershell", "администрирование windows"],
"networking": ["network", "сеть", "tcp", "ip", "osi", "маршрутизация", "vlan", "протокол"],
"databases": ["database", "sql", "база данных", "postgresql", "mysql"],
"web": ["web", "html", "css", "javascript", "http", "frontend", "backend"],
"cryptography": ["cryptography", "криптография", "шифрование", "rsa", "aes"],
"cybersecurity": ["cybersecurity", "безопасность", "owasp", "уязвимость", "pentest"],
"pentest": ["pentest", "pentesting", "red team", "тестирование на проникновение"],
"soc": ["soc", "blue team", "security operations", "siem"],
"devsecops": ["devsecops", "ci/cd", "security automation"],
"tools": ["tools", "инструменты", "nmap", "burp", "metasploit", "wireshark"],
"certifications": ["certification", "сертификация", "ceh", "oscp", "cissp"],
"professional": ["github", "portfolio", "linkedin", "блог", "конференция"]
}
SYSTEM_PROMPT = """Ты — наставник-преподаватель по кибербезопасности. Твоя цель — довести ученика с уровня "пользователь ПК" до уровня junior в кибербезопасности.
КУРСОВАЯ СТРУКТУРА (17 модулей):
1) Computer Science: с полного нуля до уровня стандарта мировых вузов
2) Математика: с полного нуля до уровня стандарта мировых вузов
3) Английский язык: с полного нуля до уровня B2
4) Основы программирования: с полного нуля до уровня стандарта мировых вузов
5) Операционные системы Linux: с полного нуля до уровня стандарта мировых вузов
6) Операционные системы Windows: с уровня пользователя до уровня стандарта мировых вузов
7) Сетевые технологии: с полного нуля до уровня стандарта мировых вузов
8) Базы данных и SQL: с полного нуля до уровня стандарта мировых вузов
9) Веб-технологии: с полного нуля до уровня стандарта мировых вузов
10) Криптография: с полного нуля до уровня стандарта мировых вузов
11) Базовые принципы кибербезопасности: с полного нуля до уровня стандарта мировых вузов
12) Тестирование на проникновение (Red Team): с полного нуля до уровня стандарта мировых вузов
13) SOC и Blue Team: с полного нуля до уровня стандарта мировых вузов
14) DevSecOps: с полного нуля до уровня стандарта мировых вузов
15) Инструменты и практика: список тем для изучения, без практических
16) Сертификации и карьера: список тем для изучения, без практических
17) Профессиональное развитие: GitHub портфолио, блог, нетворкинг, конференции
МЕТОДОЛОГИЯ:
- Каждый предмет = числовая прямая от 0 до ∞
- Темы = точки на прямой (например: "цифры" = 0.01, "дроби" = 0.04)
- Без усвоения базы — не переходить дальше
- Адаптация вектора обучения по прогрессу
ФОРМАТ ОТВЕТА:
"В [ПРЕДМЕТ] будем проходить [ТЕМА_1] и [ТЕМА_2].
[Дополнительные инструкции по структуре изучения]"
ПРАВИЛА:
- Проверяй усвоение предыдущих тем
- Не суди по одному слову вне контекста
- Учитывай межпредметные связи
- Корректируй траекторию обучения динамически
- Отвечай всегда на русском языке"""
USER_PROMPT_TEMPLATE = """Текущий прогресс обучения:
{progress}
Контекст из заметок по предмету:
{context}
Вопрос ученика: {question}"""
# ========================= # =========================
# DATA STRUCTURES # DATA STRUCTURES
# ========================= # =========================
@dataclass @dataclass
class SubjectProgress: class SubjectProgress:
"""Track progress for a specific subject"""
name: str name: str
topics_covered: Set[str] topics_covered: Set[str]
last_studied: Optional[str] last_studied: Optional[str]
confidence_level: float = 0.0 # 0.0 to 1.0 confidence_level: float = 0.0
def to_dict(self): def to_dict(self):
return { return {
@@ -169,7 +166,6 @@ class SubjectProgress:
@dataclass @dataclass
class KnowledgeState: class KnowledgeState:
"""Complete learning state across all subjects"""
subjects: Dict[str, SubjectProgress] subjects: Dict[str, SubjectProgress]
last_analysis: str last_analysis: str
file_hashes: Dict[str, str] file_hashes: Dict[str, str]
@@ -196,22 +192,18 @@ class KnowledgeState:
# UTILITY FUNCTIONS # UTILITY FUNCTIONS
# ========================= # =========================
def get_file_hash(file_path: str) -> str: def get_file_hash(file_path: str) -> str:
"""Generate MD5 hash for file change detection"""
return hashlib.md5(Path(file_path).read_bytes()).hexdigest() return hashlib.md5(Path(file_path).read_bytes()).hexdigest()
def load_json_cache(file_path: str) -> dict: def load_json_cache(file_path: str) -> dict:
"""Load JSON cache with error handling"""
Path(file_path).parent.mkdir(parents=True, exist_ok=True) Path(file_path).parent.mkdir(parents=True, exist_ok=True)
if Path(file_path).exists(): if Path(file_path).exists():
try: try:
return json.loads(Path(file_path).read_text()) return json.loads(Path(file_path).read_text())
except json.JSONDecodeError: except json.JSONDecodeError:
console.print(f"[yellow]⚠️ Corrupted cache: {file_path}. Resetting.[/yellow]")
return {} return {}
return {} return {}
def save_json_cache(data, file_path: str): def save_json_cache(data, file_path: str):
"""Save JSON cache with error handling"""
try: try:
Path(file_path).write_text(json.dumps(data, indent=2, ensure_ascii=False)) Path(file_path).write_text(json.dumps(data, indent=2, ensure_ascii=False))
except Exception as e: except Exception as e:
@@ -220,20 +212,20 @@ def save_json_cache(data, file_path: str):
# ========================= # =========================
# SUBJECT DETECTION # SUBJECT DETECTION
# ========================= # =========================
def detect_subject_from_query(query: str) -> Optional[str]: def detect_subjects_from_query(query: str) -> List[str]:
"""Detect which subject the user wants to study"""
query_lower = query.lower() query_lower = query.lower()
detected = []
# Check for explicit subject mentions
for subject, keywords in SUBJECT_KEYWORDS.items(): for subject, keywords in SUBJECT_KEYWORDS.items():
for keyword in keywords: for keyword in keywords:
if keyword.lower() in query_lower: if keyword.lower() in query_lower:
return subject if subject not in detected:
detected.append(subject)
break
return None return detected
def detect_subject_from_content(text: str) -> Optional[str]: def detect_subject_from_content(text: str) -> Optional[str]:
"""Detect subject from note content"""
text_lower = text.lower() text_lower = text.lower()
subject_scores = {subject: 0 for subject in SUBJECT_KEYWORDS.keys()} subject_scores = {subject: 0 for subject in SUBJECT_KEYWORDS.keys()}
@@ -242,148 +234,13 @@ def detect_subject_from_content(text: str) -> Optional[str]:
if keyword.lower() in text_lower: if keyword.lower() in text_lower:
subject_scores[subject] += 1 subject_scores[subject] += 1
# Return subject with highest score, if any matches
best_subject = max(subject_scores.items(), key=lambda x: x[1]) best_subject = max(subject_scores.items(), key=lambda x: x[1])
return best_subject[0] if best_subject[1] > 0 else None return best_subject[0] if best_subject[1] > 0 else None
# =========================
# KNOWLEDGE ANALYSIS
# =========================
class KnowledgeAnalyzer:
"""Analyze learning progress from notes"""
def __init__(self, vectorstore):
self.vectorstore = vectorstore
async def analyze_all_notes(self, file_hashes: Dict[str, str]) -> KnowledgeState:
"""Analyze all notes to build complete knowledge state"""
console.print("[cyan]🔍 Analyzing all notes for learning progress...[/cyan]")
# Initialize subjects
subjects = {
name: SubjectProgress(name=name, topics_covered=set(), last_studied=None)
for name in SUBJECT_KEYWORDS.keys()
}
# Get all documents from vectorstore
try:
db_data = await asyncio.to_thread(self.vectorstore.get)
if not db_data or not db_data['documents']:
console.print("[yellow]⚠️ No documents found in vectorstore[/yellow]")
return KnowledgeState(subjects, datetime.now().isoformat(), file_hashes)
# Process each document
for text, metadata in zip(db_data['documents'], db_data['metadatas']):
if not metadata or 'source' not in metadata:
continue
# Detect subject
subject = detect_subject_from_content(text)
if subject:
subjects[subject].topics_covered.add(text[:100]) # Use first 100 chars as topic identifier
# Update last studied timestamp
file_path = metadata['source']
if file_path in file_hashes:
subjects[subject].last_studied = file_hashes[file_path]
# Calculate confidence levels based on topic coverage
for subject in subjects.values():
subject.confidence_level = min(len(subject.topics_covered) / 10.0, 1.0)
console.print(f"[green]✓ Analysis complete. Found progress in {len([s for s in subjects.values() if s.topics_covered])} subjects[/green]")
except Exception as e:
console.print(f"[red]✗ Error during analysis: {e}[/red]")
return KnowledgeState(subjects, datetime.now().isoformat(), file_hashes)
def get_progress_summary(self, knowledge_state: KnowledgeState, subject: Optional[str] = None) -> str:
"""Generate human-readable progress summary"""
if subject and subject in knowledge_state.subjects:
subj = knowledge_state.subjects[subject]
return f"Предмет: {subj.name}\n" \
f"Тем изучено: {len(subj.topics_covered)}\n" \
f"Уровень уверенности: {subj.confidence_level:.1%}"
# Return all subjects summary
summary = "Текущий прогресс обучения:\n"
for subj in knowledge_state.subjects.values():
if subj.topics_covered:
summary += f"- {subj.name}: {len(subj.topics_covered)} тем, уверенность {subj.confidence_level:.1%}\n"
return summary
# =========================
# DOCUMENT PROCESSING
# =========================
class DocumentProcessor:
"""Process markdown documents for the learning system"""
def __init__(self, vectorstore):
self.vectorstore = vectorstore
self.text_splitter = RecursiveCharacterTextSplitter(
chunk_size=CHUNK_SIZE,
chunk_overlap=CHUNK_OVERLAP,
separators=["\n\n", "\n", ". ", " "]
)
async def process_file(self, file_path: str) -> List[Document]:
"""Process a single markdown file"""
try:
loader = UnstructuredMarkdownLoader(file_path)
documents = loader.load()
if not documents:
return []
# Add source metadata
for doc in documents:
doc.metadata["source"] = file_path
# Split into chunks
chunks = self.text_splitter.split_documents(documents)
return chunks
except Exception as e:
console.print(f"[red]✗ Error processing {Path(file_path).name}: {e}[/red]")
return []
async def index_files(self, file_paths: List[str]) -> bool:
"""Index multiple files with batching"""
all_chunks = []
for file_path in file_paths:
chunks = await self.process_file(file_path)
all_chunks.extend(chunks)
if not all_chunks:
return False
batch_size = 20
total_batches = (len(all_chunks) + batch_size - 1) // batch_size
try:
await asyncio.to_thread(self.vectorstore.reset_collection)
for i in range(0, len(all_chunks), batch_size):
batch = all_chunks[i:i + batch_size]
await asyncio.to_thread(self.vectorstore.add_documents, batch)
console.print(f" [dim]Пакет {i//batch_size + 1}/{total_batches} проиндексирован[/dim]")
return True
except Exception as e:
console.print(f"[red]✗ Error indexing documents: {e}[/red]")
return False
# ========================= # =========================
# LEARNING ASSISTANT # LEARNING ASSISTANT
# ========================= # =========================
class LearningAssistant: class LearningAssistant:
"""Main learning assistant class"""
def __init__(self): def __init__(self):
self.embeddings = OllamaEmbeddings( self.embeddings = OllamaEmbeddings(
model=EMBEDDING_MODEL, model=EMBEDDING_MODEL,
@@ -408,58 +265,44 @@ class LearningAssistant:
]) ])
self.chain = self.prompt | self.llm | StrOutputParser() self.chain = self.prompt | self.llm | StrOutputParser()
self.processor = DocumentProcessor(self.vectorstore) self.text_splitter = RecursiveCharacterTextSplitter(
self.analyzer = KnowledgeAnalyzer(self.vectorstore) chunk_size=CHUNK_SIZE,
chunk_overlap=CHUNK_OVERLAP,
separators=["\n\n", "\n", ". ", " "]
)
async def initialize(self): async def initialize(self):
"""Initialize the learning system""" console.print("[bold cyan]🎓 RAG Learning System - Educational Assistant[/bold cyan]")
console.print(Panel.fit( console.print(f"📂 Notes Directory: {MD_DIRECTORY}")
"[bold cyan]🎓 RAG Learning System - Educational Assistant[/bold cyan]\n" console.print(f"🧠 Model: {LLM_MODEL}\n")
"📂 Notes Directory: {}\n"
"🧠 Model: {}\n"
"[dim]Analyzing your learning progress...[/dim]".format(
MD_DIRECTORY, LLM_MODEL
),
border_style="cyan"
))
# Load or create knowledge state
knowledge_state = await self.load_or_analyze_knowledge() knowledge_state = await self.load_or_analyze_knowledge()
console.print("[green]✓ System initialized successfully![/green]") console.print("[green]✓ System initialized successfully![/green]")
console.print("[dim]💡 Tip: /help[/dim]\n") console.print("[dim]💡 Tip: Просто напишите 'изучаем английский' или 'учим математику'[/dim]\n")
return knowledge_state return knowledge_state
async def load_or_analyze_knowledge(self) -> KnowledgeState: async def load_or_analyze_knowledge(self) -> KnowledgeState:
"""Load existing knowledge state or analyze all notes"""
# Load file hashes
file_hashes = self.get_file_hashes() file_hashes = self.get_file_hashes()
# Load knowledge state
state_data = load_json_cache(KNOWLEDGE_STATE_PATH) state_data = load_json_cache(KNOWLEDGE_STATE_PATH)
if state_data: if state_data:
knowledge_state = KnowledgeState.from_dict(state_data) knowledge_state = KnowledgeState.from_dict(state_data)
# Check if files have changed
if self.have_files_changed(file_hashes, knowledge_state.file_hashes): if self.have_files_changed(file_hashes, knowledge_state.file_hashes):
console.print("[yellow]📁 Files changed, re-analyzing knowledge...[/yellow]") console.print("[yellow]📁 Files changed, re-analyzing knowledge...[/yellow]")
knowledge_state = await self.analyzer.analyze_all_notes(file_hashes) knowledge_state = await self.analyze_all_notes(file_hashes)
save_json_cache(knowledge_state.to_dict(), KNOWLEDGE_STATE_PATH) save_json_cache(knowledge_state.to_dict(), KNOWLEDGE_STATE_PATH)
else: else:
console.print("[green]✓ Knowledge state up to date[/green]") console.print("[green]✓ Knowledge state up to date[/green]")
else: else:
console.print("[yellow]📊 First time setup - analyzing all notes...[/yellow]") console.print("[yellow]📊 First time setup - analyzing all notes...[/yellow]")
knowledge_state = await self.analyzer.analyze_all_notes(file_hashes) knowledge_state = await self.analyze_all_notes(file_hashes)
save_json_cache(knowledge_state.to_dict(), KNOWLEDGE_STATE_PATH) save_json_cache(knowledge_state.to_dict(), KNOWLEDGE_STATE_PATH)
return knowledge_state return knowledge_state
def get_file_hashes(self) -> Dict[str, str]: def get_file_hashes(self) -> Dict[str, str]:
"""Get hashes for all markdown files"""
file_hashes = {} file_hashes = {}
for root, _, files in os.walk(MD_DIRECTORY): for root, _, files in os.walk(MD_DIRECTORY):
for file in files: for file in files:
if file.endswith(".md"): if file.endswith(".md"):
@@ -468,24 +311,53 @@ class LearningAssistant:
file_hashes[file_path] = get_file_hash(file_path) file_hashes[file_path] = get_file_hash(file_path)
except Exception as e: except Exception as e:
console.print(f"[red]✗ Error reading {file}: {e}[/red]") console.print(f"[red]✗ Error reading {file}: {e}[/red]")
return file_hashes return file_hashes
def have_files_changed(self, current_hashes: Dict[str, str], cached_hashes: Dict[str, str]) -> bool: def have_files_changed(self, current: Dict[str, str], cached: Dict[str, str]) -> bool:
"""Check if any files have changed""" if len(current) != len(cached):
if len(current_hashes) != len(cached_hashes):
return True return True
for path, hash_val in current.items():
for file_path, current_hash in current_hashes.items(): if path not in cached or cached[path] != hash_val:
if file_path not in cached_hashes or cached_hashes[file_path] != current_hash:
return True return True
return False return False
async def get_relevant_context(self, subject: str, knowledge_state: KnowledgeState) -> str: async def analyze_all_notes(self, file_hashes: Dict[str, str]) -> KnowledgeState:
"""Get context relevant to the specified subject""" console.print("[cyan]🔍 Analyzing all notes for learning progress...[/cyan]")
subjects = {
name: SubjectProgress(name=name, topics_covered=set(), last_studied=None)
for name in SUBJECTS.keys()
}
try:
db_data = await asyncio.to_thread(self.vectorstore.get)
if db_data and db_data['documents']:
for text, metadata in zip(db_data['documents'], db_data['metadatas']):
if not metadata or 'source' not in metadata:
continue
subject = detect_subject_from_content(text)
if subject:
subjects[subject].topics_covered.add(text[:100])
file_path = metadata['source']
if file_path in file_hashes:
subjects[subject].last_studied = file_hashes[file_path]
for subject in subjects.values():
subject.confidence_level = min(len(subject.topics_covered) / 10.0, 1.0)
studied_count = len([s for s in subjects.values() if s.topics_covered])
console.print(f"[green]✓ Analysis complete. Found progress in {studied_count} subjects[/green]")
except Exception as e:
console.print(f"[red]✗ Error during analysis: {e}[/red]")
return KnowledgeState(subjects, datetime.now().isoformat(), file_hashes)
async def get_relevant_context(self, subject: str) -> str:
try: try:
# Get all documents and filter by subject
db_data = await asyncio.to_thread(self.vectorstore.get) db_data = await asyncio.to_thread(self.vectorstore.get)
if not db_data or not db_data['documents']: if not db_data or not db_data['documents']:
@@ -502,11 +374,10 @@ class LearningAssistant:
"source": Path(metadata.get('source', 'unknown')).name "source": Path(metadata.get('source', 'unknown')).name
}) })
# Build context string
context = f"Найдено {len(relevant_docs)} заметок по предмету:\n" context = f"Найдено {len(relevant_docs)} заметок по предмету:\n"
char_count = len(context) char_count = len(context)
for doc in relevant_docs[:TOP_K]: # Limit to top K documents for doc in relevant_docs[:TOP_K]:
doc_text = f"\n---\nИсточник: {doc['source']}\n{doc['text']}\n" doc_text = f"\n---\nИсточник: {doc['source']}\n{doc['text']}\n"
if char_count + len(doc_text) > MAX_CONTEXT_CHARS: if char_count + len(doc_text) > MAX_CONTEXT_CHARS:
@@ -517,7 +388,7 @@ class LearningAssistant:
char_count += len(doc_text) char_count += len(doc_text)
if not relevant_docs: if not relevant_docs:
return f"Заметок по предмету '{subject}' не найдено." return f"Заметок по предмету '{SUBJECTS.get(subject, subject)}' не найдено."
return context return context
@@ -525,74 +396,108 @@ class LearningAssistant:
console.print(f"[red]✗ Error getting context: {e}[/red]") console.print(f"[red]✗ Error getting context: {e}[/red]")
return "Ошибка при получении контекста." return "Ошибка при получении контекста."
def get_progress_summary(self, knowledge_state: KnowledgeState, subjects: List[str]) -> str:
summary = "Текущий прогресс обучения:\n"
for subject in subjects:
if subject in knowledge_state.subjects:
subj = knowledge_state.subjects[subject]
if subj.topics_covered:
summary += f"- {SUBJECTS[subject]}: {len(subj.topics_covered)} тем, уверенность {subj.confidence_level:.1%}\n"
else:
summary += f"- {SUBJECTS[subject]}: изучение с нуля\n"
return summary
async def process_learning_query(self, query: str, knowledge_state: KnowledgeState) -> str: async def process_learning_query(self, query: str, knowledge_state: KnowledgeState) -> str:
"""Process a learning query""" subjects = detect_subjects_from_query(query)
# Detect subject from query
subject = detect_subject_from_query(query)
if not subject: if not subjects:
# Try to infer from broader context or ask for clarification
return "Пожалуйста, уточните предмет для изучения (например: 'изучаем английский', 'учим математику')." return "Пожалуйста, уточните предмет для изучения (например: 'изучаем английский', 'учим математику')."
# Get relevant context responses = []
context = await self.get_relevant_context(subject, knowledge_state)
# Get progress summary for subject in subjects:
progress = self.analyzer.get_progress_summary(knowledge_state, subject) context = await self.get_relevant_context(subject)
progress = self.get_progress_summary(knowledge_state, [subject])
console.print(f"[blue]🔍 Анализирую прогресс по предмету: {SUBJECTS[subject]}[/blue]")
response = ""
console.print("[bold blue]Ассистент:[/bold blue] ", end="")
async for chunk in self.chain.astream({
"context": context,
"question": query,
"progress": progress
}):
console.print(chunk, end="", style="blue")
response += chunk
console.print("\n")
responses.append(response)
# Generate response return "\n\n".join(responses) if len(responses) > 1 else responses[0]
console.print(f"[blue]🔍 Анализирую прогресс по предмету: {subject}[/blue]")
console.print(f"[dim]Контекст: {len(context)} символов[/dim]\n") async def index_files(self, file_paths: List[str]) -> bool:
all_chunks = []
response = "" for file_path in file_paths:
console.print("[bold blue]Ассистент:[/bold blue] ", end="") try:
loader = UnstructuredMarkdownLoader(file_path)
documents = loader.load()
if documents:
for doc in documents:
doc.metadata["source"] = file_path
chunks = self.text_splitter.split_documents(documents)
all_chunks.extend(chunks)
except Exception as e:
console.print(f"[red]✗ Error processing {Path(file_path).name}: {e}[/red]")
async for chunk in self.chain.astream({ if not all_chunks:
"context": context, return False
"question": query,
"progress": progress
}):
console.print(chunk, end="", style=ANSWER_COLOR)
response += chunk
console.print("\n") try:
return response await asyncio.to_thread(self.vectorstore.reset_collection)
batch_size = 20
for i in range(0, len(all_chunks), batch_size):
batch = all_chunks[i:i + batch_size]
await asyncio.to_thread(self.vectorstore.add_documents, batch)
return True
except Exception as e:
console.print(f"[red]✗ Error indexing documents: {e}[/red]")
return False
# ========================= # =========================
# MAIN APPLICATION # MAIN APPLICATION
# ========================= # =========================
async def main(): async def main():
"""Main application entry point"""
# Setup directories
Path(MD_DIRECTORY).mkdir(parents=True, exist_ok=True) Path(MD_DIRECTORY).mkdir(parents=True, exist_ok=True)
assistant = LearningAssistant() assistant = LearningAssistant()
try: try:
# Initialize system
knowledge_state = await assistant.initialize() knowledge_state = await assistant.initialize()
# Main interaction loop
while True: while True:
# Get user input
query = await session.prompt_async("> ", style=style) query = await session.prompt_async("> ", style=style)
query = query.strip() query = query.strip()
if not query: if not query:
continue continue
# Handle exit commands
if query.lower() in ['/exit', '/quit', 'exit', 'quit', 'выход']: if query.lower() in ['/exit', '/quit', 'exit', 'quit', 'выход']:
console.print("\n👋 До свидания! Удачи в обучении!", style="yellow") console.print("\n👋 До свидания! Удачи в обучении!", style="yellow")
break break
# Handle help
if query.lower() in ['/help', 'help', 'помощь']: if query.lower() in ['/help', 'help', 'помощь']:
await show_help() await show_help()
continue continue
# Handle reindex command
if query.lower() in ['/reindex', 'reindex']: if query.lower() in ['/reindex', 'reindex']:
console.print("[yellow]🔄 Переиндексирую все файлы...[/yellow]") console.print("[yellow]🔄 Переиндексирую все файлы...[/yellow]")
@@ -603,12 +508,11 @@ async def main():
console.print("[yellow]⚠️ Markdown файлы не найдены[/yellow]") console.print("[yellow]⚠️ Markdown файлы не найдены[/yellow]")
continue continue
# Вызовите index_files напрямую — он сам напечатает прогресс success = await assistant.index_files(files)
success = await assistant.processor.index_files(files)
if success: if success:
console.print("[cyan]📊 Анализирую знания...[/cyan]") console.print("[cyan]📊 Анализирую знания...[/cyan]")
knowledge_state = await assistant.analyzer.analyze_all_notes( knowledge_state = await assistant.analyze_all_notes(
assistant.get_file_hashes() assistant.get_file_hashes()
) )
save_json_cache(knowledge_state.to_dict(), KNOWLEDGE_STATE_PATH) save_json_cache(knowledge_state.to_dict(), KNOWLEDGE_STATE_PATH)
@@ -618,30 +522,27 @@ async def main():
continue continue
# Process learning query
await assistant.process_learning_query(query, knowledge_state) await assistant.process_learning_query(query, knowledge_state)
except KeyboardInterrupt: except KeyboardInterrupt:
console.print("\n👋 До свидания! Удачи в обучении!", style="yellow") console.print("\n👋 До свидания! Удачи в обучении!", style="yellow")
except Exception as e: except Exception as e:
console.print(f"[red]✗ Unexpected error: {e}[/red]") console.print(f"[red]✗ Unexpected error: {e}[/red]")
console.print_exception()
async def show_help(): async def show_help():
"""Display help information"""
console.print("\n[bold cyan]🎓 RAG Learning System - Справка[/bold cyan]") console.print("\n[bold cyan]🎓 RAG Learning System - Справка[/bold cyan]")
console.print("=" * 60, style="dim") console.print("=" * 60, style="dim")
console.print("\n[bold green]Использование:[/bold green]") console.print("\n[bold green]Использование:[/bold green]")
console.print("Просто напишите, что хотите изучать:") console.print("Просто напишите, что хотите изучать:")
console.print(" • 'изучаем английский'") console.print(" • 'изучаем английский'")
console.print(" • 'учим математику'") console.print(" • 'учим математику и программирование'")
console.print(" • 'погнали по сетям'") console.print(" • 'давай по сетям'")
console.print(" • 'давай python'\n") console.print(" • 'пора изучать кибербезопасность'\n")
console.print("[bold green]Доступные предметы:[/bold green]") console.print("[bold green]Доступные предметы:[/bold green]")
for subject, keywords in SUBJECT_KEYWORDS.items(): for key, name in SUBJECTS.items():
console.print(f" • {subject}: {', '.join(keywords[:3])}...") console.print(f" • {name}")
console.print("\n[bold green]Команды:[/bold green]") console.print("\n[bold green]Команды:[/bold green]")
console.print(" • /help или помощь - показать эту справку") console.print(" • /help или помощь - показать эту справку")
@@ -663,7 +564,3 @@ if __name__ == "__main__":
asyncio.run(main()) asyncio.run(main())
except KeyboardInterrupt: except KeyboardInterrupt:
console.print("\n👋 До свидания! Удачи в обучении!", style="yellow") console.print("\n👋 До свидания! Удачи в обучении!", style="yellow")
sys.exit(0)
except Exception as e:
console.print(f"[red]✗ Unexpected error: {e}[/red]")
sys.exit(1)

127
monitor.py Normal file
View File

@@ -0,0 +1,127 @@
#!/usr/bin/env python3
"""
Real-time GPU and RAM monitoring utility
"""
import psutil
import time
import threading
from rich.console import Console
from rich.live import Live
from rich.table import Table
try:
import pynvml
NVIDIA_AVAILABLE = True
except ImportError:
NVIDIA_AVAILABLE = False
console = Console()
class SystemMonitor:
def __init__(self):
self.running = False
self.gpu_available = False
if NVIDIA_AVAILABLE:
try:
pynvml.nvmlInit()
self.gpu_count = pynvml.nvmlDeviceGetCount()
self.gpu_available = self.gpu_count > 0
except:
pass
def get_ram_usage(self):
memory = psutil.virtual_memory()
return {
"used_gb": memory.used / 1024**3,
"total_gb": memory.total / 1024**3,
"percent": memory.percent
}
def get_gpu_usage(self):
if not self.gpu_available:
return None
gpu_info = []
try:
for i in range(self.gpu_count):
handle = pynvml.nvmlDeviceGetHandleByIndex(i)
info = pynvml.nvmlDeviceGetMemoryInfo(handle)
gpu_info.append({
"id": i,
"used_gb": info.used / 1024**3,
"total_gb": info.total / 1024**3,
"percent": (info.used / info.total) * 100
})
except:
pass
return gpu_info
def create_table(self):
table = Table(title="System Resources")
table.add_column("Resource", style="cyan")
table.add_column("Used", style="yellow")
table.add_column("Total", style="green")
table.add_column("Usage %", style="red")
# RAM
ram = self.get_ram_usage()
table.add_row(
"RAM",
f"{ram['used_gb']:.2f} GB",
f"{ram['total_gb']:.2f} GB",
f"{ram['percent']:.1f}%"
)
# GPU
if self.gpu_available:
gpus = self.get_gpu_usage()
if gpus:
for gpu in gpus:
table.add_row(
f"GPU {gpu['id']}",
f"{gpu['used_gb']:.2f} GB",
f"{gpu['total_gb']:.2f} GB",
f"{gpu['percent']:.1f}%"
)
else:
table.add_row("GPU", "N/A", "N/A", "N/A")
return table
def start_monitoring(self):
self.running = True
with Live(self.create_table(), refresh_per_second=1, console=console) as live:
while self.running:
live.update(self.create_table())
time.sleep(1)
def stop_monitoring(self):
self.running = False
if self.gpu_available:
try:
pynvml.nvmlShutdown()
except:
pass
def monitor_resources():
"""Start monitoring in a separate thread"""
monitor = SystemMonitor()
thread = threading.Thread(target=monitor.start_monitoring, daemon=True)
thread.start()
return monitor
if __name__ == "__main__":
console.print("[bold cyan]System Resource Monitor[/bold cyan]")
console.print("Press Ctrl+C to stop\n")
monitor = SystemMonitor()
try:
monitor.start_monitoring()
except KeyboardInterrupt:
console.print("\n[yellow]Monitoring stopped.[/yellow]")
monitor.stop_monitoring()

8
pyproject.toml
View File

@@ -6,17 +6,15 @@ readme = "README.md"
requires-python = ">=3.13" requires-python = ">=3.13"
dependencies = [ dependencies = [
"chromadb>=1.4.0", "chromadb>=1.4.0",
"langchain>=1.2.0",
"langchain-chroma>=1.1.0", "langchain-chroma>=1.1.0",
"langchain-community>=0.4.1", "langchain-community>=0.4.1",
"langchain-ollama>=1.0.1", "langchain-ollama>=1.0.1",
"langchain-text-splitters>=1.1.0", "langchain-text-splitters>=1.1.0",
"nest-asyncio>=1.6.0",
"nvidia-ml-py>=13.590.44",
"prompt-toolkit>=3.0.52", "prompt-toolkit>=3.0.52",
"python-dotenv>=1.2.1", "python-dotenv>=1.2.1",
"pyyaml>=6.0.3",
"rich>=14.2.0", "rich>=14.2.0",
"unstructured[md]>=0.18.21", "unstructured[md]>=0.18.21",
"watchdog>=6.0.0", "psutil>=7.2.1",
"nvidia-ml-py>=13.590.44",
"nest-asyncio>=1.6.0",
] ]

196
uv.lock generated
View File

@@ -952,20 +952,6 @@ wheels = [
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] ]
[[package]]
name = "langchain"
version = "1.2.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "langchain-core" },
{ name = "langgraph" },
{ name = "pydantic" },
]
sdist = { url = "https://files.pythonhosted.org/packages/b1/12/3a74c22abdfddd877dfc2ee666d516f9132877fcd25eb4dd694835c59c79/langchain-1.2.0.tar.gz", hash = "sha256:a087d1e2b2969819e29a91a6d5f98302aafe31bd49ba377ecee3bf5a5dcfe14a", size = 536126, upload-time = "2025-12-15T14:51:42.24Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/23/00/4e3fa0d90f5a5c376ccb8ca983d0f0f7287783dfac48702e18f01d24673b/langchain-1.2.0-py3-none-any.whl", hash = "sha256:82f0d17aa4fbb11560b30e1e7d4aeb75e3ad71ce09b85c90ab208b181a24ffac", size = 102828, upload-time = "2025-12-15T14:51:40.802Z" },
]
[[package]] [[package]]
name = "langchain-chroma" name = "langchain-chroma"
version = "1.1.0" version = "1.1.0"
@@ -1074,62 +1060,6 @@ dependencies = [
] ]
sdist = { url = "https://files.pythonhosted.org/packages/0e/72/a3add0e4eec4eb9e2569554f7c70f4a3c27712f40e3284d483e88094cc0e/langdetect-1.0.9.tar.gz", hash = "sha256:cbc1fef89f8d062739774bd51eda3da3274006b3661d199c2655f6b3f6d605a0", size = 981474, upload-time = "2021-05-07T07:54:13.562Z" } sdist = { url = "https://files.pythonhosted.org/packages/0e/72/a3add0e4eec4eb9e2569554f7c70f4a3c27712f40e3284d483e88094cc0e/langdetect-1.0.9.tar.gz", hash = "sha256:cbc1fef89f8d062739774bd51eda3da3274006b3661d199c2655f6b3f6d605a0", size = 981474, upload-time = "2021-05-07T07:54:13.562Z" }
[[package]]
name = "langgraph"
version = "1.0.5"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "langchain-core" },
{ name = "langgraph-checkpoint" },
{ name = "langgraph-prebuilt" },
{ name = "langgraph-sdk" },
{ name = "pydantic" },
{ name = "xxhash" },
]
sdist = { url = "https://files.pythonhosted.org/packages/7d/47/28f4d4d33d88f69de26f7a54065961ac0c662cec2479b36a2db081ef5cb6/langgraph-1.0.5.tar.gz", hash = "sha256:7f6ae59622386b60fe9fa0ad4c53f42016b668455ed604329e7dc7904adbf3f8", size = 493969, upload-time = "2025-12-12T23:05:48.224Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/23/1b/e318ee76e42d28f515d87356ac5bd7a7acc8bad3b8f54ee377bef62e1cbf/langgraph-1.0.5-py3-none-any.whl", hash = "sha256:b4cfd173dca3c389735b47228ad8b295e6f7b3df779aba3a1e0c23871f81281e", size = 157056, upload-time = "2025-12-12T23:05:46.499Z" },
]
[[package]]
name = "langgraph-checkpoint"
version = "3.0.1"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "langchain-core" },
{ name = "ormsgpack" },
]
sdist = { url = "https://files.pythonhosted.org/packages/0f/07/2b1c042fa87d40cf2db5ca27dc4e8dd86f9a0436a10aa4361a8982718ae7/langgraph_checkpoint-3.0.1.tar.gz", hash = "sha256:59222f875f85186a22c494aedc65c4e985a3df27e696e5016ba0b98a5ed2cee0", size = 137785, upload-time = "2025-11-04T21:55:47.774Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/48/e3/616e3a7ff737d98c1bbb5700dd62278914e2a9ded09a79a1fa93cf24ce12/langgraph_checkpoint-3.0.1-py3-none-any.whl", hash = "sha256:9b04a8d0edc0474ce4eaf30c5d731cee38f11ddff50a6177eead95b5c4e4220b", size = 46249, upload-time = "2025-11-04T21:55:46.472Z" },
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[[package]]
name = "langgraph-prebuilt"
version = "1.0.5"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "langchain-core" },
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