├── Dockerfile ├── LICENSE ├── README.md ├── agents ├── base.py ├── code_exec_agent.py ├── default_agent.py ├── pdf_file_agent.py ├── pdf_link_agent.py └── selector.py ├── config.py ├── llm └── phi_wrapper.py ├── main.py ├── requirements.txt ├── sanitizer └── prompt_sanitizer.py ├── society_mind └── autogen_society.py ├── templates ├── code_instruction.txt ├── critic_instruction.txt ├── default_instruction.txt ├── finalizer_instruction.txt ├── generator_instruction.txt └── pdf_instruction.txt └── utils ├── cache.py ├── docker_sandbox.py ├── exceptions.py ├── io.py ├── logger.py └── pdf_utils.py /Dockerfile: -------------------------------------------------------------------------------- 1 | # 2 | # NOTE: THIS DOCKERFILE IS GENERATED VIA "apply-templates.sh" 3 | # 4 | # PLEASE DO NOT EDIT IT DIRECTLY. 5 | # 6 | 7 | FROM debian:bookworm-slim 8 | 9 | # ensure local python is preferred over distribution python 10 | ENV PATH /usr/local/bin:$PATH 11 | 12 | # runtime dependencies 13 | RUN set -eux; \ 14 | apt-get update; \ 15 | apt-get install -y --no-install-recommends \ 16 | ca-certificates \ 17 | netbase \ 18 | tzdata \ 19 | ; \ 20 | rm -rf /var/lib/apt/lists/* 21 | 22 | ENV PYTHON_VERSION 3.14.0a7 23 | ENV PYTHON_SHA256 71adbcec3ac9edf93308e55cfb4184f2eb4b16fda2bb0a5a382929ed29c8386d 24 | 25 | RUN set -eux; \ 26 | \ 27 | savedAptMark="$(apt-mark showmanual)"; \ 28 | apt-get update; \ 29 | apt-get install -y --no-install-recommends \ 30 | dpkg-dev \ 31 | gcc \ 32 | gnupg \ 33 | libbluetooth-dev \ 34 | libbz2-dev \ 35 | libc6-dev \ 36 | libdb-dev \ 37 | libffi-dev \ 38 | libgdbm-dev \ 39 | liblzma-dev \ 40 | libncursesw5-dev \ 41 | libreadline-dev \ 42 | libsqlite3-dev \ 43 | libssl-dev \ 44 | make \ 45 | tk-dev \ 46 | uuid-dev \ 47 | wget \ 48 | xz-utils \ 49 | zlib1g-dev \ 50 | ; \ 51 | \ 52 | wget -O python.tar.xz "https://www.python.org/ftp/python/${PYTHON_VERSION%%[a-z]*}/Python-$PYTHON_VERSION.tar.xz"; \ 53 | echo "$PYTHON_SHA256 *python.tar.xz" | sha256sum -c -; \ 54 | mkdir -p /usr/src/python; \ 55 | tar --extract --directory /usr/src/python --strip-components=1 --file python.tar.xz; \ 56 | rm python.tar.xz; \ 57 | \ 58 | cd /usr/src/python; \ 59 | gnuArch="$(dpkg-architecture --query DEB_BUILD_GNU_TYPE)"; \ 60 | ./configure \ 61 | --build="$gnuArch" \ 62 | --enable-loadable-sqlite-extensions \ 63 | --enable-optimizations \ 64 | --enable-option-checking=fatal \ 65 | --enable-shared \ 66 | --with-lto \ 67 | --with-ensurepip \ 68 | ; \ 69 | nproc="$(nproc)"; \ 70 | EXTRA_CFLAGS="$(dpkg-buildflags --get CFLAGS)"; \ 71 | LDFLAGS="$(dpkg-buildflags --get LDFLAGS)"; \ 72 | LDFLAGS="${LDFLAGS:--Wl},--strip-all"; \ 73 | arch="$(dpkg --print-architecture)"; arch="${arch##*-}"; \ 74 | # https://docs.python.org/3.12/howto/perf_profiling.html 75 | # https://github.com/docker-library/python/pull/1000#issuecomment-2597021615 76 | case "$arch" in \ 77 | amd64|arm64) \ 78 | # only add "-mno-omit-leaf" on arches that support it 79 | # https://gcc.gnu.org/onlinedocs/gcc-14.2.0/gcc/x86-Options.html#index-momit-leaf-frame-pointer-2 80 | # https://gcc.gnu.org/onlinedocs/gcc-14.2.0/gcc/AArch64-Options.html#index-momit-leaf-frame-pointer 81 | EXTRA_CFLAGS="${EXTRA_CFLAGS:-} -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer"; \ 82 | ;; \ 83 | i386) \ 84 | # don't enable frame-pointers on 32bit x86 due to performance drop. 85 | ;; \ 86 | *) \ 87 | # other arches don't support "-mno-omit-leaf" 88 | EXTRA_CFLAGS="${EXTRA_CFLAGS:-} -fno-omit-frame-pointer"; \ 89 | ;; \ 90 | esac; \ 91 | make -j "$nproc" \ 92 | "EXTRA_CFLAGS=${EXTRA_CFLAGS:-}" \ 93 | "LDFLAGS=${LDFLAGS:-}" \ 94 | ; \ 95 | # https://github.com/docker-library/python/issues/784 96 | # prevent accidental usage of a system installed libpython of the same version 97 | rm python; \ 98 | make -j "$nproc" \ 99 | "EXTRA_CFLAGS=${EXTRA_CFLAGS:-}" \ 100 | "LDFLAGS=${LDFLAGS:--Wl},-rpath='\$\$ORIGIN/../lib'" \ 101 | python \ 102 | ; \ 103 | make install; \ 104 | \ 105 | cd /; \ 106 | rm -rf /usr/src/python; \ 107 | \ 108 | find /usr/local -depth \ 109 | \( \ 110 | \( -type d -a \( -name test -o -name tests -o -name idle_test \) \) \ 111 | -o \( -type f -a \( -name '*.pyc' -o -name '*.pyo' -o -name 'libpython*.a' \) \) \ 112 | \) -exec rm -rf '{}' + \ 113 | ; \ 114 | \ 115 | ldconfig; \ 116 | \ 117 | apt-mark auto '.*' > /dev/null; \ 118 | apt-mark manual $savedAptMark; \ 119 | find /usr/local -type f -executable -not \( -name '*tkinter*' \) -exec ldd '{}' ';' \ 120 | | awk '/=>/ { so = $(NF-1); if (index(so, "/usr/local/") == 1) { next }; gsub("^/(usr/)?", "", so); printf "*%s\n", so }' \ 121 | | sort -u \ 122 | | xargs -r dpkg-query --search \ 123 | | cut -d: -f1 \ 124 | | sort -u \ 125 | | xargs -r apt-mark manual \ 126 | ; \ 127 | apt-get purge -y --auto-remove -o APT::AutoRemove::RecommendsImportant=false; \ 128 | rm -rf /var/lib/apt/lists/*; \ 129 | \ 130 | export PYTHONDONTWRITEBYTECODE=1; \ 131 | python3 --version; \ 132 | pip3 --version 133 | 134 | # make some useful symlinks that are expected to exist ("/usr/local/bin/python" and friends) 135 | RUN set -eux; \ 136 | for src in idle3 pip3 pydoc3 python3 python3-config; do \ 137 | dst="$(echo "$src" | tr -d 3)"; \ 138 | [ -s "/usr/local/bin/$src" ]; \ 139 | [ ! -e "/usr/local/bin/$dst" ]; \ 140 | ln -svT "$src" "/usr/local/bin/$dst"; \ 141 | done 142 | 143 | CMD ["python3"] -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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-------------------------------------------------------------------------------- 1 | # 🧠 MindForсe: Intelligent Context-Aware Assistant [beta-version] 2 | 3 | **Умный ассистент с поддержкой контекста, анализом документов и безопасным выполнением кода** 4 | 5 | --- 6 | 7 | ## 🌟 Особенности 8 | 9 | - 📄 **Анализ PDF** (по ссылкам и локальным файлам) 10 | - ⌨️ **Безопасное выполнение кода** в Docker-песочнице 11 | - 🤖 **Итеративное улучшение ответов** (Society of Mind) 12 | - 🔒 **Защита от инъекций** с помощью BERT-модели 13 | - 🧩 **Модульная архитектура** с переиспользуемыми агентами 14 | - ⚡ **Умное кэширование** с учетом версий и данных 15 | 16 | --- 17 | 18 | ## 🏗 Архитектура системы 19 | 20 | ```mermaid 21 | graph TD 22 | A[Пользовательский запрос] --> B{Тип контента} 23 | B -->|PDF-ссылка| C[PDFLinkAgent] 24 | B -->|Код| D[CodeExecutionAgent] 25 | B -->|Локальный PDF| E[PDFFileAgent] 26 | B -->|Текст| F[DefaultAgent] 27 | C --> G[Извлечение текста] 28 | D --> H[Запуск в Docker] 29 | E --> G 30 | G --> I[Семантический поиск] 31 | H --> J[Сбор результатов] 32 | I --> K[Society of Mind] 33 | J --> K 34 | K --> L[Генерация ответа] 35 | L --> M[Кэширование] 36 | M --> N[Пользователь] 37 | -------------------------------------------------------------------------------- /agents/base.py: -------------------------------------------------------------------------------- 1 | from abc import ABC, abstractmethod 2 | from typing import Any, Dict 3 | from utils.exceptions import ProcessingError 4 | 5 | class Agent(ABC): 6 | def __init__(self, config: Dict[str, Any] = None): 7 | self.config = config or {} 8 | self._validate_config() 9 | 10 | def _validate_config(self): 11 | """Проверка конфигурации агента""" 12 | required_params = self.required_params() 13 | for param in required_params: 14 | if param not in self.config: 15 | raise ProcessingError(f"Missing required parameter: {param}") 16 | 17 | @staticmethod 18 | @abstractmethod 19 | def required_params() -> list: 20 | """Список обязательных параметров конфигурации""" 21 | return [] 22 | 23 | @abstractmethod 24 | async def execute(self, input_data: str) -> str: 25 | """Основной метод выполнения задачи""" 26 | pass 27 | 28 | def __repr__(self): 29 | return f"<{self.__class__.__name__} config={self.config}>" -------------------------------------------------------------------------------- /agents/code_exec_agent.py: -------------------------------------------------------------------------------- 1 | import re 2 | from typing import Dict, Any 3 | from .base import Agent 4 | from utils.docker_sandbox import DockerSandbox 5 | from utils.exceptions import (CodeExecutionError, ResourceLimitExceeded, 6 | DockerSecurityException) 7 | 8 | class CodeExecutionAgent(Agent): 9 | MAX_OUTPUT_LENGTH = 10000 10 | BLACKLIST_PATTERNS = [ 11 | r"os\.system", 12 | r"subprocess\.", 13 | r"open\(", 14 | r"import\s+(os|sys|subprocess)", 15 | r"__import__", 16 | r"eval\(", 17 | r"exec\(", 18 | r"pickle\.", 19 | r"shutil\.", 20 | r"socket\." 21 | ] 22 | 23 | @staticmethod 24 | def required_params(): 25 | return ["docker_config"] 26 | 27 | def __init__(self, config: Dict[str, Any]): 28 | super().__init__(config) 29 | self.sandbox = DockerSandbox(config["docker_config"]) 30 | 31 | async def execute(self, input_data: str) -> str: 32 | """Безопасное выполнение кода""" 33 | try: 34 | self._validate_code(input_data) 35 | result = await self.sandbox.execute(input_data) 36 | return self._sanitize_output(result) 37 | except DockerSecurityException as e: 38 | raise CodeExecutionError(f"Security violation: {str(e)}") from e 39 | except Exception as e: 40 | raise CodeExecutionError(str(e)) from e 41 | 42 | def _validate_code(self, code: str): 43 | """Проверка кода на опасные паттерны""" 44 | for pattern in self.BLACKLIST_PATTERNS: 45 | if re.search(pattern, code): 46 | raise DockerSecurityException(f"Blocked pattern: {pattern}") 47 | 48 | def _sanitize_output(self, output: str) -> str: 49 | """Санобработка вывода""" 50 | if len(output) > self.MAX_OUTPUT_LENGTH: 51 | raise ResourceLimitExceeded("Output too large") 52 | 53 | # Удаление чувствительной информации 54 | cleaned = re.sub(r"(API_KEY|SECRET|PASSWORD)\s*=\s*'.*?'", "[REDACTED]", output) 55 | return cleaned[:self.MAX_OUTPUT_LENGTH] -------------------------------------------------------------------------------- /agents/default_agent.py: -------------------------------------------------------------------------------- 1 | from typing import Dict, Any 2 | from .base import Agent 3 | from utils.exceptions import ProcessingError 4 | 5 | class DefaultAgent(Agent): 6 | async def execute(self, input_data: str) -> str: 7 | """Дефолтная обработка запроса""" 8 | try: 9 | return input_data 10 | except Exception as e: 11 | raise ProcessingError(f"Default processing failed: {str(e)}") from e -------------------------------------------------------------------------------- /agents/pdf_file_agent.py: -------------------------------------------------------------------------------- 1 | import fitz 2 | import os 3 | from pathlib import Path 4 | from typing import Dict, Any 5 | from .base import Agent 6 | from utils.exceptions import PDFProcessingError, ResourceLimitExceeded 7 | 8 | class PDFFileAgent(Agent): 9 | MAX_FILE_SIZE = 50 * 1024 * 1024 # 50MB 10 | ALLOWED_MIME_TYPES = ["application/pdf"] 11 | 12 | @staticmethod 13 | def required_params(): 14 | return ["upload_dir", "embedding_model"] 15 | 16 | def __init__(self, config: Dict[str, Any]): 17 | super().__init__(config) 18 | self.upload_dir = Path(config["upload_dir"]) 19 | self.embedding_model = config["embedding_model"] 20 | self._validate_upload_dir() 21 | 22 | def _validate_upload_dir(self): 23 | """Проверка директории для загрузок""" 24 | if not self.upload_dir.exists(): 25 | self.upload_dir.mkdir(parents=True) 26 | if not os.access(self.upload_dir, os.W_OK): 27 | raise PDFProcessingError("Upload directory not writable") 28 | 29 | async def execute(self, input_data: str) -> str: 30 | """Обработка загруженного PDF""" 31 | try: 32 | file_path = self._validate_file(input_data) 33 | text = self._parse_pdf(file_path) 34 | return self._find_relevant_sections(text, input_data) 35 | except Exception as e: 36 | raise PDFProcessingError(str(e)) from e 37 | 38 | def _validate_file(self, input_data: str) -> Path: 39 | """Валидация загруженного файла""" 40 | file_match = re.search(r"(.+?)", input_data) 41 | if not file_match: 42 | raise PDFProcessingError("Invalid file format") 43 | 44 | file_path = self.upload_dir / file_match.group(1) 45 | if not file_path.exists(): 46 | raise PDFProcessingError("File not found") 47 | 48 | if file_path.stat().st_size > self.MAX_FILE_SIZE: 49 | raise ResourceLimitExceeded("File size exceeds limit") 50 | 51 | return file_path 52 | 53 | def _parse_pdf(self, file_path: Path) -> str: 54 | """Парсинг PDF файла""" 55 | try: 56 | doc = fitz.open(file_path) 57 | return "\n".join([page.get_text() for page in doc]) 58 | except fitz.FileDataError: 59 | raise PDFProcessingError("Invalid PDF file structure") 60 | except Exception as e: 61 | raise PDFProcessingError(f"PDF parsing error: {str(e)}") 62 | 63 | def _find_relevant_sections(self, text: str, query: str) -> str: 64 | """Поиск релевантных разделов""" 65 | try: 66 | chunks = text.split("\n\n") 67 | query_embedding = self.embedding_model.encode(query) 68 | doc_embeddings = self.embedding_model.encode(chunks) 69 | 70 | scores = util.pytorch_cos_sim(query_embedding, doc_embeddings)[0] 71 | top_indices = scores.argsort(descending=True)[:5] 72 | 73 | return "\n".join([chunks[i] for i in top_indices]) 74 | except Exception as e: 75 | raise PDFProcessingError(f"Relevance search failed: {str(e)}") -------------------------------------------------------------------------------- /agents/pdf_link_agent.py: -------------------------------------------------------------------------------- 1 | import re 2 | import requests 3 | import fitz 4 | from io import BytesIO 5 | from typing import Optional 6 | from sentence_transformers import util 7 | from .base import Agent 8 | from utils.exceptions import (PDFProcessingError, NetworkError, 9 | ResourceLimitExceeded, SecurityException) 10 | 11 | class PDFLinkAgent(Agent): 12 | MAX_PDF_SIZE = 10 * 1024 * 1024 # 10MB 13 | TIMEOUT = 15 14 | 15 | @staticmethod 16 | def required_params(): 17 | return ["embedding_model"] 18 | 19 | def __init__(self, config: Dict[str, Any]): 20 | super().__init__(config) 21 | self.embedding_model = config["embedding_model"] 22 | 23 | async def execute(self, input_data: str) -> str: 24 | """Основной метод обработки PDF по ссылке""" 25 | try: 26 | url = self._extract_url(input_data) 27 | content = await self._download_pdf(url) 28 | text = self._parse_pdf(content) 29 | return self._find_relevant_sections(text, input_data) 30 | except Exception as e: 31 | raise PDFProcessingError(str(e)) from e 32 | 33 | def _extract_url(self, text: str) -> str: 34 | """Извлечение PDF URL из текста""" 35 | match = re.search(r'(https?://\S+\.pdf)', text) 36 | if not match: 37 | raise PDFProcessingError("No valid PDF URL found") 38 | return match.group(1) 39 | 40 | async def _download_pdf(self, url: str) -> bytes: 41 | """Безопасная загрузка PDF""" 42 | try: 43 | async with requests.Session() as session: 44 | response = await session.get( 45 | url, 46 | stream=True, 47 | timeout=self.TIMEOUT, 48 | headers={"User-Agent": "Mozilla/5.0"} 49 | ) 50 | response.raise_for_status() 51 | 52 | if int(response.headers.get('Content-Length', 0)) > self.MAX_PDF_SIZE: 53 | raise ResourceLimitExceeded("PDF file size exceeds limit") 54 | 55 | return await response.content.read() 56 | 57 | except requests.RequestException as e: 58 | raise NetworkError(f"Failed to download PDF: {str(e)}") from e 59 | 60 | def _parse_pdf(self, content: bytes) -> str: 61 | """Парсинг PDF контента""" 62 | try: 63 | with BytesIO(content) as buffer: 64 | doc = fitz.open("pdf", buffer) 65 | return "\n".join([page.get_text() for page in doc]) 66 | except fitz.FileDataError: 67 | raise PDFProcessingError("Invalid PDF file structure") 68 | except Exception as e: 69 | raise PDFProcessingError(f"PDF parsing error: {str(e)}") 70 | 71 | def _find_relevant_sections(self, text: str, query: str) -> str: 72 | """Поиск релевантных разделов""" 73 | try: 74 | chunks = text.split("\n\n") 75 | query_embedding = self.embedding_model.encode(query) 76 | doc_embeddings = self.embedding_model.encode(chunks) 77 | 78 | scores = util.pytorch_cos_sim(query_embedding, doc_embeddings)[0] 79 | top_indices = scores.argsort(descending=True)[:5] 80 | 81 | return "\n".join([chunks[i] for i in top_indices]) 82 | except Exception as e: 83 | raise PDFProcessingError(f"Relevance search failed: {str(e)}") -------------------------------------------------------------------------------- /agents/selector.py: -------------------------------------------------------------------------------- 1 | import re 2 | import mimetypes 3 | from typing import Optional 4 | from .base import Agent 5 | from .pdf_link_agent import PDFLinkAgent 6 | from .code_exec_agent import CodeExecutionAgent 7 | from .pdf_file_agent import PDFFileAgent 8 | from .default_agent import DefaultAgent 9 | from utils.exceptions import AgentSelectionError, SecurityException 10 | 11 | class AgentSelector: 12 | def __init__(self): 13 | self.code_patterns = [ 14 | r'(def\s+\w+\s*\(.*\):)', 15 | r'(class\s+\w+)', 16 | r'(import\s+\w+)', 17 | r'(print\(.*\))', 18 | r'(\#\!.*python)' 19 | ] 20 | self.url_pattern = r'(https?:\/\/(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+\.pdf)' 21 | 22 | def select_agent(self, prompt: str) -> Agent: 23 | try: 24 | # Проверка безопасности перед выбором агента 25 | self._check_prompt_safety(prompt) 26 | 27 | # Определение типа задачи 28 | if self._is_pdf_url(prompt): 29 | return PDFLinkAgent() 30 | 31 | if self._is_code(prompt): 32 | return CodeExecutionAgent() 33 | 34 | if self._has_uploaded_file(prompt): 35 | return self._handle_file_upload(prompt) 36 | 37 | return DefaultAgent() 38 | 39 | except Exception as e: 40 | raise AgentSelectionError(f"Agent selection failed: {str(e)}") 41 | 42 | def _check_prompt_safety(self, prompt: str): 43 | forbidden_patterns = [ 44 | r'(\/etc\/passwd)', 45 | r'(file:\/\/)', 46 | r'(localhost:\d+)' 47 | ] 48 | for pattern in forbidden_patterns: 49 | if re.search(pattern, prompt): 50 | raise SecurityException(f"Dangerous pattern detected: {pattern}") 51 | 52 | def _is_pdf_url(self, text: str) -> bool: 53 | return bool(re.search(self.url_pattern, text)) 54 | 55 | def _is_code(self, text: str) -> bool: 56 | return any(re.search(pattern, text) for pattern in self.code_patterns) 57 | 58 | def _has_uploaded_file(self, text: str) -> bool: 59 | return '' in text 60 | 61 | def _handle_file_upload(self, prompt: str) -> Agent: 62 | file_info = self._parse_upload(prompt) 63 | mime_type, _ = mimetypes.guess_type(file_info['name']) 64 | 65 | if mime_type == 'application/pdf': 66 | return PDFFileAgent() 67 | elif mime_type in ['text/plain', 'text/x-python']: 68 | return CodeExecutionAgent() 69 | 70 | raise AgentSelectionError(f"Unsupported file type: {mime_type}") 71 | 72 | def _parse_upload(self, prompt: str) -> dict: 73 | match = re.search(r'(?P.+?)', prompt) 74 | if not match: 75 | raise AgentSelectionError("Invalid file upload format") 76 | return {'name': match.group('name')} -------------------------------------------------------------------------------- /config.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LastGuardian89/Mind-Force/6fa3ed29f17b451cdb2f76d925a9efc4ec2f72c9/config.py -------------------------------------------------------------------------------- /llm/phi_wrapper.py: -------------------------------------------------------------------------------- 1 | from transformers import AutoTokenizer, AutoModelForCausalLM 2 | import torch 3 | import os 4 | 5 | class PhiLLM: 6 | def __init__(self, model_id="microsoft/phi-2"): 7 | self.tokenizer = AutoTokenizer.from_pretrained(model_id) 8 | self.model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).cuda() 9 | 10 | def _load_template(self, template_name): 11 | path = os.path.join("templates", template_name) 12 | with open(path) as f: 13 | return f.read() 14 | 15 | def generate(self, prompt, context="", mode="auto"): 16 | if mode == "pdf": 17 | template = self._load_template("pdf_instruction.txt") 18 | filled = template.format(context=context, question=prompt) 19 | elif mode == "code": 20 | template = self._load_template("code_instruction.txt") 21 | filled = template.format(code=prompt, question="What does this code do?") 22 | else: 23 | template = self._load_template("default_instruction.txt") 24 | filled = template.format(question=prompt) 25 | 26 | inputs = self.tokenizer(filled, return_tensors="pt").to(self.model.device) 27 | outputs = self.model.generate(**inputs, max_new_tokens=300) 28 | return self.tokenizer.decode(outputs[0], skip_special_tokens=True) 29 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | from typing import Optional 3 | from agents.selector import AgentSelector 4 | from .llm.phi_wrapper import PhiLLM 5 | from .society_mind.autogen_society import SocietyMind 6 | from .sanitizer.prompt_sanitizer import SanitizationPipeline 7 | from .utils.io import get_input_data, send_response, log_request 8 | from .utils.cache import check_cache, save_cache 9 | from .utils.logger import setup_logging, RequestLogger 10 | from utils.exceptions import (SecurityException, ProcessingError, 11 | NetworkError, ResourceLimitExceeded) 12 | 13 | class AIOrchestrator: 14 | def __init__(self): 15 | setup_logging() 16 | self.logger = RequestLogger() 17 | self.selector = AgentSelector() 18 | self.llm = PhiLLM() 19 | self.society = SocietyMind(self.llm) 20 | self.cache_enabled = True 21 | 22 | async def process_request(self, user_input: str) -> str: 23 | try: 24 | # Шаг 1: Санитайзинг ввода 25 | clean_input = await SanitizationPipeline.process(user_input) 26 | 27 | # Шаг 2: Проверка кэша 28 | if self.cache_enabled: 29 | cached = check_cache(clean_input) 30 | if cached: 31 | self.logger.log("CACHE_HIT", {"input": clean_input}) 32 | return cached 33 | 34 | # Шаг 3: Выбор и выполнение агента 35 | agent = self.selector.select_agent(clean_input) 36 | context = await agent.execute(clean_input) 37 | 38 | # Шаг 4: Генерация ответа 39 | raw_response = await self.llm.generate_async(clean_input, context) 40 | 41 | # Шаг 5: Обсуждение в SocietyMind 42 | final_response = await self.society.refine( 43 | prompt=clean_input, 44 | context=context, 45 | initial_response=raw_response 46 | ) 47 | 48 | # Шаг 6: Сохранение и возврат результата 49 | save_cache(clean_input, final_response) 50 | return final_response 51 | 52 | except SecurityException as e: 53 | self.logger.log("SECURITY_BLOCK", { 54 | "input": user_input, 55 | "reason": str(e) 56 | }) 57 | return "Request blocked for security reasons" 58 | 59 | except ProcessingError as e: 60 | self.logger.log("PROCESSING_ERROR", { 61 | "input": user_input, 62 | "error": str(e) 63 | }) 64 | return "Error processing your request" 65 | 66 | except Exception as e: 67 | self.logger.log("INTERNAL_ERROR", { 68 | "input": user_input, 69 | "error": str(e) 70 | }) 71 | return "Internal server error" 72 | 73 | finally: 74 | log_request(user_input, final_response if 'final_response' in locals() else None) 75 | 76 | async def main_flow(): 77 | orchestrator = AIOrchestrator() 78 | while True: 79 | try: 80 | user_input = get_input_data() 81 | response = await orchestrator.process_request(user_input) 82 | send_response(response) 83 | except KeyboardInterrupt: 84 | break 85 | 86 | if __name__ == "__main__": 87 | asyncio.run(main_flow()) -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | transformers>=4.30 2 | torch>=2.0 3 | python-dotenv>=0.19 4 | sentence-transformers>=2.2 5 | pymupdf>=1.22 6 | docker>=6.0 7 | aiohttp>=3.8 -------------------------------------------------------------------------------- /sanitizer/prompt_sanitizer.py: -------------------------------------------------------------------------------- 1 | import re 2 | import torch 3 | from transformers import BertTokenizer, BertForSequenceClassification 4 | from typing import Optional 5 | from utils.exceptions import InjectionAttemptError, SecurityException 6 | 7 | class PromptSanitizer: 8 | def __init__(self, model_path: str = "bert-prompt-sanitizer"): 9 | self.patterns = [ 10 | (r'(?i)(delete|drop|truncate)', "SQL injection"), 11 | (r'', "HTML injection"), 12 | (r'\{%|%\}', "Template injection"), 13 | (r'__import__|eval\(|exec\(', "Code injection"), 14 | (r'(ftp|ssh|sftp)://', "Dangerous protocol"), 15 | (r'/etc/passwd', "Sensitive file access") 16 | ] 17 | 18 | try: 19 | self.tokenizer = BertTokenizer.from_pretrained(model_path) 20 | self.model = BertForSequenceClassification.from_pretrained(model_path).eval() 21 | except Exception as e: 22 | raise RuntimeError(f"Failed to load security model: {str(e)}") 23 | 24 | def sanitize(self, prompt: str) -> str: 25 | self._check_patterns(prompt) 26 | self._check_ml(prompt) 27 | return prompt 28 | 29 | def _check_patterns(self, text: str): 30 | for pattern, description in self.patterns: 31 | if re.search(pattern, text): 32 | raise InjectionAttemptError(f"Pattern detected: {description} - {pattern}") 33 | 34 | def _check_ml(self, text: str): 35 | try: 36 | inputs = self.tokenizer( 37 | text, 38 | return_tensors="pt", 39 | max_length=512, 40 | truncation=True 41 | ) 42 | 43 | with torch.no_grad(): 44 | outputs = self.model(**inputs) 45 | 46 | probs = torch.softmax(outputs.logits, dim=1) 47 | if probs[0][1].item() > 0.85: 48 | raise SecurityException("ML model detected malicious intent") 49 | 50 | except Exception as e: 51 | raise SecurityException(f"Security check failed: {str(e)}") 52 | 53 | class SanitizationPipeline: 54 | @staticmethod 55 | async def process(prompt: str) -> str: 56 | try: 57 | sanitizer = PromptSanitizer() 58 | return sanitizer.sanitize(prompt) 59 | except Exception as e: 60 | raise SecurityException(str(e)) -------------------------------------------------------------------------------- /society_mind/autogen_society.py: -------------------------------------------------------------------------------- 1 | import os 2 | import asyncio 3 | import torch 4 | import re 5 | from typing import Optional, Tuple 6 | from sentence_transformers import SentenceTransformer, util 7 | from llm.phi_wrapper import PhiLLM 8 | from utils.exceptions import QualityThresholdReached 9 | 10 | class SocietyMind: 11 | def __init__( 12 | self, 13 | model: PhiLLM, 14 | max_rounds: int = 3, 15 | similarity_threshold: float = 0.85, 16 | quality_threshold: float = 0.7 17 | ): 18 | self.model = model 19 | self.max_rounds = max_rounds 20 | self.similarity_threshold = similarity_threshold 21 | self.quality_threshold = quality_threshold 22 | self.similarity_model = SentenceTransformer('all-MiniLM-L6-v2') 23 | 24 | self.templates = { 25 | 'generator': self._load_template("generator_instruction.txt"), 26 | 'critic': self._load_template("critic_instruction.txt"), 27 | 'finalizer': self._load_template("finalizer_instruction.txt") 28 | } 29 | 30 | async def refine_response( 31 | self, 32 | query: str, 33 | context: str, 34 | initial_response: str 35 | ) -> str: 36 | current_response = initial_response 37 | previous_response = "" 38 | iteration = 0 39 | 40 | while iteration < self.max_rounds: 41 | # 1. Generate critique with context 42 | critique = await self._generate_critique(query, current_response, context) 43 | 44 | # 2. Check stopping conditions 45 | stop_reason = self._check_stopping_conditions( 46 | current_response, 47 | previous_response, 48 | context 49 | ) 50 | if stop_reason: 51 | print(f"Stopping iteration: {stop_reason}") 52 | break 53 | 54 | # 3. Generate improved response 55 | previous_response = current_response 56 | current_response = await self._generate_improved( 57 | query, 58 | context, 59 | critique 60 | ) 61 | 62 | iteration += 1 63 | 64 | return await self._finalize_response(current_response, context) 65 | 66 | def _check_stopping_conditions( 67 | self, 68 | current: str, 69 | previous: str, 70 | context: str 71 | ) -> Optional[str]: 72 | # 1. Check similarity between iterations 73 | iteration_similarity = self._calculate_similarity(current, previous) 74 | if iteration_similarity > self.similarity_threshold: 75 | return f"Iteration similarity {iteration_similarity:.2f}" 76 | 77 | # 2. Check quality score 78 | quality_score = self._calculate_quality_score(current, context) 79 | if quality_score >= self.quality_threshold: 80 | return f"Quality threshold {quality_score:.2f}" 81 | 82 | return None 83 | 84 | def _calculate_quality_score(self, response: str, context: str) -> float: 85 | 86 | context_sim = self._calculate_similarity(response, context) 87 | 88 | key_terms = self._extract_key_terms(context) 89 | coverage = sum(1 for term in key_terms if term in response) / len(key_terms) 90 | 91 | 92 | length_factor = min(max(len(response)/500, 0.5), 1.0) 93 | 94 | 95 | return 0.6*context_sim + 0.3*coverage + 0.1*length_factor 96 | 97 | def _extract_key_terms(self, text: str, top_n: int = 10) -> list: 98 | words = re.findall(r'\w+', text.lower()) 99 | freq = {} 100 | for word in words: 101 | if word in freq: 102 | freq[word] += 1 103 | else: 104 | freq[word] = 1 105 | return sorted(freq.items(), key=lambda x: x[1], reverse=True)[:top_n] 106 | 107 | async def _generate_critique( 108 | self, 109 | query: str, 110 | response: str, 111 | context: str 112 | ) -> str: 113 | prompt = self.templates['critic'].format( 114 | query=query, 115 | response=response, 116 | context=context 117 | ) 118 | return await self._safe_generate(prompt) 119 | 120 | async def _generate_improved( 121 | self, 122 | query: str, 123 | context: str, 124 | critique: str 125 | ) -> str: 126 | prompt = self.templates['generator'].format( 127 | query=query, 128 | context=context, 129 | feedback=critique 130 | ) 131 | return await self._safe_generate(prompt) 132 | 133 | def _calculate_similarity(self, text1: str, text2: str) -> float: 134 | if not text1 or not text2: 135 | return 0.0 136 | embeddings = self.similarity_model.encode([text1, text2]) 137 | return util.pytorch_cos_sim(embeddings[0], embeddings[1]).item() 138 | 139 | async def _safe_generate(self, prompt: str) -> str: 140 | try: 141 | inputs = self.model.tokenizer( 142 | prompt, 143 | return_tensors="pt", 144 | max_length=1024, 145 | truncation=True 146 | ).to(self.model.device) 147 | 148 | outputs = await asyncio.to_thread( 149 | self.model.model.generate, 150 | **inputs, 151 | max_new_tokens=500, 152 | temperature=0.7, 153 | top_p=0.9, 154 | repetition_penalty=1.1 155 | ) 156 | 157 | return self.model.tokenizer.decode( 158 | outputs[0], 159 | skip_special_tokens=True 160 | ).strip() 161 | except Exception as e: 162 | raise RuntimeError(f"Generation failed: {str(e)}") 163 | 164 | def _load_template(self, filename: str) -> str: 165 | template_path = os.path.join("templates", filename) 166 | with open(template_path, "r") as f: 167 | return f.read() 168 | 169 | async def _finalize_response(self, response: str, context: str) -> str: 170 | prompt = self.templates['finalizer'].format( 171 | response=response, 172 | context=context 173 | ) 174 | return await self._safe_generate(prompt) -------------------------------------------------------------------------------- /templates/code_instruction.txt: -------------------------------------------------------------------------------- 1 | Instruction: You are an AI engineer. The user will send a code snippet and possibly a related question. You must simulate the code in a secure Python environment, return the result, and optionally explain it. 2 | Input (Code): 3 | {code} 4 | 5 | Question: {question} 6 | Output: -------------------------------------------------------------------------------- /templates/critic_instruction.txt: -------------------------------------------------------------------------------- 1 | [ROLE] 2 | You are a quality assurance expert. Analyze this response considering the context from user documents/code. 3 | 4 | [USER QUESTION] 5 | {query} 6 | 7 | [RESPONSE TO CRITIQUE] 8 | {response} 9 | 10 | [CONTEXT FROM USER DATA] 11 | {context} 12 | 13 | [INSTRUCTIONS] 14 | 1. Identify factual inconsistencies with context 15 | 2. Check technical accuracy 16 | 3. Verify source citations 17 | 4. Assess clarity for non-experts 18 | 5. Rate 1-5 with justification -------------------------------------------------------------------------------- /templates/default_instruction.txt: -------------------------------------------------------------------------------- 1 | Instruction: You are a general-purpose assistant. Provide clear, accurate, and helpful answers to the user's questions. 2 | Question: {question} 3 | Output: -------------------------------------------------------------------------------- /templates/finalizer_instruction.txt: -------------------------------------------------------------------------------- 1 | [ROLE] 2 | You are an editor. Refine this response to meet quality standards. 3 | 4 | [RESPONSE DRAFT] 5 | {response} 6 | 7 | [CONTEXT] 8 | {context} 9 | 10 | [INSTRUCTIONS] 11 | - Fix grammar and style issues 12 | - Ensure proper formatting 13 | - Add disclaimer if needed 14 | - Keep under 500 words -------------------------------------------------------------------------------- /templates/generator_instruction.txt: -------------------------------------------------------------------------------- 1 | [ROLE] 2 | You are an AI assistant. Improve this response using feedback and context. 3 | 4 | [ORIGINAL QUESTION] 5 | {query} 6 | 7 | [CRITIQUE FEEDBACK] 8 | {feedback} 9 | 10 | [USER PROVIDED CONTEXT] 11 | {context} 12 | 13 | [INSTRUCTIONS] 14 | 1. Address all feedback points 15 | 2. Cite relevant context sections 16 | 3. Maintain technical accuracy 17 | 4. Use markdown formatting -------------------------------------------------------------------------------- /templates/pdf_instruction.txt: -------------------------------------------------------------------------------- 1 | Instruction: You are a helpful assistant. The user will provide a question and a PDF document (either via link or upload). Your task is to answer the question using only the information relevant from the PDF document. 2 | Input: {context} 3 | Question: {question} 4 | Output: 5 | -------------------------------------------------------------------------------- /utils/cache.py: -------------------------------------------------------------------------------- 1 | import hashlib 2 | import json 3 | import os 4 | from datetime import datetime 5 | from pathlib import Path 6 | from typing import Optional, Dict, Any 7 | 8 | class SmartCache: 9 | def __init__(self, cache_dir: str = "cache", ttl: int = 86400): 10 | self.cache_dir = Path(cache_dir) 11 | self.ttl = ttl # Время жизни записи в секундах (по умолчанию 24 часа) 12 | self._init_cache_dir() 13 | 14 | def _init_cache_dir(self): 15 | self.cache_dir.mkdir(exist_ok=True, parents=True) 16 | 17 | def _get_key_path(self, key: str) -> Path: 18 | return self.cache_dir / f"{key}.json" 19 | 20 | def generate_key( 21 | self, 22 | prompt: str, 23 | context: str, 24 | model_version: str, 25 | data_hash: str 26 | ) -> str: 27 | """Генерация уникального ключа кэша""" 28 | key_data = f"{prompt}-{context}-{model_version}-{data_hash}" 29 | return hashlib.sha256(key_data.encode()).hexdigest() 30 | 31 | def check_cache(self, key: str) -> Optional[Dict[str, Any]]: 32 | """Проверка наличия записи в кэше""" 33 | key_path = self._get_key_path(key) 34 | 35 | if not key_path.exists(): 36 | return None 37 | 38 | with open(key_path, 'r') as f: 39 | entry = json.load(f) 40 | 41 | if self._is_expired(entry['timestamp']): 42 | key_path.unlink() 43 | return None 44 | 45 | return entry['response'] 46 | 47 | def save_cache( 48 | self, 49 | key: str, 50 | response: str, 51 | metadata: Optional[Dict] = None 52 | ): 53 | """Сохранение записи в кэш""" 54 | entry = { 55 | 'timestamp': datetime.now().isoformat(), 56 | 'response': response, 57 | 'metadata': metadata or {} 58 | } 59 | 60 | with open(self._get_key_path(key), 'w') as f: 61 | json.dump(entry, f) 62 | 63 | def _is_expired(self, timestamp: str) -> bool: 64 | """Проверка истечения срока жизни записи""" 65 | entry_time = datetime.fromisoformat(timestamp) 66 | return (datetime.now() - entry_time).total_seconds() > self.ttl 67 | 68 | class DataHasher: 69 | @staticmethod 70 | def hash_content(content: bytes) -> str: 71 | return hashlib.sha256(content).hexdigest() 72 | 73 | @classmethod 74 | def hash_file(cls, file_path: Path) -> str: 75 | with open(file_path, 'rb') as f: 76 | return cls.hash_content(f.read()) 77 | 78 | @classmethod 79 | def hash_code(cls, code: str) -> str: 80 | return cls.hash_content(code.encode()) 81 | 82 | class CacheManager: 83 | def __init__(self, model: PhiLLM): #? Решить вопрос с PhiLLM 84 | self.cache = SmartCache() 85 | self.model = model 86 | self.hasher = DataHasher() 87 | 88 | async def process_request( 89 | self, 90 | prompt: str, 91 | context: str, 92 | data_source: Optional[Path] = None, 93 | code: Optional[str] = None 94 | ) -> Optional[str]: 95 | data_hash = self._get_data_hash(data_source, code) 96 | 97 | cache_key = self.cache.generate_key( 98 | prompt=prompt, 99 | context=context, 100 | model_version=self.model.version, 101 | data_hash=data_hash 102 | ) 103 | 104 | if cached := self.cache.check_cache(cache_key): 105 | return cached 106 | 107 | return None 108 | 109 | def _get_data_hash( 110 | self, 111 | data_source: Optional[Path], 112 | code: Optional[str] 113 | ) -> str: 114 | if data_source: 115 | return self.hasher.hash_file(data_source) 116 | if code: 117 | return self.hasher.hash_code(code) 118 | return "no_data" 119 | 120 | async def handle_user_request(prompt: str, context: str, file_path: Path): 121 | model = PhiLLM() 122 | cache_manager = CacheManager(model) 123 | 124 | cached_response = await cache_manager.process_request( 125 | prompt=prompt, 126 | context=context, 127 | data_source=file_path 128 | ) 129 | 130 | if cached_response: 131 | return cached_response 132 | 133 | response = await process_request(prompt, context, file_path) 134 | 135 | cache_manager.cache.save_cache( 136 | key=cache_manager.cache.generate_key( 137 | prompt=prompt, 138 | context=context, 139 | model_version=model.version, 140 | data_hash=cache_manager.hasher.hash_file(file_path) 141 | ), 142 | response=response, 143 | metadata={ 144 | 'source': str(file_path), 145 | 'model_version': model.version 146 | } 147 | ) 148 | 149 | return response -------------------------------------------------------------------------------- /utils/docker_sandbox.py: -------------------------------------------------------------------------------- 1 | import docker 2 | from docker.errors import DockerException 3 | from .exceptions import DockerSecurityException, ResourceLimitExceeded, CodeExecutionError 4 | 5 | class DockerSandbox: 6 | def __init__(self): 7 | self.client = docker.from_env() 8 | self._validate_docker() 9 | 10 | def _validate_docker(self): 11 | try: 12 | self.client.ping() 13 | except DockerException: 14 | raise RuntimeError("Docker daemon not available") 15 | 16 | async def execute(self, code: str, timeout=10, mem_limit='100m') -> str: 17 | self._check_code_safety(code) 18 | 19 | try: 20 | container = self.client.containers.run( 21 | image="python-sandbox:secure", 22 | command=f"timeout -s KILL {timeout} python -c '{code}'", 23 | mem_limit=mem_limit, 24 | network_mode="none", 25 | pids_limit=100, 26 | read_only=True, 27 | detach=True 28 | ) 29 | 30 | try: 31 | result = container.wait(timeout=timeout + 2) 32 | if result['StatusCode'] != 0: 33 | raise CodeExecutionError(f"Exit code {result['StatusCode']}") 34 | 35 | logs = container.logs().decode() 36 | self._check_output_safety(logs) 37 | return logs 38 | 39 | except docker.errors.ContainerError as e: 40 | raise CodeExecutionError(str(e)) 41 | finally: 42 | container.remove(force=True) 43 | 44 | except docker.errors.ImageNotFound: 45 | raise CodeExecutionError("Sandbox image not found") 46 | except Exception as e: 47 | raise CodeExecutionError(str(e)) 48 | 49 | def _check_code_safety(self, code: str): 50 | dangerous_patterns = [ 51 | 'os.system', 'subprocess', 'open(', 52 | 'import socket', 'import shutil', 53 | '__import__', 'eval(', 'exec(' 54 | ] 55 | 56 | if any(pattern in code for pattern in dangerous_patterns): 57 | raise DockerSecurityException(code) 58 | 59 | def _check_output_safety(self, output: str): 60 | if len(output) > 10_000: 61 | raise ResourceLimitExceeded("Output size") -------------------------------------------------------------------------------- /utils/exceptions.py: -------------------------------------------------------------------------------- 1 | class SecurityException(Exception): 2 | def __init__(self, message="Security violation detectted"): 3 | super().__init__(message) 4 | 5 | class InjectionAttemptError(SecurityException): 6 | def __init__(self, pattern): 7 | super().__init__(f"Potential injection attempt detect: {pattern}") 8 | 9 | class DockerSecurityException(SecurityException): 10 | def __init__(self, code_snippet): 11 | super().__init__(f"Dangerous code blocked: {code_snippet}") 12 | 13 | class ProcessingError(Exception): 14 | """Base class for processing errors""" 15 | def __init__(self, message="Processing failed"): 16 | super().__init__(message) 17 | 18 | class PDFProcessingError(ProcessingError): 19 | """PDF-related errors""" 20 | def __init__(self, reason): 21 | super().__init__(f"PDF processing failed: {reason}") 22 | 23 | class CodeExecutionError(ProcessingError): 24 | """Code execution errors""" 25 | def __init__(self, reason): 26 | super().__init__(f"Code execution failed: {reason}") 27 | 28 | class ResourceLimitExceeded(ProcessingError): 29 | """Resource limitation errors""" 30 | def __init__(self, resource_type): 31 | super().__init__(f"{resource_type} limit exceeded") 32 | 33 | class NetworkError(ProcessingError): 34 | """Network-related errors""" 35 | def __init__(self, url): 36 | super().__init__(f"Network operation failed for: {url}") -------------------------------------------------------------------------------- /utils/io.py: -------------------------------------------------------------------------------- 1 | import datetime 2 | import os 3 | 4 | def get_input_data(): 5 | return input("Enter your question/code/link or upload: ") 6 | 7 | def send_response_to_user(response): 8 | print("\n\n[Final Response]:\n", response) 9 | 10 | def log_request(prompt, response): 11 | log_line = f"{datetime.datetime.now().isoformat()} | PROMPT: {prompt}\nRESPONSE: {response}\n{'='*80}\n" 12 | with open("logs/request_log.txt", "a") as log_file: 13 | log_file.write(log_line) -------------------------------------------------------------------------------- /utils/logger.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import json 3 | from datetime import datetime 4 | 5 | def setup_logging(): 6 | logging.basicConfig( 7 | level=logging.INFO, 8 | format='%(asctime)s - %(levelname)s - %(message)s', 9 | handlers=[ 10 | logging.FileHandler('app.log'), 11 | logging.StreamHandler() 12 | ] 13 | ) 14 | 15 | class RequestLogger: 16 | def __init__(self): 17 | self.logger = logging.getLogger('security') 18 | 19 | def log(self, event_type: str, details: dict): 20 | log_entry = { 21 | 'timestamp': datetime.utcnow().isoformat(), 22 | 'type': event_type, 23 | 'details': details 24 | } 25 | self.logger.info(json.dumps(log_entry)) -------------------------------------------------------------------------------- /utils/pdf_utils.py: -------------------------------------------------------------------------------- 1 | import fitz # PyMuPDF 2 | import requests 3 | from sentence_transformers import SentenceTransformer, util 4 | 5 | model = SentenceTransformer('all-MiniLM-L6-v2') 6 | 7 | def extract_text_from_url_pdf(url): 8 | response = requests.get(url) 9 | with open("temp.pdf", 'wb') as f: 10 | f.write(response.content) 11 | return extract_text_from_uploaded_pdf("temp.pdf") 12 | 13 | def extract_text_from_uploaded_pdf(path='temp.pdf'): 14 | doc = fitz.open(path) 15 | return "\n".join([page.get_text() for page in doc]) 16 | 17 | def find_relevant_passages(text, question, k=5): 18 | chunks = text.split("\n\n") 19 | embeddings = model.encode(chunks, convert_to_tensor=True) 20 | question_emb = model.encode(question, convert_to_tensor=True) 21 | top_k = util.semantic_search(question_emb, embeddings, top_k=k)[0] 22 | return "\n".join([chunks[idx['corpus_id']] for idx in top_k]) --------------------------------------------------------------------------------