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import fastapi |
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from typing import Optional, Dict, Any |
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import copy |
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import requests |
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import json |
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import os |
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import sys |
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from io import StringIO |
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import ctypes |
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import subprocess |
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import logging |
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from pathlib import Path |
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from llama_cpp import Llama |
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from concurrent.futures import ThreadPoolExecutor, as_completed |
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import random |
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import time |
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import inspect |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("meetkai/functionary-small-v3.2-GGUF", trust_remote_code=True) |
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model_path = AutoModelForCausalLM.from_pretrained("sentence-transformers/all-MiniLM-L6-v2", trust_remote_code=True) |
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') |
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logger = logging.getLogger(__name__) |
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class Tool(fastapi.FastAPI): |
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def __init__( |
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self, |
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tool_name: str, |
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description: str, |
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name_for_human: Optional[str] = None, |
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name_for_model: Optional[str] = None, |
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description_for_human: Optional[str] = None, |
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description_for_model: Optional[str] = None, |
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logo_url: Optional[str] = None, |
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author_github: Optional[str] = None, |
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contact_email: str = "", |
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legal_info_url: str = "", |
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version: str = "0.1.0", |
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): |
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super().__init__( |
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title=tool_name, |
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description=description, |
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version=version, |
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) |
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if name_for_human is None: |
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name_for_human = tool_name |
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if name_for_model is None: |
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name_for_model = name_for_human |
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if description_for_human is None: |
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description_for_human = description |
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if description_for_model is None: |
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description_for_model = description_for_human |
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self.api_info = { |
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"schema_version": "v1", |
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"name_for_human": name_for_human, |
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"name_for_model": name_for_model, |
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"description_for_human": description_for_human, |
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"description_for_model": description_for_model, |
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"auth": { |
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"type": "none", |
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}, |
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"api": { |
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"type": "openapi", |
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"url": "/openapi.json", |
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"is_user_authenticated": False, |
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}, |
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"author_github": author_github, |
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"logo_url": logo_url, |
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"contact_email": contact_email, |
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"legal_info_url": legal_info_url, |
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} |
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@self.get("/.well-known/ai-plugin.json", include_in_schema=False) |
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def get_api_info(request: fastapi.Request): |
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openapi_path = str(request.url).replace("/.well-known/ai-plugin.json", "/openapi.json") |
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info = copy.deepcopy(self.api_info) |
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info["api"]["url"] = str(openapi_path) |
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return info |
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class SelfLearningTool: |
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def __init__(self): |
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self.tools = {} |
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def add_tool(self, name: str, func: callable): |
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self.tools[name] = func |
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def use_tool(self, name: str, *args, **kwargs): |
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if name in self.tools: |
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return self.tools[name](*args, **kwargs) |
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else: |
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return f"Tool '{name}' not found." |
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def list_tools(self): |
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return list(self.tools.keys()) |
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def remove_tool(self, name: str): |
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if name in self.tools: |
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del self.tools[name] |
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return f"Tool '{name}' removed successfully." |
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else: |
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return f"Tool '{name}' not found." |
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class PythonREPL: |
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def __init__(self): |
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self.globals = {} |
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self.locals = {} |
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self.output_buffer = StringIO() |
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self.self_learning_tool = SelfLearningTool() |
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def run(self, command: str) -> str: |
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old_stdout = sys.stdout |
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sys.stdout = self.output_buffer |
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try: |
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exec(command, self.globals, self.locals) |
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output = self.output_buffer.getvalue() |
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except Exception as e: |
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output = f"Error: {repr(e)}" |
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finally: |
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sys.stdout = old_stdout |
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self.output_buffer.truncate(0) |
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self.output_buffer.seek(0) |
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return output |
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def add_tool(self, name: str, func: callable): |
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self.self_learning_tool.add_tool(name, func) |
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def use_tool(self, name: str, *args, **kwargs): |
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return self.self_learning_tool.use_tool(name, *args, **kwargs) |
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def list_tools(self): |
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return self.self_learning_tool.list_tools() |
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def remove_tool(self, name: str): |
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return self.self_learning_tool.remove_tool(name) |
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def self_reflect(self): |
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reflection = "Self-reflection:\n" |
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reflection += f"Number of defined variables: {len(self.locals)}\n" |
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reflection += f"Number of available tools: {len(self.list_tools())}\n" |
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reflection += "Available tools:\n" |
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for tool in self.list_tools(): |
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reflection += f"- {tool}\n" |
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return reflection |
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def self_inspect(self): |
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inspection = "Self-inspection:\n" |
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for name, value in self.locals.items(): |
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inspection += f"{name}: {type(value)}\n" |
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if callable(value): |
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try: |
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signature = inspect.signature(value) |
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inspection += f" Signature: {signature}\n" |
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except ValueError: |
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inspection += " Signature: Unable to inspect\n" |
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return inspection |
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def initialize_llm(model_path: str, n_ctx: int, n_threads: int = 4, n_batch: int = 512) -> Llama: |
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try: |
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return Llama(model_path=model_path, n_ctx=n_ctx, n_threads=n_threads, n_batch=n_batch, verbose=True) |
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except Exception as e: |
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logger.error(f"Failed to initialize LLM: {e}") |
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raise |
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llm = initialize_llm(model_path, 4096) |
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def build_tool(config) -> Tool: |
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tool = Tool( |
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"Advanced Python REPL", |
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"Execute sophisticated Python commands with self-learning capabilities", |
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name_for_model="Advanced Python REPL", |
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description_for_model=( |
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"An advanced Python shell for executing complex Python commands. " |
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"Input should be a valid Python command or script. " |
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"Use print(...) to see the output of expressions. " |
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"Capable of handling multi-line code, advanced Python features, " |
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"and self-learning tools." |
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), |
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logo_url="https://your-app-url.com/.well-known/logo.png", |
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contact_email="hello@contact.com", |
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legal_info_url="hello@legal.com" |
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) |
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python_repl = PythonREPL() |
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def sanitize_input(query: str) -> str: |
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return query.strip().strip("```").strip() |
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@tool.get("/run_python") |
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def run_python(query: str): |
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sanitized_query = sanitize_input(query) |
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result = python_repl.run(sanitized_query) |
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return {"result": result, "execution_time": time.time()} |
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@tool.get("/add_tool") |
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def add_tool(name: str, code: str): |
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sanitized_code = sanitize_input(code) |
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try: |
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exec(f"def {name}({sanitized_code})", python_repl.globals, python_repl.locals) |
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python_repl.add_tool(name, python_repl.locals[name]) |
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return f"Tool '{name}' added successfully." |
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except Exception as e: |
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return f"Error adding tool: {str(e)}" |
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@tool.get("/use_tool") |
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def use_tool(name: str, args: str): |
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try: |
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result = python_repl.use_tool(name, *eval(args)) |
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return {"result": result} |
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except Exception as e: |
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return {"error": str(e)} |
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@tool.get("/list_tools") |
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def list_tools(): |
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return {"tools": python_repl.list_tools()} |
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@tool.get("/remove_tool") |
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def remove_tool(name: str): |
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return {"result": python_repl.remove_tool(name)} |
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@tool.get("/self_reflect") |
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def self_reflect(): |
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return {"reflection": python_repl.self_reflect()} |
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@tool.get("/self_inspect") |
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def self_inspect(): |
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return {"inspection": python_repl.self_inspect()} |
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@tool.get("/write_file") |
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def write_file(file_path: str, text: str) -> str: |
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write_path = Path(file_path) |
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try: |
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write_path.parent.mkdir(exist_ok=True, parents=False) |
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with write_path.open("w", encoding="utf-8") as f: |
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f.write(text) |
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return f"File written successfully to {file_path}." |
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except Exception as e: |
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return "Error: " + str(e) |
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@tool.get("/read_file") |
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def read_file(file_path: str) -> str: |
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read_path = Path(file_path) |
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try: |
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with read_path.open("r", encoding="utf-8") as f: |
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content = f.read() |
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return content |
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except Exception as e: |
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return "Error: " + str(e) |
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return tool |
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if __name__ == "__main__": |
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config = {} |
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advanced_python_repl = build_tool(config) |
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import uvicorn |
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uvicorn.run(advanced_python_repl, host="0.0.0.0", port=8000) |
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