diff --git a/prototypes/arc1_prototype/.gitignore b/prototypes/arc1_prototype/.gitignore new file mode 100644 index 0000000..be054ba --- /dev/null +++ b/prototypes/arc1_prototype/.gitignore @@ -0,0 +1,4 @@ +.env +__pycache__ +.venv +.roproject diff --git a/prototypes/arc1_prototype/.python-version b/prototypes/arc1_prototype/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/prototypes/arc1_prototype/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/prototypes/arc1_prototype/README.md b/prototypes/arc1_prototype/README.md new file mode 100644 index 0000000..e69de29 diff --git a/prototypes/arc1_prototype/client.py b/prototypes/arc1_prototype/client.py new file mode 100644 index 0000000..1e8188e --- /dev/null +++ b/prototypes/arc1_prototype/client.py @@ -0,0 +1,473 @@ +import asyncio +import json +import logging +import os +import shutil +from contextlib import AsyncExitStack +from typing import Any +from openai import AzureOpenAI + +import httpx +from dotenv import load_dotenv +from mcp import ClientSession, StdioServerParameters +from mcp.client.stdio import stdio_client + +# Configure logging +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" +) + + +class Configuration: + """Manages configuration and environment variables for the MCP client.""" + + def __init__(self) -> None: + """Initialize configuration with environment variables.""" + self.load_env() + self.api_key = os.getenv("LLM_API_KEY") + + @staticmethod + def load_env() -> None: + """Load environment variables from .env file.""" + load_dotenv() + + @staticmethod + def load_config(file_path: str) -> dict[str, Any]: + """Load server configuration from JSON file. + + Args: + file_path: Path to the JSON configuration file. + + Returns: + Dict containing server configuration. + + Raises: + FileNotFoundError: If configuration file doesn't exist. + JSONDecodeError: If configuration file is invalid JSON. + """ + with open(file_path, "r") as f: + return json.load(f) + + @property + def llm_api_key(self) -> str: + """Get the LLM API key. + + Returns: + The API key as a string. + + Raises: + ValueError: If the API key is not found in environment variables. + """ + if not self.api_key: + raise ValueError("LLM_API_KEY not found in environment variables") + return self.api_key + + +class Server: + """Manages MCP server connections and tool execution.""" + + def __init__(self, name: str, config: dict[str, Any]) -> None: + self.name: str = name + self.config: dict[str, Any] = config + self.stdio_context: Any | None = None + self.session: ClientSession | None = None + self._cleanup_lock: asyncio.Lock = asyncio.Lock() + self.exit_stack: AsyncExitStack = AsyncExitStack() + + async def initialize(self) -> None: + """Initialize the server connection.""" + command = ( + shutil.which("npx") + if self.config["command"] == "npx" + else self.config["command"] + ) + if command is None: + raise ValueError("The command must be a valid string and cannot be None.") + + server_params = StdioServerParameters( + command=command, + args=self.config["args"], + env={**os.environ, **self.config["env"]} + if self.config.get("env") + else None, + ) + try: + stdio_transport = await self.exit_stack.enter_async_context( + stdio_client(server_params) + ) + read, write = stdio_transport + session = await self.exit_stack.enter_async_context( + ClientSession(read, write) + ) + await session.initialize() + self.session = session + except Exception as e: + logging.error(f"Error initializing server {self.name}: {e}") + await self.cleanup() + raise + + async def list_tools(self) -> list[Any]: + """List available tools from the server. + + Returns: + A list of available tools. + + Raises: + RuntimeError: If the server is not initialized. + """ + if not self.session: + raise RuntimeError(f"Server {self.name} not initialized") + + tools_response = await self.session.list_tools() + tools = [] + + for item in tools_response: + if isinstance(item, tuple) and item[0] == "tools": + tools.extend( + Tool(tool.name, tool.description, tool.inputSchema) + for tool in item[1] + ) + + return tools + + async def execute_tool( + self, + tool_name: str, + arguments: dict[str, Any], + retries: int = 2, + delay: float = 1.0, + ) -> Any: + """Execute a tool with retry mechanism. + + Args: + tool_name: Name of the tool to execute. + arguments: Tool arguments. + retries: Number of retry attempts. + delay: Delay between retries in seconds. + + Returns: + Tool execution result. + + Raises: + RuntimeError: If server is not initialized. + Exception: If tool execution fails after all retries. + """ + if not self.session: + raise RuntimeError(f"Server {self.name} not initialized") + + attempt = 0 + while attempt < retries: + try: + logging.info(f"Executing {tool_name}...") + result = await self.session.call_tool(tool_name, arguments) + + return result + + except Exception as e: + attempt += 1 + logging.warning( + f"Error executing tool: {e}. Attempt {attempt} of {retries}." + ) + if attempt < retries: + logging.info(f"Retrying in {delay} seconds...") + await asyncio.sleep(delay) + else: + logging.error("Max retries reached. Failing.") + raise + + async def cleanup(self) -> None: + """Clean up server resources.""" + async with self._cleanup_lock: + try: + await self.exit_stack.aclose() + self.session = None + self.stdio_context = None + except Exception as e: + logging.error(f"Error during cleanup of server {self.name}: {e}") + + +class Tool: + """Represents a tool with its properties and formatting.""" + + def __init__( + self, name: str, description: str, input_schema: dict[str, Any] + ) -> None: + self.name: str = name + self.description: str = description + self.input_schema: dict[str, Any] = input_schema + + def format_for_llm(self) -> str: + """Format tool information for LLM. + + Returns: + A formatted string describing the tool. + """ + args_desc = [] + if "properties" in self.input_schema: + for param_name, param_info in self.input_schema["properties"].items(): + arg_desc = ( + f"- {param_name}: {param_info.get('description', 'No description')}" + ) + if param_name in self.input_schema.get("required", []): + arg_desc += " (required)" + args_desc.append(arg_desc) + + return f""" +Tool: {self.name} +Description: {self.description} +Arguments: +{chr(10).join(args_desc)} +""" + + +class LLMClient: + """Manages communication with the LLM provider.""" + + def __init__(self, api_key: str) -> None: + self.api_key: str = api_key + + def get_response(self, messages: list[dict[str, str]]) -> str: + """Get a response from the LLM. + + Args: + messages: A list of message dictionaries. + + Returns: + The LLM's response as a string. + + Raises: + httpx.RequestError: If the request to the LLM fails. + """ + url = "https://ai.exxeta.com/api/v2/azure/openai" + + # Convert messages to the correct format expected by the API + formatted_messages = [] + for msg in messages: + # print(msg) + formatted_messages.append({ + "role": msg["role"], + "content": msg["content"] + }) + + client = AzureOpenAI( + api_key=self.api_key, + api_version="2023-07-01-preview", + base_url=url + ) + response = client.chat.completions.create( + messages=formatted_messages, + model="gpt-4o-mini", + # response_format={"type": "json_object"} + # temperature=0.7, + # top_p=0.95, + # frequency_penalty=0, + # presence_penalty=0, + # max_tokens=800, + # stop="", + # stream=False + ) + if response.choices[0].message.content: + # print("response: " + response.choices[0].message.content) + return response.choices[0].message.content + + print("No response from Azure OpenAI") + + # url = "https://api.groq.com/openai/v1/chat/completions" + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {self.api_key}", + } + payload = { + "messages": messages, + "model": "gpt-4o-mini", + "temperature": 0.7, + "max_tokens": 4096, + "top_p": 1, + "stream": False, + "stop": None, + } + + try: + with httpx.Client() as client: + response = client.post(url, headers=headers, json=payload) + response.raise_for_status() + data = response.json() + return data["choices"][0]["message"]["content"] + + except httpx.RequestError as e: + error_message = f"Error getting LLM response: {str(e)}" + logging.error(error_message) + + if isinstance(e, httpx.HTTPStatusError): + status_code = e.response.status_code + logging.error(f"Status code: {status_code}") + logging.error(f"Response details: {e.response.text}") + + return ( + f"I encountered an error: {error_message}. " + "Please try again or rephrase your request." + ) + + +class ChatSession: + """Orchestrates the interaction between user, LLM, and tools.""" + + def __init__(self, servers: list[Server], llm_client: LLMClient) -> None: + self.servers: list[Server] = servers + self.llm_client: LLMClient = llm_client + + async def cleanup_servers(self) -> None: + """Clean up all servers properly.""" + cleanup_tasks = [ + asyncio.create_task(server.cleanup()) for server in self.servers + ] + if cleanup_tasks: + try: + await asyncio.gather(*cleanup_tasks, return_exceptions=True) + except Exception as e: + logging.warning(f"Warning during final cleanup: {e}") + + async def process_llm_response(self, llm_response: str) -> str: + """Process the LLM response and execute tools if needed. + + Args: + llm_response: The response from the LLM. + + Returns: + The result of tool execution or the original response. + """ + import json + + try: + tool_call = json.loads(llm_response) + if "tool" in tool_call and "arguments" in tool_call: + logging.info(f"Executing tool: {tool_call['tool']}") + logging.info(f"With arguments: {tool_call['arguments']}") + + for server in self.servers: + tools = await server.list_tools() + if any(tool.name == tool_call["tool"] for tool in tools): + try: + result = await server.execute_tool( + tool_call["tool"], tool_call["arguments"] + ) + + if isinstance(result, dict) and "progress" in result: + progress = result["progress"] + total = result["total"] + percentage = (progress / total) * 100 + logging.info( + f"Progress: {progress}/{total} " + f"({percentage:.1f}%)" + ) + + return f"Tool execution result: {result}" + except Exception as e: + error_msg = f"Error executing tool: {str(e)}" + logging.error(error_msg) + return error_msg + + return f"No server found with tool: {tool_call['tool']}" + return llm_response + except json.JSONDecodeError: + return llm_response + + async def start(self) -> None: + """Main chat session handler.""" + try: + for server in self.servers: + try: + await server.initialize() + except Exception as e: + logging.error(f"Failed to initialize server: {e}") + await self.cleanup_servers() + return + + all_tools = [] + for server in self.servers: + tools = await server.list_tools() + all_tools.extend(tools) + + tools_description = "\n".join([tool.format_for_llm() for tool in all_tools]) + + # print("All tools:", tools_description) + + system_message = ( + "You are a helpful assistant with access to these tools:\n\n" + f"{tools_description}\n" + "Choose the appropriate tool based on the user's question. " + "If no tool is needed, reply directly.\n\n" + "IMPORTANT: When you need to use a tool, you must ONLY respond with " + "the exact JSON object format below, nothing else:\n" + "{\n" + ' "tool": "tool-name",\n' + ' "arguments": {\n' + ' "argument-name": "value"\n' + " }\n" + "}\n\n" + "After receiving a tool's response:\n" + "1. Transform the raw data into a natural, conversational response\n" + "2. Keep responses concise but informative\n" + "3. Focus on the most relevant information\n" + "4. Use appropriate context from the user's question\n" + "5. Avoid simply repeating the raw data\n\n" + "Please use only the tools that are explicitly defined above." + "You can call another tool, but call them in the next call. Answer 'next tool' if you need another tool." + ) + + messages = [{"role": "system", "content": system_message}] + + while True: + try: + user_input = input("You: ").strip().lower() + if user_input in ["quit", "exit"]: + logging.info("\nExiting...") + break + + messages.append({"role": "user", "content": user_input}) + + llm_response = self.llm_client.get_response(messages) + logging.info("\nAssistant: %s", llm_response) + + result = await self.process_llm_response(llm_response) + + while result != llm_response: + messages.append({"role": "assistant", "content": llm_response}) + messages.append({"role": "system", "content": result}) + + llm_response = self.llm_client.get_response(messages) + logging.info("\nAssistant: %s", llm_response) + + result = await self.process_llm_response(llm_response) + + # logging.info("\nFinal response: %s", final_response) + # messages.append( + # {"role": "assistant", "content": final_response} + # ) + + # messages.append({"role": "assistant", "content": llm_response}) + # logging.info("\nFinal response: %s", llm_response) + + + except KeyboardInterrupt: + logging.info("\nExiting...") + break + + finally: + await self.cleanup_servers() + + +async def main() -> None: + """Initialize and run the chat session.""" + config = Configuration() + server_config = config.load_config("servers_config.json") + servers = [ + Server(name, srv_config) + for name, srv_config in server_config["mcpServers"].items() + ] + llm_client = LLMClient(config.llm_api_key) + chat_session = ChatSession(servers, llm_client) + await chat_session.start() + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/prototypes/arc1_prototype/pyproject.toml b/prototypes/arc1_prototype/pyproject.toml new file mode 100644 index 0000000..9f356cb --- /dev/null +++ b/prototypes/arc1_prototype/pyproject.toml @@ -0,0 +1,10 @@ +[project] +name = "arc1-prototype" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "mcp[cli]>=1.7.1", + "openai>=1.77.0", +] diff --git a/prototypes/arc1_prototype/server.py b/prototypes/arc1_prototype/server.py new file mode 100644 index 0000000..d8ab52a --- /dev/null +++ b/prototypes/arc1_prototype/server.py @@ -0,0 +1,43 @@ +# server.py +from mcp.server.fastmcp import FastMCP +import random + +# Create an MCP server +mcp = FastMCP("Demo") + +risikoProfile = ["Core/Core+, Core", "Value Add"] +risikoProfileSpacy = ["Core/Core+, Core", "Value Add", "3.2", "e au uae"] + +# Add an addition tool +@mcp.tool() +def add(a: int, b: int) -> int: + """Add two numbers""" + return a + b + +@mcp.tool() +def getFromSpaCy() -> list: + """Get data from SpaCy""" + return [{"page":random.randint(1, 35), "value": random.choice(risikoProfileSpacy), "key": "Risiko"}, + {"page":random.randint(1, 35), "value": "Real Estate", "key": "FondName"}] + +@mcp.tool() +def getFromChatGPT() -> list: + """Get data from ChatGPT""" + return [{"page":random.randint(1, 35), "value": random.choice(risikoProfile), "key": "Risiko"}, + {"page":random.randint(1, 35), "value": "Real False Name", "key": "FondName"}] + +@mcp.tool() +def checkSpacyResult() -> dict: + """This tool checks the result of SpaCy, ensuring it meets certain criteria.""" + return {"page":random.randint(1, 35), "value": random.choice(risikoProfile), "key": "Risiko"} + +@mcp.tool() +def getFromChatGPTSingle(value: str) -> dict: + """This tool get a single value from ChatGPT. You can use the value to specify for which key the value should calculated""" + return {"page":random.randint(1, 35), "value": random.choice(risikoProfile), "key": value} + +# Add a dynamic greeting resource +@mcp.resource("greeting://{name}") +def get_greeting(name: str) -> str: + """Get a personalized greeting""" + return f"Hello, {name}!" diff --git a/prototypes/arc1_prototype/servers_config.json b/prototypes/arc1_prototype/servers_config.json new file mode 100644 index 0000000..70f256a --- /dev/null +++ b/prototypes/arc1_prototype/servers_config.json @@ -0,0 +1,8 @@ +{ + "mcpServers": { + "sqlite": { + "command": "uv", + "args": ["run", "mcp", "run", "server.py"] + } + } +} diff --git a/prototypes/arc1_prototype/shared.py b/prototypes/arc1_prototype/shared.py new file mode 100644 index 0000000..e69de29 diff --git a/prototypes/arc1_prototype/uv.lock b/prototypes/arc1_prototype/uv.lock new file mode 100644 index 0000000..40ea761 --- /dev/null +++ b/prototypes/arc1_prototype/uv.lock 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