294 lines
10 KiB
Python
294 lines
10 KiB
Python
"""
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API client for Clover - A terminal assistant for AI-powered project management
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Handles communication with the OpenAI-compatible LLM server
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"""
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import json
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import os
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import time
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from typing import Any, Dict, Optional
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import requests
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from config.settings import load_config
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class APIClient:
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"""
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API client for communicating with the LLM server
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"""
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def __init__(self):
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"""Initialize the API client with configuration"""
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self.config = load_config()
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self.base_url = self.config.get(
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"base_url", "error-set-CLOVER_BASE_URL-in-.env-file"
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)
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self.api_key = self.config.get("api_key")
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self.ssl_verify = self.config.get("ssl_verify", True)
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# Use requests.Session for connection pooling and consistent SSL settings
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self.session = requests.Session()
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self.session.headers.update(
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{
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"Content-Type": "application/json",
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"User-Agent": "clover-cli/1.0",
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"Accept": "application/json",
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}
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)
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if self.api_key:
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self.session.headers["Authorization"] = f"Bearer {self.api_key}"
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# Warn if using HTTP (non-TLS) connection
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if self.base_url.startswith("http://") and not self.base_url.startswith(
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"http://localhost"
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):
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import warnings
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warnings.warn(
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f"Using non-TLS connection to {self.base_url}. "
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"Consider using HTTPS for remote endpoints. "
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"Set CLOVER_SSL_VERIFY=false to disable SSL verification for self-signed certs.",
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UserWarning,
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)
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def _make_request(
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self, endpoint: str, method: str = "GET", data: Optional[Dict] = None
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) -> Dict[str, Any]:
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"""
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Make a request to the LLM API server with retry and exponential backoff.
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Retries up to 3 times with exponential backoff (2s base delay) for
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transient failures (5xx errors, connection errors, timeouts).
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Uses requests.Session with explicit SSL verification control.
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Args:
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endpoint (str): API endpoint
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method (str): HTTP method (GET, POST)
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data (dict): Request data
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Returns:
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dict: Response from the API
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"""
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max_retries = self.config.get("max_retries", 3)
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base_delay = self.config.get("retry_base_delay", 2)
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last_exception = None
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for attempt in range(max_retries + 1):
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try:
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kwargs = {
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"timeout": self.config.get("timeout", 300),
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"verify": self.ssl_verify,
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}
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if method == "GET":
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response = self.session.get(url, **kwargs)
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elif method == "POST":
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response = self.session.post(url, json=data, **kwargs)
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else:
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raise ValueError(f"Unsupported HTTP method: {method}")
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# Retry on 5xx server errors
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if response.status_code >= 500 and attempt < max_retries:
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delay = base_delay * (2**attempt)
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print(
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f"Server error {response.status_code}, "
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f"retrying in {delay}s (attempt {attempt + 1}/{max_retries})"
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)
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time.sleep(delay)
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continue
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response.raise_for_status()
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return {"success": True, "data": response.json()}
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except requests.exceptions.Timeout as e:
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last_exception = e
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if attempt < max_retries:
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delay = base_delay * (2**attempt)
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print(
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f"Timeout, retrying in {delay}s "
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f"(attempt {attempt + 1}/{max_retries})"
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)
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time.sleep(delay)
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continue
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except requests.exceptions.ConnectionError as e:
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last_exception = e
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if attempt < max_retries:
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delay = base_delay * (2**attempt)
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print(
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f"Connection error, retrying in {delay}s "
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f"(attempt {attempt + 1}/{max_retries})"
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)
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time.sleep(delay)
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continue
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except requests.exceptions.RequestException as e:
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return {"success": False, "error": f"HTTP Request failed: {str(e)}"}
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except json.JSONDecodeError as e:
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return {"success": False, "error": f"Invalid JSON response: {str(e)}"}
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except Exception as e:
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return {"success": False, "error": f"Unexpected error: {str(e)}"}
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# All retries exhausted
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if last_exception:
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return {
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"success": False,
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"error": f"Request failed after {max_retries} retries: {str(last_exception)}",
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}
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return {"success": False, "error": "Request failed after maximum retries"}
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def list_models(self) -> Dict[str, Any]:
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"""
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Get list of available models from the LLM server
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Returns:
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dict: Available models information
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"""
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# Use Ollama standard endpoint for model listing
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result = self._make_request("/api/tags", "GET")
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if not result["success"]:
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return {"error": result["error"], "models": []}
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try:
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data = result["data"]
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# Handle Ollama format correctly - the response has a "models" key with array
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if isinstance(data, dict) and "models" in data:
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models_list = data["models"]
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else:
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# If it's already just a list of models
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models_list = data if isinstance(data, list) else []
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return {"models": models_list}
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except Exception as e:
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return {"error": f"Failed to parse models response: {str(e)}", "models": []}
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def chat_completion(
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self, messages: list, model: str = None, **kwargs
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) -> Dict[str, Any]:
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"""
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Get a completion from the LLM using Ollama chat endpoint.
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Uses /api/chat with proper message list format to preserve
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multi-turn conversation semantics (system, user, assistant roles).
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Args:
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messages (list): List of message dictionaries with 'role' and 'content'
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model (str): Model to use
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**kwargs: Additional parameters for the API
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Returns:
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dict: Response from the LLM in OpenAI-compatible format
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"""
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if model is None:
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# Reload config to get latest model setting
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from config.settings import load_config
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current_config = load_config()
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model = current_config.get("model", "qwen3-coder:30b")
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# Filter messages to only include valid roles for chat endpoint
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chat_messages = []
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for message in messages:
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role = message.get("role", "user")
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content = message.get("content", "")
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if role in ("system", "user", "assistant"):
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chat_messages.append({"role": role, "content": content})
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if not chat_messages:
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return {"error": "No valid messages provided for chat completion"}
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# Use Ollama chat endpoint with proper message format
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data = {
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"model": model,
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"messages": chat_messages,
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"stream": False,
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**kwargs,
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}
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result = self._make_request("/api/chat", "POST", data)
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if not result["success"]:
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return {"error": result["error"]}
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# Extract the response from Ollama's chat format
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try:
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response_data = result["data"]
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# Ollama chat endpoint returns message in message.content
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if "message" in response_data:
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return {
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"choices": [
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{
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"message": {
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"role": response_data["message"].get(
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"role", "assistant"
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),
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"content": response_data["message"].get(
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"content", ""
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),
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}
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}
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]
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}
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elif "response" in response_data:
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return {
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"choices": [{"message": {"content": response_data["response"]}}]
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}
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else:
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return response_data
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except Exception as e:
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return {"error": f"Failed to process chat completion result: {str(e)}"}
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def generate_text(self, prompt: str, model: str = None, **kwargs) -> Dict[str, Any]:
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"""
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Generate text using the LLM
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Args:
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prompt (str): Prompt to send to the LLM
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model (str): Model to use
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**kwargs: Additional parameters for the API
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Returns:
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dict: Generated response
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"""
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if model is None:
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model = self.config.get("model", "qwen3-coder:30b")
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# Format as Ollama generate request
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data = {"model": model, "prompt": prompt, "stream": False, **kwargs}
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result = self._make_request("/api/generate", "POST", data)
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if not result["success"]:
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return {"error": result["error"]}
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try:
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response_data = result["data"]
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# Extract the actual response text for generate endpoint
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if "response" in response_data:
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return {
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"choices": [{"message": {"content": response_data["response"]}}]
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}
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else:
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# If we got back something else, return it as-is
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return response_data
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except Exception as e:
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return {"error": f"Failed to process generate result: {str(e)}"}
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def get_model_info(self, model_name: str) -> Dict[str, Any]:
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"""
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Get information about a specific model
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Args:
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model_name (str): Name of the model
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Returns:
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dict: Model information
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"""
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# Use Ollama's show endpoint
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result = self._make_request(f"/api/show/{model_name}", "POST")
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if not result["success"]:
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return {"error": result["error"]}
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return result["data"]
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