188 lines
6.4 KiB
Python
188 lines
6.4 KiB
Python
"""Ollama LLM client with robust error handling."""
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import logging
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import os
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from typing import Any
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import requests
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from requests.exceptions import HTTPError, RequestException, Timeout
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from ..config import settings
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from .exceptions import LLMAPIError, LLMConfigurationError
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logger = logging.getLogger(__name__)
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class OllamaClient:
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"""
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Client for interacting with Ollama API.
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Ollama runs locally and provides an OpenAI-compatible API.
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Example:
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>>> client = OllamaClient(model="llama3.2")
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>>> messages = [{"role": "user", "content": "Hello!"}]
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>>> response = client.complete(messages)
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>>> print(response)
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"""
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def __init__(
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self,
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base_url: str | None = None,
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model: str | None = None,
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timeout: int | None = None,
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temperature: float | None = None,
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):
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"""
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Initialize Ollama client.
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Args:
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base_url: Ollama API base URL (defaults to http://localhost:11434)
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model: Model name to use (e.g., "llama3.2", "mistral", "codellama")
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timeout: Request timeout in seconds (defaults to settings)
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temperature: Temperature for generation (defaults to settings)
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Raises:
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LLMConfigurationError: If configuration is invalid
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"""
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self.base_url = base_url or os.getenv(
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"OLLAMA_BASE_URL", "http://localhost:11434"
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)
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self.model = model or os.getenv("OLLAMA_MODEL", "llama3.2")
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self.timeout = timeout or settings.request_timeout
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self.temperature = (
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temperature if temperature is not None else settings.temperature
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)
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if not self.base_url:
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raise LLMConfigurationError(
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"Ollama base URL is required. Set OLLAMA_BASE_URL environment variable."
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)
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if not self.model:
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raise LLMConfigurationError(
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"Ollama model is required. Set OLLAMA_MODEL environment variable."
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)
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logger.info(f"Ollama client initialized with model: {self.model}")
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def complete(self, messages: list[dict[str, Any]], tools: list[dict[str, Any]] | None = None) -> dict[str, Any]:
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"""
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Generate a completion from the LLM.
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Args:
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messages: List of message dicts with 'role' and 'content' keys
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tools: Optional list of tool specifications (OpenAI format)
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Returns:
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OpenAI-compatible message dict with 'role', 'content', and optionally 'tool_calls'
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Raises:
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LLMAPIError: If API request fails
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ValueError: If messages format is invalid
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"""
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# Validate messages format
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if not messages:
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raise ValueError("Messages list cannot be empty")
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for msg in messages:
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if not isinstance(msg, dict):
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raise ValueError(f"Each message must be a dict, got {type(msg)}")
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if "role" not in msg:
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raise ValueError(f"Message must have 'role' key, got {msg.keys()}")
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# Allow system, user, assistant, and tool roles
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if msg["role"] not in ("system", "user", "assistant", "tool"):
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raise ValueError(f"Invalid role: {msg['role']}")
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# Content is optional for tool messages (they may have tool_call_id instead)
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if msg["role"] != "tool" and "content" not in msg:
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raise ValueError(f"Non-tool message must have 'content' key, got {msg.keys()}")
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url = f"{self.base_url}/api/chat"
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payload = {
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"model": self.model,
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"messages": messages,
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"stream": False,
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"options": {
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"temperature": self.temperature,
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},
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}
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# Add tools if provided
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if tools:
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payload["tools"] = tools
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try:
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logger.debug(f"Sending request to {url} with {len(messages)} messages and {len(tools) if tools else 0} tools")
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response = requests.post(url, json=payload, timeout=self.timeout)
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response.raise_for_status()
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data = response.json()
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# Validate response structure
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if "message" not in data:
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raise LLMAPIError("Invalid API response: missing 'message'")
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# Return the full message dict (OpenAI format)
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message = data["message"]
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logger.debug(f"Received response: {message.get('content', '')[:100]}...")
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return message
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except Timeout as e:
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logger.error(f"Request timeout after {self.timeout}s: {e}")
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raise LLMAPIError(f"Request timeout after {self.timeout} seconds") from e
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except HTTPError as e:
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logger.error(f"HTTP error from Ollama API: {e}")
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if e.response is not None:
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try:
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error_data = e.response.json()
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error_msg = error_data.get("error", str(e))
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except Exception:
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error_msg = str(e)
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raise LLMAPIError(f"Ollama API error: {error_msg}") from e
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raise LLMAPIError(f"HTTP error: {e}") from e
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except RequestException as e:
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logger.error(f"Request failed: {e}")
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raise LLMAPIError(f"Failed to connect to Ollama API: {e}") from e
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except (KeyError, IndexError, TypeError) as e:
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logger.error(f"Failed to parse API response: {e}")
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raise LLMAPIError(f"Invalid API response format: {e}") from e
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def list_models(self) -> list[str]:
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"""
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List available models in Ollama.
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Returns:
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List of model names
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"""
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url = f"{self.base_url}/api/tags"
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try:
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response = requests.get(url, timeout=self.timeout)
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response.raise_for_status()
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data = response.json()
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models = [model["name"] for model in data.get("models", [])]
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logger.info(f"Found {len(models)} models: {models}")
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return models
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except Exception as e:
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logger.error(f"Failed to list models: {e}")
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return []
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def is_available(self) -> bool:
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"""
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Check if Ollama is running and accessible.
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Returns:
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True if Ollama is available, False otherwise
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"""
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try:
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url = f"{self.base_url}/api/tags"
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response = requests.get(url, timeout=5)
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return response.status_code == 200
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except Exception:
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return False
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