95 lines
2.3 KiB
Python
95 lines
2.3 KiB
Python
"""Ollama backend implementation."""
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import ollama as ollama_client
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from typing import Optional
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from .base import LLMBackend
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class OllamaBackend(LLMBackend):
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"""Ollama API backend for local LLM inference."""
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def __init__(
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self,
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host: str = "localhost:11434",
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model: str = "codellama",
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temperature: float = 0.1,
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max_tokens: int = 500,
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):
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"""Initialize the Ollama backend.
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Args:
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host: Ollama server host.
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model: Model name to use.
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temperature: Generation temperature.
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max_tokens: Maximum tokens to generate.
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"""
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self.host = host
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self._model = model
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self.temperature = temperature
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self.max_tokens = max_tokens
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self._client = None
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@property
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def client(self):
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"""Get or create Ollama client."""
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if self._client is None:
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self._client = ollama_client.Client(host=self.host)
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return self._client
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def generate(self, prompt: str) -> str:
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"""Generate response using Ollama API.
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Args:
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prompt: The prompt to send.
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Returns:
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Generated response text.
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"""
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try:
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response = self.client.generate(
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model=self._model,
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prompt=prompt,
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options={
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"temperature": self.temperature,
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"num_predict": self.max_tokens,
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},
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)
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return response.get("response", "")
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except Exception as e:
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raise ConnectionError(f"Ollama API error: {e}")
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def is_available(self) -> bool:
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"""Check if Ollama is running and model is available.
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Returns:
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True if backend is available.
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"""
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try:
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self.client.ps()
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return True
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except Exception:
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return False
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def get_model_name(self) -> str:
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"""Get the current model name.
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Returns:
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Model name string.
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"""
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return self._model
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def set_model(self, model: str) -> None:
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"""Set the model to use.
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Args:
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model: Name of the model.
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"""
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self._model = model
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def close(self) -> None:
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"""Clean up client resources."""
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self._client = None
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