71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
from typing import Dict, List, Optional
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try:
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from ollama import Client
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except ImportError:
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raise ImportError(
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"The 'ollama' library is required. Please install it using 'pip install ollama'."
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)
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from mem0.configs.llms.base import BaseLlmConfig
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from mem0.llms.base import LLMBase
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class OllamaLLM(LLMBase):
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"""
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A class for interacting with Ollama's language models using the specified configuration.
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"""
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def __init__(self, config: Optional[BaseLlmConfig] = None):
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"""
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Initializes the OllamaLLM instance with the given configuration.
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Args:
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config (Optional[BaseLlmConfig]): Configuration settings for the language model.
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"""
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super().__init__(config)
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if not self.config.model:
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self.config.model = "llama3.1:70b"
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self.client = Client(host=self.config.ollama_base_url)
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self._ensure_model_exists()
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def _ensure_model_exists(self):
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"""
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Ensures the specified model exists locally. If not, pulls it from Ollama.
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"""
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local_models = self.client.list()["models"]
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if not any(model.get("name") == self.config.model for model in local_models):
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self.client.pull(self.config.model)
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def generate_response(
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self,
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messages: List[Dict[str, str]],
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response_format: Optional[str] = None,
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) -> str:
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"""
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Generates a response using Ollama based on the provided messages.
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Args:
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messages (List[Dict[str, str]]): A list of dictionaries, each containing a 'role' and 'content' key.
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response_format (Optional[str]): The desired format of the response. Defaults to None.
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Returns:
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str: The generated response from the model.
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"""
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params = {
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"model": self.config.model,
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"messages": messages,
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"options": {
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"temperature": self.config.temperature,
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"num_predict": self.config.max_tokens,
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"top_p": self.config.top_p,
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},
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}
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if response_format:
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params["format"] = "json"
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response = self.client.chat(**params)
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return response["message"]["content"]
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