95 lines
3.1 KiB
Python
95 lines
3.1 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("The 'ollama' library is required. Please install it using 'pip install ollama'.")
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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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def __init__(self, config: Optional[BaseLlmConfig] = None):
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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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Ensure the specified model exists locally. If not, pull 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 _parse_response(self, response, tools):
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"""
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Process the response based on whether tools are used or not.
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Args:
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response: The raw response from API.
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tools: The list of tools provided in the request.
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Returns:
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str or dict: The processed response.
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"""
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if tools:
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processed_response = {
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"content": response["message"]["content"],
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"tool_calls": [],
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}
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if response["message"].get("tool_calls"):
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for tool_call in response["message"]["tool_calls"]:
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processed_response["tool_calls"].append(
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{
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"name": tool_call["function"]["name"],
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"arguments": tool_call["function"]["arguments"],
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}
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)
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return processed_response
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else:
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return response["message"]["content"]
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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=None,
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tools: Optional[List[Dict]] = None,
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tool_choice: str = "auto",
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):
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"""
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Generate a response based on the given messages using OpenAI.
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Args:
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messages (list): List of message dicts containing 'role' and 'content'.
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response_format (str or object, optional): Format of the response. Defaults to "text".
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tools (list, optional): List of tools that the model can call. Defaults to None.
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tool_choice (str, optional): Tool choice method. Defaults to "auto".
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Returns:
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str: The generated response.
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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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if tools:
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params["tools"] = tools
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response = self.client.chat(**params)
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return self._parse_response(response, tools)
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