import json
from openai import OpenAI
from selfmemory.llms.base import LLMBase
from selfmemory.llms.configs import BaseLlmConfig, LMStudioConfig
from selfmemory.memory.utils import extract_json
class LMStudioLLM(LLMBase):
def __init__(self, config: BaseLlmConfig | LMStudioConfig | dict | None = None):
# Convert to LMStudioConfig if needed
if config is None:
config = LMStudioConfig()
elif isinstance(config, dict):
config = LMStudioConfig(**config)
elif isinstance(config, BaseLlmConfig) and not isinstance(
config, LMStudioConfig
):
# Convert BaseLlmConfig to LMStudioConfig
config = LMStudioConfig(
model=config.model,
temperature=config.temperature,
api_key=config.api_key,
max_tokens=config.max_tokens,
top_p=config.top_p,
top_k=config.top_k,
enable_vision=config.enable_vision,
vision_details=config.vision_details,
http_client_proxies=config.http_client,
)
super().__init__(config)
self.config.model = (
self.config.model
or "lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF/Meta-Llama-3.1-70B-Instruct-IQ2_M.gguf"
)
self.config.api_key = self.config.api_key or "lm-studio"
self.client = OpenAI(
base_url=self.config.lmstudio_base_url, api_key=self.config.api_key
)
def _parse_response(self, response, tools):
"""
Process the response based on whether tools are used or not.
Args:
response: The raw response from API.
tools: The list of tools provided in the request.
Returns:
str or dict: The processed response.
"""
if tools:
processed_response = {
"content": response.choices[0].message.content,
"tool_calls": [],
}
if response.choices[0].message.tool_calls:
for tool_call in response.choices[0].message.tool_calls:
processed_response["tool_calls"].append(
{
"name": tool_call.function.name,
"arguments": json.loads(
extract_json(tool_call.function.arguments)
),
}
)
return processed_response
return response.choices[0].message.content
def generate_response(
self,
messages: list[dict[str, str]],
response_format=None,
tools: list[dict] | None = None,
tool_choice: str = "auto",
**kwargs,
):
"""
Generate a response based on the given messages using LM Studio.
Args:
messages (list): List of message dicts containing 'role' and 'content'.
response_format (str or object, optional): Format of the response. Defaults to "text".
tools (list, optional): List of tools that the model can call. Defaults to None.
tool_choice (str, optional): Tool choice method. Defaults to "auto".
**kwargs: Additional LM Studio-specific parameters.
Returns:
str: The generated response.
"""
params = self._get_supported_params(messages=messages, **kwargs)
params.update(
{
"model": self.config.model,
"messages": messages,
}
)
if self.config.lmstudio_response_format:
params["response_format"] = self.config.lmstudio_response_format
elif response_format:
params["response_format"] = response_format
else:
params["response_format"] = {"type": "json_object"}
if tools:
params["tools"] = tools
params["tool_choice"] = tool_choice
response = self.client.chat.completions.create(**params)
return self._parse_response(response, tools)
