import json
import os
from openai import OpenAI
from selfmemory.configs.llms.base import BaseLlmConfig
from selfmemory.configs.llms.vllm import VllmConfig
from selfmemory.llms.base import LLMBase
from selfmemory.memory.utils import extract_json
class VllmLLM(LLMBase):
def __init__(self, config: BaseLlmConfig | VllmConfig | dict | None = None):
# Convert to VllmConfig if needed
if config is None:
config = VllmConfig()
elif isinstance(config, dict):
config = VllmConfig(**config)
elif isinstance(config, BaseLlmConfig) and not isinstance(config, VllmConfig):
# Convert BaseLlmConfig to VllmConfig
config = VllmConfig(
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)
if not self.config.model:
self.config.model = "Qwen/Qwen2.5-32B-Instruct"
self.config.api_key = (
self.config.api_key or os.getenv("VLLM_API_KEY") or "vllm-api-key"
)
base_url = self.config.vllm_base_url or os.getenv("VLLM_BASE_URL")
self.client = OpenAI(api_key=self.config.api_key, base_url=base_url)
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 vLLM.
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 vLLM-specific parameters.
Returns:
str: The generated response.
"""
params = self._get_supported_params(messages=messages, **kwargs)
params.update(
{
"model": self.config.model,
"messages": messages,
}
)
if tools:
params["tools"] = tools
params["tool_choice"] = tool_choice
response = self.client.chat.completions.create(**params)
return self._parse_response(response, tools)
