import json
import logging
import os
from openai import OpenAI
from selfmemory.llms.base import LLMBase
from selfmemory.llms.configs import BaseLlmConfig, OpenAIConfig
from selfmemory.memory.utils import extract_json
class OpenAILLM(LLMBase):
def __init__(self, config: BaseLlmConfig | OpenAIConfig | dict | None = None):
# Convert to OpenAIConfig if needed
if config is None:
config = OpenAIConfig()
elif isinstance(config, dict):
config = OpenAIConfig(**config)
elif isinstance(config, BaseLlmConfig) and not isinstance(config, OpenAIConfig):
# Convert BaseLlmConfig to OpenAIConfig
config = OpenAIConfig(
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 = "gpt-4.1-nano-2025-04-14"
if os.environ.get("OPENROUTER_API_KEY"): # Use OpenRouter
self.client = OpenAI(
api_key=os.environ.get("OPENROUTER_API_KEY"),
base_url=self.config.openrouter_base_url
or os.getenv("OPENROUTER_API_BASE")
or "https://openrouter.ai/api/v1",
)
else:
api_key = self.config.api_key or os.getenv("OPENAI_API_KEY")
base_url = (
self.config.openai_base_url
or os.getenv("OPENAI_BASE_URL")
or "https://api.openai.com/v1"
)
self.client = OpenAI(api_key=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 JSON response based on the given messages using OpenAI.
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 OpenAI-specific parameters.
Returns:
json: The generated response.
"""
params = self._get_supported_params(messages=messages, **kwargs)
params.update(
{
"model": self.config.model,
"messages": messages,
}
)
if os.getenv("OPENROUTER_API_KEY"):
openrouter_params = {}
if self.config.models:
openrouter_params["models"] = self.config.models
openrouter_params["route"] = self.config.route
params.pop("model")
if self.config.site_url and self.config.app_name:
extra_headers = {
"HTTP-Referer": self.config.site_url,
"X-Title": self.config.app_name,
}
openrouter_params["extra_headers"] = extra_headers
params.update(**openrouter_params)
else:
openai_specific_generation_params = ["store"]
for param in openai_specific_generation_params:
if hasattr(self.config, param):
params[param] = getattr(self.config, param)
if response_format:
params["response_format"] = response_format
if (
tools
): # TODO: Remove tools if no issues found with new memory addition logic
params["tools"] = tools
params["tool_choice"] = tool_choice
response = self.client.chat.completions.create(**params)
parsed_response = self._parse_response(response, tools)
if self.config.response_callback:
try:
self.config.response_callback(self, response, params)
except Exception as e:
# Log error but don't propagate
logging.error(f"Error due to callback: {e}")
pass
return parsed_response
