249 lines
8.2 KiB
Python
249 lines
8.2 KiB
Python
# Project EmbodiedGen
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#
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# Copyright (c) 2025 Horizon Robotics. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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# implied. See the License for the specific language governing
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# permissions and limitations under the License.
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import base64
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import logging
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import os
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from io import BytesIO
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from typing import Optional
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import yaml
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from openai import AzureOpenAI, OpenAI # pip install openai
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from PIL import Image
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from tenacity import (
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retry,
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stop_after_attempt,
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stop_after_delay,
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wait_random_exponential,
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)
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from embodied_gen.utils.process_media import combine_images_to_grid
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logging.getLogger("httpx").setLevel(logging.WARNING)
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logging.basicConfig(level=logging.WARNING)
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logger = logging.getLogger(__name__)
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__all__ = [
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"GPTclient",
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]
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CONFIG_FILE = "embodied_gen/utils/gpt_config.yaml"
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class GPTclient:
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"""A client to interact with GPT models via OpenAI or Azure API.
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Supports text and image prompts, connection checking, and configurable parameters.
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Args:
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endpoint (str): API endpoint URL.
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api_key (str): API key for authentication.
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model_name (str, optional): Model name to use.
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api_version (str, optional): API version (for Azure).
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check_connection (bool, optional): Whether to check API connection.
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verbose (bool, optional): Enable verbose logging.
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Example:
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```sh
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export ENDPOINT="https://yfb-openai-sweden.openai.azure.com"
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export API_KEY="xxxxxx"
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export API_VERSION="2025-03-01-preview"
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export MODEL_NAME="yfb-gpt-4o-sweden"
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```
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```py
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from embodied_gen.utils.gpt_clients import GPT_CLIENT
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response = GPT_CLIENT.query("Describe the physics of a falling apple.")
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response = GPT_CLIENT.query(
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text_prompt="Describe the content in each image."
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image_base64=["path/to/image1.png", "path/to/image2.jpg"],
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)
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```
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"""
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def __init__(
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self,
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endpoint: str,
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api_key: str,
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model_name: str = "yfb-gpt-4o",
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api_version: str = None,
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check_connection: bool = True,
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verbose: bool = False,
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):
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if api_version is not None:
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self.client = AzureOpenAI(
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azure_endpoint=endpoint,
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api_key=api_key,
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api_version=api_version,
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)
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else:
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self.client = OpenAI(
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base_url=endpoint,
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api_key=api_key,
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)
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self.endpoint = endpoint
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self.model_name = model_name
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self.image_formats = {".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif"}
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self.verbose = verbose
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if check_connection:
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self.check_connection()
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logger.info(f"Using GPT model: {self.model_name}.")
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@retry(
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wait=wait_random_exponential(min=1, max=20),
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stop=(stop_after_attempt(10) | stop_after_delay(30)),
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)
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def completion_with_backoff(self, **kwargs):
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"""Performs a chat completion request with retry/backoff."""
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return self.client.chat.completions.create(**kwargs)
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def query(
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self,
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text_prompt: str,
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image_base64: Optional[list[str | Image.Image]] = None,
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system_role: Optional[str] = None,
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params: Optional[dict] = None,
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) -> Optional[str]:
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"""Queries the GPT model with text and optional image prompts.
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Args:
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text_prompt (str): Main text input.
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image_base64 (Optional[list[str | Image.Image]], optional): List of image base64 strings, file paths, or PIL Images.
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system_role (Optional[str], optional): System-level instructions.
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params (Optional[dict], optional): Additional GPT parameters.
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Returns:
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Optional[str]: Model response content, or None if error.
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"""
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if system_role is None:
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system_role = "You are a highly knowledgeable assistant specializing in physics, engineering, and object properties." # noqa
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content_user = [
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{
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"type": "text",
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"text": text_prompt,
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},
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]
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# Process images if provided
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if image_base64 is not None:
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if not isinstance(image_base64, list):
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image_base64 = [image_base64]
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# Hardcode tmp because of the openrouter can't input multi images.
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if "openrouter" in self.endpoint:
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image_base64 = combine_images_to_grid(image_base64)
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for img in image_base64:
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if isinstance(img, Image.Image):
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buffer = BytesIO()
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img.save(buffer, format=img.format or "PNG")
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buffer.seek(0)
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image_binary = buffer.read()
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img = base64.b64encode(image_binary).decode("utf-8")
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elif (
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len(os.path.splitext(img)) > 1
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and os.path.splitext(img)[-1].lower() in self.image_formats
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):
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if not os.path.exists(img):
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raise FileNotFoundError(f"Image file not found: {img}")
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with open(img, "rb") as f:
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img = base64.b64encode(f.read()).decode("utf-8")
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content_user.append(
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{
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"type": "image_url",
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"image_url": {"url": f"data:image/png;base64,{img}"},
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}
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)
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payload = {
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"messages": [
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{"role": "system", "content": system_role},
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{"role": "user", "content": content_user},
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],
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"temperature": 0.1,
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"max_tokens": 500,
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"top_p": 0.1,
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"frequency_penalty": 0,
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"presence_penalty": 0,
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"stop": None,
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"model": self.model_name,
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}
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if params:
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payload.update(params)
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response = None
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try:
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response = self.completion_with_backoff(**payload)
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response = response.choices[0].message.content
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except Exception as e:
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logger.error(f"Error GPTclint {self.endpoint} API call: {e}")
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response = None
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if self.verbose:
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logger.info(f"Prompt: {text_prompt}")
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logger.info(f"Response: {response}")
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return response
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def check_connection(self) -> None:
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"""Checks whether the GPT API connection is working.
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Raises:
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ConnectionError: If connection fails.
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"""
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try:
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response = self.completion_with_backoff(
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messages=[
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{"role": "system", "content": "You are a test system."},
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{"role": "user", "content": "Hello"},
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],
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model=self.model_name,
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temperature=0,
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max_tokens=100,
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)
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content = response.choices[0].message.content
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logger.info(f"Connection check success.")
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except Exception as e:
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raise ConnectionError(
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f"Failed to connect to GPT API at {self.endpoint}, "
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f"please check setting in `{CONFIG_FILE}` and `README`."
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)
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with open(CONFIG_FILE, "r") as f:
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config = yaml.safe_load(f)
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agent_type = config["agent_type"]
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agent_config = config.get(agent_type, {})
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# Prefer environment variables, fallback to YAML config
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endpoint = os.environ.get("ENDPOINT", agent_config.get("endpoint"))
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api_key = os.environ.get("API_KEY", agent_config.get("api_key"))
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api_version = os.environ.get("API_VERSION", agent_config.get("api_version"))
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model_name = os.environ.get("MODEL_NAME", agent_config.get("model_name"))
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GPT_CLIENT = GPTclient(
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endpoint=endpoint,
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api_key=api_key,
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api_version=api_version,
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model_name=model_name,
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check_connection=False,
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)
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