mirror of
https://github.com/Pythagora-io/gpt-pilot.git
synced 2026-01-08 12:53:50 -05:00
5b474ccc1f
This is a complete rewrite of the GPT Pilot core, from the ground up, making the agentic architecture front and center, and also fixing some long-standing problems with the database architecture that weren't feasible to solve without breaking compatibility. As the database structure and config file syntax have changed, we have automatic imports for projects and current configs, see the README.md file for details. This also relicenses the project to FSL-1.1-MIT license.
91 lines
2.9 KiB
Python
91 lines
2.9 KiB
Python
from os.path import dirname, exists, join
|
|
|
|
from dotenv import dotenv_values
|
|
|
|
from core.config import Config, LLMProvider, ProviderConfig, loader
|
|
|
|
|
|
def import_from_dotenv(new_config_path: str) -> bool:
|
|
"""
|
|
Import configuration from old gpt-pilot .env file and save it to a new format.
|
|
|
|
If the configuration is already loaded, does nothing. If the target file
|
|
already exists, it's parsed as is (it's not overwritten).
|
|
|
|
Otherwise, loads the values from `pilot/.env` file and creates a new configuration
|
|
with the relevant settings.
|
|
|
|
This intentionally DOES NOT load the .env variables into the current process
|
|
environments, to avoid polluting it with old settings.
|
|
|
|
:param new_config_path: Path to save the new configuration file.
|
|
:return: True if the configuration was imported, False otherwise.
|
|
"""
|
|
if loader.config_path or exists(new_config_path):
|
|
# Config already exists, nothing to do
|
|
return True
|
|
|
|
env_path = join(dirname(__file__), "..", "..", "pilot", ".env")
|
|
if not exists(env_path):
|
|
return False
|
|
|
|
values = dotenv_values(env_path)
|
|
if not values:
|
|
return False
|
|
|
|
config = convert_config(values)
|
|
|
|
with open(new_config_path, "w", encoding="utf-8") as fp:
|
|
fp.write(config.model_dump_json(indent=2))
|
|
|
|
return True
|
|
|
|
|
|
def convert_config(values: dict) -> Config:
|
|
config = Config()
|
|
|
|
for provider in LLMProvider:
|
|
endpoint = values.get(f"{provider.value.upper()}_ENDPOINT")
|
|
key = values.get(f"{provider.value.upper()}_API_KEY")
|
|
|
|
if provider == LLMProvider.OPENAI:
|
|
# OpenAI is also used for Azure and OpenRouter and local LLMs
|
|
if endpoint is None:
|
|
endpoint = values.get("AZURE_ENDPOINT")
|
|
if endpoint is None:
|
|
endpoint = values.get("OPENROUTER_ENDPOINT")
|
|
|
|
if key is None:
|
|
key = values.get("AZURE_API_KEY")
|
|
if key is None:
|
|
key = values.get("OPENROUTER_API_KEY")
|
|
if key and endpoint is None:
|
|
endpoint = "https://openrouter.ai/api/v1/chat/completions"
|
|
|
|
if endpoint or key and provider not in config.llm:
|
|
config.llm[provider] = ProviderConfig()
|
|
|
|
if endpoint:
|
|
endpoint = endpoint.replace("chat/completions", "")
|
|
config.llm[provider].base_url = endpoint
|
|
if key:
|
|
config.llm[provider].api_key = key
|
|
|
|
provider = "openai"
|
|
model = values.get("MODEL_NAME", "gpt-4-turbo")
|
|
if "/" in model:
|
|
provider, model = model.split("/", 1)
|
|
|
|
try:
|
|
agent_provider = LLMProvider(provider.upper())
|
|
except ValueError:
|
|
agent_provider = LLMProvider.OPENAI
|
|
|
|
config.agent["default"].model = model
|
|
config.agent["default"].provider = agent_provider
|
|
|
|
ignore_paths = [p for p in values.get("IGNORE_PATHS", "").split(",") if p]
|
|
if ignore_paths:
|
|
config.fs.ignore_paths += ignore_paths
|
|
return config
|