Unified Configuration File in TOML Format

config.toml

@@ -0,0 +1,34 @@
+# config.toml
+# Unified configuration file for the gpt-engineer project
+
+# API Configuration
+[api]
+# API key for OpenAPI
+# OPENAI_API_KEY=Your personal OpenAI API key from https://platform.openai.com/account/api-keys
+OPENAI_API_KEY = "your_api_key_here"
+ANTHROPIC_API_KEY = "your_anthropic_api_key_here"
+# Model configurations
+[model]
+model_name = "gpt-4o"
+# Controls randomness: lower values for more focused, deterministic outputs
+temperature = 0.1
+# Endpoint for your Azure OpenAI Service (https://xx.openai.azure.com).
+# In that case, the given model is the deployment name chosen in the Azure AI Studio.
+azure_endpoint = ""
+
+# improve mode Configuration
+[improve]
+# Enable or disable linting (true/false)
+is_linting = true
+# Enable or disable file selection. "true" will open your default editor to select the file. (true/false)
+is_file_selection = true
+
+# Git Filter Configuration
+[git_filter]
+# File extension settings for the git filter
+file_extensions = ["py", "toml", "md"]
+
+# Self-Healing Mechanism Configuration
+[self_healing]
+# Number of retry attempts for self-healing mechanisms (0-2)
+retry_attempts = 1

gpt_engineer/applications/cli/main.py

@@ -57,6 +57,7 @@
 from gpt_engineer.core.files_dict import FilesDict
 from gpt_engineer.core.git import stage_uncommitted_to_git
 from gpt_engineer.core.preprompts_holder import PrepromptsHolder
+from gpt_engineer.core.project_config import Config
 from gpt_engineer.core.prompt import Prompt
 from gpt_engineer.tools.custom_steps import clarified_gen, lite_gen, self_heal
 
@@ -250,7 +251,7 @@ def prompt_yesno() -> bool:
 def main(
     project_path: str = typer.Argument(".", help="path"),
     model: str = typer.Option(
-        os.environ.get("MODEL_NAME", "gpt-4o"), "--model", "-m", help="model id string"
+        os.environ.get("MODEL_NAME", "gpt-4"), "--model", "-m", help="model id string"
     ),
     temperature: float = typer.Option(
         0.1,
@@ -410,6 +411,11 @@ def main(
     path = Path(project_path)
     print("Running gpt-engineer in", path.absolute(), "\n")
 
+    # read the configuration file from the root directory
+    config = Config()
+    config.from_toml(Path(os.getcwd()) / "config.toml").to_dict()
+    # todo: apply configuration here
+
     prompt = load_prompt(
         DiskMemory(path),
         improve_mode,

gpt_engineer/core/project_config.py

@@ -8,7 +8,7 @@
 
 import tomlkit
 
-default_config_filename = "gpt-engineer.toml"
+default_config_filename = "config.toml"
 
 example_config = """
 [run]
@@ -38,22 +38,22 @@ class _PathsConfig:
 
 
 @dataclass
-class _RunConfig:
-    build: str | None = None
-    test: str | None = None
-    lint: str | None = None
-    format: str | None = None
+class _ApiConfig:
+    OPENAI_API_KEY: str | None = None
+    ANTHROPIC_API_KEY: str | None = None
 
 
 @dataclass
-class _OpenApiConfig:
-    url: str
+class _ModelConfig:
+    model_name: str | None = None
+    temperature: float | None = None
+    azure_endpoint: str | None = None
 
 
 @dataclass
-class _GptEngineerAppConfig:
-    project_id: str
-    openapi: list[_OpenApiConfig] | None = None
+class _ImproveConfig:
+    is_linting: bool | None = None
+    is_file_selection: bool | None = None
 
 
 def filter_none(d: dict) -> dict:
@@ -74,8 +74,9 @@ class Config:
     """Configuration for the GPT Engineer CLI and gptengineer.app via `gpt-engineer.toml`."""
 
     paths: _PathsConfig = field(default_factory=_PathsConfig)
-    run: _RunConfig = field(default_factory=_RunConfig)
-    gptengineer_app: _GptEngineerAppConfig | None = None
+    api_config: _ApiConfig = field(default_factory=_ApiConfig)
+    model_config: _ModelConfig = field(default_factory=_ModelConfig)
+    improve_config: _ImproveConfig = field(default_factory=_ImproveConfig)
 
     @classmethod
     def from_toml(cls, config_file: Path | str):
@@ -86,31 +87,36 @@ def from_toml(cls, config_file: Path | str):
 
     @classmethod
     def from_dict(cls, config_dict: dict):
-        run = _RunConfig(**config_dict.get("run", {}))
-        paths = _PathsConfig(**config_dict.get("paths", {}))
-
-        # load optional gptengineer-app section
-        gptengineer_app_dict = config_dict.get("gptengineer-app", {})
-        gptengineer_app = None
-        if gptengineer_app_dict:
-            assert (
-                "project_id" in gptengineer_app_dict
-            ), "project_id is required in gptengineer-app section"
-            gptengineer_app = _GptEngineerAppConfig(
-                # required if gptengineer-app section is present
-                project_id=gptengineer_app_dict["project_id"],
-                openapi=[
-                    _OpenApiConfig(**openapi)
-                    for openapi in gptengineer_app_dict.get("openapi", [])
-                ]
-                or None,
+        paths = _PathsConfig(**config_dict.get("paths", {"base": None, "src": None}))
+        api_config = _ApiConfig(
+            **config_dict.get(
+                "api", {"OPENAI_API_KEY": None, "ANTHROPIC_API_KEY": None}
             )
+        )
+        model_config = _ModelConfig(
+            **config_dict.get(
+                "model",
+                {"model_name": None, "temperature": None, "azure_endpoint": None},
+            )
+        )
+        improve_config = _ImproveConfig(
+            **config_dict.get(
+                "improve", {"is_linting": None, "is_file_selection": None}
+            )
+        )
 
-        return cls(paths=paths, run=run, gptengineer_app=gptengineer_app)
+        return cls(
+            paths=paths,
+            api_config=api_config,
+            model_config=model_config,
+            improve_config=improve_config,
+        )
 
     def to_dict(self) -> dict:
         d = asdict(self)
-        d["gptengineer-app"] = d.pop("gptengineer_app", None)
+        d["api"] = d.pop("api_config", None)
+        d["model"] = d.pop("model_config", None)
+        d["improve"] = d.pop("improve_config", None)
 
         # Drop None values and empty dictionaries
         # Needed because tomlkit.dumps() doesn't handle None values,