Run python autoformatter on entire Python libraries

cookbooks/Basic-Prompt-Routing/assistant_app.py

@@ -1,14 +1,14 @@
 import asyncio
 import os
 
+# Create ~/.env file with this line: export OPENAI_API_KEY=<your key here>
+# You can get your key from https://platform.openai.com/api-keys
+import dotenv
 import openai
 import streamlit as st
 
 from aiconfig import AIConfigRuntime
 
-# Create ~/.env file with this line: export OPENAI_API_KEY=<your key here>
-# You can get your key from https://platform.openai.com/api-keys 
-import dotenv
 dotenv.load_dotenv()
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
@@ -32,12 +32,8 @@ async def assistant_response(prompt):
 
 # Streamlit Setup
 st.title("AI Teaching Assistant")
-st.markdown(
-    "Ask a math, physics, or general question. Based on your question, an AI math prof, physics prof, or general assistant will respond."
-)
-st.markdown(
-    "**This is a simple demo of prompt routing - based on your question, an LLM decides which AI teacher responds.**"
-)
+st.markdown("Ask a math, physics, or general question. Based on your question, an AI math prof, physics prof, or general assistant will respond.")
+st.markdown("**This is a simple demo of prompt routing - based on your question, an LLM decides which AI teacher responds.**")
 
 # Chat setup
 if "messages" not in st.session_state:

cookbooks/Cli-Mate/cli-mate.py

@@ -53,19 +53,13 @@ async def query(aiconfig_path: str, question: str) -> list[ExecuteResult]:
     return result
 
 
-async def get_mod_result(
-    aiconfig_path: str, source_code: str, question: str
-) -> list[ExecuteResult]:
-    question_about_code = (
-        f"QUERY ABOUT SOURCE CODE:\n{question}\nSOURCE CODE:\n```{source_code}\n```"
-    )
+async def get_mod_result(aiconfig_path: str, source_code: str, question: str) -> list[ExecuteResult]:
+    question_about_code = f"QUERY ABOUT SOURCE CODE:\n{question}\nSOURCE CODE:\n```{source_code}\n```"
 
     return await query(aiconfig_path, question_about_code)
 
 
-async def mod_code(
-    aiconfig_path: str, source_code_file: str, question: str, update_file: bool = False
-):
+async def mod_code(aiconfig_path: str, source_code_file: str, question: str, update_file: bool = False):
     # read source code from file
     with open(source_code_file, "r", encoding="utf8") as file:
         source_code = file.read()
@@ -99,9 +93,7 @@ def signal_handler(_: int, __: FrameType | None):
     i = 0
     while True:
         try:
-            user_input = await event_loop.run_in_executor(
-                None, session.prompt, "Query: [ctrl-D to exit] "
-            )
+            user_input = await event_loop.run_in_executor(None, session.prompt, "Query: [ctrl-D to exit] ")
         except KeyboardInterrupt:
             continue
         except EOFError:
@@ -152,9 +144,7 @@ async def main():
     subparsers = parser.add_subparsers(dest="command")
 
     loop_parser = subparsers.add_parser("loop")
-    loop_parser.add_argument(
-        "-scf", "--source-code-file", help="Specify a source code file."
-    )
+    loop_parser.add_argument("-scf", "--source-code-file", help="Specify a source code file.")
 
     args = parser.parse_args()
 

cookbooks/HuggingFace/hf.py

@@ -3,10 +3,7 @@
 
 # HuggingFace API imports
 from huggingface_hub import InferenceClient
-from huggingface_hub.inference._text_generation import (
-    TextGenerationResponse,
-    TextGenerationStreamResponse,
-)
+from huggingface_hub.inference._text_generation import TextGenerationResponse, TextGenerationStreamResponse
 
 # ModelParser Utils
 # Type hint imports
@@ -166,14 +163,7 @@ def id(self) -> str:
         """
         return "HuggingFaceTextParser"
 
-    def serialize(
-        self,
-        prompt_name: str,
-        data: Any,
-        ai_config: "AIConfigRuntime",
-        parameters: Optional[Dict] = None,
-        **kwargs
-    ) -> List[Prompt]:
+    def serialize(self, prompt_name: str, data: Any, ai_config: "AIConfigRuntime", parameters: Optional[Dict] = None, **kwargs) -> List[Prompt]:
         """
         Defines how a prompt and model inference settings get serialized in the .aiconfig.
 
@@ -196,9 +186,7 @@ def serialize(
         prompt = Prompt(
             name=prompt_name,
             input=prompt_input,
-            metadata=PromptMetadata(
-                model=model_metadata, parameters=parameters, **kwargs
-            ),
+            metadata=PromptMetadata(model=model_metadata, parameters=parameters, **kwargs),
         )
         return [prompt]
 
@@ -230,9 +218,7 @@ async def deserialize(
 
         return completion_data
 
-    async def run_inference(
-        self, prompt: Prompt, aiconfig, options, parameters
-    ) -> List[Output]:
+    async def run_inference(self, prompt: Prompt, aiconfig, options, parameters) -> List[Output]:
         """
         Invoked to run a prompt in the .aiconfig. This method should perform
         the actual model inference based on the provided prompt and inference settings.
@@ -247,9 +233,7 @@ async def run_inference(
         completion_data = await self.deserialize(prompt, aiconfig, options, parameters)
 
         # if stream enabled in runtime options and config, then stream. Otherwise don't stream.
-        stream = (options.stream if options else False) and (
-            not "stream" in completion_data or completion_data.get("stream") != False
-        )
+        stream = (options.stream if options else False) and (not "stream" in completion_data or completion_data.get("stream") != False)
 
         response = self.client.text_generation(**completion_data)
         response_is_detailed = completion_data.get("details", False)

cookbooks/HuggingFace/python/hf.py

@@ -3,10 +3,7 @@
 
 # HuggingFace API imports
 from huggingface_hub import InferenceClient
-from huggingface_hub.inference._text_generation import (
-    TextGenerationResponse,
-    TextGenerationStreamResponse,
-)
+from huggingface_hub.inference._text_generation import TextGenerationResponse, TextGenerationStreamResponse
 
 # ModelParser Utils
 # Type hint imports
@@ -166,14 +163,7 @@ def id(self) -> str:
         """
         return "HuggingFaceTextParser"
 
-    def serialize(
-        self,
-        prompt_name: str,
-        data: Any,
-        ai_config: "AIConfigRuntime",
-        parameters: Optional[Dict] = None,
-        **kwargs
-    ) -> List[Prompt]:
+    def serialize(self, prompt_name: str, data: Any, ai_config: "AIConfigRuntime", parameters: Optional[Dict] = None, **kwargs) -> List[Prompt]:
         """
         Defines how a prompt and model inference settings get serialized in the .aiconfig.
 
@@ -196,9 +186,7 @@ def serialize(
         prompt = Prompt(
             name=prompt_name,
             input=prompt_input,
-            metadata=PromptMetadata(
-                model=model_metadata, parameters=parameters, **kwargs
-            ),
+            metadata=PromptMetadata(model=model_metadata, parameters=parameters, **kwargs),
         )
         return [prompt]
 
@@ -230,9 +218,7 @@ async def deserialize(
 
         return completion_data
 
-    async def run_inference(
-        self, prompt: Prompt, aiconfig, options, parameters
-    ) -> List[Output]:
+    async def run_inference(self, prompt: Prompt, aiconfig, options, parameters) -> List[Output]:
         """
         Invoked to run a prompt in the .aiconfig. This method should perform
         the actual model inference based on the provided prompt and inference settings.
@@ -247,9 +233,7 @@ async def run_inference(
         completion_data = await self.deserialize(prompt, aiconfig, options, parameters)
 
         # if stream enabled in runtime options and config, then stream. Otherwise don't stream.
-        stream = (options.stream if options else False) and (
-            not "stream" in completion_data or completion_data.get("stream") != False
-        )
+        stream = (options.stream if options else False) and (not "stream" in completion_data or completion_data.get("stream") != False)
 
         response = self.client.text_generation(**completion_data)
         response_is_detailed = completion_data.get("details", False)

cookbooks/Wizard-GPT/wizard-gpt.py

@@ -1,12 +1,14 @@
 import asyncio
+import os
 
-from aiconfig import AIConfigRuntime, InferenceOptions, Prompt
+import dotenv
 
 # Create ~/.env file with this line: export OPENAI_API_KEY=<your key here>
-# You can get your key from https://platform.openai.com/api-keys 
+# You can get your key from https://platform.openai.com/api-keys
 import openai
-import dotenv
-import os
+
+from aiconfig import AIConfigRuntime, InferenceOptions, Prompt
+
 dotenv.load_dotenv()
 openai.api_key = os.getenv("OPENAI_API_KEY")
 

python/src/aiconfig/ChatCompletion.py

@@ -29,10 +29,7 @@ def validate_and_add_prompts_to_config(prompts: List[Prompt], aiconfig) -> None:
         in_config = False
         for config_prompt in aiconfig.prompts:
             # check for duplicates (same input and settings.)
-            if (
-                config_prompt.input == new_prompt.input
-                and new_prompt.metadata == config_prompt.metadata
-            ):
+            if config_prompt.input == new_prompt.input and new_prompt.metadata == config_prompt.metadata:
                 in_config = True
                 # update outputs if different
                 if config_prompt.outputs != new_prompt.outputs:
@@ -58,9 +55,7 @@ def extract_outputs_from_response(response) -> List[Output]:
 
     response = response.model_dump(exclude_none=True)
 
-    response_without_choices = {
-        key: copy.deepcopy(value) for key, value in response.items() if key != "choices"
-    }
+    response_without_choices = {key: copy.deepcopy(value) for key, value in response.items() if key != "choices"}
     for i, choice in enumerate(response.get("choices")):
         response_without_choices.update({"finish_reason": choice.get("finish_reason")})
         output = ExecuteResult(
@@ -77,7 +72,7 @@ def extract_outputs_from_response(response) -> List[Output]:
 
 
 def async_run_serialize_helper(
-    aiconfig: AIConfigRuntime, 
+    aiconfig: AIConfigRuntime,
     request_kwargs: Dict,
 ) -> List[Prompt]:
     """
@@ -88,9 +83,7 @@ def async_run_serialize_helper(
     serialized_prompts = None
 
     async def run_and_await_serialize():
-        result = await aiconfig.serialize(
-            request_kwargs.get("model"), request_kwargs, "prompt"
-        )
+        result = await aiconfig.serialize(request_kwargs.get("model"), request_kwargs, "prompt")
         return result
 
     # serialize prompts from ChatCompletion kwargs
@@ -125,17 +118,9 @@ def _get_aiconfig_runtime(output_aiconfig_path: str) -> AIConfigRuntime:
         except IOError:
             return AIConfigRuntime.create(**(aiconfig_settings or {}))
 
-    output_aiconfig = (
-        output_aiconfig_ref
-        if isinstance(output_aiconfig_ref, AIConfigRuntime)
-        else _get_aiconfig_runtime(output_aiconfig_ref)
-    )
+    output_aiconfig = output_aiconfig_ref if isinstance(output_aiconfig_ref, AIConfigRuntime) else _get_aiconfig_runtime(output_aiconfig_ref)
 
-    output_config_file_path = (
-        output_aiconfig_ref
-        if isinstance(output_aiconfig_ref, str)
-        else output_aiconfig_ref.file_path
-    )
+    output_config_file_path = output_aiconfig_ref if isinstance(output_aiconfig_ref, str) else output_aiconfig_ref.file_path
 
     # TODO: openai makes it hard to statically annotate.
     def _create_chat_completion_with_config_saving(*args, **kwargs) -> Any:  # type: ignore
@@ -147,9 +132,7 @@ def _create_chat_completion_with_config_saving(*args, **kwargs) -> Any:  # type:
         outputs = []
 
         # Check if response is a stream
-        stream = kwargs.get("stream", False) is True and isinstance(
-            response, openai.Stream
-        )
+        stream = kwargs.get("stream", False) is True and isinstance(response, openai.Stream)
 
         # Convert Response to output for last prompt
         if not stream:
@@ -189,9 +172,7 @@ def generate_streamed_response() -> Generator[Any, None, None]:
                         )
                         stream_outputs[index] = output
                     yield chunk
-                stream_outputs = [
-                    stream_outputs[i] for i in sorted(list(stream_outputs.keys()))
-                ]
+                stream_outputs = [stream_outputs[i] for i in sorted(list(stream_outputs.keys()))]
 
                 # Add outputs to last prompt
                 serialized_prompts[-1].outputs = stream_outputs

python/src/aiconfig/Config.py

@@ -1,22 +1,20 @@
 import json
 import os
-import yaml
 from typing import Any, Dict, List, Literal, Optional, Tuple
 
 import requests
+import yaml
 from aiconfig.callback import CallbackEvent, CallbackManager
 from aiconfig.default_parsers.anyscale_endpoint import DefaultAnyscaleEndpointParser
 from aiconfig.default_parsers.openai import DefaultOpenAIParser
 from aiconfig.default_parsers.palm import PaLMChatParser, PaLMTextParser
 from aiconfig.model_parser import InferenceOptions, ModelParser
+
 from aiconfig.schema import JSONObject
 
 from .default_parsers.dalle import DalleImageGenerationParser
 from .default_parsers.hf import HuggingFaceTextGenerationParser
-from .registry import (
-    ModelParserRegistry,
-    update_model_parser_registry_with_config_runtime,
-)
+from .registry import ModelParserRegistry, update_model_parser_registry_with_config_runtime
 from .schema import AIConfig, Prompt
 from .util.config_utils import is_yaml_ext
 

python/src/aiconfig/callback.py

@@ -2,17 +2,7 @@
 import asyncio
 import logging
 import time
-from typing import (
-    Any,
-    Awaitable,
-    Callable,
-    Coroutine,
-    Final,
-    List,
-    Sequence,
-    TypeAlias,
-    Union,
-)
+from typing import Any, Awaitable, Callable, Coroutine, Final, List, Sequence, TypeAlias, Union
 
 from pydantic import BaseModel, ConfigDict
 
@@ -43,9 +33,7 @@ def __init__(self, name: str, file: str, data: Any, ts_ns: int = time.time_ns())
 Result: TypeAlias = Union[Ok[Any], Err[Any]]
 
 
-async def execute_coroutine_with_timeout(
-    coroutine: Coroutine[Any, Any, Any], timeout: int
-) -> Result:
+async def execute_coroutine_with_timeout(coroutine: Coroutine[Any, Any, Any], timeout: int) -> Result:
     """
     Execute a coroutine with a timeout, return an Ok result or an Err on Exception
 
@@ -123,9 +111,7 @@ def setup_logger():
         name = "my-logger"
         log_file = "aiconfig.log"
 
-        formatter = logging.Formatter(
-            "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-        )
+        formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
         handler = logging.FileHandler(log_file)
         handler.setFormatter(formatter)
 

python/src/aiconfig/editor/server/server.py

@@ -1,6 +1,6 @@
 import logging
-from typing import Any, Dict, Type, Union
 import webbrowser
+from typing import Any, Dict, Type, Union
 
 import lastmile_utils.lib.core.api as core_utils
 import result