pre-commit version update and a few spelling fixes (#2913)

.pre-commit-config.yaml

@@ -8,7 +8,7 @@ ci:
 
 repos:
  - repo: https://github.com/pre-commit/pre-commit-hooks
- rev: v4.5.0
+ rev: v4.6.0
  hooks:
  - id: check-added-large-files
  - id: check-ast
@@ -23,21 +23,21 @@ repos:
  - id: end-of-file-fixer
  - id: no-commit-to-branch
  - repo: https://github.com/psf/black
- rev: 24.3.0
+ rev: 24.4.2
  hooks:
  - id: black
  - repo: https://github.com/astral-sh/ruff-pre-commit
- rev: v0.3.4
+ rev: v0.4.8
  hooks:
  - id: ruff
  types_or: [ python, pyi, jupyter ]
  args: ["--fix", "--ignore=E402"]
  exclude: notebook/agentchat_databricks_dbrx.ipynb
  - repo: https://github.com/codespell-project/codespell
- rev: v2.2.6
+ rev: v2.3.0
  hooks:
  - id: codespell
- args: ["-L", "ans,linar,nam,tread,ot,"]
+ args: ["-L", "ans,linar,nam,tread,ot,assertIn,dependin,socio-economic"]
  exclude: |
  (?x)^(
  pyproject.toml |

autogen/oai/gemini.py

@@ -151,7 +151,7 @@ def create(self, params: Dict) -> ChatCompletion:
  if not model_name:
  raise ValueError(
  "Please provide a model name for the Gemini Client. "
- "You can configurate it in the OAI Config List file. "
+ "You can configure it in the OAI Config List file. "
  "See this [LLM configuration tutorial](https://microsoft.github.io/autogen/docs/topics/llm_configuration/) for more details."
  )
 

notebook/agentchat_groupchat_stateflow.ipynb

@@ -74,7 +74,7 @@
  "- Scientist: Read the papers and write a summary.\n",
  "\n",
  "\n",
- "In the Figure, we define a simple workflow for research with 4 states: Init, Retrieve, Reserach and End. Within each state, we will call different agents to perform the tasks.\n",
+ "In the Figure, we define a simple workflow for research with 4 states: Init, Retrieve, Research and End. Within each state, we will call different agents to perform the tasks.\n",
  "- Init: We use the initializer to start the workflow.\n",
  "- Retrieve: We will first call the coder to write code and then call the executor to execute the code.\n",
  "- Research: We will call the scientist to read the papers and write a summary.\n",

test/oai/_test_completion.py

@@ -4,7 +4,6 @@
 import os
 import sys
 from functools import partial
-from test.oai.test_utils import KEY_LOC, OAI_CONFIG_LIST
 
 import datasets
 import numpy as np
@@ -18,6 +17,7 @@
  implement,
 )
 from autogen.math_utils import eval_math_responses, solve_problem
+from test.oai.test_utils import KEY_LOC, OAI_CONFIG_LIST
 
 here = os.path.abspath(os.path.dirname(__file__))
 

website/blog/2024-01-23-Code-execution-in-docker/index.mdx

@@ -33,7 +33,7 @@ user_proxy.initiate_chat(assistant, message="Plot a chart of NVDA and TESLA stoc
 
 To opt out of from this default behaviour there are some options.
 
-### Diasable code execution entirely
+### Disable code execution entirely
 
 - Set `code_execution_config` to `False` for each code-execution agent. E.g.:
 

website/blog/2024-02-29-StateFlow/index.mdx

@@ -102,7 +102,7 @@ scientist = autogen.AssistantAgent(
 )
 ```
 
-In the Figure, we define a simple workflow for research with 4 states: Init, Retrieve, Reserach, and End. Within each state, we will call different agents to perform the tasks.
+In the Figure, we define a simple workflow for research with 4 states: Init, Retrieve, Research, and End. Within each state, we will call different agents to perform the tasks.
 - Init: We use the initializer to start the workflow.
 - Retrieve: We will first call the coder to write code and then call the executor to execute the code.
 - Research: We will call the scientist to read the papers and write a summary.

website/blog/2024-05-24-Agent/index.mdx

@@ -141,7 +141,7 @@ better with low cost. [EcoAssistant](/blog/2023/11/09/EcoAssistant) is a good ex
 
 - [AutoDefense](/blog/2024/03/11/AutoDefense/Defending%20LLMs%20Against%20Jailbreak%20Attacks%20with%20AutoDefense) demonstrates that using multi-agents reduces the risk of suffering from jailbreak attacks.
 
-There are certainly tradeoffs to make. The large design space of multi-agents offers these tradeoffs and opens up new opportunites for optimization.
+There are certainly tradeoffs to make. The large design space of multi-agents offers these tradeoffs and opens up new opportunities for optimization.
 
 > Over a year since the debut of Ask AT&T, the generative AI platform to which we’ve onboarded over 80,000 users, AT&T has been enhancing its capabilities by incorporating 'AI Agents'. These agents, powered by the Autogen framework pioneered by Microsoft (https://microsoft.github.io/autogen/blog/2023/12/01/AutoGenStudio/), are designed to tackle complicated workflows and tasks that traditional language models find challenging. To drive collaboration, AT&T is contributing back to the open-source project by introducing features that facilitate enhanced security and role-based access for various projects and data.
 >