merge main

.pre-commit-config.yaml

@@ -14,13 +14,24 @@ repos:
     hooks:
       - id: black
         args: ['--line-length=88']
+        exclude: ^docs/|.*\.(json|yaml|md|txt)$
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
     rev: v0.4.2
     hooks:
       # Run the linter.
       - id: ruff
-        args: ['--fix', '--extend-ignore=E402']
+        args: ['--fix']
+        exclude: ^docs/|.*\.(json|yaml|md|txt)$
+
+  # Add local hooks to run custom commands
+  - repo: local
+    hooks:
+      - id: run-make-format
+        name: Run Make Format
+        entry: make format
+        language: system
+        pass_filenames: false
   # - repo: https://github.com/pycqa/flake8
   #   rev: 4.0.1
   #   hooks:

Makefile

@@ -0,0 +1,51 @@
+# Define variables for common directories and commands
+PYTHON = poetry run
+SRC_DIR = .
+
+# Default target: Show help
+.PHONY: help
+help:
+	@echo "Available targets:"
+	@echo "  setup            Install dependencies and set up pre-commit hooks"
+	@echo "  format           Run Black and Ruff to format the code"
+	@echo "  lint             Run Ruff to check code quality"
+	@echo "  test             Run tests with pytest"
+	@echo "  precommit        Run pre-commit hooks on all files"
+	@echo "  clean            Clean up temporary files and build artifacts"
+
+# Install dependencies and set up pre-commit hooks
+.PHONY: setup
+setup:
+	poetry install
+	poetry run pre-commit install
+
+# Format code using Black and Ruff
+.PHONY: format
+format:
+	$(PYTHON) black $(SRC_DIR)
+	git ls-files | xargs pre-commit run black --files
+
+# Run lint checks using Ruff
+.PHONY: lint
+lint:
+	$(PYTHON) ruff check $(SRC_DIR)
+
+# Run all pre-commit hooks on all files
+.PHONY: precommit
+precommit:
+	$(PYTHON) pre-commit run --all-files
+
+# Run tests
+.PHONY: test
+test:
+	$(PYTHON) pytest
+
+# Clean up temporary files and build artifacts
+.PHONY: clean
+clean:
+	rm -rf .pytest_cache
+	rm -rf .mypy_cache
+	rm -rf __pycache__
+	rm -rf build dist *.egg-info
+	find . -type d -name "__pycache__" -exec rm -r {} +
+	find . -type f -name "*.pyc" -delete

adalflow/adalflow/components/model_client/anthropic_client.py

@@ -15,7 +15,8 @@
 anthropic = safe_import(
     OptionalPackages.ANTHROPIC.value[0], OptionalPackages.ANTHROPIC.value[1]
 )
-import anthropic
+
+# import anthropic
 from anthropic import (
     RateLimitError,
     APITimeoutError,

adalflow/adalflow/optim/optimizer.py

@@ -67,7 +67,7 @@ def __init__(
         dataset: Sequence[DataClass] = None,
         exclude_input_fields_from_bootstrap_demos: bool = False,
         *args,
-        **kwargs
+        **kwargs,
     ):
         self._weighted = weighted
         self.dataset = dataset

adalflow/tests/test_random_sample.py

@@ -1,13 +1,12 @@
 import unittest
 from typing import TypeVar
+from adalflow.core.functional import random_sample
+
 
 # Assuming the random_sample function is defined here or imported
 T_co = TypeVar("T_co", covariant=True)
 
 
-from adalflow.core.functional import random_sample
-
-
 class TestRandomSample(unittest.TestCase):
 
     def setUp(self):

benchmarks/hotpot_qa/adal_exp/train_vanilla.py

@@ -114,7 +114,7 @@ def train(
         **gpt_3_model,
         teacher_model_config=gpt_4o_model,
         text_optimizer_model_config=gpt_4o_model,
-        backward_engine_model_config=gpt_4o_model
+        backward_engine_model_config=gpt_4o_model,
     )
     print(adal_component)
     trainer = adal.Trainer(

docs/source/contributor/index.rst

@@ -1,19 +1,20 @@
 Contributor Guide
 =============================
-Welcome to the AdalFlow community! We're building the most user-friendly, modular, and powerful library for building and auto-optimizing LLM applications, rom Chatbots and RAGs to Agents.
+Welcome to the AdalFlow community! We're building the most user-friendly, modular, and powerful library for building and auto-optimizing LLM applications, from Chatbots and RAGs to Agents.
 *Think of AdalFlow for LLM applications and prompt engineering as the PyTorch/TensorFlow/JAX equivalent for AI modeling.*
-The goal is to provide basic and fundamental building blocks to create advanced applications with auto-optimization out of the box.
+
+The goal of the library is to provide basic and fundamental building blocks to create advanced applications with auto-optimization out of the box.
 As we mature, we anticipate that more RAG, memory-based chatbots, or agent frameworks will be built on top of AdalFlow’s building blocks, such as `retriever` and `generator`.
 We highly suggest you read our :ref:`design principle<lightrag_design_philosophy>` before you start contributing.
 
-We only accept high quality contributions.
-We appreciate contributors, but we have to hold our libary responsible for our users.
-Once you decide to contribute, we hope it’s not just to list your name on the repo.
-More importantly, we want you to learn and improve your own skills, support your favorite projects, and contribute to the community!
+We greatly appreciate all contributions, from bug fixes to new features, and value every contributor.
+However, we must be selective to ensure our library remains reliable for users.
+We hope your contributions go beyond listing your name on the repo—our goal is for you to learn, grow your skills, support your favorite projects, and give back to the community!
 
+The goal of this guide is to design the best process for maintaining the quality of our library while enabling the community to make meaningful contributions.
 It took us three months to set up this contributor guide, as we first tested the process with early contributors.
-Our goal is to design the best process for maintaining the quality of our library while enabling the community to make meaningful contributions.
-We are determined to make AdalFlow as great and legendary as PyTorch.
+*We are determined to make AdalFlow as great and legendary as PyTorch.*
+
 This guide covers the overall contributing process, along with development essentials for environment setup, coding, testing, and documentation.
 
 Here’s to the future of LLM applications!