Remove black (#2178)

1. Remove black
2. Applied `ruff format` to code which was inconsistent with ruff and
black
3. Relevant edits in Makefile

Author: NirantK

.github/workflows/ci.yaml

@@ -179,8 +179,8 @@ jobs:
           # Note: We use direct commands here instead of the standalone Makefiles
           # to have precise control over CI-specific options like --check for dry-run
           echo "Checking ragas formatting..."
-          black --check --config ragas/pyproject.toml ragas/src ragas/tests docs
-          ruff check ragas/src docs ragas/tests
+          ruff format --check ragas/src ragas/tests docs --exclude ragas/src/ragas/_version.py
+          ruff check ragas/src docs ragas/tests --exclude ragas/src/ragas/_version.py
 
       - name: Type check
         run: make type

DEVELOPMENT.md

@@ -232,7 +232,7 @@ cd ragas && uv run pytest tests/unit -k "test_name"
 ### Code Quality Pipeline
 The `make format` command runs:
 1. **isort**: Import sorting
-2. **black**: Code formatting
+2. **ruff format**: Code formatting
 3. **ruff --fix-only**: Auto-fix issues (including unused imports)
 4. **ruff check**: Final linting validation
 

Makefile

@@ -69,7 +69,7 @@ run-ci: ## Run complete CI pipeline (mirrors GitHub CI exactly)
 run-ci-format-check: ## Run format check in dry-run mode (like GitHub CI)
 	@echo "Running format check (dry-run, like GitHub CI)..."
 	@echo "Checking ragas formatting..."
-	$(Q)black --check --config ragas/pyproject.toml ragas/src ragas/tests docs
+	$(Q)uv run ruff format --check ragas/src ragas/tests docs
 	$(Q)ruff check ragas/src docs ragas/tests
 
 run-ci-type: ## Run type checking (matches GitHub CI)
@@ -83,7 +83,7 @@ run-ci-tests: ## Run all tests with CI options
 run-ci-fast: ## Fast CI check for quick local validation (2-3 minutes)
 	@echo "Running fast CI check for quick feedback..."
 	@echo "Format check..."
-	$(Q)black --check --config ragas/pyproject.toml ragas/src ragas/tests docs
+	$(Q)uv run ruff format --check ragas/src ragas/tests docs
 	$(Q)ruff check ragas/src docs ragas/tests
 	@echo "Core unit tests (no nbmake for speed)..."
 	$(Q)cd ragas && pytest tests/unit tests/experimental --dist loadfile -n auto -x

docs/howtos/customizations/testgenerator/testgen-custom-single-hop.ipynb

@@ -239,11 +239,9 @@
     "\n",
     "@dataclass\n",
     "class MySingleHopScenario(SingleHopQuerySynthesizer):\n",
-    "\n",
     "    theme_persona_matching_prompt = ThemesPersonasMatchingPrompt()\n",
     "\n",
     "    async def _generate_scenarios(self, n, knowledge_graph, persona_list, callbacks):\n",
-    "\n",
     "        property_name = \"keyphrases\"\n",
     "        nodes = []\n",
     "        for node in knowledge_graph.nodes:\n",

docs/howtos/customizations/testgenerator/testgen-customisation.ipynb

@@ -258,7 +258,6 @@
     "\n",
     "@dataclass\n",
     "class MyMultiHopQuery(MultiHopQuerySynthesizer):\n",
-    "\n",
     "    theme_persona_matching_prompt = ThemesPersonasMatchingPrompt()\n",
     "\n",
     "    async def _generate_scenarios(\n",
@@ -268,7 +267,6 @@
     "        persona_list,\n",
     "        callbacks,\n",
     "    ) -> t.List[MultiHopScenario]:\n",
-    "\n",
     "        # query and get (node_a, rel, node_b) to create multi-hop queries\n",
     "        results = kg.find_two_nodes_single_rel(\n",
     "            relationship_condition=lambda rel: (\n",
@@ -284,7 +282,6 @@
     "                node_a, node_b = triplet[0], triplet[-1]\n",
     "                overlapped_keywords = triplet[1].properties[\"overlapped_items\"]\n",
     "                if overlapped_keywords:\n",
-    "\n",
     "                    # match the keyword with a persona for query creation\n",
     "                    themes = list(dict(overlapped_keywords).keys())\n",
     "                    prompt_input = ThemesPersonasInput(\n",

ragas/Makefile

@@ -15,10 +15,8 @@ help: ## Show all Makefile targets
 
 format: ## Format and lint ragas code
 	@echo "Formatting and linting ragas code..."
-	@echo "(black) Formatting ragas..."
-	$(Q)uv run black --config pyproject.toml $(RAGAS_PATHS)
-	@echo "(black) Formatting stubs..."
-	$(Q)find src -name "*.pyi" ! -name "*_pb2*" -exec uv run black --pyi --config pyproject.toml {} \;
+	@echo "(ruff format) Formatting ragas..."
+	$(Q)uv run ruff format $(RAGAS_PATHS)
 	@echo "(ruff) Auto-fixing ragas (includes import sorting and unused imports)..."
 	$(Q)uv run ruff check $(RAGAS_PATHS) --fix-only
 	@echo "(ruff) Final linting check for ragas..."
@@ -39,7 +37,7 @@ test: ## Run ragas unit tests (including experimental)
 run-ci: ## Run complete CI pipeline for ragas
 	@echo "Running ragas CI pipeline..."
 	@echo "Format check..."
-	$(Q)uv run black --check --config pyproject.toml $(RAGAS_PATHS)
+	$(Q)uv run ruff format --check $(RAGAS_PATHS)
 	$(Q)uv run ruff check $(RAGAS_PATHS)
 	@echo "Type check..."
 	$(Q)$(MAKE) type

ragas/pyproject.toml

@@ -96,7 +96,6 @@ markers = [
 [dependency-groups]
 dev = [
     "ruff",
-    "black[jupyter]",
     "pyright>=1.1.403",
     "pre-commit>=4.3.0",
     "pytest",

ragas/src/ragas/callbacks.py

@@ -135,7 +135,6 @@ def parse_run_traces(
     traces: t.Dict[str, ChainRun],
     parent_run_id: t.Optional[str] = None,
 ) -> t.List[t.Dict[str, t.Any]]:
-
     root_traces = [
         chain_trace
         for chain_trace in traces.values()

ragas/src/ragas/embeddings/base.py

@@ -95,7 +95,8 @@ def __get_pydantic_core_schema__(
         Define how Pydantic generates a schema for BaseRagasEmbeddings.
         """
         return core_schema.no_info_after_validator_function(
-            cls, core_schema.is_instance_schema(cls)  # The validator function
+            cls,
+            core_schema.is_instance_schema(cls),  # The validator function
         )
 
 
@@ -267,9 +268,9 @@ def embed_documents(self, texts: t.List[str]) -> t.List[t.List[float]]:
         from sentence_transformers.SentenceTransformer import SentenceTransformer
         from torch import Tensor
 
-        assert isinstance(
-            self.model, SentenceTransformer
-        ), "Model is not of the type Bi-encoder"
+        assert isinstance(self.model, SentenceTransformer), (
+            "Model is not of the type Bi-encoder"
+        )
         embeddings = self.model.encode(
             texts, normalize_embeddings=True, **self.encode_kwargs
         )
@@ -284,9 +285,9 @@ def predict(self, texts: t.List[t.List[str]]) -> t.List[t.List[float]]:
         from sentence_transformers.cross_encoder import CrossEncoder
         from torch import Tensor
 
-        assert isinstance(
-            self.model, CrossEncoder
-        ), "Model is not of the type CrossEncoder"
+        assert isinstance(self.model, CrossEncoder), (
+            "Model is not of the type CrossEncoder"
+        )
 
         predictions = self.model.predict(texts, **self.encode_kwargs)
 

ragas/src/ragas/embeddings/haystack_wrapper.py

@@ -117,7 +117,9 @@ def __repr__(self) -> str:
         except ImportError:
             return f"{self.__class__.__name__}(embeddings=Unknown(...))"
 
-        if isinstance(self.embedder, (OpenAITextEmbedder, SentenceTransformersTextEmbedder)):  # type: ignore
+        if isinstance(
+            self.embedder, (OpenAITextEmbedder, SentenceTransformersTextEmbedder)
+        ):  # type: ignore
             model_info = self.embedder.model
         elif isinstance(self.embedder, AzureOpenAITextEmbedder):  # type: ignore
             model_info = self.embedder.azure_deployment