style: apply ALL ruff rules

pyproject.toml

@@ -144,8 +144,8 @@ target-version = "py310"
 docstring-code-format = true
 
 [tool.ruff.lint]
-select = ["A", "ASYNC", "B", "BLE", "C4", "C90", "D", "DTZ", "E", "EM", "ERA", "F", "FBT", "FLY", "FURB", "G", "I", "ICN", "INP", "INT", "ISC", "LOG", "N", "NPY", "PERF", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "Q", "RET", "RSE", "RUF", "S", "SIM", "SLF", "SLOT", "T10", "T20", "TCH", "TID", "TRY", "UP", "W", "YTT"]
-ignore = ["D203", "D213", "E501", "RET504", "RUF002", "RUF022", "S101", "S307", "TC004"]
+select = ["ALL"]
+ignore = ["CPY", "FIX", "ARG001", "COM812", "D203", "D213", "E501", "PD008", "PD009", "RET504", "S101", "TD003"]
 unfixable = ["ERA001", "F401", "F841", "T201", "T203"]
 
 [tool.ruff.lint.flake8-tidy-imports]

src/raglite/__init__.py

@@ -17,7 +17,7 @@
     vector_search,
 )
 
-__all__ = [
+__all__ = [  # noqa: RUF022
     # Config
     "RAGLiteConfig",
     # Insert

src/raglite/_bench.py

@@ -94,7 +94,7 @@ def __init__(
         insert_variant: str | None = None,
         search_variant: str | None = None,
         config: RAGLiteConfig | None = None,
-    ):
+    ) -> None:
         super().__init__(
             dataset,
             num_results=num_results,
@@ -145,7 +145,7 @@ def __init__(
         num_results: int = 10,
         insert_variant: str | None = None,
         search_variant: str | None = None,
-    ):
+    ) -> None:
         super().__init__(
             dataset,
             num_results=num_results,
@@ -156,7 +156,7 @@ def __init__(
         self.embedder_dim = 3072
         self.persist_path = self.cwd / self.insert_id
 
-    def insert_documents(self, max_workers: int | None = None) -> None:
+    def insert_documents(self, max_workers: int | None = None) -> None:  # noqa: ARG002
         # Adapted from https://docs.llamaindex.ai/en/stable/examples/vector_stores/FaissIndexDemo/.
         import faiss
         from llama_index.core import Document, StorageContext, VectorStoreIndex
@@ -178,14 +178,15 @@ def insert_documents(self, max_workers: int | None = None) -> None:
         index.storage_context.persist(persist_dir=self.persist_path)
 
     @cached_property
-    def index(self) -> Any:
+    def index(self) -> Any:  # noqa: ANN401
         from llama_index.core import StorageContext, load_index_from_storage
         from llama_index.embeddings.openai import OpenAIEmbedding
         from llama_index.vector_stores.faiss import FaissVectorStore
 
         vector_store = FaissVectorStore.from_persist_dir(persist_dir=self.persist_path.as_posix())
         storage_context = StorageContext.from_defaults(
-            vector_store=vector_store, persist_dir=self.persist_path.as_posix()
+            vector_store=vector_store,
+            persist_dir=self.persist_path.as_posix(),
         )
         embed_model = OpenAIEmbedding(model=self.embedder, dimensions=self.embedder_dim)
         index = load_index_from_storage(storage_context, embed_model=embed_model)
@@ -215,7 +216,7 @@ def __init__(
         num_results: int = 10,
         insert_variant: str | None = None,
         search_variant: str | None = None,
-    ):
+    ) -> None:
         super().__init__(
             dataset,
             num_results=num_results,
@@ -227,7 +228,7 @@ def __init__(
         )
 
     @cached_property
-    def client(self) -> Any:
+    def client(self) -> Any:  # noqa: ANN401
         import openai
 
         return openai.OpenAI()
@@ -269,7 +270,9 @@ def insert_documents(self, max_workers: int | None = None) -> None:
                 files.append(temp_file.open("rb"))
                 if len(files) == max_files_per_batch or (i == self.dataset.docs_count() - 1):
                     self.client.vector_stores.file_batches.upload_and_poll(
-                        vector_store_id=vector_store.id, files=files, max_concurrency=max_workers
+                        vector_store_id=vector_store.id,
+                        files=files,
+                        max_concurrency=max_workers,
                     )
                     for f in files:
                         f.close()
@@ -283,7 +286,9 @@ def search(self, query_id: str, query: str, *, num_results: int = 10) -> list[Sc
         if not self.vector_store_id:
             return []
         response = self.client.vector_stores.search(
-            vector_store_id=self.vector_store_id, query=query, max_num_results=2 * num_results
+            vector_store_id=self.vector_store_id,
+            query=query,
+            max_num_results=2 * num_results,
         )
         scored_docs = [
             ScoredDoc(

src/raglite/_chainlit.py

@@ -39,7 +39,7 @@ async def start_chat() -> None:
             TextInput(id="llm", label="LLM", initial=config.llm),
             TextInput(id="embedder", label="Embedder", initial=config.embedder),
             Switch(id="vector_search_query_adapter", label="Query adapter", initial=True),
-        ]
+        ],
     ).send()
     await update_config(settings)
 
@@ -95,7 +95,9 @@ async def handle_message(user_message: cl.Message) -> None:
     messages: list[dict[str, str]] = cl.chat_context.to_openai()[:-1]  # type: ignore[no-untyped-call]
     messages.append({"role": "user", "content": user_prompt})
     async for token in async_rag(
-        messages, on_retrieval=lambda x: chunk_spans.extend(x), config=config
+        messages,
+        on_retrieval=lambda x: chunk_spans.extend(x),
+        config=config,
     ):
         await assistant_message.stream_token(token)
     # Append RAG sources, if any.

src/raglite/_chatml_function_calling.py

@@ -98,9 +98,9 @@ def _convert_chunks_to_completion(
             {
                 "text": text,
                 "index": 0,
-                "logprobs": logprobs,  # TODO: Improve accumulation of logprobs
+                "logprobs": logprobs,  # TODO(lsorber): Improve accumulation of logprobs
                 "finish_reason": finish_reason,  # type: ignore[typeddict-item]
-            }
+            },
         ],
     }
     # Add usage section if present in the chunks
@@ -131,7 +131,8 @@ def _stream_tool_calls(
         prompt += f"functions.{tool_name}:\n"
         try:
             grammar = llama_grammar.LlamaGrammar.from_json_schema(
-                json.dumps(tool["function"]["parameters"]), verbose=llama.verbose
+                json.dumps(tool["function"]["parameters"]),
+                verbose=llama.verbose,
             )
         except Exception as e:
             warnings.warn(
@@ -140,7 +141,8 @@ def _stream_tool_calls(
                 stacklevel=2,
             )
             grammar = llama_grammar.LlamaGrammar.from_string(
-                llama_grammar.JSON_GBNF, verbose=llama.verbose
+                llama_grammar.JSON_GBNF,
+                verbose=llama.verbose,
             )
         completion_or_chunks = llama.create_completion(
             prompt=prompt,
@@ -182,7 +184,8 @@ def _stream_tool_calls(
                     "stop": [*completion_kwargs["stop"], ":", "</function_calls>"],
                     "max_tokens": None,
                     "grammar": llama_grammar.LlamaGrammar.from_string(
-                        follow_up_gbnf_tool_grammar, verbose=llama.verbose
+                        follow_up_gbnf_tool_grammar,
+                        verbose=llama.verbose,
                     ),
                 },
             ),
@@ -253,7 +256,7 @@ def chatml_function_calling_with_streaming(
     grammar: Optional[llama.LlamaGrammar] = None,  # type: ignore[name-defined]
     logprobs: Optional[bool] = None,
     top_logprobs: Optional[int] = None,
-    **kwargs: Any,
+    **kwargs: Any,  # noqa: ANN401
 ) -> Union[
     llama_types.CreateChatCompletionResponse,
     Iterator[llama_types.CreateChatCompletionStreamResponse],
@@ -381,7 +384,10 @@ def chatml_function_calling_with_streaming(
         or len(tools) == 0
     ):
         prompt = template_renderer.render(
-            messages=messages, tools=[], tool_calls=None, add_generation_prompt=True
+            messages=messages,
+            tools=[],
+            tool_calls=None,
+            add_generation_prompt=True,
         )
         return llama_chat_format._convert_completion_to_chat(  # noqa: SLF001
             llama.create_completion(
@@ -404,7 +410,10 @@ def chatml_function_calling_with_streaming(
         assert tools
     function_names = " | ".join([f'''"functions.{t["function"]["name"]}:"''' for t in tools])
     prompt = template_renderer.render(
-        messages=messages, tools=tools, tool_calls=True, add_generation_prompt=True
+        messages=messages,
+        tools=tools,
+        tool_calls=True,
+        add_generation_prompt=True,
     )
     initial_gbnf_tool_grammar = (
         (
@@ -429,7 +438,8 @@ def chatml_function_calling_with_streaming(
                 "stream": False,
                 "max_tokens": None,
                 "grammar": llama_grammar.LlamaGrammar.from_string(
-                    initial_gbnf_tool_grammar, verbose=llama.verbose
+                    initial_gbnf_tool_grammar,
+                    verbose=llama.verbose,
                 ),
             },
         ),
@@ -449,7 +459,10 @@ def chatml_function_calling_with_streaming(
     # Case 2 step 2A: Respond with a message
     if tool_name is None:
         prompt = template_renderer.render(
-            messages=messages, tools=[], tool_calls=None, add_generation_prompt=True
+            messages=messages,
+            tools=[],
+            tool_calls=None,
+            add_generation_prompt=True,
         )
         prompt += think
         return llama_chat_format._convert_completion_to_chat(  # noqa: SLF001
@@ -469,7 +482,12 @@ def chatml_function_calling_with_streaming(
     prompt += "<function_calls>\n"
     if stream:
         return _stream_tool_calls(
-            llama, prompt, tools, tool_name, completion_kwargs, follow_up_gbnf_tool_grammar
+            llama,
+            prompt,
+            tools,
+            tool_name,
+            completion_kwargs,
+            follow_up_gbnf_tool_grammar,
         )
     tool = next((tool for tool in tools if tool["function"]["name"] == tool_name), None)
     completions: List[llama_types.CreateCompletionResponse] = []
@@ -479,7 +497,8 @@ def chatml_function_calling_with_streaming(
         prompt += f"functions.{tool_name}:\n"
         try:
             grammar = llama_grammar.LlamaGrammar.from_json_schema(
-                json.dumps(tool["function"]["parameters"]), verbose=llama.verbose
+                json.dumps(tool["function"]["parameters"]),
+                verbose=llama.verbose,
             )
         except Exception as e:
             warnings.warn(
@@ -488,7 +507,8 @@ def chatml_function_calling_with_streaming(
                 stacklevel=2,
             )
             grammar = llama_grammar.LlamaGrammar.from_string(
-                llama_grammar.JSON_GBNF, verbose=llama.verbose
+                llama_grammar.JSON_GBNF,
+                verbose=llama.verbose,
             )
         completion_or_chunks = llama.create_completion(
             prompt=prompt,
@@ -515,7 +535,8 @@ def chatml_function_calling_with_streaming(
                     "stop": [*completion_kwargs["stop"], ":", "</function_calls>"],  # type: ignore[misc]
                     "max_tokens": None,
                     "grammar": llama_grammar.LlamaGrammar.from_string(
-                        follow_up_gbnf_tool_grammar, verbose=llama.verbose
+                        follow_up_gbnf_tool_grammar,
+                        verbose=llama.verbose,
                     ),
                 },
             ),
@@ -533,7 +554,7 @@ def chatml_function_calling_with_streaming(
                 "finish_reason": "tool_calls",
                 "index": 0,
                 "logprobs": _convert_text_completion_logprobs_to_chat(
-                    completion["choices"][0]["logprobs"]
+                    completion["choices"][0]["logprobs"],
                 ),
                 "message": {
                     "role": "assistant",
@@ -548,11 +569,11 @@ def chatml_function_calling_with_streaming(
                             },
                         }
                         for i, (tool_name, completion) in enumerate(
-                            zip(completions_tool_name, completions, strict=True)
+                            zip(completions_tool_name, completions, strict=True),
                         )
                     ],
                 },
-            }
+            },
         ],
         "usage": {
             "completion_tokens": sum(

src/raglite/_cli.py

@@ -14,7 +14,9 @@ class RAGLiteCLIConfig(BaseSettings):
     """RAGLite CLI config."""
 
     model_config: ClassVar[SettingsConfigDict] = SettingsConfigDict(
-        env_prefix="RAGLITE_", env_file=".env", extra="allow"
+        env_prefix="RAGLITE_",
+        env_file=".env",
+        extra="allow",
     )
 
     mcp_server_name: str = "RAGLite"
@@ -67,7 +69,7 @@ def install_mcp_server(
     claude_config_path = get_claude_config_path()
     if not claude_config_path:
         typer.echo(
-            "Please download the Claude desktop app from https://claude.ai/download before installing an MCP server."
+            "Please download the Claude desktop app from https://claude.ai/download before installing an MCP server.",
         )
         return
     claude_config_filepath = claude_config_path / "claude_desktop_config.json"
@@ -88,7 +90,7 @@ def install_mcp_server(
             "--python",
             "3.11",
             "--with",
-            "numpy<2.0.0",  # TODO: Remove this constraint when uv no longer needs it to solve the environment.
+            "numpy<2.0.0",  # TODO(lsorber): Remove this constraint when uv no longer needs it to solve the environment.
             "raglite",
             "mcp",
             "run",
@@ -112,7 +114,9 @@ def run_mcp_server(
     from raglite._mcp import create_mcp_server
 
     config = RAGLiteConfig(
-        db_url=ctx.obj["db_url"], llm=ctx.obj["llm"], embedder=ctx.obj["embedder"]
+        db_url=ctx.obj["db_url"],
+        llm=ctx.obj["llm"],
+        embedder=ctx.obj["embedder"],
     )
     mcp = create_mcp_server(server_name, config=config)
     mcp.run()
@@ -122,7 +126,10 @@ def run_mcp_server(
 def bench(
     ctx: typer.Context,
     dataset_name: str = typer.Option(
-        "nano-beir/hotpotqa", "--dataset", "-d", help="Dataset to use from https://ir-datasets.com/"
+        "nano-beir/hotpotqa",
+        "--dataset",
+        "-d",
+        help="Dataset to use from https://ir-datasets.com/",
     ),
     measure: str = typer.Option(
         "AP@10",
@@ -157,7 +164,9 @@ def bench(
     )
     dataset = ir_datasets.load(dataset_name)
     evaluator = RAGLiteEvaluator(
-        dataset, insert_variant=f"single-vector-{chunk_max_size // 4}t", config=config
+        dataset,
+        insert_variant=f"single-vector-{chunk_max_size // 4}t",
+        config=config,
     )
     index.append("RAGLite (single-vector)")
     results.append(ir_measures.calc_aggregate(measures, dataset.qrels_iter(), evaluator.score()))
@@ -170,7 +179,9 @@ def bench(
     )
     dataset = ir_datasets.load(dataset_name)
     evaluator = RAGLiteEvaluator(
-        dataset, insert_variant=f"multi-vector-{chunk_max_size // 4}t", config=config
+        dataset,
+        insert_variant=f"multi-vector-{chunk_max_size // 4}t",
+        config=config,
     )
     index.append("RAGLite (multi-vector)")
     results.append(ir_measures.calc_aggregate(measures, dataset.qrels_iter(), evaluator.score()))

src/raglite/_config.py

@@ -24,9 +24,12 @@
 
 
 # Lazily load the default search method to avoid circular imports.
-# TODO: Replace with search_and_rerank_chunk_spans after benchmarking.
+# TODO(lsorber): Replace with search_and_rerank_chunk_spans after benchmarking.
 def _vector_search(
-    query: str, *, num_results: int = 8, config: "RAGLiteConfig | None" = None
+    query: str,
+    *,
+    num_results: int = 8,
+    config: "RAGLiteConfig | None" = None,
 ) -> tuple[list[ChunkId], list[float]]:
     from raglite._search import vector_search
 
@@ -45,7 +48,7 @@ class RAGLiteConfig:
             "llama-cpp-python/unsloth/Qwen3-8B-GGUF/*Q4_K_M.gguf@8192"
             if llama_supports_gpu_offload()
             else "llama-cpp-python/unsloth/Qwen3-4B-GGUF/*Q4_K_M.gguf@8192"
-        )
+        ),
     )
     llm_max_tries: int = 4
     # Embedder config used for indexing.
@@ -54,7 +57,7 @@ class RAGLiteConfig:
             "llama-cpp-python/lm-kit/bge-m3-gguf/*F16.gguf@512"
             if llama_supports_gpu_offload() or (os.cpu_count() or 1) >= 4  # noqa: PLR2004
             else "llama-cpp-python/lm-kit/bge-m3-gguf/*Q4_K_M.gguf@512"
-        )
+        ),
     )
     embedder_normalize: bool = True
     # Chunk config used to partition documents into chunks.