ci: Simplify Python code with ruff rules SIM

e2e/pipelines/test_standard_pipelines.py

@@ -306,7 +306,7 @@ def test_summarization_pipeline():
     output = pipeline.run(query=query, params={"Retriever": {"top_k": 1}})
     answers = output["answers"]
     assert len(answers) == 1
-    assert "The Eiffel Tower is one of the world's tallest structures." == answers[0]["answer"].strip()
+    assert answers[0]["answer"].strip() == "The Eiffel Tower is one of the world's tallest structures."
 
 
 def test_summarization_pipeline_one_summary():

e2e/preview/components/test_gpt35_generator.py

@@ -17,7 +17,7 @@ def test_gpt35_generator_run(generator_class, model_name):
     assert "Paris" in results["replies"][0]
     assert len(results["metadata"]) == 1
     assert model_name in results["metadata"][0]["model"]
-    assert "stop" == results["metadata"][0]["finish_reason"]
+    assert results["metadata"][0]["finish_reason"] == "stop"
 
 
 @pytest.mark.skipif(
@@ -54,6 +54,6 @@ def __call__(self, chunk):
 
     assert len(results["metadata"]) == 1
     assert model_name in results["metadata"][0]["model"]
-    assert "stop" == results["metadata"][0]["finish_reason"]
+    assert results["metadata"][0]["finish_reason"] == "stop"
 
     assert callback.responses == results["replies"][0]

e2e/preview/components/test_whisper_local.py

@@ -14,14 +14,14 @@ def test_whisper_local_transcriber(preview_samples_path):
     docs = output["documents"]
     assert len(docs) == 3
 
-    assert "this is the content of the document." == docs[0].text.strip().lower()
+    assert docs[0].text.strip().lower() == "this is the content of the document."
     assert preview_samples_path / "audio" / "this is the content of the document.wav" == docs[0].metadata["audio_file"]
 
-    assert "the context for this answer is here." == docs[1].text.strip().lower()
+    assert docs[1].text.strip().lower() == "the context for this answer is here."
     assert (
         str((preview_samples_path / "audio" / "the context for this answer is here.wav").absolute())
         == docs[1].metadata["audio_file"]
     )
 
-    assert "answer." == docs[2].text.strip().lower()
-    assert "<<binary stream>>" == docs[2].metadata["audio_file"]
+    assert docs[2].text.strip().lower() == "answer."
+    assert docs[2].metadata["audio_file"] == "<<binary stream>>"

e2e/preview/components/test_whisper_remote.py

@@ -22,14 +22,14 @@ def test_whisper_remote_transcriber(preview_samples_path):
     docs = output["documents"]
     assert len(docs) == 3
 
-    assert "this is the content of the document." == docs[0].text.strip().lower()
+    assert docs[0].text.strip().lower() == "this is the content of the document."
     assert preview_samples_path / "audio" / "this is the content of the document.wav" == docs[0].metadata["audio_file"]
 
-    assert "the context for this answer is here." == docs[1].text.strip().lower()
+    assert docs[1].text.strip().lower() == "the context for this answer is here."
     assert (
         str((preview_samples_path / "audio" / "the context for this answer is here.wav").absolute())
         == docs[1].metadata["audio_file"]
     )
 
-    assert "answer." == docs[2].text.strip().lower()
-    assert "<<binary stream>>" == docs[2].metadata["audio_file"]
+    assert docs[2].text.strip().lower() == "answer."
+    assert docs[2].metadata["audio_file"] == "<<binary stream>>"

haystack-linter/haystack_linter/linting.py

@@ -37,16 +37,13 @@ def leave_functiondef(self, node: nodes.FunctionDef) -> None:
         self._function_stack.pop()
 
     def visit_call(self, node: nodes.Call) -> None:
-        if isinstance(node.func, nodes.Attribute) and isinstance(node.func.expr, nodes.Name):
-            if node.func.expr.name == "logging" and node.func.attrname in [
-                "debug",
-                "info",
-                "warning",
-                "error",
-                "critical",
-                "exception",
-            ]:
-                self.add_message("no-direct-logging", args=node.func.attrname, node=node)
+        if (
+            isinstance(node.func, nodes.Attribute)
+            and isinstance(node.func.expr, nodes.Name)
+            and node.func.expr.name == "logging"
+            and node.func.attrname in ["debug", "info", "warning", "error", "critical", "exception"]
+        ):
+            self.add_message("no-direct-logging", args=node.func.attrname, node=node)
 
 
 class NoLoggingConfigurationChecker(BaseChecker):
@@ -71,9 +68,13 @@ def leave_functiondef(self, node: nodes.FunctionDef) -> None:
         self._function_stack.pop()
 
     def visit_call(self, node: nodes.Call) -> None:
-        if isinstance(node.func, nodes.Attribute) and isinstance(node.func.expr, nodes.Name):
-            if node.func.expr.name == "logging" and node.func.attrname in ["basicConfig"]:
-                self.add_message("no-logging-basicconfig", node=node)
+        if (
+            isinstance(node.func, nodes.Attribute)
+            and isinstance(node.func.expr, nodes.Name)
+            and node.func.expr.name == "logging"
+            and node.func.attrname in ["basicConfig"]
+        ):
+            self.add_message("no-logging-basicconfig", node=node)
 
 
 def register(linter: "PyLinter") -> None:

haystack/agents/base.py

@@ -346,7 +346,7 @@ def run(
                         You can only pass parameters to tools that are pipelines, but not nodes.
         """
         try:
-            if not self.hash == self.last_hash:
+            if self.hash != self.last_hash:
                 self.last_hash = self.hash
                 send_event(event_name="Agent", event_properties={"llm.agent_hash": self.hash})
         except Exception as exc:

haystack/document_stores/elasticsearch/es8.py

@@ -299,9 +299,10 @@ def _init_elastic_client(
         return client
 
     def _index_exists(self, index_name: str, headers: Optional[Dict[str, str]] = None) -> bool:
-        if logger.isEnabledFor(logging.DEBUG):
-            if self.client.options(headers=headers).indices.exists_alias(name=index_name):
-                logger.debug("Index name %s is an alias.", index_name)
+        if logger.isEnabledFor(logging.DEBUG) and self.client.options(headers=headers).indices.exists_alias(
+            name=index_name
+        ):
+            logger.debug("Index name %s is an alias.", index_name)
 
         return self.client.options(headers=headers).indices.exists(index=index_name)
 

haystack/document_stores/es_converter.py

@@ -228,9 +228,8 @@ def elasticsearch_index_to_document_store(
         content = record["_source"].pop(original_content_field, "")
         if content:
             meta = {}
-            if original_name_field is not None:
-                if original_name_field in record["_source"]:
-                    meta["name"] = record["_source"].pop(original_name_field)
+            if original_name_field is not None and original_name_field in record["_source"]:
+                meta["name"] = record["_source"].pop(original_name_field)
             # Only add selected metadata fields
             if included_metadata_fields is not None:
                 for metadata_field in included_metadata_fields:

haystack/document_stores/faiss.py

@@ -447,9 +447,8 @@ def get_all_documents_generator(
             return_embedding = self.return_embedding
 
         for doc in documents:
-            if return_embedding:
-                if doc.meta and doc.meta.get("vector_id") is not None:
-                    doc.embedding = self.faiss_indexes[index].reconstruct(int(doc.meta["vector_id"]))
+            if return_embedding and doc.meta and doc.meta.get("vector_id") is not None:
+                doc.embedding = self.faiss_indexes[index].reconstruct(int(doc.meta["vector_id"]))
             yield doc
 
     def get_documents_by_id(

haystack/document_stores/opensearch.py

@@ -382,10 +382,9 @@ def write_documents(
             self.index_type in ["ivf", "ivf_pq"]
             and not index.startswith(".")
             and not self._ivf_model_exists(index=index)
-        ):
-            if self.get_embedding_count(index=index, headers=headers) >= self.ivf_train_size:
-                train_docs = self.get_all_documents(index=index, return_embedding=True, headers=headers)
-                self._train_ivf_index(index=index, documents=train_docs, headers=headers)
+        ) and self.get_embedding_count(index=index, headers=headers) >= self.ivf_train_size:
+            train_docs = self.get_all_documents(index=index, return_embedding=True, headers=headers)
+            self._train_ivf_index(index=index, documents=train_docs, headers=headers)
 
     def _embed_documents(self, documents: List[Document], retriever: DenseRetriever) -> np.ndarray:
         """

haystack/document_stores/pinecone.py

@@ -487,7 +487,7 @@ def write_documents(
             documents=document_objects, index=index, duplicate_documents=duplicate_documents
         )
         if document_objects:
-            add_vectors = False if document_objects[0].embedding is None else True
+            add_vectors = document_objects[0].embedding is not None
             # If these are not labels, we need to find the correct value for `doc_type` metadata field
             if not labels:
                 type_metadata = DOCUMENT_WITH_EMBEDDING if add_vectors else DOCUMENT_WITHOUT_EMBEDDING

haystack/document_stores/search_engine.py

@@ -1620,9 +1620,8 @@ def delete_index(self, index: str):
         self._index_delete(index)
 
     def _index_exists(self, index_name: str, headers: Optional[Dict[str, str]] = None) -> bool:
-        if logger.isEnabledFor(logging.DEBUG):
-            if self.client.indices.exists_alias(name=index_name):
-                logger.debug("Index name %s is an alias.", index_name)
+        if logger.isEnabledFor(logging.DEBUG) and self.client.indices.exists_alias(name=index_name):
+            logger.debug("Index name %s is an alias.", index_name)
 
         return self.client.indices.exists(index=index_name, headers=headers)
 

haystack/document_stores/utils.py

@@ -40,9 +40,8 @@ def eval_data_from_json(
             logger.warning("No title information found for documents in QA file: %s", filename)
 
         for squad_document in data["data"]:
-            if max_docs:
-                if len(docs) > max_docs:
-                    break
+            if max_docs and len(docs) > max_docs:
+                break
             # Extracting paragraphs and their labels from a SQuAD document dict
             cur_docs, cur_labels, cur_problematic_ids = _extract_docs_and_labels_from_dict(
                 squad_document, preprocessor, open_domain
@@ -84,9 +83,8 @@ def eval_data_from_jsonl(
 
     with open(filename, "r", encoding="utf-8") as file:
         for document in file:
-            if max_docs:
-                if len(docs) > max_docs:
-                    break
+            if max_docs and len(docs) > max_docs:
+                break
             # Extracting paragraphs and their labels from a SQuAD document dict
             squad_document = json.loads(document)
             cur_docs, cur_labels, cur_problematic_ids = _extract_docs_and_labels_from_dict(
@@ -96,19 +94,18 @@ def eval_data_from_jsonl(
             labels.extend(cur_labels)
             problematic_ids.extend(cur_problematic_ids)
 
-            if batch_size is not None:
-                if len(docs) >= batch_size:
-                    if len(problematic_ids) > 0:
-                        logger.warning(
-                            "Could not convert an answer for %s questions.\n"
-                            "There were conversion errors for question ids: %s",
-                            len(problematic_ids),
-                            problematic_ids,
-                        )
-                    yield docs, labels
-                    docs = []
-                    labels = []
-                    problematic_ids = []
+            if batch_size is not None and len(docs) >= batch_size:
+                if len(problematic_ids) > 0:
+                    logger.warning(
+                        "Could not convert an answer for %s questions.\n"
+                        "There were conversion errors for question ids: %s",
+                        len(problematic_ids),
+                        problematic_ids,
+                    )
+                yield docs, labels
+                docs = []
+                labels = []
+                problematic_ids = []
 
     yield docs, labels
 

haystack/document_stores/weaviate.py

@@ -661,10 +661,9 @@ def write_documents(
                             if isinstance(v, dict):
                                 json_fields.append(k)
                                 v = json.dumps(v)
-                            elif isinstance(v, list):
-                                if len(v) > 0 and isinstance(v[0], dict):
-                                    json_fields.append(k)
-                                    v = [json.dumps(item) for item in v]
+                            elif isinstance(v, list) and len(v) > 0 and isinstance(v[0], dict):
+                                json_fields.append(k)
+                                v = [json.dumps(item) for item in v]
                             _doc[k] = v
                         _doc.pop("meta")
 
@@ -734,9 +733,8 @@ def update_document_meta(
         # Weaviate requires dates to be in RFC3339 format
         date_fields = self._get_date_properties(index)
         for date_field in date_fields:
-            if date_field in meta:
-                if isinstance(meta[date_field], str):
-                    meta[date_field] = convert_date_to_rfc3339(str(meta[date_field]))
+            if date_field in meta and isinstance(meta[date_field], str):
+                meta[date_field] = convert_date_to_rfc3339(str(meta[date_field]))
 
         self.weaviate_client.data_object.update(meta, class_name=index, uuid=id)
 
@@ -771,10 +769,8 @@ def get_document_count(
         else:
             result = self.weaviate_client.query.aggregate(index).with_meta_count().do()
 
-        if "data" in result:
-            if "Aggregate" in result.get("data"):
-                if result.get("data").get("Aggregate").get(index):
-                    doc_count = result.get("data").get("Aggregate").get(index)[0]["meta"]["count"]
+        if "data" in result and "Aggregate" in result.get("data") and result.get("data").get("Aggregate").get(index):
+            doc_count = result.get("data").get("Aggregate").get(index)[0]["meta"]["count"]
 
         return doc_count
 
@@ -1153,9 +1149,13 @@ def query(
             query_output = self.weaviate_client.query.raw(gql_query)
 
         results = []
-        if query_output and "data" in query_output and "Get" in query_output.get("data"):
-            if query_output.get("data").get("Get").get(index):
-                results = query_output.get("data").get("Get").get(index)
+        if (
+            query_output
+            and "data" in query_output
+            and "Get" in query_output.get("data")
+            and query_output.get("data").get("Get").get(index)
+        ):
+            results = query_output.get("data").get("Get").get(index)
 
         # We retrieve the JSON properties from the schema and convert them back to the Python dicts
         json_properties = self._get_json_properties(index=index)
@@ -1421,9 +1421,13 @@ def query_by_embedding(
             )
 
         results = []
-        if query_output and "data" in query_output and "Get" in query_output.get("data"):
-            if query_output.get("data").get("Get").get(index):
-                results = query_output.get("data").get("Get").get(index)
+        if (
+            query_output
+            and "data" in query_output
+            and "Get" in query_output.get("data")
+            and query_output.get("data").get("Get").get(index)
+        ):
+            results = query_output.get("data").get("Get").get(index)
 
         # We retrieve the JSON properties from the schema and convert them back to the Python dicts
         json_properties = self._get_json_properties(index=index)

haystack/modeling/data_handler/data_silo.py

@@ -111,10 +111,12 @@ def _get_dataset(self, filename: Optional[Union[str, Path]], dicts: Optional[Lis
         if dicts is None:
             dicts = list(self.processor.file_to_dicts(filename))  # type: ignore
             # shuffle list of dicts here if we later want to have a random dev set split from train set
-            if str(self.processor.train_filename) in str(filename):
-                if not self.processor.dev_filename:
-                    if self.processor.dev_split > 0.0:
-                        random.shuffle(dicts)
+            if (
+                str(self.processor.train_filename) in str(filename)
+                and not self.processor.dev_filename
+                and self.processor.dev_split > 0.0
+            ):
+                random.shuffle(dicts)
 
         num_dicts = len(dicts)
         datasets = []

haystack/modeling/data_handler/processor.py

@@ -488,9 +488,8 @@ def dataset_from_dicts(
         dataset, tensor_names, baskets = self._create_dataset(baskets)
 
         # Logging
-        if indices:
-            if 0 in indices:
-                self._log_samples(n_samples=1, baskets=baskets)
+        if indices and 0 in indices:
+            self._log_samples(n_samples=1, baskets=baskets)
 
         # During inference we need to keep the information contained in baskets.
         if return_baskets:

haystack/modeling/evaluation/eval.py

@@ -194,12 +194,15 @@ def log_results(
             logger.info("\n _________ %s _________", head["task_name"])
             for metric_name, metric_val in head.items():
                 # log with experiment tracking framework (e.g. Mlflow)
-                if logging:
-                    if not metric_name in ["preds", "labels"] and not metric_name.startswith("_"):
-                        if isinstance(metric_val, numbers.Number):
-                            tracker.track_metrics(
-                                metrics={f"{dataset_name}_{metric_name}_{head['task_name']}": metric_val}, step=steps
-                            )
+                if (
+                    logging
+                    and not metric_name in ["preds", "labels"]
+                    and not metric_name.startswith("_")
+                    and isinstance(metric_val, numbers.Number)
+                ):
+                    tracker.track_metrics(
+                        metrics={f"{dataset_name}_{metric_name}_{head['task_name']}": metric_val}, step=steps
+                    )
                 # print via standard python logger
                 if print:
                     if metric_name == "report":