chore: make Haystack warnings consistent (#9083)

* chore: make Haystack warnings consistent

* more structured logging

* small fixes

haystack/components/converters/openapi_functions.py

@@ -84,7 +84,7 @@ class OpenAPIServiceToFunctions:
                             "IO error reading OpenAPI specification file: {source}. Error: {e}", source=source, e=e
                         )
                 else:
-                    logger.warning(f"OpenAPI specification file not found: {source}")
+                    logger.warning("OpenAPI specification file not found: {source}", source=source)
             elif isinstance(source, ByteStream):
                 openapi_spec_content = source.data.decode("utf-8")
                 if not openapi_spec_content:

haystack/components/converters/output_adapter.py

@@ -5,7 +5,6 @@
 import ast
 import contextlib
 from typing import Any, Callable, Dict, Optional, Set
-from warnings import warn
 
 import jinja2.runtime
 from jinja2 import Environment, TemplateSyntaxError, meta
@@ -13,9 +12,11 @@ from jinja2.nativetypes import NativeEnvironment
 from jinja2.sandbox import SandboxedEnvironment
 from typing_extensions import TypeAlias
 
-from haystack import component, default_from_dict, default_to_dict
+from haystack import component, default_from_dict, default_to_dict, logging
 from haystack.utils import deserialize_callable, deserialize_type, serialize_callable, serialize_type
 
+logger = logging.getLogger(__name__)
+
 
 class OutputAdaptationException(Exception):
     """Exception raised when there is an error during output adaptation."""
@@ -76,7 +77,7 @@ class OutputAdapter:
                 "Unsafe mode is enabled. This allows execution of arbitrary code in the Jinja template. "
                 "Use this only if you trust the source of the template."
             )
-            warn(msg)
+            logger.warning(msg)
         self._env = (
             NativeEnvironment() if self._unsafe else SandboxedEnvironment(undefined=jinja2.runtime.StrictUndefined)
         )

haystack/components/embedders/hugging_face_api_document_embedder.py

@@ -2,12 +2,11 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-import warnings
 from typing import Any, Dict, List, Optional, Union
 
 from tqdm import tqdm
 
-from haystack import component, default_from_dict, default_to_dict
+from haystack import component, default_from_dict, default_to_dict, logging
 from haystack.dataclasses import Document
 from haystack.lazy_imports import LazyImport
 from haystack.utils import Secret, deserialize_secrets_inplace
@@ -17,6 +16,8 @@ from haystack.utils.url_validation import is_valid_http_url
 with LazyImport(message="Run 'pip install \"huggingface_hub>=0.27.0\"'") as huggingface_hub_import:
     from huggingface_hub import InferenceClient
 
+logger = logging.getLogger(__name__)
+
 
 @component
 class HuggingFaceAPIDocumentEmbedder:
@@ -241,11 +242,11 @@ class HuggingFaceAPIDocumentEmbedder:
         if self.api_type == HFEmbeddingAPIType.SERVERLESS_INFERENCE_API:
             if truncate is not None:
                 msg = "`truncate` parameter is not supported for Serverless Inference API. It will be ignored."
-                warnings.warn(msg)
+                logger.warning(msg)
                 truncate = None
             if normalize is not None:
                 msg = "`normalize` parameter is not supported for Serverless Inference API. It will be ignored."
-                warnings.warn(msg)
+                logger.warning(msg)
                 normalize = None
 
         all_embeddings = []

haystack/components/embedders/hugging_face_api_text_embedder.py

@@ -2,10 +2,9 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-import warnings
 from typing import Any, Dict, List, Optional, Union
 
-from haystack import component, default_from_dict, default_to_dict
+from haystack import component, default_from_dict, default_to_dict, logging
 from haystack.lazy_imports import LazyImport
 from haystack.utils import Secret, deserialize_secrets_inplace
 from haystack.utils.hf import HFEmbeddingAPIType, HFModelType, check_valid_model
@@ -14,6 +13,8 @@ from haystack.utils.url_validation import is_valid_http_url
 with LazyImport(message="Run 'pip install \"huggingface_hub>=0.27.0\"'") as huggingface_hub_import:
     from huggingface_hub import InferenceClient
 
+logger = logging.getLogger(__name__)
+
 
 @component
 class HuggingFaceAPITextEmbedder:
@@ -200,11 +201,11 @@ class HuggingFaceAPITextEmbedder:
         if self.api_type == HFEmbeddingAPIType.SERVERLESS_INFERENCE_API:
             if truncate is not None:
                 msg = "`truncate` parameter is not supported for Serverless Inference API. It will be ignored."
-                warnings.warn(msg)
+                logger.warning(msg)
                 truncate = None
             if normalize is not None:
                 msg = "`normalize` parameter is not supported for Serverless Inference API. It will be ignored."
-                warnings.warn(msg)
+                logger.warning(msg)
                 normalize = None
 
         text_to_embed = self.prefix + text + self.suffix

haystack/components/evaluators/llm_evaluator.py

@@ -4,15 +4,16 @@
 
 import json
 from typing import Any, Dict, List, Optional, Tuple, Type
-from warnings import warn
 
 from tqdm import tqdm
 
-from haystack import component, default_from_dict, default_to_dict
+from haystack import component, default_from_dict, default_to_dict, logging
 from haystack.components.builders import PromptBuilder
 from haystack.components.generators import OpenAIGenerator
 from haystack.utils import Secret, deserialize_secrets_inplace, deserialize_type, serialize_type
 
+logger = logging.getLogger(__name__)
+
 
 @component
 class LLMEvaluator:
@@ -206,10 +207,9 @@ class LLMEvaluator:
             try:
                 result = self.generator.run(prompt=prompt["prompt"])
             except Exception as e:
-                msg = f"Error while generating response for prompt: {prompt}. Error: {e}"
                 if self.raise_on_failure:
-                    raise ValueError(msg)
+                    raise ValueError(f"Error while generating response for prompt: {prompt}. Error: {e}")
-                warn(msg)
+                logger.warning("Error while generating response for prompt: {prompt}. Error: {e}", prompt=prompt, e=e)
                 results.append(None)
                 errors += 1
                 continue
@@ -225,8 +225,11 @@ class LLMEvaluator:
                 metadata = result["meta"]
 
         if errors > 0:
-            msg = f"LLM evaluator failed for {errors} out of {len(list_of_input_names_to_values)} inputs."
+            logger.warning(
-            warn(msg)
+                "LLM evaluator failed for {errors} out of {len(list_of_input_names_to_values)} inputs.",
+                errors=errors,
+                len=len(list_of_input_names_to_values),
+            )
 
         return {"results": results, "meta": metadata}
 
@@ -374,14 +377,19 @@ class LLMEvaluator:
             msg = "Response from LLM evaluator is not a valid JSON."
             if self.raise_on_failure:
                 raise ValueError(msg)
-            warn(msg)
+            logger.warning(msg)
             return False
 
         if not all(output in parsed_output for output in expected):
-            msg = f"Expected response from LLM evaluator to be JSON with keys {expected}, got {received}."
             if self.raise_on_failure:
-                raise ValueError(msg)
+                raise ValueError(
-            warn(msg)
+                    f"Expected response from LLM evaluator to be JSON with keys {expected}, got {{received}}."
+                )
+            logger.warning(
+                "Expected response from LLM evaluator to be JSON with keys {expected}, got {received}.",
+                expected=expected,
+                received=received,
+            )
             return False
 
         return True

haystack/components/readers/extractive.py

@@ -3,7 +3,6 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import math
-import warnings
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
 
@@ -215,9 +214,10 @@ class ExtractiveReader:
         document_contents = []
         for i, doc in enumerate(documents):
             if doc.content is None:
-                warnings.warn(
+                logger.warning(
-                    f"Document with id {doc.id} was passed to ExtractiveReader. The Document doesn't "
+                    "Document with id {doc_id} was passed to ExtractiveReader. The Document doesn't "
-                    f"contain any text and it will be ignored."
+                    "contain any text and it will be ignored.",
+                    doc_id=doc.id,
                 )
                 continue
             texts.append(doc.content)

haystack/components/routers/conditional_router.py

@@ -5,7 +5,6 @@
 import ast
 import contextlib
 from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Set, Union, get_args, get_origin
-from warnings import warn
 
 from jinja2 import Environment, TemplateSyntaxError, meta
 from jinja2.nativetypes import NativeEnvironment
@@ -192,7 +191,7 @@ class ConditionalRouter:
                 "Unsafe mode is enabled. This allows execution of arbitrary code in the Jinja template. "
                 "Use this only if you trust the source of the template."
             )
-            warn(msg)
+            logger.warning(msg)
 
         self._env = NativeEnvironment() if self._unsafe else SandboxedEnvironment()
         self._env.filters.update(self.custom_filters)
@@ -216,13 +215,11 @@ class ConditionalRouter:
         # warn about unused optional variables
         unused_optional_vars = set(self.optional_variables) - input_types if self.optional_variables else None
         if unused_optional_vars:
-            msg = (
+            logger.warning(
-                f"The following optional variables are specified but not used in any route: {unused_optional_vars}. "
+                "The following optional variables are specified but not used in any route: {unused_optional_vars}. "
-                "Check if there's a typo in variable names."
+                "Check if there's a typo in variable names.",
+                unused_optional_vars=unused_optional_vars,
             )
-            # intentionally using both warn and logger
-            warn(msg, UserWarning)
-            logger.warning(msg)
 
         # add mandatory input types
         component.set_input_types(self, **dict.fromkeys(mandatory_input_types, Any))

haystack/evaluation/eval_run_result.py

@@ -5,13 +5,15 @@
 import csv
 from copy import deepcopy
 from typing import Any, Dict, List, Literal, Optional, Union
-from warnings import warn
 
+from haystack import logging
 from haystack.lazy_imports import LazyImport
 
 with LazyImport("Run 'pip install pandas'") as pandas_import:
     from pandas import DataFrame
 
+logger = logging.getLogger(__name__)
+
 
 class EvaluationRunResult:
     """
@@ -188,10 +190,15 @@ class EvaluationRunResult:
             raise ValueError("The 'other' parameter must have 'run_name', 'inputs', and 'results' attributes.")
 
         if self.run_name == other.run_name:
-            warn(f"The run names of the two evaluation results are the same ('{self.run_name}')")
+            logger.warning(
+                "The run names of the two evaluation results are the same ('{run_name}')", run_name=self.run_name
+            )
 
         if self.inputs.keys() != other.inputs.keys():
-            warn(f"The input columns differ between the results; using the input columns of '{self.run_name}'.")
+            logger.warning(
+                "The input columns differ between the results; using the input columns of '{run_name}'",
+                run_name=self.run_name,
+            )
 
         # got both detailed reports
         detailed_a = self.detailed_report(output_format="json")

test/components/embedders/test_hugging_face_api_document_embedder.py

@@ -203,7 +203,7 @@ class TestHuggingFaceAPIDocumentEmbedder:
             "my_prefix document number 4 my_suffix",
         ]
 
-    def test_embed_batch(self, mock_check_valid_model, recwarn):
+    def test_embed_batch(self, mock_check_valid_model, caplog):
         texts = ["text 1", "text 2", "text 3", "text 4", "text 5"]
 
         with patch("huggingface_hub.InferenceClient.feature_extraction") as mock_embedding_patch:
@@ -225,10 +225,10 @@ class TestHuggingFaceAPIDocumentEmbedder:
             assert len(embedding) == 384
             assert all(isinstance(x, float) for x in embedding)
 
-        # Check that warnings about ignoring truncate and normalize are raised
+        # Check that logger warnings about ignoring truncate and normalize are raised
-        assert len(recwarn) == 2
+        assert len(caplog.records) == 2
-        assert "truncate" in str(recwarn[0].message)
+        assert "truncate" in caplog.records[0].message
-        assert "normalize" in str(recwarn[1].message)
+        assert "normalize" in caplog.records[1].message
 
     def test_embed_batch_wrong_embedding_shape(self, mock_check_valid_model):
         texts = ["text 1", "text 2", "text 3", "text 4", "text 5"]

test/components/embedders/test_hugging_face_api_text_embedder.py

@@ -136,7 +136,7 @@ class TestHuggingFaceAPITextEmbedder:
         with pytest.raises(TypeError):
             embedder.run(text=list_integers_input)
 
-    def test_run(self, mock_check_valid_model, recwarn):
+    def test_run(self, mock_check_valid_model, caplog):
         with patch("huggingface_hub.InferenceClient.feature_extraction") as mock_embedding_patch:
             mock_embedding_patch.return_value = array([[random.random() for _ in range(384)]])
 
@@ -158,9 +158,9 @@ class TestHuggingFaceAPITextEmbedder:
         assert all(isinstance(x, float) for x in result["embedding"])
 
         # Check that warnings about ignoring truncate and normalize are raised
-        assert len(recwarn) == 2
+        assert len(caplog.records) == 2
-        assert "truncate" in str(recwarn[0].message)
+        assert "truncate" in caplog.records[0].message
-        assert "normalize" in str(recwarn[1].message)
+        assert "normalize" in caplog.records[1].message
 
     def test_run_wrong_embedding_shape(self, mock_check_valid_model):
         # embedding ndim > 2

test/components/routers/test_conditional_router.py

@@ -525,7 +525,7 @@ class TestRouter:
         result = pipe.run(data={"router": {"question": "What?", "mode": "chat", "language": "en", "source": "doc"}})
         assert result["router"] == {"en_doc_chat": "What?"}, "Pipeline should handle all parameters"
 
-    def test_warns_on_unused_optional_variables(self):
+    def test_warns_on_unused_optional_variables(self, caplog):
         """
         Test that a warning is raised when optional_variables contains variables
         that are not used in any route conditions or outputs.
@@ -536,8 +536,8 @@ class TestRouter:
         ]
 
         # Initialize with unused optional variables and capture warning
-        with pytest.warns(UserWarning, match="optional variables"):
         router = ConditionalRouter(routes=routes, optional_variables=["unused_var1", "unused_var2"])
+        assert "optional variables" in caplog.records[0].message
 
         # Verify router still works normally
         result = router.run(question="What?", mode="chat")