fix: Allow passing boto3 config to all AWS Bedrock classes

integrations/amazon_bedrock/src/haystack_integrations/components/embedders/amazon_bedrock/document_embedder.py

@@ -2,6 +2,7 @@
 import logging
 from typing import Any, Dict, List, Literal, Optional
 
+from botocore.config import Config
 from botocore.exceptions import ClientError
 from haystack import component, default_from_dict, default_to_dict
 from haystack.dataclasses import Document
@@ -73,6 +74,7 @@ def __init__(
         progress_bar: bool = True,
         meta_fields_to_embed: Optional[List[str]] = None,
         embedding_separator: str = "\n",
+        boto3_config: Optional[Dict[str, Any]] = None,
         **kwargs,
     ):
         """
@@ -98,6 +100,7 @@ def __init__(
             to keep the logs clean.
         :param meta_fields_to_embed: List of meta fields that should be embedded along with the Document text.
         :param embedding_separator: Separator used to concatenate the meta fields to the Document text.
+        :param boto3_config: The configuration for the boto3 client.
         :param kwargs: Additional parameters to pass for model inference. For example, `input_type` and `truncate` for
             Cohere models.
         :raises ValueError: If the model is not supported.
@@ -110,6 +113,19 @@ def __init__(
             )
             raise ValueError(msg)
 
+        self.model = model
+        self.aws_access_key_id = aws_access_key_id
+        self.aws_secret_access_key = aws_secret_access_key
+        self.aws_session_token = aws_session_token
+        self.aws_region_name = aws_region_name
+        self.aws_profile_name = aws_profile_name
+        self.batch_size = batch_size
+        self.progress_bar = progress_bar
+        self.meta_fields_to_embed = meta_fields_to_embed or []
+        self.embedding_separator = embedding_separator
+        self.boto3_config = boto3_config
+        self.kwargs = kwargs
+
         def resolve_secret(secret: Optional[Secret]) -> Optional[str]:
             return secret.resolve_value() if secret else None
 
@@ -121,26 +137,17 @@ def resolve_secret(secret: Optional[Secret]) -> Optional[str]:
                 aws_region_name=resolve_secret(aws_region_name),
                 aws_profile_name=resolve_secret(aws_profile_name),
             )
-            self._client = session.client("bedrock-runtime")
+            config: Optional[Config] = None
+            if self.boto3_config:
+                config = Config(**self.boto3_config)
+            self._client = session.client("bedrock-runtime", config=config)
         except Exception as exception:
             msg = (
                 "Could not connect to Amazon Bedrock. Make sure the AWS environment is configured correctly. "
                 "See https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html#configuration"
             )
             raise AmazonBedrockConfigurationError(msg) from exception
 
-        self.model = model
-        self.aws_access_key_id = aws_access_key_id
-        self.aws_secret_access_key = aws_secret_access_key
-        self.aws_session_token = aws_session_token
-        self.aws_region_name = aws_region_name
-        self.aws_profile_name = aws_profile_name
-        self.batch_size = batch_size
-        self.progress_bar = progress_bar
-        self.meta_fields_to_embed = meta_fields_to_embed or []
-        self.embedding_separator = embedding_separator
-        self.kwargs = kwargs
-
     def _prepare_texts_to_embed(self, documents: List[Document]) -> List[str]:
         """
         Prepare the texts to embed by concatenating the Document text with the metadata fields to embed.
@@ -269,6 +276,7 @@ def to_dict(self) -> Dict[str, Any]:
             progress_bar=self.progress_bar,
             meta_fields_to_embed=self.meta_fields_to_embed,
             embedding_separator=self.embedding_separator,
+            boto3_config=self.boto3_config,
             **self.kwargs,
         )
 

integrations/amazon_bedrock/src/haystack_integrations/components/embedders/amazon_bedrock/text_embedder.py

@@ -2,6 +2,7 @@
 import logging
 from typing import Any, Dict, List, Literal, Optional
 
+from botocore.config import Config
 from botocore.exceptions import ClientError
 from haystack import component, default_from_dict, default_to_dict
 from haystack.utils.auth import Secret, deserialize_secrets_inplace
@@ -62,6 +63,7 @@ def __init__(
         aws_session_token: Optional[Secret] = Secret.from_env_var("AWS_SESSION_TOKEN", strict=False),  # noqa: B008
         aws_region_name: Optional[Secret] = Secret.from_env_var("AWS_DEFAULT_REGION", strict=False),  # noqa: B008
         aws_profile_name: Optional[Secret] = Secret.from_env_var("AWS_PROFILE", strict=False),  # noqa: B008
+        boto3_config: Optional[Dict[str, Any]] = None,
         **kwargs,
     ):
         """
@@ -81,6 +83,7 @@ def __init__(
         :param aws_session_token: AWS session token.
         :param aws_region_name: AWS region name.
         :param aws_profile_name: AWS profile name.
+        :param boto3_config: The configuration for the boto3 client.
         :param kwargs: Additional parameters to pass for model inference. For example, `input_type` and `truncate` for
             Cohere models.
         :raises ValueError: If the model is not supported.
@@ -92,6 +95,15 @@ def __init__(
             )
             raise ValueError(msg)
 
+        self.model = model
+        self.aws_access_key_id = aws_access_key_id
+        self.aws_secret_access_key = aws_secret_access_key
+        self.aws_session_token = aws_session_token
+        self.aws_region_name = aws_region_name
+        self.aws_profile_name = aws_profile_name
+        self.boto3_config = boto3_config
+        self.kwargs = kwargs
+
         def resolve_secret(secret: Optional[Secret]) -> Optional[str]:
             return secret.resolve_value() if secret else None
 
@@ -103,22 +115,17 @@ def resolve_secret(secret: Optional[Secret]) -> Optional[str]:
                 aws_region_name=resolve_secret(aws_region_name),
                 aws_profile_name=resolve_secret(aws_profile_name),
             )
-            self._client = session.client("bedrock-runtime")
+            config: Optional[Config] = None
+            if self.boto3_config:
+                config = Config(**self.boto3_config)
+            self._client = session.client("bedrock-runtime", config=config)
         except Exception as exception:
             msg = (
                 "Could not connect to Amazon Bedrock. Make sure the AWS environment is configured correctly. "
                 "See https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html#configuration"
             )
             raise AmazonBedrockConfigurationError(msg) from exception
 
-        self.model = model
-        self.aws_access_key_id = aws_access_key_id
-        self.aws_secret_access_key = aws_secret_access_key
-        self.aws_session_token = aws_session_token
-        self.aws_region_name = aws_region_name
-        self.aws_profile_name = aws_profile_name
-        self.kwargs = kwargs
-
     @component.output_types(embedding=List[float])
     def run(self, text: str):
         """Embeds the input text using the Amazon Bedrock model.
@@ -185,6 +192,7 @@ def to_dict(self) -> Dict[str, Any]:
             aws_region_name=self.aws_region_name.to_dict() if self.aws_region_name else None,
             aws_profile_name=self.aws_profile_name.to_dict() if self.aws_profile_name else None,
             model=self.model,
+            boto3_config=self.boto3_config,
             **self.kwargs,
         )
 

integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/chat/chat_generator.py

@@ -3,6 +3,7 @@
 import re
 from typing import Any, Callable, ClassVar, Dict, List, Optional, Type
 
+from botocore.config import Config
 from botocore.exceptions import ClientError
 from haystack import component, default_from_dict, default_to_dict
 from haystack.dataclasses import ChatMessage, StreamingChunk
@@ -77,6 +78,7 @@ def __init__(
         stop_words: Optional[List[str]] = None,
         streaming_callback: Optional[Callable[[StreamingChunk], None]] = None,
         truncate: Optional[bool] = True,
+        boto3_config: Optional[Dict[str, Any]] = None,
     ):
         """
         Initializes the `AmazonBedrockChatGenerator` with the provided parameters. The parameters are passed to the
@@ -110,6 +112,11 @@ def __init__(
           [StreamingChunk](https://docs.haystack.deepset.ai/docs/data-classes#streamingchunk) object and
         switches the streaming mode on.
         :param truncate: Whether to truncate the prompt messages or not.
+        :param boto3_config: The configuration for the boto3 client.
+
+        :raises ValueError: If the model name is empty or None.
+        :raises AmazonBedrockConfigurationError: If the AWS environment is not configured correctly or the model is
+            not supported.
         """
         if not model:
             msg = "'model' cannot be None or empty string"
@@ -120,7 +127,10 @@ def __init__(
         self.aws_session_token = aws_session_token
         self.aws_region_name = aws_region_name
         self.aws_profile_name = aws_profile_name
+        self.stop_words = stop_words or []
+        self.streaming_callback = streaming_callback
         self.truncate = truncate
+        self.boto3_config = boto3_config
 
         # get the model adapter for the given model
         model_adapter_cls = self.get_model_adapter(model=model)
@@ -141,17 +151,17 @@ def resolve_secret(secret: Optional[Secret]) -> Optional[str]:
                 aws_region_name=resolve_secret(aws_region_name),
                 aws_profile_name=resolve_secret(aws_profile_name),
             )
-            self.client = session.client("bedrock-runtime")
+            config: Optional[Config] = None
+            if self.boto3_config:
+                config = Config(**self.boto3_config)
+            self.client = session.client("bedrock-runtime", config=config)
         except Exception as exception:
             msg = (
                 "Could not connect to Amazon Bedrock. Make sure the AWS environment is configured correctly. "
                 "See https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html#configuration"
             )
             raise AmazonBedrockConfigurationError(msg) from exception
 
-        self.stop_words = stop_words or []
-        self.streaming_callback = streaming_callback
-
     @component.output_types(replies=List[ChatMessage])
     def run(
         self,
@@ -256,6 +266,7 @@ def to_dict(self) -> Dict[str, Any]:
             generation_kwargs=self.model_adapter.generation_kwargs,
             streaming_callback=callback_name,
             truncate=self.truncate,
+            boto3_config=self.boto3_config,
         )
 
     @classmethod

integrations/amazon_bedrock/tests/test_chat_generator.py

@@ -1,7 +1,7 @@
 import json
 import logging
 import os
-from typing import Optional, Type
+from typing import Any, Dict, Optional, Type
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -26,14 +26,24 @@
 ]
 
 
-def test_to_dict(mock_boto3_session):
+@pytest.mark.parametrize(
+    "boto3_config",
+    [
+        None,
+        {
+            "read_timeout": 1000,
+        },
+    ],
+)
+def test_to_dict(mock_boto3_session: Any, boto3_config: Optional[Dict[str, Any]]):
     """
     Test that the to_dict method returns the correct dictionary without aws credentials
     """
     generator = AmazonBedrockChatGenerator(
         model="anthropic.claude-v2",
         generation_kwargs={"temperature": 0.7},
         streaming_callback=print_streaming_chunk,
+        boto3_config=boto3_config,
     )
     expected_dict = {
         "type": KLASS,
@@ -48,13 +58,23 @@ def test_to_dict(mock_boto3_session):
             "stop_words": [],
             "streaming_callback": "haystack.components.generators.utils.print_streaming_chunk",
             "truncate": True,
+            "boto3_config": boto3_config,
         },
     }
 
     assert generator.to_dict() == expected_dict
 
 
-def test_from_dict(mock_boto3_session):
+@pytest.mark.parametrize(
+    "boto3_config",
+    [
+        None,
+        {
+            "read_timeout": 1000,
+        },
+    ],
+)
+def test_from_dict(mock_boto3_session: Any, boto3_config: Optional[Dict[str, Any]]):
     """
     Test that the from_dict method returns the correct object
     """
@@ -71,12 +91,14 @@ def test_from_dict(mock_boto3_session):
                 "generation_kwargs": {"temperature": 0.7},
                 "streaming_callback": "haystack.components.generators.utils.print_streaming_chunk",
                 "truncate": True,
+                "boto3_config": boto3_config,
             },
         }
     )
     assert generator.model == "anthropic.claude-v2"
     assert generator.model_adapter.generation_kwargs == {"temperature": 0.7}
     assert generator.streaming_callback == print_streaming_chunk
+    assert generator.boto3_config == boto3_config
 
 
 def test_default_constructor(mock_boto3_session, set_env_variables):

integrations/amazon_bedrock/tests/test_document_embedder.py

@@ -1,4 +1,5 @@
 import io
+from typing import Any, Dict, Optional
 from unittest.mock import patch
 
 import pytest
@@ -66,10 +67,20 @@ def test_connection_error(self, mock_boto3_session):
                 input_type="fake_input_type",
             )
 
-    def test_to_dict(self, mock_boto3_session):
+    @pytest.mark.parametrize(
+        "boto3_config",
+        [
+            None,
+            {
+                "read_timeout": 1000,
+            },
+        ],
+    )
+    def test_to_dict(self, mock_boto3_session: Any, boto3_config: Optional[Dict[str, Any]]):
         embedder = AmazonBedrockDocumentEmbedder(
             model="cohere.embed-english-v3",
             input_type="search_document",
+            boto3_config=boto3_config,
         )
 
         expected_dict = {
@@ -86,12 +97,22 @@ def test_to_dict(self, mock_boto3_session):
                 "progress_bar": True,
                 "meta_fields_to_embed": [],
                 "embedding_separator": "\n",
+                "boto3_config": boto3_config,
             },
         }
 
         assert embedder.to_dict() == expected_dict
 
-    def test_from_dict(self, mock_boto3_session):
+    @pytest.mark.parametrize(
+        "boto3_config",
+        [
+            None,
+            {
+                "read_timeout": 1000,
+            },
+        ],
+    )
+    def test_from_dict(self, mock_boto3_session: Any, boto3_config: Optional[Dict[str, Any]]):
         data = {
             "type": TYPE,
             "init_parameters": {
@@ -106,6 +127,7 @@ def test_from_dict(self, mock_boto3_session):
                 "progress_bar": True,
                 "meta_fields_to_embed": [],
                 "embedding_separator": "\n",
+                "boto3_config": boto3_config,
             },
         }
 
@@ -117,6 +139,7 @@ def test_from_dict(self, mock_boto3_session):
         assert embedder.progress_bar
         assert embedder.meta_fields_to_embed == []
         assert embedder.embedding_separator == "\n"
+        assert embedder.boto3_config == boto3_config
 
     def test_init_invalid_model(self):
         with pytest.raises(ValueError):