fix: support streaming_callback param in amazon bedrock generators

integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/adapters.py

@@ -1,8 +1,8 @@
 import json
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional
 
-from .handlers import TokenStreamingHandler
+from haystack.dataclasses import StreamingChunk
 
 
 class BedrockModelAdapter(ABC):
@@ -39,22 +39,24 @@ def get_responses(self, response_body: Dict[str, Any]) -> List[str]:
         responses = [completion.lstrip() for completion in completions]
         return responses
 
-    def get_stream_responses(self, stream, stream_handler: TokenStreamingHandler) -> List[str]:
+    def get_stream_responses(self, stream, streaming_callback: Callable[[StreamingChunk], None]) -> List[str]:
         """
         Extracts the responses from the Amazon Bedrock streaming response.
 
         :param stream: The streaming response from the Amazon Bedrock request.
-        :param stream_handler: The handler for the streaming response.
+        :param streaming_callback: The handler for the streaming response.
         :returns: A list of string responses.
         """
-        tokens: List[str] = []
+        streaming_chunks: List[StreamingChunk] = []
         for event in stream:
             chunk = event.get("chunk")
             if chunk:
                 decoded_chunk = json.loads(chunk["bytes"].decode("utf-8"))
-                token = self._extract_token_from_stream(decoded_chunk)
-                tokens.append(stream_handler(token, event_data=decoded_chunk))
-        responses = ["".join(tokens).lstrip()]
+                streaming_chunk: StreamingChunk = self._build_streaming_chunk(decoded_chunk)
+                streaming_chunks.append(streaming_chunk)
+                streaming_callback(streaming_chunk)
+
+        responses = ["".join(streaming_chunk.content for streaming_chunk in streaming_chunks).lstrip()]
         return responses
 
     def _get_params(self, inference_kwargs: Dict[str, Any], default_params: Dict[str, Any]) -> Dict[str, Any]:
@@ -84,12 +86,12 @@ def _extract_completions_from_response(self, response_body: Dict[str, Any]) -> L
         """
 
     @abstractmethod
-    def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
+    def _build_streaming_chunk(self, chunk: Dict[str, Any]) -> StreamingChunk:
         """
-        Extracts the token from a streaming chunk.
+        Extracts the content and meta from a streaming chunk.
 
-        :param chunk: The streaming chunk.
-        :returns: A string token.
+        :param chunk: The streaming chunk as dict.
+        :returns: A StreamingChunk object.
         """
 
 
@@ -150,17 +152,17 @@ def _extract_completions_from_response(self, response_body: Dict[str, Any]) -> L
 
         return [response_body["completion"]]
 
-    def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
+    def _build_streaming_chunk(self, chunk: Dict[str, Any]) -> StreamingChunk:
         """
-        Extracts the token from a streaming chunk.
+        Extracts the content and meta from a streaming chunk.
 
-        :param chunk: The streaming chunk.
-        :returns: A string token.
+        :param chunk: The streaming chunk as dict.
+        :returns: A StreamingChunk object.
         """
         if self.use_messages_api:
-            return chunk.get("delta", {}).get("text", "")
+            return StreamingChunk(content=chunk.get("delta", {}).get("text", ""), meta=chunk)
 
-        return chunk.get("completion", "")
+        return StreamingChunk(content=chunk.get("completion", ""), meta=chunk)
 
 
 class MistralAdapter(BedrockModelAdapter):
@@ -199,17 +201,18 @@ def _extract_completions_from_response(self, response_body: Dict[str, Any]) -> L
         """
         return [output.get("text", "") for output in response_body.get("outputs", [])]
 
-    def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
+    def _build_streaming_chunk(self, chunk: Dict[str, Any]) -> StreamingChunk:
         """
-        Extracts the token from a streaming chunk.
+        Extracts the content and meta from a streaming chunk.
 
-        :param chunk: The streaming chunk.
-        :returns: A string token.
+        :param chunk: The streaming chunk as dict.
+        :returns: A StreamingChunk object.
         """
+        content = ""
         chunk_list = chunk.get("outputs", [])
         if chunk_list:
-            return chunk_list[0].get("text", "")
-        return ""
+            content = chunk_list[0].get("text", "")
+        return StreamingChunk(content=content, meta=chunk)
 
 
 class CohereCommandAdapter(BedrockModelAdapter):
@@ -254,14 +257,14 @@ def _extract_completions_from_response(self, response_body: Dict[str, Any]) -> L
         responses = [generation["text"] for generation in response_body["generations"]]
         return responses
 
-    def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
+    def _build_streaming_chunk(self, chunk: Dict[str, Any]) -> StreamingChunk:
         """
-        Extracts the token from a streaming chunk.
+        Extracts the content and meta from a streaming chunk.
 
-        :param chunk: The streaming chunk.
-        :returns: A string token.
+        :param chunk: The streaming chunk as dict.
+        :returns: A StreamingChunk object.
         """
-        return chunk.get("text", "")
+        return StreamingChunk(content=chunk.get("text", ""), meta=chunk)
 
 
 class CohereCommandRAdapter(BedrockModelAdapter):
@@ -313,15 +316,15 @@ def _extract_completions_from_response(self, response_body: Dict[str, Any]) -> L
         responses = [response_body["text"]]
         return responses
 
-    def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
+    def _build_streaming_chunk(self, chunk: Dict[str, Any]) -> StreamingChunk:
         """
-        Extracts the token from a streaming chunk.
+        Extracts the content and meta from a streaming chunk.
 
-        :param chunk: The streaming chunk.
-        :returns: A string token.
+        :param chunk: The streaming chunk as dict.
+        :returns: A StreamingChunk object.
         """
         token: str = chunk.get("text", "")
-        return token
+        return StreamingChunk(content=token, meta=chunk)
 
 
 class AI21LabsJurassic2Adapter(BedrockModelAdapter):
@@ -357,7 +360,7 @@ def _extract_completions_from_response(self, response_body: Dict[str, Any]) -> L
         responses = [completion["data"]["text"] for completion in response_body["completions"]]
         return responses
 
-    def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
+    def _build_streaming_chunk(self, chunk: Dict[str, Any]) -> StreamingChunk:
         msg = "Streaming is not supported for AI21 Jurassic 2 models."
         raise NotImplementedError(msg)
 
@@ -398,14 +401,14 @@ def _extract_completions_from_response(self, response_body: Dict[str, Any]) -> L
         responses = [result["outputText"] for result in response_body["results"]]
         return responses
 
-    def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
+    def _build_streaming_chunk(self, chunk: Dict[str, Any]) -> StreamingChunk:
         """
-        Extracts the token from a streaming chunk.
+        Extracts the content and meta from a streaming chunk.
 
-        :param chunk: The streaming chunk.
-        :returns: A string token.
+        :param chunk: The streaming chunk as dict.
+        :returns: A StreamingChunk object.
         """
-        return chunk.get("outputText", "")
+        return StreamingChunk(content=chunk.get("outputText", ""), meta=chunk)
 
 
 class MetaLlamaAdapter(BedrockModelAdapter):
@@ -442,11 +445,11 @@ def _extract_completions_from_response(self, response_body: Dict[str, Any]) -> L
         """
         return [response_body["generation"]]
 
-    def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
+    def _build_streaming_chunk(self, chunk: Dict[str, Any]) -> StreamingChunk:
         """
-        Extracts the token from a streaming chunk.
+        Extracts the content and meta from a streaming chunk.
 
-        :param chunk: The streaming chunk.
-        :returns: A string token.
+        :param chunk: The streaming chunk as dict.
+        :returns: A StreamingChunk object.
         """
-        return chunk.get("generation", "")
+        return StreamingChunk(content=chunk.get("generation", ""), meta=chunk)

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

@@ -48,19 +48,18 @@ def get_responses(self, response_body: Dict[str, Any]) -> List[ChatMessage]:
         return self._extract_messages_from_response(response_body)
 
     def get_stream_responses(
-        self, stream: EventStream, stream_handler: Callable[[StreamingChunk], None]
+        self, stream: EventStream, streaming_callback: Callable[[StreamingChunk], None]
     ) -> List[ChatMessage]:
-        tokens: List[str] = []
+        streaming_chunks: List[StreamingChunk] = []
         last_decoded_chunk: Dict[str, Any] = {}
         for event in stream:
             chunk = event.get("chunk")
             if chunk:
                 last_decoded_chunk = json.loads(chunk["bytes"].decode("utf-8"))
-                token = self._extract_token_from_stream(last_decoded_chunk)
-                stream_chunk = StreamingChunk(content=token)  # don't extract meta, we care about tokens only
-                stream_handler(stream_chunk)  # callback the stream handler with StreamingChunk
-                tokens.append(token)
-        responses = ["".join(tokens).lstrip()]
+                streaming_chunk = self._build_streaming_chunk(last_decoded_chunk)
+                streaming_callback(streaming_chunk)  # callback the stream handler with StreamingChunk
+                streaming_chunks.append(streaming_chunk)
+        responses = ["".join(chunk.content for chunk in streaming_chunks).lstrip()]
         return [ChatMessage.from_assistant(response, meta=last_decoded_chunk) for response in responses]
 
     @staticmethod
@@ -142,12 +141,12 @@ def _extract_messages_from_response(self, response_body: Dict[str, Any]) -> List
         """
 
     @abstractmethod
-    def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
+    def _build_streaming_chunk(self, chunk: Dict[str, Any]) -> StreamingChunk:
         """
-        Extracts the token from a streaming chunk.
+        Extracts the content and meta from a streaming chunk.
 
-        :param chunk: The streaming chunk.
-        :returns: The extracted token.
+        :param chunk: The streaming chunk as dict.
+        :returns: A StreamingChunk object.
         """
 
 
@@ -252,16 +251,16 @@ def _extract_messages_from_response(self, response_body: Dict[str, Any]) -> List
                     messages.append(ChatMessage.from_assistant(content["text"], meta=meta))
         return messages
 
-    def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
+    def _build_streaming_chunk(self, chunk: Dict[str, Any]) -> StreamingChunk:
         """
-        Extracts the token from a streaming chunk.
+        Extracts the content and meta from a streaming chunk.
 
-        :param chunk: The streaming chunk.
-        :returns: The extracted token.
+        :param chunk: The streaming chunk as dict.
+        :returns: A StreamingChunk object.
         """
         if chunk.get("type") == "content_block_delta" and chunk.get("delta", {}).get("type") == "text_delta":
-            return chunk.get("delta", {}).get("text", "")
-        return ""
+            return StreamingChunk(content=chunk.get("delta", {}).get("text", ""), meta=chunk)
+        return StreamingChunk(content="", meta=chunk)
 
     def _to_anthropic_message(self, m: ChatMessage) -> Dict[str, Any]:
         """
@@ -425,17 +424,17 @@ def _extract_messages_from_response(self, response_body: Dict[str, Any]) -> List
             messages.append(ChatMessage.from_assistant(response["text"], meta=meta))
         return messages
 
-    def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
+    def _build_streaming_chunk(self, chunk: Dict[str, Any]) -> StreamingChunk:
         """
-        Extracts the token from a streaming chunk.
+        Extracts the content and meta from a streaming chunk.
 
-        :param chunk: The streaming chunk.
-        :returns: The extracted token.
+        :param chunk: The streaming chunk as dict.
+        :returns: A StreamingChunk object.
         """
         response_chunk = chunk.get("outputs", [])
         if response_chunk:
-            return response_chunk[0].get("text", "")
-        return ""
+            return StreamingChunk(content=response_chunk[0].get("text", ""), meta=chunk)
+        return StreamingChunk(content="", meta=chunk)
 
 
 class MetaLlama2ChatAdapter(BedrockModelChatAdapter):
@@ -543,11 +542,11 @@ def _extract_messages_from_response(self, response_body: Dict[str, Any]) -> List
         metadata = {k: v for (k, v) in response_body.items() if k != message_tag}
         return [ChatMessage.from_assistant(response_body[message_tag], meta=metadata)]
 
-    def _extract_token_from_stream(self, chunk: Dict[str, Any]) -> str:
+    def _build_streaming_chunk(self, chunk: Dict[str, Any]) -> StreamingChunk:
         """
-        Extracts the token from a streaming chunk.
+        Extracts the content and meta from a streaming chunk.
 
-        :param chunk: The streaming chunk.
-        :returns: The extracted token.
+        :param chunk: The streaming chunk as dict.
+        :returns: A StreamingChunk object.
         """
-        return chunk.get("generation", "")
+        return StreamingChunk(content=chunk.get("generation", ""), meta=chunk)

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

@@ -156,18 +156,29 @@ def resolve_secret(secret: Optional[Secret]) -> Optional[str]:
             stacklevel=2,
         )
 
-    def invoke(self, *args, **kwargs):
+    @component.output_types(replies=List[ChatMessage])
+    def run(
+        self,
+        messages: List[ChatMessage],
+        streaming_callback: Optional[Callable[[StreamingChunk], None]] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+    ):
         """
-        Invokes the Amazon Bedrock LLM with the given parameters. The parameters are passed to the Amazon Bedrock
-        client.
+        Generates a list of `ChatMessage` response to the given messages using the Amazon Bedrock LLM.
 
-        :param args: The positional arguments passed to the generator.
-        :param kwargs: The keyword arguments passed to the generator.
-        :returns: List of `ChatMessage` generated by LLM.
+        :param messages: The messages to generate a response to.
+        :param streaming_callback:
+            A callback function that is called when a new token is received from the stream.
+        :param generation_kwargs: Additional generation keyword arguments passed to the model.
+        :returns: A dictionary with the following keys:
+            - `replies`: The generated List of `ChatMessage` objects.
         """
+        generation_kwargs = generation_kwargs or {}
+        generation_kwargs = generation_kwargs.copy()
+
+        streaming_callback = streaming_callback or self.streaming_callback
+        generation_kwargs["stream"] = streaming_callback is not None
 
-        kwargs = kwargs.copy()
-        messages: List[ChatMessage] = kwargs.pop("messages", [])
         # check if the prompt is a list of ChatMessage objects
         if not (
             isinstance(messages, list)
@@ -177,39 +188,29 @@ def invoke(self, *args, **kwargs):
             msg = f"The model {self.model} requires a list of ChatMessage objects as a prompt."
             raise ValueError(msg)
 
-        body = self.model_adapter.prepare_body(messages=messages, **{"stop_words": self.stop_words, **kwargs})
+        body = self.model_adapter.prepare_body(
+            messages=messages, **{"stop_words": self.stop_words, **generation_kwargs}
+        )
         try:
-            if self.streaming_callback:
+            if streaming_callback:
                 response = self.client.invoke_model_with_response_stream(
                     body=json.dumps(body), modelId=self.model, accept="application/json", contentType="application/json"
                 )
                 response_stream = response["body"]
-                responses = self.model_adapter.get_stream_responses(
-                    stream=response_stream, stream_handler=self.streaming_callback
+                replies = self.model_adapter.get_stream_responses(
+                    stream=response_stream, streaming_callback=streaming_callback
                 )
             else:
                 response = self.client.invoke_model(
                     body=json.dumps(body), modelId=self.model, accept="application/json", contentType="application/json"
                 )
                 response_body = json.loads(response.get("body").read().decode("utf-8"))
-                responses = self.model_adapter.get_responses(response_body=response_body)
+                replies = self.model_adapter.get_responses(response_body=response_body)
         except ClientError as exception:
             msg = f"Could not inference Amazon Bedrock model {self.model} due: {exception}"
             raise AmazonBedrockInferenceError(msg) from exception
 
-        return responses
-
-    @component.output_types(replies=List[ChatMessage])
-    def run(self, messages: List[ChatMessage], generation_kwargs: Optional[Dict[str, Any]] = None):
-        """
-        Generates a list of `ChatMessage` responses to the given messages using the Amazon Bedrock LLM.
-
-        :param messages: The messages to generate a response to.
-        :param generation_kwargs: Additional generation keyword arguments passed to the model.
-        :returns: A dictionary with the following keys:
-            - `replies`: The generated list of `ChatMessage` objects.
-        """
-        return {"replies": self.invoke(messages=messages, **(generation_kwargs or {}))}
+        return {"replies": replies}
 
     @classmethod
     def get_model_adapter(cls, model: str) -> Optional[Type[BedrockModelChatAdapter]]: