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Beispiele für die Converse-API
Die folgende Tabelle zeigt Beispiele für die Verwendung der Operationen Converse und ConverseStream.
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Dieses Beispiel zeigt, wie die Operation
Conversemit dem Anthropic Claude 3 Sonnet-Modell aufgerufen wird. Das Beispiel zeigt, wie der Eingabetext, die Inferenzparameter und zusätzliche Parameter gesendet werden, die für das Modell einzigartig sind. Durch den Code wird eine Konversation gestartet, in der das Modell aufgefordert wird, eine Liste mit Liedern zu erstellen. Anschließend wird das Gespräch mit der Aufforderung, dass die Lieder von Künstlern aus dem Vereinigtes Königreich stammen sollen, fortgesetzt.# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Shows how to use the <noloc>Converse</noloc> API with Anthropic Claude 3 Sonnet (on demand). """ import logging import boto3 from botocore.exceptions import ClientError logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def generate_conversation(bedrock_client, model_id, system_prompts, messages): """ Sends messages to a model. Args: bedrock_client: The Boto3 Bedrock runtime client. model_id (str): The model ID to use. system_prompts (JSON) : The system prompts for the model to use. messages (JSON) : The messages to send to the model. Returns: response (JSON): The conversation that the model generated. """ logger.info("Generating message with model %s", model_id) # Inference parameters to use. temperature = 0.5 top_k = 200 # Base inference parameters to use. inference_config = {"temperature": temperature} # Additional inference parameters to use. additional_model_fields = {"top_k": top_k} # Send the message. response = bedrock_client.converse( modelId=model_id, messages=messages, system=system_prompts, inferenceConfig=inference_config, additionalModelRequestFields=additional_model_fields ) # Log token usage. token_usage = response['usage'] logger.info("Input tokens: %s", token_usage['inputTokens']) logger.info("Output tokens: %s", token_usage['outputTokens']) logger.info("Total tokens: %s", token_usage['totalTokens']) logger.info("Stop reason: %s", response['stopReason']) return response def main(): """ Entrypoint for Anthropic Claude 3 Sonnet example. """ logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") model_id = "anthropic.claude-3-sonnet-20240229-v1:0" # Setup the system prompts and messages to send to the model. system_prompts = [{"text": "You are an app that creates playlists for a radio station that plays rock and pop music. Only return song names and the artist."}] message_1 = { "role": "user", "content": [{"text": "Create a list of 3 pop songs."}] } message_2 = { "role": "user", "content": [{"text": "Make sure the songs are by artists from the United Kingdom."}] } messages = [] try: bedrock_client = boto3.client(service_name='bedrock-runtime') # Start the conversation with the 1st message. messages.append(message_1) response = generate_conversation( bedrock_client, model_id, system_prompts, messages) # Add the response message to the conversation. output_message = response['output']['message'] messages.append(output_message) # Continue the conversation with the 2nd message. messages.append(message_2) response = generate_conversation( bedrock_client, model_id, system_prompts, messages) output_message = response['output']['message'] messages.append(output_message) # Show the complete conversation. for message in messages: print(f"Role: {message['role']}") for content in message['content']: print(f"Text: {content['text']}") print() except ClientError as err: message = err.response['Error']['Message'] logger.error("A client error occurred: %s", message) print(f"A client error occured: {message}") else: print( f"Finished generating text with model {model_id}.") if __name__ == "__main__": main() - Image
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Im Beispiel wird gezeigt, wie ein Bild als Teil einer Nachricht gesendet wird, und das Modell aufgefordert wird, das Bild zu beschreiben. Im Beispiel werden die Operation
Converseund das Anthropic-Claude 3 Sonnet-Modell verwendet.# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Shows how to send an image with the <noloc>Converse</noloc> API with an accompanying text prompt to Anthropic Claude 3 Sonnet (on demand). """ import logging import boto3 from botocore.exceptions import ClientError logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def generate_conversation(bedrock_client, model_id, input_text, input_image): """ Sends a message to a model. Args: bedrock_client: The Boto3 Bedrock runtime client. model_id (str): The model ID to use. input text : The text prompt accompanying the image. input_image : The path to the input image. Returns: response (JSON): The conversation that the model generated. """ logger.info("Generating message with model %s", model_id) # Get image extension and read in image as bytes image_ext = input_image.split(".")[-1] with open(input_image, "rb") as f: image = f.read() message = { "role": "user", "content": [ { "text": input_text }, { "image": { "format": image_ext, "source": { "bytes": image } } } ] } messages = [message] # Send the message. response = bedrock_client.converse( modelId=model_id, messages=messages ) return response def main(): """ Entrypoint for Anthropic Claude 3 Sonnet example. """ logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") model_id = "anthropic.claude-3-sonnet-20240229-v1:0" input_text = "What's in this image?" input_image = "path/to/image" try: bedrock_client = boto3.client(service_name="bedrock-runtime") response = generate_conversation( bedrock_client, model_id, input_text, input_image) output_message = response['output']['message'] print(f"Role: {output_message['role']}") for content in output_message['content']: print(f"Text: {content['text']}") token_usage = response['usage'] print(f"Input tokens: {token_usage['inputTokens']}") print(f"Output tokens: {token_usage['outputTokens']}") print(f"Total tokens: {token_usage['totalTokens']}") print(f"Stop reason: {response['stopReason']}") except ClientError as err: message = err.response['Error']['Message'] logger.error("A client error occurred: %s", message) print(f"A client error occured: {message}") else: print( f"Finished generating text with model {model_id}.") if __name__ == "__main__": main() - Document
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Im Beispiel wird gezeigt, wie ein Bild als Teil einer Nachricht gesendet wird, und das Modell aufgefordert wird, das Bild zu beschreiben. Im Beispiel werden die Operation
Converseund das Anthropic-Claude 3 Sonnet-Modell verwendet.# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Shows how to send an document as part of a message to Anthropic Claude 3 Sonnet (on demand). """ import logging import boto3 from botocore.exceptions import ClientError logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def generate_message(bedrock_client, model_id, input_text, input_document_path): """ Sends a message to a model. Args: bedrock_client: The Boto3 Bedrock runtime client. model_id (str): The model ID to use. input text : The input message. input_document_path : The path to the input document. Returns: response (JSON): The conversation that the model generated. """ logger.info("Generating message with model %s", model_id) # Get format from path and read the path input_document_format = input_document_path.split(".")[-1] with open(input_document_path, 'rb') as input_document_file: input_document = input_document_file.read() # Message to send. message = { "role": "user", "content": [ { "text": input_text }, { "document": { "name": "MyDocument", "format": input_document_format, "source": { "bytes": input_document } } } ] } messages = [message] # Send the message. response = bedrock_client.converse( modelId=model_id, messages=messages ) return response def main(): """ Entrypoint for Anthropic Claude 3 Sonnet example. """ logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") model_id = "anthropic.claude-3-sonnet-20240229-v1:0" input_text = "What's in this document?" input_document_path = "path/to/document" try: bedrock_client = boto3.client(service_name="bedrock-runtime") response = generate_message( bedrock_client, model_id, input_text, input_document_path) output_message = response['output']['message'] print(f"Role: {output_message['role']}") for content in output_message['content']: print(f"Text: {content['text']}") token_usage = response['usage'] print(f"Input tokens: {token_usage['inputTokens']}") print(f"Output tokens: {token_usage['outputTokens']}") print(f"Total tokens: {token_usage['totalTokens']}") print(f"Stop reason: {response['stopReason']}") except ClientError as err: message = err.response['Error']['Message'] logger.error("A client error occurred: %s", message) print(f"A client error occured: {message}") else: print( f"Finished generating text with model {model_id}.") if __name__ == "__main__": main() - Streaming
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Dieses Beispiel zeigt, wie die Operation
ConverseStreammit dem Anthropic-Claude 3 Sonnet-Modell aufgerufen wird. Das Beispiel zeigt, wie der Eingabetext, die Inferenzparameter und zusätzliche Parameter gesendet werden, die für das Modell einzigartig sind.# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Shows how to use the <noloc>Converse</noloc> API to stream a response from Anthropic Claude 3 Sonnet (on demand). """ import logging import boto3 from botocore.exceptions import ClientError logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def stream_conversation(bedrock_client, model_id, messages, system_prompts, inference_config, additional_model_fields): """ Sends messages to a model and streams the response. Args: bedrock_client: The Boto3 Bedrock runtime client. model_id (str): The model ID to use. messages (JSON) : The messages to send. system_prompts (JSON) : The system prompts to send. inference_config (JSON) : The inference configuration to use. additional_model_fields (JSON) : Additional model fields to use. Returns: Nothing. """ logger.info("Streaming messages with model %s", model_id) response = bedrock_client.converse_stream( modelId=model_id, messages=messages, system=system_prompts, inferenceConfig=inference_config, additionalModelRequestFields=additional_model_fields ) stream = response.get('stream') if stream: for event in stream: if 'messageStart' in event: print(f"\nRole: {event['messageStart']['role']}") if 'contentBlockDelta' in event: print(event['contentBlockDelta']['delta']['text'], end="") if 'messageStop' in event: print(f"\nStop reason: {event['messageStop']['stopReason']}") if 'metadata' in event: metadata = event['metadata'] if 'usage' in metadata: print("\nToken usage") print(f"Input tokens: {metadata['usage']['inputTokens']}") print( f":Output tokens: {metadata['usage']['outputTokens']}") print(f":Total tokens: {metadata['usage']['totalTokens']}") if 'metrics' in event['metadata']: print( f"Latency: {metadata['metrics']['latencyMs']} milliseconds") def main(): """ Entrypoint for streaming message API response example. """ logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") model_id = "anthropic.claude-3-sonnet-20240229-v1:0" system_prompt = """You are an app that creates playlists for a radio station that plays rock and pop music. Only return song names and the artist.""" # Message to send to the model. input_text = "Create a list of 3 pop songs." message = { "role": "user", "content": [{"text": input_text}] } messages = [message] # System prompts. system_prompts = [{"text" : system_prompt}] # inference parameters to use. temperature = 0.5 top_k = 200 # Base inference parameters. inference_config = { "temperature": temperature } # Additional model inference parameters. additional_model_fields = {"top_k": top_k} try: bedrock_client = boto3.client(service_name='bedrock-runtime') stream_conversation(bedrock_client, model_id, messages, system_prompts, inference_config, additional_model_fields) except ClientError as err: message = err.response['Error']['Message'] logger.error("A client error occurred: %s", message) print("A client error occured: " + format(message)) else: print( f"Finished streaming messages with model {model_id}.") if __name__ == "__main__": main() - Video
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Im Beispiel wird gezeigt, wie ein Video als Teil einer Nachricht gesendet wird, und das Modell aufgefordert wird, das Video zu beschreiben. Im Beispiel werden die Operation
Converseund das Amazon Nova Pro-Modell verwendet.# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Shows how to send a video with the <noloc>Converse</noloc> API to Amazon Nova Pro (on demand). """ import logging import boto3 from botocore.exceptions import ClientError logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def generate_conversation(bedrock_client, model_id, input_text, input_video): """ Sends a message to a model. Args: bedrock_client: The Boto3 Bedrock runtime client. model_id (str): The model ID to use. input text : The input message. input_video : The input video. Returns: response (JSON): The conversation that the model generated. """ logger.info("Generating message with model %s", model_id) # Message to send. with open(input_video, "rb") as f: video = f.read() message = { "role": "user", "content": [ { "text": input_text }, { "video": { "format": 'mp4', "source": { "bytes": video } } } ] } messages = [message] # Send the message. response = bedrock_client.converse( modelId=model_id, messages=messages ) return response def main(): """ Entrypoint for Amazon Nova Pro example. """ logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") model_id = "amazon.nova-pro-v1:0" input_text = "What's in this video?" input_video = "path/to/video" try: bedrock_client = boto3.client(service_name="bedrock-runtime") response = generate_conversation( bedrock_client, model_id, input_text, input_video) output_message = response['output']['message'] print(f"Role: {output_message['role']}") for content in output_message['content']: print(f"Text: {content['text']}") token_usage = response['usage'] print(f"Input tokens: {token_usage['inputTokens']}") print(f"Output tokens: {token_usage['outputTokens']}") print(f"Total tokens: {token_usage['totalTokens']}") print(f"Stop reason: {response['stopReason']}") except ClientError as err: message = err.response['Error']['Message'] logger.error("A client error occurred: %s", message) print(f"A client error occured: {message}") else: print( f"Finished generating text with model {model_id}.") if __name__ == "__main__": main()
Verwenden der Converse-API
Ein Tool verwenden, um eine Modellantwort zu vervollständigen