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Add document understanding examples for text generation models
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‎.doc_gen/metadata/bedrock-runtime_metadata.yaml

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@@ -1522,6 +1522,109 @@ bedrock-runtime_InvokeModelWithResponseStream_TitanTextEmbeddings:
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services:
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bedrock-runtime: {InvokeModel}
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# Document understanding
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bedrock-runtime_DocumentUnderstanding_AmazonNova:
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title: Send and process a document with Amazon Nova on &BR;
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title_abbrev: "Document understanding"
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synopsis: send and process a document with Amazon Nova on &BR;
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category: Amazon Nova
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languages:
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Python:
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versions:
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- sdk_version: 3
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github: python/example_code/bedrock-runtime
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excerpts:
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- description: Send and process a document with Amazon Nova on &BR;
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snippet_tags:
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- python.example_code.bedrock-runtime.DocumentUnderstanding_AmazonNovaText
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services:
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bedrock-runtime: {Converse}
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bedrock-runtime_DocumentUnderstanding_AnthropicClaude:
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title: Send and process a document with Anthropic Claude on &BR;
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title_abbrev: "Document understanding"
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synopsis: send and process a document with Anthropic Claude on &BR;
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category: Anthropic Claude
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languages:
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Python:
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versions:
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- sdk_version: 3
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github: python/example_code/bedrock-runtime
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excerpts:
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- description: Send and process a document with Anthropic Claude on &BR;
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snippet_tags:
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- python.example_code.bedrock-runtime.DocumentUnderstanding_AnthropicClaude
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services:
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bedrock-runtime: {Converse}
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bedrock-runtime_DocumentUnderstanding_CohereCommand:
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title: Send and process a document with Cohere Command models on &BR;
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title_abbrev: "Document understanding"
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synopsis: send and process a document with Cohere Command models on &BR;
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category: Cohere Command
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languages:
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Python:
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versions:
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- sdk_version: 3
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github: python/example_code/bedrock-runtime
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excerpts:
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- description: Send and process a document with Cohere Command models on &BR;
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snippet_tags:
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- python.example_code.bedrock-runtime.DocumentUnderstanding_CohereCommand
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services:
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bedrock-runtime: {Converse}
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bedrock-runtime_DocumentUnderstanding_DeepSeek:
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title: Send and process a document with DeepSeek on &BR;
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title_abbrev: "Document understanding"
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synopsis: send and process a document with DeepSeek on &BR;
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category: DeepSeek
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languages:
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Python:
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versions:
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- sdk_version: 3
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github: python/example_code/bedrock-runtime
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excerpts:
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- description: Send and process a document with DeepSeek on &BR;
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snippet_tags:
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- python.example_code.bedrock-runtime.DocumentUnderstanding_DeepSeek
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services:
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bedrock-runtime: {Converse}
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bedrock-runtime_DocumentUnderstanding_MetaLlama:
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title: Send and process a document with Llama on &BR;
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title_abbrev: "Document understanding"
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synopsis: send and process a document with Llama on &BR;
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category: Meta Llama
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languages:
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Python:
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versions:
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- sdk_version: 3
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github: python/example_code/bedrock-runtime
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excerpts:
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- description: Send and process a document with Llama on &BR;
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snippet_tags:
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- python.example_code.bedrock-runtime.DocumentUnderstanding_MetaLlama
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services:
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bedrock-runtime: {Converse}
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bedrock-runtime_DocumentUnderstanding_Mistral:
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title: Send and process a document with Mistral models on &BR;
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title_abbrev: "Document understanding"
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synopsis: send and process a document with Mistral models on &BR;
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category: Meta Llama
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languages:
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Python:
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versions:
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- sdk_version: 3
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github: python/example_code/bedrock-runtime
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excerpts:
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- description: Send and process a document with Mistral models on &BR;
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snippet_tags:
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- python.example_code.bedrock-runtime.DocumentUnderstanding_Mistral
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services:
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bedrock-runtime: {Converse}
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# Tool use scenarios
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bedrock-runtime_Scenario_ToolUseDemo_AmazonNova:
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title: "A tool use demo illustrating how to connect AI models on &BR; with a custom tool or API"

‎python/example_code/bedrock-runtime/models/amazon_nova/amazon_nova_text/document_understanding.py

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# SPDX-License-Identifier: Apache-2.0
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# snippet-start:[python.example_code.bedrock-runtime.DocumentUnderstanding_AmazonNovaText]
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# Use the Conversation API to send a text message to Amazon Nova.
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# Send and process a document with Amazon Nova on Amazon Bedrock.
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import boto3
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from botocore.exceptions import ClientError
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# Create a Bedrock Runtime client in the AWS Region you want to use.
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client = boto3.client("bedrock-runtime", region_name="us-east-1")
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# Set the model ID, e.g., Amazon Nova Lite.
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# Set the model ID, e.g. Amazon Nova Lite.
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model_id = "amazon.nova-lite-v1:0"
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# Load the document
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{
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"role": "user",
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"content": [
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{
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"text": "Briefly compare the models described in this document"
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},
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{"text": "Briefly compare the models described in this document"},
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{
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"document": {
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# Available formats: html, md, pdf, doc/docx, xls/xlsx, csv, and txt
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"format": "pdf",
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"name": "Amazon Nova Service Cards",
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"source": {"bytes": document_bytes}
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"source": {"bytes": document_bytes},
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}
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}
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},
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],
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}
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]

‎python/example_code/bedrock-runtime/models/anthropic_claude/document_understanding.py

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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# SPDX-License-Identifier: Apache-2.0
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# snippet-start:[python.example_code.bedrock-runtime.DocumentUnderstanding_AnthropicClaude]
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# Send and process a document with Anthropic Claude on Amazon Bedrock.
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import boto3
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from botocore.exceptions import ClientError
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# Create a Bedrock Runtime client in the AWS Region you want to use.
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client = boto3.client("bedrock-runtime", region_name="us-east-1")
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# Set the model ID, e.g. Claude 3 Haiku.
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model_id = "anthropic.claude-3-haiku-20240307-v1:0"
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# Load the document
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with open("example-data/amazon-nova-service-cards.pdf", "rb") as file:
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document_bytes = file.read()
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# Start a conversation with a user message and the document
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conversation = [
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{
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"role": "user",
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"content": [
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{"text": "Briefly compare the models described in this document"},
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{
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"document": {
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# Available formats: html, md, pdf, doc/docx, xls/xlsx, csv, and txt
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"format": "pdf",
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"name": "Amazon Nova Service Cards",
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"source": {"bytes": document_bytes},
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}
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},
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],
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}
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]
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try:
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# Send the message to the model, using a basic inference configuration.
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response = client.converse(
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modelId=model_id,
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messages=conversation,
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inferenceConfig={"maxTokens": 500, "temperature": 0.3},
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)
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# Extract and print the response text.
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response_text = response["output"]["message"]["content"][0]["text"]
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print(response_text)
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except (ClientError, Exception) as e:
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print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
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exit(1)
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# snippet-end:[python.example_code.bedrock-runtime.DocumentUnderstanding_AnthropicClaude]

‎python/example_code/bedrock-runtime/models/cohere_command/document_understanding.py

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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# SPDX-License-Identifier: Apache-2.0
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# snippet-start:[python.example_code.bedrock-runtime.DocumentUnderstanding_CohereCommand]
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# Send and process a document with Cohere Command models on Amazon Bedrock.
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import boto3
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from botocore.exceptions import ClientError
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# Create a Bedrock Runtime client in the AWS Region you want to use.
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client = boto3.client("bedrock-runtime", region_name="us-east-1")
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# Set the model ID, e.g. Command R+.
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model_id = "cohere.command-r-plus-v1:0"
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# Load the document
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with open("example-data/amazon-nova-service-cards.pdf", "rb") as file:
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document_bytes = file.read()
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# Start a conversation with a user message and the document
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conversation = [
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{
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"role": "user",
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"content": [
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{"text": "Briefly compare the models described in this document"},
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{
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"document": {
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# Available formats: html, md, pdf, doc/docx, xls/xlsx, csv, and txt
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"format": "pdf",
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"name": "Amazon Nova Service Cards",
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"source": {"bytes": document_bytes},
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}
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},
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],
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}
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]
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try:
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# Send the message to the model, using a basic inference configuration.
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response = client.converse(
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modelId=model_id,
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messages=conversation,
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inferenceConfig={"maxTokens": 500, "temperature": 0.3},
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)
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# Extract and print the response text.
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response_text = response["output"]["message"]["content"][0]["text"]
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print(response_text)
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except (ClientError, Exception) as e:
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print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
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exit(1)
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# snippet-end:[python.example_code.bedrock-runtime.DocumentUnderstanding_CohereCommand]

‎python/example_code/bedrock-runtime/models/deepseek/document_understanding.py

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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# SPDX-License-Identifier: Apache-2.0
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# snippet-start:[python.example_code.bedrock-runtime.DocumentUnderstanding_DeepSeek]
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# Send and process a document with DeepSeek on Amazon Bedrock.
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import boto3
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from botocore.exceptions import ClientError
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# Create a Bedrock Runtime client in the AWS Region you want to use.
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client = boto3.client("bedrock-runtime", region_name="us-east-1")
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# Set the model ID, e.g. DeepSeek-R1
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model_id = "us.deepseek.r1-v1:0"
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# Load the document
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with open("example-data/amazon-nova-service-cards.pdf", "rb") as file:
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document_bytes = file.read()
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# Start a conversation with a user message and the document
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conversation = [
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{
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"role": "user",
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"content": [
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{"text": "Briefly compare the models described in this document"},
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{
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"document": {
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# Available formats: html, md, pdf, doc/docx, xls/xlsx, csv, and txt
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"format": "pdf",
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"name": "Amazon Nova Service Cards",
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"source": {"bytes": document_bytes},
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}
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},
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],
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}
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]
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try:
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# Send the message to the model, using a basic inference configuration.
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response = client.converse(
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modelId=model_id,
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messages=conversation,
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inferenceConfig={"maxTokens": 2000, "temperature": 0.3},
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)
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# Extract and print the reasoning and response text.
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reasoning, response_text = "", ""
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for item in response["output"]["message"]["content"]:
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for key, value in item.items():
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if key == "reasoningContent":
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reasoning = value["reasoningText"]["text"]
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elif key == "text":
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response_text = value
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print(f"\nReasoning:\n{reasoning}")
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print(f"\nResponse:\n{response_text}")
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except (ClientError, Exception) as e:
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print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
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exit(1)
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# snippet-end:[python.example_code.bedrock-runtime.DocumentUnderstanding_DeepSeek]

‎python/example_code/bedrock-runtime/models/meta_llama/document_understanding.py

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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# SPDX-License-Identifier: Apache-2.0
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# snippet-start:[python.example_code.bedrock-runtime.DocumentUnderstanding_MetaLlama]
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# Send and process a document with Llama on Amazon Bedrock.
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import boto3
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from botocore.exceptions import ClientError
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# Create a Bedrock Runtime client in the AWS Region you want to use.
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client = boto3.client("bedrock-runtime", region_name="us-east-1")
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# Set the model ID, e.g. Llama 3.1 8B Instruct.
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model_id = "us.meta.llama3-1-8b-instruct-v1:0"
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# Load the document
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with open("example-data/amazon-nova-service-cards.pdf", "rb") as file:
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document_bytes = file.read()
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# Start a conversation with a user message and the document
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conversation = [
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{
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"role": "user",
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"content": [
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{"text": "Briefly compare the models described in this document"},
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{
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"document": {
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# Available formats: html, md, pdf, doc/docx, xls/xlsx, csv, and txt
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"format": "pdf",
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"name": "Amazon Nova Service Cards",
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"source": {"bytes": document_bytes},
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}
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},
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],
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}
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]
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try:
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# Send the message to the model, using a basic inference configuration.
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response = client.converse(
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modelId=model_id,
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messages=conversation,
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inferenceConfig={"maxTokens": 500, "temperature": 0.3},
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)
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# Extract and print the response text.
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response_text = response["output"]["message"]["content"][0]["text"]
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print(response_text)
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except (ClientError, Exception) as e:
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print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
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exit(1)
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# snippet-end:[python.example_code.bedrock-runtime.DocumentUnderstanding_MetaLlama]

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