Preface
Introduction to Amazon Bedrock Chat with Image File using Guide recipe
- Amazon Bedrock Chat with Image File using Guide recipe contents
  - Amazon Bedrock Chat with Image File using Guide recipe assets
Using the Amazon Bedrock Chat with Image File using Guide recipe

Amazon Bedrock Chat with Image File using Guide

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Step 3. Configure and publish the processes

Configure the deployment details of the LLM model and publish the processes.

To publish the

Get Context from Image File

process, click Actions
in the row that contains the process and select Publish
.

To publish the

Check Image Format

process, click Actions
in the row that contains the process and select Publish
.

Open the

Chat with Image File

process.

On the Temp Fields
tab of the Start step, in the Model_id
field, enter the deployment ID of the LLM model that was deployed.

Optionally, in the

Configure Request Parameters

step, enter the prompt instructions in the

Assignments

field by updating the

Prompt_Configuration

field using the Expression Editor, as shown in the following sample code:

For Prompt_Configuration: 
<GenerationConfig_AwsBedrock_PO-1>
   <Temperature>0.5</Temperature>
   <TopP>0.6</TopP>
   <MaxTokens>500</MaxTokens>
</GenerationConfig_AwsBedrock_PO-1>

For the

Prompt_Configuration

field, enter values for the following properties:

Property	Description
temperature	Controls the randomness of the model's output. A lower value close to 0 makes the output more deterministic, while a higher value close to 1 increases randomness and creativity. For example, if temperature is set to 0.5, the model balances between deterministic and creative outputs.
topP	Determines the cumulative probability threshold for token selection. The model considers the smallest set of tokens whose cumulative probability meets or exceeds topP . For example, if topP is set to 0.1, the model considers only the top 10% most probable tokens at each step.
max_tokens	Defines the token count of your prompt. The value can't exceed the model's context length. Most of the models have a context length of 2048 tokens.

Optionally, in the

Prepare Request

step, enter the prompt instructions in the

Assignments

field by updating the

Request

field using the Expression Editor, as shown in the following sample code:

For Request:
<root xmlns:m="urn:informatica:ae:xquery:json2xml:meta-data">
<messages m:isArray="true">
    <role>user</role>
    <content m:isArray="true">
        <text>Image: </text>
    </content>
    <content m:isArray="true">
        <image>
            <format>jpeg</format>
            <source>
                <bytes>{$temp.Context_From_File }</bytes>
            </source>
        </image>
    </content>
	    <content m:isArray="true">
        <image>
            <format>png</format>
            <source>
                <bytes>{$temp.Context_From_File }</bytes>
            </source>
        </image>
    </content>
    <content m:isArray="true">
        <image>
            <format>gif</format>
            <source>
                <bytes>{$temp.Context_From_File }</bytes>
            </source>
        </image>
    </content>
    <content m:isArray="true">
        <image>
            <format>webp</format>
            <source>
                <bytes>{$temp.Context_From_File }</bytes>
            </source>
        </image>
    </content>
    <content>
        <text>{$input.User_Prompt }</text>
    </content>
</messages>
<interfenceConfig>
<max_tokens m:type="xs:int">{$temp.Prompt_Configuration[1]/MaxTokens }</max_tokens>
<temperature m:type="xs:double">{$temp.Prompt_Configuration[1]/Temperature }</temperature>
<topP m:type="xs:double">{$temp.Prompt_Configuration[1]/TopP }</topP>
</interfenceConfig>
</root>

Save and publish the process.

Using the Amazon Bedrock Chat with Image File using Guide recipe

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