Configure the deployment details of the LLM model and publish the processes.
1Open the Read History from File process.
2On the Start tab of the Start step, select the same Secure Agent that you had selected in the Run On field for the FileConnectionChatHistory connection.
3 Save and publish the process.
4Open the Write Chat History in File process.
5Optionally, in the Prepare History to Save in File step, click the Assignments tab. Open the Expression Editor for the File_Name field and enter the format to save the file. For example, .txt
6 Save and publish the process.
7Open the Amazon Bedrock Chat with History parent process.
8On the Temp Fields tab of the Start step, in the Model_id field, enter the user-specific deployment ID of the LLM model.
The default model id is anthropic.claude-3-sonnet-20240229-v1:0. You can optionally edit the model id.
9Optionally, in the Configure Request Parameters step, click the Assignments tab. Open the Expression Editor for the Prompt_Configuration field and enter the prompt instructions as shown in the following sample code:
For the Prompt_Configuration field, enter values for the following properties:
Property
Description
maxTokens
Defines the maximum number of tokens that the model can generate in its response. Setting a limit ensures that the response is concise and fits within the desired length constraints.
temperature
Controls the randomness of the model's output. A lower value makes the output more deterministic, while a higher value increases randomness and creativity. For example, a temperature of 0.9 balances between deterministic and creative outputs.
topP
An alternative to sampling with temperature where the model considers the results of the token with topP probability. For example, if topP is set to 0.1, the model considers only the top 10% most probable tokens at each step.