Anthropic

To process a large amount of data based on context and input from the user using Anthropic Provider.

Description

The Anthropic model, developed by Anthropic, is a language model that combines the power of GPT (Generative Pre-trained Transformer) with LLM (Language Model for Legal Text). It is specifically designed to understand and generate legal text, making it useful for tasks such as legal document analysis, contract generation, and legal research. The model has been trained on a large corpus of legal documents to ensure accuracy and relevance in its responses.

The Anthropic component allows you to integrate Anthropic into your flows. You can customize the parameters used by Anthropic component, and also specify the context of knowledge that the Anthropic component operates on, as well as provide the input query. Both the context and the query are given to the Anthropic component by specifying Diaflow component identifiers. For example, the above screenshot shows the default user message of trigger.text which is a Text Input component.

The Anthropic component has the identifier of an-X, where X represents the instance number of the Anthropic component.

Inputs

The Anthropic component has the following input connections.

Input Name	Description	Constraints
From data Loaders/ Data source/Vector DB	This input connection represents the context information for the Anthropic model.	Must originate from a Data Loader/Data Source or VectorDB component.
From Input	This input connection represents the user query for the Anthropic model.	Must originate from a component that generates a text string as output such as a Python or Text Input component.

Component settings

Parameter Name	Description
Credentials	You can specify to use your own Anthropic credentials or alternatively you can use Diaflow's default credentials.
Model	This parameter specifies the version of Anthropic that the component should use. Available values: - Claude-3 - haiku - Claude-3 - opus - Claude-3 - sonnet - Claude-2-1
Prompt	Describes how you want the Anthropic model to respond. For example, you can specify the role, manner and rules that Anthropic should adhere to. Also mention the component ID to connect the components.
Image source	Adding an image to your prompt by identify a trigger file in this configuration.

Advanced configurations

Options	Description
Enable caching	This option determines whether the results of the component are cached. This means that on the next run of the Flow, Diaflow will utilize the previous computed component output, as long as the inputs have not changed.
Caching time	Only applicable if the "Enable Caching" option has been enabled. This parameter controls how long Diaflow will wait before automatically clearing the cache.
Memory	The ability of the model to remember and utilize context within a single session. The context window represent the maximum amount of text the model can consider.
Temperature	The temperature is used to control the randomness of the output. When you set it higher, you'll get more random outputs. When you set it lower, towards 0, the values are more deterministic. Valid range for this parameter is 0 to 1.
Max lenght	The Max Length parameter in OpenAI refers to the maximum number of tokens allowed in the input text. Tokens can be individual words or characters. By setting the max length, you can control the length of the response generated by the model. It's important to note that longer texts may result in higher costs and longer response times. Valid range for this parameter is 0 to 3097.
Top P	Top-p sampling, involves selecting the next word from the smallest possible set of words whose cumulative probability is greater than or equal to the specified probability p, typically between 0 and 1.

Outputs

The Anthropic component has the following output connections.

Output Name	Description	Constraints
To Output	This output connection contains the text result of the Anthropic component.	Can be connected to any component that accepts a string input.

Use case

Here is a simple use case of the Anthropic component, where the Anthropic component is being used to provide the user the ability to ask the Anthropic component questions via a Text Input component.