Generate Predictive Text with OpenHermes-2 Mistral 7B Cognitive Actions

24 Apr 2025
Generate Predictive Text with OpenHermes-2 Mistral 7B Cognitive Actions

Integrating advanced language models into your applications can significantly enhance user interaction and content generation. The OpenHermes-2-Mistral-7B-AWQ Cognitive Actions provide powerful tools for generating predictive text responses, tailored for developers who wish to leverage AI-driven text generation capabilities. In this blog post, we will explore the Generate Predictive Text Responses action, detailing its functionality, input requirements, output structure, and a conceptual usage example.

Prerequisites

Before you dive into integrating Cognitive Actions, ensure you have the following:

  • An API key for the Cognitive Actions platform.
  • A basic understanding of handling JSON data and making API calls.

Authentication typically involves passing your API key in the headers of your requests, allowing secure access to the action capabilities.

Cognitive Actions Overview

Generate Predictive Text Responses

Description: This action generates predictive text responses using the OpenHermes-2-Mistral-7B-AWQ model. It is specifically designed to provide efficient, high-quality predictions based on a given JSON-formatted message prompt.

  • Category: Text Generation

Input

The input for this action is structured as follows:

{
  "topK": 50,
  "topP": 0.9,
  "prompt": "[{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}, {\"role\": \"user\", \"content\": \"What is Slite?\"}]",
  "temperature": 0.75,
  "maxNewTokens": 512,
  "useBeamSearch": false
}
  • Required Fields:
    • prompt: A JSON-stringified array of message objects indicating roles and content.
  • Optional Fields:
    • topK: Samples from the top k most likely tokens during text decoding (default: 50).
    • topP: Samples from the top p percentage of most likely tokens (default: 0.9).
    • temperature: Controls the randomness of outputs (default: 0.75).
    • maxNewTokens: Maximum number of new tokens to generate (default: 512).
    • useBeamSearch: Determines whether to use beam search for decoding (default: false).

Output

The output of the action typically looks like this:

[
  "Slite is a knowledge management tool that helps teams to collaborate, share, and organize their knowledge effectively. It provides a centralized platform for teams to create, store, and find information, making it easier for them to work together and make informed decisions. Slite offers features such as note-taking, document management, task management, and real-time collaboration, making it an essential tool for teams that need to keep their knowledge organized and easily accessible."
]

This output consists of a list of generated text responses based on the input prompt.

Conceptual Usage Example (Python)

Here’s how a developer might implement the Generate Predictive Text Responses action in Python:

import requests
import json

# Replace with your Cognitive Actions API key and endpoint
COGNITIVE_ACTIONS_API_KEY = "YOUR_COGNITIVE_ACTIONS_API_KEY"
COGNITIVE_ACTIONS_EXECUTE_URL = "https://api.cognitiveactions.com/actions/execute" # Hypothetical endpoint

action_id = "52f02207-f155-4ae9-b736-b9929456e86d" # Action ID for Generate Predictive Text Responses

# Construct the input payload based on the action's requirements
payload = {
    "topK": 50,
    "topP": 0.9,
    "prompt": "[{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}, {\"role\": \"user\", \"content\": \"What is Slite?\"}]",
    "temperature": 0.75,
    "maxNewTokens": 512,
    "useBeamSearch": false
}

headers = {
    "Authorization": f"Bearer {COGNITIVE_ACTIONS_API_KEY}",
    "Content-Type": "application/json"
}

try:
    response = requests.post(
        COGNITIVE_ACTIONS_EXECUTE_URL,
        headers=headers,
        json={"action_id": action_id, "inputs": payload} # Hypothetical structure
    )
    response.raise_for_status() # Raise an exception for bad status codes (4xx or 5xx)

    result = response.json()
    print("Action executed successfully:")
    print(json.dumps(result, indent=2))

except requests.exceptions.RequestException as e:
    print(f"Error executing action {action_id}: {e}")
    if e.response is not None:
        print(f"Response status: {e.response.status_code}")
        try:
            print(f"Response body: {e.response.json()}")
        except json.JSONDecodeError:
            print(f"Response body: {e.response.text}")

In this code snippet:

  • Replace YOUR_COGNITIVE_ACTIONS_API_KEY with your actual API key.
  • The payload variable is structured according to the input schema, while the action_id corresponds to the specific action being called.
  • The endpoint URL and request structure are illustrative; you will need to adjust them according to your actual API setup.

Conclusion

The Generate Predictive Text Responses action offers developers a robust way to integrate advanced text generation capabilities into their applications. By understanding how to structure your inputs and handle the outputs effectively, you can create highly interactive and intelligent user experiences. Consider exploring additional use cases such as chatbots, content creation, and personalized responses to maximize the potential of Cognitive Actions in your projects.

← Back to All Articles