Mistral Completion Example

Welcome to this fun and informative example of using the Mistral language model with LangChain in Go! 🚀🌙

This example demonstrates how to generate text completions using the Mistral AI model through the LangChain Go library. It showcases two different approaches: streaming and non-streaming completions.

What This Example Does

1. Setup: The code initializes a Mistral language model using the mistral.New() function, specifying the "open-mistral-7b" model.

2. Streaming Completion:

   • It generates a completion for the question "Who was the first man to walk on the moon?"
   • The response is streamed in real-time, with each chunk printed to the console as it arrives.
   • This demonstrates how to handle streaming responses, which can be useful for displaying results progressively.

3. Non-Streaming Completion:

   • It generates a completion for the question "Who was the first man to go to space?"
   • This time, it waits for the full response before printing it.
   • It uses a different model ("mistral-small-latest") and a lower temperature setting for variety.

Key Features

• Model Selection: Shows how to specify different Mistral models.
• Temperature Control: Demonstrates adjusting the randomness of outputs using the temperature parameter.
• Streaming vs. Non-Streaming: Illustrates both real-time and batch completion methods.

Running the Example

To run this example, make sure you have the necessary dependencies installed and your Mistral API credentials set up. Then, simply execute the Go file:

go run mistral_completion_example.go

Enjoy exploring the capabilities of Mistral AI with LangChain in Go! 🎉👨‍🚀