Mistral

Unstable API

0.7.0

@project-lakechain/bedrock-text-processors

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Bedrock text processors expose different constructs that leverage the Generative AI text models powered by Amazon Bedrock. Using these constructs, you can use prompt engineering techniques to transform text documents, including, text summarization, text translation, information extraction, and more!

The Mistral text processor is part of the Bedrock text processors and allows you to leverage the Mistral and Mixtral large-language models provided by Mistral AI within your pipelines.

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📝 Text Generation

To start using Mistral models in your pipelines, you import the MistralTextProcessor construct in your CDK stack, and specify the specific text model you want to use.

💁 The below example demonstrates how to use the Mistral text processor to summarize input documents uploaded to an S3 bucket.

import { S3EventTrigger } from '@project-lakechain/s3-event-trigger';
import { MistralTextProcessor, MistralTextModel } from '@project-lakechain/bedrock-text-processors';
import { CacheStorage } from '@project-lakechain/core';

class Stack extends cdk.Stack {
  constructor(scope: cdk.Construct, id: string) {
    const cache = new CacheStorage(this, 'Cache');

    // Create the S3 event trigger.
    const trigger = new S3EventTrigger.Builder()
      .withScope(this)
      .withIdentifier('Trigger')
      .withCacheStorage(cache)
      .withBucket(bucket)
      .build();

    // Transforms input documents using a Mistral model.
    const mistral = new MistralTextProcessor.Builder()
      .withScope(this)
      .withIdentifier('MistralTextProcessor')
      .withCacheStorage(cache)
      .withSource(source)
      .withModel(MistralTextModel.MIXTRAL_8x7B_INSTRUCT)
      .withPrompt(`
        Give a detailed summary of the text with the following constraints:
        - Write the summary in the same language as the original text.
        - Keep the original meaning, style, and tone of the text in the summary.
      `)
      .withModelParameters({
        temperature: 0.3,
        max_tokens: 4096
      })
      .build();
  }
}

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🤖 Model Selection

You can select the specific Mistral model to use with this middleware using the .withModel API.

import { MistralTextProcessor, MistralTextModel } from '@project-lakechain/bedrock-text-processors';

const mistral = new MistralTextProcessor.Builder()
  .withScope(this)
  .withIdentifier('MistralTextProcessor')
  .withCacheStorage(cache)
  .withSource(source)
  .withModel(MistralTextModel.MISTRAL_LARGE) // 👈 Specify a model
  .withPrompt(prompt)
  .build();

You can choose amongst the following models : MISTRAL_7B_INSTRUCT, MIXTRAL_8x7B_INSTRUCT and MISTRAL_LARGE.

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🌐 Region Selection

You can specify the AWS region in which you want to invoke Amazon Bedrock using the .withRegion API. This can be helpful if Amazon Bedrock is not yet available in your deployment region.

💁 By default, the middleware will use the current region in which it is deployed.

import { MistralTextProcessor, MistralTextModel } from '@project-lakechain/bedrock-text-processors';

const mistral = new MistralTextProcessor.Builder()
  .withScope(this)
  .withIdentifier('MistralTextProcessor')
  .withCacheStorage(cache)
  .withSource(source)
  .withRegion('eu-central-1') // 👈 Alternate region
  .withModel(MistralTextModel.MISTRAL_LARGE)
  .withPrompt(prompt)
  .build();

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⚙️ Model Parameters

You can forward specific parameters to the text models using the .withModelParameters method. Below is a description of the supported parameters.

Parameter	Description	Min	Max	Default
temperature	Controls the randomness of the generated text.	0	1	N/A
max_tokens	The maximum number of tokens to generate.	1	4096	4096
top_p	Controls the diversity of text that the model generates by setting the percentage of most-likely candidates that the model considers for the next token.	0	1	N/A
top_k	Controls the number of most-likely candidates that the model considers for the next token.	1	200	N/A

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🏗️ Architecture

This middleware is based on a Lambda compute running on an ARM64 architecture, and integrate with Amazon Bedrock to generate text based on the given prompt and input documents.

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🏷️ Properties

Supported Inputs

Mime Type	Description
text/plain	UTF-8 text documents.
text/markdown	Markdown documents.
text/csv	CSV documents.
text/html	HTML documents.
application/x-subrip	SubRip subtitles.
text/vtt	Web Video Text Tracks (WebVTT) subtitles.
application/json	JSON documents.
application/xml	XML documents.

Supported Outputs

Mime Type	Description
text/plain	UTF-8 text documents.

Supported Compute Types

Type	Description
CPU	This middleware only supports CPU compute.