IntelliNode v1.3.7

New Changes 🌟

• Support Llama Code and Python Models: now you can utilize Llama code and python-based models in the intelliNode module.
• Large Data Memory Semantic Search: introducing enhanced semantic search with large data using pagination.

Code Examples 💻

• Install the module: npm i intellinode
• The llama coder sample: test_llama_chatbot.
• The large data semantic search using SemanticSearchPaging: test_semantic_search_pagination.

IntelliNode v1.3.0

v1.3.0

New Changes 🌟

• Introduced the Large Learning Model (LLM) Evaluator: a quick comparison among multiple models' results based on your target answers. This feature simplifies the assessing of LLMs such as LaMMA V2 and GPT4, providing valuable insights into model performance.
• Prompt Engineering Feature: an efficient way to automate the process of creating new prompts.

Code Examples 💻

LLM Evaluator

const { LLMEvaluation } = require('intellinode');

// create the LLMEvaluation with the desired embedding for unified comparison.
const llmEvaluation = new LLMEvaluation(openaiKey, 'openai');

const results = await llmEvaluation.compareModels(inputString, targetAnswers, providerSets)

Check the model evaluation documentation.

Prompt Engineering

Import the Prompt class:

const Prompt = require('intellinode');

Generate a new prompt using an existing model:

const promptTemp = await Prompt.fromChatGPT("generate a fantasy image with ninja jumping across buildings", openaiApiKey);

Automatically fill and format a prompt with dynamic data:

const promptTemp = await Prompt.fromText("information retrieval about ${reference} text with ${query} user input");
const inputString = promptTemp.format({reference: '<reference_text>', query: '<user_query>'})

IntelliNode v1.2.3 🦙

v1.2.3

Update Chatbot with Llama V2 Support 🌟

IntelliNode library now supports Llama 🦙 v2 chat models, enhancing the capabilities of the chatbot function even further.

The Llama model available with two options:

1. Replica API Integration: a seamless and fast implementation requiring only an API key. Users can integrate Llama v2 into their existing apps with minimal configuration using intelli-node module.

2. AWS SageMaker Integration: a more advanced and flexible integration adaptability for enterprise-level projects.

Code Examples 💻

The documentation on how to switch the chatbot between ChatGPT and LLama can be found in the IntelliNode chatbot wiki.

IntelliNode v1.1.0

v1.1.0

New Changes 🌟

• Upgraded the chatbot to support functions call which empowers the automation.
• Added an automation sample for AWS S3.
• Openai updated to support the organization as an optional parameter.

Code Examples 💻

Automation

import:

const { Chatbot, ChatGPTInput, ChatGPTMessage, FunctionModelInput } = require('intellinode');

call:

// define the functions details
const functions_desc = [
  new FunctionModelInput('list_buckets', 'List all available S3 buckets')
];

// prepare the input
const systemMsg = "Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous.";
const input = new ChatGPTInput(systemMsg, {model: "gpt-3.5-turbo-0613"});
input.addMessage(new ChatGPTMessage("List my s3 buckets.", "user"));

// call the chatbot
const bot = new Chatbot(apiKey, SupportedChatModels.OPENAI);
const responses = await bot.chat(input, functions_desc.map(f => f.getFunctionModelInput()));

parse the output:

// if the response type is an object, the model response a function name and arguments to call.
// otherwise, the model returned a text response for clarification.
responses.forEach((response) => {
  if (typeof response === "object") {
    console.log("- Function call: ", JSON.stringify(response.function_call, null, 2));
  } else {
    console.log("- " + response);
  }
});

Check the full automation example under the samples directory.

IntelliNode v1.0.0

v1.0.0

🎉 The first stable release of IntelliNode, after testing with hundreds of users via the online playground show.intellinode.ai

New Changes 🌟

• Update the Gen function to support the new 16k context version of gpt-3.5-turbo.
• Security enhancements.
• Add test runner to validate the code before publishing to npm repository.

IntelliNode v0.0.31

v0.0.31

New Changes 🌟

• Support Azure OpenAI backend.
• Add an optional openai proxy URL to bypass regional restrictions (request by the users).
• Improve the Gen function by accepting backend engine to switch between models in one line code.
• Enhance the csv to html charts function.

Code Examples 💻

Openai Azure Service

1. Set Azure backend as proxy

const { ProxyHelper } = require('intellinode');
ProxyHelper.getInstance().setAzureOpenai(resourceName);

2. Call any openai function

const { Gen } = require('intellinode');
// generate blog post
result = await Gen.get_blog_post(prompt, openaiApiKey, provider='openai');

Gen content with multi backend example

• Generate marketing content using openai

marketingDesc = await Gen.get_marketing_desc(prompt, openaiApiKey);

• Or using cohere

marketingDesc = await Gen.get_marketing_desc(prompt, cohereApiKey, provider='cohere');

Gen image with multi backend example

• Generate prompt description using chatGPT and the image using stable diffusion:

image = await Gen.generate_image_from_desc(prompt, openaiApiKey, stabilityApiKey, true);

• or generate the image using DALL·E:

image = await Gen.generate_image_from_desc(prompt, openaiApiKey, openaiApiKey, true, provider='openai');

IntelliNode v0.0.21

v0.0.21

New Changes 🌟

• Introducing a new Gen function: Generate charts directly from CSV data.
• Enhanced the HTML generation: we have further enhanced the Gen function to generate HTML pages based on user prompts.

Code Examples 💻

• import:

const { Gen } = require('intellinode');

Generate charts

const csv_str_data = '<your csv as string>'
const topic = "<the csv topic>";

const htmlCode = await Gen.generate_dashboard(csv_str_data, topic, openaiKey, num_graphs=2);

Output example:

Generate html files

text = 'a registration page with flat modern theme.'
await Gen.save_html_page(text, folder='tempFolder', file_name='register_page', openaiKey='<key>');

Output example:

Note that Gen function in beta, the output generated may not always meet your expectations.

For more code examples, check the sample.

IntelliNode v0.0.19

v0.0.19

New Features 🌟

• Add a new Gen function to generate and save html pages from users prompt.

Code Examples 💻

• import:

const { Gen } = require('intellinode');

• execute the one line command to generate html:

text = 'a registration page with flat modern theme.'
await Gen.save_html_page(text, folder='tempFolder', file_name='register_page', openaiKey='<key>');

Output example:

• other existing functions

// one line to generate blog post
const blogPost = await Gen.get_blog_post(prompt, openaiApiKey);

For more code examples, check the sample.

IntelliNode v0.0.18

v0.0.18

New Features 🌟

• Introduce the Gen function, an efficient addition to the IntelliNode to generate text content, images, and audio in the shortest way possible.

Code Examples 💻

1. import:

const { Gen } = require('intellinode');

2. execute the one line command:

// one line to generate marketing description 
const marketingDesc = await Gen.get_marketing_desc('gaming chair', openaiApiKey);

// or one line to generate blog post
const blogPost = await Gen.get_blog_post(prompt, openaiApiKey);

// or generate images from production description
const image = await Gen.generate_image_from_desc(prompt, openaiApiKey, stabilityApiKey, is_base64=true);

// or one line to generate speech synthesis
const audioContent = await Gen.generate_speech_synthesis(text, apiKey);

For more details, check the sample.

IntelliNode v0.0.15

v0.0.15

New Features 🌟

• Add an image to image model to the stable diffusion wrapper (used for image styling or edits).

Changes 🔧

• Update the remote image controller to use stable diffusion XL model.

Code Examples 💻

Remote image model

const { RemoteImageModel, SupportedImageModels } =  require('intellinode');
const imgModel = new RemoteImageModel('stability-ai-key', SupportedImageModels.STABILITY);
result_base64 = await imgModel.generateImages(new ImageModelInput({ 
                                               prompt: imageText,
                                               numberOfImages: 3 }));

Output after XL model change:

Output before the change: