najiaboo/superknowa

This repository is intended for IBM Ecosystem partners. It contains pluggable components designed to tackle various Generative AI use cases using Large Language Models (LLMs).

SuperKnowa

Fast Framework to build RAG (Retriever Augmented Generation) Pipelines at Scale - powered by watsonx

Welcome to the SuperKnowa GitHub repository! Here, you will find a diverse collection of pluggable components designed to tackle various Generative AI use cases using Large Language Models (LLMs). Think of these components as building blocks, much like Lego pieces, that you can assemble to address a wide range of challenges in the realm of AI-driven text generation.

SuperKnowa is a powerful framework developed using watsonx (watch the video on watsonx.ai here) that harnesses the capabilities of Large Language Models (LLMs) to offer a range of advanced Generative AI use cases. This repository introduces you to the various use cases covered by SuperKnowa.

Live Q&A Application

Try SuperKnowa live (requires an IBM ID): https://superknowa.tsglwatson.buildlab.cloud/.

Getting Started

You can get started by updating the config.yaml file and run the LLMQnA.py script for quickly configuring your RAG pipeline:

retriever:
  indexName: superknowa
  query: What is IBM Cloud?
  ....

reranker:
  query: What is IBM Data and Analytics Reference Architecture?
  ...

LLMQnA:
  question: What is IBM Data and Analytics Reference Architecture?
  ...

To explore SuperKnowa's features and capabilities, refer to the blog series, code examples, and resources provided in this repository.

For detailed instructions and examples, navigate to each component's directory. Unleash the potential of Large Language Models in your projects using SuperKnowa's Generative AI Lego Components!

Let's unlock the potential of Generative AI with SuperKnowa and shape the future of AI-powered knowledge processing!

Repository Contents

1. Indexing Documents

   1. Elastic Search

   2. Solr

   3. Watson Discovery

2. Neural Retriever

   1. Elastic Search

   2. Solr

3. Re-Ranker

4. In-context learning using LLM

5. LLM Evaluations

   1. LLM Model Evaluation

   2. MLFLOW Integration

6. Fine-Tuning

   1. Instruct DB

   2. Fine Tuning Falcon 7B using QLORA

   3. Fine Tuning LLAMA2 7B using QLORA

7. RLHF Model

8. Deploy & Infer

   1. Backend

   2. Deployment

9. AI Alignment Tool

   1. Capture Human Feedback
   2. Admin Dashboard for AI Alignment Results

10. Enterprise LLM Use Cases

AI Alignment Tool

Measure the alignment of AI models on the metrics of helpfulness, harmfulness and accuracy by capturing human inputs.

Results Dashboard

Build your various online & offline experiments for evaluations and compare the AI alignment results using an interactive dashboard.

LLM Eval Toolkit

Eval_Package

The Eval_Package is a tool designed to evaluate the performance of the LLM (Language Model) on a dataset containing questions, context, and ideal answers. It allows you to run evaluations on various datasets and assess how well the Model generates the answer.

Features

• Evaluate LLM Model on custom datasets: Use the Eval_Package to assess the performance of your Model on datasets of your choice.
• Measure model accuracy: The package provides metrics to gauge the accuracy of the model-generated answers against the ideal answers.

MLflow_Package

The MLflow_Package is a comprehensive toolkit designed to integrate the results from the Eval_Package and efficiently track and manage experiments. It also enables you to create a leaderboard for evaluation comparisons and visualize metrics through a dashboard.

Features

• Experiment tracking: Utilize MLflow to keep a record of experiments, including parameters, metrics, and model artifacts generated during evaluations.
• Leaderboard creation: The package allows you to create a leaderboard, making it easy to compare the performance of different Models across multiple datasets.
• Metric visualization: Generate insightful charts and graphs through the dashboard, allowing you to visualize and analyze evaluation metrics easily.

Enterprise LLM Use Cases

Below is a list of Generative AI use cases built using the SuperKnowa framework.

1. Conversational Q&A on Private Knowledge Base

Engage in natural language conversations with SuperKnowa's conversational Question & Answer (Q&A) system. Ask questions based on the private enterprise knowledge base, and receive detailed, context-aware responses.

2. Ask Your PDF/Documents

Leverage SuperKnowa's "Ask your documents" feature to unlock the potential of your PDFs and text documents. SuperKnowa can help you extract relevant information, answer specific questions, and assist in information retrieval.

3. Summarisation

Effortlessly generate coherent and informative summaries with SuperKnowa's summarization feature across large text corpus using FlanT5 and UL2. Extract the main points and essential details from articles, reports, and other texts, allowing for efficient content comprehension.

4. Key Points from your PDF

SuperKnowa's abstractive summarisation feature goes beyond simple extraction using FlanUL2, and LLAMA2. It can analyze lengthy PDF documents and generate concise abstractive summaries, capturing the essence of the content. Additionally, SuperKnowa identifies key points, making it easier to comprehend and communicate complex information.

5. Text to SQL

Experience the power of SuperKnowa's Text-to-SQL capability, which transforms natural language queries into structured SQL queries. Interact with databases using plain language, eliminating the need for expertise in SQL.

Build Lab Team

Owner: Kunal Sawarkar, Chief Data Scientist

Builders

• Shivam Solanki, Senior Advisory Data Scientist
• Michael Spriggs, Principal Architect
• Kevin Huang, Sr. ML-Ops Engineer
• Abhilasha Mangal, Senior Data Scientist
• Sahil Desai, Data Scientist
• Amit Khandelwal- Senior Data Scientist
• Himadri Talukder - Senior Software Engineer
• Tyler Berns- Data Scientist

Disclaimer - Please note that this content is made available to foster Embedded AI technology adoption. The content may include systems & methods pending patent with USPTO and protected under US Patent Laws. Copyright - 2023 IBM Corporation. In case of any questions or support needed, please reach out at [email protected]