Supported Examples

This document introduces the supported examples of GenAIExamples. The supported Vector Database, LLM models, serving frameworks and hardwares are listed as below.

ChatQnA

ChatQnA is an example of chatbot for question and answering through retrieval augmented generation (RAG).

Framework LLM Embedding Vector Database Serving HW Description
LangChain/LlamaIndex NeuralChat-7B BGE-Base Redis TGI TEI Xeon/Gaudi2/GPU Chatbot
NeuralChat-7B BGE-Base Chroma TGI TEI Xeon/Gaudi2 Chatbot
Mistral-7B BGE-Base Redis TGI TEI Xeon/Gaudi2 Chatbot
Mistral-7B BGE-Base Qdrant TGI TEI Xeon/Gaudi2 Chatbot
Qwen2-7B BGE-Base Redis TGI Xeon/Gaudi2 Chatbot

CodeGen

CodeGen is an example of copilot designed for code generation in Visual Studio Code.

Framework

LLM

Serving

HW

Description

LangChain/LlamaIndex

meta-llama/CodeLlama-7b-hf

TGI

Xeon/Gaudi2

Copilot

CodeTrans

CodeTrans is an example of chatbot for converting code written in one programming language to another programming language while maintaining the same functionality.

Framework

LLM

Serving

HW

Description

LangChain/LlamaIndex

HuggingFaceH4/mistral-7b-grok

TGI

Xeon/Gaudi2

Code Translation

DocSum

DocSum is an example of chatbot for summarizing the content of documents or reports.

Framework

LLM

Serving

HW

Description

LangChain/LlamaIndex

NeuralChat-7B

TGI

Xeon/Gaudi2

Chatbot

LangChain/LlamaIndex

Mistral-7B

TGI

Xeon/Gaudi2

Chatbot

Language Translation

Language Translation is an example of chatbot for converting a source-language text to an equivalent target-language text.

Framework

LLM

Serving

HW

Description

LangChain/LlamaIndex

haoranxu/ALMA-13B

TGI

Xeon/Gaudi2

Language Translation

SearchQnA

SearchQnA is an example of chatbot for using search engine to enhance QA quality.

Framework

LLM

Serving

HW

Description

LangChain/LlamaIndex

NeuralChat-7B

TGI

Xeon/Gaudi2

Chatbot

LangChain/LlamaIndex

Mistral-7B

TGI

Xeon/Gaudi2

Chatbot

VisualQnA

VisualQnA is an example of chatbot for question and answering based on the images.

LVM

HW

Description

llava-hf/llava-v1.6-mistral-7b-hf

Gaudi2

Chatbot

VideoQnA

VideoQnA is an example of chatbot for question and answering based on the videos. It retrieves video based on provided user prompt. It uses only the video embeddings to perform vector similarity search in Intel’s VDMS vector database and performs all operations on Intel Xeon CPU. The pipeline supports long form videos and time-based search.

By default, the embedding and LVM models are set to a default value as listed below:

Service

Model

HW

Description

Embedding

openai/clip-vit-base-patch32

Xeon

Video embeddings service

LVM

DAMO-NLP-SG/Video-LLaMA

Xeon

LVM service

RerankFinetuning

Rerank model finetuning example is for training rerank model on a dataset for improving its capability on specific field.

By default, the base model is set to a default value as listed below:

Service

Base Model

HW

Description

Rerank Finetuning

BAAI/bge-reranker-large

Xeon

Rerank model finetuning service

InstructionTuning

The Instruction Tuning example is designed to further train large language models (LLMs) on a dataset consisting of (instruction, output) pairs using supervised learning. This process bridges the gap between the LLM’s original objective of next-word prediction and the user’s objective of having the model follow human instructions accurately. By leveraging Instruction Tuning, this example enhances the LLM’s ability to better understand and execute specific tasks, improving the model’s alignment with user instructions and its overall performance.

By default, the base model is set to a default value as listed below:

Service

Base Model

HW

Description

InstructionTuning

meta-llama/Llama-2-7b-chat-hf

Xeon/Gaudi

LLM model Instruction Tuning service

DocIndexRetriever

The DocRetriever example demonstrates how to match user queries with free-text records using various retrieval methods. It plays a key role in Retrieval-Augmented Generation (RAG) systems by dynamically fetching relevant information from external sources, ensuring responses are factual and up-to-date. Powered by vector databases, DocRetriever enables efficient, semantic retrieval by storing data as vectors and quickly identifying the most relevant documents based on similarity.

Framework

Embedding

Vector Database

Serving

HW

Description

LangChain/LlamaIndex

BGE-Base

Redis

TEI

Xeon/Gaudi2

Document Retrieval service

AgentQnA

The AgentQnA example demonstrates a hierarchical, multi-agent system designed for question-answering tasks. A supervisor agent interacts directly with the user, delegating tasks to a worker agent and utilizing various tools to gather information and generate answers. The worker agent primarily uses a retrieval tool to respond to the supervisor’s queries. Additionally, the supervisor can access other tools, such as APIs to query knowledge graphs, SQL databases, or external knowledge bases, to enhance the accuracy and relevance of its responses.

Worker agent uses open-source websearch tool (duckduckgo), agents use OpenAI GPT-4o-mini as llm backend.

NOTE: This example is in active development. The code structure of these use cases are subject to change.

AudioQnA

The AudioQnA example demonstrates the integration of Generative AI (GenAI) models for performing question-answering (QnA) on audio files, with the added functionality of Text-to-Speech (TTS) for generating spoken responses. The example showcases how to convert audio input to text using Automatic Speech Recognition (ASR), generate answers to user queries using a language model, and then convert those answers back to speech using Text-to-Speech (TTS).

ASR TTS LLM HW Description
openai/whisper-small microsoft/SpeechT5 TGI Xeon/Gaudi2 Talkingbot service

FaqGen

FAQ Generation Application leverages the power of large language models (LLMs) to revolutionize the way you interact with and comprehend complex textual data. By harnessing cutting-edge natural language processing techniques, our application can automatically generate comprehensive and natural-sounding frequently asked questions (FAQs) from your documents, legal texts, customer queries, and other sources. In this example use case, we utilize LangChain to implement FAQ Generation and facilitate LLM inference using Text Generation Inference on Intel Xeon and Gaudi2 processors.

Framework

LLM

Serving

HW

Description

LangChain/LlamaIndex

Meta-Llama-3-8B-Instruct

TGI

Xeon/Gaudi2

Chatbot

MultimodalQnA

MultimodalQnA addresses your questions by dynamically fetching the most pertinent multimodal information (frames, transcripts, and/or captions) from your collection of videos.

ProductivitySuite

Productivity Suite streamlines your workflow to boost productivity. It leverages the power of OPEA microservices to deliver a comprehensive suite of features tailored to meet the diverse needs of modern enterprises.