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ChatQnA Application

Chatbots are the most widely adopted use case for leveraging the powerful chat and reasoning capabilities of large language models (LLMs). The retrieval augmented generation (RAG) architecture is quickly becoming the industry standard for chatbots development. It combines the benefits of a knowledge base (via a vector store) and generative models to reduce hallucinations, maintain up-to-date information, and leverage domain-specific knowledge.

RAG bridges the knowledge gap by dynamically fetching relevant information from external sources, ensuring that responses generated remain factual and current. The core of this architecture are vector databases, which are instrumental in enabling efficient and semantic retrieval of information. These databases store data as vectors, allowing RAG to swiftly access the most pertinent documents or data points based on semantic similarity.

ChatQnA architecture shows below:

architecture

ChatQnA is implemented on top of GenAIComps, the ChatQnA Flow Chart shows below:

Flow Chart

This ChatQnA use case performs RAG using LangChain, Redis VectorDB and Text Generation Inference on Intel Gaudi2 or Intel XEON Scalable Processors. The Intel Gaudi2 accelerator supports both training and inference for deep learning models in particular for LLMs. Please visit Habana AI products for more details.

Deploy ChatQnA Service

The ChatQnA service can be effortlessly deployed on either Intel Gaudi2 or Intel XEON Scalable Processors.

Currently we support two ways of deploying ChatQnA services with docker compose:

  1. Start services using the docker image on docker hub:
docker pull opea/chatqna:latest

Two type of UI are supported now, choose one you like and pull the referred docker image.

If you choose conversational UI, follow the instruction and modify the docker_compose.yaml.

docker pull opea/chatqna-ui:latest
# or
docker pull opea/chatqna-conversation-ui:latest
  1. Start services using the docker images built from source: Guide

Setup Environment Variable

To set up environment variables for deploying ChatQnA services, follow these steps:

  1. Set the required environment variables:
# Example: host_ip="192.168.1.1"
export host_ip="External_Public_IP"
# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
export no_proxy="Your_No_Proxy"
export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
  1. If you are in a proxy environment, also set the proxy-related environment variables:
export http_proxy="Your_HTTP_Proxy"
export https_proxy="Your_HTTPs_Proxy"
  1. Set up other environment variables:

Notice that you can only choose one command below to set up envs according to your hardware. Other that the port numbers may be set incorrectly.

# on Gaudi
source ./docker/gaudi/set_env.sh
# on Xeon
source ./docker/xeon/set_env.sh
# on Nvidia GPU
source ./docker/gpu/set_env.sh

Deploy ChatQnA on Gaudi

Please find corresponding docker_compose.yaml.

cd GenAIExamples/ChatQnA/docker/gaudi/
docker compose -f docker_compose.yaml up -d

Notice: Currently only the Habana Driver 1.16.x is supported for Gaudi.

Please refer to the Gaudi Guide to build docker images from source.

Deploy ChatQnA on Xeon

Please find corresponding docker_compose.yaml.

cd GenAIExamples/ChatQnA/docker/xeon/
docker compose -f docker_compose.yaml up -d

Refer to the Xeon Guide for more instructions on building docker images from source.

Deploy ChatQnA on NVIDIA GPU

cd GenAIExamples/ChatQnA/docker/gpu/
docker compose -f docker_compose.yaml up -d

Refer to the NVIDIA GPU Guide for more instructions on building docker images from source.

Deploy ChatQnA into Kubernetes on Xeon & Gaudi with GMC

Refer to the Kubernetes Guide for instructions on deploying ChatQnA into Kubernetes on Xeon & Gaudi with GMC.

Deploy ChatQnA into Kubernetes on Xeon & Gaudi without GMC

Refer to the Kubernetes Guide for instructions on deploying ChatQnA into Kubernetes on Xeon & Gaudi without GMC.

Deploy ChatQnA into Kubernetes using Helm Chart

Install Helm (version >= 3.15) first. Please refer to the Helm Installation Guide for more information.

Refer to the ChatQnA helm chart for instructions on deploying ChatQnA into Kubernetes on Xeon & Gaudi.

Deploy ChatQnA on AI PC

Refer to the AI PC Guide for instructions on deploying ChatQnA on AI PC.

Consume ChatQnA Service

Two ways of consuming ChatQnA Service:

  1. Use cURL command on terminal
curl http://${host_ip}:8888/v1/chatqna \
    -H "Content-Type: application/json" \
    -d '{
        "messages": "What is the revenue of Nike in 2023?"
    }'
  1. Access via frontend

To access the frontend, open the following URL in your browser: http://{host_ip}:5173

By default, the UI runs on port 5173 internally.

If you choose conversational UI, use this URL: http://{host_ip}:5174

Troubleshooting

  1. If you get errors like "Access Denied", please validate micro service first. A simple example:
http_proxy="" curl ${host_ip}:6006/embed -X POST  -d '{"inputs":"What is Deep Learning?"}' -H 'Content-Type: application/json'

  1. (Docker only) If all microservices work well, please check the port ${host_ip}:8888, the port may be allocated by other users, you can modify the docker_compose.yaml.

  2. (Docker only) If you get errors like "The container name is in use", please change container name in docker_compose.yaml.