This document outlines the deployment process for a Document Summarization application utilizing the GenAIComps microservice pipeline on Intel Gaudi server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as llm
. We will publish the Docker images to Docker Hub soon, which will simplify the deployment process for this service.
First of all, you need to build Docker Images locally and install the python package of it.
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
The Audio to text Service is another service for converting audio to text. Follow these steps to build and run the service:
docker build -t opea/dataprep-audio2text:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/multimedia2text/audio2text/Dockerfile .
The Video to Audio Service extracts audio from video files. Follow these steps to build and run the service:
docker build -t opea/dataprep-video2audio:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/multimedia2text/video2audio/Dockerfile .
The Multimedia to Text Service transforms multimedia data to text data. Follow these steps to build and run the service:
docker build -t opea/dataprep-multimedia2text:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/multimedia2text/Dockerfile .
To construct the Mega Service, we utilize the GenAIComps microservice pipeline within the docsum.py
Python script. Build the MegaService Docker image via below command:
git clone https://github.com/opea-project/GenAIExamples
cd GenAIExamples/DocSum/
docker build -t opea/docsum:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
Several UI options are provided. If you need to work with multimedia documents, .doc, or .pdf files, suggested to use Gradio UI.
Build the Gradio UI frontend Docker image using the following command:
cd GenAIExamples/DocSum/ui
docker build -t opea/docsum-gradio-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f docker/Dockerfile.gradio .
Build the frontend Docker image via below command:
cd GenAIExamples/DocSum/ui
docker build -t opea/docsum-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f docker/Dockerfile .
Build the frontend Docker image via below command:
cd GenAIExamples/DocSum/ui
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8888/v1/docsum"
docker build -t opea/docsum-react-ui:latest --build-arg BACKEND_SERVICE_ENDPOINT=$BACKEND_SERVICE_ENDPOINT -f ./docker/Dockerfile.react .
docker build -t opea/docsum-react-ui:latest --build-arg BACKEND_SERVICE_ENDPOINT=$BACKEND_SERVICE_ENDPOINT --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react .
Default model is "Intel/neural-chat-7b-v3-3". Change "LLM_MODEL_ID" environment variable in commands below if you want to use another model.
export LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
When using gated models, you also need to provide HuggingFace token to "HUGGINGFACEHUB_API_TOKEN" environment variable.
To set up environment variables for deploying Document Summarization services, follow these steps:
-
Set the required environment variables:
# Example: host_ip="192.168.1.1" export host_ip="External_Public_IP" # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1" export no_proxy="Your_No_Proxy" export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
-
If you are in a proxy environment, also set the proxy-related environment variables:
export http_proxy="Your_HTTP_Proxy" export https_proxy="Your_HTTPs_Proxy"
-
Set up other environment variables:
source GenAIExamples/DocSum/docker_compose/set_env.sh
cd GenAIExamples/DocSum/docker_compose/intel/hpu/gaudi
docker compose -f compose.yaml up -d
You will have the following Docker Images:
opea/docsum-ui:latest
opea/docsum:latest
opea/llm-docsum-tgi:latest
opea/whisper:latest
opea/dataprep-audio2text:latest
opea/dataprep-multimedia2text:latest
opea/dataprep-video2audio:latest
-
TGI Service
curl http://${host_ip}:8008/generate \ -X POST \ -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \ -H 'Content-Type: application/json'
-
LLM Microservice
curl http://${host_ip}:9000/v1/chat/docsum \ -X POST \ -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \ -H 'Content-Type: application/json'
-
Whisper Microservice
curl http://${host_ip}:7066/v1/asr \ -X POST \ -d '{"audio":"UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}' \ -H 'Content-Type: application/json'
Expected output:
{"asr_result":"you"}
-
Audio2Text Microservice
curl http://${host_ip}:9199/v1/audio/transcriptions \ -X POST \ -d '{"byte_str":"UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}' \ -H 'Content-Type: application/json'
Expected output:
{"downstream_black_list":[],"id":"--> this will be different id number for each run <--","query":"you"}
-
Multimedia to text Microservice
curl http://${host_ip}:7079/v1/multimedia2text \ -X POST \ -d '{"audio":"UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}' \ -H 'Content-Type: application/json'
Expected output:
{"downstream_black_list":[],"id":"--> this will be different id number for each run <--","query":"you"}
-
MegaService
Text:
curl -X POST http://${host_ip}:8888/v1/docsum \ -H "Content-Type: application/json" \ -d '{"type": "text", "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' # Use English mode (default). curl http://${host_ip}:8888/v1/docsum \ -H "Content-Type: multipart/form-data" \ -F "type=text" \ -F "messages=Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5." \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true" # Use Chinese mode. curl http://${host_ip}:8888/v1/docsum \ -H "Content-Type: multipart/form-data" \ -F "type=text" \ -F "messages=2024年9月26日,北京——今日,英特尔正式发布英特尔® 至强® 6性能核处理器(代号Granite Rapids),为AI、数据分析、科学计算等计算密集型业务提供卓越性能。" \ -F "max_tokens=32" \ -F "language=zh" \ -F "stream=true" # Upload file curl http://${host_ip}:8888/v1/docsum \ -H "Content-Type: multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
Audio and Video file uploads are not supported in docsum with curl request, please use the Gradio-UI.
Audio:
curl -X POST http://${host_ip}:8888/v1/docsum \ -H "Content-Type: application/json" \ -d '{"type": "audio", "messages": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}' curl http://${host_ip}:8888/v1/docsum \ -H "Content-Type: multipart/form-data" \ -F "type=audio" \ -F "messages=UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA" \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
Video:
curl -X POST http://${host_ip}:8888/v1/docsum \ -H "Content-Type: application/json" \ -d '{"type": "video", "messages": "convert your video to base64 data type"}' curl http://${host_ip}:8888/v1/docsum \ -H "Content-Type: multipart/form-data" \ -F "type=video" \ -F "messages=convert your video to base64 data type" \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
More detailed tests can be found here
cd GenAIExamples/DocSum/test
Several UI options are provided. If you need to work with multimedia documents, .doc, or .pdf files, suggested to use Gradio UI.
Open this URL http://{host_ip}:5173
in your browser to access the Gradio based frontend.
Open this URL http://{host_ip}:5173
in your browser to access the Svelte based frontend.
Here is an example for summarizing a article.
To access the React-based frontend, modify the UI service in the compose.yaml
file. Replace docsum-xeon-ui-server
service with the docsum-xeon-react-ui-server
service as per the config below:
docsum-gaudi-react-ui-server:
image: ${REGISTRY:-opea}/docsum-react-ui:${TAG:-latest}
container_name: docsum-gaudi-react-ui-server
depends_on:
- docsum-gaudi-backend-server
ports:
- "5174:80"
environment:
- no_proxy=${no_proxy}
- https_proxy=${https_proxy}
- http_proxy=${http_proxy}
- DOC_BASE_URL=${BACKEND_SERVICE_ENDPOINT}
Open this URL http://{host_ip}:5175
in your browser to access the frontend.