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DocSum example doc review update (#203)

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Signed-off-by: ying.hu <ying.hu@intel.com>
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Ying Hu
2024-05-29 11:19:02 +08:00
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DocSum/README.md
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# Document Summarization Application
In a world where data, information, and legal complexities is prevalent, the volume of legal documents is growing rapidly. Law firms, legal professionals, and businesses are dealing with an ever-increasing number of legal texts, including contracts, court rulings, statutes, and regulations.
These documents contain important insights, but understanding them can be overwhelming. This is where the demand for legal document summarization comes in.
In a world where data, information, and legal complexities are prevalent, the volume of legal documents is growing rapidly. Law firms, legal professionals, and businesses are dealing with an ever-increasing number of legal texts, including contracts, court rulings, statutes, and regulations. These documents contain important insights, but understanding them can be overwhelming. This is where the demand for legal document summarization comes in.
Large Language Models (LLMs) have revolutionized the way we interact with text, LLMs can be used to create summaries of news articles, research papers, technical documents, and other types of text. Suppose you have a set of documents (PDFs, Notion pages, customer questions, etc.) and you want to summarize the content. In this example use case, we use LangChain to apply some summarization strategies and run LLM inference using Text Generation Inference on Intel Xeon and Gaudi2.
Large Language Models (LLMs) have revolutionized the way we interact with text. These models can be used to create summaries of news articles, research papers, technical documents, and other types of text. Suppose you have a set of documents (PDFs, Notion pages, customer questions, etc.) and you want to summarize the content. In this example use case, we utilize LangChain to implement summarization strategies and facilitate LLM inference using Text Generation Inference on Intel Xeon and Gaudi2 processors.
The document summarization architecture shows below:
The architecture for document summarization will be illustrated/described below:
![Architecture](https://i.imgur.com/XT0YUhu.png)

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DocSum/docker-composer/gaudi/README.md
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# Build MegaService of Document Summarization on Gaudi
This document outlines the deployment process for a Document Summarization application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) 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, it will simplify the deployment process for this service.
This document outlines the deployment process for a Document Summarization application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) 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, which will simplify the deployment process for this service.
## 🚀 Build Docker Images
First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.
First of all, you need to build Docker Images locally. This step can be ignored once the Docker images are published to Docker hub.
```bash
git clone https://github.com/opea-project/GenAIComps.git
@@ -13,7 +13,7 @@ cd GenAIComps
### 1. Pull TGI Gaudi Image
As TGI Gaudi has been officially published as a Docker image, we simply need to pull it.
As TGI Gaudi has been officially published as a Docker image, we simply need to pull it:
```bash
docker pull ghcr.io/huggingface/tgi-gaudi:1.2.1
@@ -68,7 +68,7 @@ export LLM_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8888/v1/docsum"
```
Note: Please replace with `host_ip` with you external IP address, do not use localhost.
Note: Please replace with `host_ip` with your external IP address, do not use localhost.
### Start Microservice Docker Containers
@@ -106,9 +106,9 @@ curl http://${host_ip}:8888/v1/docsum -H "Content-Type: application/json" -d '{
}'
```
## Enable LangSmith for Monotoring Application (Optional)
## Enable LangSmith to Monitor an Application (Optional)
LangSmith offers tools to debug, evaluate, and monitor language models and intelligent agents. It can be used to assess benchmark data for each microservice. Before launching your services with `docker compose -f docker_compose.yaml up -d`, you need to enable LangSmith tracing by setting the `LANGCHAIN_TRACING_V2` environment variable to true and configuring your LangChain API key.
LangSmith offers a suite of tools to debug, evaluate, and monitor language models and intelligent agents. It can be used to assess benchmark data for each microservice. Before launching your services with `docker compose -f docker_compose.yaml up -d`, you need to enable LangSmith tracing by setting the `LANGCHAIN_TRACING_V2` environment variable to true and configuring your LangChain API key.
Here's how you can do it:

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DocSum/docker-composer/xeon/README.md
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# Build Mega Service of Document Summarization on Xeon
# Build Mega Service of Document Summarization on Intel Xeon Processor
This document outlines the deployment process for a Document Summarization application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon 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, it will simplify the deployment process for this service.
This document outlines the deployment process for a Document Summarization application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on an Intel Xeon 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.
## 🚀 Apply Xeon Server on AWS
## 🚀 Apply Intel Xeon Server on AWS
To apply a Xeon server on AWS, start by creating an AWS account if you don't have one already. Then, head to the [EC2 Console](https://console.aws.amazon.com/ec2/v2/home) to begin the process. Within the EC2 service, select the Amazon EC2 M7i or M7i-flex instance type to leverage the power of 4th Generation Intel Xeon Scalable processors. These instances are optimized for high-performance computing and demanding workloads.
To apply a Intel Xeon server on AWS, start by creating an AWS account if you don't have one already. Then, head to the [EC2 Console](https://console.aws.amazon.com/ec2/v2/home) to begin the process. Within the EC2 service, select the Amazon EC2 M7i or M7i-flex instance type to leverage the power of 4th Generation Intel Xeon Scalable processors. These instances are optimized for high-performance computing and demanding workloads.
For detailed information about these instance types, you can refer to this [link](https://aws.amazon.com/ec2/instance-types/m7i/). Once you've chosen the appropriate instance type, proceed with configuring your instance settings, including network configurations, security groups, and storage options.
@@ -29,7 +29,7 @@ Then run the command `docker images`, you will have the following four Docker Im
### 2. Build MegaService Docker Image
To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `docsum.py` Python script. Build MegaService Docker image via below command:
To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `docsum.py` Python script. Build the MegaService Docker image via below command:
```bash
git clone https://github.com/opea-project/GenAIExamples
@@ -39,7 +39,7 @@ docker build -t opea/docsum:latest --build-arg https_proxy=$https_proxy --build-
### 3. Build UI Docker Image
Build frontend Docker image via below command:
Build the frontend Docker image via below command:
```bash
cd GenAIExamples/DocSum/ui/
@@ -69,7 +69,7 @@ export LLM_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8888/v1/docsum"
```
Note: Please replace with `host_ip` with you external IP address, do not use localhost.
Note: Please replace with `host_ip` with your external IP address, do not use localhost.
### Start Microservice Docker Containers
@@ -109,7 +109,7 @@ curl http://${host_ip}:8888/v1/docsum -H "Content-Type: application/json" -d '{
Following the validation of all aforementioned microservices, we are now prepared to construct a mega-service.
## Enable LangSmith for Monotoring Application (Optional)
## Enable LangSmith to Monitor an Application (Optional)
LangSmith offers tools to debug, evaluate, and monitor language models and intelligent agents. It can be used to assess benchmark data for each microservice. Before launching your services with `docker compose -f docker_compose.yaml up -d`, you need to enable LangSmith tracing by setting the `LANGCHAIN_TRACING_V2` environment variable to true and configuring your LangChain API key.