Deploying language-detection Service

This document provides a comprehensive guide to deploying the language-detection microservice pipeline on Intel platforms.

This guide covers two deployment methods:

🚀 1. Quick Start with Docker Compose

This method uses Docker Compose to start all necessary services with a single command. It is the fastest and easiest way to get the service running.

1.1. Access the Code

Clone the repository and navigate to the deployment directory:

git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps/comps/language_detection/deployment/docker_compose

1.2. Deploy the Service

Choose the command corresponding to your target platform.

docker compose -f compose.yaml up language-detection -d

1.3. Validate the Service

1.3.1 Pipeline Mode

The input request consists of the answer that has to be translated and a prompt containing the user’s query.

Example Input

curl -X POST -H "Content-Type: application/json" -d @- http://localhost:8069/v1/language_detection <<JSON_DATA
{
  "text": "Hi. I am doing fine.",
  "prompt": "### You are a helpful, respectful, and honest assistant to help the user with questions. \
Please refer to the search results obtained from the local knowledge base. \
But be careful to not incorporate information that you think is not relevant to the question. \
If you don't know the answer to a question, please don't share false information. \
### Search results:   \n
### Question: 你好。你好吗？ \n
### Answer:"
}
JSON_DATA

Example Output

The output contains the answer, prompt template, source language and target language.

{
  "id": "1b16e065a1fcbdb4d999fd3d09a619cb",
  "data": { "text": "Hi. I am doing fine.", "source_lang": "English", "target_lang": "Chinese" },
  "prompt_template": "\n Translate this from {source_lang} to {target_lang}:\n   {source_lang}:\n   {text}\n\n  {target_lang}: \n "
}

1.3.2 Standalone Mode

The input request consists of the text that has to be translated and the target language.

Example Input

curl -X POST -H "Content-Type: application/json" -d @- http://localhost:8069/v1/language_detection <<JSON_DATA
{
  "text": "Hi. I am doing fine.",
  "target_language": "Chinese"
}
JSON_DATA

Example Output

The output contains the original text, prompt template, source language and target language.

{
  "id": "1b16e065a1fcbdb4d999fd3d09a619cb",
  "data": { "text": "Hi. I am doing fine.", "source_lang": "English", "target_lang": "Chinese" },
  "prompt_template": "\n Translate this from {source_lang} to {target_lang}:\n   {source_lang}:\n   {text}\n\n  {target_lang}: \n "
}

1.4. Clean Up the Deployment

To stop and remove the containers, run the following command from the comps/language_detection/deployment/docker_compose directory:

docker compose down

🚀 2. Manual Step-by-Step Deployment (Advanced)

This section provides detailed instructions for building the Docker images and running each microservice container individually.

2.1. Clone the Repository

If you haven’t already, clone the repository and navigate to the root directory:

git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps

2.2. Build the Docker Images

Use the below docker build command to create the image:

docker build -t opea/language-detection:latest -f comps/language_detection/src/Dockerfile .

Please note that the building process may take a while to complete.

2.3. Configuration Options

The configuration for the Language Detection Microservice can be adjusted by exporting environment variable.

Environment Variable

Description

LANG_DETECT_STANDALONE

Set this to True for Standalone mode

2.4. Run the Microservice Containers

docker run -d --name="language-detection-microservice" \
  -p 8069:8069\
  --net=host \
  --ipc=host \
  opea/language-detection:latest

2.5. Validate the Service

Reference to（validate the service）

2.6. Clean Up the Deployment

To stop and remove the containers you started manually, use the docker stop and docker rm commands.

docker stop language-detection
docker rm language-detection

🚀 3. Start Language Detection Microservice with Python

To start the Language Detection microservice, you need to install python packages first.

3.1. Install Requirements

pip install -r requirements.txt

3.2. Start Microservice

python opea_language_detection_microservice.py