Language Detection microservice¶

The Language Detection microservice can be run in 2 modes:

Pipeline: This mode adds multilingual support to ChatQnA pipelines. The microservice detects the language of the user’s query as well as the LLM generated response to set up a prompt for translation.
Standalone: This mode supports standalone translation. The microservice detects the language of the provided text. It then sets up a prompt for translating the provided text from the source language (detected language) to the provided target language.

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

Getting started¶

🚀1. Start Language Detection Microservice with Python (Option 1)¶

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

1.1. Install Requirements¶

pip install -r requirements.txt

1.2. Start Microservice¶

python opea_language_detection_microservice.py

🚀2. Start Language Detection Microservice with Docker (Option 2)¶

2.1. Build the Docker Image:¶

Use the below docker build command to create the image:

cd ../../../
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.

2.2. Run the Docker Container:¶

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

3. Verify the Language Detection Microservice¶

3.1. Check Status¶

curl http://localhost:8069/v1/health_check \
  -X GET \
  -H 'Content-Type: application/json'

3.2. Sending a Request¶

3.2.1 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 "
}

3.2.2 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 "
}