🛢🔗 Text-to-SQL Microservice with Langchain¶

This README provides set-up instructions and comprehensive details regarding the Text-to-SQL microservices via LangChain. In this configuration, we will employ PostgresDB as our example database to showcase this microservice.

🚀 Start Microservice with Python（Option 1）¶

Install Requirements¶

pip install -r requirements.txt

Start PostgresDB Service¶

We will use Chinook sample database as a default to test the Text-to-SQL microservice. Chinook database is a sample database ideal for demos and testing ORM tools targeting single and multiple database servers.

export POSTGRES_USER=postgres
export POSTGRES_PASSWORD=testpwd
export POSTGRES_DB=chinook

cd comps/texttosql/langchain

docker run --name postgres-db --ipc=host -e POSTGRES_USER=${POSTGRES_USER} -e POSTGRES_HOST_AUTH_METHOD=trust -e POSTGRES_DB=${POSTGRES_DB} -e POSTGRES_PASSWORD=${POSTGRES_PASSWORD} -p 5442:5432 -d -v ./chinook.sql:/docker-entrypoint-initdb.d/chinook.sql postgres:latest

Start TGI Service¶

export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export LLM_MODEL_ID="mistralai/Mistral-7B-Instruct-v0.3"
export TGI_PORT=8008

docker run -d --name="texttosql-tgi-endpoint" --ipc=host -p $TGI_PORT:80 -v ./data:/data --shm-size 1g -e HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e model=${LLM_MODEL_ID} ghcr.io/huggingface/text-generation-inference:2.1.0 --model-id $LLM_MODEL_ID

Verify the TGI Service¶

export your_ip=$(hostname -I | awk '{print $1}')
curl http://${your_ip}:${TGI_PORT}/generate \
  -X POST \
  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
  -H 'Content-Type: application/json'

Setup Environment Variables¶

export TGI_LLM_ENDPOINT="http://${your_ip}:${TGI_PORT}"

Start Text-to-SQL Microservice with Python Script¶

Start Text-to-SQL microservice with below command.

python3 main.py

🚀 Start Microservice with Docker (Option 2)¶

Start PostGreSQL Database Service¶

Please refer to section Start PostgresDB Service

Start TGI Service¶

Please refer to section Start TGI Service

Setup Environment Variables¶

export TGI_LLM_ENDPOINT="http://${your_ip}:${TGI_PORT}"

Build Docker Image¶

cd GenAIComps/
docker build -t opea/texttosql:latest -f comps/texttosql/langchain/Dockerfile .

Run Docker with CLI (Option A)¶

export TGI_LLM_ENDPOINT="http://${your_ip}:${TGI_PORT}"

docker run  --runtime=runc --name="comps-langchain-texttosql"  -p 9090:8080 --ipc=host -e llm_endpoint_url=${TGI_LLM_ENDPOINT} opea/texttosql:latest

Run via docker compose (Option B)¶

Setup Environment Variables.

export TGI_LLM_ENDPOINT=http://${your_ip}:${TGI_PORT}
export HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export LLM_MODEL_ID="mistralai/Mistral-7B-Instruct-v0.3"
export POSTGRES_USER=postgres
export POSTGRES_PASSWORD=testpwd
export POSTGRES_DB=chinook

Start the services.

docker compose -f docker_compose_texttosql.yaml up

✅ Invoke the microservice.¶

The Text-to-SQL microservice exposes the following API endpoints:

Test Database Connection

curl --location http://${your_ip}:9090/v1/postgres/health \
--header 'Content-Type: application/json' \
--data '{"user": "'${POSTGRES_USER}'","password": "'${POSTGRES_PASSWORD}'","host": "'${your_ip}'", "port": "5442", "database": "'${POSTGRES_DB}'"}'

Execute SQL Query from input text

curl http://${your_ip}:9090/v1/texttosql\
        -X POST \
        -d '{"input_text": "Find the total number of Albums.","conn_str": {"user": "'${POSTGRES_USER}'","password": "'${POSTGRES_PASSWORD}'","host": "'${your_ip}'", "port": "5442", "database": "'${POSTGRES_DB}'"}}' \
        -H 'Content-Type: application/json'