🛢 Text-to-SQL Microservice

In today’s data-driven world, the ability to efficiently extract insights from databases is crucial. However, querying databases often requires specialized knowledge of SQL(Structured Query Language) and database schemas, which can be a barrier for non-technical users. This is where the Text-to-SQL microservice comes into play, leveraging the power of LLMs and agentic frameworks to bridge the gap between human language and database queries. This microservice is built on LangChain/LangGraph frameworks.

The microservice enables a wide range of use cases, making it a versatile tool for businesses, researchers, and individuals alike. Users can generate queries based on natural language questions, enabling them to quickly retrieve relevant data from their databases. Additionally, the service can be integrated into ChatBots, allowing for natural language interactions and providing accurate responses based on the underlying data. Furthermore, it can be utilized to build custom dashboards, enabling users to visualize and analyze insights based on their specific requirements, all through the power of natural language.

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🛠️ Features

Implement SQL Query based on input text: Transform user-provided natural language into SQL queries, subsequently executing them to retrieve data from SQL databases.

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⚙️ Implementation

The text-to-sql microservice able to implement with various framework and support various types of SQL databases.

🔗 Utilizing Text-to-SQL with Langchain framework

The follow guide 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.

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🚀 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/text2sql

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="text2sql-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 opea_text2sql_microservice.py

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🚀 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/text2sql:latest -f comps/text2sql/src/Dockerfile .

Run Docker with CLI (Option A)

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

docker run  --runtime=runc --name="comps-langchain-text2sql"  -p 9090:8080 --ipc=host -e llm_endpoint_url=${TGI_LLM_ENDPOINT} opea/text2sql: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_text2sql.yaml up
  

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✅ 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://${your_ip}:9090/v1/text2sql\
          -X POST \
          -d '{"input_text": "Find the total number of Albums.","conn_str": {"user": "'${POSTGRES_USER}'","password": "'${POSTGRES_PASSWORD}'","host": "'${your_ip}'", "port": "5442", "database": "'${POSTGRES_DB}'"}}' \
          -H 'Content-Type: application/json'