madangehlot88/sqldatabasechain-langchain-demo

SQLDatabaseChain LangChain Demo

This repository contains code for basics interaction with postgres database using SQLDatabaseChain. Also added examples for langchain demo to demonstrate the use of langchain simple llm calls and running chains using templates.

Install the dependencies

• Create a new virtual environment

python -m venv venv
source venv/bin/activate #for ubuntu
venv/Scripts/activate #for windows

• Install langchain,openai, python-environ and psycopg2 libraries using pip.

pip install langchain openai python-environ psycopg2

LangChain Simple LLM Demo

Source code

from langchain.llms import OpenAI

# Accessing the OPENAI KEY
import environ
env = environ.Env()
environ.Env.read_env()
API_KEY = env('OPENAI_API_KEY')

# Simple LLM call Using LangChain
llm = OpenAI(model_name="text-davinci-003", openai_api_key=API_KEY)
question = "Which language is used to create chatgpt ?"
print(question, llm(question))

Output

LangChain Chain and Templates Demo

Source code

from langchain.llms import OpenAI

# Accessing the OPENAI KEY
import environ
env = environ.Env()
environ.Env.read_env()
API_KEY = env('OPENAI_API_KEY')

# Creating a prompt template and running the LLM chain
from langchain import PromptTemplate, LLMChain
template = "What are the top {n} resources to learn {language} programming?"
prompt = PromptTemplate(template=template,input_variables=['n','language'])
chain = LLMChain(llm=llm,prompt=prompt)
input = {'n':3,'language':'Python'}
print(chain.run(input))

Output

SQLDatabaseChain Demo

• Download and install postgres in your machine.
• Create a database called tasks in pgAdmin4
• Run the db.py file to create the database table tasks and insert values in it.

python db.py

• Run the app.py file to run the SQL Database Chain.

python app.py

Output