Generative AI Series
Retrieval Augmented Generation(RAG) — Chatbot for database with LlamaIndex (Text2SQL)
Implement the RAG technique using Langchain, and LlamaIndex for conversational chatbot on a database, using Text-SQL.
This blog is an ongoing series on GenerativeAI and is a continuation of the previous blog, which talks about the RAG pattern and how RAG is used to augment prompts and enhance the content and context of an LLM, with specific data
I covered the RAG pattern in my previous blog “Prompt Engineering: Retrieval Augmented Generation(RAG)”. I introduced LlamaIndex to implement RAG, and we built a chatbot to ask-the-documents in another blog. In this blog, we will explore how we can build a chatbot that converts chat conversations to SQL and queries the database.
I have installed Postgres on my MacBook and created 2 tables.
product_master: Where I store the product_id and the name of the product.
inventor: where I use the product_id and store the inventory of that product.
The following screenshots show the schema and the data that I inserted
Lets now create a
requirements.txt with all the dependent libraries that we need for this project.
The key libraries here other than
sqlalchemy, which is the Python SQL toolkit, and
psycopg2, which is the adapter we will be using to connect to Postgres.
Let's set the environment variables in
.env file. In the env file, we provide the OPENAI_API_KEY and the database variables, as shown below
Let's now walk through the application code.