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This template enables a user to interact with a SQL database using natural language.

It uses Zephyr-7b via Ollama to run inference locally on a Mac laptop.

Environment Setup​

Before using this template, you need to set up Ollama and SQL database.

  1. Follow instructions here to download Ollama.

  2. Download your LLM of interest:

    • This package uses zephyr: ollama pull zephyr
    • You can choose from many LLMs here
  3. This package includes an example DB of 2023 NBA rosters. You can see instructions to build this DB here.


To use this package, you should first have the LangChain CLI installed:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package sql-ollama

If you want to add this to an existing project, you can just run:

langchain app add sql-ollama

And add the following code to your file:

from sql_ollama import chain as sql_ollama_chain

add_routes(app, sql_ollama_chain, path="/sql-ollama")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"

If you are inside this directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all templates at We can access the playground at

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/sql-ollama")

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