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

It uses Mistral-7b via llama.cpp to run inference locally on a Mac laptop.

Environment Setup​

To set up the environment, use the following steps:

conda create -n llama python=3.9.16
conda activate /Users/rlm/miniforge3/envs/llama
CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dir


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-llamacpp

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

langchain app add sql-llamacpp

And add the following code to your file:

from sql_llamacpp import chain as sql_llamacpp_chain

add_routes(app, sql_llamacpp_chain, path="/sql-llamacpp")

The package will download the Mistral-7b model from here. You can select other files and specify their download path (browse here).

This package includes an example DB of 2023 NBA rosters. You can see instructions to build this DB here.

(Optional) Configure LangSmith for tracing, monitoring and debugging 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 running locally at http://localhost:8000

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

You can access the template from code with:

from langserve.client import RemoteRunnable

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

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