# sql-llamacpp This template enables a user to interact with a SQL database using natural language. It uses [Mistral-7b](https://mistral.ai/news/announcing-mistral-7b/) via [llama.cpp](https://github.com/ggerganov/llama.cpp) to run inference locally on a Mac laptop. ## Environment Setup To set up the environment, use the following steps: ```shell wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh bash Miniforge3-MacOSX-arm64.sh 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 ``` ## Usage To use this package, you should first have the LangChain CLI installed: ```shell pip install -U langchain-cli ``` To create a new LangChain project and install this as the only package, you can do: ```shell langchain app new my-app --package sql-llamacpp ``` If you want to add this to an existing project, you can just run: ```shell langchain app add sql-llamacpp ``` And add the following code to your `server.py` file: ```python 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](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF). You can select other files and specify their download path (browse [here](https://huggingface.co/TheBloke)). This package includes an example DB of 2023 NBA rosters. You can see instructions to build this DB [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb). (Optional) Configure LangSmith for tracing, monitoring and debugging LangChain applications. LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). If you don't have access, you can skip this section ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_PROJECT= # if not specified, defaults to "default" ``` If you are inside this directory, then you can spin up a LangServe instance directly by: ```shell langchain serve ``` This will start the FastAPI app with a server running locally at [http://localhost:8000](http://localhost:8000) You can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) You can access the playground at [http://127.0.0.1:8000/sql-llamacpp/playground](http://127.0.0.1:8000/sql-llamacpp/playground) You can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/sql-llamacpp") ```