# sql-ollama This template enables a user to interact with a SQL database using natural language. It uses [Zephyr-7b](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) via [Ollama](https://ollama.ai/library/zephyr) 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](https://python.langchain.com/docs/integrations/chat/ollama) to download Ollama. 2. Download your LLM of interest: * This package uses `zephyr`: `ollama pull zephyr` * You can choose from many LLMs [here](https://ollama.ai/library) 3. 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). ## 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-ollama ``` If you want to add this to an existing project, you can just run: ```shell langchain app add sql-ollama ``` And add the following code to your `server.py` file: ```python 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](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 is running locally at [http://localhost:8000](http://localhost:8000) We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) We can access the playground at [http://127.0.0.1:8000/sql-ollama/playground](http://127.0.0.1:8000/sql-ollama/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/sql-ollama") ```