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163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
78 lines
2.4 KiB
Markdown
78 lines
2.4 KiB
Markdown
# SQL - Ollama
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This template enables a user to interact with a SQL database using natural language.
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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.
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## Environment Setup
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Before using this template, you need to set up Ollama and SQL database.
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1. Follow instructions [here](https://python.langchain.com/docs/integrations/chat/ollama) to download Ollama.
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2. Download your LLM of interest:
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* This package uses `zephyr`: `ollama pull zephyr`
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* You can choose from many LLMs [here](https://ollama.ai/library)
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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).
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package sql-ollama
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add sql-ollama
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```
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And add the following code to your `server.py` file:
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```python
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from sql_ollama import chain as sql_ollama_chain
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add_routes(app, sql_ollama_chain, path="/sql-ollama")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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You can sign up for LangSmith [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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We can access the playground at [http://127.0.0.1:8000/sql-ollama/playground](http://127.0.0.1:8000/sql-ollama/playground)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/sql-ollama")
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``` |