mirror of
https://github.com/hwchase17/langchain
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78 lines
2.6 KiB
Markdown
78 lines
2.6 KiB
Markdown
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# sql-llamacpp
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This template enables a user to interact with a SQL database using natural language.
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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.
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## Environment Setup
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To set up the environment, use the following steps:
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```shell
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wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
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bash Miniforge3-MacOSX-arm64.sh
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conda create -n llama python=3.9.16
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conda activate /Users/rlm/miniforge3/envs/llama
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CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dir
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```
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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-llamacpp
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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-llamacpp
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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_llamacpp import chain as sql_llamacpp_chain
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add_routes(app, sql_llamacpp_chain, path="/sql-llamacpp")
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```
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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)).
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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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(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
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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 running locally at
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[http://localhost:8000](http://localhost:8000)
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You can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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You can access the playground at [http://127.0.0.1:8000/sql-llamacpp/playground](http://127.0.0.1:8000/sql-llamacpp/playground)
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You 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-llamacpp")
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```
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