An example application built with LangChain CLI and LangServe
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An example application built with LangChain CLI and LangServe
 langserve_launch_example
<!--- This is a LangChain project bootstrapped by [LangChain CLI](https://github.com/langchain-ai/langchain). --->
## Customise
To customise this project, edit the following files:
- `langserve_launch_example/chain.py` contains an example chain, which you can edit to suit your needs.
- `langserve_launch_example/server.py` contains a FastAPI app that serves that chain using `langserve`. You can edit this to add more endpoints or customise your server.
- `tests/test_chain.py` contains tests for the chain. You can edit this to add more tests.
- `pyproject.toml` contains the project metadata, including the project name, version, and dependencies. You can edit this to add more dependencies or customise your project metadata.
## Install dependencies
If using poetry:
```bash
poetry install
```
If using vanilla pip:
```bash
pip install .
```
## Usage
By default, this uses OpenAI. So you will need to set your OpenAI API key:
```
export OPENAI_API_KEY="sk-..."
```
To run the project locally, run
```
make start
```
This will launch a webserver on port 8001.
Or via docker compose (does not use hot reload by default):
```
docker compose up
```
## Deploy
To deploy the project, first build the docker image:
```
docker build . -t langserve_launch_example:latest
```
Then run the image:
```
docker run -p 8001:8001 -e PORT=8001 langserve_launch_example:latest
```
Don't forget to add any needed environment variables!
## Deploy to GCP
You can deploy to GCP Cloud Run using the following command:
First create a `.env.gcp.yaml` file with the contents from `.env.gcp.yaml.example` and fill in the values. Then run:
```
make deploy_gcp
```
## Contributing
For information on how to set up your dev environment and contribute, see [here](.github/CONTRIBUTING.md).