langchain/templates/plate-chain
Erick Friis 78da34153e
TEMPLATES Metadata (#13691)
Co-authored-by: Lance Martin <lance@langchain.dev>
2023-11-22 01:41:12 -05:00
..
examples Sphinxbio nls/add plate chain template (#12502) 2023-10-28 21:48:17 -07:00
plate_chain fix plate chain (#12673) 2023-10-31 13:45:09 -07:00
tests Sphinxbio nls/add plate chain template (#12502) 2023-10-28 21:48:17 -07:00
LICENSE Sphinxbio nls/add plate chain template (#12502) 2023-10-28 21:48:17 -07:00
poetry.lock IMPROVEMENT Allow openai v1 in all templates that require it (#13489) 2023-11-16 17:10:08 -08:00
pyproject.toml TEMPLATES Metadata (#13691) 2023-11-22 01:41:12 -05:00
README.md Update readmes with new cli install (#12847) 2023-11-03 12:10:32 -07:00

plate-chain

This template enables parsing of data from laboratory plates.

In the context of biochemistry or molecular biology, laboratory plates are commonly used tools to hold samples in a grid-like format.

This can parse the resulting data into standardized (e.g., JSON) format for further processing.

Environment Setup

Set the OPENAI_API_KEY environment variable to access the OpenAI models.

Usage

To utilize plate-chain, you must have the LangChain CLI installed:

pip install -U langchain-cli

Creating a new LangChain project and installing plate-chain as the only package can be done with:

langchain app new my-app --package plate-chain

If you wish to add this to an existing project, simply run:

langchain app add plate-chain

Then add the following code to your server.py file:

from plate_chain import chain as plate_chain

add_routes(app, plate_chain, path="/plate-chain")

(Optional) For configuring LangSmith, which helps trace, monitor and debug LangChain applications, use the following code:

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"

If you're in this directory, you can start a LangServe instance directly by:

langchain serve

This starts the FastAPI app with a server running locally at http://localhost:8000

All templates can be viewed at http://127.0.0.1:8000/docs Access the playground at http://127.0.0.1:8000/plate-chain/playground

You can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/plate-chain")