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…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ``` |
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| examples | ||
| plate_chain | ||
| tests | ||
| LICENSE | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
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")