langchain/libs/cli/langchain_cli/integration_template
ccurme 3823daa0b9
cli: update integration doc template for tools (#30188)
Chain example -> langgraph agent
2025-03-09 21:14:43 +00:00
..
docs cli: update integration doc template for tools (#30188) 2025-03-09 21:14:43 +00:00
integration_template core: basemessage.text() (#29078) 2025-02-18 17:45:44 -08:00
scripts multiple: pydantic 2 compatibility, v0.3 (#26443) 2024-09-13 14:38:45 -07:00
tests docs: standard tests to markdown, load templates from files (#28603) 2024-12-07 01:37:21 +00:00
.gitignore cli[patch]: integration template (#14571) 2023-12-13 08:55:30 -08:00
LICENSE cli[patch]: copyright 2024 default (#17204) 2024-02-07 14:52:37 -08:00
Makefile cli: standard tests in cli, test that they run, skip vectorstore tests (#28521) 2024-12-05 00:38:32 -08:00
pyproject.toml cli: release 0.0.34 (#28525) 2024-12-05 15:35:49 +00:00
README.md cli[patch], google-vertexai[patch]: readme template (#16470) 2024-01-23 12:08:17 -07:00

package_name

This package contains the LangChain integration with ModuleName

Installation

pip install -U __package_name__

And you should configure credentials by setting the following environment variables:

  • TODO: fill this out

Chat Models

Chat__ModuleName__ class exposes chat models from ModuleName.

from __module_name__ import Chat__ModuleName__

llm = Chat__ModuleName__()
llm.invoke("Sing a ballad of LangChain.")

Embeddings

__ModuleName__Embeddings class exposes embeddings from ModuleName.

from __module_name__ import __ModuleName__Embeddings

embeddings = __ModuleName__Embeddings()
embeddings.embed_query("What is the meaning of life?")

LLMs

__ModuleName__LLM class exposes LLMs from ModuleName.

from __module_name__ import __ModuleName__LLM

llm = __ModuleName__LLM()
llm.invoke("The meaning of life is")