This template creates an agent that uses Google Gemini function calling to communicate its decisions on what actions to take.
This example creates an agent that can optionally look up information on the internet using Tavily's search engine.
The following environment variables need to be set:
TAVILY_API_KEY environment variable to access Tavily
GOOGLE_API_KEY environment variable to access the Google Gemini APIs.
To use this package, you should first have the LangChain CLI installed:
pip install -U langchain-cli
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package gemini-functions-agent
If you want to add this to an existing project, you can just run:
langchain app add gemini-functions-agent
And add the following code to your
from gemini_functions_agent import agent_executor as gemini_functions_agent_chain
add_routes(app, gemini_functions_agent_chain, path="/openai-functions-agent")
(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
If you are inside this directory, then you can spin up a LangServe instance directly by:
This will start the FastAPI app with a server is running locally at http://localhost:8000
We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/gemini-functions-agent/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/gemini-functions-agent")