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https://github.com/hwchase17/langchain
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163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
73 lines
2.2 KiB
Markdown
73 lines
2.2 KiB
Markdown
# Gemini functions - agent
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This template creates an agent that uses `Google Gemini function calling` to communicate its decisions on what actions to take.
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This example creates an agent that optionally looks up information on the internet using `Tavily's` search engine.
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[See an example LangSmith trace here](https://smith.langchain.com/public/0ebf1bd6-b048-4019-b4de-25efe8d3d18c/r)
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## Environment Setup
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The following environment variables need to be set:
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Set the `TAVILY_API_KEY` environment variable to access Tavily
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Set the `GOOGLE_API_KEY` environment variable to access the Google Gemini APIs.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package gemini-functions-agent
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add gemini-functions-agent
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```
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And add the following code to your `server.py` file:
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```python
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from gemini_functions_agent import agent_executor as gemini_functions_agent_chain
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add_routes(app, gemini_functions_agent_chain, path="/openai-functions-agent")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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You can sign up for LangSmith [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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We can access the playground at [http://127.0.0.1:8000/gemini-functions-agent/playground](http://127.0.0.1:8000/gemini-functions-agent/playground)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/gemini-functions-agent")
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``` |