Make Tools own model, add ToolKit Concept (#1095 )

Follow-up of @hinthornw's PR:

- Migrate the Tool abstraction to a separate file (`BaseTool`).
- `Tool` implementation of `BaseTool` takes in function and coroutine to
more easily maintain backwards compatibility
- Add a Toolkit abstraction that can own the generation of tools around
a shared concept or state

---------

Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Francisco Ingham <fpingham@gmail.com>
Co-authored-by: Dhruv Anand <105786647+dhruv-anand-aintech@users.noreply.github.com>
Co-authored-by: cragwolfe <cragcw@gmail.com>
Co-authored-by: Anton Troynikov <atroyn@users.noreply.github.com>
Co-authored-by: Oliver Klingefjord <oliver@klingefjord.com>
Co-authored-by: William Fu-Hinthorn <whinthorn@Williams-MBP-3.attlocal.net>
Co-authored-by: Bruno Bornsztein <bruno.bornsztein@gmail.com>

2023-02-18 13:40:43 -08:00

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Google Serper Wrapper

This page covers how to use the Serper Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search. It is broken into two parts: setup, and then references to the specific Google Serper wrapper.

Setup

Go to serper.dev to sign up for a free account
Get the api key and set it as an environment variable (SERPER_API_KEY)

Wrappers

Utility

There exists a GoogleSerperAPIWrapper utility which wraps this API. To import this utility:

from langchain.utilities import GoogleSerperAPIWrapper

You can use it as part of a Self Ask chain:

from langchain.utilities import GoogleSerperAPIWrapper
from langchain.llms.openai import OpenAI
from langchain.agents import initialize_agent, Tool

import os

os.environ["SERPER_API_KEY"] = ""
os.environ['OPENAI_API_KEY'] = ""

llm = OpenAI(temperature=0)
search = GoogleSerperAPIWrapper()
tools = [
    Tool(
        name="Intermediate Answer",
        func=search.run
    )
]

self_ask_with_search = initialize_agent(tools, llm, agent="self-ask-with-search", verbose=True)
self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?")

Output

Entering new AgentExecutor chain...
 Yes.
Follow up: Who is the reigning men's U.S. Open champion?
Intermediate answer: Current champions Carlos Alcaraz, 2022 men's singles champion.
Follow up: Where is Carlos Alcaraz from?
Intermediate answer: El Palmar, Spain
So the final answer is: El Palmar, Spain

> Finished chain.

'El Palmar, Spain'

For a more detailed walkthrough of this wrapper, see this notebook.

Tool

You can also easily load this wrapper as a Tool (to use with an Agent). You can do this with:

from langchain.agents import load_tools
tools = load_tools(["google-serper"])

For more information on this, see this page

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