langchain/docs/ecosystem/google_serper.md
Shrined 10dab053b4
Add Enum for agent types (#2321)
This pull request adds an enum class for the various types of agents
used in the project, located in the `agent_types.py` file. Currently,
the project is using hardcoded strings for the initialization of these
agents, which can lead to errors and make the code harder to maintain.
With the introduction of the new enums, the code will be more readable
and less error-prone.

The new enum members include:

- ZERO_SHOT_REACT_DESCRIPTION
- REACT_DOCSTORE
- SELF_ASK_WITH_SEARCH
- CONVERSATIONAL_REACT_DESCRIPTION
- CHAT_ZERO_SHOT_REACT_DESCRIPTION
- CHAT_CONVERSATIONAL_REACT_DESCRIPTION

In this PR, I have also replaced the hardcoded strings with the
appropriate enum members throughout the codebase, ensuring a smooth
transition to the new approach.
2023-04-03 21:56:20 -07:00

2.2 KiB

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
from langchain.agents.agent_types import AgentType

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,
        description="useful for when you need to ask with search"
    )
]

self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.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