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Leonid Ganeline e2d7677526

docs: compound ecosystem and integrations (#4870 )

# Docs: compound ecosystem and integrations

**Problem statement:** We have a big overlap between the
References/Integrations and Ecosystem/LongChain Ecosystem pages. It
confuses users. It creates a situation when new integration is added
only on one of these pages, which creates even more confusion.
- removed References/Integrations page (but move all its information
into the individual integration pages - in the next PR).
- renamed Ecosystem/LongChain Ecosystem into Integrations/Integrations.
I like the Ecosystem term. It is more generic and semantically richer
than the Integration term. But it mentally overloads users. The
`integration` term is more concrete.
UPDATE: after discussion, the Ecosystem is the term.
Ecosystem/Integrations is the page (in place of Ecosystem/LongChain
Ecosystem).

As a result, a user gets a single place to start with the individual
integration.

2023-05-18 09:29:57 -07:00

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Google Serper

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 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

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