Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:

- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.

There is also a full reference section, as well as extra resources
(glossary, gallery, etc)

Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>

2023-01-02 08:24:09 -08:00

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Google Search Wrapper

This page covers how to use the Google Search API within LangChain. It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers.

Installation and Setup

Install requirements with pip install google-api-python-client
Set up a Custom Search Engine, following these instructions
Get an API Key and Custom Search Engine ID from the previous step, and set them as environment variables GOOGLE_API_KEY and GOOGLE_CSE_ID respectively

Wrappers

Utility

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

from langchain.utilities import GoogleSearchAPIWrapper

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-search"])

For more information on this, see this page

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