LlamaHub Demostration¶

Here we give a simple overview of how to use data loaders and tools (for agents) within LlamaHub.

NOTES:

• You can learn how to use everything in LlamaHub by clicking into each module and looking at the code snippet.
• Also, you can find a full list of notebooks around agent tools here.
• In this guide we'll show how to use download_loader and download_tool. You can also install llama-hub as a package.

Using a Data Loader¶

In this example we show how to use SimpleWebPageReader.

NOTE: for any module on LlamaHub, to use with download_ functions, note down the class name.

In [ ]:

%pip install llama-index-agent-openai
%pip install llama-index-readers-web
%pip install llama-index-tools-google

%pip install llama-index-agent-openai %pip install llama-index-readers-web %pip install llama-index-tools-google

In [ ]:

from llama_index.readers.web import SimpleWebPageReader

from llama_index.readers.web import SimpleWebPageReader

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reader = SimpleWebPageReader(html_to_text=True)

reader = SimpleWebPageReader(html_to_text=True)

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docs = reader.load_data(urls=["https://eugeneyan.com/writing/llm-patterns/"])

docs = reader.load_data(urls=["https://eugeneyan.com/writing/llm-patterns/"])

In [ ]:

print(docs[0].get_content()[:400])

print(docs[0].get_content()[:400])

# [eugeneyan](/)

  * [Start Here](/start-here/ "Start Here")
  * [Writing](/writing/ "Writing")
  * [Speaking](/speaking/ "Speaking")
  * [Prototyping](/prototyping/ "Prototyping")
  * [About](/about/ "About")

# Patterns for Building LLM-based Systems & Products

[ [llm](/tag/llm/) [engineering](/tag/engineering/)
[production](/tag/production/) ]  · 66 min read

> Discussions on [HackerNews](htt

Now you can plug these docs into your downstream LlamaIndex pipeline.

In [ ]:

from llama_index.core import VectorStoreIndex

index = VectorStoreIndex.from_documents(docs)
query_engine = index.as_query_engine()

from llama_index.core import VectorStoreIndex index = VectorStoreIndex.from_documents(docs) query_engine = index.as_query_engine()

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response = query_engine.query("What are ways to evaluate LLMs?")
print(str(response))

response = query_engine.query("What are ways to evaluate LLMs?") print(str(response))

Using an Agent Tool Spec¶

In this example we show how to load an agent tool.

In [ ]:

from llama_index.tools.google import GmailToolSpec

from llama_index.tools.google import GmailToolSpec

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tool_spec = GmailToolSpec()

tool_spec = GmailToolSpec()

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# plug into your agent
from llama_index.agent.openai import OpenAIAgent

# plug into your agent from llama_index.agent.openai import OpenAIAgent

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agent = OpenAIAgent.from_tools(tool_spec.to_tool_list())

agent = OpenAIAgent.from_tools(tool_spec.to_tool_list())

In [ ]:

agent.chat("What is my most recent email")

agent.chat("What is my most recent email")