from llama_index.llms.gemini import Gemini
from llama_index.llms.gemini import Gemini is correct. This line of code is used to import the Gemini class from the llama_index.llms.gemini module. You can use this class to create an instance of the Gemini model and use its methods for various tasks such as generating text completions or having a chat conversation.from llama_index.llms.gemini import Gemini
llm = Gemini(model_name="models/gemini-ultra", api_key="YOUR_API_KEY")
resp = llm.complete("Write a poem about a magic backpack")
print(resp)Gemini class is created with a specific model name and an API key. The complete method is then called on this instance with a prompt to generate a text completion."YOUR_API_KEY" with your actual API key.from llama_index.core.agent import ReActAgent
from llama_index.core.tools import FunctionTool
from llama_index.llms.gemini import Gemini
# Define a sample Tool
def multiply(a: int, b: int) -> int:
"""Multiply two integers and return the result integer"""
return a * b
# Create a Tool instance for the multiply function
multiply_tool = FunctionTool.from_defaults(fn=multiply)
# Initialize the language model
llm = Gemini(model_name="models/gemini-ultra", api_key="
", transport="rest")
# Initialize the ReAct agent with the multiply tool
agent = ReActAgent.from_tools([multiply_tool], llm=llm, verbose=True)
# Query the agent to calculate 85 multiplied by 9
resp = agent.query("Calculate 85 multiplied by 9")
# Print the response from the agent
print(resp.response)FunctionTool instance."YOUR_API_KEY" with your actual API key.from llama_index.core.agent import ReActAgent
from llama_index.core.tools import FunctionTool
from llama_index.llms.gemini import Gemini
# Define a sample Tool
def multiply(a: int, b: int) -> int:
"""Multiply two integers and return the result integer"""
return a * b
# Create a Tool instance for the multiply function
multiply_tool = FunctionTool.from_defaults(fn=multiply)
# Initialize the language model
llm = Gemini(model_name="models/gemini-ultra", api_key="YOUR_API_KEY", transport="rest")
# Initialize the ReAct agent with the multiply tool
agent = ReActAgent.from_tools([multiply_tool], llm=llm, verbose=True)
# Query the agent to calculate 85 multiplied by 9
resp = agent.query("Calculate 85 multiplied by 9")
# Print the response from the agent
print(resp.response)
FunctionTool and LlamaParse are two different components used in the LlamaIndex library, each serving a different purpose.FunctionTool is a class that allows users to easily convert any user-defined function into a tool that can be used by an agent. It takes a function as an argument and can also auto-infer the function schema. This is useful when you want to create a tool that performs a specific operation or task. For example, you can create a FunctionTool that multiplies two numbers, adds two numbers, etc. Here is an example of how to use it:from llama_index.core.tools import FunctionTool
def multiply(a: int, b: int) -> int:
"""Multiply two integers and return the result integer"""
return a * b
multiply_tool = FunctionTool.from_defaults(fn=multiply)LlamaParse is not mentioned in the provided knowledge sources. It's possible that you may be referring to a different component or concept in the LlamaIndex library. If you could provide more context or details about LlamaParse, I would be able to give a more accurate comparison.FunctionTool, LlamaParse, or possibly both.