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Updated 8 months ago

is this correct ```from llama_index.llms.gemini import Gemini ```

@kapa.ai is this correct
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from llama_index.llms.gemini import Gemini  
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14 comments
Please stand by @andysingal as I search the knowledge base. I normally take about 20 seconds or so. :wave:
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Yes, the code 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.

Here is an example of how you can use it:

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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)


In this example, an instance of the 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.

Please replace "YOUR_API_KEY" with your actual API key.

Sources:
  • LlamaIndex API Reference ()
  • LlamaIndex Examples ()
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@kapa.ai is this correct
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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)
Let me check the knowledge base for you. One moment! :books:
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Yes, the code you provided is correct and follows the correct usage of the LlamaIndex library. This code creates a ReAct agent with a simple multiplication tool and uses the Gemini language model to process queries.

Here's a breakdown of what the code does:

  1. It imports the necessary classes from the LlamaIndex library.
  2. It defines a simple multiplication function and wraps it in a FunctionTool instance.
  3. It initializes the Gemini language model with a specific model name and API key.
  4. It creates a ReAct agent with the multiplication tool and the Gemini language model.
  5. It sends a query to the agent to calculate the product of 85 and 9.
  6. It prints the response from the agent.
Please replace "YOUR_API_KEY" with your actual API key.

Here is the code:

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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)
Sources:
  • LlamaIndex API Reference ()
  • LlamaIndex Examples ()
  • LlamaIndex Agent Module Guide ()
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i do not understand what is wrong when i run "Context-Augmented OpenAI Agent" https://docs.llamaindex.ai/en/stable/examples/agent/openai_agent_context_retrieval/ . Any one else having challenges?
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@kapa.ai what is difference between using Functool vs LlamaParse?
Let me check the knowledge base for you. One moment! :books:
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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:

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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)

(Source: LlamaIndex API Reference ())

On the other hand, 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.

Remember, it's important to use the right tool for the right job. Depending on what you're trying to achieve, you might need to use FunctionTool, LlamaParse, or possibly both.
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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FunctionTool and LlamaParse are two fundamentally different things

FunctionTool allows you to define tools to be used and called by an agent

LlamaParse loads documents
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