llama-index-tools-hive 0.2.0

LlamaIndex Tool for Hive Intelligence

Hive Tool for LlamaIndex

This tool provides integration with Hive Intelligence, enabling powerful crypto and Web3 intelligence capabilities.

Installation

Install the package using pip:

pip install llama-index-tools-hive

Setup

Set your Hive API key as an environment variable:

export HIVE_API_KEY=your_api_key_here

On Windows:

set HIVE_API_KEY=your_api_key_here

Basic Usage

Import and Initialize

from llama_index.tools.hive import HiveToolSpec, HiveSearchMessage

# Initialize with API key and optional search parameters
hive_tool = HiveToolSpec(
    api_key="your_api_key_here",  # or it will use HIVE_API_KEY env var
)

Simple Search

# Single prompt search
results = hive_tool.search(
    prompt="What is the current price of Ethereum?", include_data_sources=True
)
print("Search results:", results)

Chat-style Conversation

# Create conversation messages
chat_msgs = [
    HiveSearchMessage(role="user", content="Price of what?"),
    HiveSearchMessage(role="assistant", content="Please specify asset."),
    HiveSearchMessage(role="user", content="BTC"),
]

# Execute chat search
results = hive_tool.search(messages=chat_msgs, include_data_sources=True)
print("Chat response:", results)

Advanced Usage

Search with Custom Parameters

results = hive_tool.search(
    prompt="Analyze the latest trends in DeFi",
    include_data_sources=True,  # Include sources in response
)

API Reference

HiveToolSpec

__init__(self, api_key: str, temperature: Optional[float] = None, top_k: Optional[int] = None, top_p: Optional[float] = None)

• api_key: Your Hive Intelligence API key
• temperature: Controls randomness (0.0 to 1.0) - optional
• top_k: Limit number of results returned - optional
• top_p: Nucleus sampling parameter - optional

search(self, prompt: str = None, messages: List[HiveSearchMessage] = None, include_data_sources: bool = False) -> HiveSearchResponse

Parameters:

• prompt: The search query or question (string)
• messages: List of HiveSearchMessage objects for chat-style conversations
• include_data_sources: Whether to include data sources in the response

Returns:

• HiveSearchResponse containing the search results

Examples

Full Example Script

from llama_index.tools.hive import HiveToolSpec, HiveSearchMessage


def main():
    # Initialize with API key and search parameters
    hive = HiveToolSpec(api_key="your_api_key_here")

    # Simple search
    print("--- Simple Search ---")
    results = hive.search(
        prompt="What is the current price of Bitcoin?",
        include_data_sources=True,
    )
    print("Results:", results)

    # Chat conversation
    print("\n--- Chat Conversation ---")
    chat = [
        HiveSearchMessage(role="user", content="Tell me about"),
        HiveSearchMessage(
            role="assistant", content="What would you like to know about?"
        ),
        HiveSearchMessage(role="user", content="Ethereum upgrades"),
    ]
    results = hive.search(messages=chat)
    print("Chat response:", results)


if __name__ == "__main__":
    main()