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Using Tavily Search with Langroid

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1. Set Up Tavily

1. Access Tavily Platform
   Go to the Tavily Platform.

2. Sign Up or Log In
   Create an account or log in if you already have one.

3. Get Your API Key

4. Navigate to your dashboard

5. Copy your API key

6. Set Environment Variable
   Add the following variable to your .env file: ```env TAVILY_API_KEY=

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2. Use Tavily Search with Langroid

Installation

uv add tavily-python
# or
pip install tavily-python

Code Example

import langroid as lr
from langroid.agent.chat_agent import ChatAgent, ChatAgentConfig
from langroid.agent.tools.tavily_search_tool import TavilySearchTool

# Configure the ChatAgent
config = ChatAgentConfig(
    name="search-agent",
    llm=lr.language_models.OpenAIGPTConfig(
        chat_model=lr.language_models.OpenAIChatModel.GPT4
    ),
    use_tools=True
)

# Create the agent
agent = ChatAgent(config)

# Enable Tavily search tool
agent.enable_message(TavilySearchTool)

3. Perform Web Searches

Use the agent to perform web searches using Tavily's AI-powered search.

# Simple search query
response = agent.llm_response(
    "What are the latest developments in quantum computing?"
)
print(response)

# Search with specific number of results
response = agent.llm_response(
    "Find 5 recent news articles about artificial intelligence."
)
print(response)

4. Custom Search Requests

You can also customize the search behavior by creating a TavilySearchTool instance directly:

from langroid.agent.tools.tavily_search_tool import TavilySearchTool

# Create a custom search request
search_request = TavilySearchTool(
    query="Latest breakthroughs in fusion energy",
    num_results=3
)

# Get search results
results = search_request.handle()
print(results)

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