# *This is the old routing agent implementation.* Current implementation of the routing agent can be found in src/agent/base_agent.py

Llama Index Tool Specification Example Usage
This document provides an overview of how to utilize custom tool specifications with the Llama Index framework to handle specialized tasks like vision and location-based queries.

Installation
Ensure that the necessary Python packages are installed:

python
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%pip install pydantic
%pip install googlemaps
%pip install llama_index
These commands install pydantic for data validation, googlemaps for handling location queries, and llama_index for integrating AI tools and agents.

Configuration
The following classes are defined but commented out for clarity. You should uncomment them in your implementation if you plan to use their functionality:

python
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# from main import handle_location_question, handle_vision_question, app
# from llama_index.agent.openai import OpenAIAgent
These lines would typically import custom functions and classes necessary for handling specific types of questions within an application framework like Flask.

Tool Specifications
Define tool specifications to modularize the functionality for vision and location queries:

python
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from llama_index.core.tools.tool_spec.base import BaseToolSpec

class CVToolSpec(BaseToolSpec):
    spec_functions = ['handle_vision_question']

    def __init__(self):  # , form):
        # self.form = form
        pass

    def handle_vision_question(request):
        return handle_vision_question(request)

class LocationToolSpec(BaseToolSpec):
    spec_functions = ['handle_location_question']

    def __init__(self):  # , form):
        # self.form = form
        pass

    def handle_location_question(request):
        return handle_location_question(request)
The CVToolSpec and LocationToolSpec classes inherit from BaseToolSpec and specify functions that handle vision and location questions, respectively.

Integration with Llama Index
Create an OpenAI language model agent and bind it with the specified tools:

python
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from llama_index.agent.openai import OpenAIAgent
from llama_index.llms.openai import OpenAI

cv_tool = CVToolSpec()
loc_tool = LocationToolSpec()
full_tool_list = cv_tool.to_tool_list() + loc_tool.to_tool_list()

llm = OpenAI(model="gpt-3.5-turbo")
agent = OpenAIAgent.from_tools(full_tool_list, verbose=True)
This setup initializes an OpenAI LLM with a custom agent that incorporates both vision and location handling tools.

Execution
The agent can now handle complex queries that require integration of different functionalities:

python
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# This is a hypothetical example and would need a real request object to work:
# response = agent.handle(request)
# print(response)
Replace agent.handle(request) with an appropriate method call to handle real requests. Ensure the request object is constructed properly according to the needs of the handle_vision_question and handle_location_question functions.

Extending Functionality
The framework allows for easy extension by adding new tool specifications or modifying existing ones to handle different types of data or queries.

Remember to handle API keys and sensitive data securely, especially when dealing with location data or integrating third-party services like Google Maps.







