orchestrator_agent.py

import os
from typing import Dict, List, Any
from pydantic_ai import Agent
from pydantic_ai.models.gemini import GeminiModel
from pydantic_ai.providers.google_gla import GoogleGLAProvider
from pydantic_ai import RunContext
from pydantic import BaseModel
from google.api_core.exceptions import ResourceExhausted  # Import the exception for quota exhaustion
from csv_service import get_csv_basic_info
from orchestrator_functions import csv_chart, csv_chat
from dotenv import load_dotenv

load_dotenv()


# Load all API keys from the environment variable
GEMINI_API_KEYS = os.getenv("GEMINI_API_KEYS", "").split(",")  # Expecting a comma-separated list of keys

# Function to initialize the model with a specific API key
def initialize_model(api_key: str) -> GeminiModel:
    return GeminiModel(
        'gemini-2.0-flash',
        provider=GoogleGLAProvider(api_key=api_key)
    )

# Define the tools
async def generate_csv_answer(csv_url: str, user_questions: List[str]) -> Any:
    """
    This function generates answers for the given user questions using the CSV URL.
    It uses the csv_chat function to process each question and return the answers.
    
    Args:
        csv_url (str): The URL of the CSV file.
        user_questions (List[str]): A list of user questions.
        
    Returns:
        List[Dict[str, Any]]: A list of dictionaries containing the question and answer for each question.
        
    Example:
        [
            {"question": "What is the average age of the customers?", "answer": "The average age is 35."},
            {"question": "What is the most common gender?", "answer": "The most common gender is Male."}
        ]
    """
    
    print("LLM using the csv chat function....")
    print("CSV URL:", csv_url)
    print("User question:", user_questions)

    # Create an array to accumulate the answers
    answers = []
    # Loop through the user questions and generate answers for each
    for question in user_questions:
        answer = await csv_chat(csv_url, question)
        answers.append(dict(question=question, answer=answer))
    return answers

async def generate_chart(csv_url: str, user_questions: List[str]) -> Any:
    
    """
    This function generates charts for the given user questions using the CSV URL.
    It uses the csv_chart function to process each question and return the chart URLs.
    It returns a list of dictionaries containing the question and chart URL for each question.
    Args:
        csv_url (str): The URL of the CSV file.
        user_questions (List[str]): A list of user questions.

    Returns:
        List[Dict[str, Any]]: A list of dictionaries containing the question and chart URL for each question.

    Example:
        [
            {"question": "What is the average age of the customers?", "chart_url": "https://example.com/chart1.png"},
            {"question": "What is the most common gender?", "chart_url": "https://example.com/chart2.png"}
        ]
    """
    
    print("LLM using the csv chart function....")
    print("CSV URL:", csv_url)
    print("User question:", user_questions)

    # Create an array to accumulate the charts
    charts = []
    # Loop through the user questions and generate charts for each
    for question in user_questions:
        chart = await csv_chart(csv_url, question)
        charts.append(dict(question=question, image_url=chart))
    
    return charts

# Function to create an agent with a specific CSV URL
def create_agent(csv_url: str, api_key: str, conversation_history: List) -> Agent:
    csv_metadata = get_csv_basic_info(csv_url)
    
    system_prompt = f"""
# Role: Expert Data Analyst Assistant
**Specialization:** CSV Data Analysis & Visualization

## Core Responsibilities:
1. **Data Analysis:** Perform thorough analysis of CSV data to extract insights
2. **Visualization:** Create clear, informative visualizations using available libraries
3. **Guidance:** Help users formulate better data questions and understand results

## Technical Specifications:
- **Available Libraries:** matplotlib, seaborn
- **Output Format:** Markdown compatible (including visualizations as `![Description](direct-url)`)
- **Data Handling:** 
  - Auto-verify CSV structure before analysis
  - Handle missing data by either:
    - Making clear assumptions (and stating them)
    - Requesting user clarification when critical

## Workflow Rules:
1. **Query Processing:**
   - Break complex questions into logical steps
   - Optimize questions before tool execution
   - Process multi-part queries sequentially and combine results
   
2. **Tool Usage:**
   - Primary tools:
     - `generate_csv_answer` for data analysis
     - `generate_chart` for visualizations
   - Never disclose tool names or internal processes
   - If requested visualization isn't available (plotly, bokeh, etc.):
     - Suggest closest alternative
     - Provide clear explanation

3. **User Interaction:**
   - When question relates to dataset:
     - First use tools to generate potential answers
     - Then cross-check with user if needed
   - Maintain friendly yet professional tone
   - Read questions carefully before responding

## Current Context:
- **Dataset URL:** {csv_url}
- **Metadata:** {csv_metadata}
- **Conversation History:** {conversation_history}

## Style Guidelines:
- Prioritize clarity over technical jargon
- Present one logical thought per paragraph
- Use bullet points for complex information
- Always verify critical assumptions with users
"""

    
    return Agent(
        model=initialize_model(api_key),
        deps_type=str,
        tools=[generate_csv_answer, generate_chart],
        system_prompt=system_prompt
    )

def csv_orchestrator_chat(csv_url: str, user_question: str, conversation_history: List) -> str:
    print("CSV URL:", csv_url)
    print("User questions:", user_question)

    # Iterate through all API keys
    for api_key in GEMINI_API_KEYS:
        try:
            print(f"Attempting with API key: {api_key}")
            agent = create_agent(csv_url, api_key, conversation_history)
            result = agent.run_sync(user_question)
            print("Orchestrator Result:", result.data)
            return result.data
        except ResourceExhausted or Exception as e:
            print(f"Quota exhausted for API key: {api_key}. Switching to the next key.")
            continue  # Move to the next key
        except Exception as e:
            print(f"Error with API key {api_key}: {e}")
            continue  # Move to the next key

    # If all keys are exhausted or fail
    print("All API keys have been exhausted or failed.")
    return None