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Langchain AI agent with Gemini AI Api

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Integrating Gemini AI API with LangChain AI agents allows you to create a more dynamic and intelligent data science pipeline. Below is an example of how to accomplish this:

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Steps to Integrate Gemini AI with LangChain

1. Set Up Gemini API: Use the Gemini AI API for specific tasks like preprocessing, training, and evaluating.


2. Define Tools: Use LangChain's Tool class to wrap Gemini API functions.


3. Create a LangChain Agent: The agent orchestrates the tools and interacts with the Gemini API.




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Python Code

Prerequisites

Install the required libraries:

pip install langchain openai requests pandas

Code

import requests
import pandas as pd
from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI

# Set your Gemini API key and endpoint
API_KEY = "your_gemini_api_key"
BASE_URL = "https://api.gemini.ai/v1" # Replace with actual Gemini API base URL

# Define helper functions for the Gemini AI API
def preprocess_data_gemini(data):
    """Preprocess data using Gemini API."""
    url = f"{BASE_URL}/preprocess"
    headers = {"Authorization": f"Bearer {API_KEY}"}
    payload = {"data": data.to_json(orient="split")}

    response = requests.post(url, headers=headers, json=payload)

    if response.status_code == 200:
        processed_data = pd.DataFrame(response.json()["data"])
        return f"Data preprocessing completed. Processed data: {processed_data.head()}"
    else:
        return f"Error in preprocessing: {response.json()}"

def train_model_gemini(data):
    """Train model using Gemini API."""
    url = f"{BASE_URL}/train"
    headers = {"Authorization": f"Bearer {API_KEY}"}
    payload = {"data": data.to_json(orient="split")}

    response = requests.post(url, headers=headers, json=payload)

    if response.status_code == 200:
        model_id = response.json()["model_id"]
        return f"Model training started. Model ID: {model_id}"
    else:
        return f"Error in model training: {response.json()}"

def evaluate_model_gemini(model_id, test_data):
    """Evaluate model using Gemini API."""
    url = f"{BASE_URL}/evaluate"
    headers = {"Authorization": f"Bearer {API_KEY}"}
    payload = {
        "model_id": model_id,
        "test_data": test_data.to_json(orient="split"),
    }

    response = requests.post(url, headers=headers, json=payload)

    if response.status_code == 200:
        metrics = response.json()["metrics"]
        return f"Model evaluation completed. Metrics: {metrics}"
    else:
        return f"Error in model evaluation: {response.json()}"

# Define LangChain tools
tools = [
    Tool(
        name="PreprocessData",
        func=lambda data: preprocess_data_gemini(pd.read_csv(data)),
        description="Preprocess data using the Gemini AI API. Provide the path to a CSV file.",
    ),
    Tool(
        name="TrainModel",
        func=lambda data: train_model_gemini(pd.read_csv(data)),
        description="Train a model using the Gemini AI API. Provide the path to the training CSV file.",
    ),
    Tool(
        name="EvaluateModel",
        func=lambda args: evaluate_model_gemini(args['model_id'], pd.read_csv(args['test_file'])),
        description="Evaluate a model using the Gemini AI API. Provide the model ID and the path to the test data CSV file.",
    ),
]

# Initialize the LangChain agent
llm = OpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)

# Run the agent
if __name__ == "__main__":
    # Example usage
    file_path = "data.csv" # Replace with the path to your dataset
    test_file_path = "test_data.csv" # Replace with the path to your test dataset

    # Preprocess data
    preprocess_result = agent.run(f"Preprocess the data from the file {file_path}.")
    print(preprocess_result)

    # Train model
    train_result = agent.run(f"Train a model using the data from the file {file_path}.")
    print(train_result)

    # Evaluate model
    model_id = "your_model_id" # Replace with actual model ID
    evaluate_result = agent.run(f"Evaluate the model with ID {model_id} using the test data from the file {test_file_path}.")
    print(evaluate_result)


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Explanation

1. Gemini API Functions:

preprocess_data_gemini: Preprocess data via Gemini API.

train_model_gemini: Train a model using the Gemini API.

evaluate_model_gemini: Evaluate the trained model.



2. LangChain Tools:

Each tool wraps a Gemini API function and provides an interface for the LangChain agent.



3. LangChain Agent:

The agent orchestrates the pipeline and takes user instructions dynamically.

Example commands like "Preprocess the data from the file" are interpreted and executed by the agent.





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Use Case

1. Interactive Workflow:

The agent allows dynamic interaction with the pipeline (e.g., re-run a specific step).



2. Extensibility:

Add more tools (e.g., hyperparameter tuning) or handle specific Gemini AI endpoints.







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