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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