Step by Step Guide to Building a Fireflies.ai API Integration in Python

Introduction

Hey there, fellow developer! Ready to supercharge your workflow with Fireflies.ai? This nifty AI-powered tool transcribes and analyzes your meetings, and today we're diving into how to integrate it into your Python projects. Buckle up!

Prerequisites

Before we jump in, make sure you've got:

• A Python environment (3.6+)
• A Fireflies.ai account with an API key (if you don't have one, hop over to their website and grab it)

Installation

Let's kick things off by installing the fireflyai package:

pip install fireflyai

Easy peasy, right?

Authentication

Now, let's get you authenticated:

from fireflyai import Fireflies

ff = Fireflies('YOUR_API_KEY')

Replace 'YOUR_API_KEY' with your actual API key, and you're good to go!

Basic API Operations

Fetching Transcripts

Want to grab a transcript? Here's how:

transcript = ff.get_transcript('meeting_id')
print(transcript.text)

Searching for Specific Content

Need to find something specific? Try this:

results = ff.search('keyword', limit=10)
for result in results:
    print(result.text)

Retrieving Meeting Metadata

Get the lowdown on a meeting:

metadata = ff.get_meeting('meeting_id')
print(metadata.title, metadata.duration)

Advanced Features

Working with Custom Vocabularies

Got some jargon? Teach it to Fireflies:

ff.add_custom_vocabulary(['AI', 'ML', 'NLP'])

Handling Webhooks

Stay in the loop with real-time updates:

from flask import Flask, request

app = Flask(__name__)

@app.route('/webhook', methods=['POST'])
def handle_webhook():
    data = request.json
    # Process the webhook data
    return '', 200

if __name__ == '__main__':
    app.run(port=5000)

Error Handling and Best Practices

Dealing with Rate Limits

Don't get caught out by rate limits:

import time

def rate_limited_request(func, *args, **kwargs):
    try:
        return func(*args, **kwargs)
    except RateLimitError:
        time.sleep(60)  # Wait for a minute
        return func(*args, **kwargs)

Implementing Retry Logic

When at first you don't succeed, try, try again:

from retrying import retry

@retry(stop_max_attempt_number=3, wait_fixed=2000)
def make_api_call():
    # Your API call here
    pass

Example Use Case: Meeting Summary Generator

Let's put it all together:

def generate_meeting_summary(meeting_id):
    transcript = ff.get_transcript(meeting_id)
    metadata = ff.get_meeting(meeting_id)
    
    summary = f"Meeting: {metadata.title}\n"
    summary += f"Duration: {metadata.duration} minutes\n"
    summary += f"Key points:\n"
    
    # Use NLP to extract key points (simplified)
    key_points = extract_key_points(transcript.text)
    for point in key_points:
        summary += f"- {point}\n"
    
    return summary

# Usage
print(generate_meeting_summary('your_meeting_id'))

Testing and Debugging

Unit Testing

Keep your code ship-shape:

import unittest

class TestFirefliesIntegration(unittest.TestCase):
    def setUp(self):
        self.ff = Fireflies('TEST_API_KEY')
    
    def test_get_transcript(self):
        transcript = self.ff.get_transcript('test_meeting_id')
        self.assertIsNotNone(transcript)
        # Add more assertions

if __name__ == '__main__':
    unittest.main()

Troubleshooting

If you're hitting a wall, check these common issues:

• API key validity
• Network connectivity
• Correct meeting IDs

Conclusion

And there you have it! You're now equipped to harness the power of Fireflies.ai in your Python projects. Remember, the API documentation is your best friend for diving deeper. Now go forth and build something awesome!

Happy coding! 🚀