Unlocking Large Language Models with LaunchDarkly & Vercel's AI Gateway

Building a Production LLM Data Extraction Pipeline

As machine learning (ML) models become increasingly pervasive in our industry, we're faced with an intriguing challenge: extracting meaningful data from unstructured text. In this article, we'll explore how to build a production-ready data extraction pipeline using LaunchDarkly and Vercel AI Gateway.

The Problem of Unstructured Text

Every conversation your organization has contains signals that your ML models need:

• Customer calls reveal buying intent
• Support tickets expose product friction
• Interview transcripts capture technical depth

However, these signals are buried in thousands of words of unstructured text. This is where traditional tools like Gong and Chorus shine, but they're not designed for extracting specific features with a custom schema.

Requirements for a Production-Ready Pipeline

To extract meaningful data from unstructured text, our pipeline should meet the following requirements:

• Scalability: Handle large volumes of text data without compromising performance
• Customizability: Support a wide range of feature extraction use cases with custom schemas
• Flexibility: Integrate with various ML frameworks and libraries

Solution Overview

We'll leverage LaunchDarkly, a popular feature flagging platform, to manage our pipeline's configuration and Vercel AI Gateway, a serverless platform for building AI-powered applications.

Step 1: Setting up the Pipeline

First, we need to set up our pipeline using LaunchDarkly. We'll create a new project and configure it with the following settings:

• Feature flag: text_extraction
• Environment variable: VERCEL_AI_GATEWAY_API_KEY

Here's an example of how you might do this in your configuration file:

projects:
  my-project:
    features:
      - name: text_extraction
        description: Extract meaningful data from unstructured text

Step 2: Creating a Vercel AI Gateway Function

Next, we'll create a Vercel AI Gateway function to handle the feature extraction logic. This function will be triggered by the text_extraction feature flag.

Here's an example of what your function might look like:

from vercel.ai.gateway import request
import json

def extract_features(text):
    # Your custom feature extraction logic goes here
    return {"features": [{"name": "buying_intent", "value": 0.8}, {"name": "product_friction", "value": 0.2}]}

async def lambda_handler(event):
    if event["featureFlags"]["text_extraction"]:
        text = event["data"]
        features = extract_features(text)
        return json.dumps(features)
    else:
        return json.dumps({"error": "Feature extraction not enabled"})

Step 3: Integrate with Your ML Framework

Finally, we'll integrate our pipeline with your chosen ML framework. This will involve calling the Vercel AI Gateway function from within your ML model.

Here's an example of how you might do this in PyTorch:

import torch
from vercel.ai.gateway import client

# Load pre-trained model and weights
model = torch.load("model.pth")
weights = torch.load("weights.pth")

# Set up Vercel AI Gateway client
client.set_api_key(os.environ["VERCEL_AI_GATEWAY_API_KEY"])

# Call the extract_features function
features = json.loads(client.extract_features(text))

# Use the extracted features in your ML model
output = model(features)

Best Practices and Implementation Details

Here are some best practices to keep in mind when building your pipeline:

• Use a robust data ingestion process: Ensure that your pipeline can handle large volumes of text data without compromising performance.
• Implement feature engineering: Use techniques like tokenization, stemming, and lemmatization to extract meaningful features from unstructured text.
• Monitor and optimize pipeline performance: Regularly monitor your pipeline's performance and make adjustments as needed to ensure optimal results.

By following these guidelines and leveraging the power of LaunchDarkly and Vercel AI Gateway, you'll be well on your way to building a production-ready LLM data extraction pipeline that meets the needs of your organization.

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By Malik Abualzait