All Too Swift: Real-Time Reddit Processing Simplified with AI

What if you could instantly spot and respond to millions of Reddit comments, all in real-time? No delays, no limits—just fast, seamless insights as they happen.

In this guide, we’ll show you how to set up a real-time data processing system using powerful AI models with LLM Workers. To bring it to life, we’ll use a Taylor Swift bot as an example, a bot that scans Reddit comments in real-time to find and respond to discussions about Taylor Swift. ✨

Table of Contents 🗂️

1. The Power of Real-Time Data Processing and Dedicated Inference
2. Building the Reddit Bot
   • Step 1: Set Up Your Environment
   • Step 2: Setting Up Reddit and Initializing Cortecs Model
   • Step 3: Define the Classification and Response Chains
   • Step 4: Stream and Process Reddit Comments in Real-Time
3. Conclusion

---

The Power of Real-Time Data Processing and Dedicated Inference

We all know that real-time applications demand high performance, especially when you're dealing with large amounts of data. However, the challenge of processing data quickly and efficiently is easily resolved by using dedicated inference and this is where Cortecs really shines.

By leveraging Cortecs' dedicated inference models, you get a system that:

• Handles High Volumes: Process hundreds of requests per second without throttling with the ability to scale seamlessly using LLM Workers dedicated to specific tasks.
• Maintains Consistency: With dedicated resources like LLM Workers, you can count on stable latency, no matter the load.
• Is Easy to Implement: You don’t need to worry about complex infrastructure or performance fine-tuning; it just works.

Why Dedicated Inference Matters

Traditional inference models often share resources with other users, leading to bottlenecks during peak times. With dedicated inference, you get exclusive access to computational resources, ensuring that your system remains reliable and fast even under heavy loads. This makes it ideal for applications like fraud detection, customer service automation, and content moderation.

---

Building the Reddit Bot 🛠️

Step 1: Set Up Your Environment

Before diving into the code, you need to install a few libraries:

pip install praw langchain-core cortecs-py

These libraries serve the following purposes:

• praw: The Python Reddit API Wrapper.
• langchain: A framework that helps you work with language models.
• cortecs: The platform that provides high-performance models for real-time inference.

After that, to authenticate and access the Cortecs models, you need to create an account at Cortecs.ai.
Once you’ve signed up, go to your profile page, generate your access credentials, and set them as environment variables in your code.

import os

# Set the Cortecs API credentials as environment variables
os.environ["OPENAI_API_KEY"] = "your_openai_api_key"
os.environ["CORTECS_CLIENT_ID"] = "your_cortecs_client_id"
os.environ["CORTECS_CLIENT_SECRET"] = "your_cortecs_client_secret"

Step 2: Setting up Reddit and Initializing Cortecs Model

Then, you'll need to create a Reddit account and register your application to get API access. To do this, visit Reddit's API page and create a new application to obtain your Client ID and Client Secret.

Once you have your Client ID and Client Secret, you can initialize the Reddit API client and set up the Cortecs model for real-time inference as follows

import praw
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from cortecs_py import Cortecs
from cortecs_py.integrations.langchain import DedicatedLLM

if __name__ == '__main__':
   # Choose the model for real-time inference
   model_name = 'cortecs/phi-4-FP8-Dynamic'
   cortecs = Cortecs()
   # Set up Reddit API credentials
   reddit = praw.Reddit(
       client_id="YOUR_CLIENT_ID",       # Replace with your Client ID
       client_secret="YOUR_CLIENT_SECRET",  # Replace with your Client Secret
       user_agent="YOUR_USER_AGENT"     # Replace with your User Agent
)

Note that model_name refers to the model you choose for inference. In this example, we’ve selected the cortecs/phi-4-FP8-Dynamic model, which is suitable for many general-purpose tasks. You can find a list of models here.

Step 3: Define the Classification and Response Chains

In this step, we initialize the model for real-time processing and define the classification and response chains that will be used to process the posts and generate responses.

    with DedicatedLLM(cortecs, model_name, context_length=1500, temperature=0.) as llm:  
        prompt = ChatPromptTemplate.from_template("""
        Given the reddit post below, classify it as either `Art`, `Finance`, `Science`, `Taylor Swift` or `Other`.
        Do not provide an explanation.

        {channel}: {title}\n Classification:""")
        classification_chain = prompt | llm | StrOutputParser()

        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are the biggest Taylor Swift fan."),
            ("user", "Respond to this post:\n {comment}")
        ])
        response_chain = prompt | llm

So we defined two main tasks (or "chains"):

• Classification Chain: The first prompt defines the classification logic for Reddit posts. It takes the post title and subreddit as input and classifies the post into categories such as Art, Finance, Science, Taylor Swift, or Other. The StrOutputParser() ensures that the output is in the desired format.

• Response Chain: The second prompt generates a response if the post is about Taylor Swift. We use a system message to indicate that the model should behave as a "biggest Taylor Swift fan" and a user message to define the format for the response.

Step 4: Stream and Process Reddit Comments in Real-Time

With the classification and response chains in place, the next step is to continuously stream Reddit comments and process them in real time. This allows the bot to react to posts as they come in.

        # scan reddit in realtime 
        for post in reddit.subreddit("all").stream.comments():
            topic = classification_chain.invoke({'channel': post.subreddit_name_prefixed, 'title': post.link_title})
            print(f'{post.subreddit_name_prefixed} {post.link_title}')
            if topic == 'Taylor Swift':
                response = response_chain.invoke({'comment': post.body})
                print(post.body + '\n---> ' + response.content)

This code:

• Stream Comments: Continuously monitor Reddit for new comments.
• Classify Posts: Use the classification chain to categorize each post.
• Respond to Specific Topics: If a post is classified as related to Taylor Swift, the bot responds with a pre-defined message.

While running the code, you can monitor the progression of the model execution on the console page of the Cortecs web interface, as shown in the image below.

---

Conclusion 🎉

Building real-time applications can be challenging, but with the right tools, they become much more manageable. By using LLM Workers, you can process high volumes of data without compromising performance. Whether you're classifying content, detecting trends, or automating responses, the approach shown here can be easily adapted to fit your needs.

Now, it’s your turn to try it out. Start experimenting with real-time data processing and explore the possibilities! 🚀