Real-Time AI Applications with Open-Source LLMs

Ahmad W KhanAhmad W Khan
8 min read

The landscape of real-time AI applications has been transformed by open-source Large Language Models (LLMs) like LLaMA, Falcon, and GPT-J. These models, when combined with powerful tools like Hugging Face, LangChain, and Ollama, enable developers to process and act upon live data streams in milliseconds.

This guide will take you through building real-time AI applications.

Table of Contents

  1. Understanding Open-Source LLMs and Real-Time Applications

    • The Rise of Open-Source LLMs

    • Real-Time Applications in Action

    • Key Challenges and Considerations

  2. The Ecosystem of Tools and Frameworks

    • Hugging Face Transformers

    • LangChain

    • Ollama

    • Complementary Tools: Kafka, WebSockets, FastAPI

  3. Preparing Your Environment

    • System Requirements

    • Setting Up Hardware and Software

    • Installing Necessary Libraries

    • Testing Your Environment

  4. Designing Real-Time AI Applications

    • Core Principles

    • Architectural Patterns

    • Use Case Analysis

  5. Building a Real-Time LLM Pipeline

    • Connecting to Streaming Data Sources

    • Preprocessing Real-Time Data

    • Creating Real-Time Inference APIs

  6. Real-Time Applications with LangChain

    • Using LangChain Memory for Stateful Interactions

    • Advanced Workflows with LangChain Agents

    • Real-Time Chain Composition

  7. End-to-End Project: Sentiment Analysis System

    • Twitter Streaming API Setup

    • Building a Sentiment Prediction Pipeline

    • Visualizing Live Results

  8. Optimizing Performance and Scalability

    • Techniques for Low Latency

    • Distributed Inference Systems

    • Batch Processing and Parallelism

  9. Deployment and Real-World Use Cases

    • Deployment Strategies: Cloud, Edge, and Hybrid

    • Real-World Applications: Customer Support, Content Generation, IoT

  10. Conclusion

1. Understanding Open-Source LLMs and Real-Time Applications

Open-source LLMs have made advanced AI accessible to developers, businesses, and researchers worldwide. Unlike proprietary models like GPT-4, open-source LLMs are free to use, transparent, and customizable.

Why Open-Source LLMs?

  1. Transparency: Understand and modify the model’s architecture.

  2. Customizability: Fine-tune models for specific use cases.

  3. Cost-Effectiveness: Avoid high subscription fees for proprietary models.

Key Players in Open-Source LLMs

  • LLaMA 2 (Meta): Lightweight and efficient, ideal for low-resource environments.

  • Falcon: High-performance model optimized for text generation.

  • GPT-J and GPT-NeoX (EleutherAI): Versatile models with strong community support.

Real-Time Applications in Action

Real-time applications use AI to process and respond to live data streams. Some prominent use cases include:

  1. Chatbots and Virtual Assistants: Provide instant, context-aware responses.

  2. Sentiment Analysis: Monitor public opinion or brand sentiment in real-time.

  3. Event Detection: Identify anomalies or trends in streaming data.

  4. Content Generation: Adapt content based on user preferences.

Key Challenges and Considerations

  • Latency: Minimizing response time is critical.

  • Scalability: Handling large volumes of data in real-time.

  • Model Performance: Balancing accuracy with speed.

  • Ethical Issues: Ensuring unbiased and safe AI outputs.

2. The Ecosystem of Tools and Frameworks

Creating real-time AI applications requires integrating multiple tools for LLM inference, data streaming, and API management.

Hugging Face Transformers

Hugging Face is a cornerstone of the open-source LLM ecosystem.

Features:

  • Support for hundreds of pretrained models.

  • APIs for text generation, translation, and summarization.

  • Fine-tuning capabilities for custom datasets.

Example: Using Hugging Face for Inference

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct")
model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-7b-instruct")

input_text = "Explain quantum mechanics in simple terms."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))

LangChain

LangChain orchestrates complex workflows by chaining LLMs with tools and APIs.

Key Features:

  1. Memory: Persistent state for conversations.

  2. Agents: Decision-making capabilities using external tools.

  3. Streaming: Process live data feeds seamlessly.

Ollama

A privacy-focused tool for running LLMs locally.

Why Use Ollama?

  • Lightweight and containerized.

  • Ideal for edge devices or secure environments.

Supporting Frameworks

  • Kafka: Scalable, distributed event streaming.

  • FastAPI: Lightweight API development framework.

  • WebSockets: Real-time client-server communication.

3. Preparing Your Environment

To build a production-ready system, it’s essential to set up the right environment.

System Requirements

  • Hardware: NVIDIA GPU with at least 12GB VRAM.

  • Software:

    • Python 3.9+

    • Docker (optional for containerization).

Installing Libraries

Install the required tools using pip:

pip install transformers langchain fastapi uvicorn kafka-python

Testing the Environment

Verify installation with a sample LLM inference:

from transformers import pipeline
generator = pipeline("text-generation", model="gpt2")
print(generator("The future of AI is", max_length=30))

Troubleshooting Tips

  1. Ensure GPU drivers are installed correctly.

  2. Use torch.cuda.is_available() to verify GPU availability.

4. Designing Real-Time AI Applications

Core Principles

  1. Low Latency: Optimize every step of the pipeline.

  2. Scalability: Plan for increasing data volumes.

  3. Modularity: Design reusable components.

Architectural Patterns

  • Event-Driven Architectures: Trigger actions based on events (e.g., Kafka consumers).

  • Microservices: Decouple components for better scalability.

5. Building a Real-Time LLM Pipeline

Step 1: Data Ingestion

Use Kafka for ingesting live data streams.

from kafka import KafkaConsumer
consumer = KafkaConsumer('my-topic', bootstrap_servers='localhost:9092')
for message in consumer:
    print(message.value)

Step 2: Real-Time Inference

Deploy Hugging Face models as a REST API:

from fastapi import FastAPI

app = FastAPI()

@app.post("/predict")
async def predict(input_text: str):
    inputs = tokenizer(input_text, return_tensors="pt")
    outputs = model.generate(**inputs)
    return {"response": tokenizer.decode(outputs[0])}

Step 3: Response Delivery

Integrate WebSockets for instant feedback:

from fastapi import WebSocket

@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    while True:
        data = await websocket.receive_text()
        result = predict(data)
        await websocket.send_text(result)

6. Real-Time Applications with LangChain

LangChain simplifies integrating LLMs into complex real-time applications by chaining multiple models, tools, and external APIs. Let’s explore its capabilities in detail.

LangChain Memory for Stateful Interactions

In real-time applications, stateful conversations (e.g., chatbots) need memory to maintain context across multiple exchanges. LangChain’s memory module is ideal for this.

Example: Conversational Memory

from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.llms import HuggingFacePipeline
from transformers import pipeline

# Load LLM
hf_pipeline = pipeline("text-generation", model="gpt2")
llm = HuggingFacePipeline(pipeline=hf_pipeline)

# Create conversation chain with memory
memory = ConversationBufferMemory()
conversation = ConversationChain(llm=llm, memory=memory)

# Simulate a conversation
print(conversation.run("What is the capital of France?"))
print(conversation.run("What about Germany?"))

Advanced Workflows with LangChain Agents

LangChain agents allow LLMs to make decisions and interact with tools like search engines, calculators, or APIs.

Example: Using an Agent with a Tool

from langchain.agents import load_tools, initialize_agent
from langchain.llms import HuggingFacePipeline

tools = load_tools(["serpapi"])  # Use SerpAPI for web searches
agent = initialize_agent(tools, llm, agent="zero-shot-react-description")

# Ask a question requiring tool usage
response = agent.run("Who won the FIFA World Cup in 2022?")
print(response)

Real-Time Chain Composition

LangChain supports building custom pipelines for real-time tasks, such as routing user queries to different APIs based on intent.

Example: Multi-Step Real-Time Chain

from langchain.chains import SequentialChain
from langchain.prompts import PromptTemplate

# Define prompt templates
intent_prompt = PromptTemplate(input_variables=["input"], template="Classify intent: {input}")
query_prompt = PromptTemplate(input_variables=["input"], template="Query: {input}")

# Create chains
intent_chain = LLMChain(llm=llm, prompt=intent_prompt)
query_chain = LLMChain(llm=llm, prompt=query_prompt)

# Combine chains
real_time_chain = SequentialChain(chains=[intent_chain, query_chain])
print(real_time_chain.run("What is the weather in Paris?"))

7. Example Project: Real-Time Sentiment Analysis System

In this project, we’ll create a complete real-time sentiment analysis system using the Twitter API, Hugging Face models, and LangChain.

Step 1: Set Up Twitter Streaming API

Authenticate with Twitter API

import tweepy

API_KEY = "your_api_key"
API_SECRET = "your_api_secret"
ACCESS_TOKEN = "your_access_token"
ACCESS_TOKEN_SECRET = "your_access_token_secret"

auth = tweepy.OAuthHandler(API_KEY, API_SECRET)
auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)
api = tweepy.API(auth)

# Stream Listener
class StreamListener(tweepy.StreamListener):
    def on_status(self, status):
        print(status.text)

# Stream tweets
listener = StreamListener()
stream = tweepy.Stream(auth=api.auth, listener=listener)
stream.filter(track=["AI", "machine learning"])

Step 2: Sentiment Analysis Pipeline

Using Hugging Face Sentiment Analysis

from transformers import pipeline

# Load sentiment analysis pipeline
sentiment_pipeline = pipeline("sentiment-analysis")

# Analyze sentiment
def analyze_sentiment(text):
    result = sentiment_pipeline(text)
    return {"label": result[0]["label"], "score": result[0]["score"]}

Step 3: Integrate with LangChain for Enriched Context

Enhance the pipeline by integrating LangChain for contextual understanding.

Example: LangChain Sentiment Contextualization

from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# Define sentiment contextualization prompt
context_prompt = PromptTemplate(
    input_variables=["input", "sentiment"],
    template="Given the text: '{input}' and its sentiment: {sentiment}, provide deeper insights."
)

# Chain for contextualization
context_chain = LLMChain(llm=llm, prompt=context_prompt)

# Enrich sentiment analysis
def enriched_analysis(text):
    sentiment = analyze_sentiment(text)
    context = context_chain.run(input=text, sentiment=sentiment["label"])
    return {"sentiment": sentiment, "context": context}

Step 4: Real-Time Dashboard

Build a real-time dashboard to visualize results using Streamlit.

epip install streamlit

Dashboard Code

import streamlit as st

st.title("Real-Time Sentiment Analysis")
st.text_area("Tweet", placeholder="Streamed tweets will appear here...", height=200)

# Add sentiment analysis results
st.write("Sentiment Results")
st.json(enriched_analysis("AI is revolutionizing the world!"))

Run the dashboard:

streamlit run app.py

8. Optimizing Performance and Scalability

Real-time applications must be optimized for low latency and high throughput. Here are the strategies to achieve this:

Optimizing Latency

  1. ONNX Conversion: Convert models to ONNX format for faster inference.

     from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from onnxruntime import InferenceSession

 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
 model.save_pretrained("onnx_model")

 session = InferenceSession("onnx_model.onnx")

  2. FP16 Precision: Use half-precision models for faster computations.

     from transformers import AutoModel
 model = AutoModel.from_pretrained("gpt2", torch_dtype=torch.float16)

Scaling for High Throughput

  1. Distributed Inference: Deploy multiple instances of the model across GPUs.

  2. Load Balancing: Use tools like NGINX to distribute traffic.

9. Deployment and Real-World Use Cases

Deployment Strategies

  1. Cloud Deployment: Use AWS SageMaker or Google AI Platform for scalable deployments.

  2. Edge Deployment: Use Ollama or TensorFlow Lite for on-device inference.

  3. Hybrid Deployment: Combine cloud and edge solutions for optimal performance.

Real-World Applications

  1. Customer Support Chatbots: Real-time query resolution with memory and personalization.

  2. IoT Event Monitoring: Analyze sensor data for actionable insights.

  3. Live Content Moderation: Filter inappropriate content in live chats.

10. Conclusion

Real-time AI applications powered by open-source LLMs have limitless potential. By mastering tools like Hugging Face, LangChain, and Kafka, developers can build scalable, responsive systems that redefine user experiences. Start small, optimize, and scale your applications to bring real-time AI to life.

If you found this article helpful or want to reach out to me to discuss anything, then feel free to reach out to me: AhmadWKhan.com.

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Written by

Ahmad W Khan
Ahmad W Khan