What the hell is CustomGPT!!?

Imagine asking a super-smart assistant about your favorite movie, and instead of searching through dusty old files, it instantly pulls out exactly what you need—like magic! That’s Retrieval Augmented Generation (RAG) in action.

RAG is like giving AI a superpower—a memory! It combines the brains of a language model (like GPT) with a retrieval system that fetches the right information from a vast ocean of data. It's the secret sauce behind the most advanced AI today!

The Power of Custom GPT Models

Now, imagine tailoring these powerful models specifically for your needs—that's where custom GPT models come into play!

Why Customize?

• Domain Specificity: Train models on your unique data to get highly relevant responses.

• Enhanced Performance: Fine-tuned models understand and generate content that aligns perfectly with your business context.

• Data Privacy: Keeping sensitive information within a custom model ensures better security.

Impact on Businesses and the World:

• Improved Customer Service: Instant, accurate responses tailored to customer needs boost satisfaction.

• Efficient Decision Making: Access to precise information helps leaders make informed choices swiftly.

• Innovation Acceleration: Customized models can process and generate ideas faster, driving innovation across industries.

• Educational Advancement: Personalized learning experiences become possible, catering to individual student needs globally.

Getting Started with Custom GPT:

• Utilize platforms like OpenAI's Fine-Tuning API to train models on your dataset.

• Combine with RAG systems for a powerhouse solution that retrieves and generates information seamlessly.

• Leverage frameworks like LangChain and Llama Index to build sophisticated applications with ease.

Lets Build:

Here's a breakdown of how you can create a full Retrieval Augmented Generation (RAG) bot using both Llama Index and LangChain.

Llama Index RAG Bot with Custom GPT

1. Document Loading

pythonCopy codefrom llama_index import SimpleDirectoryReader

# Load documents from a specified directory
documents = SimpleDirectoryReader('data/').load_data()

2. Custom GPT Setup

pythonCopy codefrom llama_index import LLMPredictor
from langchain import OpenAI

# Define a custom GPT model with specific parameters
llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.7))

3. Building the Vector Index

pythonCopy codefrom llama_index import GPTSimpleVectorIndex

# Create a vector index using the loaded documents and custom GPT
index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor)

4. Querying the Index

pythonCopy code# Define the query
query = "What are the applications of Quantum Computing?"

# Query the vector index and get a response
response = index.query(query)

# Print the response
print(response)

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-LangChain RAG Bot with Custom GPT

1. Document Loading

pythonCopy codefrom langchain.document_loaders import DirectoryLoader, TextLoader

# Load documents from a directory with specific file types
loader = DirectoryLoader('data/', glob="*.txt", loader_cls=TextLoader)
documents = loader.load()

2. Custom GPT Setup

pythonCopy codefrom langchain.llms import OpenAI

# Set up the custom GPT model with the desired parameters
llm = OpenAI(temperature=0.7)

3. Creating Embeddings and Building the Vector Store

pythonCopy codefrom langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS

# Generate embeddings for the documents
embeddings = OpenAIEmbeddings()

# Build the vector store using FAISS for similarity search
faiss_index = FAISS.from_documents(documents, embeddings)

4. Querying the Vector Store

pythonCopy code# Define the query
query = "What are the applications of Quantum Computing?"

# Perform a similarity search to find relevant documents
docs = faiss_index.similarity_search(query)

# Generate a response using the custom GPT model
response = llm.generate([doc.page_content for doc in docs])

# Print the response
print(response)

Comparison and Use Cases:

Llama Index:

• Pros: Simplified setup, directly integrated with custom GPT, good for smaller, focused applications.

• Use Case: Ideal for scenarios where you want a quick setup with minimal configuration.

LangChain:

• Pros: Highly customisable, more control over the entire process, including document loading, embedding creation, and querying.

• Use Case: Suitable for complex applications where fine-tuning and advanced configuration are required, especially when handling larger datasets.

Current State of the Art:

The tech powering RAG is evolving rapidly:

• GPT-4 Models: Delivering human-like understanding and generation.

• Google's Gemini: Combining strengths of GPT and reinforcement learning.

• Meta's Llama 3.1: Open-source model focusing on accessibility and customization.

• Vector Databases: Tools like Pinecone, FAISS, and Weaviate enable lightning-fast data searches.

Resources & Frameworks:

• LangChain: Build complex applications leveraging multiple LLMs.

• Llama Index: Simplify connecting LLMs with your custom data.

• Pinecone: Scale your vector searches effortlessly.

• Weaviate: An open-source solution for semantic searches.

Checkout the full code and implementation on GitHub : "Custom_GPT"

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By embracing RAG and custom GPT models, we're not just making smarter machines—we're crafting tools that understand and evolve with us, making the future more efficient, innovative, and exciting!

Let’s harness this technology to transform ideas into reality and drive meaningful change across the globe.

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Kunal Mohare ,

Signing Off.

LinkedIn | Github