Beyond Basic Chatbots: Leveraging LLMs and RAG for Smarter Conversations
Introduction
Tired of generic chatbots? Let's build one that truly understands your data. By combining Large Language Models (LLMs) with modern technologies like Vector Databases and Retrieval-Augmented Generation (RAG), we can create an AI assistant that gives precise, context-aware responses without hallucination.
In this guide, you’ll learn to:
Harness the power of VectorDB, RAG, LLMs, and Gradio
Build a chatbot that delivers accurate answers from your private data
Deploy your intelligent assistant step-by-step
Ready to create an AI that thinks just for you? Let’s dive in!
Vector Databases (VectorDB)
A vector database is a specialized type of database optimized for storing, retrieving, and managing vector embeddings, handling high-dimensional data unlike traditional databases designed for structured data. They are silent powerhouses for advanced ML and NLP tasks.
Workflow of VectorDB
Key components of VectorDB
1. Vector Embeddings: Numerical representations of data in high-dimensional vectors capturing semantic meaning, applicable to words, sentences, and documents.
2. Embedding Model: A machine learning model that converts raw data (text, images, or audio) into high-dimensional vectors (embeddings). Examples include Word2Vec and BERT for text and CNNs for images.
3. Similarity Search: Finding vectors in a vector database that are most similar to a query vector using metrics like Euclidean distance, cosine similarity, or inner product.es.
FAISS VectorDB
In our blog, we leverage FAISS (Facebook AI Similarity Search) to handle efficient similarity search and clustering of dense vectors. FAISS is a powerful library designed to search through large sets of vectors, even those exceeding RAM capacity. This capability is crucial for our needs, as it ensures that we can perform fast and accurate searches across extensive datasets. Additionally, FAISS provides robust tools for evaluation and parameter tuning, enabling us to optimize our search algorithms for better performance.
Retrieval-Augmented Generation (RAG)
RAG integrates information retrieval systems with generative large language models (LLMs) to provide contextually relevant and accurate responses. This approach addresses issues of hallucination and context in LLMs, ensuring more reliable and coherent outputs.
RAG = Retrieval-based model + Generative-based model
Retrieval-based models: Extract information from external knowledge sources like databases, articles, or websites.
Generative-based models: Generate text using language generation capabilities.
Key components of RAG
1. The User
2. VectorDB (technique used for retrieval)
3. Generative AI System (LLMs)
Workflow of RAG
LLM Model
To build an effective and interactive chatbot, we are utilizing the “microsoft/Phi-3-mini-4k-instruct” model from Microsoft’s Phi-3 series. This model is a compact, efficient transformer-based model optimized for various NLP tasks, especially instruction-based applications. Fine-tuned on instructional data, it generates detailed, contextually appropriate responses, making it ideal for educational tools, customer support, content creation, and interactive applications. Despite its small size, it delivers high-quality text generation, demonstrating versatility across different use cases. The model and its tokenizer are easily integrated and deployed via Hugging Face.
2. Fine-tuning: Supervised Fine-tuning (SFT) and Direct Preference Optimization (DPO) ensure alignment with human preferences and safety guidelines.
3. Inputs: Text. It is best suited for prompts using a chat format.
4.Context length: 4K tokens
5. Outputs: Generated text in response to the input
<|system|>
You are a helpful assistant.<|end|>
<|user|>
How to explain Internet for a medieval knight?<|end|>
<|assistant|>
where the model generates the text after
<|assistant|>
Gradio: Simplifying Machine Learning Interfaces
Gradio is an open-source Python package that transforms complex machine-learning models into accessible, user-friendly applications. While machine learning traditionally requires specialized hardware, software, and technical expertise, Gradio breaks down these barriers by enabling developers to create intuitive interfaces with just a few lines of code. These interfaces can be easily embedded in Python notebooks or shared through URLs, making ML models more collaborative and accessible. The framework supports a wide range of customizable UI components compatible with popular ML frameworks like TensorFlow and PyTorch, as well as standard Python functions.
How to build a Chatbot given Contextual and Non-Contextual inputs
Now that we understand our key components, let’s bring them together to build our intelligent chatbot. Our implementation follows a logical three-step architecture.
First, we’ll implement a vector database (VectorDB) as our foundation, leveraging the embedding capabilities we discussed to enable efficient similarity search.
Next, we’ll connect this VectorDB to our chosen LLM, creating a bridge between our stored knowledge and the generation capabilities we explored earlier.
Finally, we’ll wrap everything in a Gradio interface, using the UI components we covered to create an accessible user experience. This architecture ensures that our chatbot can not only understand and retrieve relevant information but also present it in a way that’s intuitive for users.
Let’s dive in!
1. Implementing a vector database (VectorDB)
1. Environmental Setup: Prepare the necessary tools and libraries, including Python, FAISS, and KeyBert library, to create a suitable development environment for implementing Vectordb.
!pip install faiss-gpu keybert
3.FAISS Implementation:
Initialize the variables and reset the global variables: To maintain a clean and predictable state in your application, particularly when handling multiple operations or datasets sequentially, it’s essential to reset global variables. Resetting the global variables ensures that previous data does not interfere with new operations, providing a clean slate for each new dataset.
!pip install faiss-gpu keybert
Embedding Model & Tokenizer: The BERT-base-uncased model serves as our text embedding solution, offering bidirectional context understanding through its 12-layer Transformer architecture. This pre-trained model, available via Hugging Face, processes up to 512 tokens and handles text in lowercase format for improved efficiency. We’ll utilize both the model and its corresponding tokenizer for converting text into meaningful vector representations.
Text Chunking: Check the number of tokens and split the text into chunks: Vectors in a Vector Database have a predefined size (e.g. 1536 bytes), and each vector should be the same size. The most common solution is to split the given text into “chunks”, create an embedding for each chunk, and store all the chunks into the vector database.
Generate the vector embeddings & implement vectordb: We will generate vector embeddings for our text chunks to prepare them for insertion into the vector database. These embeddings transform text into numerical representations that FAISS can efficiently search and cluster..
For example, using an embedding framework, text like ‘name’ can be transformed into a numerical representation like:
Create Search vector and get the relevant content: We’ll use KeyBERT for keyword extraction, which will help us identify key phrases within the text. These keywords can then be used as search queries to find the most relevant information in our vector database.
2. Integrating VectorDB with Generative AI System (LLMs)
1. LLM Model
Check if GPU support is available or not:
2. Text Generation Pipeline: Pipelines offer an efficient way to use models for inference, especially when we need to handle large-scale data or deploy models on devices with limited resources.
Quantization: It is a technique used to reduce the computational load and memory footprint of a neural network model by representing its weights and activations with lower precision data types, such as bfloat16, int8 instead of the usual float32. By reducing the precision from 32-bit floats to 16-bit floats (bfloat16), the model consumes less memory, which is critical when deploying large models on devices with limited resources.
3. Generating and Processing Text Responses: With the text generation pipeline set up, we can now focus on generating and processing text responses based on contextual and non-contextual inputs.
Contextual Input: It involves providing specific information on a particular topic, allowing you to ask the chatbot questions related solely to that given information. The bot will then generate answers based exclusively on the provided content.
Non-Contextual Inputs: It allows you to ask the chatbot questions without providing any prior specific information. The bot will generate answers based on general knowledge and available data, without being confined to a specific context.
3. Implementing UI for the Chatbot service using Gradio
1. Environmental Setup:
2. Initialize required variables:
3. Process the given text to get a relevant context: Process the input text and retrieve relevant context.
4. Format the UI Interface:
5. Process the question given a specific context: process the user’s question when a specific context is provided.
6. Process the question if no context is given: Handles cases where the user asks a question without any specific context.
7.Implement the functionality of Clear button: Clears the text and reset the interface state.
8. Launch Gradio: Launch the Gradio interface, allowing users to interact with our chatbot.
About the Author
Karthika Rajan Nair completed a Data Science internship with the Machine Learning team at Founding Minds, where she collaborated closely with the team and authored this blog as a result of her work. With a strong foundation in Python, machine learning frameworks, and cloud technologies, Karthika is passionate about advancing her skills to drive innovation and address complex data challenges. She is pursuing a Master’s degree in Data Science at the University at Buffalo, New York.
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