Unveiling RAG: Revolutionizing Information Retrieval and Content Generation.

{Retrieval Augmented Generation}

Section -1

LLMs, or large language models, serve as powerful “generator models” capable of generating text based on provided input. Whether you’re seeking creative output, business ideas, information retrieval, or code generation, LLMs are versatile tools ready to assist. Here’s how they work:

• You can ask LLM to generate poem or rap about a topic
• You can ask LLM to generate business ideas
• You can ask LLM to find out something from the huge chunk of text that you have provided
• You can provide a scenarios and ask LLM to generate python code or SQL code or any other code.

In all the above situation you will be doing the below mentioned things-

1.Prompt — What you want LLM to do {Rap about science like Jesse Pinkman from Breaking Bad}

2. Instructions — How you want LLM to respond back while working on your prompt follow while generating the text {Treat each line as one element of a list and avoid rapping about Gus Fring.}

3. Additional Information — You can provide LLM with extra context or references to aid its generation process. For example, you might provide Jesse Pinkman’s dialogue as “Ground Truth” for LLM’s reference.

Section -2

Technical Approach to Document Question Answering

When faced with the challenge of extracting information from a vast corpus of text, particularly in the context of mergers where details are spread across numerous documents, a systematic and technical approach is essential. Here’s how I would tackle this problem:

1.Text Chunking:

• Break down the 10,000 pages of information into manageable chunks. This can be done at various levels such as character-level, page-level, paragraph-level, or any other suitable granularity.
• Develop code to automate this process, ensuring efficiency and accuracy in chunk segmentation.

2.Text Embedding:

• Once the text is chunked, each chunk needs to be converted into a numerical representation for computational analysis. This is where embedding models come into play.
• Utilize an embedding model to transform the textual data into vector spaces or numerical representations. Embedding models capture semantic information and relationships between words or phrases.
• These numerical representations enable efficient comparison, similarity analysis, and retrieval of relevant information.

3.Question Answering Model:

• Implement a question answering model capable of processing the numeric representations of both the question and the chunks of text.
• This model should be trained on question-answer pairs or utilize techniques like BERT (Bidirectional Encoder Representations from Transformers) for contextual understanding.
• The model should effectively retrieve relevant chunks of text based on the query and provide the corresponding answers.

4.Scalability and Optimization:

• Consider scalability and optimization strategies to handle large volumes of text data efficiently.
• Techniques such as parallel processing, distributed computing, or using pre-trained models can enhance performance and reduce computational overhead.

Fig.1 Embedding model returning stream of vectors based on inputs

After segmenting 10,000 pages into individual chunks, you’ve fed them into an embedding model for conversion. The model has then produced a continuous stream of vectors, each representing a unique aspect of the text. These vectors were diligently captured and deposited into a specialized database known as vectorDB. Unlike conventional databases, vectorDB is tailored specifically to accommodate and manage high-dimensional vector data efficiently. Further insights into vectorDB will be provided in the subsequent chapter. In the meantime, refer to the accompanying image for a brief overview of vectorDB’s functionalities.

Normal Text

Tokens

Tokenization

Embedding or vector representation in “n” dimension

“Yo”: [0.21144, 0.63053, -0.020552, …] (N-dimensional vector)
“,”: [0.418, 0.24968, -0.41242, …]
“yo”: [0.48271, -0.31877, 0.5071, …]
“check”: [0.024074, 0.20902, 0.26508, …]
“it”: [0.60993, 0.67703, -0.01279, …]
“out”: [0.022701, 0.26177, 0.26458, …]
“I”: [0.41055, 0.89837, 0.11374, …]
“‘m”: [0.011283, 0.21698, 0.011683, …]
“breaking”: [0.086786, -0.19157, 0.11083, …]
“bad”: [-0.14788, 0.5065, -0.03543, …]
“with”: [0.009877, 0.42413, 0.046038, …]
“the”: [0.70627, 0.28223, 0.0071297, …]
“knowledge”: [0.12835, -0.42525, -0.16186, …]
“I”: [0.41055, 0.89837, 0.11374, …]
“possess”: [0.072617, -0.10663, 0.052415, …]
“,”: [0.418, 0.24968, -0.41242, …]
“Cooking”: [0.14478, 0.20488, 0.49475, …]
“up”: [0.12293, 0.80297, 0.42082, …]
“rhymes”: [-0.33935, 0.20912, 0.46348, …]
“hotter”: [-0.16856, 0.19461, -0.31518, …]
“than”: [0.27062, 0.045789, -0.6916, …]
“Walter”: [0.16942, 0.79715, -0.11892, …]
“White”: [0.059205, 0.111, -0.046535, …]
“‘s”: [0.42744, 0.16559, 0.75444, …]
“meth”: [-0.21734, 0.46515, 0.25789, …]
“success”: [-0.074976, -0.061078, 0.22443, …]
“.”: [0.43251, -0.1061, 0.012001, …]

RAG

Now, you’ve stored this comprehensive dataset in a specialized vector database (Vec DB), equipped to handle high-dimensional vector data efficiently. The process unfolds as follows:

Step 1: User queries the system with a question.

Step 2: Utilizing advanced similarity search algorithms, the system conducts a thorough comparison across all 10,000 documents, ranking them based on the similarity between the tokens present in the question and those within the documents.

Step 3: Documents are further prioritized based on their relevance, selecting the top 5 or 10 documents from the sorted list.

Step 4: These top-ranked documents are then fed into an LLM (Large Language Model) along with the original question. The LLM meticulously analyzes the text within these documents and generates a nuanced response tailored to the user’s query.

This intricate process ensures that users receive precise and informative answers drawn from the most relevant documents within the dataset, facilitated by the seamless integration of advanced search algorithms, vector database technology, and cutting-edge language models.

And that’s how you create a Simple RAG application.

Created RAG Pipeline with Draw.io.

Our observations reveal several key insights into the capabilities of LLMs:

1. Text Generation: Leveraging LLMs for text generation, we integrated organizational data to produce contextually relevant outputs. This fusion of text generation with organizational data harnesses the LLM’s prowess in generating informative content tailored to specific contexts.
2. Broad Knowledge Base: LLMs, having been trained on vast repositories of internet data, possess extensive knowledge encompassing movies, TV series, characters, and their dialogues. This rich understanding of cultural references empowers LLMs to infuse generated text with nuanced insights and references.
3. Next Token Prediction: Through its training process, LLMs have acquired the ability to discern patterns and anticipate subsequent tokens in a given sequence. This predictive capability enables them to generate coherent and contextually relevant text with remarkable accuracy.

In Practice:

To demonstrate the utility of LLMs, we developed a rudimentary application based on the Retrieval-Augmented Generation (RAG) framework, facilitating question-and-answer interactions with datasets. In the subsequent chapter, we delve deeper into various strategies and methodologies for implementing RAG applications, exploring diverse approaches to leverage the full potential of this innovative framework.

Kindly provide your feedback. Feel free to share your doubts and questions, and I’ll do my best to provide clear and concise explanations or solutions. Your understanding is my priority, so don’t hesitate to ask for clarification or assistance on any topic. Let’s work together to resolve any queries you may have!

LLM prompting guide

What are Vector Embeddings | Pinecone

What is a Vector Database & How Does it Work? Use Cases + Examples | Pinecone

Byte-Pair Encoding tokenization - Hugging Face NLP Course

Thanks

LK