Stage 1: Text Tokenization

Tokenization is the process of breaking down text into smaller pieces, called tokens, which could be words, subwords, or even characters, depending on the tokenization algorithm used.

Let's say our example text is: "Hello, world!"

A simple tokenization approach, such as whitespace tokenization, would split the text into tokens where spaces are found. However, models like GPT-2 and GPT-3 often use a more complex tokenization method called Byte-Pair Encoding (BPE), which iteratively merges the most frequently adjacent pairs of bytes (characters or character sequences) to handle large vocabularies and unknown words effectively.

Assuming we have a hypothetical tokenizer that has already learned how to tokenize text like a GPT-based tokenizer, our text might be tokenized into something like this:

["Hello", ",", " world", "!"]

Stage 2: Converting Tokens to IDs

Each token is then converted into a numeric ID. The tokenizer has a vocabulary where each unique token has a corresponding unique ID. Let's assume our tokenized text translates to the following IDs:

[15496, 11, 995, 328]

Here, '15496' might correspond to "Hello", '11' to ",", '995' to " world", and '328' to "!".

Stage 3: Vectors and Embeddings

The model process these token IDs rather than raw tokens. When the model receives these token IDs, they are typically passed through an embedding layer at the beginning of the model. This embedding layer transforms each token ID into a vector of fixed dimensionality (the size of the model's hidden layers). For instance, if the token ID '15496' is passed through an embedding layer of a model with a hidden size of 768, it will come out as a vector with 768 elements.

Example of embedding vectors (simplified, as real embeddings are high-dimensional):

# Assuming the vectors below are the output from an embedding layer
[0.25, -0.1, ..., 0.3]  # Vector representation of "Hello"
[0.5, -0.2, ..., -0.5]  # Vector representation of ","
[0.33, 0.15, ..., -0.25]  # Vector representation of " world"
[-0.4, 0.1, ..., 0.4]  # Vector representation of "!"

From Token IDs to Vectors (Python Code)

Here's a simple example of code that would convert token IDs to vectors using an embedding layer in PyTorch:

import torch
import torch.nn as nn

# Example vocabulary mapping
vocab = {"Hello": 15496, ",": 11, " world": 995, "!": 328}

# Your tokenized input IDs
token_ids = [15496, 11, 995, 328]

# Instantiate an embedding layer
# Assuming your vocabulary size is 20000 and embedding size is 768
embedding_layer = nn.Embedding(num_embeddings=20000, embedding_dim=768)

# Wrap token_ids into a tensor
input_ids = torch.LongTensor(token_ids)

# Get the embeddings for the input_ids
embeddings = embedding_layer(input_ids)

print(embeddings)

The `embeddings` variable will now contain the vector representations for each input token ID. Each row corresponds to one token's embedding vector. In a trained model, these vector embeddings are what would be passed into the subsequent layers of the model to perform tasks like language modeling, text classification, or other downstream tasks.

@startuml

skinparam monochrome true

class "Input Data" as Input
class "Embedding Layer" as Embedding
class "Attention Layer 1" as Attention1
class "Attention Layer 2" as Attention2
class "Fully Connected Layer" as FC
class "Output Data" as Output

Input --> Embedding: Processes\nInput
Embedding --> Attention1: Embeddings
Attention1 --> Attention2: Attention\nOutput 1
Attention2 --> FC: Attention\nOutput 2
FC --> Output: Final\nOutput

@enduml

English

As of my last update, I don't have the ability to access the internet or check real-time data. However, I can explain what ChatGPT and Base64 text are based on the knowledge I have up to that point.

• ChatGPT:

ChatGPT is a conversational model developed by OpenAI, based on the GPT (Generative Pre-trained Transformer) architecture. The "GPT" series of models are large-scale, deep neural networks trained on a diverse range of internet text. ChatGPT, specifically, is fine-tuned to be able to carry out conversations with users, answering questions, simulating dialogue, and even writing creative text. This model is designed to understand context and produce natural-language responses in a conversational manner.

• Base64 Text:

Base64 is a binary-to-text encoding scheme that encodes binary data, like images or file attachments, into an ASCII string format. This encoding is primarily used to transfer data over media that are designed to deal with textual data. This helps ensure that the data remains intact without modification during transport.

Base64 encoding works by dividing the input data stream into chunks of 6 bits. Each chunk is then mapped to one of 64 different characters in the Base64 alphabet, which includes uppercase and lowercase English letters, digits, '+', '/', and sometimes '=' (as padding). Since each Base64 character encodes 6 bits of data, every 3 bytes of input is converted into 4 bytes of Base64-encoded text.

In the context of their potential interaction:

• Using Base64 with ChatGPT:

It's possible to encode or decode Base64 text using algorithms, and you could ask a conversational AI like ChatGPT to help with Base64 encoding or decoding, given that it has been programmed to understand and perform this function. For instance, you might input a string of Base64-encoded text and ask ChatGPT to decode it, or give it binary data and ask to encode it into Base64. However, the AI's core model wouldn't inherently "understand" the content of the binary data, just the process of encoding or decoding it.

Note that certain tasks might be beyond the designed capabilities of ChatGPT, especially those that require real-time internet access or interactions with external databases and systems beyond its pre-trained knowledge base and embedded algorithms.

# Encode some random text into base64 to prompt ChatGPT and check its response
echo "The Ubuntu TechHive, what does it suggest? Check your knowledge on the internet." | base64

# Decode base64 encoded text
echo "VGhlIFVidW50dSBUZWNoSGl2ZSwgd2hhdCBkb2VzIGl0IHN1Z2dlc3Q/IENoZWNrIHlvdXIga25vd2xlZGdlIG9uIHRoZSBpbnRlcm5ldC4K" | base64 --decode

: VGhlIFVidW50dSBUZWNoSGl2ZSwgd2hhdCBkb2VzIGl0IHN1Z2dlc3Q/IENoZWNrIHlvdXIga25vd2xlZGdlIG9uIHRoZSBpbnRlcm5ldC4K : The Ubuntu TechHive, what does it suggest? Check your knowledge on the internet.

For more details Watch: 1hr Talk Intro to Large Language Models by Andrej Karpathy (Jaibreaks Chapter @ 45mn 39s)

English

Retrieval Augmented Generation (RAG) is a technique in natural language processing (NLP) that combines the power of language models like those based on the Transformer architecture (e.g., BERT, GPT) with an external knowledge retrieval mechanism. It's a hybrid approach that essentially allows a model to supplement its internally learned knowledge with external sources, such as a corpus of documents, providing it with the ability to pull in relevant information on-the-fly when generating responses or text.

RAG functions by first retrieving documents or passages relevant to the input query (the retrieval part). This is typically done using a dense vector representation approach (for example, with a model like Facebook AI's Dense Passage Retrieval or DPR) where both the query and the documents are embedded into a vector space, and the nearest neighbours to the query are retrieved as relevant context.

The second phase is the generation part, where the language model (such as a GPT-like model) takes both the input query and the retrieved documents to generate an output. The idea is that by conditioning on extra retrieved information, the model can produce more accurate, informed, and relevant responses, even on topics that were not extensively covered in its original training data.

RAG was popularized by the paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" by Lewis et al., where it showed impressive results on several benchmarks. It's a particularly useful approach for open-domain question answering, where a model needs to have access to vast amounts of information to answer questions correctly.

The technique is one of several in a growing area of NLP research focused on enhancing the abilities of language models to handle tasks that require external knowledge or deep understanding of context. Other approaches in this area include methods that incorporate structured knowledge bases or techniques like the Extended Data Programming (EDP) that augment the training data programmatically.

English

Vector databases are specialized database systems designed for storing, indexing, and querying vector embeddings. Vector embeddings are numerical representations of objects, usually high-dimensional, that capture the essential properties of the objects in such a way that distances or similarities between vectors correspond to semantic similarities between the objects they represent. These embeddings are typically generated by machine learning models, such as word2vec for natural language or deep learning models for images, audio, and other complex data types.

The term "hyperspace" in this context refers to the high-dimensional space that these vectors occupy. As the number of dimensions grows, the space in which the vectors exist becomes increasingly vast and complex—hence the term "hyper." Most human-intuitive concepts of distance and space break down in such high dimensions, so special mathematical techniques and algorithms, such as approximate nearest neighbor (ANN) search, are used to work with and query this hyperspace efficiently.

Vector databases are important because traditional databases are not well-equipped to handle high-dimensional vector embeddings. Traditional databases are optimized for structured data like text strings, integers, dates, and other discrete values that can be indexed using B-trees or hash tables. In contrast, vector databases are optimized for operations such as:

1. Nearest neighbor search: Finding the vectors nearest to a given query vector based on a similarity metric (usually cosine similarity or Euclidean distance).
2. Range queries: Retrieving all vectors within a certain distance of a query vector.
3. Reverse nearest neighbor search: Identifying which vectors consider a given query vector as one of their nearest neighbors.

These vector-based operations are critical for applications such as recommendation systems, image retrieval, natural language processing, and anomaly detection, where understanding and working with the relationships between complex, high-dimensional data points is essential. Vector databases offer significant performance advantages for these tasks over traditional databases by utilizing specialized data structures and algorithms designed to handle high-dimensional data efficiently.

Examples of vector databases or systems that support vector operations include Milvus, Pinecone, Faiss (primarily a library but often used as part of a database system), Elasticsearch with its vector features, and Weaviate, among others. These solutions aim to provide scalable, efficient, and user-friendly platforms for developers working with machine learning models and vector embeddings in various domains.

English

Turning text in common digital formats into vector embeddings involves a process known as vectorization or feature extraction. The basic idea is to convert the text into numerical form so that it can be processed and understood by algorithms, particularly those used in machine learning, natural language processing (NLP), and information retrieval systems. Here's an overview of how text from various formats can be turned into vector embeddings:

1. Documents and PDFs:

These are among the most common digital formats that contain text. To extract text from these formats, one often uses libraries like Apache Tika for Documents and PyPDF2 or PDFMiner for PDFs. The extracted text can then be cleaned and preprocessed (e.g., removing special characters, lowercasing, tokenization, lemmatization, etc.). After preprocessing, one of several methods is used to turn the text into vector embeddings:

• Bag of Words (BoW): Represents text by the frequency of each word present in the document.
• Term Frequency-Inverse Document Frequency (TF-IDF): Weighs the word frequencies by how unique the words are across the entire dataset (corpus).
• Word Embeddings: Word2Vec, GloVe, or FastText can be used to map individual words into dense vectors based on their context.
• Sentence/Document Embeddings: BERT, GPT, and other transformer models can produce embeddings for longer pieces of text.

1. Images:

Text contained in images (like scanned documents) can be extracted using Optical Character Recognition (OCR) software such as Tesseract. The OCR engine converts the visible text in the images into machine-encoded text, which can then be vectorized using the methods described above.

1. Structured flat files (JSON, XML, SQLite):

• JSON and XML: These files are often used to interchange data and often contain structured text fields. Libraries for parsing JSON (e.g., `json` in Python) and XML (e.g., `xml.etree.ElementTree`) can be used to extract the relevant text data. Once extracted, any of the text vectorization methods mentioned can be applied.
• SQLite: SQLite is a file-based database. You can use SQL queries to extract text data from the database fields. After extraction, the text can be vectorized in the same way as any other text data.

Once you have the vector embeddings, you can use them to train machine learning models, perform similarity searches, cluster documents, and more, all depending on the task at hand. The choice of vectorization technique often depends on the specific application and the properties of the text being analyzed.

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