What are all of the components of a RAG pipeline?

@startuml
skinparam monochrome true

actor User
User -> Preprocessing : Input Query
Preprocessing -> Retriever : Processed Query
Retriever -> DocumentStore : Retrieve Documents
DocumentStore --> Retriever : Documents
Retriever -> Generator : Documents + Query
Generator -> Postprocessing : Raw Output
Postprocessing -> User : Final Output

@enduml

This UML diagram outlines the flow of data from the user query to the final response, with each component interacting sequentially.

A Retrieval-Augmented Generation (RAG) pipeline consists of several key components. Here’s a breakdown:

Preprocessing

Purpose: Processes the input data and prepares it for the retriever and generator.
Tasks: Tokenization, normalization, and conversion to embeddings.

Tokenization

Tokenization is the process of breaking down a text into smaller units called tokens. These tokens can be words, phrases, or even characters, depending on the model and application. Here’s a detailed explanation:

Purpose

Facilitates Analysis: Tokenization makes it easier to analyze and process text by breaking it into manageable pieces.
Foundation for NLP: It’s a fundamental step in natural language processing (NLP) tasks, enabling the conversion of text into a format that models can understand.

Types of Tokenization

Word Tokenization: Splits the text into individual words. For example, “Hello, world!” becomes [“Hello”, “,”, “world”, “!”].
Subword Tokenization: Breaks words into subword units. This is useful for handling out-of-vocabulary words and is commonly used in BPE (Byte Pair Encoding) and WordPiece.
Character Tokenization: Treats each character as a token. Useful for languages without clear word boundaries.

Challenges in Tokenization

Language Variability: Different languages have different tokenization needs (e.g., Chinese vs. English).
Ambiguities: Words like “I’ve” may need to be split into [“I”, “‘ve”].
Punctuation Handling: Determining whether punctuation should be separate tokens.

Tools and Libraries

NLTK (Natural Language Toolkit): Offers various tokenization functions for different languages.
spaCy: Provides efficient and customizable tokenization strategies.
Transformers Libraries: Often include tokenizers tailored for specific models like BERT, GPT.

Applications

Text Preprocessing: Preparing data for analysis or ML models.
Search Engines: Improving text indexing and retrieval.
Sentiment Analysis: Understanding sentiment by analyzing word-level tokens.

Tokenization is a crucial step in converting unstructured text into a structured format, enabling efficient and accurate text processing and analysis.

Normalization

Normalization in text processing refers to the transformation of text into a consistent, standard format. This process involves several techniques:

Purpose

Consistency: Ensures uniformity across text data, improving the reliability of text analysis and machine learning models.
Reduction of Variability: Minimizes differences due to case, formatting, or typographical errors.

Common Techniques

Lowercasing: Converts all characters to lowercase to avoid distinctions based on capitalization (e.g., “Apple” and “apple”).
Removing Punctuation: Strips out punctuation marks to focus on the words themselves.
Stemming: Reduces words to their root forms (e.g., “running” to “run”) using algorithms like Porter or Snowball stemmers.
Lemmatization: Converts words to their base or dictionary form, considering context (e.g., “better” to “good”).
Removing Stop Words: Eliminates common words like “and”, “is”, “in” that often provide little informational value.
Unicode Normalization: Converts text into a standard Unicode format to handle characters consistently across different encodings.

Challenges

Language Dependencies: Different languages may require specific normalization techniques.
Context Sensitivity: Stemming and lemmatization may lead to loss of meaning if context is not considered.
Loss of Information: Over-normalization can strip away useful information.

Tools and Libraries

NLTK (Natural Language Toolkit): Offers modules for stemming, lemmatization, and stop word removal.
spaCy: Provides advanced lemmatization and other preprocessing functionalities.

Applications

Search Engines: Improves indexing and retrieval accuracy.
Sentiment Analysis: Enhances the understanding of sentiment by focusing on base forms of words.
Machine Translation: Ensures consistency in text, making translation more reliable.

Normalization is crucial for effective text analysis, reducing noise, and ensuring that text data is processed in a meaningful way.

Conversion to embeddings

Conversion to embeddings involves transforming text data into numerical vectors that can be processed by machine learning models. Here’s a detailed explanation:

Purpose

Numerical Representation: Converts text into a format that algorithms can understand and analyze.
Capture Semantic Meaning: Represents words or phrases in a way that captures their contextual meaning and relationships.

Process

Word Embeddings:

Maps individual words to vectors.
Examples: Word2Vec, GloVe.

Sentence Embeddings:

Converts entire sentences or paragraphs into vectors.
Examples: Universal Sentence Encoder, Sentence-BERT.

Common Techniques

Word2Vec:

Uses shallow neural networks to learn word associations.
Methods: Continuous Bag of Words (CBOW) and Skip-gram.

GloVe (Global Vectors for Word Representation):

Relies on word co-occurrence statistics from a corpus.
Generates vectors where semantic relationships are captured by vector distances.

Transformers:

Uses complex models (e.g., BERT, GPT) to create context-aware embeddings.
Capable of encoding nuanced meanings beyond individual words.

Challenges

Dimensionality: High-dimensional vectors can lead to increased computational cost.
Out-of-Vocabulary Words: New or rare words may not have pre-trained embeddings.
Context Sensitivity: Traditional embeddings like Word2Vec may lack context sensitivity, which transformers address.

Applications

Search and Information Retrieval: Improves the matching of queries with relevant documents.
Recommendation Systems: Helps in understanding user preferences based on textual data.
NLP Tasks: Powers tasks like sentiment analysis, translation, and summarization.

Embeddings are crucial in modern NLP applications, enabling complex models to perform tasks that require understanding of language at a deep, contextual level.

Retriever

Purpose: Retrieves relevant documents from the document store based on the query.
Methods: Dense retrieval (e.g., embeddings) or sparse retrieval (e.g., BM25).

Dense Retrieval

Purpose

Uses vector representations to improve retrieval by capturing semantic meaning.

Techniques

Uses embeddings to convert queries and documents to vectors.
Employs similarity measures like cosine similarity to match vectors.

Tools and Libraries

FAISS (Facebook AI Similarity Search): Efficient similarity search and clustering of dense vectors.
Transformers: Provides pre-trained models like BERT for generating dense embeddings.

Sparse Retrieval

Purpose

Matches query terms directly with document terms without capturing semantic meaning.

Techniques

Uses term frequency-inverse document frequency (TF-IDF) and BM25 for scoring.

Tools and Libraries

Lucene: High-performance text search engine library.
Elasticsearch: Distributed, RESTful search engine built on Lucene.

Challenges

Scalability: Handling large document collections efficiently.
Balance: Choosing between speed (sparse) and accuracy (dense).

Applications

Search Engines: Retrieval of relevant documents or web pages.
Question Answering: Finding potential answers from large text corpora.

Retrievers are essential for narrowing down a vast amount of information to the most relevant data for further processing.

Document Store

Purpose: Stores the documents or data that the model will reference.
Examples: Elasticsearch, PostgreSQL, or simple in-memory storage.

Generator

Purpose: Generates text or answers using retrieved documents and a language model.
Examples: Fine-tuned transformer models like BERT, GPT-3.

Postprocessing

Purpose: Refines the output generated by the model.
Tasks: Filtering, ranking, or formatting the output for the user.

Feedback Loop

Purpose: Incorporates user feedback to improve the system over time.
Methods: Reinforcement learning, user corrections.

Questions

what is “Porter or Snowball stemmers”?

what is “high-dimensional vectors”?

tillydray/rag-from-scratch

ErrorLooks like something went wrong!

About

Resources

Uh oh!

Stars

Watchers

Forks

Releases

Packages 0

Languages

Error
Looks like something went wrong!

Packages