@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:
- Purpose: Processes the input data and prepares it for the retriever and generator.
- Tasks: Tokenization, normalization, and conversion to embeddings.
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:
- 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.
- 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.
- 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.
- 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.
- 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 in text processing refers to the transformation of text into a consistent, standard format. This process involves several techniques:
- 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.
- 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.
- 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.
- NLTK (Natural Language Toolkit): Offers modules for stemming, lemmatization, and stop word removal.
- spaCy: Provides advanced lemmatization and other preprocessing functionalities.
- 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 involves transforming text data into numerical vectors that can be processed by machine learning models. Here’s a detailed explanation:
- 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.
- Maps individual words to vectors.
- Examples: Word2Vec, GloVe.
- Converts entire sentences or paragraphs into vectors.
- Examples: Universal Sentence Encoder, Sentence-BERT.
- Uses shallow neural networks to learn word associations.
- Methods: Continuous Bag of Words (CBOW) and Skip-gram.
- Relies on word co-occurrence statistics from a corpus.
- Generates vectors where semantic relationships are captured by vector distances.
- Uses complex models (e.g., BERT, GPT) to create context-aware embeddings.
- Capable of encoding nuanced meanings beyond individual words.
- 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.
- 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.
- Purpose: Retrieves relevant documents from the document store based on the query.
- Methods: Dense retrieval (e.g., embeddings) or sparse retrieval (e.g., BM25).
- Uses vector representations to improve retrieval by capturing semantic meaning.
- Uses embeddings to convert queries and documents to vectors.
- Employs similarity measures like cosine similarity to match vectors.
- FAISS (Facebook AI Similarity Search): Efficient similarity search and clustering of dense vectors.
- Transformers: Provides pre-trained models like BERT for generating dense embeddings.
- Matches query terms directly with document terms without capturing semantic meaning.
- Uses term frequency-inverse document frequency (TF-IDF) and BM25 for scoring.
- Lucene: High-performance text search engine library.
- Elasticsearch: Distributed, RESTful search engine built on Lucene.
- Scalability: Handling large document collections efficiently.
- Balance: Choosing between speed (sparse) and accuracy (dense).
- 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.
- Purpose: Stores the documents or data that the model will reference.
- Examples: Elasticsearch, PostgreSQL, or simple in-memory storage.
- Purpose: Generates text or answers using retrieved documents and a language model.
- Examples: Fine-tuned transformer models like BERT, GPT-3.
- Purpose: Refines the output generated by the model.
- Tasks: Filtering, ranking, or formatting the output for the user.
- Purpose: Incorporates user feedback to improve the system over time.
- Methods: Reinforcement learning, user corrections.