RAG, Chunking, Embeddings and Vector Stores

Retrieval Augmented Generation (RAG) is a powerful technique that enhances the capabilities of Large Language Models (LLMs) by allowing them to access and utilize information from external knowledge sources. This process enables LLMs to provide more accurate, up-to-date, and contextually relevant responses. Three fundamental pillars underpin most RAG systems: how information is segmented (chunking), how the meaning of these segments is captured (embedding models), and how these segments are efficiently searched (vector stores).

1. Strategic Segmentation: Chunk Size, Overlap, and Contextual Relevance

graph TD
    A[Original Document] --> B[Chunking Process]
    B --> C[Chunk 1: Text...]
    B --> D[Chunk 2: ...with Overlap]
    C --> E[Vector Embedding]
    D --> E

LLMs operate with a “context window”—a finite limit on the amount of text they can process simultaneously. When dealing with extensive documents, codebases, or knowledge bases, it’s impractical to feed all the information to an LLM at once. This is where strategic segmentation, or chunking, becomes essential.

What is Chunking?

Chunking involves breaking down large volumes of text or code into smaller, more manageable segments, commonly referred to as “chunks.” Various text-splitting tools and algorithms are employed for this purpose, often based on character count, token count, or structural elements like paragraphs or code blocks.

A. Chunk Size

What it is: This is a configurable parameter that defines the desired maximum size of each chunk (e.g., a common setting might be 1000 characters or a certain number of tokens).
Why it matters: The choice of chunk size significantly impacts both the retrieval process and the quality of information passed to the LLM.

Guidance on Choosing Chunk Size:

Nature of Content: Technical documentation might need larger chunks than narrative text
Embedding Model Characteristics: Consider model’s optimal input range
LLM Context Window: Total chunks + query must fit within window
Query Specificity: Smaller chunks for specific queries, larger for broad ones

B. Chunk Overlap

graph LR
    A[Chunk 1: ...end of text] --> B[Overlap Region]
    B --> C[Chunk 2: start of next...]

What it is: Content repeated between consecutive chunks (e.g., 150 characters)
Why it matters: Preserves context and prevents edge information loss

Overlap Guidelines:

10-20% of chunk size is typical
Balance between context preservation and redundancy

2. Embedding Models: Capturing the Essence of Text

graph LR
    A[Text Chunk] --> B[Embedding Model]
    B --> C[384-dimensional Vector]
    C --> D[Vector Space]
    D --> E[Similar chunks cluster together]

What are Embeddings? An embedding is a dense numerical representation (a vector) that captures semantic meaning. Similar texts produce vectors that are close in vector space.

Key Characteristics of Embedding Models:

Trained on massive text datasets
Understand contextual relationships between words/phrases
Convert text to fixed-length vectors (e.g., 384 dimensions)
Enable semantic search beyond keyword matching

Popular Options:

Sentence Transformers (e.g., ‘all-MiniLM-L6-v2’)
BERT-based models
OpenAI embeddings

3. Vector Stores: Navigating the Landscape of Meaning

sequenceDiagram
    participant User
    participant System
    participant VectorStore
    User->>System: Query
    System->>VectorStore: Convert to Embedding
    VectorStore-->>System: Top 3 Relevant Chunks
    System->>LLM: Augmented Prompt
    LLM-->>User: Informed Response

What are Vector Stores? Specialized databases optimized for:

Efficient storage of high-dimensional vectors
Rapid similarity searches (nearest neighbor)
Scalability to millions/billions of vectors

FAISS Architecture Overview:

graph TB
    A[Input Vectors] --> B[Indexing]
    B --> C[Flat Index]
    B --> D[IVF Index]
    B --> E[PQ Compression]
    C --> F[Fast Search]
    D --> F
    E --> F

Key Features:

Multiple indexing strategies (exact/approximate)
Product Quantization for memory efficiency
GPU acceleration support
Language bindings (Python/C++)

Typical Workflow:

Index construction during setup
Persistence to disk for reuse
Millisecond-level retrieval at query time

Conclusion

The careful orchestration of:

Intelligent Chunking (size/overlap balance)
Semantic Embeddings (accurate vector representations)
Efficient Vector Search (FAISS and similar technologies)

…enables RAG systems to bridge LLMs with vast knowledge bases. Continuous experimentation with chunk sizes, embedding models, and indexing strategies remains crucial for optimal performance in specific applications.

Erick Santana

Explorer