RAG

In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have become powerful tools for a wide range of applications. However, these models come with inherent limitations that need to be addressed for optimal performance. Two methods stand out for enhancing LLM capabilities: Retrieval Augmented Generation (RAG) and Fine-Tuning. But which approach is right for your specific use case? Let’s break down the differences, strengths, and ideal applications for each. ...

Document chunking is a crucial step in information retrieval and retrieval-augmented generation (RAG) pipelines, where large documents are broken into smaller, manageable segments called “chunks.” This improves retrieval efficiency, contextual understanding, and overall system performance. Retrieval Augmented Generation (RAG) Pipeline Key Terminology Chunk Size The length of a single chunk, typically measured in tokens, words, characters, or sentences. Large chunk sizes retain more context but increase computational cost. Small chunk sizes reduce processing needs but may lose context. Chunk Overlap The number of tokens/words that overlap between consecutive chunks. Helps preserve context across chunks, especially when key information spans boundaries. Typical chunk overlaps range from 10% to 30% of the chunk size. Chunking Strategies 1. Fixed-Length Chunking Splits text into equal-sized chunks based on a predefined token/word count. Example: Breaking a document into 512-token chunks with a 50-token overlap. Pros: Simple and computationally efficient. Cons: Can split sentences or paragraphs unnaturally, losing semantic meaning. 2. Sentence-Based Chunking Uses sentence boundaries to create chunks, ensuring chunks do not break in the middle of a sentence. Pros: Preserves readability and coherence. Cons: Chunk sizes may vary, requiring additional preprocessing. 3. Paragraph-Based Chunking Divides documents based on paragraph boundaries. Pros: Retains more semantic meaning than sentence-based chunking. Cons: Chunk sizes can be inconsistent, and long paragraphs may still need splitting. 4. Semantic Chunking Uses AI models to detect topic shifts and create contextually relevant chunks. Example: LlamaIndex’s semantic chunking based on sentence embeddings. Pros: Preserves meaning while keeping chunk sizes optimized. Cons: More computationally expensive than basic methods. 5. Overlapping Sliding Window Chunking Creates chunks with a fixed overlap (e.g., 512 tokens with a 128-token overlap). Pros: Reduces context loss between chunks. Cons: Introduces redundancy, increasing storage and retrieval costs. Best Practices for Chunking Optimize Chunk Size: Choose a size that balances context retention and processing efficiency. Common ranges: ...

1. Indexing Indexing prepares the knowledge base for efficient retrieval by processing and storing documents in a structured format. Load Purpose: Ingest raw data from various sources and normalize it for further processing. Common Data Sources: Structured: Databases, APIs Semi-structured: JSON, XML, CSV Unstructured: PDFs, DOCX, HTML, scraped web pages Best Practices: Extract metadata (e.g., author, timestamps) to enhance retrieval. Normalize text (remove special characters, fix encoding issues). Detect language and preprocess accordingly (stemming, lemmatization). Split Purpose: Break documents into manageable chunks for improved retrieval accuracy. Splitting Strategies: Fixed-size chunking (e.g., 512 tokens per chunk). Overlapping chunks (ensures context continuity). Semantic chunking (splitting based on topic shifts or sentence boundaries). Best Practices: Ensure chunks are not too large (to avoid irrelevant retrieval) or too small (to retain context). Use metadata tags to track chunk origin and relationships. Documents chunking strategies and best practices ...

Introduction Vector databases (or vector stores) are specialized database systems designed to store, manage, and efficiently query high-dimensional vector embeddings. Unlike traditional databases that excel at exact matching, vector databases are optimized for similarity search—finding items that are conceptually similar rather than identical. These databases have become a critical component in the machine learning ecosystem, particularly for applications like semantic search, recommendation systems, and Retrieval Augmented Generation (RAG) pipelines with large language models. ...