RAG Systems: How to Build Document Search with AI — A Complete Guide to Chunking, Embeddings, and Vector Search

## Introduction Imagine you have a corporate knowledge base, legal documents, or technical documentation spanning hundreds of pages. How do you make AI answer questions using only this data, rather than general knowledge from the internet? The answer is RAG (Retrieval Augmented Generation). This architecture combines information retrieval and text generation: the system first finds relevant fragments in your documents, then passes them to a language model to formulate a response. Without RAG, AI can "hallucinate" or provide outdated data. In this guide, we'll break down how to build an indexing pipeline from scratch: from chunking to vector search. ## What is RAG and Why Do You Need It? RAG is a pattern that allows an AI model (e.g., GPT or Llama) to work with your private data. Instead of fine-tuning the model (which is expensive and time-consuming), you create an index from documents and retrieve relevant pieces at the response stage. This solves three key problems: - **Relevance**: The model always answers based on the latest version of your documents. - **Accuracy**: Reduces "hallucinations" because the answer is grounded in facts. - **Scalability**: You can add millions of documents without retraining. ## Indexing Pipeline: From Document to Vector Building a RAG system starts with indexing—converting text into a machine-readable format. Here are the main stages: ### 1. Chunking: Splitting Documents Documents rarely fit entirely into a model's context. They need to be split into chunks—small fragments (usually 256–1024 tokens). It's important not to cut text randomly but to use semantic splitting: - **By paragraphs**: Preserves logic but can be too large. - **By sentences**: Suitable for short answers. - **With overlap**: Adds 10-20% context from neighboring chunks to maintain coherence. Example strategy: For technical documentation, use chunks of 512 tokens with 10% overlap; for legal texts, use 256 tokens without overlap. ### 2. Embeddings: Turning Text into Numbers After chunking, each fragment needs to be converted into a vector—an embedding. This is a numerical representation of the text's meaning. Popular embedding models: | Model | Dimension | Language | Feature | |--------|-------------|------|-------------| | ruBERT-tiny | 312 | Russian | Lightweight, fast | | intfloat/multilingual-e5-large | 1024 | Multilingual | High accuracy | | text-embedding-3-small | 1536 | English | From OpenAI, paid | For Russian-language documents, it's optimal to use multilingual models—they better understand language nuances. ### 3. Vector Database: Store and Search A vector database (e.g., Pinecone, Qdrant, or FAISS) stores embeddings and allows searching for the nearest ones by similarity. When a user asks a question, their text is also converted into a vector, and the database returns the top-K most similar chunks. Key search metrics: - **Cosine similarity**: Standard for text vectors. - **Euclidean distance**: Sensitive to scale. - **Dot product**: Faster but requires normalization. ## Response Pipeline: How AI Uses Retrieved Data After indexing, the system is ready to answer. The process looks like this: 1. The user writes a question: "How to configure authorization in the API?" 2. Vector search finds 3-5 relevant chunks from the documentation. 3. These chunks are inserted into the prompt along with the question: "Based on the following data: [chunk text]. Answer the question: …" 4. The language model generates an answer, referencing only the provided fragments. An important nuance is **result ranking**. Sometimes the top chunks are irrelevant, so a re-ranker (e.g., Cohere or BGE) is added to reassess similarity with a more accurate model. ## Practical Optimization Tips - **Experiment with chunking**: For FAQs, use small chunks (128 tokens); for reviews, use large chunks (1024 tokens). - **Clean data**: Remove noise (HTML tags, extra spaces) before indexing—this improves embedding quality. - **Use hybrid search**: Combine vector search with BM25 (keywords) for better coverage. - **Monitor latency**: If responses take longer than 3 seconds, reduce the number of chunks (K) or use caching. ## Conclusion RAG systems are not magic but a clear engineering pipeline: chunking → embeddings → vector database → generation. Following this guide, you can build document search that delivers accurate and contextual answers based on your data. Start small: take 10 documents, test different chunking strategies, and choose an embedding model for your language. Happy experimenting!

RAG Systems: How to Build Document Search with AI — A Complete Guide to Chunking, Embeddings, and Vector Search

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