DIY RAG System: How to Connect Your Knowledge Base to AI and Get Accurate Answers

## Introduction Imagine: your corporate AI assistant no longer "hallucinates" on general data, but responds strictly based on your documents — reports, instructions, knowledge base. This is not magic, but RAG (Retrieval Augmented Generation) — a technology that allows a language model to search for information in your personal file collection before generating a response. In 2026, RAG has become the standard for business solutions: from customer support to internal knowledge bases. In this article, we will break down how to build a RAG system yourself — from uploading PDFs to vector search. ## What is RAG and Why Do You Need It? RAG (Retrieval Augmented Generation) is a hybrid approach: the AI first finds relevant text chunks from your storage, then forms a response based on them. Without RAG, models (e.g., GPT-4 or Claude) rely solely on their training data, which may be outdated. With RAG, you get: - **Relevance**: answers strictly based on your documents. - **Confidentiality**: data is not used for model training. - **Control**: you manage what the AI "knows." Key components of RAG: document loading, chunking (splitting into fragments), creating embeddings, and vector search. Next — step by step. ## Step 1: Loading and Preparing Documents Any RAG system starts with file upload. Supported formats: PDF, DOCX, TXT, Markdown, HTML. For example, let's take three technical manuals of 50 pages each. ### Data Cleaning Before chunking, remove noise: watermarks, headers/footers, extra spaces. Use libraries like `PyMuPDF` for Python or `Unstructured` for parsing. For example, a Python script: ```python import fitz doc = fitz.open("manual.pdf") text = "".join([page.get_text() for page in doc]) ``` ## Step 2: Chunking (Splitting into Fragments) Long documents cannot be sent to an LLM entirely — the model has a context limit. Chunking solves this problem: you cut the text into semantic blocks. ### Optimal Chunking Parameters | Parameter | Recommendation | Explanation | |-----------|----------------|-------------| | Chunk size | 256-512 tokens | Balance between detail and context | | Overlap | 10-20% | Preserves connectivity of adjacent chunks | | Strategy | Semantic (by paragraphs) | Better than simple character-based cutting | Example implementation with `LangChain`: ```python from langchain.text_splitter import RecursiveCharacterTextSplitter splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) chunks = splitter.split_text(text) ``` Tip: for technical documents, use semantic chunking (by headings) to avoid breaking logical blocks. ## Step 3: Creating Embeddings Embeddings are numerical vectors that represent the meaning of text. AI does not understand words but can compare vectors. The closer the vectors in multidimensional space, the semantically closer the texts. ### Choosing an Embedding Model | Model | Dimensionality | Free? | Feature | |-------|----------------|-------|---------| | `text-embedding-ada-002` (OpenAI) | 1536 | No (pay per token) | High accuracy | | `intfloat/multilingual-e5-large` | 1024 | Yes (open-source) | Russian language support | | `BAAI/bge-m3` | 1024 | Yes | Multilingual, up to 8192 tokens | For Russian, `intfloat/multilingual-e5-large` is best. Generating an embedding for one chunk: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/multilingual-e5-large') embedding = model.encode("Your chunk text") ``` ## Step 4: Vector Search and Database Now you need to store embeddings in a vector database. Popular options: ChromaDB (lightweight, for prototypes), Pinecone (cloud, scalable), Qdrant (self-hosted). The vector database stores pairs `(vector, chunk text + metadata)`. ### Example with ChromaDB (local): ```python import chromadb client = chromadb.Client() collection = client.create_collection("my_knowledge") collection.add( embeddings=embeddings_list, documents=c ```

DIY RAG System: How to Connect Your Knowledge Base to AI and Get Accurate Answers

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