𝗧𝗵𝗶𝘀 𝗶𝘀 𝗵𝗼𝘄 𝗚𝗲𝗻𝗔𝗜 𝗳𝗶𝗻𝗱𝘀 𝗺𝗲𝗮𝗻𝗶𝗻𝗴 𝗶𝗻 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝘁𝗲𝘅𝘁. ⬇️ And yes it all starts with vector databases — not magic. This is the mechanism that powers AI Agent memory, RAG and semantic search. And this diagram below? Nails the entire flow — from raw data to relevant answers. Let's break it down (the explanation shows of how a vector database works — using the simple example prompt: “Who am I): ⬇️ 1. 𝗜𝗻𝗽𝘂𝘁: ➜ There are two inputs: Data = the source text (docs, chat history, product descriptions...) and the query = the question or prompt you’re asking. These are processed in exactly the same way — so they can be compared mathematically later. 2. 𝗪𝗼𝗿𝗱 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 ➜ Each word (like “how”, “are”, “you”) is transformed into a list of numbers — a word embedding. These word embeddings capture semantic meaning, so that for example "bank" (money) and "finance" land closer than "bank" (river). This turns raw text into numerical signals. 3. 𝗧𝗲𝘅𝘁 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 ➜ Both data and query go through this stack: - Encoder: Transforms word embeddings based on their context (e.g. transformers like BERT). - Linear Layer: Projects these high-dimensional embeddings into a more compact space. -ReLU Activation: Introduces non-linearity — helping the model focus on important features. The output? A single text embedding that represents the entire sentence or chunk. 4. 𝗠𝗲𝗮𝗻 𝗣𝗼𝗼𝗹𝗶𝗻𝗴 ➜ Now we take the average of all token embeddings — one clean vector per chunk. This is the "semantic fingerprint" of your text. 5. 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 ➜ All document vectors are indexed — meaning they’re structured for fast similarity search. This is where vector databases like FAISS or Pinecone come in. 6. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 (𝗗𝗼𝘁 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 & 𝗔𝗿𝗴𝗺𝗮𝘅) ➜ When you submit a query.: The query is also embedded and pooled into a vector. The system compares your query to all indexed vectors using dot product — a measure of similarity. Argmax finds the closest match — i.e. the most relevant chunk. This is semantic search at work. - Keyword search finds strings. - Vector search finds meaning. 7. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 ➜ All document vectors live in persistent vector storage — always ready for future retrieval and use by the LLM. This is basically the database layer behind: - RAG - Semantic search - Agent memory - Enterprise GenAI apps - etc. 𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀 — 𝘁𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝘆𝗼𝘂’𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗼𝗻. Kudos to Tom Yeh for this brilliant visualization! | 48 comments on LinkedIn