๐ง๐ต๐ถ๐ ๐ถ๐ ๐ต๐ผ๐ ๐๐ฒ๐ป๐๐ ๐ณ๐ถ๐ป๐ฑ๐ ๐บ๐ฒ๐ฎ๐ป๐ถ๐ป๐ด ๐ถ๐ป ๐๐ป๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ๐ฑ ๐๐ฒ๐ ๐. โฌ๏ธ And yes it all starts with vector databases โ not magic. This is the mechanism that powers AI Agent memory, RAG and semantic search. And this diagram below? Nails the entire flow โ from raw data to relevant answers. Let's break it down (the explanation shows of how a vector database works โ using the simple example prompt: โWho am I): โฌ๏ธ 1. ๐๐ป๐ฝ๐๐: โ There are two inputs: Data = the source text (docs, chat history, product descriptions...) and the query = the question or prompt youโre asking. These are processed in exactly the same way โ so they can be compared mathematically later. 2. ๐ช๐ผ๐ฟ๐ฑ ๐๐บ๐ฏ๐ฒ๐ฑ๐ฑ๐ถ๐ป๐ด โ Each word (like โhowโ, โareโ, โyouโ) is transformed into a list of numbers โ 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. ๐ง๐ฒ๐ ๐ ๐๐บ๐ฏ๐ฒ๐ฑ๐ฑ๐ถ๐ป๐ด ๐ฃ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ โ 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 โ helping the model focus on important features. The output? A single text embedding that represents the entire sentence or chunk. 4. ๐ ๐ฒ๐ฎ๐ป ๐ฃ๐ผ๐ผ๐น๐ถ๐ป๐ด โ Now we take the average of all token embeddings โ one clean vector per chunk. This is the "semantic fingerprint" of your text. 5. ๐๐ป๐ฑ๐ฒ๐ ๐ถ๐ป๐ด โ All document vectors are indexed โ meaning theyโre structured for fast similarity search. This is where vector databases like FAISS or Pinecone come in. 6. ๐ฅ๐ฒ๐๐ฟ๐ถ๐ฒ๐๐ฎ๐น (๐๐ผ๐ ๐ฃ๐ฟ๐ผ๐ฑ๐๐ฐ๐ & ๐๐ฟ๐ด๐บ๐ฎ๐ ) โ 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 โ a measure of similarity. Argmax finds the closest match โ i.e. the most relevant chunk. This is semantic search at work. - Keyword search finds strings. - Vector search finds meaning. 7. ๐ฉ๐ฒ๐ฐ๐๐ผ๐ฟ ๐ฆ๐๐ผ๐ฟ๐ฎ๐ด๐ฒ โ All document vectors live in persistent vector storage โ 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. ๐๐ณ ๐๐ผ๐โ๐ฟ๐ฒ ๐ฏ๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐๐๐ ๐ โ ๐๐ต๐ถ๐ ๐ถ๐ ๐๐ต๐ฒ ๐ฝ๐ฎ๐๐๐ฒ๐ฟ๐ป ๐๐ผ๐โ๐ฟ๐ฒ ๐ฏ๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐ผ๐ป. Kudos to Tom Yeh for this brilliant visualization! | 48 comments on LinkedIn