---
type: concept
title: "Vector Embeddings"
description: "Numeric representations of text that enable semantic similarity search, stored and managed in a vector database for retrieval."
tags: [embeddings, vectors, retrieval]
videos: [why-the-best-ai-coding-tools-abandoned-rag-and-what-they-use-instead, the-simplest-rag-stack-that-actually-works-complete-guide, every-rag-strategy-explained-in-13-minutes-no-fluff, turn-any-file-into-llm-knowledge-in-seconds, knowledge-graphs-in-n8n-are-finally-here, introducing-rag-20-agentic-rag-knowledge-graphs-free-template, all-the-best-rag-strategies-in-one-mcp-for-ai-coding-assistants, the-easiest-possible-strategy-for-accurate-rag-step-by-step-guide, i-built-the-ultimate-rag-mcp-server-for-ai-coding-better-than-context7, the-proven-solution-for-unbelievable-rag-performance-lightrag-guide, build-ai-agents-that-evolve-over-time, the-ultimate-n8n-rag-ai-agent-template-local-ai-edition, i-built-the-ultimate-n8n-rag-ai-agent-template, run-supabase-100-locally-for-your-ai-agents, n8n-crawl4ai-scrape-any-website-in-minutes-with-no-code, the-future-of-rag-is-agentic-learn-this-strategy-now, turn-any-website-into-llm-knowledge-in-seconds, effortless-rag-in-n8n-use-all-your-files-pdfs-excel-and-more, i-built-a-full-rag-ai-agent-with-no-code-in-4-minutes-with-vectorshift, run-all-your-ai-locally-in-minutes-llms-rag-and-more, this-rag-ai-agent-with-n8n-supabase-is-the-real-deal, no-code-rag-agents-you-have-to-check-out-n8n-langchain, this-ai-agent-with-rag-manages-my-life, the-most-value-packed-rag-guide-on-youtube-feat-llama-31-405b, the-only-ai-developer-roadmap-you-need-in-2024]
created: 2026-07-21
updated: 2026-07-21
---

# Vector Embeddings

Vector embeddings are the mathematical representation of text that makes semantic search possible: an embedding model turns each chunk into a long list of numbers so that conceptually similar text lands close together in vector space. In Cole's words, "we turn all of these chunks into vectors their mathematical representation using an embedding model and then we store that all in a vector database" [0:03:01], and a query is handled the same way, "that query is then turned into a vector representation just like the chunks were using the same embedding model" [0:03:18]. Retrieval is then just vector mathematics: find the stored chunks whose vectors are nearest the query vector. Cole calls this "the backbone of rag" [0:09:54].

## Why it matters

Embeddings are what let retrieval match on meaning rather than exact words. The embedding model and the vector database together "help us" find "synonyms and find text that's similar based on conceptual meaning" [0:02:29], the core capability that keyword search lacks. Two operational rules recur across every build. First, you must "use the same embedding model regardless of the provider" for both ingestion and query time [0:20:04], because "your model has to have the same number of dimensions for both the inserts and the retrievals" [0:28:55]. Mixing models produces vectors in incompatible spaces and retrieval silently breaks. Second, dimensionality is model-specific: OpenAI's text-embedding-3 models differ from local models like nomic-embed-text, whose "vector size" is 768 [0:26:32], and the database schema must match.

## How it works

At ingestion, chunks are embedded and stored as records alongside their content and metadata (file ID, title, source), so "when we do a rag lookup when this record is returned the metadata is included so the llm can actually use this information to cite its source" [0:21:23]. At query time the user question is embedded with that same model and a similarity search returns the top matches, for example "the top 10 chunks by similarity" [0:17:55]. A common pitfall Cole flags: many pipelines insert without upserting, so re-ingesting a document creates "duplicate vectors," which is why he deletes old vectors for a file before re-inserting. Embeddings can come from hosted providers or run fully locally for privacy.

## Builds on

- [Chunking](./chunking.md) - chunks are the unit that gets embedded; embedding quality depends on good chunk boundaries.

## Part of

- [RAG](./rag.md) - embeddings are the retrieval substrate of the whole pipeline.

## Related

- [Semantic Search](./semantic-search.md) - the meaning-based matching embeddings enable.
- [Hybrid Search](./hybrid-search.md) - combines vector similarity with keyword search for better recall.
- [Knowledge Bases](./knowledge-bases.md) - the vector store where embeddings live.
- [Fine-Tuned Embeddings](./fine-tuned-embeddings.md) - retraining the embedding model on domain data to sharpen relevance.
- [Context-Aware Chunking](./context-aware-chunking.md) - uses an embedding model to find semantic split points.
- [Contextual Retrieval](./contextual-retrieval.md) - enriches chunks before they are embedded.
- [Traditional RAG](./traditional-rag.md) - the baseline pipeline embeddings power.
- [Local AI](./local-ai.md) - running embedding models locally for a fully private pipeline.
- [Agent Deployment](./agent-deployment.md) - the vector store is a runtime dependency to provision.

## Tools

- [Supabase](../entities/tools/supabase.md) - Postgres plus the pgvector extension, "literally turn it into a vector database for rag" [0:00:00].
- [Pinecone](../entities/tools/pinecone.md) - a dedicated managed vector database Cole often points to.
- [Qdrant](../entities/tools/qdrant.md) - another dedicated vector database option.
- [Ollama](../entities/tools/ollama.md) - serves local embedding models like nomic-embed-text.
- [OpenAI](../entities/organizations/openai.md) - provider of the text-embedding-3 models used as a hosted default.
- [Crawl4AI](../entities/tools/crawl4ai.md) - produces the content that gets embedded during ingestion.
- [LangChain](../entities/tools/langchain.md) - supplies embedding and vector-store integrations in code and no-code flows.
- [Oracle AI Database](../entities/tools/oracle-ai-database.md) - The video's sponsor; a unified AI database with built-in embeddings and semantic, keyword, and knowledge-graph search in one place, used for Cole's agentic RAG demo.

## Sources

- [The Future of RAG is Agentic - Learn this Strategy NOW](../sources/the-future-of-rag-is-agentic-learn-this-strategy-now.md) - "[0:03:01] we turn all of these chunks into vectors their mathematical representation using an embedding model and then we store that all in a vector database"
- [The EASIEST Possible Strategy for Accurate RAG (Step by Step Guide)](../sources/the-easiest-possible-strategy-for-accurate-rag-step-by-step-guide.md) - "[0:20:04] It's very important for both the agent and then also within the pipeline that you are using the same embedding model regardless of the provider"
- [I Built the ULTIMATE n8n RAG AI Agent Template](../sources/i-built-the-ultimate-n8n-rag-ai-agent-template.md) - "[0:28:55] we are using the exact same embedding model that we use when we insert things into Supabase... you just have to make sure that your model has the same number of dimensions for both the inserts and the retrievals"
- [The Simplest RAG Stack That Actually Works (Complete Guide)](../sources/the-simplest-rag-stack-that-actually-works-complete-guide.md) - "[0:17:55] we create that vector representation of our query to find the most relevant chunks. And so what we get out of this is the top 10 chunks by similarity"
- [Run Supabase 100% LOCALLY for Your AI Agents](../sources/run-supabase-100-locally-for-your-ai-agents.md) - "[0:00:00] it's Postgres under the hood... you can also use the PG Vector extension to literally turn it into a vector database for rag"
- [The ULTIMATE n8n RAG AI Agent Template - Local AI Edition](../sources/the-ultimate-n8n-rag-ai-agent-template-local-ai-edition.md) - "[0:21:23] I am adding metadata for this record... when this record is returned the metadata is included so the llm can actually use this information to cite its source"
- [The ONLY AI Developer Roadmap You Need in 2024](../sources/the-only-ai-developer-roadmap-you-need-in-2024.md) - "[0:09:54] it's so important to learn how Vector databases and embeddings work because that is the backbone of rag"
- [The Most Value Packed RAG Guide on YouTube (Feat. Llama 3.1 405B!)](../sources/the-most-value-packed-rag-guide-on-youtube-feat-llama-31-405b.md) - "[0:01:46] turn each one of these into vectors that we then store as embeddings in our Vector database so now we have all of our knowledge these document chunks represented mathematically"
- [Why the Best AI Coding Tools Abandoned RAG (And What They Use Instead)](../sources/why-the-best-ai-coding-tools-abandoned-rag-and-what-they-use-instead.md) - "[0:02:29] These are the kinds of things that the embedding model in the vector database can help us with."
- [Every RAG Strategy Explained in 13 Minutes (No Fluff)](../sources/every-rag-strategy-explained-in-13-minutes-no-fluff.md) - "[0:01:22] We embed that query and then we search our vector database to find similar chunks"
- [Run ALL Your AI Locally in Minutes (LLMs, RAG, and more)](../sources/run-all-your-ai-locally-in-minutes-llms-rag-and-more.md) - "[0:14:18] when you have this inserter it is not an upsert it is just an insert... if you reinsert a document you're actually going to have duplicate vectors for that document"
- [Turn ANY File into LLM Knowledge in SECONDS](../sources/turn-any-file-into-llm-knowledge-in-seconds.md) - "[0:17:48] So we then take these chunks, we embed them with an embedding model, and we store them in our vector database."
- [Introducing RAG 2.0: Agentic RAG + Knowledge Graphs (FREE Template)](../sources/introducing-rag-20-agentic-rag-knowledge-graphs-free-template.md) - "[0:07:45] when a user query comes into our AI agent, we feed that through the embedding model as well. So we can do a match in our vector database"
- [Build AI Agents that EVOLVE Over Time](../sources/build-ai-agents-that-evolve-over-time.md) - "[0:14:09] So, it created the vector for our memory. That's why we have this massive list of numbers here"
- [Knowledge Graphs in n8n are FINALLY Here!](../sources/knowledge-graphs-in-n8n-are-finally-here.md)
- [All the BEST RAG Strategies in ONE MCP for AI Coding Assistants](../sources/all-the-best-rag-strategies-in-one-mcp-for-ai-coding-assistants.md)
- [I Built the Ultimate RAG MCP Server for AI Coding (Better than Context7)](../sources/i-built-the-ultimate-rag-mcp-server-for-ai-coding-better-than-context7.md)
- [The PROVEN Solution for Unbelievable RAG Performance (LightRAG Guide)](../sources/the-proven-solution-for-unbelievable-rag-performance-lightrag-guide.md)
- [n8n + Crawl4AI - Scrape ANY Website in Minutes with NO Code](../sources/n8n-crawl4ai-scrape-any-website-in-minutes-with-no-code.md)
- [Turn ANY Website into LLM Knowledge in SECONDS](../sources/turn-any-website-into-llm-knowledge-in-seconds.md)
- [Effortless RAG in n8n - Use ALL Your Files (PDFs, Excel, and More)](../sources/effortless-rag-in-n8n-use-all-your-files-pdfs-excel-and-more.md)
- [I Built a FULL RAG AI Agent with No Code in 4 Minutes with VectorShift](../sources/i-built-a-full-rag-ai-agent-with-no-code-in-4-minutes-with-vectorshift.md)
- [This RAG AI Agent with n8n + Supabase is the Real Deal](../sources/this-rag-ai-agent-with-n8n-supabase-is-the-real-deal.md)
- [No Code RAG Agents? You HAVE to Check out n8n + LangChain](../sources/no-code-rag-agents-you-have-to-check-out-n8n-langchain.md)
- [This AI Agent with RAG Manages MY LIFE](../sources/this-ai-agent-with-rag-manages-my-life.md)
