1. What Are Embeddings?

• Think of embeddings as numbers that capture the meaning of text.

  • Example: “car”, “automobile”, “vehicle” → embeddings of these words will be close to each other in a 3D space.

• These embeddings are stored in a vector database, so when you search, the system finds the “closest meaning” instead of exact words.

• This is how AI can answer questions even if your query wording doesn’t exactly match your documents.

👉 Analogy: Imagine a library where books are sorted by topic similarity, not alphabet. When you ask about “cars,” you’ll also get “automobile” books.

3. Creating the Embedding Workflow in n8n

We’ll now build a workflow that:

1. Starts manually

2. Downloads your text file from Google Drive

3. Embeds the text with Gemini embeddings

4. Stores the embedding into Supabase

Workflow: Manual Trigger → Google Drive → Supabase

• Manual Trigger – start the workflow by hand.

• Google Drive Node – download your text file.

• Gemini Embeddings Node (via n8n OpenAI/Gemini connector) – convert text into vectors.

• Supabase Vector Node – insert into the documents table.

4. Querying the Vector Database with an AI Agent

Now let’s make it interactive:

1. Chat Trigger Node – user can type a question.

2. AI Agent Node – configured with Supabase vector store as a tool.

   • The agent first checks Supabase for the most relevant chunks of text.

   • Then it uses those chunks as context to answer the user’s query.

👉 Example:

• User asks: “What services does the website offer?”

• The AI Agent searches the Supabase embeddings, finds the relevant website section, and answers naturally.

✅ Key Takeaways

• Embeddings = numerical meaning of text.

• Vector database = store + search by similarity, not exact words.

• Supabase with pgvector lets us easily integrate embeddings into workflows.

• With n8n, you can automate both embedding creation and query answering.