What is the main takeaway regarding Supabase pgvector Returning Zero Results: The Missing RPC Function?

Your vector database has 8,000 records and returns nothing. Here is the silent failure pattern that causes it and the SQL to fix it.

Did You Know This Supabase pgvector Error Breaks Most RAG Setups?

This one is a trap. You build your vector database. You embed thousands of records. You run a semantic search query. You get zero results. No error. Just silence.

Here is what is happening and how to fix it.

The Silent Failure Pattern

Supabase pgvector similarity search requires a custom RPC (Remote Procedure Call) function in your database - typically called something like match_knowledge_base. This function takes a query embedding, compares it against all stored vectors using cosine similarity, and returns the top matches above a threshold.

If that function doesn’t exist, the API call returns a 404 error. If your application code catches all exceptions broadly (except Exception: results = []), it silently swallows that 404 and returns an empty list. No error in your logs. No indication anything is wrong. Just zero results.

The Fix: Create the RPC Function

Run this SQL in your Supabase SQL editor, replacing the table and column names to match your schema:

CREATE OR REPLACE FUNCTION match_knowledge_base(
  query_embedding vector(1536), 
  match_count int DEFAULT 5, 
  match_threshold float DEFAULT 0.5
) 
RETURNS TABLE(id text, title text, content text, similarity float) 
LANGUAGE plpgsql AS $$ 
BEGIN 
  RETURN QUERY 
  SELECT k.id, k.title, k.content,
         1 - (k.embedding <=> query_embedding) AS similarity 
  FROM knowledge_base k 
  WHERE k.embedding IS NOT NULL 
    AND 1 - (k.embedding <=> query_embedding) > match_threshold 
  ORDER BY k.embedding <=> query_embedding 
  LIMIT match_count; 
END; 
$$;

The Lesson

Never catch exceptions broadly in production AI systems. If a semantic search returns zero results, that should throw a visible error, not silently fall back to an empty list. Build explicit error handling that distinguishes between “no results found” and “the search function doesn’t exist.”

Check your RPC functions first whenever a vector search returns nothing.

Related reading:

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