NEW: Self-Hosted AI Bot RAG In n8n & Supabase Vector Store!🤖 OpenAI/HuggingFace Embeddings PG Vector
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QUERY 1:
ALTER TABLE "YOUR TABLE NAME"
ADD COLUMN embedding VECTOR(1536),
ADD COLUMN metadata JSONB,
ADD COLUMN content TEXT;
QUERY 2 (YOU MUST PUT THE ARROWS AROUND THE '=' YOUTUBE DOESN'T ALLOW THOSE CHARACTERS - CHECK THE VIDEO TO CONFIRM THIS QUERY WHEN YOU MAKE THE CHANGES!):
CREATE OR REPLACE FUNCTION public.match_documents(
filter JSONB,
match_count INT,
query_embedding VECTOR(1536)
)
RETURNS TABLE (
id BIGINT,
content TEXT,
metadata JSONB,
embedding VECTOR(1536),
similarity FLOAT
)
LANGUAGE plpgsql AS $$
BEGIN
RETURN QUERY
SELECT
v.id,
v.content,
v.metadata,
v.embedding,
1 - (v.embedding = match_documents.query_embedding) AS similarity
FROM "📺 YouTube Videos" v
WHERE v.metadata @ filter
ORDER BY v.embedding = match_documents.query_embedding
LIMIT match_count;
END;
$$
;
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⏳ Timestamps
0:00 - Introduction to Complete RAG Setup in n8n & Supabase
0:33 - What is RAG (Retrieval Augmented Generation)?
2:00 - Tools Overview: n8n and Supabase Vector Store
3:21 - Embedding Models: OpenAI and HuggingFace
6:00 - Creating Supabase Table for Vector Storage
9:04 - n8n Workflow Setup with Airtable
11:20 - OpenAI Embedding Costs
13:00 - Testing Embeddings in Supabase Vector Store
14:40 - Q&A Chain Setup in n8n
18:00 - Adding Functions in Supabase for Vector Store
21:00 - Testing Q&A Chain with OpenAI and Supabase
25:00 - Recap of Complete RAG Setup Process in n8n & Supabase
27:30 - Closing Remarks and Additional Resources
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