1// Workflow: Index documents to Pinecone
2// Node 1: Read documents (Google Drive, HTTP, File)
3// Node 2: Text Splitter (Recursive Character)
4// Node 3: Embeddings (OpenAI)
5// Node 4: Pinecone Vector Store (Insert)
6
7// Pinecone node configuration:
8// - Operation: Upsert
9// - Index: your-index-name
10// - Namespace: "documents"

1-- Enable pgvector extension
2create extension if not exists vector;
3
4-- Create documents table
5create table documents (
6  id bigserial primary key,
7  content text,
8  metadata jsonb,
9  embedding vector(1536)
10);
11
12-- Create similarity search function
13create or replace function match_documents (
14  query_embedding vector(1536),
15  match_threshold float,
16  match_count int
17)
18returns table (
19  id bigint,
20  content text,
21  metadata jsonb,
22  similarity float
23)
24language sql stable
25as $$
26  select
27    documents.id,
28    documents.content,
29    documents.metadata,
30    1 - (documents.embedding <=> query_embedding) as similarity
31  from documents
32  where 1 - (documents.embedding <=> query_embedding) > match_threshold
33  order by similarity desc
34  limit match_count;
35$$;

1// Supabase Vector Store node in n8n
2// Operation: Insert Documents
3// Table: documents
4// Content Column: content
5// Embedding Column: embedding
6// Metadata Columns: metadata

1// Docker compose for Qdrant
2// docker-compose.yml:
3// services:
4//   qdrant:
5//     image: qdrant/qdrant
6//     ports:
7//       - "6333:6333"
8//     volumes:
9//       - qdrant_data:/qdrant/storage
10
11// n8n Qdrant credential:
12// - URL: http://localhost:6333
13// - API Key: (optional for local)
14
15// Create collection via HTTP Request node:
16const createCollection = {
17  url: "http://qdrant:6333/collections/my-docs",
18  method: "PUT",
19  body: {
20    vectors: {
21      size: 1536,
22      distance: "Cosine"
23    }
24  }
25};