nikhilsnayak.dev

• Framework: Next.js
• Styling: Tailwind CSS
• Components: Shadcn UI
• LLM: OpenAI
• Vector Store: Supabase
• RAG: Langchain
• Database: Vercel Postgres
• ORM: Drizzle ORM
• Auth: Auth.js
• Deployment: Vercel
• Analytics: Vercel Analytics

Running Locally

This application requires Node.js v18.17+.

Create a .env.local file similar to .env.example.

git clone https://github.com/nikhilsnayak/nikhilsnayak.dev.git <name-of-your-repo>
cd <name-of-your-repo>
bun install
bun run dev

Vector Store Schema

Run the following sql in your supabase database

-- Enable the pgvector extension to work with embedding vectors
create extension vector;

-- Create a table to store your documents
create table documents (
  id bigserial primary key,
  content text, -- corresponds to Document.pageContent
  metadata jsonb, -- corresponds to Document.metadata
  embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed
);

-- Create a function to search for documents
create function match_documents (
  query_embedding vector(1536),
  match_count int DEFAULT null,
  filter jsonb DEFAULT '{}'
) returns table (
  id bigint,
  content text,
  metadata jsonb,
  embedding jsonb,
  similarity float
)
language plpgsql
as $$
#variable_conflict use_column
begin
  return query
  select
    id,
    content,
    metadata,
    (embedding::text)::jsonb as embedding,
    1 - (documents.embedding <=> query_embedding) as similarity
  from documents
  where metadata @> filter
  order by documents.embedding <=> query_embedding
  limit match_count;
end;
$$;

Feel free to use this repository as a template. Please remove all of my personal information

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