mirror of
https://github.com/StanGirard/quivr.git
synced 2024-12-11 11:43:54 +03:00
288 lines
7.5 KiB
PL/PgSQL
288 lines
7.5 KiB
PL/PgSQL
-- Create users table
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CREATE TABLE IF NOT EXISTS user_daily_usage(
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user_id UUID REFERENCES auth.users (id),
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email TEXT,
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date TEXT,
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daily_requests_count INT,
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PRIMARY KEY (user_id, date)
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);
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-- Create chats table
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CREATE TABLE IF NOT EXISTS chats(
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chat_id UUID DEFAULT uuid_generate_v4() PRIMARY KEY,
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user_id UUID REFERENCES auth.users (id),
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creation_time TIMESTAMP DEFAULT current_timestamp,
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history JSONB,
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chat_name TEXT
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);
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-- Create vector extension
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CREATE EXTENSION IF NOT EXISTS vector;
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-- Create vectors table
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CREATE TABLE IF NOT EXISTS vectors (
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id UUID DEFAULT uuid_generate_v4() PRIMARY KEY,
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content TEXT,
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file_sha1 TEXT,
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metadata JSONB,
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embedding VECTOR(1536)
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);
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-- Create function to match vectors
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CREATE OR REPLACE FUNCTION match_vectors(query_embedding VECTOR(1536), match_count INT, p_brain_id UUID)
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RETURNS TABLE(
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id UUID,
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brain_id UUID,
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content TEXT,
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metadata JSONB,
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embedding VECTOR(1536),
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similarity FLOAT
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) LANGUAGE plpgsql AS $$
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#variable_conflict use_column
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BEGIN
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RETURN QUERY
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SELECT
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vectors.id,
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brains_vectors.brain_id,
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vectors.content,
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vectors.metadata,
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vectors.embedding,
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1 - (vectors.embedding <=> query_embedding) AS similarity
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FROM
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vectors
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INNER JOIN
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brains_vectors ON vectors.id = brains_vectors.vector_id
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WHERE brains_vectors.brain_id = p_brain_id
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ORDER BY
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vectors.embedding <=> query_embedding
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LIMIT match_count;
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END;
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$$;
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-- Create stats table
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CREATE TABLE IF NOT EXISTS stats (
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time TIMESTAMP,
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chat BOOLEAN,
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embedding BOOLEAN,
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details TEXT,
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metadata JSONB,
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id INTEGER PRIMARY KEY GENERATED ALWAYS AS IDENTITY
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);
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-- Create summaries table
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CREATE TABLE IF NOT EXISTS summaries (
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id BIGSERIAL PRIMARY KEY,
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document_id UUID REFERENCES vectors(id),
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content TEXT,
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metadata JSONB,
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embedding VECTOR(1536)
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);
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-- Create function to match summaries
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CREATE OR REPLACE FUNCTION match_summaries(query_embedding VECTOR(1536), match_count INT, match_threshold FLOAT)
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RETURNS TABLE(
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id BIGINT,
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document_id UUID,
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content TEXT,
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metadata JSONB,
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embedding VECTOR(1536),
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similarity FLOAT
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) LANGUAGE plpgsql AS $$
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#variable_conflict use_column
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BEGIN
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RETURN QUERY
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SELECT
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id,
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document_id,
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content,
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metadata,
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embedding,
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1 - (summaries.embedding <=> query_embedding) AS similarity
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FROM
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summaries
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WHERE 1 - (summaries.embedding <=> query_embedding) > match_threshold
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ORDER BY
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summaries.embedding <=> query_embedding
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LIMIT match_count;
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END;
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$$;
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-- Create api_keys table
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CREATE TABLE IF NOT EXISTS api_keys(
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key_id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
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user_id UUID REFERENCES auth.users (id),
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api_key TEXT UNIQUE,
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creation_time TIMESTAMP DEFAULT current_timestamp,
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deleted_time TIMESTAMP,
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is_active BOOLEAN DEFAULT true
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);
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--- Create prompts table
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CREATE TABLE IF NOT EXISTS prompts (
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id UUID DEFAULT uuid_generate_v4() PRIMARY KEY,
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title VARCHAR(255),
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content TEXT,
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status VARCHAR(255) DEFAULT 'private'
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);
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--- Create brains table
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CREATE TABLE IF NOT EXISTS brains (
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brain_id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
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name TEXT NOT NULL,
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status TEXT,
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description TEXT,
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model TEXT,
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max_tokens INT,
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temperature FLOAT,
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openai_api_key TEXT,
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prompt_id UUID REFERENCES prompts(id),
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last_update TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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);
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-- Create chat_history table
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CREATE TABLE IF NOT EXISTS chat_history (
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message_id UUID DEFAULT uuid_generate_v4(),
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chat_id UUID REFERENCES chats(chat_id),
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user_message TEXT,
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assistant TEXT,
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message_time TIMESTAMP DEFAULT current_timestamp,
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PRIMARY KEY (chat_id, message_id),
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prompt_id UUID REFERENCES prompts(id),
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brain_id UUID REFERENCES brains(brain_id)
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);
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-- Create notification table
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CREATE TABLE IF NOT EXISTS notifications (
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id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
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datetime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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chat_id UUID REFERENCES chats(chat_id),
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message TEXT,
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action VARCHAR(255) NOT NULL,
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status VARCHAR(255) NOT NULL
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);
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-- Create brains X users table
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CREATE TABLE IF NOT EXISTS brains_users (
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brain_id UUID,
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user_id UUID,
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rights VARCHAR(255),
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default_brain BOOLEAN DEFAULT false,
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PRIMARY KEY (brain_id, user_id),
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FOREIGN KEY (user_id) REFERENCES auth.users (id),
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FOREIGN KEY (brain_id) REFERENCES brains (brain_id)
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);
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-- Create brains X vectors table
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CREATE TABLE IF NOT EXISTS brains_vectors (
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brain_id UUID,
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vector_id UUID,
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file_sha1 TEXT,
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PRIMARY KEY (brain_id, vector_id),
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FOREIGN KEY (vector_id) REFERENCES vectors (id),
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FOREIGN KEY (brain_id) REFERENCES brains (brain_id)
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);
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-- Create brains X vectors table
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CREATE TABLE IF NOT EXISTS brain_subscription_invitations (
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brain_id UUID,
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email VARCHAR(255),
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rights VARCHAR(255),
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PRIMARY KEY (brain_id, email),
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FOREIGN KEY (brain_id) REFERENCES brains (brain_id)
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);
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--- Create user_identity table
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CREATE TABLE IF NOT EXISTS user_identity (
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user_id UUID PRIMARY KEY,
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openai_api_key VARCHAR(255)
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);
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CREATE OR REPLACE FUNCTION public.get_user_email_by_user_id(user_id uuid)
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RETURNS TABLE (email text)
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SECURITY definer
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AS $$
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BEGIN
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RETURN QUERY SELECT au.email::text FROM auth.users au WHERE au.id = user_id;
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END;
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$$ LANGUAGE plpgsql;
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CREATE OR REPLACE FUNCTION public.get_user_id_by_user_email(user_email text)
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RETURNS TABLE (user_id uuid)
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SECURITY DEFINER
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AS $$
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BEGIN
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RETURN QUERY SELECT au.id::uuid FROM auth.users au WHERE au.email = user_email;
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END;
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$$ LANGUAGE plpgsql;
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CREATE TABLE IF NOT EXISTS migrations (
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name VARCHAR(255) PRIMARY KEY,
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executed_at TIMESTAMPTZ DEFAULT current_timestamp
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);
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CREATE TABLE IF NOT EXISTS user_settings (
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user_id UUID PRIMARY KEY,
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models JSONB DEFAULT '["gpt-3.5-turbo","huggingface/mistralai/Mistral-7B-Instruct-v0.1"]'::jsonb,
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daily_chat_credit INT DEFAULT 20,
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max_brains INT DEFAULT 3,
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max_brain_size INT DEFAULT 10000000
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);
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-- knowledge table
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CREATE TABLE IF NOT EXISTS knowledge (
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id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
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file_name TEXT,
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url TEXT,
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brain_id UUID NOT NULL REFERENCES brains(brain_id),
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extension TEXT NOT NULL,
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CHECK ((file_name IS NOT NULL AND url IS NULL) OR (file_name IS NULL AND url IS NOT NULL))
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);
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-- knowledge_vectors table
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CREATE TABLE IF NOT EXISTS knowledge_vectors (
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knowledge_id UUID NOT NULL REFERENCES knowledge(id),
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vector_id UUID NOT NULL REFERENCES vectors(id),
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embedding_model TEXT NOT NULL,
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PRIMARY KEY (knowledge_id, vector_id, embedding_model)
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);
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-- Create the onboarding table
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CREATE TABLE IF NOT EXISTS onboardings (
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user_id UUID NOT NULL REFERENCES auth.users (id),
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onboarding_b1 BOOLEAN NOT NULL DEFAULT false,
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onboarding_b2 BOOLEAN NOT NULL DEFAULT false,
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onboarding_b3 BOOLEAN NOT NULL DEFAULT false,
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PRIMARY KEY (user_id)
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)
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insert into
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storage.buckets (id, name)
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values
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('quivr', 'quivr');
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CREATE POLICY "Access Quivr Storage 1jccrwz_0" ON storage.objects FOR INSERT TO anon WITH CHECK (bucket_id = 'quivr');
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CREATE POLICY "Access Quivr Storage 1jccrwz_1" ON storage.objects FOR SELECT TO anon USING (bucket_id = 'quivr');
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CREATE POLICY "Access Quivr Storage 1jccrwz_2" ON storage.objects FOR UPDATE TO anon USING (bucket_id = 'quivr');
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CREATE POLICY "Access Quivr Storage 1jccrwz_3" ON storage.objects FOR DELETE TO anon USING (bucket_id = 'quivr');
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INSERT INTO migrations (name)
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SELECT '20231004150000_add_onboarding_table'
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WHERE NOT EXISTS (
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SELECT 1 FROM migrations WHERE name = '20231004150000_add_onboarding_table'
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);
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