quivr/scripts/tables.sql

-- Create users table
CREATE TABLE IF NOT EXISTS users(
    user_id UUID REFERENCES auth.users (id),
    email TEXT,
    date TEXT,
    requests_count INT,
    PRIMARY KEY (user_id, date)
);

-- Create chats table
CREATE TABLE IF NOT EXISTS chats(
    chat_id UUID DEFAULT uuid_generate_v4() PRIMARY KEY,
    user_id UUID REFERENCES auth.users (id),
    creation_time TIMESTAMP DEFAULT current_timestamp,
    history JSONB,
    chat_name TEXT
);

-- Create chat_history table
CREATE TABLE IF NOT EXISTS chat_history (
    message_id UUID DEFAULT uuid_generate_v4(),
    chat_id UUID REFERENCES chats(chat_id),
    user_message TEXT,
    assistant TEXT,
    message_time TIMESTAMP DEFAULT current_timestamp,
    PRIMARY KEY (chat_id, message_id)
);

-- Create vector extension
CREATE EXTENSION IF NOT EXISTS vector;

-- Create vectors table
CREATE TABLE IF NOT EXISTS vectors (
    id BIGSERIAL PRIMARY KEY,
    content TEXT,
    metadata JSONB,
    embedding VECTOR(1536)
);

-- Create function to match vectors
CREATE OR REPLACE FUNCTION match_vectors(query_embedding VECTOR(1536), match_count INT, p_brain_id UUID)
RETURNS TABLE(
    id BIGINT,
    brain_id UUID,
    content TEXT,
    metadata JSONB,
    embedding VECTOR(1536),
    similarity FLOAT
) LANGUAGE plpgsql AS $$
#variable_conflict use_column
BEGIN
    RETURN QUERY
    SELECT
        vectors.id,
        brains_vectors.brain_id,
        vectors.content,
        vectors.metadata,
        vectors.embedding,
        1 - (vectors.embedding <=> query_embedding) AS similarity
    FROM
        vectors
    INNER JOIN
        brains_vectors ON vectors.id = brains_vectors.vector_id
    WHERE brains_vectors.brain_id = p_brain_id
    ORDER BY
        vectors.embedding <=> query_embedding
    LIMIT match_count;
END;
$$;


-- Create stats table
CREATE TABLE IF NOT EXISTS stats (
    time TIMESTAMP,
    chat BOOLEAN,
    embedding BOOLEAN,
    details TEXT,
    metadata JSONB,
    id INTEGER PRIMARY KEY GENERATED ALWAYS AS IDENTITY
);

-- Create summaries table
CREATE TABLE IF NOT EXISTS summaries (
    id BIGSERIAL PRIMARY KEY,
    document_id BIGINT REFERENCES vectors(id),
    content TEXT,
    metadata JSONB,
    embedding VECTOR(1536)
);

-- Create function to match summaries
CREATE OR REPLACE FUNCTION match_summaries(query_embedding VECTOR(1536), match_count INT, match_threshold FLOAT)
RETURNS TABLE(
    id BIGINT,
    document_id BIGINT,
    content TEXT,
    metadata JSONB,
    embedding VECTOR(1536),
    similarity FLOAT
) LANGUAGE plpgsql AS $$
#variable_conflict use_column
BEGIN
    RETURN QUERY
    SELECT
        id,
        document_id,
        content,
        metadata,
        embedding,
        1 - (summaries.embedding <=> query_embedding) AS similarity
    FROM
        summaries
    WHERE 1 - (summaries.embedding <=> query_embedding) > match_threshold
    ORDER BY
        summaries.embedding <=> query_embedding
    LIMIT match_count;
END;
$$;

-- Create api_keys table
CREATE TABLE IF NOT EXISTS api_keys(
    key_id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
    user_id UUID REFERENCES auth.users (id),
    api_key TEXT UNIQUE,
    creation_time TIMESTAMP DEFAULT current_timestamp,
    deleted_time TIMESTAMP,
    is_active BOOLEAN DEFAULT true
);

-- Create brains table
CREATE TABLE  IF NOT EXISTS brains (
  brain_id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
  name TEXT,
  status TEXT,
  model TEXT,
  max_tokens TEXT,
  temperature FLOAT
);

-- Create brains X users table
CREATE TABLE IF NOT EXISTS brains_users (
  brain_id UUID,
  user_id UUID,
  rights VARCHAR(255),
  default_brain BOOLEAN DEFAULT false,
  PRIMARY KEY (brain_id, user_id),
  FOREIGN KEY (user_id) REFERENCES auth.users (id),
  FOREIGN KEY (brain_id) REFERENCES Brains (brain_id)
);

-- Create brains X vectors table
CREATE TABLE IF NOT EXISTS brains_vectors (
  brain_id UUID,
  vector_id BIGINT,
  PRIMARY KEY (brain_id, vector_id),
  FOREIGN KEY (vector_id) REFERENCES vectors (id),
  FOREIGN KEY (brain_id) REFERENCES brains (brain_id)
);