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https://github.com/QuivrHQ/quivr.git
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33f49ee289
* feat: user can create api keys * fix: linting on build * Update backend/routes/api_key_routes.py * chore: rename and refactor AuthBearer * chore: add types
117 lines
2.7 KiB
PL/PgSQL
117 lines
2.7 KiB
PL/PgSQL
-- Create users table
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CREATE TABLE IF NOT EXISTS users(
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user_id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
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email TEXT,
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date TEXT,
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requests_count INT
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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 users(user_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 BIGSERIAL PRIMARY KEY,
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user_id TEXT,
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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 vectors
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CREATE OR REPLACE FUNCTION match_vectors(query_embedding VECTOR(1536), match_count INT, p_user_id TEXT)
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RETURNS TABLE(
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id BIGINT,
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user_id TEXT,
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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 $$
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#variable_conflict use_column
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BEGIN
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RETURN QUERY
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SELECT
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id,
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user_id,
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content,
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metadata,
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embedding,
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1 - (vectors.embedding <=> query_embedding) AS similarity
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FROM
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vectors
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WHERE vectors.user_id = p_user_id
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ORDER BY
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vectors.embedding <=> query_embedding
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LIMIT match_count;
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END;
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$$;
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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 BIGINT 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 BIGINT,
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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 users(user_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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