rckrdmrd
45e77e9a9c
DDL schemas for Trading Platform: - User management - Authentication - Payments - Education - ML predictions - Trading data Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
99 lines
3.1 KiB
SQL
99 lines
3.1 KiB
SQL
-- =====================================================
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-- LLM SCHEMA - MESSAGES TABLE
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-- =====================================================
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-- Description: Individual messages in conversations
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-- Schema: llm
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-- Author: Database Agent
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-- Date: 2025-12-06
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-- =====================================================
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CREATE TABLE llm.messages (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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-- Conversación
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conversation_id UUID NOT NULL REFERENCES llm.conversations(id) ON DELETE CASCADE,
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-- Rol y contenido
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role llm.message_role NOT NULL,
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content TEXT NOT NULL,
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-- Metadata de LLM
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model_name VARCHAR(100), -- claude-opus-4-5, gpt-4, etc.
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prompt_tokens INTEGER,
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completion_tokens INTEGER,
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total_tokens INTEGER,
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-- Contexto utilizado
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context_used JSONB, -- RAG context, market data, user profile, etc.
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-- Tools/Functions llamadas
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tool_calls JSONB, -- Function calls realizadas
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tool_results JSONB, -- Resultados de tool calls
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-- Metadata de procesamiento
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response_time_ms INTEGER,
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temperature DECIMAL(3,2),
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-- Feedback del usuario
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user_rating INTEGER CHECK (user_rating >= 1 AND user_rating <= 5),
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user_feedback TEXT,
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-- Referencias
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references_symbols VARCHAR(20)[] DEFAULT '{}',
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references_concepts TEXT[] DEFAULT '{}',
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-- Metadata
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metadata JSONB,
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created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
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);
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-- Índices
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CREATE INDEX idx_messages_conversation ON llm.messages(conversation_id);
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CREATE INDEX idx_messages_role ON llm.messages(role);
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CREATE INDEX idx_messages_created ON llm.messages(created_at DESC);
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CREATE INDEX idx_messages_conversation_created ON llm.messages(conversation_id, created_at ASC);
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CREATE INDEX idx_messages_rated ON llm.messages(user_rating) WHERE user_rating IS NOT NULL;
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-- Comentarios
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COMMENT ON TABLE llm.messages IS 'Individual messages in LLM conversations';
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COMMENT ON COLUMN llm.messages.role IS 'Message sender: user, assistant, system, or tool';
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COMMENT ON COLUMN llm.messages.content IS 'Message text content';
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COMMENT ON COLUMN llm.messages.model_name IS 'LLM model used to generate response';
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COMMENT ON COLUMN llm.messages.context_used IS 'Context provided to LLM (RAG docs, market data, user profile)';
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COMMENT ON COLUMN llm.messages.tool_calls IS 'Functions/tools called by LLM during response generation';
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COMMENT ON COLUMN llm.messages.user_rating IS 'User satisfaction rating (1-5 stars)';
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-- Ejemplo de context_used JSONB:
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COMMENT ON COLUMN llm.messages.context_used IS
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'Example: {
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"market_data": {
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"symbol": "BTCUSDT",
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"price": 45234.12,
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"change_24h": 0.0234
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},
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"user_profile": {
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"risk_profile": "moderate",
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"preferred_symbols": ["BTCUSDT", "ETHUSDT"]
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},
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"rag_documents": [
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{"doc_id": "123", "relevance": 0.89, "snippet": "..."},
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{"doc_id": "456", "relevance": 0.76, "snippet": "..."}
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]
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}';
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-- Ejemplo de tool_calls JSONB:
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COMMENT ON COLUMN llm.messages.tool_calls IS
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'Example: [
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{
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"tool": "get_market_data",
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"params": {"symbol": "BTCUSDT", "timeframe": "1h"},
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"result": {...}
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},
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{
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"tool": "calculate_indicator",
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"params": {"indicator": "rsi", "period": 14},
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"result": {"rsi": 65.42}
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}
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]';
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