Initial commit: Context Manager v1.0.0
Sistema local de gestión de contexto para IA: - Log inmutable (blockchain-style) - Algoritmos versionados y mejorables - Agnóstico al modelo (Anthropic, OpenAI, Ollama) - Sistema de métricas y A/B testing 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
ARCHITECT
235
README.md
Normal file
235
README.md
Normal file
@@ -0,0 +1,235 @@
|
||||
# Context Manager
|
||||
|
||||
Sistema local de gestión de contexto para IA, agnóstico al modelo.
|
||||
|
||||
## Características
|
||||
|
||||
- **Log inmutable**: Tabla de referencia no editable con encadenamiento de hashes (blockchain-style)
|
||||
- **Gestor de contexto mejorable**: Algoritmos versionados y configurables
|
||||
- **Agnóstico al modelo**: Soporta Anthropic, OpenAI, Ollama y cualquier otro proveedor
|
||||
- **Sistema de métricas**: Evaluación continua del rendimiento
|
||||
- **A/B Testing**: Experimentación entre versiones de algoritmos
|
||||
- **Auto-mejora**: Sugerencias automáticas basadas en métricas
|
||||
|
||||
## Arquitectura
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────┐
|
||||
│ TABLAS NO EDITABLES │
|
||||
│ ┌─────────────────┐ │
|
||||
│ │ immutable_log │ ← Log de mensajes (blockchain-style) │
|
||||
│ │ sessions │ ← Registro de sesiones │
|
||||
│ └─────────────────┘ │
|
||||
├─────────────────────────────────────────────────────────────────┤
|
||||
│ TABLAS EDITABLES │
|
||||
│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │
|
||||
│ │ context_blocks │ │ memory │ │ knowledge_base │ │
|
||||
│ │ (bloques ctx) │ │ (memoria LP) │ │ (RAG simple) │ │
|
||||
│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │
|
||||
│ ┌─────────────────┐ ┌─────────────────┐ │
|
||||
│ │ algorithms │ │ metrics │ │
|
||||
│ │ (versionados) │ │ (rendimiento) │ │
|
||||
│ └─────────────────┘ └─────────────────┘ │
|
||||
└─────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Instalación
|
||||
|
||||
```bash
|
||||
# Clonar
|
||||
git clone <repo>/context-manager
|
||||
cd context-manager
|
||||
|
||||
# Instalar dependencias
|
||||
pip install -r requirements.txt
|
||||
|
||||
# Inicializar base de datos
|
||||
python -m src.cli init --database context_manager
|
||||
```
|
||||
|
||||
## Uso básico
|
||||
|
||||
### CLI
|
||||
|
||||
```bash
|
||||
# Chat interactivo con Anthropic
|
||||
python -m src.cli chat --provider anthropic --model claude-sonnet-4-20250514
|
||||
|
||||
# Chat con Ollama (local)
|
||||
python -m src.cli chat --provider ollama --model llama3
|
||||
|
||||
# Analizar rendimiento del algoritmo
|
||||
python -m src.cli analyze --days 30
|
||||
|
||||
# Sugerir mejoras
|
||||
python -m src.cli suggest --apply
|
||||
|
||||
# Verificar integridad de sesión
|
||||
python -m src.cli verify <session_id>
|
||||
```
|
||||
|
||||
### Python API
|
||||
|
||||
```python
|
||||
from src import ContextManager
|
||||
from src.providers import AnthropicProvider
|
||||
|
||||
# Inicializar
|
||||
manager = ContextManager(
|
||||
host="localhost",
|
||||
database="context_manager"
|
||||
)
|
||||
|
||||
# Iniciar sesión
|
||||
session = manager.start_session(
|
||||
user_id="user1",
|
||||
model_provider="anthropic",
|
||||
model_name="claude-sonnet-4-20250514"
|
||||
)
|
||||
|
||||
# Obtener contexto para un mensaje
|
||||
context = manager.get_context_for_message(
|
||||
"¿Cómo configuro el servidor?",
|
||||
max_tokens=4000
|
||||
)
|
||||
|
||||
# Usar con proveedor
|
||||
provider = AnthropicProvider()
|
||||
response = provider.send_message("¿Cómo configuro el servidor?", context)
|
||||
|
||||
# Registrar en log inmutable
|
||||
manager.log_user_message("¿Cómo configuro el servidor?", context)
|
||||
manager.log_assistant_message(
|
||||
response.content,
|
||||
tokens_input=response.tokens_input,
|
||||
tokens_output=response.tokens_output
|
||||
)
|
||||
|
||||
# Verificar integridad
|
||||
result = manager.verify_session_integrity()
|
||||
assert result["is_valid"]
|
||||
```
|
||||
|
||||
### Mejora de algoritmos
|
||||
|
||||
```python
|
||||
from src.algorithm_improver import AlgorithmImprover
|
||||
|
||||
improver = AlgorithmImprover(db)
|
||||
|
||||
# Analizar rendimiento
|
||||
analysis = improver.analyze_algorithm(days=30)
|
||||
print(f"Calidad promedio: {analysis.avg_quality}")
|
||||
print(f"Sugerencias: {analysis.suggestions}")
|
||||
|
||||
# Crear experimento A/B
|
||||
experiment_id = improver.create_experiment(
|
||||
control_id=current_algorithm_id,
|
||||
treatment_id=new_algorithm_id,
|
||||
name="Test nuevo algoritmo",
|
||||
traffic_split=0.5,
|
||||
min_samples=100
|
||||
)
|
||||
|
||||
# Evaluar resultados
|
||||
result = improver.evaluate_experiment(experiment_id)
|
||||
print(f"Ganador: {result.winner}")
|
||||
print(f"Mejora: {result.improvement_pct}%")
|
||||
```
|
||||
|
||||
## Estructura de archivos
|
||||
|
||||
```
|
||||
context-manager/
|
||||
├── schemas/
|
||||
│ ├── 00_base.sql # Tipos y funciones base
|
||||
│ ├── 01_immutable_log.sql # Log inmutable (NO editable)
|
||||
│ ├── 02_context_manager.sql # Bloques, memoria, conocimiento
|
||||
│ └── 03_algorithm_engine.sql # Algoritmos y métricas
|
||||
├── src/
|
||||
│ ├── __init__.py
|
||||
│ ├── models.py # Modelos de datos
|
||||
│ ├── database.py # Conexión PostgreSQL
|
||||
│ ├── context_selector.py # Motor de selección
|
||||
│ ├── algorithm_improver.py # Sistema de mejora
|
||||
│ ├── cli.py # Interfaz de línea de comandos
|
||||
│ └── providers/
|
||||
│ ├── base.py # Clase base
|
||||
│ ├── anthropic.py # Adaptador Anthropic
|
||||
│ ├── openai.py # Adaptador OpenAI
|
||||
│ └── ollama.py # Adaptador Ollama
|
||||
├── config/
|
||||
│ └── default.yaml # Configuración por defecto
|
||||
├── tests/
|
||||
├── requirements.txt
|
||||
└── README.md
|
||||
```
|
||||
|
||||
## Configuración del algoritmo
|
||||
|
||||
```json
|
||||
{
|
||||
"max_tokens": 4000,
|
||||
"sources": {
|
||||
"system_prompts": true,
|
||||
"context_blocks": true,
|
||||
"memory": true,
|
||||
"knowledge": true,
|
||||
"history": true,
|
||||
"ambient": true
|
||||
},
|
||||
"weights": {
|
||||
"priority": 0.4,
|
||||
"relevance": 0.3,
|
||||
"recency": 0.2,
|
||||
"frequency": 0.1
|
||||
},
|
||||
"history_config": {
|
||||
"max_messages": 20,
|
||||
"summarize_after": 10,
|
||||
"include_system": false
|
||||
},
|
||||
"memory_config": {
|
||||
"max_items": 15,
|
||||
"min_importance": 30
|
||||
},
|
||||
"knowledge_config": {
|
||||
"max_items": 5,
|
||||
"require_keyword_match": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Integridad del log
|
||||
|
||||
El log inmutable usa encadenamiento de hashes similar a blockchain:
|
||||
|
||||
```
|
||||
Mensaje 1 → hash1 = SHA256(content1)
|
||||
Mensaje 2 → hash2 = SHA256(hash1 + content2)
|
||||
Mensaje 3 → hash3 = SHA256(hash2 + content3)
|
||||
```
|
||||
|
||||
Verificar integridad:
|
||||
```sql
|
||||
SELECT * FROM verify_chain_integrity('session-uuid');
|
||||
```
|
||||
|
||||
## Variables de entorno
|
||||
|
||||
```bash
|
||||
PGHOST=localhost
|
||||
PGPORT=5432
|
||||
PGDATABASE=context_manager
|
||||
PGUSER=postgres
|
||||
PGPASSWORD=
|
||||
|
||||
ANTHROPIC_API_KEY=sk-ant-...
|
||||
OPENAI_API_KEY=sk-...
|
||||
OLLAMA_HOST=localhost
|
||||
OLLAMA_PORT=11434
|
||||
```
|
||||
|
||||
## Licencia
|
||||
|
||||
MIT
|
||||
59
config/default.yaml
Normal file
59
config/default.yaml
Normal file
@@ -0,0 +1,59 @@
|
||||
# Context Manager - Configuración por defecto
|
||||
|
||||
database:
|
||||
host: ${PGHOST:localhost}
|
||||
port: ${PGPORT:5432}
|
||||
name: ${PGDATABASE:context_manager}
|
||||
user: ${PGUSER:postgres}
|
||||
password: ${PGPASSWORD:}
|
||||
pool:
|
||||
min_connections: 1
|
||||
max_connections: 10
|
||||
|
||||
algorithm:
|
||||
default:
|
||||
max_tokens: 4000
|
||||
sources:
|
||||
system_prompts: true
|
||||
context_blocks: true
|
||||
memory: true
|
||||
knowledge: true
|
||||
history: true
|
||||
ambient: true
|
||||
weights:
|
||||
priority: 0.4
|
||||
relevance: 0.3
|
||||
recency: 0.2
|
||||
frequency: 0.1
|
||||
history_config:
|
||||
max_messages: 20
|
||||
summarize_after: 10
|
||||
include_system: false
|
||||
memory_config:
|
||||
max_items: 15
|
||||
min_importance: 30
|
||||
knowledge_config:
|
||||
max_items: 5
|
||||
require_keyword_match: true
|
||||
|
||||
providers:
|
||||
anthropic:
|
||||
model: claude-sonnet-4-20250514
|
||||
max_tokens: 4096
|
||||
openai:
|
||||
model: gpt-4
|
||||
max_tokens: 4096
|
||||
ollama:
|
||||
host: ${OLLAMA_HOST:localhost}
|
||||
port: ${OLLAMA_PORT:11434}
|
||||
model: llama3
|
||||
|
||||
metrics:
|
||||
auto_evaluate: false
|
||||
evaluation_model: null # Modelo para evaluación automática
|
||||
retention_days: 90
|
||||
|
||||
experiments:
|
||||
default_traffic_split: 0.5
|
||||
min_samples: 100
|
||||
max_samples: 1000
|
||||
19
requirements.txt
Normal file
19
requirements.txt
Normal file
@@ -0,0 +1,19 @@
|
||||
# Base
|
||||
psycopg2-binary>=2.9.0
|
||||
pyyaml>=6.0
|
||||
|
||||
# Proveedores de IA (opcionales)
|
||||
anthropic>=0.18.0
|
||||
openai>=1.0.0
|
||||
requests>=2.28.0
|
||||
|
||||
# Para embeddings (opcional)
|
||||
# pgvector>=0.2.0
|
||||
|
||||
# Testing
|
||||
pytest>=7.0.0
|
||||
pytest-asyncio>=0.21.0
|
||||
|
||||
# Desarrollo
|
||||
black>=23.0.0
|
||||
mypy>=1.0.0
|
||||
39
schemas/00_base.sql
Normal file
39
schemas/00_base.sql
Normal file
@@ -0,0 +1,39 @@
|
||||
-- ============================================
|
||||
-- CONTEXT MANAGER - BASE TYPES
|
||||
-- Sistema local de gestión de contexto para IA
|
||||
-- ============================================
|
||||
|
||||
-- Extension para UUIDs
|
||||
CREATE EXTENSION IF NOT EXISTS "pgcrypto";
|
||||
|
||||
-- ============================================
|
||||
-- TIPOS ENUMERADOS
|
||||
-- ============================================
|
||||
|
||||
CREATE TYPE mensaje_role AS ENUM ('user', 'assistant', 'system', 'tool');
|
||||
CREATE TYPE context_source AS ENUM ('memory', 'knowledge', 'history', 'ambient', 'dataset');
|
||||
CREATE TYPE algorithm_status AS ENUM ('draft', 'testing', 'active', 'deprecated');
|
||||
CREATE TYPE metric_type AS ENUM ('relevance', 'token_efficiency', 'response_quality', 'latency');
|
||||
|
||||
-- ============================================
|
||||
-- FUNCIÓN: Timestamp de actualización
|
||||
-- ============================================
|
||||
|
||||
CREATE OR REPLACE FUNCTION update_updated_at_column()
|
||||
RETURNS TRIGGER AS $$
|
||||
BEGIN
|
||||
NEW.updated_at = CURRENT_TIMESTAMP;
|
||||
RETURN NEW;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
|
||||
-- ============================================
|
||||
-- FUNCIÓN: Hash SHA-256
|
||||
-- ============================================
|
||||
|
||||
CREATE OR REPLACE FUNCTION sha256_hash(content TEXT)
|
||||
RETURNS VARCHAR(64) AS $$
|
||||
BEGIN
|
||||
RETURN encode(digest(content, 'sha256'), 'hex');
|
||||
END;
|
||||
$$ LANGUAGE plpgsql IMMUTABLE;
|
||||
276
schemas/01_immutable_log.sql
Normal file
276
schemas/01_immutable_log.sql
Normal file
@@ -0,0 +1,276 @@
|
||||
-- ============================================
|
||||
-- LOG INMUTABLE - TABLA DE REFERENCIA
|
||||
-- NO EDITABLE - Solo INSERT permitido
|
||||
-- ============================================
|
||||
-- Esta tabla es la fuente de verdad del sistema.
|
||||
-- Nunca se modifica ni se borra. Solo se inserta.
|
||||
|
||||
-- ============================================
|
||||
-- TABLA: immutable_log
|
||||
-- Registro permanente de todas las interacciones
|
||||
-- ============================================
|
||||
|
||||
CREATE TABLE IF NOT EXISTS immutable_log (
|
||||
-- Identificación
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
hash VARCHAR(64) NOT NULL UNIQUE, -- SHA-256 del contenido
|
||||
hash_anterior VARCHAR(64), -- Encadenamiento (blockchain-style)
|
||||
|
||||
-- Sesión
|
||||
session_id UUID NOT NULL,
|
||||
sequence_num BIGINT NOT NULL, -- Número secuencial en la sesión
|
||||
|
||||
-- Mensaje
|
||||
role mensaje_role NOT NULL,
|
||||
content TEXT NOT NULL,
|
||||
|
||||
-- Modelo IA (agnóstico)
|
||||
model_provider VARCHAR(50), -- anthropic, openai, ollama, local, etc.
|
||||
model_name VARCHAR(100), -- claude-3-opus, gpt-4, llama-3, etc.
|
||||
model_params JSONB DEFAULT '{}', -- temperature, max_tokens, etc.
|
||||
|
||||
-- Contexto enviado (snapshot)
|
||||
context_snapshot JSONB, -- Copia del contexto usado
|
||||
context_algorithm_id UUID, -- Qué algoritmo seleccionó el contexto
|
||||
context_tokens_used INT,
|
||||
|
||||
-- Respuesta (solo para role=assistant)
|
||||
tokens_input INT,
|
||||
tokens_output INT,
|
||||
latency_ms INT,
|
||||
|
||||
-- Metadata inmutable
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP NOT NULL,
|
||||
source_ip VARCHAR(45),
|
||||
user_agent TEXT,
|
||||
|
||||
-- Integridad
|
||||
CONSTRAINT chain_integrity CHECK (
|
||||
(sequence_num = 1 AND hash_anterior IS NULL) OR
|
||||
(sequence_num > 1 AND hash_anterior IS NOT NULL)
|
||||
)
|
||||
);
|
||||
|
||||
-- Índices para consulta (no para modificación)
|
||||
CREATE INDEX IF NOT EXISTS idx_log_session ON immutable_log(session_id, sequence_num);
|
||||
CREATE INDEX IF NOT EXISTS idx_log_created ON immutable_log(created_at DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_log_model ON immutable_log(model_provider, model_name);
|
||||
CREATE INDEX IF NOT EXISTS idx_log_hash ON immutable_log(hash);
|
||||
CREATE INDEX IF NOT EXISTS idx_log_chain ON immutable_log(hash_anterior);
|
||||
|
||||
-- ============================================
|
||||
-- PROTECCIÓN: Trigger que impide UPDATE y DELETE
|
||||
-- ============================================
|
||||
|
||||
CREATE OR REPLACE FUNCTION prevent_log_modification()
|
||||
RETURNS TRIGGER AS $$
|
||||
BEGIN
|
||||
RAISE EXCEPTION 'immutable_log no permite modificaciones. Solo INSERT está permitido.';
|
||||
RETURN NULL;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
|
||||
DROP TRIGGER IF EXISTS protect_immutable_log_update ON immutable_log;
|
||||
CREATE TRIGGER protect_immutable_log_update
|
||||
BEFORE UPDATE ON immutable_log
|
||||
FOR EACH ROW EXECUTE FUNCTION prevent_log_modification();
|
||||
|
||||
DROP TRIGGER IF EXISTS protect_immutable_log_delete ON immutable_log;
|
||||
CREATE TRIGGER protect_immutable_log_delete
|
||||
BEFORE DELETE ON immutable_log
|
||||
FOR EACH ROW EXECUTE FUNCTION prevent_log_modification();
|
||||
|
||||
-- ============================================
|
||||
-- FUNCIÓN: Insertar en log con hash automático
|
||||
-- ============================================
|
||||
|
||||
CREATE OR REPLACE FUNCTION insert_log_entry(
|
||||
p_session_id UUID,
|
||||
p_role mensaje_role,
|
||||
p_content TEXT,
|
||||
p_model_provider VARCHAR DEFAULT NULL,
|
||||
p_model_name VARCHAR DEFAULT NULL,
|
||||
p_model_params JSONB DEFAULT '{}',
|
||||
p_context_snapshot JSONB DEFAULT NULL,
|
||||
p_context_algorithm_id UUID DEFAULT NULL,
|
||||
p_context_tokens_used INT DEFAULT NULL,
|
||||
p_tokens_input INT DEFAULT NULL,
|
||||
p_tokens_output INT DEFAULT NULL,
|
||||
p_latency_ms INT DEFAULT NULL,
|
||||
p_source_ip VARCHAR DEFAULT NULL,
|
||||
p_user_agent TEXT DEFAULT NULL
|
||||
)
|
||||
RETURNS UUID AS $$
|
||||
DECLARE
|
||||
v_sequence_num BIGINT;
|
||||
v_hash_anterior VARCHAR(64);
|
||||
v_content_hash VARCHAR(64);
|
||||
v_new_id UUID;
|
||||
BEGIN
|
||||
-- Obtener último hash y secuencia de la sesión
|
||||
SELECT sequence_num, hash
|
||||
INTO v_sequence_num, v_hash_anterior
|
||||
FROM immutable_log
|
||||
WHERE session_id = p_session_id
|
||||
ORDER BY sequence_num DESC
|
||||
LIMIT 1;
|
||||
|
||||
IF v_sequence_num IS NULL THEN
|
||||
v_sequence_num := 1;
|
||||
v_hash_anterior := NULL;
|
||||
ELSE
|
||||
v_sequence_num := v_sequence_num + 1;
|
||||
END IF;
|
||||
|
||||
-- Calcular hash del contenido (incluye hash anterior para encadenamiento)
|
||||
v_content_hash := sha256_hash(
|
||||
COALESCE(v_hash_anterior, '') ||
|
||||
p_session_id::TEXT ||
|
||||
v_sequence_num::TEXT ||
|
||||
p_role::TEXT ||
|
||||
p_content
|
||||
);
|
||||
|
||||
-- Insertar
|
||||
INSERT INTO immutable_log (
|
||||
session_id, sequence_num, hash, hash_anterior,
|
||||
role, content,
|
||||
model_provider, model_name, model_params,
|
||||
context_snapshot, context_algorithm_id, context_tokens_used,
|
||||
tokens_input, tokens_output, latency_ms,
|
||||
source_ip, user_agent
|
||||
) VALUES (
|
||||
p_session_id, v_sequence_num, v_content_hash, v_hash_anterior,
|
||||
p_role, p_content,
|
||||
p_model_provider, p_model_name, p_model_params,
|
||||
p_context_snapshot, p_context_algorithm_id, p_context_tokens_used,
|
||||
p_tokens_input, p_tokens_output, p_latency_ms,
|
||||
p_source_ip, p_user_agent
|
||||
) RETURNING id INTO v_new_id;
|
||||
|
||||
RETURN v_new_id;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
|
||||
-- ============================================
|
||||
-- TABLA: sessions
|
||||
-- Registro de sesiones (también inmutable)
|
||||
-- ============================================
|
||||
|
||||
CREATE TABLE IF NOT EXISTS sessions (
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
hash VARCHAR(64) NOT NULL UNIQUE,
|
||||
|
||||
-- Identificación
|
||||
user_id VARCHAR(100),
|
||||
instance_id VARCHAR(100),
|
||||
|
||||
-- Configuración inicial
|
||||
initial_model_provider VARCHAR(50),
|
||||
initial_model_name VARCHAR(100),
|
||||
initial_context_algorithm_id UUID,
|
||||
|
||||
-- Metadata
|
||||
metadata JSONB DEFAULT '{}',
|
||||
|
||||
-- Timestamps inmutables
|
||||
started_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP NOT NULL,
|
||||
ended_at TIMESTAMP,
|
||||
|
||||
-- Estadísticas finales (se actualizan solo al cerrar)
|
||||
total_messages INT DEFAULT 0,
|
||||
total_tokens_input INT DEFAULT 0,
|
||||
total_tokens_output INT DEFAULT 0,
|
||||
total_latency_ms INT DEFAULT 0
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_sessions_user ON sessions(user_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_sessions_instance ON sessions(instance_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_sessions_started ON sessions(started_at DESC);
|
||||
|
||||
-- ============================================
|
||||
-- FUNCIÓN: Crear nueva sesión
|
||||
-- ============================================
|
||||
|
||||
CREATE OR REPLACE FUNCTION create_session(
|
||||
p_user_id VARCHAR DEFAULT NULL,
|
||||
p_instance_id VARCHAR DEFAULT NULL,
|
||||
p_model_provider VARCHAR DEFAULT NULL,
|
||||
p_model_name VARCHAR DEFAULT NULL,
|
||||
p_algorithm_id UUID DEFAULT NULL,
|
||||
p_metadata JSONB DEFAULT '{}'
|
||||
)
|
||||
RETURNS UUID AS $$
|
||||
DECLARE
|
||||
v_session_id UUID;
|
||||
v_hash VARCHAR(64);
|
||||
BEGIN
|
||||
v_session_id := gen_random_uuid();
|
||||
v_hash := sha256_hash(v_session_id::TEXT || CURRENT_TIMESTAMP::TEXT);
|
||||
|
||||
INSERT INTO sessions (
|
||||
id, hash, user_id, instance_id,
|
||||
initial_model_provider, initial_model_name,
|
||||
initial_context_algorithm_id, metadata
|
||||
) VALUES (
|
||||
v_session_id, v_hash, p_user_id, p_instance_id,
|
||||
p_model_provider, p_model_name,
|
||||
p_algorithm_id, p_metadata
|
||||
);
|
||||
|
||||
RETURN v_session_id;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
|
||||
-- ============================================
|
||||
-- FUNCIÓN: Verificar integridad de la cadena
|
||||
-- ============================================
|
||||
|
||||
CREATE OR REPLACE FUNCTION verify_chain_integrity(p_session_id UUID)
|
||||
RETURNS TABLE (
|
||||
is_valid BOOLEAN,
|
||||
broken_at_sequence BIGINT,
|
||||
expected_hash VARCHAR(64),
|
||||
actual_hash VARCHAR(64)
|
||||
) AS $$
|
||||
DECLARE
|
||||
rec RECORD;
|
||||
prev_hash VARCHAR(64) := NULL;
|
||||
computed_hash VARCHAR(64);
|
||||
BEGIN
|
||||
FOR rec IN
|
||||
SELECT * FROM immutable_log
|
||||
WHERE session_id = p_session_id
|
||||
ORDER BY sequence_num
|
||||
LOOP
|
||||
-- Verificar encadenamiento
|
||||
IF rec.sequence_num = 1 AND rec.hash_anterior IS NOT NULL THEN
|
||||
RETURN QUERY SELECT FALSE, rec.sequence_num, NULL::VARCHAR(64), rec.hash_anterior;
|
||||
RETURN;
|
||||
END IF;
|
||||
|
||||
IF rec.sequence_num > 1 AND rec.hash_anterior != prev_hash THEN
|
||||
RETURN QUERY SELECT FALSE, rec.sequence_num, prev_hash, rec.hash_anterior;
|
||||
RETURN;
|
||||
END IF;
|
||||
|
||||
-- Verificar hash del contenido
|
||||
computed_hash := sha256_hash(
|
||||
COALESCE(prev_hash, '') ||
|
||||
rec.session_id::TEXT ||
|
||||
rec.sequence_num::TEXT ||
|
||||
rec.role::TEXT ||
|
||||
rec.content
|
||||
);
|
||||
|
||||
IF computed_hash != rec.hash THEN
|
||||
RETURN QUERY SELECT FALSE, rec.sequence_num, computed_hash, rec.hash;
|
||||
RETURN;
|
||||
END IF;
|
||||
|
||||
prev_hash := rec.hash;
|
||||
END LOOP;
|
||||
|
||||
RETURN QUERY SELECT TRUE, NULL::BIGINT, NULL::VARCHAR(64), NULL::VARCHAR(64);
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
243
schemas/02_context_manager.sql
Normal file
243
schemas/02_context_manager.sql
Normal file
@@ -0,0 +1,243 @@
|
||||
-- ============================================
|
||||
-- GESTOR DE CONTEXTO - TABLAS EDITABLES
|
||||
-- Estas tablas SÍ se pueden modificar
|
||||
-- ============================================
|
||||
|
||||
-- ============================================
|
||||
-- TABLA: context_blocks
|
||||
-- Bloques de contexto reutilizables
|
||||
-- ============================================
|
||||
|
||||
CREATE TABLE IF NOT EXISTS context_blocks (
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
|
||||
-- Identificación
|
||||
code VARCHAR(100) UNIQUE NOT NULL,
|
||||
name VARCHAR(255) NOT NULL,
|
||||
description TEXT,
|
||||
|
||||
-- Contenido
|
||||
content TEXT NOT NULL,
|
||||
content_hash VARCHAR(64), -- Para detectar cambios
|
||||
|
||||
-- Clasificación
|
||||
category VARCHAR(50) NOT NULL, -- system, persona, knowledge, rules, examples
|
||||
priority INT DEFAULT 50, -- 0-100, mayor = más importante
|
||||
tokens_estimated INT,
|
||||
|
||||
-- Alcance
|
||||
scope VARCHAR(50) DEFAULT 'global', -- global, project, session
|
||||
project_id UUID,
|
||||
|
||||
-- Condiciones de activación
|
||||
activation_rules JSONB DEFAULT '{}',
|
||||
/*
|
||||
Ejemplo activation_rules:
|
||||
{
|
||||
"always": false,
|
||||
"keywords": ["database", "sql"],
|
||||
"model_providers": ["anthropic"],
|
||||
"min_session_messages": 0,
|
||||
"time_of_day": null
|
||||
}
|
||||
*/
|
||||
|
||||
-- Estado
|
||||
active BOOLEAN DEFAULT true,
|
||||
version INT DEFAULT 1,
|
||||
|
||||
-- Timestamps
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_ctx_blocks_code ON context_blocks(code);
|
||||
CREATE INDEX IF NOT EXISTS idx_ctx_blocks_category ON context_blocks(category);
|
||||
CREATE INDEX IF NOT EXISTS idx_ctx_blocks_priority ON context_blocks(priority DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_ctx_blocks_active ON context_blocks(active);
|
||||
CREATE INDEX IF NOT EXISTS idx_ctx_blocks_scope ON context_blocks(scope);
|
||||
|
||||
DROP TRIGGER IF EXISTS update_ctx_blocks_updated_at ON context_blocks;
|
||||
CREATE TRIGGER update_ctx_blocks_updated_at
|
||||
BEFORE UPDATE ON context_blocks
|
||||
FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();
|
||||
|
||||
-- Trigger para calcular hash y tokens al insertar/actualizar
|
||||
CREATE OR REPLACE FUNCTION update_block_metadata()
|
||||
RETURNS TRIGGER AS $$
|
||||
BEGIN
|
||||
NEW.content_hash := sha256_hash(NEW.content);
|
||||
-- Estimación simple: ~4 caracteres por token
|
||||
NEW.tokens_estimated := CEIL(LENGTH(NEW.content) / 4.0);
|
||||
RETURN NEW;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
|
||||
DROP TRIGGER IF EXISTS calc_block_metadata ON context_blocks;
|
||||
CREATE TRIGGER calc_block_metadata
|
||||
BEFORE INSERT OR UPDATE OF content ON context_blocks
|
||||
FOR EACH ROW EXECUTE FUNCTION update_block_metadata();
|
||||
|
||||
-- ============================================
|
||||
-- TABLA: knowledge_base
|
||||
-- Base de conocimiento (RAG simple)
|
||||
-- ============================================
|
||||
|
||||
CREATE TABLE IF NOT EXISTS knowledge_base (
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
|
||||
-- Identificación
|
||||
title VARCHAR(255) NOT NULL,
|
||||
category VARCHAR(100) NOT NULL,
|
||||
tags TEXT[] DEFAULT '{}',
|
||||
|
||||
-- Contenido
|
||||
content TEXT NOT NULL,
|
||||
content_hash VARCHAR(64),
|
||||
tokens_estimated INT,
|
||||
|
||||
-- Embeddings (para búsqueda semántica futura)
|
||||
embedding_model VARCHAR(100),
|
||||
embedding VECTOR(1536), -- Requiere pgvector si se usa
|
||||
|
||||
-- Fuente
|
||||
source_type VARCHAR(50), -- file, url, manual, extracted
|
||||
source_ref TEXT,
|
||||
|
||||
-- Relevancia
|
||||
priority INT DEFAULT 50,
|
||||
access_count INT DEFAULT 0,
|
||||
last_accessed_at TIMESTAMP,
|
||||
|
||||
-- Estado
|
||||
active BOOLEAN DEFAULT true,
|
||||
|
||||
-- Timestamps
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_kb_category ON knowledge_base(category);
|
||||
CREATE INDEX IF NOT EXISTS idx_kb_tags ON knowledge_base USING GIN(tags);
|
||||
CREATE INDEX IF NOT EXISTS idx_kb_priority ON knowledge_base(priority DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_kb_active ON knowledge_base(active);
|
||||
|
||||
DROP TRIGGER IF EXISTS update_kb_updated_at ON knowledge_base;
|
||||
CREATE TRIGGER update_kb_updated_at
|
||||
BEFORE UPDATE ON knowledge_base
|
||||
FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();
|
||||
|
||||
DROP TRIGGER IF EXISTS calc_kb_metadata ON knowledge_base;
|
||||
CREATE TRIGGER calc_kb_metadata
|
||||
BEFORE INSERT OR UPDATE OF content ON knowledge_base
|
||||
FOR EACH ROW EXECUTE FUNCTION update_block_metadata();
|
||||
|
||||
-- ============================================
|
||||
-- TABLA: memory
|
||||
-- Memoria a largo plazo extraída de conversaciones
|
||||
-- ============================================
|
||||
|
||||
CREATE TABLE IF NOT EXISTS memory (
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
|
||||
-- Clasificación
|
||||
type VARCHAR(50) NOT NULL, -- fact, preference, decision, learning, procedure
|
||||
category VARCHAR(100),
|
||||
|
||||
-- Contenido
|
||||
content TEXT NOT NULL,
|
||||
summary VARCHAR(500),
|
||||
content_hash VARCHAR(64),
|
||||
|
||||
-- Origen
|
||||
extracted_from_session UUID REFERENCES sessions(id),
|
||||
extracted_from_log UUID, -- No FK para no bloquear
|
||||
|
||||
-- Relevancia
|
||||
importance INT DEFAULT 50, -- 0-100
|
||||
confidence DECIMAL(3,2) DEFAULT 1.0, -- 0.00-1.00
|
||||
uses INT DEFAULT 0,
|
||||
last_used_at TIMESTAMP,
|
||||
|
||||
-- Expiración
|
||||
expires_at TIMESTAMP,
|
||||
|
||||
-- Estado
|
||||
active BOOLEAN DEFAULT true,
|
||||
verified BOOLEAN DEFAULT false, -- Confirmado por usuario
|
||||
|
||||
-- Timestamps
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_memory_type ON memory(type);
|
||||
CREATE INDEX IF NOT EXISTS idx_memory_importance ON memory(importance DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_memory_active ON memory(active);
|
||||
CREATE INDEX IF NOT EXISTS idx_memory_expires ON memory(expires_at);
|
||||
|
||||
DROP TRIGGER IF EXISTS update_memory_updated_at ON memory;
|
||||
CREATE TRIGGER update_memory_updated_at
|
||||
BEFORE UPDATE ON memory
|
||||
FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();
|
||||
|
||||
-- ============================================
|
||||
-- TABLA: ambient_context
|
||||
-- Contexto ambiental (estado actual del sistema)
|
||||
-- ============================================
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ambient_context (
|
||||
id SERIAL PRIMARY KEY,
|
||||
|
||||
-- Snapshot
|
||||
captured_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
expires_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + INTERVAL '1 hour',
|
||||
|
||||
-- Datos ambientales
|
||||
environment JSONB DEFAULT '{}',
|
||||
/*
|
||||
{
|
||||
"timezone": "Europe/Madrid",
|
||||
"locale": "es-ES",
|
||||
"working_directory": "/home/user/project",
|
||||
"git_branch": "main",
|
||||
"active_project": "my-app"
|
||||
}
|
||||
*/
|
||||
|
||||
-- Estado del sistema
|
||||
system_state JSONB DEFAULT '{}',
|
||||
/*
|
||||
{
|
||||
"servers": {"architect": "online"},
|
||||
"services": {"gitea": "running"},
|
||||
"pending_tasks": [],
|
||||
"alerts": []
|
||||
}
|
||||
*/
|
||||
|
||||
-- Archivos/recursos activos
|
||||
active_resources JSONB DEFAULT '[]'
|
||||
/*
|
||||
[
|
||||
{"type": "file", "path": "/path/to/file.py", "modified": true},
|
||||
{"type": "url", "href": "https://docs.example.com"}
|
||||
]
|
||||
*/
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_ambient_captured ON ambient_context(captured_at DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_ambient_expires ON ambient_context(expires_at);
|
||||
|
||||
-- Limpiar contextos expirados
|
||||
CREATE OR REPLACE FUNCTION cleanup_expired_ambient()
|
||||
RETURNS INTEGER AS $$
|
||||
DECLARE
|
||||
deleted_count INTEGER;
|
||||
BEGIN
|
||||
DELETE FROM ambient_context
|
||||
WHERE expires_at < CURRENT_TIMESTAMP;
|
||||
GET DIAGNOSTICS deleted_count = ROW_COUNT;
|
||||
RETURN deleted_count;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
399
schemas/03_algorithm_engine.sql
Normal file
399
schemas/03_algorithm_engine.sql
Normal file
@@ -0,0 +1,399 @@
|
||||
-- ============================================
|
||||
-- MOTOR DE ALGORITMOS - Sistema evolutivo
|
||||
-- Permite versionar y mejorar el algoritmo de contexto
|
||||
-- ============================================
|
||||
|
||||
-- ============================================
|
||||
-- TABLA: context_algorithms
|
||||
-- Definición de algoritmos de selección de contexto
|
||||
-- ============================================
|
||||
|
||||
CREATE TABLE IF NOT EXISTS context_algorithms (
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
|
||||
-- Identificación
|
||||
code VARCHAR(100) UNIQUE NOT NULL,
|
||||
name VARCHAR(255) NOT NULL,
|
||||
description TEXT,
|
||||
version VARCHAR(20) NOT NULL DEFAULT '1.0.0',
|
||||
|
||||
-- Estado
|
||||
status algorithm_status DEFAULT 'draft',
|
||||
|
||||
-- Configuración del algoritmo
|
||||
config JSONB NOT NULL DEFAULT '{
|
||||
"max_tokens": 4000,
|
||||
"sources": {
|
||||
"system_prompts": true,
|
||||
"context_blocks": true,
|
||||
"memory": true,
|
||||
"knowledge": true,
|
||||
"history": true,
|
||||
"ambient": true
|
||||
},
|
||||
"weights": {
|
||||
"priority": 0.4,
|
||||
"relevance": 0.3,
|
||||
"recency": 0.2,
|
||||
"frequency": 0.1
|
||||
},
|
||||
"history_config": {
|
||||
"max_messages": 20,
|
||||
"summarize_after": 10,
|
||||
"include_system": false
|
||||
},
|
||||
"memory_config": {
|
||||
"max_items": 15,
|
||||
"min_importance": 30
|
||||
},
|
||||
"knowledge_config": {
|
||||
"max_items": 5,
|
||||
"require_keyword_match": true
|
||||
}
|
||||
}'::jsonb,
|
||||
|
||||
-- Código del algoritmo (Python embebido)
|
||||
selector_code TEXT,
|
||||
/*
|
||||
Ejemplo:
|
||||
def select_context(session, message, config):
|
||||
context = []
|
||||
# ... lógica de selección
|
||||
return context
|
||||
*/
|
||||
|
||||
-- Estadísticas
|
||||
times_used INT DEFAULT 0,
|
||||
avg_tokens_used DECIMAL(10,2),
|
||||
avg_relevance_score DECIMAL(3,2),
|
||||
avg_response_quality DECIMAL(3,2),
|
||||
|
||||
-- Linaje
|
||||
parent_algorithm_id UUID REFERENCES context_algorithms(id),
|
||||
fork_reason TEXT,
|
||||
|
||||
-- Timestamps
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
activated_at TIMESTAMP,
|
||||
deprecated_at TIMESTAMP
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_algo_code ON context_algorithms(code);
|
||||
CREATE INDEX IF NOT EXISTS idx_algo_status ON context_algorithms(status);
|
||||
CREATE INDEX IF NOT EXISTS idx_algo_version ON context_algorithms(version);
|
||||
CREATE INDEX IF NOT EXISTS idx_algo_parent ON context_algorithms(parent_algorithm_id);
|
||||
|
||||
DROP TRIGGER IF EXISTS update_algo_updated_at ON context_algorithms;
|
||||
CREATE TRIGGER update_algo_updated_at
|
||||
BEFORE UPDATE ON context_algorithms
|
||||
FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();
|
||||
|
||||
-- ============================================
|
||||
-- TABLA: algorithm_metrics
|
||||
-- Métricas de rendimiento por algoritmo
|
||||
-- ============================================
|
||||
|
||||
CREATE TABLE IF NOT EXISTS algorithm_metrics (
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
|
||||
-- Referencias
|
||||
algorithm_id UUID NOT NULL REFERENCES context_algorithms(id),
|
||||
session_id UUID REFERENCES sessions(id),
|
||||
log_entry_id UUID, -- Referencia al log inmutable
|
||||
|
||||
-- Métricas de contexto
|
||||
tokens_budget INT,
|
||||
tokens_used INT,
|
||||
token_efficiency DECIMAL(5,4), -- tokens_used / tokens_budget
|
||||
|
||||
-- Composición del contexto
|
||||
context_composition JSONB,
|
||||
/*
|
||||
{
|
||||
"system_prompts": {"count": 1, "tokens": 500},
|
||||
"context_blocks": {"count": 3, "tokens": 800},
|
||||
"memory": {"count": 5, "tokens": 300},
|
||||
"knowledge": {"count": 2, "tokens": 400},
|
||||
"history": {"count": 10, "tokens": 1500},
|
||||
"ambient": {"count": 1, "tokens": 100}
|
||||
}
|
||||
*/
|
||||
|
||||
-- Métricas de respuesta
|
||||
latency_ms INT,
|
||||
model_tokens_input INT,
|
||||
model_tokens_output INT,
|
||||
|
||||
-- Evaluación (puede ser automática o manual)
|
||||
relevance_score DECIMAL(3,2), -- 0.00-1.00: ¿El contexto fue relevante?
|
||||
response_quality DECIMAL(3,2), -- 0.00-1.00: ¿La respuesta fue buena?
|
||||
user_satisfaction DECIMAL(3,2), -- 0.00-1.00: Feedback del usuario
|
||||
|
||||
-- Evaluación automática
|
||||
auto_evaluated BOOLEAN DEFAULT false,
|
||||
evaluation_method VARCHAR(50), -- llm_judge, heuristic, user_feedback
|
||||
|
||||
-- Timestamp
|
||||
recorded_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_metrics_algorithm ON algorithm_metrics(algorithm_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_metrics_session ON algorithm_metrics(session_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_metrics_recorded ON algorithm_metrics(recorded_at DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_metrics_quality ON algorithm_metrics(response_quality DESC);
|
||||
|
||||
-- ============================================
|
||||
-- TABLA: algorithm_experiments
|
||||
-- A/B testing de algoritmos
|
||||
-- ============================================
|
||||
|
||||
CREATE TABLE IF NOT EXISTS algorithm_experiments (
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
|
||||
-- Identificación
|
||||
name VARCHAR(255) NOT NULL,
|
||||
description TEXT,
|
||||
|
||||
-- Algoritmos en competencia
|
||||
control_algorithm_id UUID NOT NULL REFERENCES context_algorithms(id),
|
||||
treatment_algorithm_id UUID NOT NULL REFERENCES context_algorithms(id),
|
||||
|
||||
-- Configuración
|
||||
traffic_split DECIMAL(3,2) DEFAULT 0.50, -- % para treatment
|
||||
min_samples INT DEFAULT 100,
|
||||
max_samples INT DEFAULT 1000,
|
||||
|
||||
-- Estado
|
||||
status VARCHAR(50) DEFAULT 'pending', -- pending, running, completed, cancelled
|
||||
|
||||
-- Resultados
|
||||
control_samples INT DEFAULT 0,
|
||||
treatment_samples INT DEFAULT 0,
|
||||
control_avg_quality DECIMAL(3,2),
|
||||
treatment_avg_quality DECIMAL(3,2),
|
||||
winner_algorithm_id UUID REFERENCES context_algorithms(id),
|
||||
statistical_significance DECIMAL(5,4),
|
||||
|
||||
-- Timestamps
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
started_at TIMESTAMP,
|
||||
completed_at TIMESTAMP
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_exp_status ON algorithm_experiments(status);
|
||||
CREATE INDEX IF NOT EXISTS idx_exp_control ON algorithm_experiments(control_algorithm_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_exp_treatment ON algorithm_experiments(treatment_algorithm_id);
|
||||
|
||||
-- ============================================
|
||||
-- VISTA: Resumen de rendimiento por algoritmo
|
||||
-- ============================================
|
||||
|
||||
CREATE OR REPLACE VIEW algorithm_performance AS
|
||||
SELECT
|
||||
a.id,
|
||||
a.code,
|
||||
a.name,
|
||||
a.version,
|
||||
a.status,
|
||||
a.times_used,
|
||||
COUNT(m.id) as total_metrics,
|
||||
AVG(m.token_efficiency) as avg_token_efficiency,
|
||||
AVG(m.relevance_score) as avg_relevance,
|
||||
AVG(m.response_quality) as avg_quality,
|
||||
AVG(m.user_satisfaction) as avg_satisfaction,
|
||||
AVG(m.latency_ms) as avg_latency,
|
||||
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY m.response_quality) as median_quality,
|
||||
STDDEV(m.response_quality) as quality_stddev
|
||||
FROM context_algorithms a
|
||||
LEFT JOIN algorithm_metrics m ON a.id = m.algorithm_id
|
||||
GROUP BY a.id, a.code, a.name, a.version, a.status, a.times_used;
|
||||
|
||||
-- ============================================
|
||||
-- FUNCIÓN: Activar algoritmo (desactiva el anterior)
|
||||
-- ============================================
|
||||
|
||||
CREATE OR REPLACE FUNCTION activate_algorithm(p_algorithm_id UUID)
|
||||
RETURNS BOOLEAN AS $$
|
||||
BEGIN
|
||||
-- Deprecar algoritmo activo actual
|
||||
UPDATE context_algorithms
|
||||
SET status = 'deprecated', deprecated_at = CURRENT_TIMESTAMP
|
||||
WHERE status = 'active';
|
||||
|
||||
-- Activar nuevo algoritmo
|
||||
UPDATE context_algorithms
|
||||
SET status = 'active', activated_at = CURRENT_TIMESTAMP
|
||||
WHERE id = p_algorithm_id;
|
||||
|
||||
RETURN FOUND;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
|
||||
-- ============================================
|
||||
-- FUNCIÓN: Clonar algoritmo para experimentación
|
||||
-- ============================================
|
||||
|
||||
CREATE OR REPLACE FUNCTION fork_algorithm(
|
||||
p_source_id UUID,
|
||||
p_new_code VARCHAR,
|
||||
p_new_name VARCHAR,
|
||||
p_reason TEXT DEFAULT NULL
|
||||
)
|
||||
RETURNS UUID AS $$
|
||||
DECLARE
|
||||
v_new_id UUID;
|
||||
v_source RECORD;
|
||||
BEGIN
|
||||
SELECT * INTO v_source FROM context_algorithms WHERE id = p_source_id;
|
||||
|
||||
IF NOT FOUND THEN
|
||||
RAISE EXCEPTION 'Algoritmo fuente no encontrado: %', p_source_id;
|
||||
END IF;
|
||||
|
||||
INSERT INTO context_algorithms (
|
||||
code, name, description, version,
|
||||
status, config, selector_code,
|
||||
parent_algorithm_id, fork_reason
|
||||
) VALUES (
|
||||
p_new_code,
|
||||
p_new_name,
|
||||
v_source.description,
|
||||
'1.0.0',
|
||||
'draft',
|
||||
v_source.config,
|
||||
v_source.selector_code,
|
||||
p_source_id,
|
||||
p_reason
|
||||
) RETURNING id INTO v_new_id;
|
||||
|
||||
RETURN v_new_id;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
|
||||
-- ============================================
|
||||
-- FUNCIÓN: Obtener algoritmo activo
|
||||
-- ============================================
|
||||
|
||||
CREATE OR REPLACE FUNCTION get_active_algorithm()
|
||||
RETURNS UUID AS $$
|
||||
SELECT id FROM context_algorithms
|
||||
WHERE status = 'active'
|
||||
ORDER BY activated_at DESC
|
||||
LIMIT 1;
|
||||
$$ LANGUAGE SQL STABLE;
|
||||
|
||||
-- ============================================
|
||||
-- FUNCIÓN: Registrar métrica de uso
|
||||
-- ============================================
|
||||
|
||||
CREATE OR REPLACE FUNCTION record_algorithm_metric(
|
||||
p_algorithm_id UUID,
|
||||
p_session_id UUID,
|
||||
p_log_entry_id UUID,
|
||||
p_tokens_budget INT,
|
||||
p_tokens_used INT,
|
||||
p_context_composition JSONB,
|
||||
p_latency_ms INT DEFAULT NULL,
|
||||
p_model_tokens_input INT DEFAULT NULL,
|
||||
p_model_tokens_output INT DEFAULT NULL
|
||||
)
|
||||
RETURNS UUID AS $$
|
||||
DECLARE
|
||||
v_metric_id UUID;
|
||||
v_efficiency DECIMAL(5,4);
|
||||
BEGIN
|
||||
v_efficiency := CASE
|
||||
WHEN p_tokens_budget > 0 THEN p_tokens_used::DECIMAL / p_tokens_budget
|
||||
ELSE 0
|
||||
END;
|
||||
|
||||
INSERT INTO algorithm_metrics (
|
||||
algorithm_id, session_id, log_entry_id,
|
||||
tokens_budget, tokens_used, token_efficiency,
|
||||
context_composition, latency_ms,
|
||||
model_tokens_input, model_tokens_output
|
||||
) VALUES (
|
||||
p_algorithm_id, p_session_id, p_log_entry_id,
|
||||
p_tokens_budget, p_tokens_used, v_efficiency,
|
||||
p_context_composition, p_latency_ms,
|
||||
p_model_tokens_input, p_model_tokens_output
|
||||
) RETURNING id INTO v_metric_id;
|
||||
|
||||
-- Actualizar contador del algoritmo
|
||||
UPDATE context_algorithms
|
||||
SET times_used = times_used + 1
|
||||
WHERE id = p_algorithm_id;
|
||||
|
||||
RETURN v_metric_id;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
|
||||
-- ============================================
|
||||
-- FUNCIÓN: Actualizar evaluación de métrica
|
||||
-- ============================================
|
||||
|
||||
CREATE OR REPLACE FUNCTION update_metric_evaluation(
|
||||
p_metric_id UUID,
|
||||
p_relevance DECIMAL DEFAULT NULL,
|
||||
p_quality DECIMAL DEFAULT NULL,
|
||||
p_satisfaction DECIMAL DEFAULT NULL,
|
||||
p_method VARCHAR DEFAULT 'manual'
|
||||
)
|
||||
RETURNS BOOLEAN AS $$
|
||||
BEGIN
|
||||
UPDATE algorithm_metrics
|
||||
SET
|
||||
relevance_score = COALESCE(p_relevance, relevance_score),
|
||||
response_quality = COALESCE(p_quality, response_quality),
|
||||
user_satisfaction = COALESCE(p_satisfaction, user_satisfaction),
|
||||
auto_evaluated = (p_method != 'user_feedback'),
|
||||
evaluation_method = p_method
|
||||
WHERE id = p_metric_id;
|
||||
|
||||
RETURN FOUND;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
|
||||
-- ============================================
|
||||
-- DATOS INICIALES: Algoritmo por defecto
|
||||
-- ============================================
|
||||
|
||||
INSERT INTO context_algorithms (code, name, description, version, status, config) VALUES
|
||||
(
|
||||
'ALG_DEFAULT_V1',
|
||||
'Algoritmo por defecto v1',
|
||||
'Selección de contexto basada en prioridad y tokens disponibles',
|
||||
'1.0.0',
|
||||
'active',
|
||||
'{
|
||||
"max_tokens": 4000,
|
||||
"sources": {
|
||||
"system_prompts": true,
|
||||
"context_blocks": true,
|
||||
"memory": true,
|
||||
"knowledge": true,
|
||||
"history": true,
|
||||
"ambient": true
|
||||
},
|
||||
"weights": {
|
||||
"priority": 0.4,
|
||||
"relevance": 0.3,
|
||||
"recency": 0.2,
|
||||
"frequency": 0.1
|
||||
},
|
||||
"history_config": {
|
||||
"max_messages": 20,
|
||||
"summarize_after": 10,
|
||||
"include_system": false
|
||||
},
|
||||
"memory_config": {
|
||||
"max_items": 15,
|
||||
"min_importance": 30
|
||||
},
|
||||
"knowledge_config": {
|
||||
"max_items": 5,
|
||||
"require_keyword_match": true
|
||||
}
|
||||
}'::jsonb
|
||||
) ON CONFLICT (code) DO NOTHING;
|
||||
35
setup.py
Normal file
35
setup.py
Normal file
@@ -0,0 +1,35 @@
|
||||
#!/usr/bin/env python3
|
||||
from setuptools import setup, find_packages
|
||||
|
||||
setup(
|
||||
name="context-manager",
|
||||
version="1.0.0",
|
||||
description="Sistema local de gestión de contexto para IA",
|
||||
author="TZZR System",
|
||||
packages=find_packages(),
|
||||
python_requires=">=3.9",
|
||||
install_requires=[
|
||||
"psycopg2-binary>=2.9.0",
|
||||
"pyyaml>=6.0",
|
||||
"requests>=2.28.0",
|
||||
],
|
||||
extras_require={
|
||||
"anthropic": ["anthropic>=0.18.0"],
|
||||
"openai": ["openai>=1.0.0"],
|
||||
"all": ["anthropic>=0.18.0", "openai>=1.0.0"],
|
||||
"dev": ["pytest>=7.0.0", "black>=23.0.0", "mypy>=1.0.0"],
|
||||
},
|
||||
entry_points={
|
||||
"console_scripts": [
|
||||
"context-manager=src.cli:main",
|
||||
],
|
||||
},
|
||||
classifiers=[
|
||||
"Development Status :: 4 - Beta",
|
||||
"Intended Audience :: Developers",
|
||||
"License :: OSI Approved :: MIT License",
|
||||
"Programming Language :: Python :: 3.9",
|
||||
"Programming Language :: Python :: 3.10",
|
||||
"Programming Language :: Python :: 3.11",
|
||||
],
|
||||
)
|
||||
25
src/__init__.py
Normal file
25
src/__init__.py
Normal file
@@ -0,0 +1,25 @@
|
||||
"""
|
||||
Context Manager - Sistema local de gestión de contexto para IA
|
||||
|
||||
Características:
|
||||
- Log inmutable (tabla de referencia no editable)
|
||||
- Gestor de contexto mejorable
|
||||
- Agnóstico al modelo de IA
|
||||
- Sistema de métricas para mejora continua
|
||||
"""
|
||||
|
||||
__version__ = "1.0.0"
|
||||
__author__ = "TZZR System"
|
||||
|
||||
from .database import Database
|
||||
from .context_selector import ContextSelector
|
||||
from .models import Session, Message, ContextBlock, Algorithm
|
||||
|
||||
__all__ = [
|
||||
"Database",
|
||||
"ContextSelector",
|
||||
"Session",
|
||||
"Message",
|
||||
"ContextBlock",
|
||||
"Algorithm",
|
||||
]
|
||||
608
src/algorithm_improver.py
Normal file
608
src/algorithm_improver.py
Normal file
@@ -0,0 +1,608 @@
|
||||
"""
|
||||
Sistema de mejora continua de algoritmos de contexto.
|
||||
|
||||
Permite:
|
||||
- Evaluar rendimiento de algoritmos
|
||||
- A/B testing entre versiones
|
||||
- Sugerir mejoras basadas en métricas
|
||||
- Auto-ajuste de parámetros
|
||||
"""
|
||||
|
||||
import uuid
|
||||
import statistics
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Optional, List, Dict, Any, Tuple
|
||||
from dataclasses import dataclass
|
||||
|
||||
from .models import Algorithm, AlgorithmMetric, AlgorithmStatus
|
||||
from .database import Database
|
||||
|
||||
|
||||
@dataclass
|
||||
class AlgorithmAnalysis:
|
||||
"""Análisis de rendimiento de un algoritmo"""
|
||||
algorithm_id: uuid.UUID
|
||||
algorithm_code: str
|
||||
total_uses: int
|
||||
sample_size: int
|
||||
|
||||
# Métricas principales
|
||||
avg_token_efficiency: float
|
||||
avg_relevance: Optional[float]
|
||||
avg_quality: Optional[float]
|
||||
avg_satisfaction: Optional[float]
|
||||
avg_latency_ms: Optional[float]
|
||||
|
||||
# Estadísticas avanzadas
|
||||
quality_stddev: Optional[float]
|
||||
quality_p25: Optional[float]
|
||||
quality_p50: Optional[float]
|
||||
quality_p75: Optional[float]
|
||||
|
||||
# Composición promedio
|
||||
avg_composition: Dict[str, Any]
|
||||
|
||||
# Tendencia (últimos 7 días vs anteriores)
|
||||
quality_trend: Optional[str] # "improving", "stable", "declining"
|
||||
|
||||
# Recomendaciones
|
||||
suggestions: List[str]
|
||||
|
||||
|
||||
@dataclass
|
||||
class ExperimentResult:
|
||||
"""Resultado de un experimento A/B"""
|
||||
experiment_id: uuid.UUID
|
||||
control_algorithm: str
|
||||
treatment_algorithm: str
|
||||
control_samples: int
|
||||
treatment_samples: int
|
||||
control_avg_quality: float
|
||||
treatment_avg_quality: float
|
||||
improvement_pct: float
|
||||
statistical_significance: float
|
||||
winner: Optional[str]
|
||||
recommendation: str
|
||||
|
||||
|
||||
class AlgorithmImprover:
|
||||
"""
|
||||
Motor de mejora continua de algoritmos.
|
||||
|
||||
Analiza métricas históricas y sugiere/aplica mejoras.
|
||||
"""
|
||||
|
||||
def __init__(self, db: Database):
|
||||
self.db = db
|
||||
|
||||
def analyze_algorithm(
|
||||
self,
|
||||
algorithm_id: uuid.UUID = None,
|
||||
days: int = 30
|
||||
) -> AlgorithmAnalysis:
|
||||
"""
|
||||
Analiza el rendimiento de un algoritmo.
|
||||
|
||||
Args:
|
||||
algorithm_id: ID del algoritmo (o el activo por defecto)
|
||||
days: Días de histórico a analizar
|
||||
|
||||
Returns:
|
||||
AlgorithmAnalysis con métricas y sugerencias
|
||||
"""
|
||||
# Obtener algoritmo
|
||||
if algorithm_id:
|
||||
algorithm = self.db.get_algorithm(algorithm_id)
|
||||
else:
|
||||
algorithm = self.db.get_active_algorithm()
|
||||
|
||||
if not algorithm:
|
||||
raise ValueError("No se encontró el algoritmo")
|
||||
|
||||
# Obtener métricas
|
||||
with self.db.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT
|
||||
COUNT(*) as total,
|
||||
AVG(token_efficiency) as avg_efficiency,
|
||||
AVG(relevance_score) as avg_relevance,
|
||||
AVG(response_quality) as avg_quality,
|
||||
AVG(user_satisfaction) as avg_satisfaction,
|
||||
AVG(latency_ms) as avg_latency,
|
||||
STDDEV(response_quality) as quality_stddev,
|
||||
PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY response_quality) as p25,
|
||||
PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY response_quality) as p50,
|
||||
PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY response_quality) as p75
|
||||
FROM algorithm_metrics
|
||||
WHERE algorithm_id = %s
|
||||
AND recorded_at > NOW() - INTERVAL '%s days'
|
||||
""",
|
||||
(str(algorithm.id), days)
|
||||
)
|
||||
stats = cur.fetchone()
|
||||
|
||||
# Obtener composición promedio
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT context_composition
|
||||
FROM algorithm_metrics
|
||||
WHERE algorithm_id = %s
|
||||
AND context_composition IS NOT NULL
|
||||
ORDER BY recorded_at DESC
|
||||
LIMIT 100
|
||||
""",
|
||||
(str(algorithm.id),)
|
||||
)
|
||||
compositions = [row["context_composition"] for row in cur.fetchall()]
|
||||
|
||||
# Tendencia reciente vs anterior
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT
|
||||
CASE
|
||||
WHEN recorded_at > NOW() - INTERVAL '7 days' THEN 'recent'
|
||||
ELSE 'older'
|
||||
END as period,
|
||||
AVG(response_quality) as avg_quality
|
||||
FROM algorithm_metrics
|
||||
WHERE algorithm_id = %s
|
||||
AND recorded_at > NOW() - INTERVAL '%s days'
|
||||
AND response_quality IS NOT NULL
|
||||
GROUP BY period
|
||||
""",
|
||||
(str(algorithm.id), days)
|
||||
)
|
||||
trends = {row["period"]: row["avg_quality"] for row in cur.fetchall()}
|
||||
|
||||
# Calcular composición promedio
|
||||
avg_composition = self._average_composition(compositions)
|
||||
|
||||
# Determinar tendencia
|
||||
trend = None
|
||||
if "recent" in trends and "older" in trends:
|
||||
diff = trends["recent"] - trends["older"]
|
||||
if diff > 0.05:
|
||||
trend = "improving"
|
||||
elif diff < -0.05:
|
||||
trend = "declining"
|
||||
else:
|
||||
trend = "stable"
|
||||
|
||||
# Generar sugerencias
|
||||
suggestions = self._generate_suggestions(
|
||||
stats, avg_composition, algorithm.config, trend
|
||||
)
|
||||
|
||||
return AlgorithmAnalysis(
|
||||
algorithm_id=algorithm.id,
|
||||
algorithm_code=algorithm.code,
|
||||
total_uses=algorithm.times_used,
|
||||
sample_size=stats["total"] if stats["total"] else 0,
|
||||
avg_token_efficiency=float(stats["avg_efficiency"]) if stats["avg_efficiency"] else 0,
|
||||
avg_relevance=float(stats["avg_relevance"]) if stats["avg_relevance"] else None,
|
||||
avg_quality=float(stats["avg_quality"]) if stats["avg_quality"] else None,
|
||||
avg_satisfaction=float(stats["avg_satisfaction"]) if stats["avg_satisfaction"] else None,
|
||||
avg_latency_ms=float(stats["avg_latency"]) if stats["avg_latency"] else None,
|
||||
quality_stddev=float(stats["quality_stddev"]) if stats["quality_stddev"] else None,
|
||||
quality_p25=float(stats["p25"]) if stats["p25"] else None,
|
||||
quality_p50=float(stats["p50"]) if stats["p50"] else None,
|
||||
quality_p75=float(stats["p75"]) if stats["p75"] else None,
|
||||
avg_composition=avg_composition,
|
||||
quality_trend=trend,
|
||||
suggestions=suggestions
|
||||
)
|
||||
|
||||
def _average_composition(self, compositions: List[Dict]) -> Dict[str, Any]:
|
||||
"""Calcula la composición promedio del contexto"""
|
||||
if not compositions:
|
||||
return {}
|
||||
|
||||
totals = {}
|
||||
for comp in compositions:
|
||||
for source, data in comp.items():
|
||||
if source not in totals:
|
||||
totals[source] = {"count": [], "tokens": []}
|
||||
if isinstance(data, dict):
|
||||
totals[source]["count"].append(data.get("count", 0))
|
||||
totals[source]["tokens"].append(data.get("tokens", 0))
|
||||
|
||||
return {
|
||||
source: {
|
||||
"avg_count": statistics.mean(data["count"]) if data["count"] else 0,
|
||||
"avg_tokens": statistics.mean(data["tokens"]) if data["tokens"] else 0
|
||||
}
|
||||
for source, data in totals.items()
|
||||
}
|
||||
|
||||
def _generate_suggestions(
|
||||
self,
|
||||
stats: Dict,
|
||||
composition: Dict,
|
||||
config: Dict,
|
||||
trend: str
|
||||
) -> List[str]:
|
||||
"""Genera sugerencias de mejora basadas en métricas"""
|
||||
suggestions = []
|
||||
|
||||
# Eficiencia de tokens
|
||||
if stats.get("avg_efficiency") and stats["avg_efficiency"] > 0.95:
|
||||
suggestions.append(
|
||||
"Alta eficiencia de tokens (>95%). Considera aumentar max_tokens "
|
||||
"para incluir más contexto relevante."
|
||||
)
|
||||
elif stats.get("avg_efficiency") and stats["avg_efficiency"] < 0.5:
|
||||
suggestions.append(
|
||||
"Baja eficiencia de tokens (<50%). El contexto es muy pequeño. "
|
||||
"Revisa las fuentes de datos disponibles."
|
||||
)
|
||||
|
||||
# Calidad de respuestas
|
||||
if stats.get("avg_quality"):
|
||||
if stats["avg_quality"] < 0.6:
|
||||
suggestions.append(
|
||||
"Calidad promedio baja (<0.6). Considera:\n"
|
||||
" - Aumentar la cantidad de memoria incluida\n"
|
||||
" - Mejorar los bloques de contexto\n"
|
||||
" - Revisar el filtrado de conocimiento"
|
||||
)
|
||||
elif stats.get("quality_stddev") and stats["quality_stddev"] > 0.2:
|
||||
suggestions.append(
|
||||
"Alta variabilidad en calidad (stddev > 0.2). "
|
||||
"El contexto no es consistente. Revisa las reglas de activación."
|
||||
)
|
||||
|
||||
# Tendencia
|
||||
if trend == "declining":
|
||||
suggestions.append(
|
||||
"La calidad está en declive. Considera:\n"
|
||||
" - Actualizar la base de conocimiento\n"
|
||||
" - Revisar memorias obsoletas\n"
|
||||
" - Crear un fork del algoritmo para experimentar"
|
||||
)
|
||||
|
||||
# Composición del contexto
|
||||
if composition:
|
||||
history = composition.get("history", {})
|
||||
if history.get("avg_tokens", 0) > config.get("max_tokens", 4000) * 0.7:
|
||||
suggestions.append(
|
||||
"El historial ocupa >70% del contexto. Considera:\n"
|
||||
" - Reducir max_messages en history_config\n"
|
||||
" - Activar summarize_after más temprano\n"
|
||||
" - Aumentar el presupuesto total de tokens"
|
||||
)
|
||||
|
||||
memory = composition.get("memory", {})
|
||||
if memory.get("avg_count", 0) < 3:
|
||||
suggestions.append(
|
||||
"Poca memoria incluida (<3 items). Considera:\n"
|
||||
" - Reducir min_importance en memory_config\n"
|
||||
" - Aumentar max_items de memoria"
|
||||
)
|
||||
|
||||
if not suggestions:
|
||||
suggestions.append("El algoritmo está funcionando bien. No hay sugerencias inmediatas.")
|
||||
|
||||
return suggestions
|
||||
|
||||
def create_experiment(
|
||||
self,
|
||||
control_id: uuid.UUID,
|
||||
treatment_id: uuid.UUID,
|
||||
name: str,
|
||||
traffic_split: float = 0.5,
|
||||
min_samples: int = 100
|
||||
) -> uuid.UUID:
|
||||
"""
|
||||
Crea un experimento A/B entre dos algoritmos.
|
||||
|
||||
Args:
|
||||
control_id: Algoritmo de control (actual)
|
||||
treatment_id: Algoritmo de tratamiento (nuevo)
|
||||
name: Nombre del experimento
|
||||
traffic_split: % de tráfico para treatment (0.0-1.0)
|
||||
min_samples: Muestras mínimas para concluir
|
||||
|
||||
Returns:
|
||||
ID del experimento
|
||||
"""
|
||||
with self.db.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
INSERT INTO algorithm_experiments
|
||||
(name, control_algorithm_id, treatment_algorithm_id, traffic_split, min_samples, status)
|
||||
VALUES (%s, %s, %s, %s, %s, 'running')
|
||||
RETURNING id
|
||||
""",
|
||||
(name, str(control_id), str(treatment_id), traffic_split, min_samples)
|
||||
)
|
||||
return cur.fetchone()["id"]
|
||||
|
||||
def get_experiment_algorithm(
|
||||
self,
|
||||
experiment_id: uuid.UUID
|
||||
) -> Tuple[uuid.UUID, str]:
|
||||
"""
|
||||
Obtiene qué algoritmo usar basado en el experimento activo.
|
||||
|
||||
Returns:
|
||||
Tuple de (algorithm_id, group) donde group es 'control' o 'treatment'
|
||||
"""
|
||||
import random
|
||||
|
||||
with self.db.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT * FROM algorithm_experiments
|
||||
WHERE id = %s AND status = 'running'
|
||||
""",
|
||||
(str(experiment_id),)
|
||||
)
|
||||
exp = cur.fetchone()
|
||||
|
||||
if not exp:
|
||||
raise ValueError("Experimento no encontrado o no activo")
|
||||
|
||||
# Asignar grupo basado en traffic_split
|
||||
if random.random() < exp["traffic_split"]:
|
||||
return exp["treatment_algorithm_id"], "treatment"
|
||||
else:
|
||||
return exp["control_algorithm_id"], "control"
|
||||
|
||||
def evaluate_experiment(self, experiment_id: uuid.UUID) -> ExperimentResult:
|
||||
"""
|
||||
Evalúa los resultados de un experimento.
|
||||
|
||||
Returns:
|
||||
ExperimentResult con análisis estadístico
|
||||
"""
|
||||
with self.db.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT
|
||||
e.*,
|
||||
c.code as control_code,
|
||||
t.code as treatment_code
|
||||
FROM algorithm_experiments e
|
||||
JOIN context_algorithms c ON e.control_algorithm_id = c.id
|
||||
JOIN context_algorithms t ON e.treatment_algorithm_id = t.id
|
||||
WHERE e.id = %s
|
||||
""",
|
||||
(str(experiment_id),)
|
||||
)
|
||||
exp = cur.fetchone()
|
||||
|
||||
if not exp:
|
||||
raise ValueError("Experimento no encontrado")
|
||||
|
||||
# Obtener métricas de cada grupo
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT
|
||||
algorithm_id,
|
||||
COUNT(*) as samples,
|
||||
AVG(response_quality) as avg_quality,
|
||||
STDDEV(response_quality) as stddev_quality
|
||||
FROM algorithm_metrics
|
||||
WHERE algorithm_id IN (%s, %s)
|
||||
AND response_quality IS NOT NULL
|
||||
AND recorded_at > (
|
||||
SELECT created_at FROM algorithm_experiments WHERE id = %s
|
||||
)
|
||||
GROUP BY algorithm_id
|
||||
""",
|
||||
(str(exp["control_algorithm_id"]), str(exp["treatment_algorithm_id"]),
|
||||
str(experiment_id))
|
||||
)
|
||||
results = {str(row["algorithm_id"]): row for row in cur.fetchall()}
|
||||
|
||||
control_data = results.get(str(exp["control_algorithm_id"]), {})
|
||||
treatment_data = results.get(str(exp["treatment_algorithm_id"]), {})
|
||||
|
||||
control_quality = float(control_data.get("avg_quality", 0)) if control_data else 0
|
||||
treatment_quality = float(treatment_data.get("avg_quality", 0)) if treatment_data else 0
|
||||
control_samples = control_data.get("samples", 0) if control_data else 0
|
||||
treatment_samples = treatment_data.get("samples", 0) if treatment_data else 0
|
||||
|
||||
# Calcular mejora
|
||||
if control_quality > 0:
|
||||
improvement = ((treatment_quality - control_quality) / control_quality) * 100
|
||||
else:
|
||||
improvement = 0
|
||||
|
||||
# Significancia estadística simple (z-test aproximado)
|
||||
significance = self._calculate_significance(
|
||||
control_quality, control_data.get("stddev_quality", 0.1), control_samples,
|
||||
treatment_quality, treatment_data.get("stddev_quality", 0.1), treatment_samples
|
||||
)
|
||||
|
||||
# Determinar ganador
|
||||
winner = None
|
||||
recommendation = ""
|
||||
if control_samples >= exp["min_samples"] and treatment_samples >= exp["min_samples"]:
|
||||
if significance > 0.95:
|
||||
if treatment_quality > control_quality:
|
||||
winner = exp["treatment_code"]
|
||||
recommendation = f"Activar {winner} como algoritmo principal."
|
||||
else:
|
||||
winner = exp["control_code"]
|
||||
recommendation = f"Mantener {winner}. El tratamiento no mejoró."
|
||||
else:
|
||||
recommendation = "No hay diferencia significativa. Continuar experimentando."
|
||||
else:
|
||||
recommendation = f"Faltan muestras. Control: {control_samples}/{exp['min_samples']}, Treatment: {treatment_samples}/{exp['min_samples']}"
|
||||
|
||||
return ExperimentResult(
|
||||
experiment_id=experiment_id,
|
||||
control_algorithm=exp["control_code"],
|
||||
treatment_algorithm=exp["treatment_code"],
|
||||
control_samples=control_samples,
|
||||
treatment_samples=treatment_samples,
|
||||
control_avg_quality=control_quality,
|
||||
treatment_avg_quality=treatment_quality,
|
||||
improvement_pct=improvement,
|
||||
statistical_significance=significance,
|
||||
winner=winner,
|
||||
recommendation=recommendation
|
||||
)
|
||||
|
||||
def _calculate_significance(
|
||||
self,
|
||||
mean1: float, std1: float, n1: int,
|
||||
mean2: float, std2: float, n2: int
|
||||
) -> float:
|
||||
"""Calcula significancia estadística (z-test aproximado)"""
|
||||
if n1 < 2 or n2 < 2:
|
||||
return 0.0
|
||||
|
||||
import math
|
||||
|
||||
std1 = std1 or 0.1
|
||||
std2 = std2 or 0.1
|
||||
|
||||
se = math.sqrt((std1**2 / n1) + (std2**2 / n2))
|
||||
if se == 0:
|
||||
return 0.0
|
||||
|
||||
z = abs(mean1 - mean2) / se
|
||||
|
||||
# Aproximación de la función CDF normal
|
||||
# P(Z <= z) usando aproximación de Zelen-Severo
|
||||
if z > 6:
|
||||
return 0.999
|
||||
elif z < -6:
|
||||
return 0.001
|
||||
|
||||
t = 1 / (1 + 0.2316419 * abs(z))
|
||||
d = 0.3989423 * math.exp(-z * z / 2)
|
||||
p = d * t * (0.3193815 + t * (-0.3565638 + t * (1.781478 + t * (-1.821256 + t * 1.330274))))
|
||||
|
||||
if z > 0:
|
||||
p = 1 - p
|
||||
|
||||
# Convertir a confianza (two-tailed)
|
||||
return 1 - 2 * min(p, 1 - p)
|
||||
|
||||
def suggest_improvements(self, algorithm_id: uuid.UUID = None) -> Dict[str, Any]:
|
||||
"""
|
||||
Sugiere mejoras concretas para el algoritmo.
|
||||
|
||||
Returns:
|
||||
Dict con sugerencias de configuración
|
||||
"""
|
||||
analysis = self.analyze_algorithm(algorithm_id)
|
||||
algorithm = self.db.get_algorithm(analysis.algorithm_id) if analysis.algorithm_id else None
|
||||
|
||||
if not algorithm:
|
||||
return {"error": "No se encontró el algoritmo"}
|
||||
|
||||
current_config = algorithm.config.copy()
|
||||
suggested_config = current_config.copy()
|
||||
changes = []
|
||||
|
||||
# Ajustar basado en análisis
|
||||
if analysis.avg_token_efficiency and analysis.avg_token_efficiency > 0.95:
|
||||
current_max = current_config.get("max_tokens", 4000)
|
||||
suggested_config["max_tokens"] = int(current_max * 1.25)
|
||||
changes.append(f"Aumentar max_tokens de {current_max} a {suggested_config['max_tokens']}")
|
||||
|
||||
if analysis.avg_quality and analysis.avg_quality < 0.6:
|
||||
# Más memoria
|
||||
memory_config = suggested_config.get("memory_config", {})
|
||||
memory_config["max_items"] = min(memory_config.get("max_items", 15) + 5, 30)
|
||||
memory_config["min_importance"] = max(memory_config.get("min_importance", 30) - 10, 10)
|
||||
suggested_config["memory_config"] = memory_config
|
||||
changes.append("Aumentar memoria incluida")
|
||||
|
||||
# Más conocimiento
|
||||
knowledge_config = suggested_config.get("knowledge_config", {})
|
||||
knowledge_config["max_items"] = min(knowledge_config.get("max_items", 5) + 3, 15)
|
||||
suggested_config["knowledge_config"] = knowledge_config
|
||||
changes.append("Aumentar conocimiento incluido")
|
||||
|
||||
if analysis.avg_composition:
|
||||
history = analysis.avg_composition.get("history", {})
|
||||
total_tokens = sum(
|
||||
data.get("avg_tokens", 0)
|
||||
for data in analysis.avg_composition.values()
|
||||
)
|
||||
if total_tokens > 0 and history.get("avg_tokens", 0) / total_tokens > 0.7:
|
||||
history_config = suggested_config.get("history_config", {})
|
||||
history_config["max_messages"] = max(history_config.get("max_messages", 20) - 5, 5)
|
||||
history_config["summarize_after"] = max(history_config.get("summarize_after", 10) - 3, 3)
|
||||
suggested_config["history_config"] = history_config
|
||||
changes.append("Reducir historial para dar espacio a otro contexto")
|
||||
|
||||
return {
|
||||
"algorithm_code": algorithm.code,
|
||||
"current_config": current_config,
|
||||
"suggested_config": suggested_config,
|
||||
"changes": changes,
|
||||
"analysis": {
|
||||
"avg_quality": analysis.avg_quality,
|
||||
"avg_efficiency": analysis.avg_token_efficiency,
|
||||
"trend": analysis.quality_trend,
|
||||
"suggestions": analysis.suggestions
|
||||
}
|
||||
}
|
||||
|
||||
def apply_improvements(
|
||||
self,
|
||||
algorithm_id: uuid.UUID,
|
||||
new_config: Dict[str, Any],
|
||||
create_fork: bool = True,
|
||||
fork_name: str = None
|
||||
) -> uuid.UUID:
|
||||
"""
|
||||
Aplica mejoras a un algoritmo.
|
||||
|
||||
Args:
|
||||
algorithm_id: Algoritmo a mejorar
|
||||
new_config: Nueva configuración
|
||||
create_fork: Si True, crea un fork en lugar de modificar
|
||||
fork_name: Nombre del fork (si create_fork=True)
|
||||
|
||||
Returns:
|
||||
ID del algoritmo (nuevo o existente)
|
||||
"""
|
||||
if create_fork:
|
||||
algorithm = self.db.get_algorithm(algorithm_id)
|
||||
if not algorithm:
|
||||
raise ValueError("Algoritmo no encontrado")
|
||||
|
||||
new_code = f"{algorithm.code}_v{algorithm.version.replace('.', '_')}_improved"
|
||||
new_name = fork_name or f"{algorithm.name} (mejorado)"
|
||||
|
||||
new_id = self.db.fork_algorithm(
|
||||
source_id=algorithm_id,
|
||||
new_code=new_code,
|
||||
new_name=new_name,
|
||||
reason="Mejora automática basada en métricas"
|
||||
)
|
||||
|
||||
# Actualizar config del fork
|
||||
with self.db.get_cursor() as cur:
|
||||
from psycopg2.extras import Json
|
||||
cur.execute(
|
||||
"""
|
||||
UPDATE context_algorithms
|
||||
SET config = %s, status = 'testing'
|
||||
WHERE id = %s
|
||||
""",
|
||||
(Json(new_config), str(new_id))
|
||||
)
|
||||
|
||||
return new_id
|
||||
else:
|
||||
# Modificar directamente
|
||||
with self.db.get_cursor() as cur:
|
||||
from psycopg2.extras import Json
|
||||
cur.execute(
|
||||
"""
|
||||
UPDATE context_algorithms
|
||||
SET config = %s, updated_at = NOW()
|
||||
WHERE id = %s
|
||||
""",
|
||||
(Json(new_config), str(algorithm_id))
|
||||
)
|
||||
return algorithm_id
|
||||
403
src/cli.py
Normal file
403
src/cli.py
Normal file
@@ -0,0 +1,403 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
CLI para Context Manager
|
||||
|
||||
Uso:
|
||||
context-manager init # Inicializa la base de datos
|
||||
context-manager chat [--provider X] # Inicia chat interactivo
|
||||
context-manager analyze # Analiza algoritmo activo
|
||||
context-manager experiment create # Crea experimento A/B
|
||||
context-manager block add # Añade bloque de contexto
|
||||
context-manager memory list # Lista memorias
|
||||
context-manager verify SESSION_ID # Verifica integridad
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
import uuid
|
||||
from pathlib import Path
|
||||
|
||||
try:
|
||||
from .database import Database
|
||||
from .context_selector import ContextManager
|
||||
from .algorithm_improver import AlgorithmImprover
|
||||
from .models import ContextBlock, Memory
|
||||
except ImportError:
|
||||
# Para ejecución directa
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
from src.database import Database
|
||||
from src.context_selector import ContextManager
|
||||
from src.algorithm_improver import AlgorithmImprover
|
||||
from src.models import ContextBlock, Memory
|
||||
|
||||
|
||||
def get_db_from_args(args) -> Database:
|
||||
"""Obtiene conexión a BD desde argumentos"""
|
||||
return Database(
|
||||
host=args.host or os.getenv("PGHOST", "localhost"),
|
||||
port=args.port or int(os.getenv("PGPORT", "5432")),
|
||||
database=args.database or os.getenv("PGDATABASE", "context_manager"),
|
||||
user=args.user or os.getenv("PGUSER", "postgres"),
|
||||
password=args.password or os.getenv("PGPASSWORD", "")
|
||||
)
|
||||
|
||||
|
||||
def cmd_init(args):
|
||||
"""Inicializa la base de datos"""
|
||||
import psycopg2
|
||||
|
||||
# Conectar sin base de datos específica
|
||||
conn = psycopg2.connect(
|
||||
host=args.host or os.getenv("PGHOST", "localhost"),
|
||||
port=args.port or int(os.getenv("PGPORT", "5432")),
|
||||
user=args.user or os.getenv("PGUSER", "postgres"),
|
||||
password=args.password or os.getenv("PGPASSWORD", ""),
|
||||
database="postgres"
|
||||
)
|
||||
conn.autocommit = True
|
||||
|
||||
db_name = args.database or os.getenv("PGDATABASE", "context_manager")
|
||||
|
||||
with conn.cursor() as cur:
|
||||
# Verificar si existe
|
||||
cur.execute(
|
||||
"SELECT 1 FROM pg_database WHERE datname = %s",
|
||||
(db_name,)
|
||||
)
|
||||
if not cur.fetchone():
|
||||
print(f"Creando base de datos '{db_name}'...")
|
||||
cur.execute(f'CREATE DATABASE "{db_name}"')
|
||||
else:
|
||||
print(f"Base de datos '{db_name}' ya existe")
|
||||
|
||||
conn.close()
|
||||
|
||||
# Aplicar schemas
|
||||
db = get_db_from_args(args)
|
||||
schema_dir = Path(__file__).parent.parent / "schemas"
|
||||
|
||||
print("Aplicando schemas...")
|
||||
for schema_file in sorted(schema_dir.glob("*.sql")):
|
||||
print(f" - {schema_file.name}")
|
||||
with open(schema_file) as f:
|
||||
sql = f.read()
|
||||
with db.get_cursor(dict_cursor=False) as cur:
|
||||
cur.execute(sql)
|
||||
|
||||
print("Base de datos inicializada correctamente")
|
||||
db.close()
|
||||
|
||||
|
||||
def cmd_chat(args):
|
||||
"""Inicia chat interactivo"""
|
||||
db = get_db_from_args(args)
|
||||
manager = ContextManager(db=db)
|
||||
|
||||
# Configurar proveedor
|
||||
provider = None
|
||||
if args.provider == "anthropic":
|
||||
from .providers import AnthropicProvider
|
||||
provider = AnthropicProvider(model=args.model or "claude-sonnet-4-20250514")
|
||||
elif args.provider == "openai":
|
||||
from .providers import OpenAIProvider
|
||||
provider = OpenAIProvider(model=args.model or "gpt-4")
|
||||
elif args.provider == "ollama":
|
||||
from .providers import OllamaProvider
|
||||
provider = OllamaProvider(model=args.model or "llama3")
|
||||
|
||||
if not provider:
|
||||
print("Proveedor no configurado. Usando modo demo (sin IA)")
|
||||
|
||||
# Iniciar sesión
|
||||
session = manager.start_session(
|
||||
user_id=args.user or "cli_user",
|
||||
model_provider=args.provider,
|
||||
model_name=args.model
|
||||
)
|
||||
print(f"Sesión iniciada: {session.id}")
|
||||
print("Escribe 'exit' para salir, 'verify' para verificar integridad")
|
||||
print("-" * 50)
|
||||
|
||||
while True:
|
||||
try:
|
||||
user_input = input("\nTú: ").strip()
|
||||
except (KeyboardInterrupt, EOFError):
|
||||
break
|
||||
|
||||
if not user_input:
|
||||
continue
|
||||
|
||||
if user_input.lower() == "exit":
|
||||
break
|
||||
|
||||
if user_input.lower() == "verify":
|
||||
result = manager.verify_session_integrity()
|
||||
if result["is_valid"]:
|
||||
print("Integridad verificada correctamente")
|
||||
else:
|
||||
print(f"ERROR: Integridad comprometida en secuencia {result['broken_at_sequence']}")
|
||||
continue
|
||||
|
||||
# Obtener contexto
|
||||
context = manager.get_context_for_message(user_input, max_tokens=args.max_tokens)
|
||||
print(f"[Contexto: {context.total_tokens} tokens, {len(context.items)} items]")
|
||||
|
||||
# Registrar mensaje usuario
|
||||
user_log_id = manager.log_user_message(user_input, context)
|
||||
|
||||
# Generar respuesta
|
||||
if provider:
|
||||
response = provider.send_message(user_input, context)
|
||||
print(f"\nAsistente: {response.content}")
|
||||
|
||||
# Registrar respuesta
|
||||
assistant_log_id = manager.log_assistant_message(
|
||||
content=response.content,
|
||||
tokens_input=response.tokens_input,
|
||||
tokens_output=response.tokens_output,
|
||||
latency_ms=response.latency_ms
|
||||
)
|
||||
|
||||
# Registrar métrica
|
||||
manager.record_metric(
|
||||
context=context,
|
||||
log_entry_id=assistant_log_id,
|
||||
tokens_budget=args.max_tokens,
|
||||
latency_ms=response.latency_ms,
|
||||
model_tokens_input=response.tokens_input,
|
||||
model_tokens_output=response.tokens_output
|
||||
)
|
||||
else:
|
||||
print("\n[Modo demo - sin proveedor de IA configurado]")
|
||||
print(f"Contexto seleccionado:")
|
||||
for item in context.items[:5]:
|
||||
print(f" - [{item.source.value}] {item.content[:100]}...")
|
||||
|
||||
print("\nSesión finalizada")
|
||||
manager.close()
|
||||
|
||||
|
||||
def cmd_analyze(args):
|
||||
"""Analiza rendimiento del algoritmo"""
|
||||
db = get_db_from_args(args)
|
||||
improver = AlgorithmImprover(db)
|
||||
|
||||
algorithm_id = uuid.UUID(args.algorithm) if args.algorithm else None
|
||||
analysis = improver.analyze_algorithm(algorithm_id, days=args.days)
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"ANÁLISIS DE ALGORITMO: {analysis.algorithm_code}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
print(f"\nMétricas generales:")
|
||||
print(f" - Usos totales: {analysis.total_uses}")
|
||||
print(f" - Muestras analizadas: {analysis.sample_size}")
|
||||
print(f" - Eficiencia de tokens: {analysis.avg_token_efficiency:.2%}" if analysis.avg_token_efficiency else " - Eficiencia de tokens: N/A")
|
||||
print(f" - Calidad promedio: {analysis.avg_quality:.2f}" if analysis.avg_quality else " - Calidad promedio: N/A")
|
||||
print(f" - Satisfacción: {analysis.avg_satisfaction:.2f}" if analysis.avg_satisfaction else " - Satisfacción: N/A")
|
||||
print(f" - Latencia promedio: {analysis.avg_latency_ms:.0f}ms" if analysis.avg_latency_ms else " - Latencia promedio: N/A")
|
||||
|
||||
if analysis.quality_trend:
|
||||
trend_emoji = {"improving": "📈", "stable": "➡️", "declining": "📉"}.get(analysis.quality_trend, "")
|
||||
print(f"\nTendencia: {trend_emoji} {analysis.quality_trend}")
|
||||
|
||||
if analysis.avg_composition:
|
||||
print(f"\nComposición promedio del contexto:")
|
||||
for source, data in analysis.avg_composition.items():
|
||||
print(f" - {source}: {data.get('avg_count', 0):.1f} items, {data.get('avg_tokens', 0):.0f} tokens")
|
||||
|
||||
print(f"\nSugerencias:")
|
||||
for i, suggestion in enumerate(analysis.suggestions, 1):
|
||||
print(f" {i}. {suggestion}")
|
||||
|
||||
db.close()
|
||||
|
||||
|
||||
def cmd_suggest(args):
|
||||
"""Sugiere mejoras para el algoritmo"""
|
||||
db = get_db_from_args(args)
|
||||
improver = AlgorithmImprover(db)
|
||||
|
||||
algorithm_id = uuid.UUID(args.algorithm) if args.algorithm else None
|
||||
suggestions = improver.suggest_improvements(algorithm_id)
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"SUGERENCIAS PARA: {suggestions.get('algorithm_code', 'N/A')}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
if suggestions.get("changes"):
|
||||
print("\nCambios sugeridos:")
|
||||
for i, change in enumerate(suggestions["changes"], 1):
|
||||
print(f" {i}. {change}")
|
||||
|
||||
if args.apply:
|
||||
print("\nAplicando cambios...")
|
||||
new_id = improver.apply_improvements(
|
||||
algorithm_id or db.get_active_algorithm().id,
|
||||
suggestions["suggested_config"],
|
||||
create_fork=True
|
||||
)
|
||||
print(f"Nuevo algoritmo creado: {new_id}")
|
||||
else:
|
||||
print("\nNo hay cambios sugeridos")
|
||||
|
||||
db.close()
|
||||
|
||||
|
||||
def cmd_block_add(args):
|
||||
"""Añade un bloque de contexto"""
|
||||
db = get_db_from_args(args)
|
||||
|
||||
block = ContextBlock(
|
||||
code=args.code,
|
||||
name=args.name,
|
||||
description=args.description,
|
||||
content=args.content or sys.stdin.read(),
|
||||
category=args.category,
|
||||
priority=args.priority
|
||||
)
|
||||
|
||||
block_id = db.create_context_block(block)
|
||||
print(f"Bloque creado: {block_id}")
|
||||
db.close()
|
||||
|
||||
|
||||
def cmd_block_list(args):
|
||||
"""Lista bloques de contexto"""
|
||||
db = get_db_from_args(args)
|
||||
blocks = db.get_active_context_blocks(category=args.category)
|
||||
|
||||
print(f"\n{'Code':<20} {'Name':<30} {'Category':<15} {'Priority':<10} {'Tokens':<10}")
|
||||
print("-" * 85)
|
||||
for block in blocks:
|
||||
print(f"{block.code:<20} {block.name[:28]:<30} {block.category:<15} {block.priority:<10} {block.tokens_estimated:<10}")
|
||||
|
||||
db.close()
|
||||
|
||||
|
||||
def cmd_memory_list(args):
|
||||
"""Lista memorias"""
|
||||
db = get_db_from_args(args)
|
||||
memories = db.get_memories(type=args.type, limit=args.limit)
|
||||
|
||||
print(f"\n{'Type':<15} {'Importance':<12} {'Uses':<8} {'Content':<60}")
|
||||
print("-" * 95)
|
||||
for mem in memories:
|
||||
content_preview = mem.content[:57] + "..." if len(mem.content) > 60 else mem.content
|
||||
print(f"{mem.type:<15} {mem.importance:<12} {mem.uses:<8} {content_preview:<60}")
|
||||
|
||||
db.close()
|
||||
|
||||
|
||||
def cmd_verify(args):
|
||||
"""Verifica integridad de una sesión"""
|
||||
db = get_db_from_args(args)
|
||||
|
||||
session_id = uuid.UUID(args.session_id)
|
||||
result = db.verify_chain_integrity(session_id)
|
||||
|
||||
if result["is_valid"]:
|
||||
print(f"Sesión {session_id}: INTEGRIDAD OK")
|
||||
else:
|
||||
print(f"Sesión {session_id}: INTEGRIDAD COMPROMETIDA")
|
||||
print(f" - Secuencia: {result['broken_at_sequence']}")
|
||||
print(f" - Hash esperado: {result['expected_hash']}")
|
||||
print(f" - Hash encontrado: {result['actual_hash']}")
|
||||
|
||||
db.close()
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Context Manager - Sistema de gestión de contexto para IA"
|
||||
)
|
||||
|
||||
# Argumentos globales de BD
|
||||
parser.add_argument("--host", help="Host de PostgreSQL")
|
||||
parser.add_argument("--port", type=int, help="Puerto de PostgreSQL")
|
||||
parser.add_argument("--database", help="Nombre de la base de datos")
|
||||
parser.add_argument("--user", help="Usuario de PostgreSQL")
|
||||
parser.add_argument("--password", help="Contraseña de PostgreSQL")
|
||||
|
||||
subparsers = parser.add_subparsers(dest="command", help="Comandos disponibles")
|
||||
|
||||
# init
|
||||
init_parser = subparsers.add_parser("init", help="Inicializa la base de datos")
|
||||
|
||||
# chat
|
||||
chat_parser = subparsers.add_parser("chat", help="Inicia chat interactivo")
|
||||
chat_parser.add_argument("--provider", choices=["anthropic", "openai", "ollama"],
|
||||
help="Proveedor de IA")
|
||||
chat_parser.add_argument("--model", help="Modelo a usar")
|
||||
chat_parser.add_argument("--user", help="ID de usuario")
|
||||
chat_parser.add_argument("--max-tokens", type=int, default=4000,
|
||||
help="Máximo de tokens de contexto")
|
||||
|
||||
# analyze
|
||||
analyze_parser = subparsers.add_parser("analyze", help="Analiza rendimiento del algoritmo")
|
||||
analyze_parser.add_argument("--algorithm", help="ID del algoritmo (o activo por defecto)")
|
||||
analyze_parser.add_argument("--days", type=int, default=30, help="Días de histórico")
|
||||
|
||||
# suggest
|
||||
suggest_parser = subparsers.add_parser("suggest", help="Sugiere mejoras")
|
||||
suggest_parser.add_argument("--algorithm", help="ID del algoritmo")
|
||||
suggest_parser.add_argument("--apply", action="store_true", help="Aplicar sugerencias")
|
||||
|
||||
# block
|
||||
block_parser = subparsers.add_parser("block", help="Gestión de bloques de contexto")
|
||||
block_sub = block_parser.add_subparsers(dest="block_command")
|
||||
|
||||
block_add = block_sub.add_parser("add", help="Añade bloque")
|
||||
block_add.add_argument("code", help="Código único del bloque")
|
||||
block_add.add_argument("name", help="Nombre del bloque")
|
||||
block_add.add_argument("--description", help="Descripción")
|
||||
block_add.add_argument("--content", help="Contenido (o stdin)")
|
||||
block_add.add_argument("--category", default="general", help="Categoría")
|
||||
block_add.add_argument("--priority", type=int, default=50, help="Prioridad (0-100)")
|
||||
|
||||
block_list = block_sub.add_parser("list", help="Lista bloques")
|
||||
block_list.add_argument("--category", help="Filtrar por categoría")
|
||||
|
||||
# memory
|
||||
memory_parser = subparsers.add_parser("memory", help="Gestión de memorias")
|
||||
memory_sub = memory_parser.add_subparsers(dest="memory_command")
|
||||
|
||||
memory_list = memory_sub.add_parser("list", help="Lista memorias")
|
||||
memory_list.add_argument("--type", help="Filtrar por tipo")
|
||||
memory_list.add_argument("--limit", type=int, default=20, help="Límite de resultados")
|
||||
|
||||
# verify
|
||||
verify_parser = subparsers.add_parser("verify", help="Verifica integridad de sesión")
|
||||
verify_parser.add_argument("session_id", help="ID de la sesión")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.command == "init":
|
||||
cmd_init(args)
|
||||
elif args.command == "chat":
|
||||
cmd_chat(args)
|
||||
elif args.command == "analyze":
|
||||
cmd_analyze(args)
|
||||
elif args.command == "suggest":
|
||||
cmd_suggest(args)
|
||||
elif args.command == "block":
|
||||
if args.block_command == "add":
|
||||
cmd_block_add(args)
|
||||
elif args.block_command == "list":
|
||||
cmd_block_list(args)
|
||||
else:
|
||||
block_parser.print_help()
|
||||
elif args.command == "memory":
|
||||
if args.memory_command == "list":
|
||||
cmd_memory_list(args)
|
||||
else:
|
||||
memory_parser.print_help()
|
||||
elif args.command == "verify":
|
||||
cmd_verify(args)
|
||||
else:
|
||||
parser.print_help()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
508
src/context_selector.py
Normal file
508
src/context_selector.py
Normal file
@@ -0,0 +1,508 @@
|
||||
"""
|
||||
Selector de Contexto - Motor principal
|
||||
|
||||
Selecciona el contexto óptimo para enviar al modelo de IA
|
||||
basándose en el algoritmo activo y las fuentes disponibles.
|
||||
"""
|
||||
|
||||
import re
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import Optional, List, Dict, Any, Callable
|
||||
|
||||
from .models import (
|
||||
Session, Message, MessageRole, ContextBlock, Memory,
|
||||
Knowledge, Algorithm, AmbientContext, ContextItem,
|
||||
SelectedContext, ContextSource
|
||||
)
|
||||
from .database import Database
|
||||
|
||||
|
||||
class ContextSelector:
|
||||
"""
|
||||
Motor de selección de contexto.
|
||||
|
||||
Características:
|
||||
- Agnóstico al modelo de IA
|
||||
- Basado en algoritmo configurable
|
||||
- Métricas de rendimiento
|
||||
- Soporte para múltiples fuentes
|
||||
"""
|
||||
|
||||
def __init__(self, db: Database):
|
||||
self.db = db
|
||||
self._custom_selectors: Dict[str, Callable] = {}
|
||||
|
||||
def register_selector(self, name: str, selector_fn: Callable):
|
||||
"""Registra un selector de contexto personalizado"""
|
||||
self._custom_selectors[name] = selector_fn
|
||||
|
||||
def select_context(
|
||||
self,
|
||||
session: Session,
|
||||
user_message: str,
|
||||
algorithm: Algorithm = None,
|
||||
max_tokens: int = None
|
||||
) -> SelectedContext:
|
||||
"""
|
||||
Selecciona el contexto óptimo para la sesión actual.
|
||||
|
||||
Args:
|
||||
session: Sesión activa
|
||||
user_message: Mensaje del usuario
|
||||
algorithm: Algoritmo a usar (o el activo por defecto)
|
||||
max_tokens: Límite de tokens (sobreescribe config del algoritmo)
|
||||
|
||||
Returns:
|
||||
SelectedContext con los items seleccionados
|
||||
"""
|
||||
# Obtener algoritmo
|
||||
if algorithm is None:
|
||||
algorithm = self.db.get_active_algorithm()
|
||||
|
||||
if algorithm is None:
|
||||
# Algoritmo por defecto si no hay ninguno
|
||||
algorithm = Algorithm(
|
||||
code="DEFAULT",
|
||||
name="Default",
|
||||
config={
|
||||
"max_tokens": 4000,
|
||||
"sources": {
|
||||
"system_prompts": True,
|
||||
"context_blocks": True,
|
||||
"memory": True,
|
||||
"knowledge": True,
|
||||
"history": True,
|
||||
"ambient": True
|
||||
},
|
||||
"weights": {"priority": 0.4, "relevance": 0.3, "recency": 0.2, "frequency": 0.1},
|
||||
"history_config": {"max_messages": 20, "summarize_after": 10, "include_system": False},
|
||||
"memory_config": {"max_items": 15, "min_importance": 30},
|
||||
"knowledge_config": {"max_items": 5, "require_keyword_match": True}
|
||||
}
|
||||
)
|
||||
|
||||
config = algorithm.config
|
||||
token_budget = max_tokens or config.get("max_tokens", 4000)
|
||||
sources = config.get("sources", {})
|
||||
|
||||
# Verificar si hay selector personalizado
|
||||
if algorithm.selector_code and algorithm.code in self._custom_selectors:
|
||||
return self._custom_selectors[algorithm.code](
|
||||
session, user_message, config, self.db
|
||||
)
|
||||
|
||||
# Selección estándar
|
||||
context = SelectedContext(algorithm_id=algorithm.id)
|
||||
composition = {}
|
||||
|
||||
# 1. System prompts y bloques de contexto
|
||||
if sources.get("context_blocks", True):
|
||||
blocks = self._select_context_blocks(user_message, config)
|
||||
for block in blocks:
|
||||
if context.total_tokens + block.tokens_estimated <= token_budget:
|
||||
context.items.append(ContextItem(
|
||||
source=ContextSource.DATASET,
|
||||
content=block.content,
|
||||
tokens=block.tokens_estimated,
|
||||
priority=block.priority,
|
||||
metadata={"block_code": block.code, "category": block.category}
|
||||
))
|
||||
context.total_tokens += block.tokens_estimated
|
||||
|
||||
composition["context_blocks"] = {
|
||||
"count": len([i for i in context.items if i.source == ContextSource.DATASET]),
|
||||
"tokens": sum(i.tokens for i in context.items if i.source == ContextSource.DATASET)
|
||||
}
|
||||
|
||||
# 2. Memoria a largo plazo
|
||||
if sources.get("memory", True):
|
||||
memory_config = config.get("memory_config", {})
|
||||
memories = self._select_memories(
|
||||
user_message,
|
||||
min_importance=memory_config.get("min_importance", 30),
|
||||
max_items=memory_config.get("max_items", 15)
|
||||
)
|
||||
|
||||
memory_tokens = 0
|
||||
memory_count = 0
|
||||
for mem in memories:
|
||||
tokens = len(mem.content) // 4
|
||||
if context.total_tokens + tokens <= token_budget:
|
||||
context.items.append(ContextItem(
|
||||
source=ContextSource.MEMORY,
|
||||
content=f"[Memoria - {mem.type}]: {mem.content}",
|
||||
tokens=tokens,
|
||||
priority=mem.importance,
|
||||
metadata={"memory_type": mem.type, "importance": mem.importance}
|
||||
))
|
||||
context.total_tokens += tokens
|
||||
memory_tokens += tokens
|
||||
memory_count += 1
|
||||
|
||||
composition["memory"] = {"count": memory_count, "tokens": memory_tokens}
|
||||
|
||||
# 3. Base de conocimiento
|
||||
if sources.get("knowledge", True):
|
||||
knowledge_config = config.get("knowledge_config", {})
|
||||
keywords = self._extract_keywords(user_message)
|
||||
|
||||
if keywords or not knowledge_config.get("require_keyword_match", True):
|
||||
knowledge_items = self.db.search_knowledge(
|
||||
keywords=keywords if knowledge_config.get("require_keyword_match", True) else None,
|
||||
limit=knowledge_config.get("max_items", 5)
|
||||
)
|
||||
|
||||
knowledge_tokens = 0
|
||||
knowledge_count = 0
|
||||
for item in knowledge_items:
|
||||
if context.total_tokens + item.tokens_estimated <= token_budget:
|
||||
context.items.append(ContextItem(
|
||||
source=ContextSource.KNOWLEDGE,
|
||||
content=f"[Conocimiento - {item.title}]: {item.content}",
|
||||
tokens=item.tokens_estimated,
|
||||
priority=item.priority,
|
||||
metadata={"title": item.title, "category": item.category}
|
||||
))
|
||||
context.total_tokens += item.tokens_estimated
|
||||
knowledge_tokens += item.tokens_estimated
|
||||
knowledge_count += 1
|
||||
|
||||
composition["knowledge"] = {"count": knowledge_count, "tokens": knowledge_tokens}
|
||||
|
||||
# 4. Contexto ambiental
|
||||
if sources.get("ambient", True):
|
||||
ambient = self.db.get_latest_ambient_context()
|
||||
if ambient:
|
||||
ambient_content = self._format_ambient_context(ambient)
|
||||
tokens = len(ambient_content) // 4
|
||||
if context.total_tokens + tokens <= token_budget:
|
||||
context.items.append(ContextItem(
|
||||
source=ContextSource.AMBIENT,
|
||||
content=ambient_content,
|
||||
tokens=tokens,
|
||||
priority=30,
|
||||
metadata={"captured_at": ambient.captured_at.isoformat()}
|
||||
))
|
||||
context.total_tokens += tokens
|
||||
composition["ambient"] = {"count": 1, "tokens": tokens}
|
||||
|
||||
# 5. Historial de conversación (al final para llenar espacio restante)
|
||||
if sources.get("history", True):
|
||||
history_config = config.get("history_config", {})
|
||||
history = self.db.get_session_history(
|
||||
session.id,
|
||||
limit=history_config.get("max_messages", 20),
|
||||
include_system=history_config.get("include_system", False)
|
||||
)
|
||||
|
||||
history_tokens = 0
|
||||
history_count = 0
|
||||
for msg in history:
|
||||
tokens = len(msg.content) // 4
|
||||
if context.total_tokens + tokens <= token_budget:
|
||||
context.items.append(ContextItem(
|
||||
source=ContextSource.HISTORY,
|
||||
content=msg.content,
|
||||
tokens=tokens,
|
||||
priority=10,
|
||||
metadata={"role": msg.role.value, "sequence": msg.sequence_num}
|
||||
))
|
||||
context.total_tokens += tokens
|
||||
history_tokens += tokens
|
||||
history_count += 1
|
||||
|
||||
composition["history"] = {"count": history_count, "tokens": history_tokens}
|
||||
|
||||
context.composition = composition
|
||||
return context
|
||||
|
||||
def _select_context_blocks(
|
||||
self,
|
||||
user_message: str,
|
||||
config: Dict[str, Any]
|
||||
) -> List[ContextBlock]:
|
||||
"""Selecciona bloques de contexto relevantes"""
|
||||
blocks = self.db.get_active_context_blocks()
|
||||
relevant_blocks = []
|
||||
|
||||
keywords = self._extract_keywords(user_message)
|
||||
|
||||
for block in blocks:
|
||||
rules = block.activation_rules
|
||||
|
||||
# Siempre incluir
|
||||
if rules.get("always", False):
|
||||
relevant_blocks.append(block)
|
||||
continue
|
||||
|
||||
# Verificar keywords
|
||||
block_keywords = rules.get("keywords", [])
|
||||
if block_keywords:
|
||||
if any(kw.lower() in user_message.lower() for kw in block_keywords):
|
||||
relevant_blocks.append(block)
|
||||
continue
|
||||
|
||||
# Verificar categoría system (siempre incluir)
|
||||
if block.category == "system":
|
||||
relevant_blocks.append(block)
|
||||
|
||||
# Ordenar por prioridad
|
||||
relevant_blocks.sort(key=lambda b: b.priority, reverse=True)
|
||||
return relevant_blocks
|
||||
|
||||
def _select_memories(
|
||||
self,
|
||||
user_message: str,
|
||||
min_importance: int = 30,
|
||||
max_items: int = 15
|
||||
) -> List[Memory]:
|
||||
"""Selecciona memorias relevantes"""
|
||||
memories = self.db.get_memories(
|
||||
min_importance=min_importance,
|
||||
limit=max_items * 2 # Obtener más para filtrar
|
||||
)
|
||||
|
||||
# Filtrar por relevancia al mensaje
|
||||
keywords = self._extract_keywords(user_message)
|
||||
if keywords:
|
||||
scored_memories = []
|
||||
for mem in memories:
|
||||
score = sum(1 for kw in keywords if kw.lower() in mem.content.lower())
|
||||
scored_memories.append((mem, score + mem.importance / 100))
|
||||
|
||||
scored_memories.sort(key=lambda x: x[1], reverse=True)
|
||||
return [m[0] for m in scored_memories[:max_items]]
|
||||
|
||||
return memories[:max_items]
|
||||
|
||||
def _extract_keywords(self, text: str) -> List[str]:
|
||||
"""Extrae keywords de un texto"""
|
||||
# Palabras comunes a ignorar
|
||||
stopwords = {
|
||||
"el", "la", "los", "las", "un", "una", "unos", "unas",
|
||||
"de", "del", "al", "a", "en", "con", "por", "para",
|
||||
"que", "qué", "como", "cómo", "donde", "dónde", "cuando", "cuándo",
|
||||
"es", "son", "está", "están", "ser", "estar", "tener", "hacer",
|
||||
"y", "o", "pero", "si", "no", "me", "te", "se", "nos",
|
||||
"the", "a", "an", "is", "are", "was", "were", "be", "been",
|
||||
"have", "has", "had", "do", "does", "did", "will", "would",
|
||||
"can", "could", "should", "may", "might", "must",
|
||||
"and", "or", "but", "if", "then", "else", "when", "where",
|
||||
"what", "which", "who", "whom", "this", "that", "these", "those",
|
||||
"i", "you", "he", "she", "it", "we", "they", "my", "your", "his", "her"
|
||||
}
|
||||
|
||||
# Extraer palabras
|
||||
words = re.findall(r'\b\w+\b', text.lower())
|
||||
|
||||
# Filtrar
|
||||
keywords = [
|
||||
w for w in words
|
||||
if len(w) > 2 and w not in stopwords
|
||||
]
|
||||
|
||||
return list(set(keywords))
|
||||
|
||||
def _format_ambient_context(self, ambient: AmbientContext) -> str:
|
||||
"""Formatea el contexto ambiental como texto"""
|
||||
lines = ["[Contexto del sistema]"]
|
||||
|
||||
env = ambient.environment
|
||||
if env:
|
||||
if env.get("timezone"):
|
||||
lines.append(f"- Zona horaria: {env['timezone']}")
|
||||
if env.get("working_directory"):
|
||||
lines.append(f"- Directorio: {env['working_directory']}")
|
||||
if env.get("git_branch"):
|
||||
lines.append(f"- Git branch: {env['git_branch']}")
|
||||
if env.get("active_project"):
|
||||
lines.append(f"- Proyecto activo: {env['active_project']}")
|
||||
|
||||
state = ambient.system_state
|
||||
if state:
|
||||
if state.get("servers"):
|
||||
servers = state["servers"]
|
||||
online = [k for k, v in servers.items() if v == "online"]
|
||||
if online:
|
||||
lines.append(f"- Servidores online: {', '.join(online)}")
|
||||
|
||||
if state.get("alerts"):
|
||||
alerts = state["alerts"]
|
||||
if alerts:
|
||||
lines.append(f"- Alertas activas: {len(alerts)}")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
class ContextManager:
|
||||
"""
|
||||
Gestor completo de contexto.
|
||||
|
||||
Combina el selector con el logging y métricas.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
db: Database = None,
|
||||
host: str = None,
|
||||
port: int = None,
|
||||
database: str = None,
|
||||
user: str = None,
|
||||
password: str = None
|
||||
):
|
||||
if db:
|
||||
self.db = db
|
||||
else:
|
||||
self.db = Database(
|
||||
host=host,
|
||||
port=port,
|
||||
database=database,
|
||||
user=user,
|
||||
password=password
|
||||
)
|
||||
|
||||
self.selector = ContextSelector(self.db)
|
||||
self._current_session: Optional[Session] = None
|
||||
|
||||
def start_session(
|
||||
self,
|
||||
user_id: str = None,
|
||||
instance_id: str = None,
|
||||
model_provider: str = None,
|
||||
model_name: str = None,
|
||||
metadata: Dict[str, Any] = None
|
||||
) -> Session:
|
||||
"""Inicia una nueva sesión"""
|
||||
algorithm = self.db.get_active_algorithm()
|
||||
self._current_session = self.db.create_session(
|
||||
user_id=user_id,
|
||||
instance_id=instance_id,
|
||||
model_provider=model_provider,
|
||||
model_name=model_name,
|
||||
algorithm_id=algorithm.id if algorithm else None,
|
||||
metadata=metadata
|
||||
)
|
||||
return self._current_session
|
||||
|
||||
def get_context_for_message(
|
||||
self,
|
||||
message: str,
|
||||
max_tokens: int = None,
|
||||
session: Session = None
|
||||
) -> SelectedContext:
|
||||
"""Obtiene el contexto para un mensaje"""
|
||||
session = session or self._current_session
|
||||
if not session:
|
||||
raise ValueError("No hay sesión activa. Llama a start_session() primero.")
|
||||
|
||||
return self.selector.select_context(
|
||||
session=session,
|
||||
user_message=message,
|
||||
max_tokens=max_tokens
|
||||
)
|
||||
|
||||
def log_user_message(
|
||||
self,
|
||||
content: str,
|
||||
context: SelectedContext = None,
|
||||
session: Session = None
|
||||
) -> uuid.UUID:
|
||||
"""Registra un mensaje del usuario en el log inmutable"""
|
||||
session = session or self._current_session
|
||||
if not session:
|
||||
raise ValueError("No hay sesión activa.")
|
||||
|
||||
return self.db.insert_log_entry(
|
||||
session_id=session.id,
|
||||
role=MessageRole.USER,
|
||||
content=content,
|
||||
model_provider=session.model_provider,
|
||||
model_name=session.model_name,
|
||||
context_snapshot=context.to_dict() if context else None,
|
||||
context_algorithm_id=context.algorithm_id if context else None,
|
||||
context_tokens_used=context.total_tokens if context else None
|
||||
)
|
||||
|
||||
def log_assistant_message(
|
||||
self,
|
||||
content: str,
|
||||
tokens_input: int = None,
|
||||
tokens_output: int = None,
|
||||
latency_ms: int = None,
|
||||
model_provider: str = None,
|
||||
model_name: str = None,
|
||||
model_params: Dict[str, Any] = None,
|
||||
session: Session = None
|
||||
) -> uuid.UUID:
|
||||
"""Registra una respuesta del asistente en el log inmutable"""
|
||||
session = session or self._current_session
|
||||
if not session:
|
||||
raise ValueError("No hay sesión activa.")
|
||||
|
||||
return self.db.insert_log_entry(
|
||||
session_id=session.id,
|
||||
role=MessageRole.ASSISTANT,
|
||||
content=content,
|
||||
model_provider=model_provider or session.model_provider,
|
||||
model_name=model_name or session.model_name,
|
||||
model_params=model_params,
|
||||
tokens_input=tokens_input,
|
||||
tokens_output=tokens_output,
|
||||
latency_ms=latency_ms
|
||||
)
|
||||
|
||||
def record_metric(
|
||||
self,
|
||||
context: SelectedContext,
|
||||
log_entry_id: uuid.UUID,
|
||||
tokens_budget: int,
|
||||
latency_ms: int = None,
|
||||
model_tokens_input: int = None,
|
||||
model_tokens_output: int = None,
|
||||
session: Session = None
|
||||
) -> uuid.UUID:
|
||||
"""Registra una métrica de uso del algoritmo"""
|
||||
session = session or self._current_session
|
||||
if not session or not context.algorithm_id:
|
||||
return None
|
||||
|
||||
return self.db.record_metric(
|
||||
algorithm_id=context.algorithm_id,
|
||||
session_id=session.id,
|
||||
log_entry_id=log_entry_id,
|
||||
tokens_budget=tokens_budget,
|
||||
tokens_used=context.total_tokens,
|
||||
context_composition=context.composition,
|
||||
latency_ms=latency_ms,
|
||||
model_tokens_input=model_tokens_input,
|
||||
model_tokens_output=model_tokens_output
|
||||
)
|
||||
|
||||
def rate_response(
|
||||
self,
|
||||
metric_id: uuid.UUID,
|
||||
relevance: float = None,
|
||||
quality: float = None,
|
||||
satisfaction: float = None
|
||||
):
|
||||
"""Evalúa una respuesta (feedback manual)"""
|
||||
self.db.update_metric_evaluation(
|
||||
metric_id=metric_id,
|
||||
relevance=relevance,
|
||||
quality=quality,
|
||||
satisfaction=satisfaction,
|
||||
method="user_feedback"
|
||||
)
|
||||
|
||||
def verify_session_integrity(self, session: Session = None) -> Dict[str, Any]:
|
||||
"""Verifica la integridad de la sesión"""
|
||||
session = session or self._current_session
|
||||
if not session:
|
||||
raise ValueError("No hay sesión activa.")
|
||||
|
||||
return self.db.verify_chain_integrity(session.id)
|
||||
|
||||
def close(self):
|
||||
"""Cierra las conexiones"""
|
||||
self.db.close()
|
||||
621
src/database.py
Normal file
621
src/database.py
Normal file
@@ -0,0 +1,621 @@
|
||||
"""
|
||||
Conexión a base de datos PostgreSQL
|
||||
"""
|
||||
|
||||
import os
|
||||
import uuid
|
||||
import json
|
||||
from datetime import datetime
|
||||
from typing import Optional, List, Dict, Any
|
||||
from contextlib import contextmanager
|
||||
|
||||
try:
|
||||
import psycopg2
|
||||
from psycopg2.extras import RealDictCursor, Json
|
||||
from psycopg2 import pool
|
||||
HAS_PSYCOPG2 = True
|
||||
except ImportError:
|
||||
HAS_PSYCOPG2 = False
|
||||
|
||||
from .models import (
|
||||
Session, Message, MessageRole, ContextBlock, Memory,
|
||||
Knowledge, Algorithm, AlgorithmStatus, AlgorithmMetric,
|
||||
AmbientContext
|
||||
)
|
||||
|
||||
|
||||
class Database:
|
||||
"""Gestión de conexión a PostgreSQL"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
host: str = None,
|
||||
port: int = None,
|
||||
database: str = None,
|
||||
user: str = None,
|
||||
password: str = None,
|
||||
min_connections: int = 1,
|
||||
max_connections: int = 10
|
||||
):
|
||||
if not HAS_PSYCOPG2:
|
||||
raise ImportError("psycopg2 no está instalado. Ejecuta: pip install psycopg2-binary")
|
||||
|
||||
self.host = host or os.getenv("PGHOST", "localhost")
|
||||
self.port = port or int(os.getenv("PGPORT", "5432"))
|
||||
self.database = database or os.getenv("PGDATABASE", "context_manager")
|
||||
self.user = user or os.getenv("PGUSER", "postgres")
|
||||
self.password = password or os.getenv("PGPASSWORD", "")
|
||||
|
||||
self._pool = pool.ThreadedConnectionPool(
|
||||
min_connections,
|
||||
max_connections,
|
||||
host=self.host,
|
||||
port=self.port,
|
||||
database=self.database,
|
||||
user=self.user,
|
||||
password=self.password
|
||||
)
|
||||
|
||||
@contextmanager
|
||||
def get_connection(self):
|
||||
"""Obtiene una conexión del pool"""
|
||||
conn = self._pool.getconn()
|
||||
try:
|
||||
yield conn
|
||||
conn.commit()
|
||||
except Exception:
|
||||
conn.rollback()
|
||||
raise
|
||||
finally:
|
||||
self._pool.putconn(conn)
|
||||
|
||||
@contextmanager
|
||||
def get_cursor(self, dict_cursor: bool = True):
|
||||
"""Obtiene un cursor"""
|
||||
with self.get_connection() as conn:
|
||||
cursor_factory = RealDictCursor if dict_cursor else None
|
||||
with conn.cursor(cursor_factory=cursor_factory) as cur:
|
||||
yield cur
|
||||
|
||||
def close(self):
|
||||
"""Cierra el pool de conexiones"""
|
||||
self._pool.closeall()
|
||||
|
||||
# ==========================================
|
||||
# SESIONES
|
||||
# ==========================================
|
||||
|
||||
def create_session(
|
||||
self,
|
||||
user_id: str = None,
|
||||
instance_id: str = None,
|
||||
model_provider: str = None,
|
||||
model_name: str = None,
|
||||
algorithm_id: uuid.UUID = None,
|
||||
metadata: Dict[str, Any] = None
|
||||
) -> Session:
|
||||
"""Crea una nueva sesión"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT create_session(%s, %s, %s, %s, %s, %s) as id
|
||||
""",
|
||||
(user_id, instance_id, model_provider, model_name,
|
||||
str(algorithm_id) if algorithm_id else None,
|
||||
Json(metadata or {}))
|
||||
)
|
||||
session_id = cur.fetchone()["id"]
|
||||
|
||||
return Session(
|
||||
id=session_id,
|
||||
user_id=user_id,
|
||||
instance_id=instance_id,
|
||||
model_provider=model_provider,
|
||||
model_name=model_name,
|
||||
algorithm_id=algorithm_id,
|
||||
metadata=metadata or {}
|
||||
)
|
||||
|
||||
def get_session(self, session_id: uuid.UUID) -> Optional[Session]:
|
||||
"""Obtiene una sesión por ID"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"SELECT * FROM sessions WHERE id = %s",
|
||||
(str(session_id),)
|
||||
)
|
||||
row = cur.fetchone()
|
||||
if row:
|
||||
return Session(
|
||||
id=row["id"],
|
||||
user_id=row["user_id"],
|
||||
instance_id=row["instance_id"],
|
||||
model_provider=row["initial_model_provider"],
|
||||
model_name=row["initial_model_name"],
|
||||
algorithm_id=row["initial_context_algorithm_id"],
|
||||
metadata=row["metadata"] or {},
|
||||
started_at=row["started_at"],
|
||||
ended_at=row["ended_at"],
|
||||
total_messages=row["total_messages"],
|
||||
total_tokens_input=row["total_tokens_input"],
|
||||
total_tokens_output=row["total_tokens_output"]
|
||||
)
|
||||
return None
|
||||
|
||||
# ==========================================
|
||||
# LOG INMUTABLE
|
||||
# ==========================================
|
||||
|
||||
def insert_log_entry(
|
||||
self,
|
||||
session_id: uuid.UUID,
|
||||
role: MessageRole,
|
||||
content: str,
|
||||
model_provider: str = None,
|
||||
model_name: str = None,
|
||||
model_params: Dict[str, Any] = None,
|
||||
context_snapshot: Dict[str, Any] = None,
|
||||
context_algorithm_id: uuid.UUID = None,
|
||||
context_tokens_used: int = None,
|
||||
tokens_input: int = None,
|
||||
tokens_output: int = None,
|
||||
latency_ms: int = None,
|
||||
source_ip: str = None,
|
||||
user_agent: str = None
|
||||
) -> uuid.UUID:
|
||||
"""Inserta una entrada en el log inmutable"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT insert_log_entry(
|
||||
%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s
|
||||
) as id
|
||||
""",
|
||||
(
|
||||
str(session_id),
|
||||
role.value,
|
||||
content,
|
||||
model_provider,
|
||||
model_name,
|
||||
Json(model_params or {}),
|
||||
Json(context_snapshot) if context_snapshot else None,
|
||||
str(context_algorithm_id) if context_algorithm_id else None,
|
||||
context_tokens_used,
|
||||
tokens_input,
|
||||
tokens_output,
|
||||
latency_ms,
|
||||
source_ip,
|
||||
user_agent
|
||||
)
|
||||
)
|
||||
return cur.fetchone()["id"]
|
||||
|
||||
def get_session_history(
|
||||
self,
|
||||
session_id: uuid.UUID,
|
||||
limit: int = None,
|
||||
include_system: bool = False
|
||||
) -> List[Message]:
|
||||
"""Obtiene el historial de una sesión"""
|
||||
with self.get_cursor() as cur:
|
||||
query = """
|
||||
SELECT * FROM immutable_log
|
||||
WHERE session_id = %s
|
||||
"""
|
||||
params = [str(session_id)]
|
||||
|
||||
if not include_system:
|
||||
query += " AND role != 'system'"
|
||||
|
||||
query += " ORDER BY sequence_num DESC"
|
||||
|
||||
if limit:
|
||||
query += " LIMIT %s"
|
||||
params.append(limit)
|
||||
|
||||
cur.execute(query, params)
|
||||
rows = cur.fetchall()
|
||||
|
||||
messages = []
|
||||
for row in reversed(rows):
|
||||
messages.append(Message(
|
||||
id=row["id"],
|
||||
session_id=row["session_id"],
|
||||
sequence_num=row["sequence_num"],
|
||||
role=MessageRole(row["role"]),
|
||||
content=row["content"],
|
||||
hash=row["hash"],
|
||||
hash_anterior=row["hash_anterior"],
|
||||
model_provider=row["model_provider"],
|
||||
model_name=row["model_name"],
|
||||
model_params=row["model_params"] or {},
|
||||
context_snapshot=row["context_snapshot"],
|
||||
context_algorithm_id=row["context_algorithm_id"],
|
||||
context_tokens_used=row["context_tokens_used"],
|
||||
tokens_input=row["tokens_input"],
|
||||
tokens_output=row["tokens_output"],
|
||||
latency_ms=row["latency_ms"],
|
||||
created_at=row["created_at"]
|
||||
))
|
||||
return messages
|
||||
|
||||
def verify_chain_integrity(self, session_id: uuid.UUID) -> Dict[str, Any]:
|
||||
"""Verifica la integridad de la cadena de hashes"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"SELECT * FROM verify_chain_integrity(%s)",
|
||||
(str(session_id),)
|
||||
)
|
||||
row = cur.fetchone()
|
||||
return {
|
||||
"is_valid": row["is_valid"],
|
||||
"broken_at_sequence": row["broken_at_sequence"],
|
||||
"expected_hash": row["expected_hash"],
|
||||
"actual_hash": row["actual_hash"]
|
||||
}
|
||||
|
||||
# ==========================================
|
||||
# BLOQUES DE CONTEXTO
|
||||
# ==========================================
|
||||
|
||||
def create_context_block(self, block: ContextBlock) -> uuid.UUID:
|
||||
"""Crea un bloque de contexto"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
INSERT INTO context_blocks
|
||||
(code, name, description, content, category, priority, scope, project_id, activation_rules, active)
|
||||
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
|
||||
RETURNING id
|
||||
""",
|
||||
(
|
||||
block.code, block.name, block.description, block.content,
|
||||
block.category, block.priority, block.scope,
|
||||
str(block.project_id) if block.project_id else None,
|
||||
Json(block.activation_rules), block.active
|
||||
)
|
||||
)
|
||||
return cur.fetchone()["id"]
|
||||
|
||||
def get_active_context_blocks(
|
||||
self,
|
||||
category: str = None,
|
||||
scope: str = None,
|
||||
project_id: uuid.UUID = None
|
||||
) -> List[ContextBlock]:
|
||||
"""Obtiene bloques de contexto activos"""
|
||||
with self.get_cursor() as cur:
|
||||
query = "SELECT * FROM context_blocks WHERE active = true"
|
||||
params = []
|
||||
|
||||
if category:
|
||||
query += " AND category = %s"
|
||||
params.append(category)
|
||||
if scope:
|
||||
query += " AND scope = %s"
|
||||
params.append(scope)
|
||||
if project_id:
|
||||
query += " AND (project_id = %s OR project_id IS NULL)"
|
||||
params.append(str(project_id))
|
||||
|
||||
query += " ORDER BY priority DESC"
|
||||
|
||||
cur.execute(query, params)
|
||||
return [
|
||||
ContextBlock(
|
||||
id=row["id"],
|
||||
code=row["code"],
|
||||
name=row["name"],
|
||||
description=row["description"],
|
||||
content=row["content"],
|
||||
content_hash=row["content_hash"],
|
||||
category=row["category"],
|
||||
priority=row["priority"],
|
||||
tokens_estimated=row["tokens_estimated"],
|
||||
scope=row["scope"],
|
||||
project_id=row["project_id"],
|
||||
activation_rules=row["activation_rules"] or {},
|
||||
active=row["active"],
|
||||
version=row["version"]
|
||||
)
|
||||
for row in cur.fetchall()
|
||||
]
|
||||
|
||||
# ==========================================
|
||||
# MEMORIA
|
||||
# ==========================================
|
||||
|
||||
def get_memories(
|
||||
self,
|
||||
type: str = None,
|
||||
min_importance: int = 0,
|
||||
limit: int = 20
|
||||
) -> List[Memory]:
|
||||
"""Obtiene memorias activas"""
|
||||
with self.get_cursor() as cur:
|
||||
query = """
|
||||
SELECT * FROM memory
|
||||
WHERE active = true
|
||||
AND importance >= %s
|
||||
AND (expires_at IS NULL OR expires_at > NOW())
|
||||
"""
|
||||
params = [min_importance]
|
||||
|
||||
if type:
|
||||
query += " AND type = %s"
|
||||
params.append(type)
|
||||
|
||||
query += " ORDER BY importance DESC, last_used_at DESC NULLS LAST LIMIT %s"
|
||||
params.append(limit)
|
||||
|
||||
cur.execute(query, params)
|
||||
return [
|
||||
Memory(
|
||||
id=row["id"],
|
||||
type=row["type"],
|
||||
category=row["category"],
|
||||
content=row["content"],
|
||||
summary=row["summary"],
|
||||
importance=row["importance"],
|
||||
confidence=float(row["confidence"]) if row["confidence"] else 1.0,
|
||||
uses=row["uses"],
|
||||
last_used_at=row["last_used_at"],
|
||||
verified=row["verified"]
|
||||
)
|
||||
for row in cur.fetchall()
|
||||
]
|
||||
|
||||
def save_memory(self, memory: Memory) -> uuid.UUID:
|
||||
"""Guarda una memoria"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
INSERT INTO memory
|
||||
(type, category, content, summary, extracted_from_session, importance, confidence, expires_at)
|
||||
VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
|
||||
RETURNING id
|
||||
""",
|
||||
(
|
||||
memory.type, memory.category, memory.content, memory.summary,
|
||||
str(memory.extracted_from_session) if memory.extracted_from_session else None,
|
||||
memory.importance, memory.confidence, memory.expires_at
|
||||
)
|
||||
)
|
||||
return cur.fetchone()["id"]
|
||||
|
||||
# ==========================================
|
||||
# CONOCIMIENTO
|
||||
# ==========================================
|
||||
|
||||
def search_knowledge(
|
||||
self,
|
||||
keywords: List[str] = None,
|
||||
category: str = None,
|
||||
tags: List[str] = None,
|
||||
limit: int = 5
|
||||
) -> List[Knowledge]:
|
||||
"""Busca en la base de conocimiento"""
|
||||
with self.get_cursor() as cur:
|
||||
query = "SELECT * FROM knowledge_base WHERE active = true"
|
||||
params = []
|
||||
|
||||
if category:
|
||||
query += " AND category = %s"
|
||||
params.append(category)
|
||||
|
||||
if tags:
|
||||
query += " AND tags && %s"
|
||||
params.append(tags)
|
||||
|
||||
if keywords:
|
||||
# Búsqueda simple por contenido
|
||||
keyword_conditions = []
|
||||
for kw in keywords:
|
||||
keyword_conditions.append("content ILIKE %s")
|
||||
params.append(f"%{kw}%")
|
||||
query += f" AND ({' OR '.join(keyword_conditions)})"
|
||||
|
||||
query += " ORDER BY priority DESC, access_count DESC LIMIT %s"
|
||||
params.append(limit)
|
||||
|
||||
cur.execute(query, params)
|
||||
return [
|
||||
Knowledge(
|
||||
id=row["id"],
|
||||
title=row["title"],
|
||||
category=row["category"],
|
||||
tags=row["tags"] or [],
|
||||
content=row["content"],
|
||||
tokens_estimated=row["tokens_estimated"],
|
||||
priority=row["priority"],
|
||||
access_count=row["access_count"]
|
||||
)
|
||||
for row in cur.fetchall()
|
||||
]
|
||||
|
||||
# ==========================================
|
||||
# CONTEXTO AMBIENTAL
|
||||
# ==========================================
|
||||
|
||||
def get_latest_ambient_context(self) -> Optional[AmbientContext]:
|
||||
"""Obtiene el contexto ambiental más reciente"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT * FROM ambient_context
|
||||
WHERE expires_at > NOW()
|
||||
ORDER BY captured_at DESC
|
||||
LIMIT 1
|
||||
"""
|
||||
)
|
||||
row = cur.fetchone()
|
||||
if row:
|
||||
return AmbientContext(
|
||||
id=row["id"],
|
||||
captured_at=row["captured_at"],
|
||||
expires_at=row["expires_at"],
|
||||
environment=row["environment"] or {},
|
||||
system_state=row["system_state"] or {},
|
||||
active_resources=row["active_resources"] or []
|
||||
)
|
||||
return None
|
||||
|
||||
def save_ambient_context(self, context: AmbientContext) -> int:
|
||||
"""Guarda un snapshot de contexto ambiental"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
INSERT INTO ambient_context
|
||||
(environment, system_state, active_resources, expires_at)
|
||||
VALUES (%s, %s, %s, %s)
|
||||
RETURNING id
|
||||
""",
|
||||
(
|
||||
Json(context.environment),
|
||||
Json(context.system_state),
|
||||
Json(context.active_resources),
|
||||
context.expires_at
|
||||
)
|
||||
)
|
||||
return cur.fetchone()["id"]
|
||||
|
||||
# ==========================================
|
||||
# ALGORITMOS
|
||||
# ==========================================
|
||||
|
||||
def get_active_algorithm(self) -> Optional[Algorithm]:
|
||||
"""Obtiene el algoritmo activo"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT * FROM context_algorithms
|
||||
WHERE status = 'active'
|
||||
ORDER BY activated_at DESC
|
||||
LIMIT 1
|
||||
"""
|
||||
)
|
||||
row = cur.fetchone()
|
||||
if row:
|
||||
return Algorithm(
|
||||
id=row["id"],
|
||||
code=row["code"],
|
||||
name=row["name"],
|
||||
description=row["description"],
|
||||
version=row["version"],
|
||||
status=AlgorithmStatus(row["status"]),
|
||||
config=row["config"] or {},
|
||||
selector_code=row["selector_code"],
|
||||
times_used=row["times_used"],
|
||||
avg_tokens_used=float(row["avg_tokens_used"]) if row["avg_tokens_used"] else None,
|
||||
avg_relevance_score=float(row["avg_relevance_score"]) if row["avg_relevance_score"] else None,
|
||||
avg_response_quality=float(row["avg_response_quality"]) if row["avg_response_quality"] else None,
|
||||
parent_algorithm_id=row["parent_algorithm_id"],
|
||||
activated_at=row["activated_at"]
|
||||
)
|
||||
return None
|
||||
|
||||
def get_algorithm(self, algorithm_id: uuid.UUID) -> Optional[Algorithm]:
|
||||
"""Obtiene un algoritmo por ID"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"SELECT * FROM context_algorithms WHERE id = %s",
|
||||
(str(algorithm_id),)
|
||||
)
|
||||
row = cur.fetchone()
|
||||
if row:
|
||||
return Algorithm(
|
||||
id=row["id"],
|
||||
code=row["code"],
|
||||
name=row["name"],
|
||||
description=row["description"],
|
||||
version=row["version"],
|
||||
status=AlgorithmStatus(row["status"]),
|
||||
config=row["config"] or {},
|
||||
selector_code=row["selector_code"],
|
||||
times_used=row["times_used"]
|
||||
)
|
||||
return None
|
||||
|
||||
def fork_algorithm(
|
||||
self,
|
||||
source_id: uuid.UUID,
|
||||
new_code: str,
|
||||
new_name: str,
|
||||
reason: str = None
|
||||
) -> uuid.UUID:
|
||||
"""Clona un algoritmo para experimentación"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"SELECT fork_algorithm(%s, %s, %s, %s) as id",
|
||||
(str(source_id), new_code, new_name, reason)
|
||||
)
|
||||
return cur.fetchone()["id"]
|
||||
|
||||
def activate_algorithm(self, algorithm_id: uuid.UUID) -> bool:
|
||||
"""Activa un algoritmo"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"SELECT activate_algorithm(%s) as success",
|
||||
(str(algorithm_id),)
|
||||
)
|
||||
return cur.fetchone()["success"]
|
||||
|
||||
# ==========================================
|
||||
# MÉTRICAS
|
||||
# ==========================================
|
||||
|
||||
def record_metric(
|
||||
self,
|
||||
algorithm_id: uuid.UUID,
|
||||
session_id: uuid.UUID,
|
||||
log_entry_id: uuid.UUID,
|
||||
tokens_budget: int,
|
||||
tokens_used: int,
|
||||
context_composition: Dict[str, Any],
|
||||
latency_ms: int = None,
|
||||
model_tokens_input: int = None,
|
||||
model_tokens_output: int = None
|
||||
) -> uuid.UUID:
|
||||
"""Registra una métrica de uso"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT record_algorithm_metric(
|
||||
%s, %s, %s, %s, %s, %s, %s, %s, %s
|
||||
) as id
|
||||
""",
|
||||
(
|
||||
str(algorithm_id), str(session_id), str(log_entry_id),
|
||||
tokens_budget, tokens_used, Json(context_composition),
|
||||
latency_ms, model_tokens_input, model_tokens_output
|
||||
)
|
||||
)
|
||||
return cur.fetchone()["id"]
|
||||
|
||||
def update_metric_evaluation(
|
||||
self,
|
||||
metric_id: uuid.UUID,
|
||||
relevance: float = None,
|
||||
quality: float = None,
|
||||
satisfaction: float = None,
|
||||
method: str = "manual"
|
||||
) -> bool:
|
||||
"""Actualiza la evaluación de una métrica"""
|
||||
with self.get_cursor() as cur:
|
||||
cur.execute(
|
||||
"SELECT update_metric_evaluation(%s, %s, %s, %s, %s) as success",
|
||||
(str(metric_id), relevance, quality, satisfaction, method)
|
||||
)
|
||||
return cur.fetchone()["success"]
|
||||
|
||||
def get_algorithm_performance(self, algorithm_id: uuid.UUID = None) -> List[Dict[str, Any]]:
|
||||
"""Obtiene estadísticas de rendimiento de algoritmos"""
|
||||
with self.get_cursor() as cur:
|
||||
query = "SELECT * FROM algorithm_performance"
|
||||
params = []
|
||||
|
||||
if algorithm_id:
|
||||
query += " WHERE id = %s"
|
||||
params.append(str(algorithm_id))
|
||||
|
||||
cur.execute(query, params)
|
||||
return [dict(row) for row in cur.fetchall()]
|
||||
309
src/models.py
Normal file
309
src/models.py
Normal file
@@ -0,0 +1,309 @@
|
||||
"""
|
||||
Modelos de datos para Context Manager
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime
|
||||
from typing import Optional, List, Dict, Any
|
||||
from enum import Enum
|
||||
import uuid
|
||||
import hashlib
|
||||
import json
|
||||
|
||||
|
||||
class MessageRole(Enum):
|
||||
USER = "user"
|
||||
ASSISTANT = "assistant"
|
||||
SYSTEM = "system"
|
||||
TOOL = "tool"
|
||||
|
||||
|
||||
class ContextSource(Enum):
|
||||
MEMORY = "memory"
|
||||
KNOWLEDGE = "knowledge"
|
||||
HISTORY = "history"
|
||||
AMBIENT = "ambient"
|
||||
DATASET = "dataset"
|
||||
|
||||
|
||||
class AlgorithmStatus(Enum):
|
||||
DRAFT = "draft"
|
||||
TESTING = "testing"
|
||||
ACTIVE = "active"
|
||||
DEPRECATED = "deprecated"
|
||||
|
||||
|
||||
@dataclass
|
||||
class Session:
|
||||
"""Sesión de conversación"""
|
||||
id: uuid.UUID = field(default_factory=uuid.uuid4)
|
||||
user_id: Optional[str] = None
|
||||
instance_id: Optional[str] = None
|
||||
model_provider: Optional[str] = None
|
||||
model_name: Optional[str] = None
|
||||
algorithm_id: Optional[uuid.UUID] = None
|
||||
metadata: Dict[str, Any] = field(default_factory=dict)
|
||||
started_at: datetime = field(default_factory=datetime.now)
|
||||
ended_at: Optional[datetime] = None
|
||||
total_messages: int = 0
|
||||
total_tokens_input: int = 0
|
||||
total_tokens_output: int = 0
|
||||
|
||||
@property
|
||||
def hash(self) -> str:
|
||||
content = f"{self.id}{self.started_at.isoformat()}"
|
||||
return hashlib.sha256(content.encode()).hexdigest()
|
||||
|
||||
|
||||
@dataclass
|
||||
class Message:
|
||||
"""Mensaje en el log inmutable"""
|
||||
id: uuid.UUID = field(default_factory=uuid.uuid4)
|
||||
session_id: uuid.UUID = None
|
||||
sequence_num: int = 0
|
||||
role: MessageRole = MessageRole.USER
|
||||
content: str = ""
|
||||
hash: str = ""
|
||||
hash_anterior: Optional[str] = None
|
||||
|
||||
# Modelo
|
||||
model_provider: Optional[str] = None
|
||||
model_name: Optional[str] = None
|
||||
model_params: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
# Contexto
|
||||
context_snapshot: Optional[Dict[str, Any]] = None
|
||||
context_algorithm_id: Optional[uuid.UUID] = None
|
||||
context_tokens_used: Optional[int] = None
|
||||
|
||||
# Respuesta
|
||||
tokens_input: Optional[int] = None
|
||||
tokens_output: Optional[int] = None
|
||||
latency_ms: Optional[int] = None
|
||||
|
||||
# Metadata
|
||||
created_at: datetime = field(default_factory=datetime.now)
|
||||
source_ip: Optional[str] = None
|
||||
user_agent: Optional[str] = None
|
||||
|
||||
def compute_hash(self) -> str:
|
||||
"""Calcula el hash del mensaje (blockchain-style)"""
|
||||
content = (
|
||||
(self.hash_anterior or "") +
|
||||
str(self.session_id) +
|
||||
str(self.sequence_num) +
|
||||
self.role.value +
|
||||
self.content
|
||||
)
|
||||
return hashlib.sha256(content.encode()).hexdigest()
|
||||
|
||||
|
||||
@dataclass
|
||||
class ContextBlock:
|
||||
"""Bloque de contexto reutilizable"""
|
||||
id: uuid.UUID = field(default_factory=uuid.uuid4)
|
||||
code: str = ""
|
||||
name: str = ""
|
||||
description: Optional[str] = None
|
||||
content: str = ""
|
||||
content_hash: Optional[str] = None
|
||||
category: str = "general"
|
||||
priority: int = 50
|
||||
tokens_estimated: int = 0
|
||||
scope: str = "global"
|
||||
project_id: Optional[uuid.UUID] = None
|
||||
activation_rules: Dict[str, Any] = field(default_factory=dict)
|
||||
active: bool = True
|
||||
version: int = 1
|
||||
created_at: datetime = field(default_factory=datetime.now)
|
||||
updated_at: datetime = field(default_factory=datetime.now)
|
||||
|
||||
def __post_init__(self):
|
||||
self.content_hash = hashlib.sha256(self.content.encode()).hexdigest()
|
||||
self.tokens_estimated = len(self.content) // 4
|
||||
|
||||
|
||||
@dataclass
|
||||
class Memory:
|
||||
"""Memoria a largo plazo"""
|
||||
id: uuid.UUID = field(default_factory=uuid.uuid4)
|
||||
type: str = "fact"
|
||||
category: Optional[str] = None
|
||||
content: str = ""
|
||||
summary: Optional[str] = None
|
||||
content_hash: Optional[str] = None
|
||||
extracted_from_session: Optional[uuid.UUID] = None
|
||||
importance: int = 50
|
||||
confidence: float = 1.0
|
||||
uses: int = 0
|
||||
last_used_at: Optional[datetime] = None
|
||||
expires_at: Optional[datetime] = None
|
||||
active: bool = True
|
||||
verified: bool = False
|
||||
created_at: datetime = field(default_factory=datetime.now)
|
||||
|
||||
|
||||
@dataclass
|
||||
class Knowledge:
|
||||
"""Base de conocimiento"""
|
||||
id: uuid.UUID = field(default_factory=uuid.uuid4)
|
||||
title: str = ""
|
||||
category: str = ""
|
||||
tags: List[str] = field(default_factory=list)
|
||||
content: str = ""
|
||||
content_hash: Optional[str] = None
|
||||
tokens_estimated: int = 0
|
||||
source_type: Optional[str] = None
|
||||
source_ref: Optional[str] = None
|
||||
priority: int = 50
|
||||
access_count: int = 0
|
||||
active: bool = True
|
||||
created_at: datetime = field(default_factory=datetime.now)
|
||||
|
||||
|
||||
@dataclass
|
||||
class Algorithm:
|
||||
"""Algoritmo de selección de contexto"""
|
||||
id: uuid.UUID = field(default_factory=uuid.uuid4)
|
||||
code: str = ""
|
||||
name: str = ""
|
||||
description: Optional[str] = None
|
||||
version: str = "1.0.0"
|
||||
status: AlgorithmStatus = AlgorithmStatus.DRAFT
|
||||
config: Dict[str, Any] = field(default_factory=lambda: {
|
||||
"max_tokens": 4000,
|
||||
"sources": {
|
||||
"system_prompts": True,
|
||||
"context_blocks": True,
|
||||
"memory": True,
|
||||
"knowledge": True,
|
||||
"history": True,
|
||||
"ambient": True
|
||||
},
|
||||
"weights": {
|
||||
"priority": 0.4,
|
||||
"relevance": 0.3,
|
||||
"recency": 0.2,
|
||||
"frequency": 0.1
|
||||
},
|
||||
"history_config": {
|
||||
"max_messages": 20,
|
||||
"summarize_after": 10,
|
||||
"include_system": False
|
||||
},
|
||||
"memory_config": {
|
||||
"max_items": 15,
|
||||
"min_importance": 30
|
||||
},
|
||||
"knowledge_config": {
|
||||
"max_items": 5,
|
||||
"require_keyword_match": True
|
||||
}
|
||||
})
|
||||
selector_code: Optional[str] = None
|
||||
times_used: int = 0
|
||||
avg_tokens_used: Optional[float] = None
|
||||
avg_relevance_score: Optional[float] = None
|
||||
avg_response_quality: Optional[float] = None
|
||||
parent_algorithm_id: Optional[uuid.UUID] = None
|
||||
fork_reason: Optional[str] = None
|
||||
created_at: datetime = field(default_factory=datetime.now)
|
||||
activated_at: Optional[datetime] = None
|
||||
deprecated_at: Optional[datetime] = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class AlgorithmMetric:
|
||||
"""Métrica de rendimiento de algoritmo"""
|
||||
id: uuid.UUID = field(default_factory=uuid.uuid4)
|
||||
algorithm_id: uuid.UUID = None
|
||||
session_id: Optional[uuid.UUID] = None
|
||||
log_entry_id: Optional[uuid.UUID] = None
|
||||
tokens_budget: int = 0
|
||||
tokens_used: int = 0
|
||||
token_efficiency: float = 0.0
|
||||
context_composition: Dict[str, Any] = field(default_factory=dict)
|
||||
latency_ms: Optional[int] = None
|
||||
model_tokens_input: Optional[int] = None
|
||||
model_tokens_output: Optional[int] = None
|
||||
relevance_score: Optional[float] = None
|
||||
response_quality: Optional[float] = None
|
||||
user_satisfaction: Optional[float] = None
|
||||
auto_evaluated: bool = False
|
||||
evaluation_method: Optional[str] = None
|
||||
recorded_at: datetime = field(default_factory=datetime.now)
|
||||
|
||||
|
||||
@dataclass
|
||||
class AmbientContext:
|
||||
"""Contexto ambiental del sistema"""
|
||||
id: int = 0
|
||||
captured_at: datetime = field(default_factory=datetime.now)
|
||||
expires_at: Optional[datetime] = None
|
||||
environment: Dict[str, Any] = field(default_factory=dict)
|
||||
system_state: Dict[str, Any] = field(default_factory=dict)
|
||||
active_resources: List[Dict[str, Any]] = field(default_factory=list)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ContextItem:
|
||||
"""Item individual de contexto seleccionado"""
|
||||
source: ContextSource
|
||||
content: str
|
||||
tokens: int
|
||||
priority: int
|
||||
metadata: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
|
||||
@dataclass
|
||||
class SelectedContext:
|
||||
"""Contexto seleccionado para enviar al modelo"""
|
||||
items: List[ContextItem] = field(default_factory=list)
|
||||
total_tokens: int = 0
|
||||
algorithm_id: Optional[uuid.UUID] = None
|
||||
composition: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
def to_messages(self) -> List[Dict[str, str]]:
|
||||
"""Convierte el contexto a formato de mensajes para la API"""
|
||||
messages = []
|
||||
|
||||
# System context primero
|
||||
system_content = []
|
||||
for item in self.items:
|
||||
if item.source in [ContextSource.MEMORY, ContextSource.KNOWLEDGE, ContextSource.AMBIENT]:
|
||||
system_content.append(item.content)
|
||||
|
||||
if system_content:
|
||||
messages.append({
|
||||
"role": "system",
|
||||
"content": "\n\n".join(system_content)
|
||||
})
|
||||
|
||||
# History messages
|
||||
for item in self.items:
|
||||
if item.source == ContextSource.HISTORY:
|
||||
role = item.metadata.get("role", "user")
|
||||
messages.append({
|
||||
"role": role,
|
||||
"content": item.content
|
||||
})
|
||||
|
||||
return messages
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""Serializa el contexto para snapshot"""
|
||||
return {
|
||||
"items": [
|
||||
{
|
||||
"source": item.source.value,
|
||||
"content": item.content[:500] + "..." if len(item.content) > 500 else item.content,
|
||||
"tokens": item.tokens,
|
||||
"priority": item.priority,
|
||||
"metadata": item.metadata
|
||||
}
|
||||
for item in self.items
|
||||
],
|
||||
"total_tokens": self.total_tokens,
|
||||
"algorithm_id": str(self.algorithm_id) if self.algorithm_id else None,
|
||||
"composition": self.composition
|
||||
}
|
||||
18
src/providers/__init__.py
Normal file
18
src/providers/__init__.py
Normal file
@@ -0,0 +1,18 @@
|
||||
"""
|
||||
Adaptadores para proveedores de IA
|
||||
|
||||
Permite usar el Context Manager con cualquier modelo de IA.
|
||||
"""
|
||||
|
||||
from .base import BaseProvider, ProviderResponse
|
||||
from .anthropic import AnthropicProvider
|
||||
from .openai import OpenAIProvider
|
||||
from .ollama import OllamaProvider
|
||||
|
||||
__all__ = [
|
||||
"BaseProvider",
|
||||
"ProviderResponse",
|
||||
"AnthropicProvider",
|
||||
"OpenAIProvider",
|
||||
"OllamaProvider",
|
||||
]
|
||||
110
src/providers/anthropic.py
Normal file
110
src/providers/anthropic.py
Normal file
@@ -0,0 +1,110 @@
|
||||
"""
|
||||
Adaptador para Anthropic (Claude)
|
||||
"""
|
||||
|
||||
import os
|
||||
from typing import List, Dict, Any, Optional
|
||||
|
||||
from .base import BaseProvider, ProviderResponse
|
||||
from ..models import SelectedContext, ContextSource
|
||||
|
||||
try:
|
||||
import anthropic
|
||||
HAS_ANTHROPIC = True
|
||||
except ImportError:
|
||||
HAS_ANTHROPIC = False
|
||||
|
||||
|
||||
class AnthropicProvider(BaseProvider):
|
||||
"""Proveedor para modelos de Anthropic (Claude)"""
|
||||
|
||||
provider_name = "anthropic"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
api_key: str = None,
|
||||
model: str = "claude-sonnet-4-20250514",
|
||||
max_tokens: int = 4096,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__(api_key=api_key, model=model, **kwargs)
|
||||
|
||||
if not HAS_ANTHROPIC:
|
||||
raise ImportError("anthropic no está instalado. Ejecuta: pip install anthropic")
|
||||
|
||||
self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY")
|
||||
self.model = model
|
||||
self.max_tokens = max_tokens
|
||||
self.client = anthropic.Anthropic(api_key=self.api_key)
|
||||
|
||||
def format_context(self, context: SelectedContext) -> tuple:
|
||||
"""
|
||||
Formatea el contexto para la API de Anthropic.
|
||||
|
||||
Returns:
|
||||
Tuple de (system_prompt, messages)
|
||||
"""
|
||||
system_parts = []
|
||||
messages = []
|
||||
|
||||
for item in context.items:
|
||||
if item.source in [ContextSource.MEMORY, ContextSource.KNOWLEDGE,
|
||||
ContextSource.AMBIENT, ContextSource.DATASET]:
|
||||
system_parts.append(item.content)
|
||||
elif item.source == ContextSource.HISTORY:
|
||||
role = item.metadata.get("role", "user")
|
||||
messages.append({
|
||||
"role": role,
|
||||
"content": item.content
|
||||
})
|
||||
|
||||
system_prompt = "\n\n".join(system_parts) if system_parts else None
|
||||
return system_prompt, messages
|
||||
|
||||
def send_message(
|
||||
self,
|
||||
message: str,
|
||||
context: SelectedContext = None,
|
||||
system_prompt: str = None,
|
||||
temperature: float = 1.0,
|
||||
**kwargs
|
||||
) -> ProviderResponse:
|
||||
"""Envía mensaje a Claude"""
|
||||
|
||||
# Formatear contexto
|
||||
context_system, context_messages = self.format_context(context) if context else (None, [])
|
||||
|
||||
# Combinar system prompts
|
||||
final_system = ""
|
||||
if system_prompt:
|
||||
final_system = system_prompt
|
||||
if context_system:
|
||||
final_system = f"{final_system}\n\n{context_system}" if final_system else context_system
|
||||
|
||||
# Construir mensajes
|
||||
messages = context_messages.copy()
|
||||
messages.append({"role": "user", "content": message})
|
||||
|
||||
# Llamar a la API
|
||||
response, latency_ms = self._measure_latency(
|
||||
self.client.messages.create,
|
||||
model=self.model,
|
||||
max_tokens=kwargs.get("max_tokens", self.max_tokens),
|
||||
system=final_system if final_system else anthropic.NOT_GIVEN,
|
||||
messages=messages,
|
||||
temperature=temperature
|
||||
)
|
||||
|
||||
return ProviderResponse(
|
||||
content=response.content[0].text,
|
||||
model=response.model,
|
||||
tokens_input=response.usage.input_tokens,
|
||||
tokens_output=response.usage.output_tokens,
|
||||
latency_ms=latency_ms,
|
||||
finish_reason=response.stop_reason,
|
||||
raw_response={
|
||||
"id": response.id,
|
||||
"type": response.type,
|
||||
"role": response.role
|
||||
}
|
||||
)
|
||||
85
src/providers/base.py
Normal file
85
src/providers/base.py
Normal file
@@ -0,0 +1,85 @@
|
||||
"""
|
||||
Clase base para proveedores de IA
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from dataclasses import dataclass, field
|
||||
from typing import List, Dict, Any, Optional
|
||||
import time
|
||||
|
||||
from ..models import SelectedContext
|
||||
|
||||
|
||||
@dataclass
|
||||
class ProviderResponse:
|
||||
"""Respuesta de un proveedor de IA"""
|
||||
content: str
|
||||
model: str
|
||||
tokens_input: int = 0
|
||||
tokens_output: int = 0
|
||||
latency_ms: int = 0
|
||||
finish_reason: str = "stop"
|
||||
raw_response: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
|
||||
class BaseProvider(ABC):
|
||||
"""
|
||||
Clase base para adaptadores de proveedores de IA.
|
||||
|
||||
Cada proveedor debe implementar:
|
||||
- send_message(): Enviar mensaje y recibir respuesta
|
||||
- format_context(): Formatear contexto al formato del proveedor
|
||||
"""
|
||||
|
||||
provider_name: str = "base"
|
||||
|
||||
def __init__(self, api_key: str = None, model: str = None, **kwargs):
|
||||
self.api_key = api_key
|
||||
self.model = model
|
||||
self.extra_config = kwargs
|
||||
|
||||
@abstractmethod
|
||||
def send_message(
|
||||
self,
|
||||
message: str,
|
||||
context: SelectedContext = None,
|
||||
system_prompt: str = None,
|
||||
**kwargs
|
||||
) -> ProviderResponse:
|
||||
"""
|
||||
Envía un mensaje al modelo y retorna la respuesta.
|
||||
|
||||
Args:
|
||||
message: Mensaje del usuario
|
||||
context: Contexto seleccionado
|
||||
system_prompt: Prompt de sistema adicional
|
||||
**kwargs: Parámetros adicionales del modelo
|
||||
|
||||
Returns:
|
||||
ProviderResponse con la respuesta
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def format_context(self, context: SelectedContext) -> List[Dict[str, str]]:
|
||||
"""
|
||||
Formatea el contexto al formato de mensajes del proveedor.
|
||||
|
||||
Args:
|
||||
context: Contexto seleccionado
|
||||
|
||||
Returns:
|
||||
Lista de mensajes en el formato del proveedor
|
||||
"""
|
||||
pass
|
||||
|
||||
def estimate_tokens(self, text: str) -> int:
|
||||
"""Estimación simple de tokens (4 caracteres por token)"""
|
||||
return len(text) // 4
|
||||
|
||||
def _measure_latency(self, func, *args, **kwargs):
|
||||
"""Mide la latencia de una función"""
|
||||
start = time.time()
|
||||
result = func(*args, **kwargs)
|
||||
latency_ms = int((time.time() - start) * 1000)
|
||||
return result, latency_ms
|
||||
141
src/providers/ollama.py
Normal file
141
src/providers/ollama.py
Normal file
@@ -0,0 +1,141 @@
|
||||
"""
|
||||
Adaptador para Ollama (modelos locales)
|
||||
"""
|
||||
|
||||
import os
|
||||
import requests
|
||||
from typing import List, Dict, Any, Optional
|
||||
|
||||
from .base import BaseProvider, ProviderResponse
|
||||
from ..models import SelectedContext, ContextSource
|
||||
|
||||
|
||||
class OllamaProvider(BaseProvider):
|
||||
"""Proveedor para modelos locales via Ollama"""
|
||||
|
||||
provider_name = "ollama"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model: str = "llama3",
|
||||
host: str = None,
|
||||
port: int = None,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__(model=model, **kwargs)
|
||||
|
||||
self.host = host or os.getenv("OLLAMA_HOST", "localhost")
|
||||
self.port = port or int(os.getenv("OLLAMA_PORT", "11434"))
|
||||
self.model = model
|
||||
self.base_url = f"http://{self.host}:{self.port}"
|
||||
|
||||
def format_context(self, context: SelectedContext) -> List[Dict[str, str]]:
|
||||
"""
|
||||
Formatea el contexto para Ollama.
|
||||
|
||||
Returns:
|
||||
Lista de mensajes en formato Ollama
|
||||
"""
|
||||
messages = []
|
||||
system_parts = []
|
||||
|
||||
for item in context.items:
|
||||
if item.source in [ContextSource.MEMORY, ContextSource.KNOWLEDGE,
|
||||
ContextSource.AMBIENT, ContextSource.DATASET]:
|
||||
system_parts.append(item.content)
|
||||
elif item.source == ContextSource.HISTORY:
|
||||
role = item.metadata.get("role", "user")
|
||||
messages.append({
|
||||
"role": role,
|
||||
"content": item.content
|
||||
})
|
||||
|
||||
if system_parts:
|
||||
messages.insert(0, {
|
||||
"role": "system",
|
||||
"content": "\n\n".join(system_parts)
|
||||
})
|
||||
|
||||
return messages
|
||||
|
||||
def send_message(
|
||||
self,
|
||||
message: str,
|
||||
context: SelectedContext = None,
|
||||
system_prompt: str = None,
|
||||
temperature: float = 0.7,
|
||||
**kwargs
|
||||
) -> ProviderResponse:
|
||||
"""Envía mensaje a Ollama"""
|
||||
|
||||
# Formatear contexto
|
||||
messages = self.format_context(context) if context else []
|
||||
|
||||
# Añadir system prompt
|
||||
if system_prompt:
|
||||
if messages and messages[0]["role"] == "system":
|
||||
messages[0]["content"] = f"{system_prompt}\n\n{messages[0]['content']}"
|
||||
else:
|
||||
messages.insert(0, {"role": "system", "content": system_prompt})
|
||||
|
||||
# Añadir mensaje del usuario
|
||||
messages.append({"role": "user", "content": message})
|
||||
|
||||
# Llamar a la API
|
||||
url = f"{self.base_url}/api/chat"
|
||||
payload = {
|
||||
"model": self.model,
|
||||
"messages": messages,
|
||||
"stream": False,
|
||||
"options": {
|
||||
"temperature": temperature
|
||||
}
|
||||
}
|
||||
|
||||
response, latency_ms = self._measure_latency(
|
||||
requests.post,
|
||||
url,
|
||||
json=payload,
|
||||
timeout=120
|
||||
)
|
||||
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
# Ollama no siempre retorna conteos de tokens
|
||||
tokens_input = data.get("prompt_eval_count", self.estimate_tokens(str(messages)))
|
||||
tokens_output = data.get("eval_count", self.estimate_tokens(data["message"]["content"]))
|
||||
|
||||
return ProviderResponse(
|
||||
content=data["message"]["content"],
|
||||
model=data.get("model", self.model),
|
||||
tokens_input=tokens_input,
|
||||
tokens_output=tokens_output,
|
||||
latency_ms=latency_ms,
|
||||
finish_reason=data.get("done_reason", "stop"),
|
||||
raw_response={
|
||||
"total_duration": data.get("total_duration"),
|
||||
"load_duration": data.get("load_duration"),
|
||||
"prompt_eval_duration": data.get("prompt_eval_duration"),
|
||||
"eval_duration": data.get("eval_duration")
|
||||
}
|
||||
)
|
||||
|
||||
def list_models(self) -> List[str]:
|
||||
"""Lista los modelos disponibles en Ollama"""
|
||||
response = requests.get(f"{self.base_url}/api/tags")
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
return [m["name"] for m in data.get("models", [])]
|
||||
|
||||
def pull_model(self, model_name: str):
|
||||
"""Descarga un modelo en Ollama"""
|
||||
response = requests.post(
|
||||
f"{self.base_url}/api/pull",
|
||||
json={"name": model_name},
|
||||
stream=True
|
||||
)
|
||||
response.raise_for_status()
|
||||
for line in response.iter_lines():
|
||||
if line:
|
||||
yield line.decode("utf-8")
|
||||
120
src/providers/openai.py
Normal file
120
src/providers/openai.py
Normal file
@@ -0,0 +1,120 @@
|
||||
"""
|
||||
Adaptador para OpenAI (GPT)
|
||||
"""
|
||||
|
||||
import os
|
||||
from typing import List, Dict, Any, Optional
|
||||
|
||||
from .base import BaseProvider, ProviderResponse
|
||||
from ..models import SelectedContext, ContextSource
|
||||
|
||||
try:
|
||||
import openai
|
||||
HAS_OPENAI = True
|
||||
except ImportError:
|
||||
HAS_OPENAI = False
|
||||
|
||||
|
||||
class OpenAIProvider(BaseProvider):
|
||||
"""Proveedor para modelos de OpenAI (GPT)"""
|
||||
|
||||
provider_name = "openai"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
api_key: str = None,
|
||||
model: str = "gpt-4",
|
||||
max_tokens: int = 4096,
|
||||
base_url: str = None,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__(api_key=api_key, model=model, **kwargs)
|
||||
|
||||
if not HAS_OPENAI:
|
||||
raise ImportError("openai no está instalado. Ejecuta: pip install openai")
|
||||
|
||||
self.api_key = api_key or os.getenv("OPENAI_API_KEY")
|
||||
self.model = model
|
||||
self.max_tokens = max_tokens
|
||||
self.client = openai.OpenAI(
|
||||
api_key=self.api_key,
|
||||
base_url=base_url
|
||||
)
|
||||
|
||||
def format_context(self, context: SelectedContext) -> List[Dict[str, str]]:
|
||||
"""
|
||||
Formatea el contexto para la API de OpenAI.
|
||||
|
||||
Returns:
|
||||
Lista de mensajes en formato OpenAI
|
||||
"""
|
||||
messages = []
|
||||
system_parts = []
|
||||
|
||||
for item in context.items:
|
||||
if item.source in [ContextSource.MEMORY, ContextSource.KNOWLEDGE,
|
||||
ContextSource.AMBIENT, ContextSource.DATASET]:
|
||||
system_parts.append(item.content)
|
||||
elif item.source == ContextSource.HISTORY:
|
||||
role = item.metadata.get("role", "user")
|
||||
messages.append({
|
||||
"role": role,
|
||||
"content": item.content
|
||||
})
|
||||
|
||||
# Insertar system message al inicio
|
||||
if system_parts:
|
||||
messages.insert(0, {
|
||||
"role": "system",
|
||||
"content": "\n\n".join(system_parts)
|
||||
})
|
||||
|
||||
return messages
|
||||
|
||||
def send_message(
|
||||
self,
|
||||
message: str,
|
||||
context: SelectedContext = None,
|
||||
system_prompt: str = None,
|
||||
temperature: float = 1.0,
|
||||
**kwargs
|
||||
) -> ProviderResponse:
|
||||
"""Envía mensaje a GPT"""
|
||||
|
||||
# Formatear contexto
|
||||
messages = self.format_context(context) if context else []
|
||||
|
||||
# Añadir system prompt adicional
|
||||
if system_prompt:
|
||||
if messages and messages[0]["role"] == "system":
|
||||
messages[0]["content"] = f"{system_prompt}\n\n{messages[0]['content']}"
|
||||
else:
|
||||
messages.insert(0, {"role": "system", "content": system_prompt})
|
||||
|
||||
# Añadir mensaje del usuario
|
||||
messages.append({"role": "user", "content": message})
|
||||
|
||||
# Llamar a la API
|
||||
response, latency_ms = self._measure_latency(
|
||||
self.client.chat.completions.create,
|
||||
model=self.model,
|
||||
messages=messages,
|
||||
max_tokens=kwargs.get("max_tokens", self.max_tokens),
|
||||
temperature=temperature
|
||||
)
|
||||
|
||||
choice = response.choices[0]
|
||||
|
||||
return ProviderResponse(
|
||||
content=choice.message.content,
|
||||
model=response.model,
|
||||
tokens_input=response.usage.prompt_tokens,
|
||||
tokens_output=response.usage.completion_tokens,
|
||||
latency_ms=latency_ms,
|
||||
finish_reason=choice.finish_reason,
|
||||
raw_response={
|
||||
"id": response.id,
|
||||
"created": response.created,
|
||||
"system_fingerprint": response.system_fingerprint
|
||||
}
|
||||
)
|
||||
Reference in New Issue
Block a user