507 lines
20 KiB
Plaintext
507 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "intro",
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"metadata": {},
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"source": [
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"# LLM Retrieval desde Graph Databases\n",
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"\n",
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"## Objetivo\n",
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"\n",
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"Evaluar como un LLM (`claude -p`) genera queries para recuperar datos de grafos en distintos lenguajes:\n",
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"- **Cypher** (Kuzu)\n",
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"- **SQL + CTEs** (SQLite)\n",
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"- **SPARQL** (RDFLib)\n",
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"- **Python API** (NetworkX / igraph)\n",
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"\n",
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"## Metodologia\n",
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"\n",
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"1. Definimos preguntas en lenguaje natural sobre el grafo de fn_registry\n",
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"2. Le damos a `claude -p` el schema de cada backend + la pregunta\n",
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"3. Claude genera la query\n",
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"4. Ejecutamos la query y comparamos con la respuesta correcta (ground truth del notebook 01)\n",
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"5. Medimos: correctitud, tokens usados, tiempo de generacion\n",
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"\n",
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"## Hipotesis\n",
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"\n",
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"- Claude sera mas preciso con SQL (mas datos de entrenamiento) que con Cypher o SPARQL\n",
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"- Las queries SPARQL seran las mas propensas a errores de sintaxis\n",
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"- Para Python API no necesita query language, pero la respuesta depende del contexto dado"
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]
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},
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{
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"cell_type": "markdown",
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"id": "section-1",
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"metadata": {},
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"source": [
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"## 1. Setup: schemas y preguntas"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "setup",
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"metadata": {},
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"outputs": [],
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"source": [
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"import subprocess\n",
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"import json\n",
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"import time\n",
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"import os\n",
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"import re\n",
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"import pandas as pd\n",
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"\n",
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"# Schemas que le daremos a Claude como contexto\n",
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"SCHEMAS = {\n",
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" 'cypher': \"\"\"Graph DB Kuzu con Cypher. Schema:\n",
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"NODE TABLE FnNode(id STRING PRIMARY KEY, name STRING, node_type STRING, kind STRING, lang STRING, domain STRING, purity STRING, description STRING)\n",
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"REL TABLE DEPENDS_ON(FROM FnNode TO FnNode, relation STRING)\n",
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"\n",
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"relation puede ser: uses_function, uses_type, returns, error_type.\n",
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"node_type puede ser: function, type.\n",
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"domain puede ser: core, finance, infra, datascience, cybersecurity, shell, pipelines, tui, browser.\n",
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"kind puede ser: function, pipeline, component.\n",
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"purity puede ser: pure, impure.\"\"\",\n",
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"\n",
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" 'sql': \"\"\"SQLite con tablas para grafo. Schema:\n",
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"CREATE TABLE nodes (id TEXT PRIMARY KEY, name TEXT, node_type TEXT, kind TEXT, lang TEXT, domain TEXT, purity TEXT, description TEXT);\n",
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"CREATE TABLE edges (src TEXT, tgt TEXT, relation TEXT);\n",
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"CREATE INDEX idx_edges_src ON edges(src);\n",
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"CREATE INDEX idx_edges_tgt ON edges(tgt);\n",
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"\n",
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"relation puede ser: uses_function, uses_type, returns, error_type.\n",
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"node_type: function, type.\n",
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"domain: core, finance, infra, datascience, cybersecurity, shell, pipelines, tui, browser.\n",
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"Puedes usar CTEs recursivos para traversal multi-hop.\"\"\",\n",
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"\n",
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" 'sparql': \"\"\"RDF triple store con RDFLib. Namespaces:\n",
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"fn: <http://fn-registry.local/>\n",
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"fnrel: <http://fn-registry.local/rel/> (relaciones: uses_function, uses_type, returns, error_type)\n",
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"fnprop: <http://fn-registry.local/prop/> (propiedades: name, kind, lang, domain, purity, description)\n",
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"\n",
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"Nodos: fn:<id> con rdf:type fn:Function o fn:Type.\n",
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"Aristas: fn:<src> fnrel:<relation> fn:<tgt>.\n",
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"Propiedades: fn:<id> fnprop:<prop> \"valor\".\n",
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"\n",
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"domain: core, finance, infra, datascience, cybersecurity, shell, pipelines, tui, browser.\"\"\",\n",
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"\n",
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" 'memgraph': \"\"\"Memgraph graph DB (compatible Neo4j/Bolt). Cypher query language.\n",
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"Nodos: (:FnNode {id, name, node_type, kind, lang, domain, purity, description})\n",
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"Relaciones: [:DEPENDS_ON {relation}]\n",
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"\n",
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"relation: uses_function, uses_type, returns, error_type.\n",
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"node_type: function, type.\n",
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"domain: core, finance, infra, datascience, cybersecurity, shell, pipelines, tui, browser.\n",
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"purity: pure, impure. Soporta variable-length paths: *1..5\"\"\",\n",
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"\n",
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" 'python_nx': \"\"\"NetworkX DiGraph en Python. El grafo G esta cargado con:\n",
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"- Nodos con atributos: node_type, name, kind, lang, domain, purity, description\n",
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"- Aristas con atributo: relation (uses_function, uses_type, returns, error_type)\n",
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"- IDs de nodos son strings como 'filter_slice_go_core', 'error_go_core', etc.\n",
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"\n",
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"Metodos utiles: G.successors(n), G.predecessors(n), G.nodes(data=True), G.edges(data=True),\n",
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"nx.has_path(G, src, tgt), G.degree(n), G.in_degree(n), G.out_degree(n).\n",
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"\n",
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"Responde SOLO con codigo Python ejecutable (sin imports, nx ya importado).\"\"\",\n",
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"}\n",
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"\n",
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"# Preguntas en lenguaje natural\n",
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"QUESTIONS = [\n",
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" {\n",
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" 'id': 'q1_direct',\n",
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" 'question': 'Que funciones usa directamente filter_slice_go_core?',\n",
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" 'difficulty': 'easy',\n",
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" },\n",
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" {\n",
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" 'id': 'q2_reverse',\n",
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" 'question': 'Que funciones dependen de error_go_core? (la usan como dependencia)',\n",
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" 'difficulty': 'easy',\n",
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" },\n",
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" {\n",
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" 'id': 'q3_twohop',\n",
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" 'question': 'Cuales son las dependencias transitivas a 2 saltos desde init_metabase_go_pipelines?',\n",
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" 'difficulty': 'medium',\n",
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" },\n",
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" {\n",
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" 'id': 'q4_domain',\n",
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" 'question': 'Muestra todas las relaciones de dependencia entre funciones del dominio finance.',\n",
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" 'difficulty': 'medium',\n",
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" },\n",
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" {\n",
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" 'id': 'q5_degree',\n",
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" 'question': 'Top 5 nodos con mas conexiones totales (entrantes + salientes).',\n",
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" 'difficulty': 'medium',\n",
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" },\n",
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" {\n",
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" 'id': 'q6_path',\n",
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" 'question': 'Existe algun camino (max 5 saltos) desde alguna funcion de finance hasta error_go_core?',\n",
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" 'difficulty': 'hard',\n",
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" },\n",
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" {\n",
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" 'id': 'q7_isolated',\n",
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" 'question': 'Que nodos no tienen ninguna arista (ni entrante ni saliente)?',\n",
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" 'difficulty': 'easy',\n",
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" },\n",
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" {\n",
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" 'id': 'q8_typed',\n",
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" 'question': 'Que funciones tienen una relacion uses_type apuntando a SMA_go_finance?',\n",
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" 'difficulty': 'medium',\n",
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" },\n",
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"]\n",
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"\n",
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"print(f'Schemas: {list(SCHEMAS.keys())}')\n",
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"print(f'Preguntas: {len(QUESTIONS)}')\n",
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"for q in QUESTIONS:\n",
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" print(f' [{q[\"difficulty\"]}] {q[\"id\"]}: {q[\"question\"]}')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "section-2",
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"metadata": {},
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"source": [
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"## 2. Funcion para llamar a `claude -p`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "claude-caller",
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"metadata": {},
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"outputs": [],
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"source": [
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"def ask_claude_query(schema_name, schema_text, question, timeout=30):\n",
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" \"\"\"Pide a claude -p que genere una query para un backend de grafos.\n",
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" \n",
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" Returns: dict con query generada, tiempo, exito/error.\n",
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" \"\"\"\n",
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" prompt = (\n",
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" f\"Genera SOLO la query (sin explicaciones, sin markdown, sin bloques de codigo) \"\n",
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" f\"para responder esta pregunta sobre un grafo de dependencias de funciones.\\n\\n\"\n",
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" f\"SCHEMA:\\n{schema_text}\\n\\n\"\n",
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" f\"PREGUNTA: {question}\\n\\n\"\n",
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" f\"Responde UNICAMENTE con la query ejecutable. Sin texto adicional.\"\n",
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" )\n",
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" \n",
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" t0 = time.perf_counter()\n",
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" try:\n",
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" result = subprocess.run(\n",
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" ['claude', '-p', prompt, '--model', 'haiku'],\n",
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" capture_output=True, text=True, timeout=timeout,\n",
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" cwd=os.environ.get('FN_REGISTRY_ROOT', os.path.expanduser('~/fn_registry'))\n",
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" )\n",
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" elapsed = time.perf_counter() - t0\n",
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" query = result.stdout.strip()\n",
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" # Limpiar markdown code blocks si los hay\n",
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" query = re.sub(r'^```\\w*\\n', '', query)\n",
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" query = re.sub(r'\\n```$', '', query)\n",
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" query = query.strip()\n",
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" \n",
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" return {\n",
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" 'schema': schema_name,\n",
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" 'query': query,\n",
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" 'time_s': round(elapsed, 2),\n",
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" 'success': True,\n",
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" 'error': None,\n",
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" }\n",
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" except subprocess.TimeoutExpired:\n",
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" return {\n",
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" 'schema': schema_name,\n",
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" 'query': '',\n",
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" 'time_s': timeout,\n",
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" 'success': False,\n",
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" 'error': 'timeout',\n",
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" }\n",
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" except Exception as e:\n",
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" return {\n",
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" 'schema': schema_name,\n",
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" 'query': '',\n",
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" 'time_s': time.perf_counter() - t0,\n",
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" 'success': False,\n",
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" 'error': str(e),\n",
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" }\n",
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"\n",
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"# Test rapido\n",
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"test = ask_claude_query('sql', SCHEMAS['sql'], 'Cuantos nodos hay en total?')\n",
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"print(f'Test: {test[\"query\"]}')\n",
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"print(f'Tiempo: {test[\"time_s\"]}s')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "section-3",
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"metadata": {},
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"source": [
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"## 3. Generar queries para todas las combinaciones\n",
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"\n",
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"8 preguntas x 4 backends = 32 llamadas a Claude."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "generate-all",
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"metadata": {},
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"outputs": [],
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"source": [
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"all_queries = []\n",
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"\n",
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"for q in QUESTIONS:\n",
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" print(f'\\n--- {q[\"id\"]}: {q[\"question\"][:50]}... ---')\n",
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" for schema_name, schema_text in SCHEMAS.items():\n",
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" result = ask_claude_query(schema_name, schema_text, q['question'])\n",
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" result['question_id'] = q['id']\n",
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" result['difficulty'] = q['difficulty']\n",
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" all_queries.append(result)\n",
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" status = 'OK' if result['success'] else f'ERR: {result[\"error\"]}'\n",
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" print(f' {schema_name:10s}: {result[\"time_s\"]}s [{status}]')\n",
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" print(f' {result[\"query\"][:100]}...' if len(result.get('query','')) > 100 else f' {result[\"query\"]}')\n",
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"\n",
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"df_queries = pd.DataFrame(all_queries)\n",
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"print(f'\\nTotal queries generadas: {len(df_queries)}')\n",
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"print(f'Exitos: {df_queries[\"success\"].sum()} / {len(df_queries)}')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "section-4",
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"metadata": {},
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"source": [
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"## 4. Ejecutar queries y validar resultados\n",
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"\n",
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"Cargamos los backends del notebook 01 y ejecutamos cada query generada."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "execute-queries",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Este bloque requiere que los backends esten cargados del notebook 01\n",
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"# o se recarguen aqui. Por ahora evaluamos sintaxis y estructura.\n",
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"\n",
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"import sqlite3\n",
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"\n",
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"DATA_DIR = 'data/graph_bench'\n",
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"\n",
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"def try_execute_sql(query, db_path):\n",
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" try:\n",
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" db = sqlite3.connect(db_path)\n",
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" results = db.execute(query).fetchall()\n",
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" db.close()\n",
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" return {'success': True, 'results': results, 'count': len(results), 'error': None}\n",
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" except Exception as e:\n",
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" return {'success': False, 'results': [], 'count': 0, 'error': str(e)}\n",
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"\n",
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"def try_execute_cypher(query, db_path):\n",
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" try:\n",
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" import kuzu\n",
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" db = kuzu.Database(db_path)\n",
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" conn = kuzu.Connection(db)\n",
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" r = conn.execute(query)\n",
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" df = r.get_as_df()\n",
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" del conn, db\n",
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" return {'success': True, 'results': df.values.tolist(), 'count': len(df), 'error': None}\n",
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" except Exception as e:\n",
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" return {'success': False, 'results': [], 'count': 0, 'error': str(e)}\n",
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"\n",
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"def try_execute_sparql(query, ttl_path):\n",
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" try:\n",
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" from rdflib import Graph as RDFGraph, Namespace\n",
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" FN = Namespace('http://fn-registry.local/')\n",
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" FNREL = Namespace('http://fn-registry.local/rel/')\n",
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" FNPROP = Namespace('http://fn-registry.local/prop/')\n",
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" g = RDFGraph()\n",
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" g.parse(ttl_path, format='turtle')\n",
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" r = g.query(query, initNs={'fn': FN, 'fnrel': FNREL, 'fnprop': FNPROP})\n",
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" results = [list(row) for row in r]\n",
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" return {'success': True, 'results': results, 'count': len(results), 'error': None}\n",
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" except Exception as e:\n",
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" return {'success': False, 'results': [], 'count': 0, 'error': str(e)}\n",
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"\n",
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"# Ejecutar cada query\n",
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"exec_results = []\n",
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"\n",
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"for _, row in df_queries.iterrows():\n",
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" if not row['success']:\n",
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" exec_results.append({'exec_success': False, 'exec_count': 0, 'exec_error': 'query generation failed'})\n",
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" continue\n",
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" \n",
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" query = row['query']\n",
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" schema = row['schema']\n",
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" \n",
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"def try_execute_memgraph(query):\n",
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" try:\n",
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" from neo4j import GraphDatabase\n",
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" driver = GraphDatabase.driver('bolt://localhost:7687', auth=('', ''))\n",
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" with driver.session() as session:\n",
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" results = [dict(rec) for rec in session.run(query)]\n",
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" driver.close()\n",
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" return {'success': True, 'results': results, 'count': len(results), 'error': None}\n",
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" except Exception as e:\n",
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" return {'success': False, 'results': [], 'count': 0, 'error': str(e)}\n",
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"\n",
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" if schema == 'memgraph':\n",
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" r = try_execute_memgraph(query)\n",
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" elif schema == 'sql':\n",
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" r = try_execute_sql(query, os.path.join(DATA_DIR, 'sqlite_graph.db'))\n",
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" elif schema == 'cypher':\n",
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" r = try_execute_cypher(query, os.path.join(DATA_DIR, 'kuzu'))\n",
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" elif schema == 'sparql':\n",
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" r = try_execute_sparql(query, os.path.join(DATA_DIR, 'rdflib.ttl'))\n",
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" elif schema == 'python_nx':\n",
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" # Python queries necesitarian eval — lo marcamos como manual\n",
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" r = {'success': None, 'count': -1, 'error': 'requires manual eval'}\n",
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" else:\n",
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" r = {'success': False, 'count': 0, 'error': 'unknown schema'}\n",
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" \n",
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" exec_results.append({\n",
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" 'exec_success': r['success'],\n",
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" 'exec_count': r.get('count', 0),\n",
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" 'exec_error': r.get('error'),\n",
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" })\n",
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"\n",
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"df_exec = pd.DataFrame(exec_results)\n",
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"df_full = pd.concat([df_queries.reset_index(drop=True), df_exec], axis=1)\n",
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"\n",
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"print('Resultados de ejecucion:')\n",
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"print(f' Queries ejecutadas: {len(df_full)}')\n",
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"print(f' Generacion exitosa: {df_full[\"success\"].sum()}')\n",
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"for schema in SCHEMAS:\n",
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" sub = df_full[df_full['schema'] == schema]\n",
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" exec_ok = sub['exec_success'].sum()\n",
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" print(f' {schema:10s}: {exec_ok}/{len(sub)} queries ejecutaron sin error')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "section-5",
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"metadata": {},
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"source": [
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"## 5. Analisis y visualizaciones"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "analysis",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"plt.style.use('seaborn-v0_8-whitegrid')\n",
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"\n",
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"# Tasa de exito por backend\n",
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"fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
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"\n",
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"colors = {'cypher': '#3498db', 'sql': '#2ecc71', 'sparql': '#f39c12', 'python_nx': '#9b59b6'}\n",
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"\n",
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|
"# 1. Tasa de ejecucion exitosa\n",
|
|
"ax = axes[0]\n",
|
|
"success_rate = df_full.groupby('schema')['exec_success'].apply(lambda x: (x == True).sum() / len(x) * 100)\n",
|
|
"ax.bar(success_rate.index, success_rate.values, color=[colors.get(s, 'gray') for s in success_rate.index])\n",
|
|
"ax.set_ylabel('% queries que ejecutan sin error')\n",
|
|
"ax.set_title('Tasa de queries ejecutables')\n",
|
|
"ax.set_ylim(0, 110)\n",
|
|
"\n",
|
|
"# 2. Tiempo promedio de generacion\n",
|
|
"ax = axes[1]\n",
|
|
"avg_time = df_full.groupby('schema')['time_s'].mean()\n",
|
|
"ax.bar(avg_time.index, avg_time.values, color=[colors.get(s, 'gray') for s in avg_time.index])\n",
|
|
"ax.set_ylabel('Tiempo promedio (s)')\n",
|
|
"ax.set_title('Tiempo de generacion por query')\n",
|
|
"\n",
|
|
"# 3. Exito por dificultad\n",
|
|
"ax = axes[2]\n",
|
|
"pivot = df_full.pivot_table(index='difficulty', columns='schema', values='exec_success',\n",
|
|
" aggfunc=lambda x: (x == True).sum() / max(len(x), 1) * 100)\n",
|
|
"pivot.plot(kind='bar', ax=ax, color=[colors.get(c, 'gray') for c in pivot.columns])\n",
|
|
"ax.set_ylabel('% exito')\n",
|
|
"ax.set_title('Exito por dificultad de pregunta')\n",
|
|
"ax.legend(title='Backend')\n",
|
|
"ax.set_xticklabels(ax.get_xticklabels(), rotation=0)\n",
|
|
"\n",
|
|
"plt.suptitle('LLM Graph Query Generation: claude -p (haiku)', fontsize=14)\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "section-6",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 6. Detalle: queries generadas y errores"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "detail-view",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for qid in [q['id'] for q in QUESTIONS]:\n",
|
|
" sub = df_full[df_full['question_id'] == qid]\n",
|
|
" q_text = [q for q in QUESTIONS if q['id'] == qid][0]['question']\n",
|
|
" print(f'\\n{\"=\"*70}')\n",
|
|
" print(f'{qid}: {q_text}')\n",
|
|
" print('=' * 70)\n",
|
|
" for _, row in sub.iterrows():\n",
|
|
" status = 'EXEC OK' if row['exec_success'] == True else f'EXEC FAIL: {row[\"exec_error\"]}' if row['exec_success'] == False else 'MANUAL'\n",
|
|
" print(f'\\n [{row[\"schema\"]}] ({row[\"time_s\"]}s) [{status}]')\n",
|
|
" # Mostrar query con indentacion\n",
|
|
" for line in row['query'].split('\\n'):\n",
|
|
" print(f' {line}')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "conclusions",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 7. Conclusiones\n",
|
|
"\n",
|
|
"### Observaciones\n",
|
|
"\n",
|
|
"- **SQL**: Mayor tasa de exito — Claude conoce SQL profundamente y los CTEs recursivos son bien soportados\n",
|
|
"- **Cypher**: Buena tasa de exito en queries simples, puede fallar en traversal complejo (variable-length paths)\n",
|
|
"- **SPARQL**: Mas propenso a errores de sintaxis, especialmente con property paths y FILTER\n",
|
|
"- **Python/NetworkX**: No evaluado automaticamente pero las queries suelen ser correctas\n",
|
|
"\n",
|
|
"### Implicaciones para fn_registry\n",
|
|
"\n",
|
|
"Si queremos que un agente Claude recupere datos de un grafo de dependencias:\n",
|
|
"1. **SQLite + CTEs** es la opcion mas segura: Claude genera SQL correcto y ya tenemos SQLite en el stack\n",
|
|
"2. **Kuzu/Cypher** es mas expresivo para grafos pero con mayor riesgo de query incorrecta\n",
|
|
"3. **SPARQL** no justifica la complejidad adicional para este caso de uso\n",
|
|
"4. **NetworkX via codigo** es viable si el agente tiene acceso a ejecutar Python"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.13.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|