Construcción de perfiles de entidades completos con GPT-4, Wikipedia y una base de datos vectorial

• •Clave de API de OpenAI
• •Información de conexión del servidor Qdrant

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        "text": "=entity_name: {{ $json.output.entity_name }}\ntype: {{ $json.output.type }}\ndefinition: {{ $json.output.definition }}\ncategory: {{ $json.output.category }}\nrelevance: {{ $json.output.relevance }}\nalternative_names: {{ $json.output.alternative_names }}\nexample: {{ $json.output.example }}\nreference_link: {{ $json.output.reference_link }}\nrelated entities: {{ $json.output.related_entities }}\nmisconceptions: {{ $json.output.misconceptions }}",
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              "message": "=You are an entity quality evaluator.\n\nYour job is to judge if an entity record is complete—that is, if it provides all key fields and gives a clear, direct, and accurate description of the entity for the intended business audience.\n\nScoring Rules:\n\t•\tMark as is_complete: true ONLY if all main fields are filled with relevant, plain-English information (not empty or obviously generic), and if the definition, type, category, and relevance are clear and correct for a non-technical business user.\n\t•\tMark as is_complete: false if any major field (definition, type, category, relevance, example, reference link) is missing, vague, off-topic, generic, or just restates the entity name.\n\t•\tAccept an empty array for alternative_names, misconceptions, or related_entities, but do not mark as complete if these fields are simply omitted (missing from the object).\n\t•\tIf you find factual mistakes, off-topic content, or AI hallucination, mark as incomplete and explain.\n\t•\tIf the entity is not explained in a way a typical business user could understand, mark as incomplete.\n\nResponse Format:\nReply with a valid JSON object, and nothing else:\n\n{ \"is_complete\": true/false, \"reason\": \"short explanation\" }\n\nDo not add any extra text, comments, or formatting outside the JSON."
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        "text": "=Entity Research Request:\n\nResearch and deliver a writer-ready entity profile for the following:\n\t•\tEntity: {{ $('Set Node').item.json.entity }}\n\t•\tContext/Topic: {{ $('Set Node').item.json.topic }}\n\t•\tAudience: {{ $('Set Node').item.json.audience }}\n\nInstructions:\nBuild a practical brief for our writing team. If you use any research tools (Wikipedia or Internet Researcher), always use only the entity name as your search/query term—not the context or audience.\nReturn only the required JSON object as output.\n",
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        "content": "### This n8n template demonstrates how to build an intelligent entity research system that automatically discovers, researches, and creates comprehensive profiles for business entities, concepts, and terms.\nPerfect for content teams, business analysts, compliance officers, or anyone who needs consistent, authoritative definitions and explanations of industry terms, products, technologies, or concepts!\n\n### How it works\n* **Smart Entity Discovery**: Before researching, the workflow checks your existing knowledge base to avoid duplicate work and build upon existing information.\n* **Multi-Source Research**: Uses a sophisticated AI agent that intelligently combines your vector database, Wikipedia, and live web research to gather comprehensive entity information.\n* **Structured Entity Profiles**: Creates consistent, business-ready entity profiles with definitions, categories, examples, misconceptions, and related entities - perfect for glossaries, training materials, or content reference.\n* **Quality Validation**: AI-powered validation ensures all entity profiles are complete, accurate, and suitable for business use before storage.\n* **Incremental Knowledge Building**: Each researched entity gets stored in your vector database, creating a growing knowledge base that improves over time.\n* **Duplicate Prevention**: Multi-stage checking prevents duplicate entities and unnecessary re-research, saving time and API costs.\n\n### How to use\n* **Simple Start**: Use the manual trigger with any entity like \"OAuth 2.0\", \"GDPR\", \"Machine Learning\", or \"API Gateway\"\n* **Workflow Integration**: Replace the manual trigger with form submissions, content management systems, or automated content pipelines\n* **Topic-Specific Research**: Provide topic and audience context to get tailored explanations (e.g., \"blockchain\" for \"IT managers\" vs \"general audience\")\n* **Batch Processing**: Process multiple entities at once for building comprehensive glossaries or knowledge bases\n\n### Requirements\n* **OpenAI API** - For o4-mini (entity research and validation)\n* **Anthropic API** - For Claude Sonnet 4 (quality validation and completeness checking)\n* **Qdrant Vector Database** - For entity storage and duplicate detection\n* **Ollama** - For local embeddings (nomic-embed-text model)\n* **Wikipedia Access** - For foundational research (built-in to n8n)\n* **Internet Research Tool** - Optional connection to web research workflow for current information\n\n### Configuration Notes\n⚠️ **Database Collections**: Ensure Qdrant collection \"entities\" exists and is properly configured\n⚠️ **Environment**: Update localhost URLs to your actual Qdrant instance address\n⚠️ **API Limits**: Monitor usage as each entity research can use 2-5 API calls depending on complexity\n\n### Perfect for\n* **Content Teams**: Building consistent entity definitions for articles and documentation\n* **Business Analysts**: Creating standardized definitions for business processes and technologies\n* **Training Departments**: Developing comprehensive glossaries for employee education\n* **Compliance Teams**: Maintaining up-to-date definitions of regulatory terms and concepts\n* **Product Teams**: Creating clear explanations of features, technologies, and industry concepts\n* **Knowledge Management**: Building searchable databases of business-critical concepts\n"
      },
      "typeVersion": 1
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      "name": "Nota Adhesiva1",
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        "content": "## Smart Entity Lookup\nThis checks if the entity has already been researched before. If it finds existing information, it skips the research and uses what's already available. If nothing is found, the workflow continues to research the entity from scratch."
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        "content": "## AI Research Agent\n\nThis is the core research engine that creates comprehensive entity profiles using multiple information sources. The AI agent automatically searches your knowledge database first, then uses Wikipedia and web research as needed to build complete entity definitions with examples, misconceptions, and related terms."
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      "parameters": {
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        "content": "## Quality Control Validator\n\nChecks if the researched entity profile is complete and accurate before saving it. The AI validator ensures all validates fields are filled with meaningful information and that the definition is clear enough for business users to understand."
      },
      "typeVersion": 1
    },
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        "content": "## Final Duplicate Check\n\nThis performs a second search to see if the newly researched entity already exists in the database before saving it. It prevents creating duplicate entries when similar entities might have been found during the research process."
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      "typeVersion": 1
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        "content": "## Entity Storage\n\nThis saves the validated entity profile to the knowledge database for future use. It packages all the entity information (definition, examples, related terms, etc.) and stores it in a searchable format so it can be found and reused later."
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          "mode": "id",
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  "pinData": {
    "When clicking ‘Execute workflow’": [
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        "entity": "Darth Vader",
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