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Technologies"},"license":"MIT","homepage":"https://wrtnlabs.io","keywords":["openai","chatgpt","anthropic","claude","ai","chatbot","nestia","swagger","openapi"],"repository":{"url":"git+https://github.com/wrtnlabs/agentica.git","type":"git"},"description":"[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/wrtnlabs/pg-vector-selector/blob/master/LICENSE) [![npm version](https://img.shields.io/npm/v/pg-vector-selector.svg)](https://www.npmjs.com/package/pg-vector-select","maintainers":[{"email":"samchon.github@gmail.com","name":"samchon"}],"readme":"# pg-vector-selector\n\n[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/wrtnlabs/pg-vector-selector/blob/master/LICENSE)\n[![npm version](https://img.shields.io/npm/v/pg-vector-selector.svg)](https://www.npmjs.com/package/pg-vector-selector)\n\nA library that significantly accelerates AI function selection through vector embeddings.\n\n## Overview\n\n`@agentica/pg-vector-selector` drastically improves function selection speed compared to traditional LLM-based methods. By leveraging vector embeddings and semantic similarity, it can identify the most appropriate functions for a given context multiple times faster than conventional approaches.\n\n```typescript\nimport { Agentica } from \"@agentica/core\";\nimport { AgenticaPgVectorSelector } from \"@agentica/pg-vector-selector\";\nimport typia from \"typia\";\n\n// Initialize with connector-hive server\nconst selectorExecute = AgenticaPgVectorSelector.boot<\"chatgpt\">(\n  \"https://your-connector-hive-server.com\"\n);\n\nconst agent = new Agentica({\n  model: \"chatgpt\",\n  vendor: {\n    model: \"gpt-4o-mini\",\n    api: new OpenAI({\n      apiKey: process.env.CHATGPT_API_KEY,\n    }),\n  },\n  controllers: [\n    await fetch(\n      \"https://shopping-be.wrtn.ai/editor/swagger.json\",\n    ).then(r => r.json()),\n    typia.llm.application<ShoppingCounselor>(),\n    typia.llm.application<ShoppingPolicy>(),\n    typia.llm.application<ShoppingSearchRag>(),\n  ],\n  config: {\n    executor: {\n      select: selectorExecute,\n    }\n  }\n});\nawait agent.conversate(\"I wanna buy MacBook Pro\");\n```\n\n## How to Use\n\n### Setup\n\n```bash\nnpm install @agentica/core @agentica/pg-vector-selector typia\nnpx typia setup\n```\n\nTo use pg-vector-selector, you need:\n\n1. A running [connector-hive](https://github.com/wrtnlabs/connector-hive) server\n2. `PostgreSQL` database connected to the `connector-hive` server\n3. pgvector extension installed in `PostgreSQL`\n\n### Initialization\n\nFirst, initialize the library with your connector-hive server:\n\n```typescript\nimport { AgenticaPgVectorSelector } from \"pg-vector-selector\";\n\nconst selectorExecute = AgenticaPgVectorSelector.boot<YourSchemaModel>(\n  \"https://your-connector-hive-server.com\"\n);\n```\n\n### Just apply Selector and Start conversation\n\nSelect the most appropriate functions for a given context:\n\n```typescript\nconst agent = new Agentica({\n  model: \"chatgpt\",\n  vendor: {\n    model: \"gpt-4o-mini\",\n    api: new OpenAI({\n      apiKey: process.env.CHATGPT_API_KEY,\n    }),\n  },\n  controllers: [\n    await fetch(\n      \"https://shopping-be.wrtn.ai/editor/swagger.json\",\n    ).then(r => r.json()),\n    typia.llm.application<ShoppingCounselor>(),\n    typia.llm.application<ShoppingPolicy>(),\n    typia.llm.application<ShoppingSearchRag>(),\n  ],\n  config: {\n    executor: {\n      select: selectorExecute,\n    }\n  }\n});\nawait agent.conversate(\"I wanna buy MacBook Pro\");\n```\n","readmeFilename":"README.md"}