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googleAI.embedder('gemini-embedding-001'),\n      },\n    ]),\n  ],\n  model: googleAI.model('gemini-2.5-flash'),\n});\n\n// Reference to a local vector database storing Genkit documentation\nconst indexer = devLocalIndexerRef('BobFacts');\nconst retriever = devLocalRetrieverRef('BobFacts');\n\nasync function main() {\n  // Add documents to the index. Only do it once.\n  await ai.index({\n    indexer: indexer,\n    documents: [\n      Document.fromText('Bob lives on the moon.'),\n      Document.fromText('Bob is 42 years old.'),\n      Document.fromText('Bob likes bananas.'),\n      Document.fromText('Bob has 11 cats.'),\n    ],\n  });\n\n  const question = 'How old is Bob?';\n\n  // Consistent API to retrieve most relevant documents based on semantic similarity to query\n  const docs = await ai.retrieve({\n    retriever: retriever,\n    query: question,\n  });\n\n  const result = await ai.generate({\n    prompt: `Use the provided context from the Genkit documentation to answer this query: ${question}`,\n    docs, // Pass retrieved documents to the model\n  });\n\n  console.log(result.text);\n}\n\nmain();\n```\n\nThe sources for this package are in the main [Genkit](https://github.com/genkit-ai/genkit) repo. Please file issues and pull requests against that repo.\n\nUsage information and reference details can be found in [official Genkit documentation](https://genkit.dev/docs/get-started/).\n\nLicense: Apache 2.0\n","readmeFilename":"README.md"}