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It routes all LLM requests through a single API endpoint, simplifying access to multiple models with a unified API, centralized management of features like routing, caching, and guardrails, and built-in observability.\n\nUse the Apiary TypeScript SDK in your applications to easily send client requests to multiple LLM providers.\n\n## Supported Providers\n\n* Anthropic\n* Gemini\n* OpenAI\n\n## Setup\n\n### Prerequisites\nTo send LLM requests using the Apiary SDK, you must have a running Apiary instance set up\nin your AWS account. Follow the setup instructions in our [installation guide](https://github.com/Apiary-gateway/cli) to easily deploy Apiary's infrastructure using our CLI tool.\n\nThe deployment process will provide an `AiGatewayRestApiEndpoint` URL and API key that you can use\nto send LLM requests with the Apiary SDK.\n\n### Installation\n\nTo install the Apiary SDK, run the following command:\n```bash\nnpm install @apiary-gateway/sdk\n```\n\n### Usage\n\nImport the Apiary client and initialize an instance with your configuration details. \n\nCall the `chatCompletion` method with a prompt (required). \nA `threadID` may also be provided to include conversation context with the prompt. \nOptionally, specify the LLM provider and model to override your configuration details for these fields. \n\n```ts\nimport Apiary from '@apiary-gateway/sdk';\n\n// Initialize the Apiary client\nconst client = new Apiary({\n apiKey: '',\n baseUrl: 'https://.com',\n // Optionally include a userId to partition cache responses based on userId\n userId: '',\n // Optionally include a metadata object with your custom routing configuration groups\n metadata: {\n user-type: 'pro-users'\n },\n});\n\nconst main = async () => {\n const completion = await client.chatCompletion(\n // Example prompt\n 'Do all bees make honey?',\n // Optionally include a `threadID` to manage conversation context \n '1745267450008',\n // Optionally specify the provider and model to override your configuration defaults\n 'openai',\n 'gpt-4',\n );\n\n // Log the returned LLM response\n console.log(completion.response.text);\n\n // Log the threadID for the returned LLM response\n console.log(completion.threadID);\n}\n\nmain();\n```\n","readmeFilename":"README.md"}