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Can Özcan"},"license":"ISC","homepage":"https://github.com/HidayetCanOzcan/VorionSDK#readme","keywords":["pair","vorion","llm","typescript","api","sdk","ai","websockets"],"repository":{"type":"git","url":"git+https://github.com/HidayetCanOzcan/VorionSDK.git"},"description":"A comprehensive TypeScript SDK for interacting with the VORION Rag & LLM  Apis, supporting both REST and WebSocket interfaces.","maintainers":[{"name":"hidayetcanozcan","email":"hidayetcan@gmail.com"}],"readme":"# Vorion SDK\r\n\r\nVorion SDK is a comprehensive TypeScript SDK for interacting with the VORION RAG & LLM APIs. It supports both REST and WebSocket interfaces and provides an extensible solution for server-side applications.\r\n\r\n## Features\r\n\r\n- Interact with VORION RAG (Retrieval-Augmented Generation) API\r\n- Interact with VORION LLM (Language Model) API\r\n- WebSocket support for real-time communication\r\n- TypeScript support for enhanced type safety and developer experience\r\n- Extensible Elysia-based server creation for server-side applications\r\n- Dapr integration for advanced distributed system capabilities\r\n- Rich type definitions for improved code quality and autocompletion\r\n\r\n## Installation\r\n\r\n```bash\r\nnpm install vorion-sdk\r\n```\r\n\r\n## Usage\r\n\r\n### Importing the SDK\r\n\r\n```typescript\r\nimport {\r\n  VorionRAGSDK,\r\n  VorionLLMSDK,\r\n  VorionWebSocket,\r\n  createVorionServer,\r\n} from \"vorion-sdk\";\r\n```\r\n\r\n### RAG API Usage\r\n\r\n```typescript\r\nconst ragSdk = new VorionRAGSDK(\"https://your-rag-api-base-url.com\");\r\n\r\n// Example: Embedding documents\r\nconst embedResult = await ragSdk.embed({\r\n  documents: [\"Your document content here\"],\r\n  embedder_name: EmbedderOptions.Azure,\r\n});\r\n\r\n// Example: Ingesting documents\r\nconst ingestResult = await ragSdk.ingest({\r\n  data_sources: [\r\n    {\r\n      type: \"txt\",\r\n      target: \"path/to/your/file.txt\",\r\n      metadata: { subject: \"Example\" },\r\n      parameters: {},\r\n    },\r\n  ],\r\n  embedder_name: EmbedderOptions.Azure,\r\n  vectorstore_name: VectorStoreOptions.Elastic,\r\n  collection_name: \"your_collection_name\",\r\n  preferred_splitter_type: SplitterTypeOptions.Recursive,\r\n});\r\n\r\n// Example: Retrieving documents\r\nconst retrieveResult = await ragSdk.retrieve({\r\n  embedder_name: EmbedderOptions.Azure,\r\n  vectorstore_name: VectorStoreOptions.Elastic,\r\n  collection_name: \"your_collection_name\",\r\n  query: \"Your search query\",\r\n  k: 5,\r\n});\r\n```\r\n\r\n### LLM API Usage\r\n\r\n```typescript\r\nconst llmSdk = new VorionLLMSDK(\"https://your-llm-api-base-url.com\");\r\n\r\n// Example: Making a prediction\r\nconst predictResult = await llmSdk.predict({\r\n  conversation_state_key: \"unique_conversation_id\",\r\n  prompt: {\r\n    text: \"Your prompt text here\",\r\n    sensitive_info: false,\r\n  },\r\n  llm_name: LLMOptions.OpenAI,\r\n  llm_group_name: LLMGroupNameOptions[\"gpt-4\"],\r\n  memory_strategy_name: MemoryStrategyOptions.FullSummarize,\r\n  memory_type: MemoryOptions.Redis,\r\n  load_balancer_strategy_name:\r\n    LoadBalanceStrategyOptions.DynamicWeightedRoundRobin,\r\n});\r\n\r\n// Example: Using Dapr for prediction\r\nimport { DaprClient } from \"@dapr/dapr\";\r\n\r\nconst daprClient = new DaprClient();\r\nconst predictResultWithDapr = await llmSdk.predict(\r\n  {\r\n    // ... prediction request parameters ...\r\n  },\r\n  true, // Set to true to use Dapr\r\n  daprClient\r\n);\r\n\r\n// Example: Using a basic AI agent\r\nconst agentResult = await llmSdk.agentBasic({\r\n  team_id: \"your_team_id\",\r\n  assistant_name: \"Assistant\",\r\n  assistant_sys_message: \"You are a helpful assistant.\",\r\n  task: \"Your task description\",\r\n  conversation_state_key: \"unique_conversation_id\",\r\n  llm_name: LLMOptions.OpenAI,\r\n  llm_group_name: LLMGroupNameOptions[\"gpt-4\"],\r\n  load_balancer_strategy_name: LoadBalanceStrategyOptions.RoundRobin,\r\n});\r\n```\r\n\r\n### WebSocket Usage\r\n\r\n```typescript\r\nconst socket = VorionWebSocket(\r\n  \"wss://your-websocket-url.com\",\r\n  \"unique-session-id.c\"\r\n);\r\n\r\nsocket.on(VorionEvents.PREDICTION_COMPLETE, (payload) => {\r\n  // console.log('Prediction complete:', payload);\r\n});\r\n\r\nsocket.on(VorionEvents.INGEST_DOCUMENTS_SUCCEEDED, (payload) => {\r\n  // console.log('Document ingestion succeeded:', payload);\r\n});\r\n```\r\n\r\n### Server Creation\r\n\r\n```typescript\r\nimport { createVorionServer, DaprEvents } from \"vorion-sdk\";\r\n\r\nconst { start, app } = createVorionServer({\r\n  port: 3000,\r\n  wsServerResponses: {\r\n    [DaprEvents.PREDICTION_COMPLETE]: async (data) => {\r\n      // Handle prediction completion\r\n      return {\r\n        event: \"custom-prediction-event\",\r\n        payload: { result: data.answer },\r\n      };\r\n    },\r\n    // Other event handlers...\r\n  },\r\n  listenCallback: () => {\r\n    // console.log('Vorion Server started');\r\n  },\r\n});\r\n\r\n// Add custom routes or logic\r\napp.get(\"/custom-route\", () => \"Custom route response\");\r\n\r\n// Start the server\r\nstart();\r\n```\r\n\r\n## API Reference\r\n\r\nThe SDK provides methods corresponding to various API endpoints:\r\n\r\n### RAG SDK Methods\r\n\r\n- `embed`: Embed documents or queries\r\n- `aembed`: Asynchronous embedding\r\n- `ingest`: Ingest documents into the vector store\r\n- `aingest`: Asynchronous document ingestion\r\n- `ingestMultipart`: Ingest documents using multipart form data\r\n- `aingestMultipart`: Asynchronous multipart document ingestion\r\n- `queryIngestState`: Query the state of an ingestion task\r\n- `rollbackIngest`: Rollback a document ingestion\r\n- `load`: Load documents\r\n- `aload`: Asynchronous document loading\r\n- `retrieve`: Retrieve relevant documents\r\n\r\n### LLM SDK Methods\r\n\r\n- `predict`: Make a prediction using the LLM\r\n- `apredict`: Asynchronous prediction\r\n- `comparePredict`: Compare predictions from different models\r\n- `agentBasic`: Use a basic AI agent\r\n- `agentAbasic`: Asynchronous basic AI agent\r\n- `agentTeam`: Use a team of AI agents\r\n- `getHistory`: Retrieve conversation history\r\n- `getLlmConfig`: Get LLM configuration\r\n- `getFile`: Retrieve a specific file\r\n- `getAllFiles`: Retrieve all files for a conversation\r\n\r\n## Key Types and Enums\r\n\r\nThe SDK includes a rich set of types and enums for better type safety and autocompletion:\r\n\r\n### RAG Types\r\n\r\n- `EmbedRequest`, `EmbedResponse`: Types for embedding requests and responses\r\n- `IngestRequest`, `IngestResponse`: Types for document ingestion\r\n- `RetrieveRequest`, `RetrieveResponse`: Types for document retrieval\r\n- `LoadRequest`, `LoadResultModel`: Types for document loading\r\n\r\n### LLM Types\r\n\r\n- `PredictRequest`, `PredictResponse`: Types for LLM predictions\r\n- `AgentBasicRequest`, `AgentBasicResponse`: Types for basic agent interactions\r\n- `GetHistoryRequest`, `GetHistoryResponse`: Types for retrieving conversation history\r\n\r\n### Enums\r\n\r\n- `EmbedderOptions`: Options for embedding models (e.g., `Azure`, `OpenAI`)\r\n- `VectorStoreOptions`: Options for vector stores (e.g., `Redis`, `Chroma`)\r\n- `LLMOptions`: Options for language models (e.g., `Azure`, `OpenAI`, `Google`)\r\n- `LLMGroupNameOptions`: Options for LLM group names (e.g., `gpt-4`, `claude-3-5-sonnet`)\r\n- `LoadBalanceStrategyOptions`: Options for load balancing strategies\r\n- `MemoryOptions`: Options for memory types (e.g., `InMemory`, `Redis`)\r\n- `MemoryStrategyOptions`: Options for memory strategies (e.g., `FullSummarize`, `RemoveTop`)\r\n- `SplitterTypeOptions`: Options for text splitting methods\r\n\r\n### WebSocket Events\r\n\r\n- `VorionEvents`: Enum of WebSocket events (e.g., `PREDICTION_COMPLETE`, `INGEST_DOCUMENTS_SUCCEEDED`)\r\n\r\n### Server Types\r\n\r\n- `VorionServerParams`: Type for server creation parameters\r\n- `wsServerResponses`: Type for WebSocket server response handlers\r\n\r\n## Error Handling\r\n\r\nAll SDK methods return a promise that resolves to an object with the following structure:\r\n\r\n```typescript\r\n{\r\n  isSuccess: boolean;\r\n  errors?: ApiError;\r\n  response?: ResponseType;\r\n  code: number | null;\r\n  createdAt: Date;\r\n}\r\n```\r\n\r\nHandle potential errors by checking the `isSuccess` flag and the `errors` object.\r\n\r\n## Server Creation and Extension\r\n\r\nThe `createVorionServer` function allows you to create an Elysia-based server that can be extended to handle WebSocket events and add custom logic:\r\n\r\n```typescript\r\nconst { start, app } = createVorionServer({\r\n  port: 3000,\r\n  wsServerResponses: {\r\n    // Define event handlers here\r\n  },\r\n});\r\n\r\n// Add custom routes or logic\r\napp.get(\"/custom-route\", () => \"Custom route response\");\r\n\r\n// Start the server\r\nstart();\r\n```\r\n\r\n## License\r\n\r\nThis project is licensed under the ISC License.\r\n","readmeFilename":"README.md","bugs":{"url":"https://github.com/HidayetCanOzcan/VorionSDK/issues"}}