vorion-sdk

Vorion SDK

Vorion 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.

Features

• Interact with VORION RAG (Retrieval-Augmented Generation) API
• Interact with VORION LLM (Language Model) API
• WebSocket support for real-time communication
• TypeScript support for enhanced type safety and developer experience
• Extensible Elysia-based server creation for server-side applications
• Dapr integration for advanced distributed system capabilities
• Rich type definitions for improved code quality and autocompletion

Installation

npm install vorion-sdk

Usage

Importing the SDK

import {
  VorionRAGSDK,
  VorionLLMSDK,
  VorionWebSocket,
  createVorionServer,
} from "vorion-sdk";

RAG API Usage

const ragSdk = new VorionRAGSDK("https://your-rag-api-base-url.com");

// Example: Embedding documents
const embedResult = await ragSdk.embed({
  documents: ["Your document content here"],
  embedder_name: EmbedderOptions.Azure,
});

// Example: Ingesting documents
const ingestResult = await ragSdk.ingest({
  data_sources: [
    {
      type: "txt",
      target: "path/to/your/file.txt",
      metadata: { subject: "Example" },
      parameters: {},
    },
  ],
  embedder_name: EmbedderOptions.Azure,
  vectorstore_name: VectorStoreOptions.Elastic,
  collection_name: "your_collection_name",
  preferred_splitter_type: SplitterTypeOptions.Recursive,
});

// Example: Retrieving documents
const retrieveResult = await ragSdk.retrieve({
  embedder_name: EmbedderOptions.Azure,
  vectorstore_name: VectorStoreOptions.Elastic,
  collection_name: "your_collection_name",
  query: "Your search query",
  k: 5,
});

LLM API Usage

const llmSdk = new VorionLLMSDK("https://your-llm-api-base-url.com");

// Example: Making a prediction
const predictResult = await llmSdk.predict({
  conversation_state_key: "unique_conversation_id",
  prompt: {
    text: "Your prompt text here",
    sensitive_info: false,
  },
  llm_name: LLMOptions.OpenAI,
  llm_group_name: LLMGroupNameOptions["gpt-4"],
  memory_strategy_name: MemoryStrategyOptions.FullSummarize,
  memory_type: MemoryOptions.Redis,
  load_balancer_strategy_name:
    LoadBalanceStrategyOptions.DynamicWeightedRoundRobin,
});

// Example: Using Dapr for prediction
import { DaprClient } from "@dapr/dapr";

const daprClient = new DaprClient();
const predictResultWithDapr = await llmSdk.predict(
  {
    // ... prediction request parameters ...
  },
  true, // Set to true to use Dapr
  daprClient
);

// Example: Using a basic AI agent
const agentResult = await llmSdk.agentBasic({
  team_id: "your_team_id",
  assistant_name: "Assistant",
  assistant_sys_message: "You are a helpful assistant.",
  task: "Your task description",
  conversation_state_key: "unique_conversation_id",
  llm_name: LLMOptions.OpenAI,
  llm_group_name: LLMGroupNameOptions["gpt-4"],
  load_balancer_strategy_name: LoadBalanceStrategyOptions.RoundRobin,
});

WebSocket Usage

const socket = VorionWebSocket(
  "wss://your-websocket-url.com",
  "unique-session-id.c"
);

socket.on(VorionEvents.PREDICTION_COMPLETE, (payload) => {
  // console.log('Prediction complete:', payload);
});

socket.on(VorionEvents.INGEST_DOCUMENTS_SUCCEEDED, (payload) => {
  // console.log('Document ingestion succeeded:', payload);
});

Server Creation

import { createVorionServer, DaprEvents } from "vorion-sdk";

const { start, app } = createVorionServer({
  port: 3000,
  wsServerResponses: {
    [DaprEvents.PREDICTION_COMPLETE]: async (data) => {
      // Handle prediction completion
      return {
        event: "custom-prediction-event",
        payload: { result: data.answer },
      };
    },
    // Other event handlers...
  },
  listenCallback: () => {
    // console.log('Vorion Server started');
  },
});

// Add custom routes or logic
app.get("/custom-route", () => "Custom route response");

// Start the server
start();

API Reference

The SDK provides methods corresponding to various API endpoints:

RAG SDK Methods

• embed: Embed documents or queries
• aembed: Asynchronous embedding
• ingest: Ingest documents into the vector store
• aingest: Asynchronous document ingestion
• ingestMultipart: Ingest documents using multipart form data
• aingestMultipart: Asynchronous multipart document ingestion
• queryIngestState: Query the state of an ingestion task
• rollbackIngest: Rollback a document ingestion
• load: Load documents
• aload: Asynchronous document loading
• retrieve: Retrieve relevant documents

LLM SDK Methods

• predict: Make a prediction using the LLM
• apredict: Asynchronous prediction
• comparePredict: Compare predictions from different models
• agentBasic: Use a basic AI agent
• agentAbasic: Asynchronous basic AI agent
• agentTeam: Use a team of AI agents
• getHistory: Retrieve conversation history
• getLlmConfig: Get LLM configuration
• getFile: Retrieve a specific file
• getAllFiles: Retrieve all files for a conversation

Key Types and Enums

The SDK includes a rich set of types and enums for better type safety and autocompletion:

RAG Types

• EmbedRequest, EmbedResponse: Types for embedding requests and responses
• IngestRequest, IngestResponse: Types for document ingestion
• RetrieveRequest, RetrieveResponse: Types for document retrieval
• LoadRequest, LoadResultModel: Types for document loading

LLM Types

• PredictRequest, PredictResponse: Types for LLM predictions
• AgentBasicRequest, AgentBasicResponse: Types for basic agent interactions
• GetHistoryRequest, GetHistoryResponse: Types for retrieving conversation history

Enums

• EmbedderOptions: Options for embedding models (e.g., Azure, OpenAI)
• VectorStoreOptions: Options for vector stores (e.g., Redis, Chroma)
• LLMOptions: Options for language models (e.g., Azure, OpenAI, Google)
• LLMGroupNameOptions: Options for LLM group names (e.g., gpt-4, claude-3-5-sonnet)
• LoadBalanceStrategyOptions: Options for load balancing strategies
• MemoryOptions: Options for memory types (e.g., InMemory, Redis)
• MemoryStrategyOptions: Options for memory strategies (e.g., FullSummarize, RemoveTop)
• SplitterTypeOptions: Options for text splitting methods

WebSocket Events

• VorionEvents: Enum of WebSocket events (e.g., PREDICTION_COMPLETE, INGEST_DOCUMENTS_SUCCEEDED)

Server Types

• VorionServerParams: Type for server creation parameters
• wsServerResponses: Type for WebSocket server response handlers

Error Handling

All SDK methods return a promise that resolves to an object with the following structure:

{
  isSuccess: boolean;
  errors?: ApiError;
  response?: ResponseType;
  code: number | null;
  createdAt: Date;
}

Handle potential errors by checking the isSuccess flag and the errors object.

Server Creation and Extension

The createVorionServer function allows you to create an Elysia-based server that can be extended to handle WebSocket events and add custom logic:

const { start, app } = createVorionServer({
  port: 3000,
  wsServerResponses: {
    // Define event handlers here
  },
});

// Add custom routes or logic
app.get("/custom-route", () => "Custom route response");

// Start the server
start();

License

This project is licensed under the ISC License.