Mirror chat/completions from Python SDK; drop ML inference

.github/workflows/ci.yml

@@ -0,0 +1,30 @@
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  ci:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        node-version: [18.x, 20.x]
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Use Node.js ${{ matrix.node-version }}
+        uses: actions/setup-node@v4
+        with:
+          node-version: ${{ matrix.node-version }}
+          cache: npm
+
+      - run: npm ci
+
+      - run: npm run lint
+
+      - run: npm test
+
+      - run: npm run build

README.md

@@ -1,55 +1,123 @@
 # OpenGradient TypeScript SDK
 
-A TypeScript/JavaScript SDK for performing on-chain inference using the OpenGradient network. Run machine learning models and LLMs directly on the blockchain with robust transaction handling and retry mechanisms.
+A TypeScript/JavaScript SDK for performing LLM chat and completion via OpenGradient's TEE (Trusted Execution Environment) with [x402](https://x402.org) payment protocol support.
 
 ## Installation
 
 ```bash
 npm install opengradient-sdk
 ```
 
+## Requirements
+
+- Node.js 18+ (for global `fetch`)
+- A funded EVM wallet on Base (settlement happens in OPG on the Base network via [x402](https://x402.org))
+
 ## Quick Start
 
 ```typescript
-import { Client, InferenceMode, LLMInferenceMode } from 'opengradient-sdk';
+import { Client, TEE_LLM } from "opengradient-sdk";
 
-// Initialize the client
 const client = new Client({
-  privateKey: 'your-private-key'
+  privateKey: process.env.PRIVATE_KEY!, // EVM private key (with or without 0x prefix)
+});
+
+// Non-streaming chat
+const result = await client.llm.chat({
+  model: TEE_LLM.CLAUDE_3_5_HAIKU,
+  messages: [{ role: "user", content: "Hello!" }],
+  maxTokens: 100,
+});
+console.log(result.chatOutput?.content);
+console.log("payment hash:", result.paymentHash);
+```
+
+### Streaming chat
+
+```typescript
+import { Client, TEE_LLM } from "opengradient-sdk";
+
+const client = new Client({ privateKey: process.env.PRIVATE_KEY! });
+
+const stream = client.llm.chat({
+  model: TEE_LLM.CLAUDE_3_5_HAIKU,
+  messages: [{ role: "user", content: "Stream me a haiku." }],
+  stream: true,
+});
+
+for await (const chunk of stream) {
+  process.stdout.write(chunk.choices[0]?.delta.content ?? "");
+}
+```
+
+### Tool / function calling
+
+```typescript
+const result = await client.llm.chat({
+  model: TEE_LLM.GPT_4O,
+  messages: [{ role: "user", content: "What's the weather in Paris?" }],
+  tools: [
+    {
+      type: "function",
+      function: {
+        name: "get_weather",
+        description: "Get current weather for a city",
+        parameters: {
+          type: "object",
+          properties: { city: { type: "string" } },
+          required: ["city"],
+        },
+      },
+    },
+  ],
+});
+console.log(result.chatOutput?.tool_calls);
+```
+
+### Completion
+
+```typescript
+const result = await client.llm.completion({
+  model: TEE_LLM.CLAUDE_3_5_HAIKU,
+  prompt: "The capital of France is",
+  maxTokens: 20,
+});
+console.log(result.completionOutput);
+```
+
+## x402 Settlement Modes
+
+```typescript
+import { X402SettlementMode } from "opengradient-sdk";
+
+await client.llm.chat({
+  model: TEE_LLM.GPT_4O,
+  messages: [{ role: "user", content: "Hi" }],
+  x402SettlementMode: X402SettlementMode.SETTLE_BATCH, // default
 });
+```
+
+- `SETTLE` — records input/output hashes only (most privacy-preserving).
+- `SETTLE_METADATA` — records full model info, complete input/output, and metadata.
+- `SETTLE_BATCH` — aggregates multiple inferences into a single on-chain settlement (most cost-efficient, default).
 
-// Run LLM chat inference
-const [txHash, finishReason, response] = await client.llmChat(
-  'Qwen/Qwen2.5-72B-Instruct',
-  LLMInferenceMode.VANILLA,
-  [{ role: 'user', content: 'Hello!' }],
-  100 // max tokens
-);
-
-// Run general model inference
-const modelInput = {
-  num_input1: [1.0, 2.0, 3.0],
-  num_input2: 10,
-  str_input1: ["hello", "ONNXY"],
-  str_input2: " world"
-};
-
-const [txHash, output] = await client.infer(
-  "QmbUqS93oc4JTLMHwpVxsE39mhNxy6hpf6Py3r9oANr8aZ",
-  InferenceMode.VANILLA,
-  modelInput
-);
+## Development
+
+```bash
+npm install      # install deps
+npm run lint     # ESLint over src/
+npm test         # Jest unit tests
+npm run build    # tsc → dist/
+npm run format   # prettier --write
 ```
 
-## Features
+CI runs `lint`, `test`, and `build` on Node 18 and 20 — see `.github/workflows/ci.yml`.
 
-- On-chain ML model inference
-- LLM completion and chat interfaces
-- Support for vanilla, ZKML and TEE (Trusted Execution Environment) inference modes
-- Automatic transaction retry with configurable parameters
-- Built-in gas estimation and management
-- Tool calling support for LLM chat
+## Available models
 
-## Contributing
+See `TEE_LLM` for the supported models, including:
 
-We welcome contributions! Please check our contribution guidelines for more details.
+- `TEE_LLM.GPT_4O`, `TEE_LLM.GPT_4_1_2025_04_14`, `TEE_LLM.O4_MINI`
+- `TEE_LLM.CLAUDE_3_5_HAIKU`, `TEE_LLM.CLAUDE_3_7_SONNET`, `TEE_LLM.CLAUDE_4_0_SONNET`
+- `TEE_LLM.GEMINI_2_0_FLASH`, `TEE_LLM.GEMINI_2_5_FLASH`, `TEE_LLM.GEMINI_2_5_FLASH_LITE`, `TEE_LLM.GEMINI_2_5_PRO`
+- `TEE_LLM.GROK_2_1212`, `TEE_LLM.GROK_2_VISION_LATEST`, `TEE_LLM.GROK_3_BETA`, `TEE_LLM.GROK_3_MINI_BETA`, `TEE_LLM.GROK_4_1_FAST`, `TEE_LLM.GROK_4_1_FAST_NON_REASONING`

eslint.config.js

@@ -0,0 +1,29 @@
+const tseslint = require("@typescript-eslint/eslint-plugin");
+const tsparser = require("@typescript-eslint/parser");
+
+module.exports = [
+  {
+    ignores: ["dist/**", "node_modules/**", "examples/**"],
+  },
+  {
+    files: ["src/**/*.ts"],
+    languageOptions: {
+      parser: tsparser,
+      parserOptions: {
+        ecmaVersion: 2020,
+        sourceType: "module",
+      },
+    },
+    plugins: {
+      "@typescript-eslint": tseslint,
+    },
+    rules: {
+      ...tseslint.configs.recommended.rules,
+      "@typescript-eslint/no-unused-vars": [
+        "error",
+        { argsIgnorePattern: "^_", varsIgnorePattern: "^_" },
+      ],
+      "@typescript-eslint/no-explicit-any": "off",
+    },
+  },
+];

examples/llm_chat.ts

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+// Run a non-streaming chat completion against a TEE-hosted LLM through
+// OpenGradient with x402 payments.
+//
+// Run with: OG_PRIVATE_KEY=0x... npx ts-node examples/llm_chat.ts
+
+import { Client, TEE_LLM, X402SettlementMode } from "../src";
+
+async function main() {
+  const privateKey = process.env.OG_PRIVATE_KEY;
+  if (!privateKey) {
+    throw new Error("OG_PRIVATE_KEY environment variable is not set");
+  }
+
+  const client = new Client({ privateKey });
+
+  const messages = [
+    { role: "user", content: "What is Python?" },
+    { role: "assistant", content: "Python is a high-level programming language." },
+    { role: "user", content: "What makes it good for beginners?" },
+  ];
+
+  const result = await client.llm.chat({
+    model: TEE_LLM.GPT_4_1_2025_04_14,
+    messages,
+    x402SettlementMode: X402SettlementMode.SETTLE_METADATA,
+  });
+
+  console.log(`Response: ${result.chatOutput?.content}`);
+  console.log(`Payment hash: ${result.paymentHash}`);
+}
+
+main().catch((err) => {
+  console.error(err);
+  process.exit(1);
+});

examples/llm_chat_stream.ts

@@ -0,0 +1,38 @@
+// Stream a chat completion from a TEE-hosted LLM through OpenGradient
+// with x402 payments.
+//
+// Run with: OG_PRIVATE_KEY=0x... npx ts-node examples/llm_chat_stream.ts
+
+import { Client, TEE_LLM, X402SettlementMode } from "../src";
+
+async function main() {
+  const privateKey = process.env.OG_PRIVATE_KEY;
+  if (!privateKey) {
+    throw new Error("OG_PRIVATE_KEY environment variable is not set");
+  }
+
+  const client = new Client({ privateKey });
+
+  const messages = [
+    { role: "user", content: "Describe to me the 7 network layers?" },
+  ];
+
+  const stream = client.llm.chat({
+    model: TEE_LLM.GPT_4_1_2025_04_14,
+    messages,
+    x402SettlementMode: X402SettlementMode.SETTLE_METADATA,
+    stream: true,
+    maxTokens: 1000,
+  });
+
+  for await (const chunk of stream) {
+    const content = chunk.choices[0]?.delta.content;
+    if (content) process.stdout.write(content);
+  }
+  process.stdout.write("\n");
+}
+
+main().catch((err) => {
+  console.error(err);
+  process.exit(1);
+});