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Add OpenGradient plugin (#451)
* Add OpenGradient plugin for on-chain ML and LLM inference Co-Authored-By: joyce@paella.dev <joyce@paella.dev> * Update OpenGradient service file to use secure credential handling Co-Authored-By: joyce@paella.dev <joyce@paella.dev> * Update pnpm-lock.yaml for OpenGradient plugin Co-Authored-By: joyce@paella.dev <joyce@paella.dev> * Update readme * Fix package * Add changeset --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: joyce@paella.dev <joyce@paella.dev> Co-authored-by: Agustin Armellini Fischer <armellini13@gmail.com>
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‎typescript/.changeset/tall-pants-turn.md

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---
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"@goat-sdk/plugin-opengradient": patch
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---
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Release package

‎typescript/packages/plugins/opengradient/README.md

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<div align="center">
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<a href="https://github.com/goat-sdk/goat">
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<img src="https://github.com/user-attachments/assets/5fc7f121-259c-492c-8bca-f15fe7eb830c" alt="GOAT" width="100px" height="auto" style="object-fit: contain;">
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</a>
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</div>
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# OpenGradient GOAT Plugin
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This plugin integrates the OpenGradient service with the GOAT SDK, providing on-chain ML model inference and LLM interactions.
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## Installation
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```bash
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npm install @goat-sdk/plugin-opengradient
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yarn add @goat-sdk/plugin-opengradient
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pnpm add @goat-sdk/plugin-opengradient
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```
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## Usage
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```typescript
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import { opengradient } from "@goat-sdk/plugin-opengradient";
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const tools = await getOnChainTools({
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wallet: viem(wallet),
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plugins: [
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opengradient(),
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],
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});
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```
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## Tools
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* `opengradient_model_inference` - Run inference on machine learning models using OpenGradient
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* `opengradient_llm_completion` - Generate text completions using LLMs through OpenGradient
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* `opengradient_llm_chat` - Interact with LLMs using a chat interface through OpenGradient
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<footer>
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<br/>
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<br/>
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<div>
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<a href="https://github.com/goat-sdk/goat">
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<img src="https://github.com/user-attachments/assets/59fa5ddc-9d47-4d41-a51a-64f6798f94bd" alt="GOAT" width="100%" height="auto" style="object-fit: contain; max-width: 800px;">
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</a>
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</div>
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</footer>

‎typescript/packages/plugins/opengradient/package.json

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{
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"name": "@goat-sdk/plugin-opengradient",
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"version": "0.1.0",
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"files": ["dist/**/*", "README.md", "package.json"],
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"scripts": {
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"build": "tsup",
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"clean": "rm -rf dist",
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"test": "vitest run --passWithNoTests"
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},
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"main": "./dist/index.js",
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"module": "./dist/index.mjs",
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"types": "./dist/index.d.ts",
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"sideEffects": false,
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"homepage": "https://ohmygoat.dev",
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"repository": {
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"type": "git",
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"url": "git+https://github.com/goat-sdk/goat.git"
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},
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"license": "MIT",
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"bugs": {
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"url": "https://github.com/goat-sdk/goat/issues"
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},
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"keywords": ["ai", "agents", "web3"],
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"dependencies": {
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"@goat-sdk/core": "workspace:*",
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"opengradient-sdk": "1.0.0",
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"web3": "4.16.0",
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"zod": "catalog:"
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},
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"peerDependencies": {
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"@goat-sdk/core": "workspace:*"
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}
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}

‎typescript/packages/plugins/opengradient/src/index.ts

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export * from "./opengradient.plugin";
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export * from "./parameters";
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export * from "./types";

‎typescript/packages/plugins/opengradient/src/opengradient.plugin.ts

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import { type Chain, PluginBase } from "@goat-sdk/core";
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import { OpengradientService } from "./opengradient.service";
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import { OpenGradientConfig } from "./types";
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export type OpengradientPluginCtorParams = {
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config: OpenGradientConfig;
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};
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export class OpengradientPlugin extends PluginBase {
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constructor({ config }: OpengradientPluginCtorParams) {
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super("opengradient", [new OpengradientService(config)]);
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}
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supportsChain = (_chain: Chain) => true;
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}
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export function opengradient(params: OpengradientPluginCtorParams) {
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return new OpengradientPlugin(params);
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}

‎typescript/packages/plugins/opengradient/src/opengradient.service.ts

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import { Tool } from "@goat-sdk/core";
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import { WalletClientBase } from "@goat-sdk/core";
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import { Client } from "opengradient-sdk";
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import { LLMChatParameters, LLMCompletionParameters, ModelInferenceParameters } from "./parameters";
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import { OpenGradientConfig } from "./types";
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export class OpengradientService {
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private client: Client | null = null;
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constructor(private config: OpenGradientConfig) {}
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private getClient(clientConfig: OpenGradientConfig): Client {
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if (!this.client) {
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this.client = new Client({
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privateKey: clientConfig.privateKey,
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});
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}
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return this.client;
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}
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@Tool({
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name: "opengradient_model_inference",
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description: "Run inference on a machine learning model using OpenGradient",
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})
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async runModelInference(walletClient: WalletClientBase, parameters: ModelInferenceParameters) {
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const client = this.getClient(this.config);
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const [txHash, modelOutput] = await client.infer(
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parameters.modelCid,
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parameters.inferenceMode,
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parameters.modelInput,
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);
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return {
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transactionHash: txHash,
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output: modelOutput,
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};
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}
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@Tool({
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name: "opengradient_llm_completion",
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description: "Generate text completions using an LLM through OpenGradient",
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})
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async runLLMCompletion(walletClient: WalletClientBase, parameters: LLMCompletionParameters) {
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const client = this.getClient(this.config);
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const [txHash, completion] = await client.llmCompletion(
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parameters.modelCid,
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parameters.inferenceMode,
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parameters.prompt,
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parameters.maxTokens,
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parameters.stopSequence,
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parameters.temperature,
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);
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return {
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transactionHash: txHash,
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completion,
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};
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}
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@Tool({
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name: "opengradient_llm_chat",
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description: "Interact with an LLM using a chat interface through OpenGradient",
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})
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async runLLMChat(walletClient: WalletClientBase, parameters: LLMChatParameters) {
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const client = this.getClient(this.config);
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const [txHash, finishReason, message] = await client.llmChat(
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parameters.modelCid,
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parameters.inferenceMode,
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parameters.messages,
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parameters.maxTokens,
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parameters.stopSequence,
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parameters.temperature,
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parameters.tools,
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parameters.toolChoice,
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);
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return {
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transactionHash: txHash,
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finishReason,
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message,
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};
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}
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}

‎typescript/packages/plugins/opengradient/src/parameters.ts

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import { createToolParameters } from "@goat-sdk/core";
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import { z } from "zod";
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export class ModelInferenceParameters extends createToolParameters(
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z.object({
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modelCid: z.string().describe("CID of the model to run inference with"),
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inferenceMode: z.number().describe("Inference mode to use (0 for VANILLA, 1 for TEE)"),
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modelInput: z.record(z.any()).describe("Input to the model in the form of a JSON object"),
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}),
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) {}
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export class LLMCompletionParameters extends createToolParameters(
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z.object({
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modelCid: z.string().describe("CID of the LLM model to use"),
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inferenceMode: z.number().describe("Inference mode to use (0 for VANILLA, 1 for TEE)"),
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prompt: z.string().describe("Text prompt for the LLM"),
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maxTokens: z.number().default(100).describe("Maximum number of tokens to generate"),
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stopSequence: z.array(z.string()).default([]).describe("Sequences that will stop generation if encountered"),
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temperature: z.number().default(0).describe("Temperature for sampling (0.0 to 1.0)"),
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}),
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) {}
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export class LLMChatParameters extends createToolParameters(
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z.object({
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modelCid: z.string().describe("CID of the LLM model to use"),
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inferenceMode: z.number().describe("Inference mode to use (0 for VANILLA, 1 for TEE)"),
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messages: z
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.array(
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z.object({
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role: z.enum(["system", "user", "assistant", "tool"]).describe("Role of the message sender"),
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content: z.string().describe("Content of the message"),
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toolCalls: z.array(z.any()).optional().describe("Tool calls made in this message"),
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toolCallId: z.string().optional().describe("ID of the tool call this message is responding to"),
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name: z.string().optional().describe("Name of the entity sending the message"),
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}),
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)
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.describe("Array of conversation messages"),
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maxTokens: z.number().default(100).describe("Maximum number of tokens to generate"),
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stopSequence: z.array(z.string()).default([]).describe("Sequences that will stop generation if encountered"),
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temperature: z.number().default(0).describe("Temperature for sampling (0.0 to 1.0)"),
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tools: z.array(z.any()).default([]).describe("Tools available to the model"),
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toolChoice: z.string().optional().describe("Tool choice strategy ('auto', 'none', or specific tool name)"),
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}),
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) {}
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export class ClientConfigParameters extends createToolParameters(
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z.object({
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privateKey: z.string().describe("Private key for authentication with OpenGradient"),
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}),
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) {}

‎typescript/packages/plugins/opengradient/src/types/index.ts

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export type OpenGradientConfig = {
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privateKey: string;
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};

‎typescript/packages/plugins/opengradient/tsconfig.json

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{
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"$schema": "https://json.schemastore.org/tsconfig",
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"extends": "../../../tsconfig.base.json",
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"include": ["src/**/*"],
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"exclude": ["node_modules", "dist"]
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}

‎typescript/packages/plugins/opengradient/tsup.config.ts

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import { defineConfig } from "tsup";
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import { treeShakableConfig } from "../../../tsup.config.base";
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export default defineConfig({
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...treeShakableConfig,
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});

‎typescript/packages/plugins/opengradient/turbo.json

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{
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"$schema": "https://turbo.build/schema.json",
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"extends": ["//"],
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"tasks": {
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"build": {
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"inputs": ["src/**", "tsup.config.ts", "!./**/*.test.{ts,tsx}", "tsconfig.json"],
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"dependsOn": ["^build"],
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"outputs": ["dist/**"]
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}
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}
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}

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