diff --git a/.env b/.env new file mode 100644 index 0000000..da1d86d --- /dev/null +++ b/.env @@ -0,0 +1 @@ +OPENROUTER_API_KEY=your-api-key-here diff --git a/README.md b/README.md index 63dfcba..5f25f90 100644 --- a/README.md +++ b/README.md @@ -55,6 +55,38 @@ This list is not a definitive list of models supported by OpenRouter, as it cons You can find the latest list of tool-supported models supported by OpenRouter [here](https://openrouter.ai/models?order=newest&supported_parameters=tools). (Note: This list may contain models that are not compatible with the AI SDK.) +For detailed information about specific models, including text generation capabilities and advanced usage examples, see our [Models Documentation](./docs/models.md). + +### 0G Compute Network Models + +The OpenRouter provider now supports models from the [0G Compute Network](https://docs.0g.ai/0g-compute/for-developers/inference-sdk), a decentralized AI inference network with verified computation capabilities: + +| Model | Provider Address | Description | Verification | +|-------|------------------|-------------|--------------| +| `0g/llama-3.3-70b-instruct` | `0xf07240Efa67755B5311bc75784a061eDB47165Dd` | State-of-the-art 70B parameter model for general AI tasks | TEE (TeeML) | +| `0g/deepseek-r1-70b` | `0x3feE5a4dd5FDb8a32dDA97Bed899830605dBD9D3` | Advanced reasoning model optimized for complex problem solving | TEE (TeeML) | + +These models run on the 0G Compute Network and provide verified AI inference through Trusted Execution Environments (TEE). For detailed text generation capabilities and examples, see the [Models Documentation](./docs/models.md#0g-compute-network-models). + +#### Example Usage with 0G Compute Models + +```typescript +import { openrouter } from '@openrouter/ai-sdk-provider'; +import { generateText } from 'ai'; + +// Using the Llama 3.3 70B model from 0G Compute Network +const { text } = await generateText({ + model: openrouter('0g/llama-3.3-70b-instruct'), + prompt: 'Explain quantum computing in simple terms.', +}); + +// Using the DeepSeek R1 70B reasoning model +const { text: reasoning } = await generateText({ + model: openrouter('0g/deepseek-r1-70b'), + prompt: 'Solve this step by step: If a train travels 120 km in 2 hours, what is its average speed?', +}); +``` + ## Passing Extra Body to OpenRouter There are 3 ways to pass extra body to OpenRouter: diff --git a/bun.lock b/bun.lock new file mode 100644 index 0000000..6eacfa1 --- /dev/null +++ b/bun.lock @@ -0,0 +1,431 @@ +{ + "lockfileVersion": 1, + 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Provider. + +## Overview + +The OpenRouter provider gives you access to over 300 large language models through the OpenRouter API. You can find the complete and up-to-date list of models at [openrouter.ai/models](https://openrouter.ai/models). + +## 0G Compute Network Models + +The OpenRouter provider supports models from the [0G Compute Network](https://docs.0g.ai/0g-compute/for-developers/inference-sdk), a decentralized AI inference network with verified computation capabilities. + +### Available Models + +#### Llama 3.3 70B Instruct (`0g/llama-3.3-70b-instruct`) + +- **Provider Address**: `0xf07240Efa67755B5311bc75784a061eDB47165Dd` +- **Parameters**: 70 billion +- **Type**: Instruction-tuned language model +- **Verification**: TEE (Trusted Execution Environment) via TeeML +- **Best For**: General AI tasks, conversation, instruction following, creative writing + +**Text Generation Capabilities:** + +- High-quality conversational responses +- Code generation and explanation +- Creative writing (stories, poems, scripts) +- Educational content and explanations +- Problem-solving and analysis +- Multi-language support + +**Example Usage:** + +```typescript +import { openrouter } from '@openrouter/ai-sdk-provider'; +import { generateText } from 'ai'; + +// General conversation +const { text } = await generateText({ + model: openrouter('0g/llama-3.3-70b-instruct'), + prompt: 'Explain the concept of machine learning to a 10-year-old.', +}); + +// Code generation +const { text: code } = await generateText({ + model: openrouter('0g/llama-3.3-70b-instruct'), + prompt: 'Write a Python function to calculate the factorial of a number.', +}); + +// Creative writing +const { text: story } = await generateText({ + model: openrouter('0g/llama-3.3-70b-instruct'), + prompt: 'Write a short science fiction story about AI and humans working together.', +}); +``` + +#### DeepSeek R1 70B (`0g/deepseek-r1-70b`) + +- **Provider Address**: `0x3feE5a4dd5FDb8a32dDA97Bed899830605dBD9D3` +- **Parameters**: 70 billion +- **Type**: Advanced reasoning model +- **Verification**: TEE (Trusted Execution Environment) via TeeML +- **Best For**: Complex problem solving, mathematical reasoning, logical analysis + +**Text Generation Capabilities:** + +- Step-by-step reasoning and problem solving +- Mathematical computations and proofs +- Logical analysis and deduction +- Scientific explanations with detailed reasoning +- Code debugging and optimization +- Complex question answering with reasoning chains + +**Example Usage:** + +```typescript +import { openrouter } from '@openrouter/ai-sdk-provider'; +import { generateText } from 'ai'; + +// Mathematical reasoning +const { text } = await generateText({ + model: openrouter('0g/deepseek-r1-70b'), + prompt: 'Solve this step by step: If a train travels 120 km in 2 hours, what is its average speed in km/h and m/s?', +}); + +// Logical problem solving +const { text: logic } = await generateText({ + model: openrouter('0g/deepseek-r1-70b'), + prompt: 'Three friends have different ages. Alice is older than Bob but younger than Charlie. If their ages sum to 60 and are consecutive integers, what are their ages?', +}); + +// Code analysis and debugging +const { text: analysis } = await generateText({ + model: openrouter('0g/deepseek-r1-70b'), + prompt: `Analyze this code and explain why it might be inefficient: + + def find_duplicates(arr): + duplicates = [] + for i in range(len(arr)): + for j in range(i+1, len(arr)): + if arr[i] == arr[j] and arr[i] not in duplicates: + duplicates.append(arr[i]) + return duplicates`, +}); +``` + +### Model Configuration Options + +Both 0G Compute models support all standard OpenRouter configuration options: + +```typescript +import { openrouter } from '@openrouter/ai-sdk-provider'; +import { generateText } from 'ai'; + +const { text } = await generateText({ + model: openrouter('0g/llama-3.3-70b-instruct', { + // Control randomness (0.0 = deterministic, 1.0 = very random) + temperature: 0.7, + + // Maximum tokens to generate + maxTokens: 500, + + // Stop generation at these sequences + stop: ['\n\n', '###'], + + // Nucleus sampling parameter + topP: 0.9, + + // Frequency penalty to reduce repetition + frequencyPenalty: 0.1, + + // Presence penalty to encourage new topics + presencePenalty: 0.1, + }), + prompt: 'Write a technical explanation of blockchain technology.', +}); +``` + +### Streaming Text Generation + +Both models support streaming for real-time text generation: + +```typescript +import { openrouter } from '@openrouter/ai-sdk-provider'; +import { streamText } from 'ai'; + +const { textStream } = await streamText({ + model: openrouter('0g/llama-3.3-70b-instruct'), + prompt: 'Write a detailed explanation of quantum computing.', +}); + +for await (const delta of textStream) { + process.stdout.write(delta); +} +``` + +### Verification and Trust + +All 0G Compute Network models run in Trusted Execution Environments (TEE) using TeeML technology, which provides: + +- **Computational Integrity**: Cryptographic proof that computations were performed correctly +- **Data Privacy**: Input and output data is protected during processing +- **Transparency**: Verifiable execution without revealing sensitive information +- **Decentralization**: Distributed across multiple independent providers + +### Performance Characteristics + +| Model | Latency | Throughput | Context Length | Best Use Cases | +|-------|---------|------------|----------------|----------------| +| `0g/llama-3.3-70b-instruct` | Medium | High | 8K tokens | General chat, creative writing, code generation | +| `0g/deepseek-r1-70b` | Medium-High | Medium | 8K tokens | Complex reasoning, math, analysis, debugging | + +### Pricing and Availability + +0G Compute Network models are available through OpenRouter's standard pricing model. Check [openrouter.ai/models](https://openrouter.ai/models) for current pricing information. + +The decentralized nature of the 0G network often provides competitive pricing compared to centralized alternatives while maintaining high availability through distributed infrastructure. + +## Other Supported Models + +For a complete list of all supported models including OpenAI, Anthropic, Google, Meta, and other providers, visit [openrouter.ai/models](https://openrouter.ai/models). + +Popular model families include: + +- **OpenAI**: GPT-4, GPT-3.5, GPT-4 Turbo +- **Anthropic**: Claude 3.5 Sonnet, Claude 3 Haiku, Claude 3 Opus +- **Google**: Gemini Pro, Gemini Flash +- **Meta**: Llama 2, Llama 3, Code Llama +- **Mistral**: Mistral 7B, Mixtral 8x7B, Mistral Large +- **And many more...** + +## Tool Support + +Many models support function calling and tool use. Check the [tool-supported models list](https://openrouter.ai/models?order=newest&supported_parameters=tools) for models compatible with the AI SDK's tool functionality. + +Both 0G Compute models support tool calling for enhanced functionality: + +```typescript +import { openrouter } from '@openrouter/ai-sdk-provider'; +import { generateText, tool } from 'ai'; +import { z } from 'zod'; + +const { text } = await generateText({ + model: openrouter('0g/llama-3.3-70b-instruct'), + prompt: 'What is the weather like in San Francisco?', + tools: { + getWeather: tool({ + description: 'Get the current weather for a location', + parameters: z.object({ + location: z.string().describe('The city and state'), + }), + execute: async ({ location }) => { + // Implementation would call a weather API + return `The weather in ${location} is sunny and 72°F`; + }, + }), + }, +}); +``` diff --git a/examples/0g-compute-example.ts b/examples/0g-compute-example.ts new file mode 100644 index 0000000..fe47d84 --- /dev/null +++ b/examples/0g-compute-example.ts @@ -0,0 +1,68 @@ +/** + * Example usage of 0G Compute Network models with OpenRouter AI SDK Provider + * + * This example demonstrates how to use the 0G Compute Network models + * through the OpenRouter provider for the Vercel AI SDK. + */ + +import { openrouter } from '@openrouter/ai-sdk-provider'; +import { generateText } from 'ai'; + +async function main() { + // Example 1: Using Llama 3.3 70B Instruct model from 0G Compute Network + console.log('🦙 Testing 0G Llama 3.3 70B Instruct model...'); + + try { + const { text } = await generateText({ + model: openrouter('0g/llama-3.3-70b-instruct'), + prompt: 'Explain quantum computing in simple terms for a beginner.', + }); + + console.log('Response from 0G Llama 3.3 70B:'); + console.log(text); + console.log('\n' + '='.repeat(80) + '\n'); + } catch (error) { + console.error('Error with 0G Llama model:', error); + } + + // Example 2: Using DeepSeek R1 70B reasoning model from 0G Compute Network + console.log('🧠 Testing 0G DeepSeek R1 70B reasoning model...'); + + try { + const { text } = await generateText({ + model: openrouter('0g/deepseek-r1-70b'), + prompt: 'Solve this step by step: If a train travels 120 km in 2 hours, what is its average speed in km/h and m/s?', + }); + + console.log('Response from 0G DeepSeek R1 70B:'); + console.log(text); + console.log('\n' + '='.repeat(80) + '\n'); + } catch (error) { + console.error('Error with 0G DeepSeek model:', error); + } + + // Example 3: Using 0G models with custom settings + console.log('⚙️ Testing 0G model with custom settings...'); + + try { + const { text } = await generateText({ + model: openrouter('0g/llama-3.3-70b-instruct', { + temperature: 0.7, + maxTokens: 150, + }), + prompt: 'Write a short poem about artificial intelligence and decentralization.', + }); + + console.log('Response from 0G Llama with custom settings:'); + console.log(text); + } catch (error) { + console.error('Error with custom settings:', error); + } +} + +// Run the examples +if (require.main === module) { + main().catch(console.error); +} + +export { main }; diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 0000000..23e38e9 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,60 @@ +# OpenRouter AI SDK Provider Examples + +This directory contains example usage of the OpenRouter AI SDK Provider with various models and configurations. + +## 0G Compute Network Examples + +### `0g-compute-example.ts` + +Basic introduction to using the 0G Compute Network models through the OpenRouter provider: + +- **0G Llama 3.3 70B Instruct**: State-of-the-art 70B parameter model for general AI tasks +- **0G DeepSeek R1 70B**: Advanced reasoning model optimized for complex problem solving + +### `text-generation-0g.ts` + +Comprehensive text generation examples showcasing the full capabilities of 0G Compute models: + +- **Creative Writing**: Short stories, poetry, dialogue generation +- **Technical Explanations**: Complex concepts explained simply +- **Code Generation**: Python, JavaScript, and other programming languages +- **Reasoning & Problem Solving**: Mathematical problems, logic puzzles, code debugging +- **Streaming Generation**: Real-time text streaming +- **Multi-turn Conversations**: Context-aware dialogue + +Both models run on the 0G Compute Network and provide verified AI inference through Trusted Execution Environments (TEE). + +### Running the Examples + +1. Install dependencies: + + ```bash + npm install + ``` + +2. Set your OpenRouter API key: + + ```bash + export OPENROUTER_API_KEY="your-api-key-here" + ``` + +3. Run the examples: + + ```bash + # Basic 0G Compute example + npx tsx examples/0g-compute-example.ts + + # Comprehensive text generation examples + npx tsx examples/text-generation-0g.ts + ``` + +### About 0G Compute Network + +The 0G Compute Network is a decentralized AI inference network that provides: + +- **Verified Computation**: All inference runs in Trusted Execution Environments (TEE) +- **Decentralized Infrastructure**: Distributed across multiple providers +- **Cost-Effective**: Competitive pricing through decentralized competition +- **High Performance**: State-of-the-art models with fast inference + +Learn more at [0G Compute Documentation](https://docs.0g.ai/0g-compute/for-developers/inference-sdk). diff --git a/examples/text-generation-0g.ts b/examples/text-generation-0g.ts new file mode 100644 index 0000000..9e86eea --- /dev/null +++ b/examples/text-generation-0g.ts @@ -0,0 +1,248 @@ +/** + * Text Generation Examples with 0G Compute Network Models + * + * This file demonstrates various text generation capabilities of 0G Compute models + * including creative writing, technical explanations, code generation, and reasoning. + */ + +import { openrouter } from '@openrouter/ai-sdk-provider'; +import { generateText, streamText } from 'ai'; + +// Configuration for different text generation scenarios +const MODELS = { + LLAMA: '0g/llama-3.3-70b-instruct', + DEEPSEEK: '0g/deepseek-r1-70b', +} as const; + +/** + * Creative Writing Examples + */ +async function creativeWritingExamples() { + console.log('🎨 Creative Writing Examples\n'); + + // Short story generation + console.log('📚 Generating a short story...'); + const { text: story } = await generateText({ + model: openrouter(MODELS.LLAMA), + prompt: 'Write a short science fiction story (300 words) about an AI that discovers emotions for the first time.', + temperature: 0.8, + maxTokens: 400, + }); + console.log(story); + console.log('\n' + '='.repeat(80) + '\n'); + + // Poetry generation + console.log('🎭 Generating a poem...'); + const { text: poem } = await generateText({ + model: openrouter(MODELS.LLAMA), + prompt: 'Write a haiku about the beauty of decentralized technology and human connection.', + temperature: 0.7, + maxTokens: 100, + }); + console.log(poem); + console.log('\n' + '='.repeat(80) + '\n'); + + // Dialogue generation + console.log('💬 Generating dialogue...'); + const { text: dialogue } = await generateText({ + model: openrouter(MODELS.LLAMA), + prompt: 'Write a dialogue between a curious child and a wise AI about what makes humans special.', + temperature: 0.6, + maxTokens: 300, + }); + console.log(dialogue); + console.log('\n' + '='.repeat(80) + '\n'); +} + +/** + * Technical Explanation Examples + */ +async function technicalExplanationExamples() { + console.log('🔬 Technical Explanation Examples\n'); + + // Complex concept explanation + console.log('🧠 Explaining quantum computing...'); + const { text: quantum } = await generateText({ + model: openrouter(MODELS.LLAMA), + prompt: 'Explain quantum computing in simple terms, using analogies that a high school student would understand.', + temperature: 0.3, + maxTokens: 400, + }); + console.log(quantum); + console.log('\n' + '='.repeat(80) + '\n'); + + // Blockchain explanation + console.log('⛓️ Explaining blockchain technology...'); + const { text: blockchain } = await generateText({ + model: openrouter(MODELS.LLAMA), + prompt: 'Explain how blockchain technology works, focusing on decentralization and trust mechanisms.', + temperature: 0.2, + maxTokens: 350, + }); + console.log(blockchain); + console.log('\n' + '='.repeat(80) + '\n'); +} + +/** + * Code Generation Examples + */ +async function codeGenerationExamples() { + console.log('💻 Code Generation Examples\n'); + + // Python function + console.log('🐍 Generating Python code...'); + const { text: pythonCode } = await generateText({ + model: openrouter(MODELS.LLAMA), + prompt: 'Write a Python class for a simple blockchain implementation with methods to add blocks, validate the chain, and calculate proof of work.', + temperature: 0.1, + maxTokens: 500, + }); + console.log(pythonCode); + console.log('\n' + '='.repeat(80) + '\n'); + + // JavaScript function + console.log('🟨 Generating JavaScript code...'); + const { text: jsCode } = await generateText({ + model: openrouter(MODELS.LLAMA), + prompt: 'Write a JavaScript function that implements a simple neural network with forward propagation for binary classification.', + temperature: 0.1, + maxTokens: 400, + }); + console.log(jsCode); + console.log('\n' + '='.repeat(80) + '\n'); +} + +/** + * Reasoning and Problem Solving Examples + */ +async function reasoningExamples() { + console.log('🧮 Reasoning and Problem Solving Examples\n'); + + // Mathematical reasoning + console.log('📐 Solving a math problem...'); + const { text: mathSolution } = await generateText({ + model: openrouter(MODELS.DEEPSEEK), + prompt: 'Solve this step by step: A company\'s profit increases by 15% each year. If they made $100,000 in profit in 2020, what will their profit be in 2025? Show all calculations.', + temperature: 0.1, + maxTokens: 300, + }); + console.log(mathSolution); + console.log('\n' + '='.repeat(80) + '\n'); + + // Logic puzzle + console.log('🧩 Solving a logic puzzle...'); + const { text: logicSolution } = await generateText({ + model: openrouter(MODELS.DEEPSEEK), + prompt: 'Solve this logic puzzle: Five friends (Alice, Bob, Charlie, Diana, Eve) sit in a row. Alice is not at either end. Bob is somewhere to the left of Charlie. Diana is next to Eve. Charlie is not next to Alice. What is the seating arrangement?', + temperature: 0.1, + maxTokens: 400, + }); + console.log(logicSolution); + console.log('\n' + '='.repeat(80) + '\n'); + + // Code debugging + console.log('🐛 Debugging code...'); + const { text: debugging } = await generateText({ + model: openrouter(MODELS.DEEPSEEK), + prompt: `Analyze this Python code and explain the bugs, then provide a corrected version: + +def calculate_average(numbers): + total = 0 + for i in range(len(numbers)): + total += numbers[i] + average = total / len(numbers) + return average + +# Test +nums = [1, 2, 3, 4, 5] +print(calculate_average(nums)) +print(calculate_average([])) # This will cause an error`, + temperature: 0.1, + maxTokens: 400, + }); + console.log(debugging); + console.log('\n' + '='.repeat(80) + '\n'); +} + +/** + * Streaming Text Generation Example + */ +async function streamingExample() { + console.log('🌊 Streaming Text Generation Example\n'); + + console.log('📝 Streaming a detailed explanation...'); + const { textStream } = await streamText({ + model: openrouter(MODELS.LLAMA), + prompt: 'Write a comprehensive explanation of how machine learning models are trained, including data preparation, model architecture, training process, and evaluation.', + temperature: 0.4, + maxTokens: 600, + }); + + for await (const delta of textStream) { + process.stdout.write(delta); + } + + console.log('\n\n' + '='.repeat(80) + '\n'); +} + +/** + * Multi-turn Conversation Example + */ +async function conversationExample() { + console.log('💭 Multi-turn Conversation Example\n'); + + const messages = [ + { role: 'user' as const, content: 'What is artificial intelligence?' }, + { role: 'assistant' as const, content: 'Artificial Intelligence (AI) is a branch of computer science that aims to create machines capable of performing tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and understanding natural language.' }, + { role: 'user' as const, content: 'How does it relate to the 0G network?' }, + ]; + + console.log('🤖 AI explaining its relationship to 0G network...'); + const { text } = await generateText({ + model: openrouter(MODELS.LLAMA), + messages, + temperature: 0.5, + maxTokens: 300, + }); + + console.log(text); + console.log('\n' + '='.repeat(80) + '\n'); +} + +/** + * Main function to run all examples + */ +async function main() { + console.log('🚀 0G Compute Network Text Generation Examples\n'); + console.log('This demo showcases various text generation capabilities of 0G models.\n'); + console.log('='.repeat(80) + '\n'); + + try { + await creativeWritingExamples(); + await technicalExplanationExamples(); + await codeGenerationExamples(); + await reasoningExamples(); + await streamingExample(); + await conversationExample(); + + console.log('✅ All examples completed successfully!'); + } catch (error) { + console.error('❌ Error running examples:', error); + } +} + +// Export for use in other files +export { + creativeWritingExamples, + technicalExplanationExamples, + codeGenerationExamples, + reasoningExamples, + streamingExample, + conversationExample, + main, +}; + +// Run examples if this file is executed directly +if (require.main === module) { + main().catch(console.error); +} diff --git a/src/chat/index.test.ts b/src/chat/index.test.ts index 8dca952..876df5e 100644 --- a/src/chat/index.test.ts +++ b/src/chat/index.test.ts @@ -1,4 +1,5 @@ import type { LanguageModelV2Prompt } from '@ai-sdk/provider'; +import type { ImageResponse } from '../schemas/image'; import type { ReasoningDetailUnion } from '../schemas/reasoning-details'; import { @@ -7,7 +8,6 @@ import { } from '@ai-sdk/provider-utils/test'; import { createOpenRouter } from '../provider'; import { ReasoningDetailType } from '../schemas/reasoning-details'; -import type { ImageResponse } from '../schemas/image'; const TEST_PROMPT: LanguageModelV2Prompt = [ { role: 'user', content: [{ type: 'text', text: 'Hello' }] }, @@ -412,6 +412,32 @@ describe('doGenerate', () => { }); }); + it('should support 0G Compute Network models', async () => { + prepareJsonResponse({ content: 'Test response from 0G model' }); + + // Test Llama 3.3 70B model + const llamaModel = provider.chat('0g/llama-3.3-70b-instruct'); + await llamaModel.doGenerate({ + prompt: TEST_PROMPT, + }); + + expect(await server.calls[0]!.requestBodyJson).toStrictEqual({ + model: '0g/llama-3.3-70b-instruct', + messages: [{ role: 'user', content: 'Hello' }], + }); + + // Test DeepSeek R1 70B model + const deepseekModel = provider.chat('0g/deepseek-r1-70b'); + await deepseekModel.doGenerate({ + prompt: TEST_PROMPT, + }); + + expect(await server.calls[1]!.requestBodyJson).toStrictEqual({ + model: '0g/deepseek-r1-70b', + messages: [{ role: 'user', content: 'Hello' }], + }); + }); + it('should pass settings', async () => { prepareJsonResponse(); @@ -899,8 +925,8 @@ describe('doStream', () => { // 5. text-delta (2 times) // 6. text-end (when stream finishes) - const streamOrder = elements.map(el => el.type); - + const streamOrder = elements.map((el) => el.type); + // Find the positions of key events const reasoningStartIndex = streamOrder.indexOf('reasoning-start'); const reasoningEndIndex = streamOrder.indexOf('reasoning-end'); @@ -926,10 +952,7 @@ describe('doStream', () => { .filter((el) => el.type === 'text-delta') .map((el) => (el as { type: 'text-delta'; delta: string }).delta); - expect(textDeltas).toEqual([ - 'Hello! ', - 'How can I help you today?', - ]); + expect(textDeltas).toEqual(['Hello! ', 'How can I help you today?']); }); it('should stream tool deltas', async () => {