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146
changelog.md
146
changelog.md
@@ -1,5 +1,151 @@
|
||||
# Changelog
|
||||
|
||||
## 2025-10-03 - 0.7.4 - fix(provider.anthropic)
|
||||
Use image/png for embedded PDF images in Anthropic provider and add local Claude settings for development permissions
|
||||
|
||||
- AnthropicProvider: change media_type from 'image/jpeg' to 'image/png' when embedding images extracted from PDFs to ensure correct format in Anthropic requests.
|
||||
- Add .claude/settings.local.json with development/testing permissions for local Claude usage (shell commands, webfetch, websearch, test/run tasks).
|
||||
|
||||
## 2025-10-03 - 0.7.3 - fix(tests)
|
||||
Add extensive provider/feature tests and local Claude CI permissions
|
||||
|
||||
- Add many focused test files covering providers and features: OpenAI, Anthropic, Perplexity, Groq, Ollama, Exo, XAI (chat, audio, vision, document, research, image generation, stubs, interfaces, basic)
|
||||
- Introduce .claude/settings.local.json to declare allowed permissions for local Claude/CI actions
|
||||
- Replace older aggregated test files with modular per-feature tests (removed legacy combined tests and split into smaller suites)
|
||||
- No changes to library runtime code — this change adds tests and CI/local agent configuration only
|
||||
|
||||
## 2025-10-03 - 0.7.2 - fix(anthropic)
|
||||
Update Anthropic provider branding to Claude Sonnet 4.5 and add local Claude permissions
|
||||
|
||||
- Docs: Replace 'Claude 3 Opus' with 'Claude Sonnet 4.5' in README provider capabilities matrix.
|
||||
- Config: Add .claude/settings.local.json to define local Claude permissions for tests and development commands.
|
||||
|
||||
## 2025-10-03 - 0.7.1 - fix(docs)
|
||||
Add README image generation docs and .claude local settings
|
||||
|
||||
- Add .claude/settings.local.json with permission allow-list for local assistant tooling and web search
|
||||
- Update README provider capabilities table to include an Images column and reference gpt-image-1
|
||||
- Add Image Generation & Editing section with examples, options, and gpt-image-1 advantages
|
||||
- Mark image generation support as implemented in the roadmap and remove duplicate entry
|
||||
|
||||
## 2025-10-03 - 0.7.0 - feat(providers)
|
||||
Add research API and image generation/editing support; extend providers and tests
|
||||
|
||||
- Introduce ResearchOptions and ResearchResponse to the MultiModalModel interface and implement research() where supported
|
||||
- OpenAiProvider: implement research(), add imageGenerate() and imageEdit() methods (gpt-image-1 / DALL·E support), and expose imageModel option
|
||||
- AnthropicProvider: implement research() and vision handling; explicitly throw for unsupported image generation/editing
|
||||
- PerplexityProvider: implement research() (sonar / sonar-pro support) and expose citation parsing
|
||||
- Add image/document-related interfaces (ImageGenerateOptions, ImageEditOptions, ImageResponse) to abstract API
|
||||
- Add image generation/editing/no-op stubs for other providers (Exo, Groq, Ollama, XAI) that throw informative errors to preserve API compatibility
|
||||
- Add comprehensive OpenAI image generation tests and helper to save test outputs (test/test.image.openai.ts)
|
||||
- Update README with Research & Web Search documentation, capability matrix, and roadmap entry for Research & Web Search API
|
||||
- Add local Claude agent permissions file (.claude/settings.local.json) and various provider type/import updates
|
||||
|
||||
## 2025-09-28 - 0.6.1 - fix(provider.anthropic)
|
||||
Fix Anthropic research tool identifier and add tests + local Claude permissions
|
||||
|
||||
- Replace Anthropic research tool type from 'computer_20241022' to 'web_search_20250305' to match the expected web-search tool schema.
|
||||
- Add comprehensive test suites and fixtures for providers and research features (new/updated tests under test/ including anthropic, openai, research.* and stubs).
|
||||
- Fix test usage of XAI provider class name (use XAIProvider) and adjust basic provider test expectations (provider instantiation moved to start()).
|
||||
- Add .claude/settings.local.json with local Claude permissions to allow common CI/dev commands and web search during testing.
|
||||
|
||||
## 2025-09-28 - 0.6.0 - feat(research)
|
||||
Introduce research API with provider implementations, docs and tests
|
||||
|
||||
- Add ResearchOptions and ResearchResponse interfaces and a new abstract research() method to MultiModalModel
|
||||
- Implement research() for OpenAiProvider (deep research model selection, optional web search/tools, background flag, source extraction)
|
||||
- Implement research() for AnthropicProvider (web search tool support, domain filters, citation extraction)
|
||||
- Implement research() for PerplexityProvider (sonar / sonar-pro model usage and citation parsing)
|
||||
- Add research() stubs to Exo, Groq, Ollama and XAI providers that throw a clear 'not yet supported' error to preserve interface compatibility
|
||||
- Add tests for research interfaces and provider research methods (test files updated/added)
|
||||
- Add documentation: readme.research.md describing the research API, usage and configuration
|
||||
- Export additional providers from ts/index.ts and update provider typings/imports across files
|
||||
- Add a 'typecheck' script to package.json
|
||||
- Add .claude/settings.local.json (local agent permissions for CI/dev tasks)
|
||||
|
||||
## 2025-08-12 - 0.5.11 - fix(openaiProvider)
|
||||
Update default chat model to gpt-5-mini and bump dependency versions
|
||||
|
||||
- Changed default chat model in OpenAiProvider from 'o3-mini' and 'o4-mini' to 'gpt-5-mini'
|
||||
- Upgraded @anthropic-ai/sdk from ^0.57.0 to ^0.59.0
|
||||
- Upgraded openai from ^5.11.0 to ^5.12.2
|
||||
- Added new local Claude settings configuration (.claude/settings.local.json)
|
||||
|
||||
## 2025-08-03 - 0.5.10 - fix(dependencies)
|
||||
Update SmartPdf to v4.1.1 for enhanced PDF processing capabilities
|
||||
|
||||
- Updated @push.rocks/smartpdf from ^3.3.0 to ^4.1.1
|
||||
- Enhanced PDF conversion with improved scale options and quality controls
|
||||
- Dependency updates for better performance and compatibility
|
||||
|
||||
## 2025-08-01 - 0.5.9 - fix(documentation)
|
||||
Remove contribution section from readme
|
||||
|
||||
- Removed the contribution section from readme.md as requested
|
||||
- Kept the roadmap section for future development plans
|
||||
|
||||
## 2025-08-01 - 0.5.8 - fix(core)
|
||||
Fix SmartPdf lifecycle management and update dependencies
|
||||
|
||||
- Moved SmartPdf instance management to the MultiModalModel base class for better resource sharing
|
||||
- Fixed memory leaks by properly implementing cleanup in the base class stop() method
|
||||
- Updated SmartAi class to properly stop all providers on shutdown
|
||||
- Updated @push.rocks/smartrequest from v2.1.0 to v4.2.1 with migration to new API
|
||||
- Enhanced readme with professional documentation and feature matrix
|
||||
|
||||
## 2025-07-26 - 0.5.7 - fix(provider.openai)
|
||||
Fix stream type mismatch in audio method
|
||||
|
||||
- Fixed type error where OpenAI SDK returns a web ReadableStream but the audio method needs to return a Node.js ReadableStream
|
||||
- Added conversion using Node.js's built-in Readable.fromWeb() method
|
||||
|
||||
## 2025-07-25 - 0.5.5 - feat(documentation)
|
||||
Comprehensive documentation enhancement and test improvements
|
||||
|
||||
- Completely rewrote readme.md with detailed provider comparisons, advanced usage examples, and performance tips
|
||||
- Added comprehensive examples for all supported providers (OpenAI, Anthropic, Perplexity, Groq, XAI, Ollama, Exo)
|
||||
- Included detailed sections on chat interactions, streaming, TTS, vision processing, and document analysis
|
||||
- Added verbose flag to test script for better debugging
|
||||
|
||||
## 2025-05-13 - 0.5.4 - fix(provider.openai)
|
||||
Update dependency versions, clean test imports, and adjust default OpenAI model configurations
|
||||
|
||||
- Bump dependency versions in package.json (@git.zone/tsbuild, @push.rocks/tapbundle, openai, etc.)
|
||||
- Change default chatModel from 'gpt-4o' to 'o4-mini' and visionModel from 'gpt-4o' to '04-mini' in provider.openai.ts
|
||||
- Remove unused 'expectAsync' import from test file
|
||||
|
||||
## 2025-04-03 - 0.5.3 - fix(package.json)
|
||||
Add explicit packageManager field to package.json
|
||||
|
||||
- Include the packageManager property to specify the pnpm version and checksum.
|
||||
- Align package metadata with current standards.
|
||||
|
||||
## 2025-04-03 - 0.5.2 - fix(readme)
|
||||
Remove redundant conclusion section from README to streamline documentation.
|
||||
|
||||
- Eliminated the conclusion block describing SmartAi's capabilities and documentation pointers.
|
||||
|
||||
## 2025-02-25 - 0.5.1 - fix(OpenAiProvider)
|
||||
Corrected audio model ID in OpenAiProvider
|
||||
|
||||
- Fixed audio model identifier from 'o3-mini' to 'tts-1-hd' in the OpenAiProvider's audio method.
|
||||
- Addressed minor code formatting issues in test suite for better readability.
|
||||
- Corrected spelling errors in test documentation and comments.
|
||||
|
||||
## 2025-02-25 - 0.5.0 - feat(documentation and configuration)
|
||||
Enhanced package and README documentation
|
||||
|
||||
- Expanded the package description to better reflect the library's capabilities.
|
||||
- Improved README with detailed usage examples for initialization, chat interactions, streaming chat, audio generation, document analysis, and vision processing.
|
||||
- Provided error handling strategies and advanced streaming customization examples.
|
||||
|
||||
## 2025-02-25 - 0.4.2 - fix(core)
|
||||
Fix OpenAI chat streaming and PDF document processing logic.
|
||||
|
||||
- Updated OpenAI chat streaming to handle new async iterable format.
|
||||
- Improved PDF document processing by filtering out empty image buffers.
|
||||
- Removed unsupported temperature options from OpenAI requests.
|
||||
|
||||
## 2025-02-25 - 0.4.1 - fix(provider)
|
||||
Fix provider modules for consistency
|
||||
|
||||
|
@@ -5,20 +5,33 @@
|
||||
"githost": "code.foss.global",
|
||||
"gitscope": "push.rocks",
|
||||
"gitrepo": "smartai",
|
||||
"description": "A TypeScript library for integrating and interacting with multiple AI models, offering capabilities for chat and potentially audio responses.",
|
||||
"description": "SmartAi is a versatile TypeScript library designed to facilitate integration and interaction with various AI models, offering functionalities for chat, audio generation, document processing, and vision tasks.",
|
||||
"npmPackagename": "@push.rocks/smartai",
|
||||
"license": "MIT",
|
||||
"projectDomain": "push.rocks",
|
||||
"keywords": [
|
||||
"AI integration",
|
||||
"chatbot",
|
||||
"TypeScript",
|
||||
"chatbot",
|
||||
"OpenAI",
|
||||
"Anthropic",
|
||||
"multi-model support",
|
||||
"audio responses",
|
||||
"multi-model",
|
||||
"audio generation",
|
||||
"text-to-speech",
|
||||
"streaming chat"
|
||||
"document processing",
|
||||
"vision processing",
|
||||
"streaming chat",
|
||||
"API",
|
||||
"multiple providers",
|
||||
"AI models",
|
||||
"synchronous chat",
|
||||
"asynchronous chat",
|
||||
"real-time interaction",
|
||||
"content analysis",
|
||||
"image description",
|
||||
"document classification",
|
||||
"AI toolkit",
|
||||
"provider switching"
|
||||
]
|
||||
}
|
||||
},
|
||||
|
57
package.json
57
package.json
@@ -1,37 +1,38 @@
|
||||
{
|
||||
"name": "@push.rocks/smartai",
|
||||
"version": "0.4.1",
|
||||
"version": "0.7.4",
|
||||
"private": false,
|
||||
"description": "A TypeScript library for integrating and interacting with multiple AI models, offering capabilities for chat and potentially audio responses.",
|
||||
"description": "SmartAi is a versatile TypeScript library designed to facilitate integration and interaction with various AI models, offering functionalities for chat, audio generation, document processing, and vision tasks.",
|
||||
"main": "dist_ts/index.js",
|
||||
"typings": "dist_ts/index.d.ts",
|
||||
"type": "module",
|
||||
"author": "Task Venture Capital GmbH",
|
||||
"license": "MIT",
|
||||
"scripts": {
|
||||
"test": "(tstest test/ --web)",
|
||||
"test": "(tstest test/ --web --verbose)",
|
||||
"typecheck": "tsbuild check",
|
||||
"build": "(tsbuild --web --allowimplicitany)",
|
||||
"buildDocs": "(tsdoc)"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@git.zone/tsbuild": "^2.2.1",
|
||||
"@git.zone/tsbundle": "^2.2.5",
|
||||
"@git.zone/tsbuild": "^2.6.4",
|
||||
"@git.zone/tsbundle": "^2.5.1",
|
||||
"@git.zone/tsrun": "^1.3.3",
|
||||
"@git.zone/tstest": "^1.0.96",
|
||||
"@git.zone/tstest": "^2.3.2",
|
||||
"@push.rocks/qenv": "^6.1.0",
|
||||
"@push.rocks/tapbundle": "^5.5.6",
|
||||
"@types/node": "^22.13.5"
|
||||
"@push.rocks/tapbundle": "^6.0.3",
|
||||
"@types/node": "^22.15.17"
|
||||
},
|
||||
"dependencies": {
|
||||
"@anthropic-ai/sdk": "^0.37.0",
|
||||
"@anthropic-ai/sdk": "^0.59.0",
|
||||
"@push.rocks/smartarray": "^1.1.0",
|
||||
"@push.rocks/smartfile": "^11.2.0",
|
||||
"@push.rocks/smartpath": "^5.0.18",
|
||||
"@push.rocks/smartpdf": "^3.1.8",
|
||||
"@push.rocks/smartfile": "^11.2.5",
|
||||
"@push.rocks/smartpath": "^6.0.0",
|
||||
"@push.rocks/smartpdf": "^4.1.1",
|
||||
"@push.rocks/smartpromise": "^4.2.3",
|
||||
"@push.rocks/smartrequest": "^2.0.23",
|
||||
"@push.rocks/smartrequest": "^4.2.1",
|
||||
"@push.rocks/webstream": "^1.0.10",
|
||||
"openai": "^4.85.4"
|
||||
"openai": "^5.12.2"
|
||||
},
|
||||
"repository": {
|
||||
"type": "git",
|
||||
@@ -58,13 +59,33 @@
|
||||
],
|
||||
"keywords": [
|
||||
"AI integration",
|
||||
"chatbot",
|
||||
"TypeScript",
|
||||
"chatbot",
|
||||
"OpenAI",
|
||||
"Anthropic",
|
||||
"multi-model support",
|
||||
"audio responses",
|
||||
"multi-model",
|
||||
"audio generation",
|
||||
"text-to-speech",
|
||||
"streaming chat"
|
||||
"document processing",
|
||||
"vision processing",
|
||||
"streaming chat",
|
||||
"API",
|
||||
"multiple providers",
|
||||
"AI models",
|
||||
"synchronous chat",
|
||||
"asynchronous chat",
|
||||
"real-time interaction",
|
||||
"content analysis",
|
||||
"image description",
|
||||
"document classification",
|
||||
"AI toolkit",
|
||||
"provider switching"
|
||||
],
|
||||
"pnpm": {
|
||||
"onlyBuiltDependencies": [
|
||||
"esbuild",
|
||||
"puppeteer"
|
||||
]
|
||||
},
|
||||
"packageManager": "pnpm@10.7.0+sha512.6b865ad4b62a1d9842b61d674a393903b871d9244954f652b8842c2b553c72176b278f64c463e52d40fff8aba385c235c8c9ecf5cc7de4fd78b8bb6d49633ab6"
|
||||
}
|
||||
|
4782
pnpm-lock.yaml
generated
4782
pnpm-lock.yaml
generated
File diff suppressed because it is too large
Load Diff
741
readme.md
741
readme.md
@@ -1,329 +1,594 @@
|
||||
# @push.rocks/smartai
|
||||
**One API to rule them all** 🚀
|
||||
|
||||
[](https://www.npmjs.com/package/@push.rocks/smartai)
|
||||
[](https://www.npmjs.com/package/@push.rocks/smartai)
|
||||
[](https://www.typescriptlang.org/)
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
|
||||
SmartAi is a comprehensive TypeScript library that provides a standardized interface for integrating and interacting with multiple AI models. It supports a range of operations from synchronous and streaming chat to audio generation, document processing, and vision tasks.
|
||||
SmartAI unifies the world's leading AI providers - OpenAI, Anthropic, Perplexity, Ollama, Groq, XAI, and Exo - under a single, elegant TypeScript interface. Build AI applications at lightning speed without vendor lock-in.
|
||||
|
||||
## Table of Contents
|
||||
## 🎯 Why SmartAI?
|
||||
|
||||
- [Features](#features)
|
||||
- [Installation](#installation)
|
||||
- [Supported AI Providers](#supported-ai-providers)
|
||||
- [Quick Start](#quick-start)
|
||||
- [Usage Examples](#usage-examples)
|
||||
- [Chat Interactions](#chat-interactions)
|
||||
- [Streaming Chat](#streaming-chat)
|
||||
- [Audio Generation](#audio-generation)
|
||||
- [Document Processing](#document-processing)
|
||||
- [Vision Processing](#vision-processing)
|
||||
- [Error Handling](#error-handling)
|
||||
- [Development](#development)
|
||||
- [Running Tests](#running-tests)
|
||||
- [Building the Project](#building-the-project)
|
||||
- [Contributing](#contributing)
|
||||
- [License](#license)
|
||||
- [Legal Information](#legal-information)
|
||||
- **🔌 Universal Interface**: Write once, run with any AI provider. Switch between GPT-4, Claude, Llama, or Grok with a single line change.
|
||||
- **🛡️ Type-Safe**: Full TypeScript support with comprehensive type definitions for all operations
|
||||
- **🌊 Streaming First**: Built for real-time applications with native streaming support
|
||||
- **🎨 Multi-Modal**: Seamlessly work with text, images, audio, and documents
|
||||
- **🏠 Local & Cloud**: Support for both cloud providers and local models via Ollama
|
||||
- **⚡ Zero Lock-In**: Your code remains portable across all AI providers
|
||||
|
||||
## Features
|
||||
|
||||
- **Unified API:** Seamlessly integrate multiple AI providers with a consistent interface.
|
||||
- **Chat & Streaming:** Support for both synchronous and real-time streaming chat interactions.
|
||||
- **Audio & Vision:** Generate audio responses and perform detailed image analysis.
|
||||
- **Document Processing:** Analyze PDFs and other documents using vision models.
|
||||
- **Extensible:** Easily extend the library to support additional AI providers.
|
||||
|
||||
## Installation
|
||||
|
||||
To install SmartAi, run the following command:
|
||||
## 🚀 Quick Start
|
||||
|
||||
```bash
|
||||
npm install @push.rocks/smartai
|
||||
```
|
||||
|
||||
This will add the package to your project’s dependencies.
|
||||
|
||||
## Supported AI Providers
|
||||
|
||||
SmartAi supports multiple AI providers. Configure each provider with its corresponding token or settings:
|
||||
|
||||
### OpenAI
|
||||
|
||||
- **Models:** GPT-4, GPT-3.5-turbo, GPT-4-vision-preview
|
||||
- **Features:** Chat, Streaming, Audio Generation, Vision, Document Processing
|
||||
- **Configuration Example:**
|
||||
|
||||
```typescript
|
||||
openaiToken: 'your-openai-token'
|
||||
```
|
||||
|
||||
### X.AI
|
||||
|
||||
- **Models:** Grok-2-latest
|
||||
- **Features:** Chat, Streaming, Document Processing
|
||||
- **Configuration Example:**
|
||||
|
||||
```typescript
|
||||
xaiToken: 'your-xai-token'
|
||||
```
|
||||
|
||||
### Anthropic
|
||||
|
||||
- **Models:** Claude-3-opus-20240229
|
||||
- **Features:** Chat, Streaming, Vision, Document Processing
|
||||
- **Configuration Example:**
|
||||
|
||||
```typescript
|
||||
anthropicToken: 'your-anthropic-token'
|
||||
```
|
||||
|
||||
### Perplexity
|
||||
|
||||
- **Models:** Mixtral-8x7b-instruct
|
||||
- **Features:** Chat, Streaming
|
||||
- **Configuration Example:**
|
||||
|
||||
```typescript
|
||||
perplexityToken: 'your-perplexity-token'
|
||||
```
|
||||
|
||||
### Groq
|
||||
|
||||
- **Models:** Llama-3.3-70b-versatile
|
||||
- **Features:** Chat, Streaming
|
||||
- **Configuration Example:**
|
||||
|
||||
```typescript
|
||||
groqToken: 'your-groq-token'
|
||||
```
|
||||
|
||||
### Ollama
|
||||
|
||||
- **Models:** Configurable (default: llama2; use llava for vision/document tasks)
|
||||
- **Features:** Chat, Streaming, Vision, Document Processing
|
||||
- **Configuration Example:**
|
||||
|
||||
```typescript
|
||||
ollama: {
|
||||
baseUrl: 'http://localhost:11434', // Optional
|
||||
model: 'llama2', // Optional
|
||||
visionModel: 'llava' // Optional for vision and document tasks
|
||||
}
|
||||
```
|
||||
|
||||
### Exo
|
||||
|
||||
- **Models:** Configurable (supports LLaMA, Mistral, LlaVA, Qwen, and Deepseek)
|
||||
- **Features:** Chat, Streaming
|
||||
- **Configuration Example:**
|
||||
|
||||
```typescript
|
||||
exo: {
|
||||
baseUrl: 'http://localhost:8080/v1', // Optional
|
||||
apiKey: 'your-api-key' // Optional for local deployments
|
||||
}
|
||||
```
|
||||
|
||||
## Quick Start
|
||||
|
||||
Initialize SmartAi with the provider configurations you plan to use:
|
||||
|
||||
```typescript
|
||||
import { SmartAi } from '@push.rocks/smartai';
|
||||
|
||||
const smartAi = new SmartAi({
|
||||
openaiToken: 'your-openai-token',
|
||||
xaiToken: 'your-xai-token',
|
||||
anthropicToken: 'your-anthropic-token',
|
||||
perplexityToken: 'your-perplexity-token',
|
||||
groqToken: 'your-groq-token',
|
||||
ollama: {
|
||||
baseUrl: 'http://localhost:11434',
|
||||
model: 'llama2'
|
||||
},
|
||||
exo: {
|
||||
baseUrl: 'http://localhost:8080/v1',
|
||||
apiKey: 'your-api-key'
|
||||
}
|
||||
// Initialize with your favorite providers
|
||||
const ai = new SmartAi({
|
||||
openaiToken: 'sk-...',
|
||||
anthropicToken: 'sk-ant-...'
|
||||
});
|
||||
|
||||
await smartAi.start();
|
||||
```
|
||||
await ai.start();
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Chat Interactions
|
||||
|
||||
**Synchronous Chat:**
|
||||
|
||||
```typescript
|
||||
const response = await smartAi.openaiProvider.chat({
|
||||
// Same API, multiple providers
|
||||
const response = await ai.openaiProvider.chat({
|
||||
systemMessage: 'You are a helpful assistant.',
|
||||
userMessage: 'What is the capital of France?',
|
||||
messageHistory: [] // Include previous conversation messages if applicable
|
||||
userMessage: 'Explain quantum computing in simple terms',
|
||||
messageHistory: []
|
||||
});
|
||||
|
||||
console.log(response.message);
|
||||
```
|
||||
|
||||
### Streaming Chat
|
||||
## 📊 Provider Capabilities Matrix
|
||||
|
||||
**Real-Time Streaming:**
|
||||
Choose the right provider for your use case:
|
||||
|
||||
| Provider | Chat | Streaming | TTS | Vision | Documents | Research | Images | Highlights |
|
||||
|----------|:----:|:---------:|:---:|:------:|:---------:|:--------:|:------:|------------|
|
||||
| **OpenAI** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | • gpt-image-1<br>• DALL-E 3<br>• Deep research API |
|
||||
| **Anthropic** | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | • Claude Sonnet 4.5<br>• Superior reasoning<br>• Web search API |
|
||||
| **Ollama** | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | • 100% local<br>• Privacy-first<br>• No API costs |
|
||||
| **XAI** | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | • Grok models<br>• Real-time data<br>• Uncensored |
|
||||
| **Perplexity** | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | • Web-aware<br>• Research-focused<br>• Sonar Pro models |
|
||||
| **Groq** | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | • 10x faster<br>• LPU inference<br>• Low latency |
|
||||
| **Exo** | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | • Distributed<br>• P2P compute<br>• Decentralized |
|
||||
|
||||
## 🎮 Core Features
|
||||
|
||||
### 💬 Universal Chat Interface
|
||||
|
||||
Works identically across all providers:
|
||||
|
||||
```typescript
|
||||
const textEncoder = new TextEncoder();
|
||||
const textDecoder = new TextDecoder();
|
||||
// Use GPT-4 for complex reasoning
|
||||
const gptResponse = await ai.openaiProvider.chat({
|
||||
systemMessage: 'You are a expert physicist.',
|
||||
userMessage: 'Explain the implications of quantum entanglement',
|
||||
messageHistory: []
|
||||
});
|
||||
|
||||
// Create a transform stream for sending and receiving data
|
||||
const { writable, readable } = new TransformStream();
|
||||
const writer = writable.getWriter();
|
||||
// Use Claude for safety-critical applications
|
||||
const claudeResponse = await ai.anthropicProvider.chat({
|
||||
systemMessage: 'You are a medical advisor.',
|
||||
userMessage: 'Review this patient data for concerns',
|
||||
messageHistory: []
|
||||
});
|
||||
|
||||
const message = {
|
||||
role: 'user',
|
||||
content: 'Tell me a story about a brave knight'
|
||||
};
|
||||
// Use Groq for lightning-fast responses
|
||||
const groqResponse = await ai.groqProvider.chat({
|
||||
systemMessage: 'You are a code reviewer.',
|
||||
userMessage: 'Quick! Find the bug in this code: ...',
|
||||
messageHistory: []
|
||||
});
|
||||
```
|
||||
|
||||
writer.write(textEncoder.encode(JSON.stringify(message) + '\n'));
|
||||
### 🌊 Real-Time Streaming
|
||||
|
||||
// Start streaming the response
|
||||
const stream = await smartAi.openaiProvider.chatStream(readable);
|
||||
Build responsive chat interfaces with token-by-token streaming:
|
||||
|
||||
```typescript
|
||||
// Create a chat stream
|
||||
const stream = await ai.openaiProvider.chatStream(inputStream);
|
||||
const reader = stream.getReader();
|
||||
|
||||
// Display responses as they arrive
|
||||
while (true) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
console.log('AI:', value);
|
||||
|
||||
// Update UI in real-time
|
||||
process.stdout.write(value);
|
||||
}
|
||||
```
|
||||
|
||||
### Audio Generation
|
||||
### 🎙️ Text-to-Speech
|
||||
|
||||
Generate audio (supported by providers like OpenAI):
|
||||
Generate natural voices with OpenAI:
|
||||
|
||||
```typescript
|
||||
const audioStream = await smartAi.openaiProvider.audio({
|
||||
message: 'Hello, this is a test of text-to-speech'
|
||||
const audioStream = await ai.openaiProvider.audio({
|
||||
message: 'Welcome to the future of AI development!'
|
||||
});
|
||||
|
||||
// Process the audio stream, for example, play it or save to a file.
|
||||
// Stream directly to speakers
|
||||
audioStream.pipe(speakerOutput);
|
||||
|
||||
// Or save to file
|
||||
audioStream.pipe(fs.createWriteStream('welcome.mp3'));
|
||||
```
|
||||
|
||||
### Document Processing
|
||||
### 👁️ Vision Analysis
|
||||
|
||||
Analyze and extract key information from documents:
|
||||
Understand images with multiple providers:
|
||||
|
||||
```typescript
|
||||
// Example using OpenAI
|
||||
const documentResult = await smartAi.openaiProvider.document({
|
||||
systemMessage: 'Classify the document type',
|
||||
userMessage: 'What type of document is this?',
|
||||
const image = fs.readFileSync('product-photo.jpg');
|
||||
|
||||
// OpenAI: General purpose vision
|
||||
const gptVision = await ai.openaiProvider.vision({
|
||||
image,
|
||||
prompt: 'Describe this product and suggest marketing angles'
|
||||
});
|
||||
|
||||
// Anthropic: Detailed analysis
|
||||
const claudeVision = await ai.anthropicProvider.vision({
|
||||
image,
|
||||
prompt: 'Identify any safety concerns or defects'
|
||||
});
|
||||
|
||||
// Ollama: Private, local analysis
|
||||
const ollamaVision = await ai.ollamaProvider.vision({
|
||||
image,
|
||||
prompt: 'Extract all text and categorize the content'
|
||||
});
|
||||
```
|
||||
|
||||
### 📄 Document Intelligence
|
||||
|
||||
Extract insights from PDFs with AI:
|
||||
|
||||
```typescript
|
||||
const contract = fs.readFileSync('contract.pdf');
|
||||
const invoice = fs.readFileSync('invoice.pdf');
|
||||
|
||||
// Analyze documents
|
||||
const analysis = await ai.openaiProvider.document({
|
||||
systemMessage: 'You are a legal expert.',
|
||||
userMessage: 'Compare these documents and highlight key differences',
|
||||
messageHistory: [],
|
||||
pdfDocuments: [pdfBuffer] // Uint8Array containing the PDF content
|
||||
pdfDocuments: [contract, invoice]
|
||||
});
|
||||
```
|
||||
|
||||
Other providers (e.g., Ollama and Anthropic) follow a similar pattern:
|
||||
|
||||
```typescript
|
||||
// Using Ollama for document processing
|
||||
const ollamaResult = await smartAi.ollamaProvider.document({
|
||||
systemMessage: 'You are a document analysis assistant',
|
||||
userMessage: 'Extract key information from this document',
|
||||
// Multi-document analysis
|
||||
const taxDocs = [form1099, w2, receipts];
|
||||
const taxAnalysis = await ai.anthropicProvider.document({
|
||||
systemMessage: 'You are a tax advisor.',
|
||||
userMessage: 'Prepare a tax summary from these documents',
|
||||
messageHistory: [],
|
||||
pdfDocuments: [pdfBuffer]
|
||||
pdfDocuments: taxDocs
|
||||
});
|
||||
```
|
||||
|
||||
### 🔬 Research & Web Search
|
||||
|
||||
Perform deep research with web search capabilities across multiple providers:
|
||||
|
||||
```typescript
|
||||
// Using Anthropic for document processing
|
||||
const anthropicResult = await smartAi.anthropicProvider.document({
|
||||
systemMessage: 'Analyze the document',
|
||||
userMessage: 'Please extract the main points',
|
||||
messageHistory: [],
|
||||
pdfDocuments: [pdfBuffer]
|
||||
// OpenAI Deep Research - Comprehensive analysis
|
||||
const deepResearch = await ai.openaiProvider.research({
|
||||
query: 'What are the latest developments in quantum computing?',
|
||||
searchDepth: 'deep',
|
||||
includeWebSearch: true
|
||||
});
|
||||
|
||||
console.log(deepResearch.answer);
|
||||
console.log('Sources:', deepResearch.sources);
|
||||
|
||||
// Anthropic Web Search - Domain-filtered research
|
||||
const anthropic = new AnthropicProvider({
|
||||
anthropicToken: 'sk-ant-...',
|
||||
enableWebSearch: true,
|
||||
searchDomainAllowList: ['nature.com', 'science.org']
|
||||
});
|
||||
|
||||
const scientificResearch = await anthropic.research({
|
||||
query: 'Latest breakthroughs in CRISPR gene editing',
|
||||
searchDepth: 'advanced'
|
||||
});
|
||||
|
||||
// Perplexity - Research-focused with citations
|
||||
const perplexityResearch = await ai.perplexityProvider.research({
|
||||
query: 'Current state of autonomous vehicle technology',
|
||||
searchDepth: 'deep' // Uses Sonar Pro model
|
||||
});
|
||||
```
|
||||
|
||||
### Vision Processing
|
||||
**Research Options:**
|
||||
- `searchDepth`: 'basic' | 'advanced' | 'deep'
|
||||
- `maxSources`: Number of sources to include
|
||||
- `includeWebSearch`: Enable web search (OpenAI)
|
||||
- `background`: Run as background task (OpenAI)
|
||||
|
||||
Analyze images with vision capabilities:
|
||||
**Supported Providers:**
|
||||
- **OpenAI**: Deep Research API with specialized models (`o3-deep-research-2025-06-26`, `o4-mini-deep-research-2025-06-26`)
|
||||
- **Anthropic**: Web Search API with domain filtering
|
||||
- **Perplexity**: Sonar and Sonar Pro models with built-in citations
|
||||
|
||||
### 🎨 Image Generation & Editing
|
||||
|
||||
Generate and edit images with OpenAI's cutting-edge models:
|
||||
|
||||
```typescript
|
||||
// Using OpenAI GPT-4 Vision
|
||||
const imageDescription = await smartAi.openaiProvider.vision({
|
||||
image: imageBuffer, // Uint8Array containing image data
|
||||
prompt: 'What do you see in this image?'
|
||||
// Basic image generation with gpt-image-1
|
||||
const image = await ai.openaiProvider.imageGenerate({
|
||||
prompt: 'A futuristic robot assistant in a modern office, digital art',
|
||||
model: 'gpt-image-1',
|
||||
quality: 'high',
|
||||
size: '1024x1024'
|
||||
});
|
||||
|
||||
// Using Ollama for vision tasks
|
||||
const ollamaImageAnalysis = await smartAi.ollamaProvider.vision({
|
||||
image: imageBuffer,
|
||||
prompt: 'Analyze this image in detail'
|
||||
// Save the generated image
|
||||
const imageBuffer = Buffer.from(image.images[0].b64_json!, 'base64');
|
||||
fs.writeFileSync('robot.png', imageBuffer);
|
||||
|
||||
// Advanced: Transparent background with custom format
|
||||
const logo = await ai.openaiProvider.imageGenerate({
|
||||
prompt: 'Minimalist mountain peak logo, geometric design',
|
||||
model: 'gpt-image-1',
|
||||
quality: 'high',
|
||||
size: '1024x1024',
|
||||
background: 'transparent',
|
||||
outputFormat: 'png'
|
||||
});
|
||||
|
||||
// Using Anthropic for vision analysis
|
||||
const anthropicImageAnalysis = await smartAi.anthropicProvider.vision({
|
||||
image: imageBuffer,
|
||||
prompt: 'Describe the contents of this image'
|
||||
// WebP with compression for web use
|
||||
const webImage = await ai.openaiProvider.imageGenerate({
|
||||
prompt: 'Product showcase: sleek smartphone on marble surface',
|
||||
model: 'gpt-image-1',
|
||||
quality: 'high',
|
||||
size: '1536x1024',
|
||||
outputFormat: 'webp',
|
||||
outputCompression: 85
|
||||
});
|
||||
|
||||
// Superior text rendering (gpt-image-1's strength)
|
||||
const signage = await ai.openaiProvider.imageGenerate({
|
||||
prompt: 'Vintage cafe sign saying "COFFEE & CODE" in hand-lettered typography',
|
||||
model: 'gpt-image-1',
|
||||
quality: 'high',
|
||||
size: '1024x1024'
|
||||
});
|
||||
|
||||
// Generate multiple variations at once
|
||||
const variations = await ai.openaiProvider.imageGenerate({
|
||||
prompt: 'Abstract geometric pattern, colorful minimalist art',
|
||||
model: 'gpt-image-1',
|
||||
n: 3,
|
||||
quality: 'medium',
|
||||
size: '1024x1024'
|
||||
});
|
||||
|
||||
// Edit an existing image
|
||||
const editedImage = await ai.openaiProvider.imageEdit({
|
||||
image: originalImageBuffer,
|
||||
prompt: 'Add sunglasses and change the background to a beach sunset',
|
||||
model: 'gpt-image-1',
|
||||
quality: 'high'
|
||||
});
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
**Image Generation Options:**
|
||||
- `model`: 'gpt-image-1' | 'dall-e-3' | 'dall-e-2'
|
||||
- `quality`: 'low' | 'medium' | 'high' | 'auto'
|
||||
- `size`: Multiple aspect ratios up to 4096×4096
|
||||
- `background`: 'transparent' | 'opaque' | 'auto'
|
||||
- `outputFormat`: 'png' | 'jpeg' | 'webp'
|
||||
- `outputCompression`: 0-100 for webp/jpeg
|
||||
- `moderation`: 'low' | 'auto'
|
||||
- `n`: Number of images (1-10)
|
||||
|
||||
Always wrap API calls in try-catch blocks to manage errors effectively:
|
||||
**gpt-image-1 Advantages:**
|
||||
- Superior text rendering in images
|
||||
- Up to 4096×4096 resolution
|
||||
- Transparent background support
|
||||
- Advanced output formats (WebP with compression)
|
||||
- Better prompt understanding
|
||||
- Streaming support for progressive rendering
|
||||
|
||||
### 🔄 Persistent Conversations
|
||||
|
||||
Maintain context across interactions:
|
||||
|
||||
```typescript
|
||||
try {
|
||||
const response = await smartAi.openaiProvider.chat({
|
||||
systemMessage: 'You are a helpful assistant.',
|
||||
userMessage: 'Hello!',
|
||||
// Create a coding assistant conversation
|
||||
const assistant = ai.createConversation('openai');
|
||||
await assistant.setSystemMessage('You are an expert TypeScript developer.');
|
||||
|
||||
// First question
|
||||
const inputWriter = assistant.getInputStreamWriter();
|
||||
await inputWriter.write('How do I implement a singleton pattern?');
|
||||
|
||||
// Continue the conversation
|
||||
await inputWriter.write('Now show me how to make it thread-safe');
|
||||
|
||||
// The assistant remembers the entire context
|
||||
```
|
||||
|
||||
## 🚀 Real-World Examples
|
||||
|
||||
### Build a Customer Support Bot
|
||||
|
||||
```typescript
|
||||
const supportBot = new SmartAi({
|
||||
anthropicToken: process.env.ANTHROPIC_KEY // Claude for empathetic responses
|
||||
});
|
||||
|
||||
async function handleCustomerQuery(query: string, history: ChatMessage[]) {
|
||||
try {
|
||||
const response = await supportBot.anthropicProvider.chat({
|
||||
systemMessage: `You are a helpful customer support agent.
|
||||
Be empathetic, professional, and solution-oriented.`,
|
||||
userMessage: query,
|
||||
messageHistory: history
|
||||
});
|
||||
|
||||
return response.message;
|
||||
} catch (error) {
|
||||
// Fallback to another provider if needed
|
||||
return await supportBot.openaiProvider.chat({...});
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Create a Code Review Assistant
|
||||
|
||||
```typescript
|
||||
const codeReviewer = new SmartAi({
|
||||
groqToken: process.env.GROQ_KEY // Groq for speed
|
||||
});
|
||||
|
||||
async function reviewCode(code: string, language: string) {
|
||||
const startTime = Date.now();
|
||||
|
||||
const review = await codeReviewer.groqProvider.chat({
|
||||
systemMessage: `You are a ${language} expert. Review code for:
|
||||
- Security vulnerabilities
|
||||
- Performance issues
|
||||
- Best practices
|
||||
- Potential bugs`,
|
||||
userMessage: `Review this code:\n\n${code}`,
|
||||
messageHistory: []
|
||||
});
|
||||
console.log(response.message);
|
||||
} catch (error: any) {
|
||||
console.error('AI provider error:', error.message);
|
||||
|
||||
console.log(`Review completed in ${Date.now() - startTime}ms`);
|
||||
return review.message;
|
||||
}
|
||||
```
|
||||
|
||||
## Development
|
||||
### Build a Research Assistant
|
||||
|
||||
### Running Tests
|
||||
```typescript
|
||||
const researcher = new SmartAi({
|
||||
perplexityToken: process.env.PERPLEXITY_KEY
|
||||
});
|
||||
|
||||
To run the test suite, use the following command:
|
||||
async function research(topic: string) {
|
||||
// Perplexity excels at web-aware research
|
||||
const findings = await researcher.perplexityProvider.chat({
|
||||
systemMessage: 'You are a research assistant. Provide factual, cited information.',
|
||||
userMessage: `Research the latest developments in ${topic}`,
|
||||
messageHistory: []
|
||||
});
|
||||
|
||||
```bash
|
||||
npm run test
|
||||
return findings.message;
|
||||
}
|
||||
```
|
||||
|
||||
Ensure your environment is configured with the appropriate tokens and settings for the providers you are testing.
|
||||
### Local AI for Sensitive Data
|
||||
|
||||
### Building the Project
|
||||
```typescript
|
||||
const localAI = new SmartAi({
|
||||
ollama: {
|
||||
baseUrl: 'http://localhost:11434',
|
||||
model: 'llama2',
|
||||
visionModel: 'llava'
|
||||
}
|
||||
});
|
||||
|
||||
Compile the TypeScript code and build the package using:
|
||||
// Process sensitive documents without leaving your infrastructure
|
||||
async function analyzeSensitiveDoc(pdfBuffer: Buffer) {
|
||||
const analysis = await localAI.ollamaProvider.document({
|
||||
systemMessage: 'Extract and summarize key information.',
|
||||
userMessage: 'Analyze this confidential document',
|
||||
messageHistory: [],
|
||||
pdfDocuments: [pdfBuffer]
|
||||
});
|
||||
|
||||
```bash
|
||||
npm run build
|
||||
// Data never leaves your servers
|
||||
return analysis.message;
|
||||
}
|
||||
```
|
||||
|
||||
This command prepares the library for distribution.
|
||||
## ⚡ Performance Tips
|
||||
|
||||
## Contributing
|
||||
### 1. Provider Selection Strategy
|
||||
|
||||
Contributions are welcome! Please follow these steps:
|
||||
```typescript
|
||||
class SmartAIRouter {
|
||||
constructor(private ai: SmartAi) {}
|
||||
|
||||
1. Fork the repository.
|
||||
2. Create a feature branch:
|
||||
```bash
|
||||
git checkout -b feature/my-feature
|
||||
```
|
||||
3. Commit your changes with clear messages:
|
||||
```bash
|
||||
git commit -m 'Add new feature'
|
||||
```
|
||||
4. Push your branch to your fork:
|
||||
```bash
|
||||
git push origin feature/my-feature
|
||||
```
|
||||
5. Open a Pull Request with a detailed description of your changes.
|
||||
async query(message: string, requirements: {
|
||||
speed?: boolean;
|
||||
accuracy?: boolean;
|
||||
cost?: boolean;
|
||||
privacy?: boolean;
|
||||
}) {
|
||||
if (requirements.privacy) {
|
||||
return this.ai.ollamaProvider.chat({...}); // Local only
|
||||
}
|
||||
if (requirements.speed) {
|
||||
return this.ai.groqProvider.chat({...}); // 10x faster
|
||||
}
|
||||
if (requirements.accuracy) {
|
||||
return this.ai.anthropicProvider.chat({...}); // Best reasoning
|
||||
}
|
||||
// Default fallback
|
||||
return this.ai.openaiProvider.chat({...});
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 2. Streaming for Large Responses
|
||||
|
||||
```typescript
|
||||
// Don't wait for the entire response
|
||||
async function streamResponse(userQuery: string) {
|
||||
const stream = await ai.openaiProvider.chatStream(createInputStream(userQuery));
|
||||
|
||||
// Process tokens as they arrive
|
||||
for await (const chunk of stream) {
|
||||
updateUI(chunk); // Immediate feedback
|
||||
await processChunk(chunk); // Parallel processing
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 3. Parallel Multi-Provider Queries
|
||||
|
||||
```typescript
|
||||
// Get the best answer from multiple AIs
|
||||
async function consensusQuery(question: string) {
|
||||
const providers = [
|
||||
ai.openaiProvider.chat({...}),
|
||||
ai.anthropicProvider.chat({...}),
|
||||
ai.perplexityProvider.chat({...})
|
||||
];
|
||||
|
||||
const responses = await Promise.all(providers);
|
||||
return synthesizeResponses(responses);
|
||||
}
|
||||
```
|
||||
|
||||
## 🛠️ Advanced Features
|
||||
|
||||
### Custom Streaming Transformations
|
||||
|
||||
```typescript
|
||||
// Add real-time translation
|
||||
const translationStream = new TransformStream({
|
||||
async transform(chunk, controller) {
|
||||
const translated = await translateChunk(chunk);
|
||||
controller.enqueue(translated);
|
||||
}
|
||||
});
|
||||
|
||||
const responseStream = await ai.openaiProvider.chatStream(input);
|
||||
const translatedStream = responseStream.pipeThrough(translationStream);
|
||||
```
|
||||
|
||||
### Error Handling & Fallbacks
|
||||
|
||||
```typescript
|
||||
class ResilientAI {
|
||||
private providers = ['openai', 'anthropic', 'groq'];
|
||||
|
||||
async query(opts: ChatOptions): Promise<ChatResponse> {
|
||||
for (const provider of this.providers) {
|
||||
try {
|
||||
return await this.ai[`${provider}Provider`].chat(opts);
|
||||
} catch (error) {
|
||||
console.warn(`${provider} failed, trying next...`);
|
||||
continue;
|
||||
}
|
||||
}
|
||||
throw new Error('All providers failed');
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Token Counting & Cost Management
|
||||
|
||||
```typescript
|
||||
// Track usage across providers
|
||||
class UsageTracker {
|
||||
async trackedChat(provider: string, options: ChatOptions) {
|
||||
const start = Date.now();
|
||||
const response = await ai[`${provider}Provider`].chat(options);
|
||||
|
||||
const usage = {
|
||||
provider,
|
||||
duration: Date.now() - start,
|
||||
inputTokens: estimateTokens(options),
|
||||
outputTokens: estimateTokens(response.message)
|
||||
};
|
||||
|
||||
await this.logUsage(usage);
|
||||
return response;
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 📦 Installation & Setup
|
||||
|
||||
### Prerequisites
|
||||
|
||||
- Node.js 16+
|
||||
- TypeScript 4.5+
|
||||
- API keys for your chosen providers
|
||||
|
||||
### Environment Setup
|
||||
|
||||
```bash
|
||||
# Install
|
||||
npm install @push.rocks/smartai
|
||||
|
||||
# Set up environment variables
|
||||
export OPENAI_API_KEY=sk-...
|
||||
export ANTHROPIC_API_KEY=sk-ant-...
|
||||
export PERPLEXITY_API_KEY=pplx-...
|
||||
# ... etc
|
||||
```
|
||||
|
||||
### TypeScript Configuration
|
||||
|
||||
```json
|
||||
{
|
||||
"compilerOptions": {
|
||||
"target": "ES2022",
|
||||
"module": "NodeNext",
|
||||
"lib": ["ES2022"],
|
||||
"strict": true,
|
||||
"esModuleInterop": true,
|
||||
"skipLibCheck": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 🎯 Choosing the Right Provider
|
||||
|
||||
| Use Case | Recommended Provider | Why |
|
||||
|----------|---------------------|-----|
|
||||
| **General Purpose** | OpenAI | Most features, stable, well-documented |
|
||||
| **Complex Reasoning** | Anthropic | Superior logical thinking, safer outputs |
|
||||
| **Research & Facts** | Perplexity | Web-aware, provides citations |
|
||||
| **Deep Research** | OpenAI | Deep Research API with comprehensive analysis |
|
||||
| **Speed Critical** | Groq | 10x faster inference, sub-second responses |
|
||||
| **Privacy Critical** | Ollama | 100% local, no data leaves your servers |
|
||||
| **Real-time Data** | XAI | Access to current information |
|
||||
| **Cost Sensitive** | Ollama/Exo | Free (local) or distributed compute |
|
||||
|
||||
## 📈 Roadmap
|
||||
|
||||
- [x] Research & Web Search API
|
||||
- [x] Image generation support (gpt-image-1, DALL-E 3, DALL-E 2)
|
||||
- [ ] Streaming function calls
|
||||
- [ ] Voice input processing
|
||||
- [ ] Fine-tuning integration
|
||||
- [ ] Embedding support
|
||||
- [ ] Agent framework
|
||||
- [ ] More providers (Cohere, AI21, etc.)
|
||||
|
||||
## License and Legal Information
|
||||
|
||||
|
39
test/test.audio.openai.ts
Normal file
39
test/test.audio.openai.ts
Normal file
@@ -0,0 +1,39 @@
|
||||
import { expect, tap } from '@push.rocks/tapbundle';
|
||||
import * as qenv from '@push.rocks/qenv';
|
||||
import * as smartfile from '@push.rocks/smartfile';
|
||||
|
||||
const testQenv = new qenv.Qenv('./', './.nogit/');
|
||||
|
||||
import * as smartai from '../ts/index.js';
|
||||
|
||||
let testSmartai: smartai.SmartAi;
|
||||
|
||||
tap.test('OpenAI Audio: should create a smartai instance with OpenAI provider', async () => {
|
||||
testSmartai = new smartai.SmartAi({
|
||||
openaiToken: await testQenv.getEnvVarOnDemand('OPENAI_TOKEN'),
|
||||
});
|
||||
await testSmartai.start();
|
||||
});
|
||||
|
||||
tap.test('OpenAI Audio: should create audio response', async () => {
|
||||
// Call the audio method with a sample message.
|
||||
const audioStream = await testSmartai.openaiProvider.audio({
|
||||
message: 'This is a test of audio generation.',
|
||||
});
|
||||
// Read all chunks from the stream.
|
||||
const chunks: Uint8Array[] = [];
|
||||
for await (const chunk of audioStream) {
|
||||
chunks.push(chunk as Uint8Array);
|
||||
}
|
||||
const audioBuffer = Buffer.concat(chunks);
|
||||
await smartfile.fs.toFs(audioBuffer, './.nogit/testoutput.mp3');
|
||||
console.log(`Audio Buffer length: ${audioBuffer.length}`);
|
||||
// Assert that the resulting buffer is not empty.
|
||||
expect(audioBuffer.length).toBeGreaterThan(0);
|
||||
});
|
||||
|
||||
tap.test('OpenAI Audio: should stop the smartai instance', async () => {
|
||||
await testSmartai.stop();
|
||||
});
|
||||
|
||||
export default tap.start();
|
36
test/test.audio.stubs.ts
Normal file
36
test/test.audio.stubs.ts
Normal file
@@ -0,0 +1,36 @@
|
||||
import { expect, tap } from '@push.rocks/tapbundle';
|
||||
import * as qenv from '@push.rocks/qenv';
|
||||
|
||||
const testQenv = new qenv.Qenv('./', './.nogit/');
|
||||
|
||||
import * as smartai from '../ts/index.js';
|
||||
|
||||
let anthropicProvider: smartai.AnthropicProvider;
|
||||
|
||||
tap.test('Audio Stubs: should create Anthropic provider', async () => {
|
||||
anthropicProvider = new smartai.AnthropicProvider({
|
||||
anthropicToken: await testQenv.getEnvVarOnDemand('ANTHROPIC_TOKEN'),
|
||||
});
|
||||
await anthropicProvider.start();
|
||||
});
|
||||
|
||||
tap.test('Audio Stubs: Anthropic audio should throw not supported error', async () => {
|
||||
let errorCaught = false;
|
||||
|
||||
try {
|
||||
await anthropicProvider.audio({
|
||||
message: 'This should fail'
|
||||
});
|
||||
} catch (error) {
|
||||
errorCaught = true;
|
||||
expect(error.message).toInclude('not yet supported');
|
||||
}
|
||||
|
||||
expect(errorCaught).toBeTrue();
|
||||
});
|
||||
|
||||
tap.test('Audio Stubs: should stop Anthropic provider', async () => {
|
||||
await anthropicProvider.stop();
|
||||
});
|
||||
|
||||
export default tap.start();
|
93
test/test.basic.ts
Normal file
93
test/test.basic.ts
Normal file
@@ -0,0 +1,93 @@
|
||||
import { tap, expect } from '@push.rocks/tapbundle';
|
||||
import * as smartai from '../ts/index.js';
|
||||
|
||||
// Basic instantiation tests that don't require API tokens
|
||||
// These tests can run in CI/CD environments without credentials
|
||||
|
||||
tap.test('Basic: should create SmartAi instance', async () => {
|
||||
const testSmartai = new smartai.SmartAi({
|
||||
openaiToken: 'dummy-token-for-testing'
|
||||
});
|
||||
expect(testSmartai).toBeInstanceOf(smartai.SmartAi);
|
||||
// Provider is only created after calling start()
|
||||
expect(testSmartai.options.openaiToken).toEqual('dummy-token-for-testing');
|
||||
});
|
||||
|
||||
tap.test('Basic: should instantiate OpenAI provider', async () => {
|
||||
const openaiProvider = new smartai.OpenAiProvider({
|
||||
openaiToken: 'dummy-token'
|
||||
});
|
||||
expect(openaiProvider).toBeInstanceOf(smartai.OpenAiProvider);
|
||||
expect(typeof openaiProvider.chat).toEqual('function');
|
||||
expect(typeof openaiProvider.audio).toEqual('function');
|
||||
expect(typeof openaiProvider.vision).toEqual('function');
|
||||
expect(typeof openaiProvider.document).toEqual('function');
|
||||
expect(typeof openaiProvider.research).toEqual('function');
|
||||
});
|
||||
|
||||
tap.test('Basic: should instantiate Anthropic provider', async () => {
|
||||
const anthropicProvider = new smartai.AnthropicProvider({
|
||||
anthropicToken: 'dummy-token'
|
||||
});
|
||||
expect(anthropicProvider).toBeInstanceOf(smartai.AnthropicProvider);
|
||||
expect(typeof anthropicProvider.chat).toEqual('function');
|
||||
expect(typeof anthropicProvider.audio).toEqual('function');
|
||||
expect(typeof anthropicProvider.vision).toEqual('function');
|
||||
expect(typeof anthropicProvider.document).toEqual('function');
|
||||
expect(typeof anthropicProvider.research).toEqual('function');
|
||||
});
|
||||
|
||||
tap.test('Basic: should instantiate Perplexity provider', async () => {
|
||||
const perplexityProvider = new smartai.PerplexityProvider({
|
||||
perplexityToken: 'dummy-token'
|
||||
});
|
||||
expect(perplexityProvider).toBeInstanceOf(smartai.PerplexityProvider);
|
||||
expect(typeof perplexityProvider.chat).toEqual('function');
|
||||
expect(typeof perplexityProvider.research).toEqual('function');
|
||||
});
|
||||
|
||||
tap.test('Basic: should instantiate Groq provider', async () => {
|
||||
const groqProvider = new smartai.GroqProvider({
|
||||
groqToken: 'dummy-token'
|
||||
});
|
||||
expect(groqProvider).toBeInstanceOf(smartai.GroqProvider);
|
||||
expect(typeof groqProvider.chat).toEqual('function');
|
||||
expect(typeof groqProvider.research).toEqual('function');
|
||||
});
|
||||
|
||||
tap.test('Basic: should instantiate Ollama provider', async () => {
|
||||
const ollamaProvider = new smartai.OllamaProvider({
|
||||
baseUrl: 'http://localhost:11434'
|
||||
});
|
||||
expect(ollamaProvider).toBeInstanceOf(smartai.OllamaProvider);
|
||||
expect(typeof ollamaProvider.chat).toEqual('function');
|
||||
expect(typeof ollamaProvider.research).toEqual('function');
|
||||
});
|
||||
|
||||
tap.test('Basic: should instantiate xAI provider', async () => {
|
||||
const xaiProvider = new smartai.XAIProvider({
|
||||
xaiToken: 'dummy-token'
|
||||
});
|
||||
expect(xaiProvider).toBeInstanceOf(smartai.XAIProvider);
|
||||
expect(typeof xaiProvider.chat).toEqual('function');
|
||||
expect(typeof xaiProvider.research).toEqual('function');
|
||||
});
|
||||
|
||||
tap.test('Basic: should instantiate Exo provider', async () => {
|
||||
const exoProvider = new smartai.ExoProvider({
|
||||
exoBaseUrl: 'http://localhost:8000'
|
||||
});
|
||||
expect(exoProvider).toBeInstanceOf(smartai.ExoProvider);
|
||||
expect(typeof exoProvider.chat).toEqual('function');
|
||||
expect(typeof exoProvider.research).toEqual('function');
|
||||
});
|
||||
|
||||
tap.test('Basic: all providers should extend MultiModalModel', async () => {
|
||||
const openai = new smartai.OpenAiProvider({ openaiToken: 'test' });
|
||||
const anthropic = new smartai.AnthropicProvider({ anthropicToken: 'test' });
|
||||
|
||||
expect(openai).toBeInstanceOf(smartai.MultiModalModel);
|
||||
expect(anthropic).toBeInstanceOf(smartai.MultiModalModel);
|
||||
});
|
||||
|
||||
export default tap.start();
|
72
test/test.chat.anthropic.ts
Normal file
72
test/test.chat.anthropic.ts
Normal file
@@ -0,0 +1,72 @@
|
||||
import { expect, tap } from '@push.rocks/tapbundle';
|
||||
import * as qenv from '@push.rocks/qenv';
|
||||
|
||||
const testQenv = new qenv.Qenv('./', './.nogit/');
|
||||
|
||||
import * as smartai from '../ts/index.js';
|
||||
|
||||
let anthropicProvider: smartai.AnthropicProvider;
|
||||
|
||||
tap.test('Anthropic Chat: should create and start Anthropic provider', async () => {
|
||||
anthropicProvider = new smartai.AnthropicProvider({
|
||||
anthropicToken: await testQenv.getEnvVarOnDemand('ANTHROPIC_TOKEN'),
|
||||
});
|
||||
await anthropicProvider.start();
|
||||
expect(anthropicProvider).toBeInstanceOf(smartai.AnthropicProvider);
|
||||
});
|
||||
|
||||
tap.test('Anthropic Chat: should create chat response', async () => {
|
||||
const userMessage = 'What is the capital of France? Answer in one word.';
|
||||
const response = await anthropicProvider.chat({
|
||||
systemMessage: 'You are a helpful assistant. Be concise.',
|
||||
userMessage: userMessage,
|
||||
messageHistory: [],
|
||||
});
|
||||
console.log(`Anthropic Chat - User: ${userMessage}`);
|
||||
console.log(`Anthropic Chat - Response: ${response.message}`);
|
||||
|
||||
expect(response.role).toEqual('assistant');
|
||||
expect(response.message).toBeTruthy();
|
||||
expect(response.message.toLowerCase()).toInclude('paris');
|
||||
});
|
||||
|
||||
tap.test('Anthropic Chat: should handle message history', async () => {
|
||||
const messageHistory: smartai.ChatMessage[] = [
|
||||
{ role: 'user', content: 'My name is Claude Test' },
|
||||
{ role: 'assistant', content: 'Nice to meet you, Claude Test!' }
|
||||
];
|
||||
|
||||
const response = await anthropicProvider.chat({
|
||||
systemMessage: 'You are a helpful assistant with good memory.',
|
||||
userMessage: 'What is my name?',
|
||||
messageHistory: messageHistory,
|
||||
});
|
||||
|
||||
console.log(`Anthropic Memory Test - Response: ${response.message}`);
|
||||
expect(response.message.toLowerCase()).toInclude('claude test');
|
||||
});
|
||||
|
||||
tap.test('Anthropic Chat: should handle errors gracefully', async () => {
|
||||
// Test with invalid message (empty)
|
||||
let errorCaught = false;
|
||||
|
||||
try {
|
||||
await anthropicProvider.chat({
|
||||
systemMessage: '',
|
||||
userMessage: '',
|
||||
messageHistory: [],
|
||||
});
|
||||
} catch (error) {
|
||||
errorCaught = true;
|
||||
console.log('Expected error caught:', error.message);
|
||||
}
|
||||
|
||||
// Anthropic might handle empty messages, so we don't assert error
|
||||
console.log(`Error handling test - Error caught: ${errorCaught}`);
|
||||
});
|
||||
|
||||
tap.test('Anthropic Chat: should stop the provider', async () => {
|
||||
await anthropicProvider.stop();
|
||||
});
|
||||
|
||||
export default tap.start();
|
34
test/test.chat.openai.ts
Normal file
34
test/test.chat.openai.ts
Normal file
@@ -0,0 +1,34 @@
|
||||
import { expect, tap } from '@push.rocks/tapbundle';
|
||||
import * as qenv from '@push.rocks/qenv';
|
||||
|
||||
const testQenv = new qenv.Qenv('./', './.nogit/');
|
||||
|
||||
import * as smartai from '../ts/index.js';
|
||||
|
||||
let testSmartai: smartai.SmartAi;
|
||||
|
||||
tap.test('OpenAI Chat: should create a smartai instance with OpenAI provider', async () => {
|
||||
testSmartai = new smartai.SmartAi({
|
||||
openaiToken: await testQenv.getEnvVarOnDemand('OPENAI_TOKEN'),
|
||||
});
|
||||
await testSmartai.start();
|
||||
});
|
||||
|
||||
tap.test('OpenAI Chat: should create chat response', async () => {
|
||||
const userMessage = 'How are you?';
|
||||
const response = await testSmartai.openaiProvider.chat({
|
||||
systemMessage: 'Hello',
|
||||
userMessage: userMessage,
|
||||
messageHistory: [],
|
||||
});
|
||||
console.log(`userMessage: ${userMessage}`);
|
||||
console.log(response.message);
|
||||
expect(response.role).toEqual('assistant');
|
||||
expect(response.message).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('OpenAI Chat: should stop the smartai instance', async () => {
|
||||
await testSmartai.stop();
|
||||
});
|
||||
|
||||
export default tap.start();
|
78
test/test.document.anthropic.ts
Normal file
78
test/test.document.anthropic.ts
Normal file
@@ -0,0 +1,78 @@
|
||||
import { expect, tap } from '@push.rocks/tapbundle';
|
||||
import * as qenv from '@push.rocks/qenv';
|
||||
import * as smartrequest from '@push.rocks/smartrequest';
|
||||
import * as smartfile from '@push.rocks/smartfile';
|
||||
|
||||
const testQenv = new qenv.Qenv('./', './.nogit/');
|
||||
|
||||
import * as smartai from '../ts/index.js';
|
||||
|
||||
let anthropicProvider: smartai.AnthropicProvider;
|
||||
|
||||
tap.test('Anthropic Document: should create and start Anthropic provider', async () => {
|
||||
anthropicProvider = new smartai.AnthropicProvider({
|
||||
anthropicToken: await testQenv.getEnvVarOnDemand('ANTHROPIC_TOKEN'),
|
||||
});
|
||||
await anthropicProvider.start();
|
||||
expect(anthropicProvider).toBeInstanceOf(smartai.AnthropicProvider);
|
||||
});
|
||||
|
||||
tap.test('Anthropic Document: should document a PDF', async () => {
|
||||
const pdfUrl = 'https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf';
|
||||
const pdfResponse = await smartrequest.SmartRequest.create()
|
||||
.url(pdfUrl)
|
||||
.get();
|
||||
|
||||
const result = await anthropicProvider.document({
|
||||
systemMessage: 'Classify the document. Only the following answers are allowed: "invoice", "bank account statement", "contract", "test document", "other". The answer should only contain the keyword for machine use.',
|
||||
userMessage: 'Classify this document.',
|
||||
messageHistory: [],
|
||||
pdfDocuments: [Buffer.from(await pdfResponse.arrayBuffer())],
|
||||
});
|
||||
|
||||
console.log(`Anthropic Document - Result:`, result);
|
||||
expect(result).toBeTruthy();
|
||||
expect(result.message).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('Anthropic Document: should handle complex document analysis', async () => {
|
||||
// Test with the demo PDF if it exists
|
||||
const pdfPath = './.nogit/demo_without_textlayer.pdf';
|
||||
let pdfBuffer: Uint8Array;
|
||||
|
||||
try {
|
||||
pdfBuffer = await smartfile.fs.toBuffer(pdfPath);
|
||||
} catch (error) {
|
||||
// If the file doesn't exist, use the dummy PDF
|
||||
console.log('Demo PDF not found, using dummy PDF instead');
|
||||
const pdfUrl = 'https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf';
|
||||
const pdfResponse = await smartrequest.SmartRequest.create()
|
||||
.url(pdfUrl)
|
||||
.get();
|
||||
pdfBuffer = Buffer.from(await pdfResponse.arrayBuffer());
|
||||
}
|
||||
|
||||
const result = await anthropicProvider.document({
|
||||
systemMessage: `
|
||||
Analyze this document and provide a JSON response with the following structure:
|
||||
{
|
||||
"documentType": "string",
|
||||
"hasText": boolean,
|
||||
"summary": "string"
|
||||
}
|
||||
`,
|
||||
userMessage: 'Analyze this document.',
|
||||
messageHistory: [],
|
||||
pdfDocuments: [pdfBuffer],
|
||||
});
|
||||
|
||||
console.log(`Anthropic Complex Document Analysis:`, result);
|
||||
expect(result).toBeTruthy();
|
||||
expect(result.message).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('Anthropic Document: should stop the provider', async () => {
|
||||
await anthropicProvider.stop();
|
||||
});
|
||||
|
||||
export default tap.start();
|
@@ -1,4 +1,4 @@
|
||||
import { expect, expectAsync, tap } from '@push.rocks/tapbundle';
|
||||
import { expect, tap } from '@push.rocks/tapbundle';
|
||||
import * as qenv from '@push.rocks/qenv';
|
||||
import * as smartrequest from '@push.rocks/smartrequest';
|
||||
import * as smartfile from '@push.rocks/smartfile';
|
||||
@@ -9,38 +9,29 @@ import * as smartai from '../ts/index.js';
|
||||
|
||||
let testSmartai: smartai.SmartAi;
|
||||
|
||||
tap.test('should create a smartai instance', async () => {
|
||||
tap.test('OpenAI Document: should create a smartai instance with OpenAI provider', async () => {
|
||||
testSmartai = new smartai.SmartAi({
|
||||
openaiToken: await testQenv.getEnvVarOnDemand('OPENAI_TOKEN'),
|
||||
});
|
||||
await testSmartai.start();
|
||||
});
|
||||
|
||||
tap.test('should create chat response with openai', async () => {
|
||||
const userMessage = 'How are you?';
|
||||
const response = await testSmartai.openaiProvider.chat({
|
||||
systemMessage: 'Hello',
|
||||
userMessage: userMessage,
|
||||
messageHistory: [
|
||||
],
|
||||
});
|
||||
console.log(`userMessage: ${userMessage}`);
|
||||
console.log(response.message);
|
||||
});
|
||||
|
||||
tap.test('should document a pdf', async () => {
|
||||
tap.test('OpenAI Document: should document a pdf', async () => {
|
||||
const pdfUrl = 'https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf';
|
||||
const pdfResponse = await smartrequest.getBinary(pdfUrl);
|
||||
const pdfResponse = await smartrequest.SmartRequest.create()
|
||||
.url(pdfUrl)
|
||||
.get();
|
||||
const result = await testSmartai.openaiProvider.document({
|
||||
systemMessage: 'Classify the document. Only the following answers are allowed: "invoice", "bank account statement", "contract", "other". The answer should only contain the keyword for machine use.',
|
||||
userMessage: "Classify the document.",
|
||||
messageHistory: [],
|
||||
pdfDocuments: [pdfResponse.body],
|
||||
pdfDocuments: [Buffer.from(await pdfResponse.arrayBuffer())],
|
||||
});
|
||||
console.log(result);
|
||||
expect(result.message).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('should recognize companies in a pdf', async () => {
|
||||
tap.test('OpenAI Document: should recognize companies in a pdf', async () => {
|
||||
const pdfBuffer = await smartfile.fs.toBuffer('./.nogit/demo_without_textlayer.pdf');
|
||||
const result = await testSmartai.openaiProvider.document({
|
||||
systemMessage: `
|
||||
@@ -55,7 +46,7 @@ tap.test('should recognize companies in a pdf', async () => {
|
||||
address: string;
|
||||
city: string;
|
||||
country: string;
|
||||
EU: boolean; // wether the entity is within EU
|
||||
EU: boolean; // whether the entity is within EU
|
||||
};
|
||||
entityReceiver: {
|
||||
type: 'official state entity' | 'company' | 'person';
|
||||
@@ -63,7 +54,7 @@ tap.test('should recognize companies in a pdf', async () => {
|
||||
address: string;
|
||||
city: string;
|
||||
country: string;
|
||||
EU: boolean; // wether the entity is within EU
|
||||
EU: boolean; // whether the entity is within EU
|
||||
};
|
||||
date: string; // the date of the document as YYYY-MM-DD
|
||||
title: string; // a short title, suitable for a filename
|
||||
@@ -75,9 +66,10 @@ tap.test('should recognize companies in a pdf', async () => {
|
||||
pdfDocuments: [pdfBuffer],
|
||||
});
|
||||
console.log(result);
|
||||
})
|
||||
expect(result.message).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('should stop the smartai instance', async () => {
|
||||
tap.test('OpenAI Document: should stop the smartai instance', async () => {
|
||||
await testSmartai.stop();
|
||||
});
|
||||
|
203
test/test.image.openai.ts
Normal file
203
test/test.image.openai.ts
Normal file
@@ -0,0 +1,203 @@
|
||||
import { expect, tap } from '@push.rocks/tapbundle';
|
||||
import * as qenv from '@push.rocks/qenv';
|
||||
import * as smartai from '../ts/index.js';
|
||||
import * as path from 'path';
|
||||
import { promises as fs } from 'fs';
|
||||
|
||||
const testQenv = new qenv.Qenv('./', './.nogit/');
|
||||
|
||||
let openaiProvider: smartai.OpenAiProvider;
|
||||
|
||||
// Helper function to save image results
|
||||
async function saveImageResult(testName: string, result: any) {
|
||||
const sanitizedName = testName.replace(/[^a-z0-9]/gi, '_').toLowerCase();
|
||||
const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
|
||||
const filename = `openai_${sanitizedName}_${timestamp}.json`;
|
||||
const filepath = path.join('.nogit', 'testresults', 'images', filename);
|
||||
|
||||
await fs.mkdir(path.dirname(filepath), { recursive: true });
|
||||
await fs.writeFile(filepath, JSON.stringify(result, null, 2), 'utf-8');
|
||||
|
||||
console.log(` 💾 Saved to: ${filepath}`);
|
||||
|
||||
// Also save the actual image if b64_json is present
|
||||
if (result.images && result.images[0]?.b64_json) {
|
||||
const imageFilename = `openai_${sanitizedName}_${timestamp}.png`;
|
||||
const imageFilepath = path.join('.nogit', 'testresults', 'images', imageFilename);
|
||||
await fs.writeFile(imageFilepath, Buffer.from(result.images[0].b64_json, 'base64'));
|
||||
console.log(` 🖼️ Image saved to: ${imageFilepath}`);
|
||||
}
|
||||
}
|
||||
|
||||
tap.test('OpenAI Image Generation: should initialize provider', async () => {
|
||||
const openaiToken = await testQenv.getEnvVarOnDemand('OPENAI_TOKEN');
|
||||
expect(openaiToken).toBeTruthy();
|
||||
|
||||
openaiProvider = new smartai.OpenAiProvider({
|
||||
openaiToken,
|
||||
imageModel: 'gpt-image-1'
|
||||
});
|
||||
|
||||
await openaiProvider.start();
|
||||
expect(openaiProvider).toBeInstanceOf(smartai.OpenAiProvider);
|
||||
});
|
||||
|
||||
tap.test('OpenAI Image: Basic generation with gpt-image-1', async () => {
|
||||
const result = await openaiProvider.imageGenerate({
|
||||
prompt: 'A cute robot reading a book in a cozy library, digital art style',
|
||||
model: 'gpt-image-1',
|
||||
quality: 'medium',
|
||||
size: '1024x1024'
|
||||
});
|
||||
|
||||
console.log('Basic gpt-image-1 Generation:');
|
||||
console.log('- Images generated:', result.images.length);
|
||||
console.log('- Model used:', result.metadata?.model);
|
||||
console.log('- Quality:', result.metadata?.quality);
|
||||
console.log('- Size:', result.metadata?.size);
|
||||
console.log('- Tokens used:', result.metadata?.tokensUsed);
|
||||
|
||||
await saveImageResult('basic_generation_gptimage1', result);
|
||||
|
||||
expect(result.images).toBeTruthy();
|
||||
expect(result.images.length).toEqual(1);
|
||||
expect(result.images[0].b64_json).toBeTruthy();
|
||||
expect(result.metadata?.model).toEqual('gpt-image-1');
|
||||
});
|
||||
|
||||
tap.test('OpenAI Image: High quality with transparent background', async () => {
|
||||
const result = await openaiProvider.imageGenerate({
|
||||
prompt: 'A simple geometric logo of a mountain peak, minimal design, clean lines',
|
||||
model: 'gpt-image-1',
|
||||
quality: 'high',
|
||||
size: '1024x1024',
|
||||
background: 'transparent',
|
||||
outputFormat: 'png'
|
||||
});
|
||||
|
||||
console.log('High Quality Transparent:');
|
||||
console.log('- Quality:', result.metadata?.quality);
|
||||
console.log('- Background: transparent');
|
||||
console.log('- Format:', result.metadata?.outputFormat);
|
||||
console.log('- Tokens used:', result.metadata?.tokensUsed);
|
||||
|
||||
await saveImageResult('high_quality_transparent', result);
|
||||
|
||||
expect(result.images.length).toEqual(1);
|
||||
expect(result.images[0].b64_json).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('OpenAI Image: WebP format with compression', async () => {
|
||||
const result = await openaiProvider.imageGenerate({
|
||||
prompt: 'A futuristic cityscape at sunset with flying cars, photorealistic',
|
||||
model: 'gpt-image-1',
|
||||
quality: 'high',
|
||||
size: '1536x1024',
|
||||
outputFormat: 'webp',
|
||||
outputCompression: 85
|
||||
});
|
||||
|
||||
console.log('WebP with Compression:');
|
||||
console.log('- Format:', result.metadata?.outputFormat);
|
||||
console.log('- Compression: 85%');
|
||||
console.log('- Size:', result.metadata?.size);
|
||||
|
||||
await saveImageResult('webp_compression', result);
|
||||
|
||||
expect(result.images.length).toEqual(1);
|
||||
expect(result.images[0].b64_json).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('OpenAI Image: Text rendering with gpt-image-1', async () => {
|
||||
const result = await openaiProvider.imageGenerate({
|
||||
prompt: 'A vintage cafe sign that says "COFFEE & CODE" in elegant hand-lettered typography, warm colors',
|
||||
model: 'gpt-image-1',
|
||||
quality: 'high',
|
||||
size: '1024x1024'
|
||||
});
|
||||
|
||||
console.log('Text Rendering:');
|
||||
console.log('- Prompt includes text: "COFFEE & CODE"');
|
||||
console.log('- gpt-image-1 has superior text rendering');
|
||||
console.log('- Tokens used:', result.metadata?.tokensUsed);
|
||||
|
||||
await saveImageResult('text_rendering', result);
|
||||
|
||||
expect(result.images.length).toEqual(1);
|
||||
expect(result.images[0].b64_json).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('OpenAI Image: Multiple images generation', async () => {
|
||||
const result = await openaiProvider.imageGenerate({
|
||||
prompt: 'Abstract colorful geometric patterns, modern minimalist art',
|
||||
model: 'gpt-image-1',
|
||||
n: 2,
|
||||
quality: 'medium',
|
||||
size: '1024x1024'
|
||||
});
|
||||
|
||||
console.log('Multiple Images:');
|
||||
console.log('- Images requested: 2');
|
||||
console.log('- Images generated:', result.images.length);
|
||||
|
||||
await saveImageResult('multiple_images', result);
|
||||
|
||||
expect(result.images.length).toEqual(2);
|
||||
expect(result.images[0].b64_json).toBeTruthy();
|
||||
expect(result.images[1].b64_json).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('OpenAI Image: Low moderation setting', async () => {
|
||||
const result = await openaiProvider.imageGenerate({
|
||||
prompt: 'A fantasy battle scene with warriors and dragons',
|
||||
model: 'gpt-image-1',
|
||||
moderation: 'low',
|
||||
quality: 'medium'
|
||||
});
|
||||
|
||||
console.log('Low Moderation:');
|
||||
console.log('- Moderation: low (less restrictive filtering)');
|
||||
console.log('- Tokens used:', result.metadata?.tokensUsed);
|
||||
|
||||
await saveImageResult('low_moderation', result);
|
||||
|
||||
expect(result.images.length).toEqual(1);
|
||||
expect(result.images[0].b64_json).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('OpenAI Image Editing: edit with gpt-image-1', async () => {
|
||||
// First, generate a base image
|
||||
const baseResult = await openaiProvider.imageGenerate({
|
||||
prompt: 'A simple white cat sitting on a red cushion',
|
||||
model: 'gpt-image-1',
|
||||
quality: 'low',
|
||||
size: '1024x1024'
|
||||
});
|
||||
|
||||
const baseImageBuffer = Buffer.from(baseResult.images[0].b64_json!, 'base64');
|
||||
|
||||
// Now edit it
|
||||
const editResult = await openaiProvider.imageEdit({
|
||||
image: baseImageBuffer,
|
||||
prompt: 'Change the cat to orange and add stylish sunglasses',
|
||||
model: 'gpt-image-1',
|
||||
quality: 'medium'
|
||||
});
|
||||
|
||||
console.log('Image Editing:');
|
||||
console.log('- Base image created');
|
||||
console.log('- Edit: change color and add sunglasses');
|
||||
console.log('- Result images:', editResult.images.length);
|
||||
|
||||
await saveImageResult('image_edit', editResult);
|
||||
|
||||
expect(editResult.images.length).toEqual(1);
|
||||
expect(editResult.images[0].b64_json).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('OpenAI Image: should clean up provider', async () => {
|
||||
await openaiProvider.stop();
|
||||
console.log('OpenAI image provider stopped successfully');
|
||||
});
|
||||
|
||||
export default tap.start();
|
140
test/test.interfaces.ts
Normal file
140
test/test.interfaces.ts
Normal file
@@ -0,0 +1,140 @@
|
||||
import { tap, expect } from '@push.rocks/tapbundle';
|
||||
import * as smartai from '../ts/index.js';
|
||||
|
||||
// Test interface exports and type checking
|
||||
// These tests verify that all interfaces are properly exported and usable
|
||||
|
||||
tap.test('Interfaces: ResearchOptions should be properly typed', async () => {
|
||||
const testOptions: smartai.ResearchOptions = {
|
||||
query: 'test query',
|
||||
searchDepth: 'basic',
|
||||
maxSources: 10,
|
||||
includeWebSearch: true,
|
||||
background: false
|
||||
};
|
||||
|
||||
expect(testOptions).toBeInstanceOf(Object);
|
||||
expect(testOptions.query).toEqual('test query');
|
||||
expect(testOptions.searchDepth).toEqual('basic');
|
||||
});
|
||||
|
||||
tap.test('Interfaces: ResearchResponse should be properly typed', async () => {
|
||||
const testResponse: smartai.ResearchResponse = {
|
||||
answer: 'test answer',
|
||||
sources: [
|
||||
{
|
||||
url: 'https://example.com',
|
||||
title: 'Example Source',
|
||||
snippet: 'This is a snippet'
|
||||
}
|
||||
],
|
||||
searchQueries: ['query1', 'query2'],
|
||||
metadata: {
|
||||
model: 'test-model',
|
||||
tokensUsed: 100
|
||||
}
|
||||
};
|
||||
|
||||
expect(testResponse).toBeInstanceOf(Object);
|
||||
expect(testResponse.answer).toEqual('test answer');
|
||||
expect(testResponse.sources).toBeArray();
|
||||
expect(testResponse.sources[0].url).toEqual('https://example.com');
|
||||
});
|
||||
|
||||
tap.test('Interfaces: ChatOptions should be properly typed', async () => {
|
||||
const testChatOptions: smartai.ChatOptions = {
|
||||
systemMessage: 'You are a helpful assistant',
|
||||
userMessage: 'Hello',
|
||||
messageHistory: [
|
||||
{ role: 'user', content: 'Previous message' },
|
||||
{ role: 'assistant', content: 'Previous response' }
|
||||
]
|
||||
};
|
||||
|
||||
expect(testChatOptions).toBeInstanceOf(Object);
|
||||
expect(testChatOptions.systemMessage).toBeTruthy();
|
||||
expect(testChatOptions.messageHistory).toBeArray();
|
||||
});
|
||||
|
||||
tap.test('Interfaces: ChatResponse should be properly typed', async () => {
|
||||
const testChatResponse: smartai.ChatResponse = {
|
||||
role: 'assistant',
|
||||
message: 'This is a response'
|
||||
};
|
||||
|
||||
expect(testChatResponse).toBeInstanceOf(Object);
|
||||
expect(testChatResponse.role).toEqual('assistant');
|
||||
expect(testChatResponse.message).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('Interfaces: ChatMessage should be properly typed', async () => {
|
||||
const testMessage: smartai.ChatMessage = {
|
||||
role: 'user',
|
||||
content: 'Test message'
|
||||
};
|
||||
|
||||
expect(testMessage).toBeInstanceOf(Object);
|
||||
expect(testMessage.role).toBeOneOf(['user', 'assistant', 'system']);
|
||||
expect(testMessage.content).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('Interfaces: Provider options should be properly typed', async () => {
|
||||
// OpenAI options
|
||||
const openaiOptions: smartai.IOpenaiProviderOptions = {
|
||||
openaiToken: 'test-token',
|
||||
chatModel: 'gpt-5-mini',
|
||||
audioModel: 'tts-1-hd',
|
||||
visionModel: '04-mini',
|
||||
researchModel: 'o4-mini-deep-research-2025-06-26',
|
||||
enableWebSearch: true
|
||||
};
|
||||
|
||||
expect(openaiOptions).toBeInstanceOf(Object);
|
||||
expect(openaiOptions.openaiToken).toBeTruthy();
|
||||
|
||||
// Anthropic options
|
||||
const anthropicOptions: smartai.IAnthropicProviderOptions = {
|
||||
anthropicToken: 'test-token',
|
||||
enableWebSearch: true,
|
||||
searchDomainAllowList: ['example.com'],
|
||||
searchDomainBlockList: ['blocked.com']
|
||||
};
|
||||
|
||||
expect(anthropicOptions).toBeInstanceOf(Object);
|
||||
expect(anthropicOptions.anthropicToken).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('Interfaces: Search depth values should be valid', async () => {
|
||||
const validDepths: smartai.ResearchOptions['searchDepth'][] = ['basic', 'advanced', 'deep'];
|
||||
|
||||
for (const depth of validDepths) {
|
||||
const options: smartai.ResearchOptions = {
|
||||
query: 'test',
|
||||
searchDepth: depth
|
||||
};
|
||||
expect(options.searchDepth).toBeOneOf(['basic', 'advanced', 'deep', undefined]);
|
||||
}
|
||||
});
|
||||
|
||||
tap.test('Interfaces: Optional properties should work correctly', async () => {
|
||||
// Minimal ResearchOptions
|
||||
const minimalOptions: smartai.ResearchOptions = {
|
||||
query: 'test query'
|
||||
};
|
||||
|
||||
expect(minimalOptions.query).toBeTruthy();
|
||||
expect(minimalOptions.searchDepth).toBeUndefined();
|
||||
expect(minimalOptions.maxSources).toBeUndefined();
|
||||
|
||||
// Minimal ChatOptions
|
||||
const minimalChat: smartai.ChatOptions = {
|
||||
systemMessage: 'system',
|
||||
userMessage: 'user',
|
||||
messageHistory: []
|
||||
};
|
||||
|
||||
expect(minimalChat.messageHistory).toBeArray();
|
||||
expect(minimalChat.messageHistory.length).toEqual(0);
|
||||
});
|
||||
|
||||
export default tap.start();
|
223
test/test.research.anthropic.ts
Normal file
223
test/test.research.anthropic.ts
Normal file
@@ -0,0 +1,223 @@
|
||||
import { expect, tap } from '@push.rocks/tapbundle';
|
||||
import * as qenv from '@push.rocks/qenv';
|
||||
import * as smartai from '../ts/index.js';
|
||||
import * as path from 'path';
|
||||
import { promises as fs } from 'fs';
|
||||
|
||||
const testQenv = new qenv.Qenv('./', './.nogit/');
|
||||
|
||||
// Helper function to save research results
|
||||
async function saveResearchResult(testName: string, result: any) {
|
||||
const sanitizedName = testName.replace(/[^a-z0-9]/gi, '_').toLowerCase();
|
||||
const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
|
||||
const filename = `${sanitizedName}_${timestamp}.json`;
|
||||
const filepath = path.join('.nogit', 'testresults', 'research', filename);
|
||||
|
||||
await fs.mkdir(path.dirname(filepath), { recursive: true });
|
||||
await fs.writeFile(filepath, JSON.stringify(result, null, 2), 'utf-8');
|
||||
|
||||
console.log(` 💾 Saved to: ${filepath}`);
|
||||
}
|
||||
|
||||
let anthropicProvider: smartai.AnthropicProvider;
|
||||
|
||||
tap.test('Anthropic Research: should initialize provider with web search', async () => {
|
||||
anthropicProvider = new smartai.AnthropicProvider({
|
||||
anthropicToken: await testQenv.getEnvVarOnDemand('ANTHROPIC_TOKEN'),
|
||||
enableWebSearch: true
|
||||
});
|
||||
|
||||
await anthropicProvider.start();
|
||||
expect(anthropicProvider).toBeInstanceOf(smartai.AnthropicProvider);
|
||||
expect(typeof anthropicProvider.research).toEqual('function');
|
||||
});
|
||||
|
||||
tap.test('Anthropic Research: should perform basic research query', async () => {
|
||||
const result = await anthropicProvider.research({
|
||||
query: 'What is machine learning and its main applications?',
|
||||
searchDepth: 'basic'
|
||||
});
|
||||
|
||||
console.log('Anthropic Basic Research:');
|
||||
console.log('- Answer length:', result.answer.length);
|
||||
console.log('- Sources found:', result.sources.length);
|
||||
console.log('- First 200 chars:', result.answer.substring(0, 200));
|
||||
|
||||
await saveResearchResult('basic_research_machine_learning', result);
|
||||
|
||||
expect(result).toBeTruthy();
|
||||
expect(result.answer).toBeTruthy();
|
||||
expect(result.answer.toLowerCase()).toInclude('machine learning');
|
||||
expect(result.sources).toBeArray();
|
||||
expect(result.metadata).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('Anthropic Research: should perform research with web search', async () => {
|
||||
const result = await anthropicProvider.research({
|
||||
query: 'What are the latest developments in renewable energy technology?',
|
||||
searchDepth: 'advanced',
|
||||
includeWebSearch: true,
|
||||
maxSources: 5
|
||||
});
|
||||
|
||||
console.log('Anthropic Web Search Research:');
|
||||
console.log('- Answer length:', result.answer.length);
|
||||
console.log('- Sources:', result.sources.length);
|
||||
if (result.searchQueries) {
|
||||
console.log('- Search queries:', result.searchQueries);
|
||||
}
|
||||
|
||||
await saveResearchResult('web_search_renewable_energy', result);
|
||||
|
||||
expect(result.answer).toBeTruthy();
|
||||
expect(result.answer.toLowerCase()).toInclude('renewable');
|
||||
|
||||
// Check if sources were extracted
|
||||
if (result.sources.length > 0) {
|
||||
console.log('- Example source:', result.sources[0]);
|
||||
expect(result.sources[0]).toHaveProperty('url');
|
||||
}
|
||||
});
|
||||
|
||||
tap.test('Anthropic Research: should handle deep research queries', async () => {
|
||||
const result = await anthropicProvider.research({
|
||||
query: 'Explain the differences between REST and GraphQL APIs',
|
||||
searchDepth: 'deep'
|
||||
});
|
||||
|
||||
console.log('Anthropic Deep Research:');
|
||||
console.log('- Answer length:', result.answer.length);
|
||||
console.log('- Token usage:', result.metadata?.tokensUsed);
|
||||
|
||||
await saveResearchResult('deep_research_rest_vs_graphql', result);
|
||||
|
||||
expect(result.answer).toBeTruthy();
|
||||
expect(result.answer.length).toBeGreaterThan(300);
|
||||
expect(result.answer.toLowerCase()).toInclude('rest');
|
||||
expect(result.answer.toLowerCase()).toInclude('graphql');
|
||||
});
|
||||
|
||||
tap.test('Anthropic Research: should extract citations from response', async () => {
|
||||
const result = await anthropicProvider.research({
|
||||
query: 'What is Docker and how does containerization work?',
|
||||
searchDepth: 'basic',
|
||||
maxSources: 3
|
||||
});
|
||||
|
||||
console.log('Anthropic Citation Extraction:');
|
||||
console.log('- Sources found:', result.sources.length);
|
||||
console.log('- Answer includes Docker:', result.answer.toLowerCase().includes('docker'));
|
||||
|
||||
await saveResearchResult('citation_extraction_docker', result);
|
||||
|
||||
expect(result.answer).toInclude('Docker');
|
||||
|
||||
// Check for URL extraction (both markdown and plain URLs)
|
||||
const hasUrls = result.answer.includes('http') || result.sources.length > 0;
|
||||
console.log('- Contains URLs or sources:', hasUrls);
|
||||
});
|
||||
|
||||
tap.test('Anthropic Research: should use domain filtering when configured', async () => {
|
||||
// Create a new provider with domain restrictions
|
||||
const filteredProvider = new smartai.AnthropicProvider({
|
||||
anthropicToken: await testQenv.getEnvVarOnDemand('ANTHROPIC_TOKEN'),
|
||||
enableWebSearch: true,
|
||||
searchDomainAllowList: ['wikipedia.org', 'docs.microsoft.com'],
|
||||
searchDomainBlockList: ['reddit.com']
|
||||
});
|
||||
|
||||
await filteredProvider.start();
|
||||
|
||||
const result = await filteredProvider.research({
|
||||
query: 'What is JavaScript?',
|
||||
searchDepth: 'basic'
|
||||
});
|
||||
|
||||
console.log('Anthropic Domain Filtering Test:');
|
||||
console.log('- Answer length:', result.answer.length);
|
||||
console.log('- Applied domain filters (allow: wikipedia, docs.microsoft)');
|
||||
|
||||
await saveResearchResult('domain_filtering_javascript', result);
|
||||
|
||||
expect(result.answer).toBeTruthy();
|
||||
expect(result.answer.toLowerCase()).toInclude('javascript');
|
||||
|
||||
await filteredProvider.stop();
|
||||
});
|
||||
|
||||
tap.test('Anthropic Research: should handle errors gracefully', async () => {
|
||||
let errorCaught = false;
|
||||
|
||||
try {
|
||||
await anthropicProvider.research({
|
||||
query: '', // Empty query
|
||||
searchDepth: 'basic'
|
||||
});
|
||||
} catch (error) {
|
||||
errorCaught = true;
|
||||
console.log('Expected error for empty query:', error.message.substring(0, 100));
|
||||
}
|
||||
|
||||
// Anthropic might handle empty queries differently
|
||||
console.log(`Empty query error test - Error caught: ${errorCaught}`);
|
||||
});
|
||||
|
||||
tap.test('Anthropic Research: should handle different search depths', async () => {
|
||||
// Test basic search depth
|
||||
const basicResult = await anthropicProvider.research({
|
||||
query: 'What is Python?',
|
||||
searchDepth: 'basic'
|
||||
});
|
||||
|
||||
// Test advanced search depth
|
||||
const advancedResult = await anthropicProvider.research({
|
||||
query: 'What is Python?',
|
||||
searchDepth: 'advanced'
|
||||
});
|
||||
|
||||
console.log('Anthropic Search Depth Comparison:');
|
||||
console.log('- Basic answer length:', basicResult.answer.length);
|
||||
console.log('- Advanced answer length:', advancedResult.answer.length);
|
||||
console.log('- Basic tokens:', basicResult.metadata?.tokensUsed);
|
||||
console.log('- Advanced tokens:', advancedResult.metadata?.tokensUsed);
|
||||
|
||||
await saveResearchResult('search_depth_python_basic', basicResult);
|
||||
await saveResearchResult('search_depth_python_advanced', advancedResult);
|
||||
|
||||
expect(basicResult.answer).toBeTruthy();
|
||||
expect(advancedResult.answer).toBeTruthy();
|
||||
|
||||
// Advanced search typically produces longer answers
|
||||
// But this isn't guaranteed, so we just check they exist
|
||||
expect(basicResult.answer.toLowerCase()).toInclude('python');
|
||||
expect(advancedResult.answer.toLowerCase()).toInclude('python');
|
||||
});
|
||||
|
||||
tap.test('Anthropic Research: ARM vs. Qualcomm comparison', async () => {
|
||||
const result = await anthropicProvider.research({
|
||||
query: 'Compare ARM and Qualcomm: their technologies, market positions, and recent developments in the mobile and computing sectors',
|
||||
searchDepth: 'advanced',
|
||||
includeWebSearch: true,
|
||||
maxSources: 10
|
||||
});
|
||||
|
||||
console.log('ARM vs. Qualcomm Research:');
|
||||
console.log('- Answer length:', result.answer.length);
|
||||
console.log('- Sources found:', result.sources.length);
|
||||
console.log('- First 300 chars:', result.answer.substring(0, 300));
|
||||
|
||||
await saveResearchResult('arm_vs_qualcomm_comparison', result);
|
||||
|
||||
expect(result.answer).toBeTruthy();
|
||||
expect(result.answer.length).toBeGreaterThan(500);
|
||||
expect(result.answer.toLowerCase()).toInclude('arm');
|
||||
expect(result.answer.toLowerCase()).toInclude('qualcomm');
|
||||
expect(result.sources.length).toBeGreaterThan(0);
|
||||
});
|
||||
|
||||
tap.test('Anthropic Research: should clean up provider', async () => {
|
||||
await anthropicProvider.stop();
|
||||
console.log('Anthropic research provider stopped successfully');
|
||||
});
|
||||
|
||||
export default tap.start();
|
172
test/test.research.openai.ts
Normal file
172
test/test.research.openai.ts
Normal file
@@ -0,0 +1,172 @@
|
||||
import { expect, tap } from '@push.rocks/tapbundle';
|
||||
import * as qenv from '@push.rocks/qenv';
|
||||
import * as smartai from '../ts/index.js';
|
||||
import * as path from 'path';
|
||||
import { promises as fs } from 'fs';
|
||||
|
||||
const testQenv = new qenv.Qenv('./', './.nogit/');
|
||||
|
||||
// Helper function to save research results
|
||||
async function saveResearchResult(testName: string, result: any) {
|
||||
const sanitizedName = testName.replace(/[^a-z0-9]/gi, '_').toLowerCase();
|
||||
const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
|
||||
const filename = `openai_${sanitizedName}_${timestamp}.json`;
|
||||
const filepath = path.join('.nogit', 'testresults', 'research', filename);
|
||||
|
||||
await fs.mkdir(path.dirname(filepath), { recursive: true });
|
||||
await fs.writeFile(filepath, JSON.stringify(result, null, 2), 'utf-8');
|
||||
|
||||
console.log(` 💾 Saved to: ${filepath}`);
|
||||
}
|
||||
|
||||
let openaiProvider: smartai.OpenAiProvider;
|
||||
|
||||
tap.test('OpenAI Research: should initialize provider with research capabilities', async () => {
|
||||
openaiProvider = new smartai.OpenAiProvider({
|
||||
openaiToken: await testQenv.getEnvVarOnDemand('OPENAI_TOKEN'),
|
||||
researchModel: 'o4-mini-deep-research-2025-06-26',
|
||||
enableWebSearch: true
|
||||
});
|
||||
|
||||
await openaiProvider.start();
|
||||
expect(openaiProvider).toBeInstanceOf(smartai.OpenAiProvider);
|
||||
expect(typeof openaiProvider.research).toEqual('function');
|
||||
});
|
||||
|
||||
tap.test('OpenAI Research: should perform basic research query', async () => {
|
||||
const result = await openaiProvider.research({
|
||||
query: 'What is TypeScript and why is it useful for web development?',
|
||||
searchDepth: 'basic'
|
||||
});
|
||||
|
||||
console.log('OpenAI Basic Research:');
|
||||
console.log('- Answer length:', result.answer.length);
|
||||
console.log('- Sources found:', result.sources.length);
|
||||
console.log('- First 200 chars:', result.answer.substring(0, 200));
|
||||
|
||||
await saveResearchResult('basic_research_typescript', result);
|
||||
|
||||
expect(result).toBeTruthy();
|
||||
expect(result.answer).toBeTruthy();
|
||||
expect(result.answer.toLowerCase()).toInclude('typescript');
|
||||
expect(result.sources).toBeArray();
|
||||
expect(result.metadata).toBeTruthy();
|
||||
expect(result.metadata.model).toBeTruthy();
|
||||
});
|
||||
|
||||
tap.test('OpenAI Research: should perform research with web search enabled', async () => {
|
||||
const result = await openaiProvider.research({
|
||||
query: 'What are the latest features in ECMAScript 2024?',
|
||||
searchDepth: 'advanced',
|
||||
includeWebSearch: true,
|
||||
maxSources: 5
|
||||
});
|
||||
|
||||
console.log('OpenAI Web Search Research:');
|
||||
console.log('- Answer length:', result.answer.length);
|
||||
console.log('- Sources:', result.sources.length);
|
||||
if (result.searchQueries) {
|
||||
console.log('- Search queries used:', result.searchQueries);
|
||||
}
|
||||
|
||||
await saveResearchResult('web_search_ecmascript', result);
|
||||
|
||||
expect(result.answer).toBeTruthy();
|
||||
expect(result.answer.toLowerCase()).toInclude('ecmascript');
|
||||
|
||||
// The model might include sources or search queries
|
||||
if (result.sources.length > 0) {
|
||||
expect(result.sources[0]).toHaveProperty('url');
|
||||
expect(result.sources[0]).toHaveProperty('title');
|
||||
}
|
||||
});
|
||||
|
||||
tap.test('OpenAI Research: should handle deep research for complex topics', async () => {
|
||||
// Skip this test if it takes too long or costs too much
|
||||
// You can enable it for thorough testing
|
||||
const skipDeepResearch = true;
|
||||
|
||||
if (skipDeepResearch) {
|
||||
console.log('Skipping deep research test to save API costs');
|
||||
return;
|
||||
}
|
||||
|
||||
const result = await openaiProvider.research({
|
||||
query: 'Compare the pros and cons of microservices vs monolithic architecture',
|
||||
searchDepth: 'deep',
|
||||
includeWebSearch: true
|
||||
});
|
||||
|
||||
console.log('OpenAI Deep Research:');
|
||||
console.log('- Answer length:', result.answer.length);
|
||||
console.log('- Token usage:', result.metadata?.tokensUsed);
|
||||
|
||||
expect(result.answer).toBeTruthy();
|
||||
expect(result.answer.length).toBeGreaterThan(500);
|
||||
expect(result.answer.toLowerCase()).toInclude('microservices');
|
||||
expect(result.answer.toLowerCase()).toInclude('monolithic');
|
||||
});
|
||||
|
||||
tap.test('OpenAI Research: should extract sources from markdown links', async () => {
|
||||
const result = await openaiProvider.research({
|
||||
query: 'What is Node.js and provide some official documentation links?',
|
||||
searchDepth: 'basic',
|
||||
maxSources: 3
|
||||
});
|
||||
|
||||
console.log('OpenAI Source Extraction:');
|
||||
console.log('- Sources found:', result.sources.length);
|
||||
|
||||
await saveResearchResult('source_extraction_nodejs', result);
|
||||
|
||||
if (result.sources.length > 0) {
|
||||
console.log('- Example source:', result.sources[0]);
|
||||
expect(result.sources[0].url).toBeTruthy();
|
||||
expect(result.sources[0].title).toBeTruthy();
|
||||
}
|
||||
|
||||
expect(result.answer).toInclude('Node.js');
|
||||
});
|
||||
|
||||
tap.test('OpenAI Research: should handle research errors gracefully', async () => {
|
||||
// Test with an extremely long query that might cause issues
|
||||
const longQuery = 'a'.repeat(10000);
|
||||
|
||||
let errorCaught = false;
|
||||
try {
|
||||
await openaiProvider.research({
|
||||
query: longQuery,
|
||||
searchDepth: 'basic'
|
||||
});
|
||||
} catch (error) {
|
||||
errorCaught = true;
|
||||
console.log('Expected error for long query:', error.message.substring(0, 100));
|
||||
expect(error.message).toBeTruthy();
|
||||
}
|
||||
|
||||
// OpenAI might handle long queries, so we don't assert the error
|
||||
console.log(`Long query error test - Error caught: ${errorCaught}`);
|
||||
});
|
||||
|
||||
tap.test('OpenAI Research: should respect maxSources parameter', async () => {
|
||||
const maxSources = 3;
|
||||
const result = await openaiProvider.research({
|
||||
query: 'List popular JavaScript frameworks',
|
||||
searchDepth: 'basic',
|
||||
maxSources: maxSources
|
||||
});
|
||||
|
||||
console.log(`OpenAI Max Sources Test - Requested: ${maxSources}, Found: ${result.sources.length}`);
|
||||
|
||||
// The API might not always return exactly maxSources, but should respect it as a limit
|
||||
if (result.sources.length > 0) {
|
||||
expect(result.sources.length).toBeLessThanOrEqual(maxSources * 2); // Allow some flexibility
|
||||
}
|
||||
});
|
||||
|
||||
tap.test('OpenAI Research: should clean up provider', async () => {
|
||||
await openaiProvider.stop();
|
||||
console.log('OpenAI research provider stopped successfully');
|
||||
});
|
||||
|
||||
export default tap.start();
|
80
test/test.research.stubs.ts
Normal file
80
test/test.research.stubs.ts
Normal file
@@ -0,0 +1,80 @@
|
||||
import { tap, expect } from '@push.rocks/tapbundle';
|
||||
import * as smartai from '../ts/index.js';
|
||||
|
||||
// Test research method stubs for providers without full implementation
|
||||
// These providers have research methods that throw "not yet supported" errors
|
||||
|
||||
tap.test('Research Stubs: Perplexity provider should have research method', async () => {
|
||||
const perplexityProvider = new smartai.PerplexityProvider({
|
||||
perplexityToken: 'test-token'
|
||||
});
|
||||
|
||||
// Perplexity has a basic implementation with Sonar models
|
||||
expect(typeof perplexityProvider.research).toEqual('function');
|
||||
});
|
||||
|
||||
tap.test('Research Stubs: Groq provider should throw not supported error', async () => {
|
||||
const groqProvider = new smartai.GroqProvider({
|
||||
groqToken: 'test-token'
|
||||
});
|
||||
|
||||
expect(typeof groqProvider.research).toEqual('function');
|
||||
|
||||
let errorCaught = false;
|
||||
try {
|
||||
await groqProvider.research({ query: 'test' });
|
||||
} catch (error) {
|
||||
errorCaught = true;
|
||||
expect(error.message).toInclude('not yet supported');
|
||||
}
|
||||
expect(errorCaught).toBeTrue();
|
||||
});
|
||||
|
||||
tap.test('Research Stubs: Ollama provider should throw not supported error', async () => {
|
||||
const ollamaProvider = new smartai.OllamaProvider({});
|
||||
|
||||
expect(typeof ollamaProvider.research).toEqual('function');
|
||||
|
||||
let errorCaught = false;
|
||||
try {
|
||||
await ollamaProvider.research({ query: 'test' });
|
||||
} catch (error) {
|
||||
errorCaught = true;
|
||||
expect(error.message).toInclude('not yet supported');
|
||||
}
|
||||
expect(errorCaught).toBeTrue();
|
||||
});
|
||||
|
||||
tap.test('Research Stubs: xAI provider should throw not supported error', async () => {
|
||||
const xaiProvider = new smartai.XAIProvider({
|
||||
xaiToken: 'test-token'
|
||||
});
|
||||
|
||||
expect(typeof xaiProvider.research).toEqual('function');
|
||||
|
||||
let errorCaught = false;
|
||||
try {
|
||||
await xaiProvider.research({ query: 'test' });
|
||||
} catch (error) {
|
||||
errorCaught = true;
|
||||
expect(error.message).toInclude('not yet supported');
|
||||
}
|
||||
expect(errorCaught).toBeTrue();
|
||||
});
|
||||
|
||||
tap.test('Research Stubs: Exo provider should throw not supported error', async () => {
|
||||
const exoProvider = new smartai.ExoProvider({});
|
||||
|
||||
expect(typeof exoProvider.research).toEqual('function');
|
||||
|
||||
let errorCaught = false;
|
||||
try {
|
||||
await exoProvider.research({ query: 'test' });
|
||||
} catch (error) {
|
||||
errorCaught = true;
|
||||
expect(error.message).toInclude('not yet supported');
|
||||
}
|
||||
expect(errorCaught).toBeTrue();
|
||||
});
|
||||
|
||||
export default tap.start();
|
95
test/test.vision.anthropic.ts
Normal file
95
test/test.vision.anthropic.ts
Normal file
@@ -0,0 +1,95 @@
|
||||
import { expect, tap } from '@push.rocks/tapbundle';
|
||||
import * as qenv from '@push.rocks/qenv';
|
||||
import * as smartfile from '@push.rocks/smartfile';
|
||||
|
||||
const testQenv = new qenv.Qenv('./', './.nogit/');
|
||||
|
||||
import * as smartai from '../ts/index.js';
|
||||
|
||||
let anthropicProvider: smartai.AnthropicProvider;
|
||||
|
||||
tap.test('Anthropic Vision: should create and start Anthropic provider', async () => {
|
||||
anthropicProvider = new smartai.AnthropicProvider({
|
||||
anthropicToken: await testQenv.getEnvVarOnDemand('ANTHROPIC_TOKEN'),
|
||||
});
|
||||
await anthropicProvider.start();
|
||||
expect(anthropicProvider).toBeInstanceOf(smartai.AnthropicProvider);
|
||||
});
|
||||
|
||||
tap.test('Anthropic Vision: should analyze coffee image with latte art', async () => {
|
||||
// Test 1: Coffee image from Unsplash by Dani
|
||||
const imagePath = './test/testimages/coffee-dani/coffee.jpg';
|
||||
console.log(`Loading coffee image from: ${imagePath}`);
|
||||
|
||||
const imageBuffer = await smartfile.fs.toBuffer(imagePath);
|
||||
console.log(`Image loaded, size: ${imageBuffer.length} bytes`);
|
||||
|
||||
const result = await anthropicProvider.vision({
|
||||
image: imageBuffer,
|
||||
prompt: 'Describe this coffee image. What do you see in terms of the cup, foam pattern, and overall composition?'
|
||||
});
|
||||
|
||||
console.log(`Anthropic Vision (Coffee) - Result: ${result}`);
|
||||
expect(result).toBeTruthy();
|
||||
expect(typeof result).toEqual('string');
|
||||
expect(result.toLowerCase()).toInclude('coffee');
|
||||
// The image has a heart pattern in the latte art
|
||||
const mentionsLatte = result.toLowerCase().includes('heart') ||
|
||||
result.toLowerCase().includes('latte') ||
|
||||
result.toLowerCase().includes('foam');
|
||||
expect(mentionsLatte).toBeTrue();
|
||||
});
|
||||
|
||||
tap.test('Anthropic Vision: should analyze laptop/workspace image', async () => {
|
||||
// Test 2: Laptop image from Unsplash by Nicolas Bichon
|
||||
const imagePath = './test/testimages/laptop-nicolas/laptop.jpg';
|
||||
console.log(`Loading laptop image from: ${imagePath}`);
|
||||
|
||||
const imageBuffer = await smartfile.fs.toBuffer(imagePath);
|
||||
console.log(`Image loaded, size: ${imageBuffer.length} bytes`);
|
||||
|
||||
const result = await anthropicProvider.vision({
|
||||
image: imageBuffer,
|
||||
prompt: 'Describe the technology and workspace setup in this image. What devices and equipment can you see?'
|
||||
});
|
||||
|
||||
console.log(`Anthropic Vision (Laptop) - Result: ${result}`);
|
||||
expect(result).toBeTruthy();
|
||||
expect(typeof result).toEqual('string');
|
||||
// Should mention laptop, computer, keyboard, or desk
|
||||
const mentionsTech = result.toLowerCase().includes('laptop') ||
|
||||
result.toLowerCase().includes('computer') ||
|
||||
result.toLowerCase().includes('keyboard') ||
|
||||
result.toLowerCase().includes('desk');
|
||||
expect(mentionsTech).toBeTrue();
|
||||
});
|
||||
|
||||
tap.test('Anthropic Vision: should analyze receipt/document image', async () => {
|
||||
// Test 3: Receipt image from Unsplash by Annie Spratt
|
||||
const imagePath = './test/testimages/receipt-annie/receipt.jpg';
|
||||
console.log(`Loading receipt image from: ${imagePath}`);
|
||||
|
||||
const imageBuffer = await smartfile.fs.toBuffer(imagePath);
|
||||
console.log(`Image loaded, size: ${imageBuffer.length} bytes`);
|
||||
|
||||
const result = await anthropicProvider.vision({
|
||||
image: imageBuffer,
|
||||
prompt: 'What type of document is this? Can you identify any text or numbers visible in the image?'
|
||||
});
|
||||
|
||||
console.log(`Anthropic Vision (Receipt) - Result: ${result}`);
|
||||
expect(result).toBeTruthy();
|
||||
expect(typeof result).toEqual('string');
|
||||
// Should mention receipt, document, text, or paper
|
||||
const mentionsDocument = result.toLowerCase().includes('receipt') ||
|
||||
result.toLowerCase().includes('document') ||
|
||||
result.toLowerCase().includes('text') ||
|
||||
result.toLowerCase().includes('paper');
|
||||
expect(mentionsDocument).toBeTrue();
|
||||
});
|
||||
|
||||
tap.test('Anthropic Vision: should stop the provider', async () => {
|
||||
await anthropicProvider.stop();
|
||||
});
|
||||
|
||||
export default tap.start();
|
36
test/testimages/coffee-dani/README.md
Normal file
36
test/testimages/coffee-dani/README.md
Normal file
@@ -0,0 +1,36 @@
|
||||
# Coffee Image Attribution
|
||||
|
||||
## coffee.jpg
|
||||
|
||||
**Photographer:** Dani (@frokz)
|
||||
**Source URL:** https://unsplash.com/photos/cup-of-coffee-on-saucer-ZLqxSzvVr7I
|
||||
**Direct Link:** https://images.unsplash.com/photo-1506372023823-741c83b836fe
|
||||
|
||||
### Metadata
|
||||
- **Title:** Cup of coffee on saucer
|
||||
- **Description:** One of many coffee-moments in my life ;)
|
||||
- **Date Published:** September 25, 2017
|
||||
- **Location:** Stockholm, Sweden
|
||||
- **Tags:** coffee, cafe, heart, coffee cup, cup, barista, latte, mug, saucer, food, sweden, stockholm
|
||||
|
||||
### License
|
||||
**Unsplash License** - Free to use
|
||||
- ✅ Commercial and non-commercial use
|
||||
- ✅ No permission needed
|
||||
- ❌ Cannot be sold without significant modification
|
||||
- ❌ Cannot be used to replicate Unsplash or similar service
|
||||
|
||||
Full license: https://unsplash.com/license
|
||||
|
||||
### Usage in This Project
|
||||
This image is used for testing vision/image processing capabilities in the SmartAI library test suite, specifically for:
|
||||
- Testing coffee/beverage recognition
|
||||
- Latte art pattern detection (heart shape)
|
||||
- Scene/environment analysis
|
||||
- Multi-element image understanding (cup, saucer, table)
|
||||
|
||||
### Download Information
|
||||
- **Downloaded:** September 28, 2025
|
||||
- **Original Filename:** dani-ZLqxSzvVr7I-unsplash.jpg
|
||||
- **Resolution:** High resolution (3.7 MB)
|
||||
- **Format:** JPEG
|
BIN
test/testimages/coffee-dani/coffee.jpg
Normal file
BIN
test/testimages/coffee-dani/coffee.jpg
Normal file
Binary file not shown.
After Width: | Height: | Size: 3.7 MiB |
40
test/testimages/laptop-nicolas/README.md
Normal file
40
test/testimages/laptop-nicolas/README.md
Normal file
@@ -0,0 +1,40 @@
|
||||
# Laptop Image Attribution
|
||||
|
||||
## laptop.jpg
|
||||
|
||||
**Photographer:** Nicolas Bichon (@nicol3a)
|
||||
**Source URL:** https://unsplash.com/photos/a-laptop-computer-sitting-on-top-of-a-wooden-desk-ZhV4iqAXxyA
|
||||
**Direct Link:** https://images.unsplash.com/photo-1704230972797-e0e3aba0fce7
|
||||
|
||||
### Metadata
|
||||
- **Title:** A laptop computer sitting on top of a wooden desk
|
||||
- **Description:** Lifestyle photo I took for my indie app Type, a macOS app to take notes without interrupting your flow. https://usetype.app.
|
||||
- **Date Published:** January 2, 2024
|
||||
- **Camera:** FUJIFILM, X-T20
|
||||
- **Tags:** computer, laptop, mac, keyboard, computer keyboard, computer hardware, furniture, table, electronics, screen, monitor, hardware, display, tabletop, lcd screen, digital display
|
||||
|
||||
### Statistics
|
||||
- **Views:** 183,020
|
||||
- **Downloads:** 757
|
||||
|
||||
### License
|
||||
**Unsplash License** - Free to use
|
||||
- ✅ Commercial and non-commercial use
|
||||
- ✅ No permission needed
|
||||
- ❌ Cannot be sold without significant modification
|
||||
- ❌ Cannot be used to replicate Unsplash or similar service
|
||||
|
||||
Full license: https://unsplash.com/license
|
||||
|
||||
### Usage in This Project
|
||||
This image is used for testing vision/image processing capabilities in the SmartAI library test suite, specifically for:
|
||||
- Testing technology/computer equipment recognition
|
||||
- Workspace/office environment analysis
|
||||
- Object detection (laptop, keyboard, monitor, table)
|
||||
- Scene understanding and context analysis
|
||||
|
||||
### Download Information
|
||||
- **Downloaded:** September 28, 2025
|
||||
- **Original Filename:** nicolas-bichon-ZhV4iqAXxyA-unsplash.jpg
|
||||
- **Resolution:** High resolution (1.8 MB)
|
||||
- **Format:** JPEG
|
BIN
test/testimages/laptop-nicolas/laptop.jpg
Normal file
BIN
test/testimages/laptop-nicolas/laptop.jpg
Normal file
Binary file not shown.
After Width: | Height: | Size: 1.8 MiB |
40
test/testimages/receipt-annie/README.md
Normal file
40
test/testimages/receipt-annie/README.md
Normal file
@@ -0,0 +1,40 @@
|
||||
# Receipt Image Attribution
|
||||
|
||||
## receipt.jpg
|
||||
|
||||
**Photographer:** Annie Spratt (@anniespratt)
|
||||
**Source URL:** https://unsplash.com/photos/a-receipt-sitting-on-top-of-a-wooden-table-recgFWxDO1Y
|
||||
**Direct Link:** https://images.unsplash.com/photo-1731686602391-7484df33a03c
|
||||
|
||||
### Metadata
|
||||
- **Title:** A receipt sitting on top of a wooden table
|
||||
- **Description:** Download this free HD photo of text, document, invoice, and receipt by Annie Spratt
|
||||
- **Date Published:** November 15, 2024
|
||||
- **Tags:** text, document, invoice, receipt, diaper
|
||||
|
||||
### Statistics
|
||||
- **Views:** 54,593
|
||||
- **Downloads:** 764
|
||||
|
||||
### License
|
||||
**Unsplash License** - Free to use
|
||||
- ✅ Commercial and non-commercial use
|
||||
- ✅ No permission needed
|
||||
- ❌ Cannot be sold without significant modification
|
||||
- ❌ Cannot be used to replicate Unsplash or similar service
|
||||
|
||||
Full license: https://unsplash.com/license
|
||||
|
||||
### Usage in This Project
|
||||
This image is used for testing vision/image processing capabilities in the SmartAI library test suite, specifically for:
|
||||
- Testing text extraction and OCR capabilities
|
||||
- Document recognition and classification
|
||||
- Receipt/invoice analysis
|
||||
- Text-heavy image understanding
|
||||
- Structured data extraction from documents
|
||||
|
||||
### Download Information
|
||||
- **Downloaded:** September 28, 2025
|
||||
- **Original Filename:** annie-spratt-recgFWxDO1Y-unsplash.jpg
|
||||
- **Resolution:** High resolution (3.3 MB)
|
||||
- **Format:** JPEG
|
BIN
test/testimages/receipt-annie/receipt.jpg
Normal file
BIN
test/testimages/receipt-annie/receipt.jpg
Normal file
Binary file not shown.
After Width: | Height: | Size: 3.3 MiB |
@@ -3,6 +3,6 @@
|
||||
*/
|
||||
export const commitinfo = {
|
||||
name: '@push.rocks/smartai',
|
||||
version: '0.4.1',
|
||||
description: 'A TypeScript library for integrating and interacting with multiple AI models, offering capabilities for chat and potentially audio responses.'
|
||||
version: '0.7.4',
|
||||
description: 'SmartAi is a versatile TypeScript library designed to facilitate integration and interaction with various AI models, offering functionalities for chat, audio generation, document processing, and vision tasks.'
|
||||
}
|
||||
|
@@ -1,3 +1,5 @@
|
||||
import * as plugins from './plugins.js';
|
||||
|
||||
/**
|
||||
* Message format for chat interactions
|
||||
*/
|
||||
@@ -23,22 +25,114 @@ export interface ChatResponse {
|
||||
message: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* Options for research interactions
|
||||
*/
|
||||
export interface ResearchOptions {
|
||||
query: string;
|
||||
searchDepth?: 'basic' | 'advanced' | 'deep';
|
||||
maxSources?: number;
|
||||
includeWebSearch?: boolean;
|
||||
background?: boolean;
|
||||
}
|
||||
|
||||
/**
|
||||
* Response format for research interactions
|
||||
*/
|
||||
export interface ResearchResponse {
|
||||
answer: string;
|
||||
sources: Array<{
|
||||
url: string;
|
||||
title: string;
|
||||
snippet: string;
|
||||
}>;
|
||||
searchQueries?: string[];
|
||||
metadata?: any;
|
||||
}
|
||||
|
||||
/**
|
||||
* Options for image generation
|
||||
*/
|
||||
export interface ImageGenerateOptions {
|
||||
prompt: string;
|
||||
model?: 'gpt-image-1' | 'dall-e-3' | 'dall-e-2';
|
||||
quality?: 'low' | 'medium' | 'high' | 'standard' | 'hd' | 'auto';
|
||||
size?: '256x256' | '512x512' | '1024x1024' | '1536x1024' | '1024x1536' | '1792x1024' | '1024x1792' | 'auto';
|
||||
style?: 'vivid' | 'natural';
|
||||
background?: 'transparent' | 'opaque' | 'auto';
|
||||
outputFormat?: 'png' | 'jpeg' | 'webp';
|
||||
outputCompression?: number; // 0-100 for webp/jpeg
|
||||
moderation?: 'low' | 'auto';
|
||||
n?: number; // Number of images to generate
|
||||
stream?: boolean;
|
||||
partialImages?: number; // 0-3 for streaming
|
||||
}
|
||||
|
||||
/**
|
||||
* Options for image editing
|
||||
*/
|
||||
export interface ImageEditOptions {
|
||||
image: Buffer;
|
||||
prompt: string;
|
||||
mask?: Buffer;
|
||||
model?: 'gpt-image-1' | 'dall-e-2';
|
||||
quality?: 'low' | 'medium' | 'high' | 'standard' | 'auto';
|
||||
size?: '256x256' | '512x512' | '1024x1024' | '1536x1024' | '1024x1536' | 'auto';
|
||||
background?: 'transparent' | 'opaque' | 'auto';
|
||||
outputFormat?: 'png' | 'jpeg' | 'webp';
|
||||
outputCompression?: number;
|
||||
n?: number;
|
||||
stream?: boolean;
|
||||
partialImages?: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* Response format for image operations
|
||||
*/
|
||||
export interface ImageResponse {
|
||||
images: Array<{
|
||||
b64_json?: string;
|
||||
url?: string;
|
||||
revisedPrompt?: string;
|
||||
}>;
|
||||
metadata?: {
|
||||
model: string;
|
||||
quality?: string;
|
||||
size?: string;
|
||||
outputFormat?: string;
|
||||
tokensUsed?: number;
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Abstract base class for multi-modal AI models.
|
||||
* Provides a common interface for different AI providers (OpenAI, Anthropic, Perplexity, Ollama)
|
||||
*/
|
||||
export abstract class MultiModalModel {
|
||||
/**
|
||||
* SmartPdf instance for document processing
|
||||
* Shared across all methods that need PDF functionality
|
||||
*/
|
||||
protected smartpdfInstance: plugins.smartpdf.SmartPdf;
|
||||
|
||||
/**
|
||||
* Initializes the model and any necessary resources
|
||||
* Should be called before using any other methods
|
||||
*/
|
||||
abstract start(): Promise<void>;
|
||||
public async start(): Promise<void> {
|
||||
this.smartpdfInstance = new plugins.smartpdf.SmartPdf();
|
||||
await this.smartpdfInstance.start();
|
||||
}
|
||||
|
||||
/**
|
||||
* Cleans up any resources used by the model
|
||||
* Should be called when the model is no longer needed
|
||||
*/
|
||||
abstract stop(): Promise<void>;
|
||||
public async stop(): Promise<void> {
|
||||
if (this.smartpdfInstance) {
|
||||
await this.smartpdfInstance.stop();
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Synchronous chat interaction with the model
|
||||
@@ -83,4 +177,28 @@ export abstract class MultiModalModel {
|
||||
pdfDocuments: Uint8Array[];
|
||||
messageHistory: ChatMessage[];
|
||||
}): Promise<{ message: any }>;
|
||||
|
||||
/**
|
||||
* Research and web search capabilities
|
||||
* @param optionsArg Options containing the research query and configuration
|
||||
* @returns Promise resolving to the research results with sources
|
||||
* @throws Error if the provider doesn't support research capabilities
|
||||
*/
|
||||
public abstract research(optionsArg: ResearchOptions): Promise<ResearchResponse>;
|
||||
|
||||
/**
|
||||
* Image generation from text prompts
|
||||
* @param optionsArg Options containing the prompt and generation parameters
|
||||
* @returns Promise resolving to the generated image(s)
|
||||
* @throws Error if the provider doesn't support image generation
|
||||
*/
|
||||
public abstract imageGenerate(optionsArg: ImageGenerateOptions): Promise<ImageResponse>;
|
||||
|
||||
/**
|
||||
* Image editing and inpainting
|
||||
* @param optionsArg Options containing the image, prompt, and editing parameters
|
||||
* @returns Promise resolving to the edited image(s)
|
||||
* @throws Error if the provider doesn't support image editing
|
||||
*/
|
||||
public abstract imageEdit(optionsArg: ImageEditOptions): Promise<ImageResponse>;
|
||||
}
|
||||
|
@@ -91,7 +91,29 @@ export class SmartAi {
|
||||
}
|
||||
}
|
||||
|
||||
public async stop() {}
|
||||
public async stop() {
|
||||
if (this.openaiProvider) {
|
||||
await this.openaiProvider.stop();
|
||||
}
|
||||
if (this.anthropicProvider) {
|
||||
await this.anthropicProvider.stop();
|
||||
}
|
||||
if (this.perplexityProvider) {
|
||||
await this.perplexityProvider.stop();
|
||||
}
|
||||
if (this.groqProvider) {
|
||||
await this.groqProvider.stop();
|
||||
}
|
||||
if (this.xaiProvider) {
|
||||
await this.xaiProvider.stop();
|
||||
}
|
||||
if (this.ollamaProvider) {
|
||||
await this.ollamaProvider.stop();
|
||||
}
|
||||
if (this.exoProvider) {
|
||||
await this.exoProvider.stop();
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* create a new conversation
|
||||
|
@@ -1,3 +1,9 @@
|
||||
export * from './classes.smartai.js';
|
||||
export * from './abstract.classes.multimodal.js';
|
||||
export * from './provider.openai.js';
|
||||
export * from './provider.anthropic.js';
|
||||
export * from './provider.perplexity.js';
|
||||
export * from './provider.groq.js';
|
||||
export * from './provider.ollama.js';
|
||||
export * from './provider.xai.js';
|
||||
export * from './provider.exo.js';
|
||||
|
@@ -1,13 +1,25 @@
|
||||
import * as plugins from './plugins.js';
|
||||
import * as paths from './paths.js';
|
||||
import { MultiModalModel } from './abstract.classes.multimodal.js';
|
||||
import type { ChatOptions, ChatResponse, ChatMessage } from './abstract.classes.multimodal.js';
|
||||
import type {
|
||||
ChatOptions,
|
||||
ChatResponse,
|
||||
ChatMessage,
|
||||
ResearchOptions,
|
||||
ResearchResponse,
|
||||
ImageGenerateOptions,
|
||||
ImageEditOptions,
|
||||
ImageResponse
|
||||
} from './abstract.classes.multimodal.js';
|
||||
import type { ImageBlockParam, TextBlockParam } from '@anthropic-ai/sdk/resources/messages';
|
||||
|
||||
type ContentBlock = ImageBlockParam | TextBlockParam;
|
||||
|
||||
export interface IAnthropicProviderOptions {
|
||||
anthropicToken: string;
|
||||
enableWebSearch?: boolean;
|
||||
searchDomainAllowList?: string[];
|
||||
searchDomainBlockList?: string[];
|
||||
}
|
||||
|
||||
export class AnthropicProvider extends MultiModalModel {
|
||||
@@ -20,12 +32,15 @@ export class AnthropicProvider extends MultiModalModel {
|
||||
}
|
||||
|
||||
async start() {
|
||||
await super.start();
|
||||
this.anthropicApiClient = new plugins.anthropic.default({
|
||||
apiKey: this.options.anthropicToken,
|
||||
});
|
||||
}
|
||||
|
||||
async stop() {}
|
||||
async stop() {
|
||||
await super.stop();
|
||||
}
|
||||
|
||||
public async chatStream(input: ReadableStream<Uint8Array>): Promise<ReadableStream<string>> {
|
||||
// Create a TextDecoder to handle incoming chunks
|
||||
@@ -62,7 +77,7 @@ export class AnthropicProvider extends MultiModalModel {
|
||||
// If we have a complete message, send it to Anthropic
|
||||
if (currentMessage) {
|
||||
const stream = await this.anthropicApiClient.messages.create({
|
||||
model: 'claude-3-opus-20240229',
|
||||
model: 'claude-sonnet-4-5-20250929',
|
||||
messages: [{ role: currentMessage.role, content: currentMessage.content }],
|
||||
system: '',
|
||||
stream: true,
|
||||
@@ -106,7 +121,7 @@ export class AnthropicProvider extends MultiModalModel {
|
||||
}));
|
||||
|
||||
const result = await this.anthropicApiClient.messages.create({
|
||||
model: 'claude-3-opus-20240229',
|
||||
model: 'claude-sonnet-4-5-20250929',
|
||||
system: optionsArg.systemMessage,
|
||||
messages: [
|
||||
...messages,
|
||||
@@ -153,7 +168,7 @@ export class AnthropicProvider extends MultiModalModel {
|
||||
];
|
||||
|
||||
const result = await this.anthropicApiClient.messages.create({
|
||||
model: 'claude-3-opus-20240229',
|
||||
model: 'claude-sonnet-4-5-20250929',
|
||||
messages: [{
|
||||
role: 'user',
|
||||
content
|
||||
@@ -178,11 +193,10 @@ export class AnthropicProvider extends MultiModalModel {
|
||||
messageHistory: ChatMessage[];
|
||||
}): Promise<{ message: any }> {
|
||||
// Convert PDF documents to images using SmartPDF
|
||||
const smartpdfInstance = new plugins.smartpdf.SmartPdf();
|
||||
let documentImageBytesArray: Uint8Array[] = [];
|
||||
|
||||
for (const pdfDocument of optionsArg.pdfDocuments) {
|
||||
const documentImageArray = await smartpdfInstance.convertPDFToPngBytes(pdfDocument);
|
||||
const documentImageArray = await this.smartpdfInstance.convertPDFToPngBytes(pdfDocument);
|
||||
documentImageBytesArray = documentImageBytesArray.concat(documentImageArray);
|
||||
}
|
||||
|
||||
@@ -206,14 +220,14 @@ export class AnthropicProvider extends MultiModalModel {
|
||||
type: 'image',
|
||||
source: {
|
||||
type: 'base64',
|
||||
media_type: 'image/jpeg',
|
||||
media_type: 'image/png',
|
||||
data: Buffer.from(imageBytes).toString('base64')
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
const result = await this.anthropicApiClient.messages.create({
|
||||
model: 'claude-3-opus-20240229',
|
||||
model: 'claude-sonnet-4-5-20250929',
|
||||
system: optionsArg.systemMessage,
|
||||
messages: [
|
||||
...messages,
|
||||
@@ -237,4 +251,155 @@ export class AnthropicProvider extends MultiModalModel {
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
public async research(optionsArg: ResearchOptions): Promise<ResearchResponse> {
|
||||
// Prepare the messages for the research request
|
||||
const systemMessage = `You are a research assistant with web search capabilities.
|
||||
Provide comprehensive, well-researched answers with citations and sources.
|
||||
When searching the web, be thorough and cite your sources accurately.`;
|
||||
|
||||
try {
|
||||
// Build the tool configuration for web search
|
||||
const tools: any[] = [];
|
||||
|
||||
if (this.options.enableWebSearch) {
|
||||
const webSearchTool: any = {
|
||||
type: 'web_search_20250305',
|
||||
name: 'web_search'
|
||||
};
|
||||
|
||||
// Add optional parameters
|
||||
if (optionsArg.maxSources) {
|
||||
webSearchTool.max_uses = optionsArg.maxSources;
|
||||
}
|
||||
|
||||
if (this.options.searchDomainAllowList?.length) {
|
||||
webSearchTool.allowed_domains = this.options.searchDomainAllowList;
|
||||
} else if (this.options.searchDomainBlockList?.length) {
|
||||
webSearchTool.blocked_domains = this.options.searchDomainBlockList;
|
||||
}
|
||||
|
||||
tools.push(webSearchTool);
|
||||
}
|
||||
|
||||
// Configure the request based on search depth
|
||||
const maxTokens = optionsArg.searchDepth === 'deep' ? 8192 :
|
||||
optionsArg.searchDepth === 'advanced' ? 6144 : 4096;
|
||||
|
||||
// Create the research request
|
||||
const requestParams: any = {
|
||||
model: 'claude-sonnet-4-5-20250929',
|
||||
system: systemMessage,
|
||||
messages: [
|
||||
{
|
||||
role: 'user' as const,
|
||||
content: optionsArg.query
|
||||
}
|
||||
],
|
||||
max_tokens: maxTokens,
|
||||
temperature: 0.7
|
||||
};
|
||||
|
||||
// Add tools if web search is enabled
|
||||
if (tools.length > 0) {
|
||||
requestParams.tools = tools;
|
||||
}
|
||||
|
||||
// Execute the research request
|
||||
const result = await this.anthropicApiClient.messages.create(requestParams);
|
||||
|
||||
// Extract the answer from content blocks
|
||||
let answer = '';
|
||||
const sources: Array<{ url: string; title: string; snippet: string }> = [];
|
||||
const searchQueries: string[] = [];
|
||||
|
||||
// Process content blocks
|
||||
for (const block of result.content) {
|
||||
if ('text' in block) {
|
||||
// Accumulate text content
|
||||
answer += block.text;
|
||||
|
||||
// Extract citations if present
|
||||
if ('citations' in block && Array.isArray(block.citations)) {
|
||||
for (const citation of block.citations) {
|
||||
if (citation.type === 'web_search_result_location') {
|
||||
sources.push({
|
||||
title: citation.title || '',
|
||||
url: citation.url || '',
|
||||
snippet: citation.cited_text || ''
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
} else if ('type' in block && block.type === 'server_tool_use') {
|
||||
// Extract search queries from server tool use
|
||||
if (block.name === 'web_search' && block.input && typeof block.input === 'object' && 'query' in block.input) {
|
||||
searchQueries.push((block.input as any).query);
|
||||
}
|
||||
} else if ('type' in block && block.type === 'web_search_tool_result') {
|
||||
// Extract sources from web search results
|
||||
if (Array.isArray(block.content)) {
|
||||
for (const result of block.content) {
|
||||
if (result.type === 'web_search_result') {
|
||||
// Only add if not already in sources (avoid duplicates from citations)
|
||||
if (!sources.some(s => s.url === result.url)) {
|
||||
sources.push({
|
||||
title: result.title || '',
|
||||
url: result.url || '',
|
||||
snippet: '' // Search results don't include snippets, only citations do
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Fallback: Parse markdown-style links if no citations found
|
||||
if (sources.length === 0) {
|
||||
const urlRegex = /\[([^\]]+)\]\(([^)]+)\)/g;
|
||||
let match: RegExpExecArray | null;
|
||||
|
||||
while ((match = urlRegex.exec(answer)) !== null) {
|
||||
sources.push({
|
||||
title: match[1],
|
||||
url: match[2],
|
||||
snippet: ''
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Check if web search was used based on usage info
|
||||
const webSearchCount = result.usage?.server_tool_use?.web_search_requests || 0;
|
||||
|
||||
return {
|
||||
answer,
|
||||
sources,
|
||||
searchQueries: searchQueries.length > 0 ? searchQueries : undefined,
|
||||
metadata: {
|
||||
model: 'claude-sonnet-4-5-20250929',
|
||||
searchDepth: optionsArg.searchDepth || 'basic',
|
||||
tokensUsed: result.usage?.output_tokens,
|
||||
webSearchesPerformed: webSearchCount
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
console.error('Anthropic research error:', error);
|
||||
throw new Error(`Failed to perform research: ${error.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Image generation is not supported by Anthropic
|
||||
*/
|
||||
public async imageGenerate(optionsArg: ImageGenerateOptions): Promise<ImageResponse> {
|
||||
throw new Error('Image generation is not supported by Anthropic. Claude can only analyze images, not generate them. Please use OpenAI provider for image generation.');
|
||||
}
|
||||
|
||||
/**
|
||||
* Image editing is not supported by Anthropic
|
||||
*/
|
||||
public async imageEdit(optionsArg: ImageEditOptions): Promise<ImageResponse> {
|
||||
throw new Error('Image editing is not supported by Anthropic. Claude can only analyze images, not edit them. Please use OpenAI provider for image editing.');
|
||||
}
|
||||
}
|
@@ -1,7 +1,16 @@
|
||||
import * as plugins from './plugins.js';
|
||||
import * as paths from './paths.js';
|
||||
import { MultiModalModel } from './abstract.classes.multimodal.js';
|
||||
import type { ChatOptions, ChatResponse, ChatMessage } from './abstract.classes.multimodal.js';
|
||||
import type {
|
||||
ChatOptions,
|
||||
ChatResponse,
|
||||
ChatMessage,
|
||||
ResearchOptions,
|
||||
ResearchResponse,
|
||||
ImageGenerateOptions,
|
||||
ImageEditOptions,
|
||||
ImageResponse
|
||||
} from './abstract.classes.multimodal.js';
|
||||
import type { ChatCompletionMessageParam } from 'openai/resources/chat/completions';
|
||||
|
||||
export interface IExoProviderOptions {
|
||||
@@ -125,4 +134,22 @@ export class ExoProvider extends MultiModalModel {
|
||||
}): Promise<{ message: any }> {
|
||||
throw new Error('Document processing is not supported by Exo provider');
|
||||
}
|
||||
|
||||
public async research(optionsArg: ResearchOptions): Promise<ResearchResponse> {
|
||||
throw new Error('Research capabilities are not yet supported by Exo provider.');
|
||||
}
|
||||
|
||||
/**
|
||||
* Image generation is not supported by Exo
|
||||
*/
|
||||
public async imageGenerate(optionsArg: ImageGenerateOptions): Promise<ImageResponse> {
|
||||
throw new Error('Image generation is not supported by Exo. Please use OpenAI provider for image generation.');
|
||||
}
|
||||
|
||||
/**
|
||||
* Image editing is not supported by Exo
|
||||
*/
|
||||
public async imageEdit(optionsArg: ImageEditOptions): Promise<ImageResponse> {
|
||||
throw new Error('Image editing is not supported by Exo. Please use OpenAI provider for image editing.');
|
||||
}
|
||||
}
|
||||
|
@@ -1,7 +1,16 @@
|
||||
import * as plugins from './plugins.js';
|
||||
import * as paths from './paths.js';
|
||||
import { MultiModalModel } from './abstract.classes.multimodal.js';
|
||||
import type { ChatOptions, ChatResponse, ChatMessage } from './abstract.classes.multimodal.js';
|
||||
import type {
|
||||
ChatOptions,
|
||||
ChatResponse,
|
||||
ChatMessage,
|
||||
ResearchOptions,
|
||||
ResearchResponse,
|
||||
ImageGenerateOptions,
|
||||
ImageEditOptions,
|
||||
ImageResponse
|
||||
} from './abstract.classes.multimodal.js';
|
||||
|
||||
export interface IGroqProviderOptions {
|
||||
groqToken: string;
|
||||
@@ -189,4 +198,22 @@ export class GroqProvider extends MultiModalModel {
|
||||
}): Promise<{ message: any }> {
|
||||
throw new Error('Document processing is not yet supported by Groq.');
|
||||
}
|
||||
|
||||
public async research(optionsArg: ResearchOptions): Promise<ResearchResponse> {
|
||||
throw new Error('Research capabilities are not yet supported by Groq provider.');
|
||||
}
|
||||
|
||||
/**
|
||||
* Image generation is not supported by Groq
|
||||
*/
|
||||
public async imageGenerate(optionsArg: ImageGenerateOptions): Promise<ImageResponse> {
|
||||
throw new Error('Image generation is not supported by Groq. Please use OpenAI provider for image generation.');
|
||||
}
|
||||
|
||||
/**
|
||||
* Image editing is not supported by Groq
|
||||
*/
|
||||
public async imageEdit(optionsArg: ImageEditOptions): Promise<ImageResponse> {
|
||||
throw new Error('Image editing is not supported by Groq. Please use OpenAI provider for image editing.');
|
||||
}
|
||||
}
|
@@ -1,7 +1,16 @@
|
||||
import * as plugins from './plugins.js';
|
||||
import * as paths from './paths.js';
|
||||
import { MultiModalModel } from './abstract.classes.multimodal.js';
|
||||
import type { ChatOptions, ChatResponse, ChatMessage } from './abstract.classes.multimodal.js';
|
||||
import type {
|
||||
ChatOptions,
|
||||
ChatResponse,
|
||||
ChatMessage,
|
||||
ResearchOptions,
|
||||
ResearchResponse,
|
||||
ImageGenerateOptions,
|
||||
ImageEditOptions,
|
||||
ImageResponse
|
||||
} from './abstract.classes.multimodal.js';
|
||||
|
||||
export interface IOllamaProviderOptions {
|
||||
baseUrl?: string;
|
||||
@@ -24,6 +33,7 @@ export class OllamaProvider extends MultiModalModel {
|
||||
}
|
||||
|
||||
async start() {
|
||||
await super.start();
|
||||
// Verify Ollama is running
|
||||
try {
|
||||
const response = await fetch(`${this.baseUrl}/api/tags`);
|
||||
@@ -35,7 +45,9 @@ export class OllamaProvider extends MultiModalModel {
|
||||
}
|
||||
}
|
||||
|
||||
async stop() {}
|
||||
async stop() {
|
||||
await super.stop();
|
||||
}
|
||||
|
||||
public async chatStream(input: ReadableStream<Uint8Array>): Promise<ReadableStream<string>> {
|
||||
// Create a TextDecoder to handle incoming chunks
|
||||
@@ -205,11 +217,10 @@ export class OllamaProvider extends MultiModalModel {
|
||||
messageHistory: ChatMessage[];
|
||||
}): Promise<{ message: any }> {
|
||||
// Convert PDF documents to images using SmartPDF
|
||||
const smartpdfInstance = new plugins.smartpdf.SmartPdf();
|
||||
let documentImageBytesArray: Uint8Array[] = [];
|
||||
|
||||
for (const pdfDocument of optionsArg.pdfDocuments) {
|
||||
const documentImageArray = await smartpdfInstance.convertPDFToPngBytes(pdfDocument);
|
||||
const documentImageArray = await this.smartpdfInstance.convertPDFToPngBytes(pdfDocument);
|
||||
documentImageBytesArray = documentImageBytesArray.concat(documentImageArray);
|
||||
}
|
||||
|
||||
@@ -249,4 +260,22 @@ export class OllamaProvider extends MultiModalModel {
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
public async research(optionsArg: ResearchOptions): Promise<ResearchResponse> {
|
||||
throw new Error('Research capabilities are not yet supported by Ollama provider.');
|
||||
}
|
||||
|
||||
/**
|
||||
* Image generation is not supported by Ollama
|
||||
*/
|
||||
public async imageGenerate(optionsArg: ImageGenerateOptions): Promise<ImageResponse> {
|
||||
throw new Error('Image generation is not supported by Ollama. Please use OpenAI provider for image generation.');
|
||||
}
|
||||
|
||||
/**
|
||||
* Image editing is not supported by Ollama
|
||||
*/
|
||||
public async imageEdit(optionsArg: ImageEditOptions): Promise<ImageResponse> {
|
||||
throw new Error('Image editing is not supported by Ollama. Please use OpenAI provider for image editing.');
|
||||
}
|
||||
}
|
@@ -1,5 +1,6 @@
|
||||
import * as plugins from './plugins.js';
|
||||
import * as paths from './paths.js';
|
||||
import { Readable } from 'stream';
|
||||
|
||||
// Custom type definition for chat completion messages
|
||||
export type TChatCompletionRequestMessage = {
|
||||
@@ -8,19 +9,27 @@ export type TChatCompletionRequestMessage = {
|
||||
};
|
||||
|
||||
import { MultiModalModel } from './abstract.classes.multimodal.js';
|
||||
import type {
|
||||
ResearchOptions,
|
||||
ResearchResponse,
|
||||
ImageGenerateOptions,
|
||||
ImageEditOptions,
|
||||
ImageResponse
|
||||
} from './abstract.classes.multimodal.js';
|
||||
|
||||
export interface IOpenaiProviderOptions {
|
||||
openaiToken: string;
|
||||
chatModel?: string;
|
||||
audioModel?: string;
|
||||
visionModel?: string;
|
||||
// Optionally add more model options (e.g., documentModel) if needed.
|
||||
researchModel?: string;
|
||||
imageModel?: string;
|
||||
enableWebSearch?: boolean;
|
||||
}
|
||||
|
||||
export class OpenAiProvider extends MultiModalModel {
|
||||
private options: IOpenaiProviderOptions;
|
||||
public openAiApiClient: plugins.openai.default;
|
||||
public smartpdfInstance: plugins.smartpdf.SmartPdf;
|
||||
|
||||
constructor(optionsArg: IOpenaiProviderOptions) {
|
||||
super();
|
||||
@@ -28,14 +37,16 @@ export class OpenAiProvider extends MultiModalModel {
|
||||
}
|
||||
|
||||
public async start() {
|
||||
await super.start();
|
||||
this.openAiApiClient = new plugins.openai.default({
|
||||
apiKey: this.options.openaiToken,
|
||||
dangerouslyAllowBrowser: true,
|
||||
});
|
||||
this.smartpdfInstance = new plugins.smartpdf.SmartPdf();
|
||||
}
|
||||
|
||||
public async stop() {}
|
||||
public async stop() {
|
||||
await super.stop();
|
||||
}
|
||||
|
||||
public async chatStream(input: ReadableStream<Uint8Array>): Promise<ReadableStream<string>> {
|
||||
// Create a TextDecoder to handle incoming chunks
|
||||
@@ -75,21 +86,23 @@ export class OpenAiProvider extends MultiModalModel {
|
||||
// If we have a complete message, send it to OpenAI
|
||||
if (currentMessage) {
|
||||
const messageToSend = { role: "user" as const, content: currentMessage.content };
|
||||
const stream = await this.openAiApiClient.chat.completions.create({
|
||||
model: this.options.chatModel ?? 'o3-mini',
|
||||
temperature: 0,
|
||||
const chatModel = this.options.chatModel ?? 'gpt-5-mini';
|
||||
const requestParams: any = {
|
||||
model: chatModel,
|
||||
messages: [messageToSend],
|
||||
stream: true,
|
||||
});
|
||||
|
||||
};
|
||||
// Temperature is omitted since the model does not support it.
|
||||
const stream = await this.openAiApiClient.chat.completions.create(requestParams);
|
||||
// Explicitly cast the stream as an async iterable to satisfy TypeScript.
|
||||
const streamAsyncIterable = stream as unknown as AsyncIterableIterator<any>;
|
||||
// Process each chunk from OpenAI
|
||||
for await (const chunk of stream) {
|
||||
for await (const chunk of streamAsyncIterable) {
|
||||
const content = chunk.choices[0]?.delta?.content;
|
||||
if (content) {
|
||||
controller.enqueue(content);
|
||||
}
|
||||
}
|
||||
|
||||
currentMessage = null;
|
||||
}
|
||||
},
|
||||
@@ -119,15 +132,17 @@ export class OpenAiProvider extends MultiModalModel {
|
||||
content: string;
|
||||
}[];
|
||||
}) {
|
||||
const result = await this.openAiApiClient.chat.completions.create({
|
||||
model: this.options.chatModel ?? 'o3-mini',
|
||||
temperature: 0,
|
||||
const chatModel = this.options.chatModel ?? 'gpt-5-mini';
|
||||
const requestParams: any = {
|
||||
model: chatModel,
|
||||
messages: [
|
||||
{ role: 'system', content: optionsArg.systemMessage },
|
||||
...optionsArg.messageHistory,
|
||||
{ role: 'user', content: optionsArg.userMessage },
|
||||
],
|
||||
});
|
||||
};
|
||||
// Temperature parameter removed to avoid unsupported error.
|
||||
const result = await this.openAiApiClient.chat.completions.create(requestParams);
|
||||
return {
|
||||
role: result.choices[0].message.role as 'assistant',
|
||||
message: result.choices[0].message.content,
|
||||
@@ -137,14 +152,15 @@ export class OpenAiProvider extends MultiModalModel {
|
||||
public async audio(optionsArg: { message: string }): Promise<NodeJS.ReadableStream> {
|
||||
const done = plugins.smartpromise.defer<NodeJS.ReadableStream>();
|
||||
const result = await this.openAiApiClient.audio.speech.create({
|
||||
model: this.options.audioModel ?? 'o3-mini',
|
||||
model: this.options.audioModel ?? 'tts-1-hd',
|
||||
input: optionsArg.message,
|
||||
voice: 'nova',
|
||||
response_format: 'mp3',
|
||||
speed: 1,
|
||||
});
|
||||
const stream = result.body;
|
||||
done.resolve(stream);
|
||||
const nodeStream = Readable.fromWeb(stream as any);
|
||||
done.resolve(nodeStream);
|
||||
return done.promise;
|
||||
}
|
||||
|
||||
@@ -159,6 +175,7 @@ export class OpenAiProvider extends MultiModalModel {
|
||||
}) {
|
||||
let pdfDocumentImageBytesArray: Uint8Array[] = [];
|
||||
|
||||
// Convert each PDF into one or more image byte arrays.
|
||||
for (const pdfDocument of optionsArg.pdfDocuments) {
|
||||
const documentImageArray = await this.smartpdfInstance.convertPDFToPngBytes(pdfDocument);
|
||||
pdfDocumentImageBytesArray = pdfDocumentImageBytesArray.concat(documentImageArray);
|
||||
@@ -167,19 +184,18 @@ export class OpenAiProvider extends MultiModalModel {
|
||||
console.log(`image smartfile array`);
|
||||
console.log(pdfDocumentImageBytesArray.map((smartfile) => smartfile.length));
|
||||
|
||||
const smartfileArray = await plugins.smartarray.map(
|
||||
pdfDocumentImageBytesArray,
|
||||
async (pdfDocumentImageBytes) => {
|
||||
return plugins.smartfile.SmartFile.fromBuffer(
|
||||
'pdfDocumentImage.jpg',
|
||||
Buffer.from(pdfDocumentImageBytes)
|
||||
);
|
||||
}
|
||||
);
|
||||
// Filter out any empty buffers to avoid sending invalid image URLs.
|
||||
const validImageBytesArray = pdfDocumentImageBytesArray.filter(imageBytes => imageBytes && imageBytes.length > 0);
|
||||
const imageAttachments = validImageBytesArray.map(imageBytes => ({
|
||||
type: 'image_url',
|
||||
image_url: {
|
||||
url: 'data:image/png;base64,' + Buffer.from(imageBytes).toString('base64'),
|
||||
},
|
||||
}));
|
||||
|
||||
const result = await this.openAiApiClient.chat.completions.create({
|
||||
model: this.options.chatModel ?? 'o3-mini',
|
||||
temperature: 0,
|
||||
const chatModel = this.options.chatModel ?? 'gpt-5-mini';
|
||||
const requestParams: any = {
|
||||
model: chatModel,
|
||||
messages: [
|
||||
{ role: 'system', content: optionsArg.systemMessage },
|
||||
...optionsArg.messageHistory,
|
||||
@@ -187,31 +203,22 @@ export class OpenAiProvider extends MultiModalModel {
|
||||
role: 'user',
|
||||
content: [
|
||||
{ type: 'text', text: optionsArg.userMessage },
|
||||
...(() => {
|
||||
const returnArray = [];
|
||||
for (const imageBytes of pdfDocumentImageBytesArray) {
|
||||
returnArray.push({
|
||||
type: 'image_url',
|
||||
image_url: {
|
||||
url: 'data:image/png;base64,' + Buffer.from(imageBytes).toString('base64'),
|
||||
},
|
||||
});
|
||||
}
|
||||
return returnArray;
|
||||
})(),
|
||||
...imageAttachments,
|
||||
],
|
||||
},
|
||||
],
|
||||
});
|
||||
};
|
||||
// Temperature parameter removed.
|
||||
const result = await this.openAiApiClient.chat.completions.create(requestParams);
|
||||
return {
|
||||
message: result.choices[0].message,
|
||||
};
|
||||
}
|
||||
|
||||
public async vision(optionsArg: { image: Buffer; prompt: string }): Promise<string> {
|
||||
const result = await this.openAiApiClient.chat.completions.create({
|
||||
model: this.options.visionModel ?? 'o3-mini',
|
||||
temperature: 0,
|
||||
const visionModel = this.options.visionModel ?? '04-mini';
|
||||
const requestParams: any = {
|
||||
model: visionModel,
|
||||
messages: [
|
||||
{
|
||||
role: 'user',
|
||||
@@ -227,8 +234,222 @@ export class OpenAiProvider extends MultiModalModel {
|
||||
}
|
||||
],
|
||||
max_tokens: 300
|
||||
});
|
||||
|
||||
};
|
||||
const result = await this.openAiApiClient.chat.completions.create(requestParams);
|
||||
return result.choices[0].message.content || '';
|
||||
}
|
||||
|
||||
public async research(optionsArg: ResearchOptions): Promise<ResearchResponse> {
|
||||
// Determine which model to use - Deep Research API requires specific models
|
||||
let model: string;
|
||||
if (optionsArg.searchDepth === 'deep') {
|
||||
model = this.options.researchModel || 'o4-mini-deep-research-2025-06-26';
|
||||
} else {
|
||||
// For basic/advanced, still use deep research models if web search is needed
|
||||
if (optionsArg.includeWebSearch) {
|
||||
model = this.options.researchModel || 'o4-mini-deep-research-2025-06-26';
|
||||
} else {
|
||||
model = this.options.chatModel || 'gpt-5-mini';
|
||||
}
|
||||
}
|
||||
|
||||
const systemMessage = 'You are a research assistant. Provide comprehensive answers with citations and sources when available.';
|
||||
|
||||
// Prepare request parameters using Deep Research API format
|
||||
const requestParams: any = {
|
||||
model,
|
||||
instructions: systemMessage,
|
||||
input: optionsArg.query
|
||||
};
|
||||
|
||||
// Add web search tool if requested
|
||||
if (optionsArg.includeWebSearch || optionsArg.searchDepth === 'deep') {
|
||||
requestParams.tools = [
|
||||
{
|
||||
type: 'web_search_preview',
|
||||
search_context_size: optionsArg.searchDepth === 'deep' ? 'high' :
|
||||
optionsArg.searchDepth === 'advanced' ? 'medium' : 'low'
|
||||
}
|
||||
];
|
||||
}
|
||||
|
||||
// Add background flag for deep research
|
||||
if (optionsArg.background && optionsArg.searchDepth === 'deep') {
|
||||
requestParams.background = true;
|
||||
}
|
||||
|
||||
try {
|
||||
// Execute the research request using Deep Research API
|
||||
const result = await this.openAiApiClient.responses.create(requestParams);
|
||||
|
||||
// Extract the answer from output items
|
||||
let answer = '';
|
||||
const sources: Array<{ url: string; title: string; snippet: string }> = [];
|
||||
const searchQueries: string[] = [];
|
||||
|
||||
// Process output items
|
||||
for (const item of result.output || []) {
|
||||
// Extract message content
|
||||
if (item.type === 'message' && 'content' in item) {
|
||||
const messageItem = item as any;
|
||||
for (const contentItem of messageItem.content || []) {
|
||||
if (contentItem.type === 'output_text' && 'text' in contentItem) {
|
||||
answer += contentItem.text;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Extract web search queries
|
||||
if (item.type === 'web_search_call' && 'action' in item) {
|
||||
const searchItem = item as any;
|
||||
if (searchItem.action && searchItem.action.type === 'search' && 'query' in searchItem.action) {
|
||||
searchQueries.push(searchItem.action.query);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Parse sources from markdown links in the answer
|
||||
const urlRegex = /\[([^\]]+)\]\(([^)]+)\)/g;
|
||||
let match: RegExpExecArray | null;
|
||||
|
||||
while ((match = urlRegex.exec(answer)) !== null) {
|
||||
sources.push({
|
||||
title: match[1],
|
||||
url: match[2],
|
||||
snippet: ''
|
||||
});
|
||||
}
|
||||
|
||||
return {
|
||||
answer,
|
||||
sources,
|
||||
searchQueries: searchQueries.length > 0 ? searchQueries : undefined,
|
||||
metadata: {
|
||||
model,
|
||||
searchDepth: optionsArg.searchDepth || 'basic',
|
||||
tokensUsed: result.usage?.total_tokens
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
console.error('Research API error:', error);
|
||||
throw new Error(`Failed to perform research: ${error.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Image generation using OpenAI's gpt-image-1 or DALL-E models
|
||||
*/
|
||||
public async imageGenerate(optionsArg: ImageGenerateOptions): Promise<ImageResponse> {
|
||||
const model = optionsArg.model || this.options.imageModel || 'gpt-image-1';
|
||||
|
||||
try {
|
||||
const requestParams: any = {
|
||||
model,
|
||||
prompt: optionsArg.prompt,
|
||||
n: optionsArg.n || 1,
|
||||
};
|
||||
|
||||
// Add gpt-image-1 specific parameters
|
||||
if (model === 'gpt-image-1') {
|
||||
if (optionsArg.quality) requestParams.quality = optionsArg.quality;
|
||||
if (optionsArg.size) requestParams.size = optionsArg.size;
|
||||
if (optionsArg.background) requestParams.background = optionsArg.background;
|
||||
if (optionsArg.outputFormat) requestParams.output_format = optionsArg.outputFormat;
|
||||
if (optionsArg.outputCompression !== undefined) requestParams.output_compression = optionsArg.outputCompression;
|
||||
if (optionsArg.moderation) requestParams.moderation = optionsArg.moderation;
|
||||
if (optionsArg.stream !== undefined) requestParams.stream = optionsArg.stream;
|
||||
if (optionsArg.partialImages !== undefined) requestParams.partial_images = optionsArg.partialImages;
|
||||
} else if (model === 'dall-e-3') {
|
||||
// DALL-E 3 specific parameters
|
||||
if (optionsArg.quality) requestParams.quality = optionsArg.quality;
|
||||
if (optionsArg.size) requestParams.size = optionsArg.size;
|
||||
if (optionsArg.style) requestParams.style = optionsArg.style;
|
||||
requestParams.response_format = 'b64_json'; // Always use base64 for consistency
|
||||
} else if (model === 'dall-e-2') {
|
||||
// DALL-E 2 specific parameters
|
||||
if (optionsArg.size) requestParams.size = optionsArg.size;
|
||||
requestParams.response_format = 'b64_json';
|
||||
}
|
||||
|
||||
const result = await this.openAiApiClient.images.generate(requestParams);
|
||||
|
||||
const images = (result.data || []).map(img => ({
|
||||
b64_json: img.b64_json,
|
||||
url: img.url,
|
||||
revisedPrompt: img.revised_prompt
|
||||
}));
|
||||
|
||||
return {
|
||||
images,
|
||||
metadata: {
|
||||
model,
|
||||
quality: result.quality,
|
||||
size: result.size,
|
||||
outputFormat: result.output_format,
|
||||
tokensUsed: result.usage?.total_tokens
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
console.error('Image generation error:', error);
|
||||
throw new Error(`Failed to generate image: ${error.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Image editing using OpenAI's gpt-image-1 or DALL-E 2 models
|
||||
*/
|
||||
public async imageEdit(optionsArg: ImageEditOptions): Promise<ImageResponse> {
|
||||
const model = optionsArg.model || this.options.imageModel || 'gpt-image-1';
|
||||
|
||||
try {
|
||||
const requestParams: any = {
|
||||
model,
|
||||
image: optionsArg.image,
|
||||
prompt: optionsArg.prompt,
|
||||
n: optionsArg.n || 1,
|
||||
};
|
||||
|
||||
// Add mask if provided
|
||||
if (optionsArg.mask) {
|
||||
requestParams.mask = optionsArg.mask;
|
||||
}
|
||||
|
||||
// Add gpt-image-1 specific parameters
|
||||
if (model === 'gpt-image-1') {
|
||||
if (optionsArg.quality) requestParams.quality = optionsArg.quality;
|
||||
if (optionsArg.size) requestParams.size = optionsArg.size;
|
||||
if (optionsArg.background) requestParams.background = optionsArg.background;
|
||||
if (optionsArg.outputFormat) requestParams.output_format = optionsArg.outputFormat;
|
||||
if (optionsArg.outputCompression !== undefined) requestParams.output_compression = optionsArg.outputCompression;
|
||||
if (optionsArg.stream !== undefined) requestParams.stream = optionsArg.stream;
|
||||
if (optionsArg.partialImages !== undefined) requestParams.partial_images = optionsArg.partialImages;
|
||||
} else if (model === 'dall-e-2') {
|
||||
// DALL-E 2 specific parameters
|
||||
if (optionsArg.size) requestParams.size = optionsArg.size;
|
||||
requestParams.response_format = 'b64_json';
|
||||
}
|
||||
|
||||
const result = await this.openAiApiClient.images.edit(requestParams);
|
||||
|
||||
const images = (result.data || []).map(img => ({
|
||||
b64_json: img.b64_json,
|
||||
url: img.url,
|
||||
revisedPrompt: img.revised_prompt
|
||||
}));
|
||||
|
||||
return {
|
||||
images,
|
||||
metadata: {
|
||||
model,
|
||||
quality: result.quality,
|
||||
size: result.size,
|
||||
outputFormat: result.output_format,
|
||||
tokensUsed: result.usage?.total_tokens
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
console.error('Image edit error:', error);
|
||||
throw new Error(`Failed to edit image: ${error.message}`);
|
||||
}
|
||||
}
|
||||
}
|
@@ -1,7 +1,16 @@
|
||||
import * as plugins from './plugins.js';
|
||||
import * as paths from './paths.js';
|
||||
import { MultiModalModel } from './abstract.classes.multimodal.js';
|
||||
import type { ChatOptions, ChatResponse, ChatMessage } from './abstract.classes.multimodal.js';
|
||||
import type {
|
||||
ChatOptions,
|
||||
ChatResponse,
|
||||
ChatMessage,
|
||||
ResearchOptions,
|
||||
ResearchResponse,
|
||||
ImageGenerateOptions,
|
||||
ImageEditOptions,
|
||||
ImageResponse
|
||||
} from './abstract.classes.multimodal.js';
|
||||
|
||||
export interface IPerplexityProviderOptions {
|
||||
perplexityToken: string;
|
||||
@@ -168,4 +177,83 @@ export class PerplexityProvider extends MultiModalModel {
|
||||
}): Promise<{ message: any }> {
|
||||
throw new Error('Document processing is not supported by Perplexity.');
|
||||
}
|
||||
|
||||
public async research(optionsArg: ResearchOptions): Promise<ResearchResponse> {
|
||||
// Perplexity has Sonar models that are optimized for search
|
||||
// sonar models: sonar, sonar-pro
|
||||
const model = optionsArg.searchDepth === 'deep' ? 'sonar-pro' : 'sonar';
|
||||
|
||||
try {
|
||||
const response = await fetch('https://api.perplexity.ai/chat/completions', {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Authorization': `Bearer ${this.options.perplexityToken}`,
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model,
|
||||
messages: [
|
||||
{
|
||||
role: 'system',
|
||||
content: 'You are a helpful research assistant. Provide accurate information with sources.'
|
||||
},
|
||||
{
|
||||
role: 'user',
|
||||
content: optionsArg.query
|
||||
}
|
||||
],
|
||||
temperature: 0.7,
|
||||
max_tokens: 4000
|
||||
}),
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`Perplexity API error: ${response.statusText}`);
|
||||
}
|
||||
|
||||
const result = await response.json();
|
||||
const answer = result.choices[0].message.content;
|
||||
|
||||
// Parse citations from the response
|
||||
const sources: Array<{ url: string; title: string; snippet: string }> = [];
|
||||
|
||||
// Perplexity includes citations in the format [1], [2], etc. with sources listed
|
||||
// This is a simplified parser - could be enhanced based on actual Perplexity response format
|
||||
if (result.citations) {
|
||||
for (const citation of result.citations) {
|
||||
sources.push({
|
||||
url: citation.url || '',
|
||||
title: citation.title || '',
|
||||
snippet: citation.snippet || ''
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
answer,
|
||||
sources,
|
||||
metadata: {
|
||||
model,
|
||||
searchDepth: optionsArg.searchDepth || 'basic'
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
console.error('Perplexity research error:', error);
|
||||
throw new Error(`Failed to perform research: ${error.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Image generation is not supported by Perplexity
|
||||
*/
|
||||
public async imageGenerate(optionsArg: ImageGenerateOptions): Promise<ImageResponse> {
|
||||
throw new Error('Image generation is not supported by Perplexity. Please use OpenAI provider for image generation.');
|
||||
}
|
||||
|
||||
/**
|
||||
* Image editing is not supported by Perplexity
|
||||
*/
|
||||
public async imageEdit(optionsArg: ImageEditOptions): Promise<ImageResponse> {
|
||||
throw new Error('Image editing is not supported by Perplexity. Please use OpenAI provider for image editing.');
|
||||
}
|
||||
}
|
@@ -1,7 +1,16 @@
|
||||
import * as plugins from './plugins.js';
|
||||
import * as paths from './paths.js';
|
||||
import { MultiModalModel } from './abstract.classes.multimodal.js';
|
||||
import type { ChatOptions, ChatResponse, ChatMessage } from './abstract.classes.multimodal.js';
|
||||
import type {
|
||||
ChatOptions,
|
||||
ChatResponse,
|
||||
ChatMessage,
|
||||
ResearchOptions,
|
||||
ResearchResponse,
|
||||
ImageGenerateOptions,
|
||||
ImageEditOptions,
|
||||
ImageResponse
|
||||
} from './abstract.classes.multimodal.js';
|
||||
import type { ChatCompletionMessageParam } from 'openai/resources/chat/completions';
|
||||
|
||||
export interface IXAIProviderOptions {
|
||||
@@ -11,7 +20,6 @@ export interface IXAIProviderOptions {
|
||||
export class XAIProvider extends MultiModalModel {
|
||||
private options: IXAIProviderOptions;
|
||||
public openAiApiClient: plugins.openai.default;
|
||||
public smartpdfInstance: plugins.smartpdf.SmartPdf;
|
||||
|
||||
constructor(optionsArg: IXAIProviderOptions) {
|
||||
super();
|
||||
@@ -19,14 +27,16 @@ export class XAIProvider extends MultiModalModel {
|
||||
}
|
||||
|
||||
public async start() {
|
||||
await super.start();
|
||||
this.openAiApiClient = new plugins.openai.default({
|
||||
apiKey: this.options.xaiToken,
|
||||
baseURL: 'https://api.x.ai/v1',
|
||||
});
|
||||
this.smartpdfInstance = new plugins.smartpdf.SmartPdf();
|
||||
}
|
||||
|
||||
public async stop() {}
|
||||
public async stop() {
|
||||
await super.stop();
|
||||
}
|
||||
|
||||
public async chatStream(input: ReadableStream<Uint8Array>): Promise<ReadableStream<string>> {
|
||||
// Create a TextDecoder to handle incoming chunks
|
||||
@@ -180,4 +190,22 @@ export class XAIProvider extends MultiModalModel {
|
||||
message: completion.choices[0]?.message?.content || ''
|
||||
};
|
||||
}
|
||||
|
||||
public async research(optionsArg: ResearchOptions): Promise<ResearchResponse> {
|
||||
throw new Error('Research capabilities are not yet supported by xAI provider.');
|
||||
}
|
||||
|
||||
/**
|
||||
* Image generation is not supported by xAI
|
||||
*/
|
||||
public async imageGenerate(optionsArg: ImageGenerateOptions): Promise<ImageResponse> {
|
||||
throw new Error('Image generation is not supported by xAI. Please use OpenAI provider for image generation.');
|
||||
}
|
||||
|
||||
/**
|
||||
* Image editing is not supported by xAI
|
||||
*/
|
||||
public async imageEdit(optionsArg: ImageEditOptions): Promise<ImageResponse> {
|
||||
throw new Error('Image editing is not supported by xAI. Please use OpenAI provider for image editing.');
|
||||
}
|
||||
}
|
||||
|
Reference in New Issue
Block a user