0.2.0

changelog.md

@@ -0,0 +1,82 @@
+# Changelog
+
+## 2025-02-03 - 0.2.0 - feat(provider.anthropic)
+Add support for vision and document processing in Anthropic provider
+
+- Implemented vision tasks for Anthropic provider using Claude-3-opus-20240229 model.
+- Implemented document processing for Anthropic provider, supporting conversion of PDF documents to images and analysis with Claude-3-opus-20240229 model.
+- Updated documentation to reflect the new capabilities of the Anthropic provider.
+
+## 2025-02-03 - 0.1.0 - feat(providers)
+Add vision and document processing capabilities to providers
+
+- OpenAI and Ollama providers now support vision tasks using GPT-4 Vision and Llava models respectively.
+- Document processing has been implemented for OpenAI and Ollama providers, converting PDFs to images for analysis.
+- Introduced abstract methods for vision and document processing in the MultiModalModel class.
+- Updated the readme file with examples for vision and document processing.
+
+## 2025-02-03 - 0.0.19 - fix(core)
+Enhanced chat streaming and error handling across providers
+
+- Refactored chatStream method to properly handle input streams and processes in Perplexity, OpenAI, Ollama, and Anthropic providers.
+- Improved error handling and message parsing in chatStream implementations.
+- Defined distinct interfaces for chat options, messages, and responses.
+- Adjusted the test logic in test/test.ts for the new classification response requirement.
+
+## 2024-09-19 - 0.0.18 - fix(dependencies)
+Update dependencies to the latest versions.
+
+- Updated @git.zone/tsbuild from ^2.1.76 to ^2.1.84
+- Updated @git.zone/tsrun from ^1.2.46 to ^1.2.49
+- Updated @push.rocks/tapbundle from ^5.0.23 to ^5.3.0
+- Updated @types/node from ^20.12.12 to ^22.5.5
+- Updated @anthropic-ai/sdk from ^0.21.0 to ^0.27.3
+- Updated @push.rocks/smartfile from ^11.0.14 to ^11.0.21
+- Updated @push.rocks/smartpromise from ^4.0.3 to ^4.0.4
+- Updated @push.rocks/webstream from ^1.0.8 to ^1.0.10
+- Updated openai from ^4.47.1 to ^4.62.1
+
+## 2024-05-29 - 0.0.17 - Documentation
+Updated project description.
+
+- Improved project description for clarity and details.
+
+## 2024-05-17 - 0.0.16 to 0.0.15 - Core
+Fixes and updates.
+
+- Various core updates and fixes for stability improvements.
+
+## 2024-04-29 - 0.0.14 to 0.0.13 - Core
+Fixes and updates.
+
+- Multiple core updates and fixes for enhanced functionality.
+
+## 2024-04-29 - 0.0.12 - Core
+Fixes and updates.
+
+- Core update and bug fixes.
+
+## 2024-04-29 - 0.0.11 - Provider
+Fix integration for anthropic provider.
+
+- Correction in the integration process with anthropic provider for better compatibility.
+
+## 2024-04-27 - 0.0.10 to 0.0.9 - Core
+Fixes and updates.
+
+- Updates and fixes to core components.
+- Updated tsconfig for improved TypeScript configuration.
+
+## 2024-04-01 - 0.0.8 to 0.0.7 - Core and npmextra
+Core updates and npmextra configuration.
+
+- Core fixes and updates.
+- Updates to npmextra.json for githost configuration.
+
+## 2024-03-31 - 0.0.6 to 0.0.2 - Core
+Initial core updates and fixes.
+
+- Multiple updates and fixes to core following initial versions.
+
+
+This summarizes the relevant updates and changes based on the provided commit messages. The changelog excludes commits that are version tags without meaningful content or repeated entries.

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@push.rocks/smartai",
-  "version": "0.0.13",
+  "version": "0.2.0",
   "private": false,
   "description": "A TypeScript library for integrating and interacting with multiple AI models, offering capabilities for chat and potentially audio responses.",
   "main": "dist_ts/index.js",
@@ -14,33 +14,33 @@
     "buildDocs": "(tsdoc)"
   },
   "devDependencies": {
-    "@git.zone/tsbuild": "^2.1.25",
+    "@git.zone/tsbuild": "^2.1.84",
     "@git.zone/tsbundle": "^2.0.5",
-    "@git.zone/tsrun": "^1.2.46",
+    "@git.zone/tsrun": "^1.2.49",
     "@git.zone/tstest": "^1.0.90",
     "@push.rocks/qenv": "^6.0.5",
-    "@push.rocks/tapbundle": "^5.0.23",
-    "@types/node": "^20.12.7"
+    "@push.rocks/tapbundle": "^5.3.0",
+    "@types/node": "^22.5.5"
   },
   "dependencies": {
-    "@anthropic-ai/sdk": "^0.20.7",
+    "@anthropic-ai/sdk": "^0.27.3",
     "@push.rocks/smartarray": "^1.0.8",
-    "@push.rocks/smartfile": "^11.0.14",
+    "@push.rocks/smartfile": "^11.0.21",
     "@push.rocks/smartpath": "^5.0.18",
-    "@push.rocks/smartpdf": "^3.1.5",
-    "@push.rocks/smartpromise": "^4.0.3",
+    "@push.rocks/smartpdf": "^3.1.6",
+    "@push.rocks/smartpromise": "^4.0.4",
     "@push.rocks/smartrequest": "^2.0.22",
-    "@push.rocks/webstream": "^1.0.8",
-    "openai": "^4.38.5"
+    "@push.rocks/webstream": "^1.0.10",
+    "openai": "^4.62.1"
   },
   "repository": {
     "type": "git",
-    "url": "git+https://code.foss.global/push.rocks/smartai.git"
+    "url": "https://code.foss.global/push.rocks/smartai.git"
   },
   "bugs": {
     "url": "https://code.foss.global/push.rocks/smartai/issues"
   },
-  "homepage": "https://code.foss.global/push.rocks/smartai#readme",
+  "homepage": "https://code.foss.global/push.rocks/smartai",
   "browserslist": [
     "last 1 chrome versions"
   ],

readme.md

@@ -1,6 +1,6 @@
 # @push.rocks/smartai
 
-Provides a standardized interface for integrating and conversing with multiple AI models, supporting operations like chat and potentially audio responses.
+Provides a standardized interface for integrating and conversing with multiple AI models, supporting operations like chat, streaming interactions, and audio responses.
 
 ## Install
 
@@ -12,84 +12,214 @@ npm install @push.rocks/smartai
 
 This command installs the package and adds it to your project's dependencies.
 
+## Supported AI Providers
+
+@push.rocks/smartai supports multiple AI providers, each with its own unique capabilities:
+
+### OpenAI
+- Models: GPT-4, GPT-3.5-turbo, GPT-4-vision-preview
+- Features: Chat, Streaming, Audio Generation, Vision, Document Processing
+- Configuration:
+  ```typescript
+  openaiToken: 'your-openai-token'
+  ```
+
+### Anthropic
+- Models: Claude-3-opus-20240229
+- Features: Chat, Streaming, Vision, Document Processing
+- Configuration:
+  ```typescript
+  anthropicToken: 'your-anthropic-token'
+  ```
+
+### Perplexity
+- Models: Mixtral-8x7b-instruct
+- Features: Chat, Streaming
+- Configuration:
+  ```typescript
+  perplexityToken: 'your-perplexity-token'
+  ```
+
+### Groq
+- Models: Llama-3.3-70b-versatile
+- Features: Chat, Streaming
+- Configuration:
+  ```typescript
+  groqToken: 'your-groq-token'
+  ```
+
+### Ollama
+- Models: Configurable (default: llama2, llava for vision/documents)
+- Features: Chat, Streaming, Vision, Document Processing
+- Configuration:
+  ```typescript
+  baseUrl: 'http://localhost:11434' // Optional
+  model: 'llama2' // Optional
+  visionModel: 'llava' // Optional, for vision and document tasks
+  ```
+
 ## Usage
 
-The `@push.rocks/smartai` package is a comprehensive solution for integrating and interacting with various AI models, designed to support operations ranging from chat interactions to possibly handling audio responses. This documentation will guide you through the process of utilizing `@push.rocks/smartai` in your applications, focusing on TypeScript and ESM syntax to demonstrate its full capabilities.
+The `@push.rocks/smartai` package is a comprehensive solution for integrating and interacting with various AI models, designed to support operations ranging from chat interactions to audio responses. This documentation will guide you through the process of utilizing `@push.rocks/smartai` in your applications.
 
 ### Getting Started
 
-Before you begin, ensure you have installed the package in your project as described in the **Install** section above. Once installed, you can start integrating AI functionalities into your application.
+Before you begin, ensure you have installed the package as described in the **Install** section above. Once installed, you can start integrating AI functionalities into your application.
 
 ### Initializing SmartAi
 
-The first step is to import and initialize the `SmartAi` class with appropriate options, including tokens for the AI services you plan to use:
+The first step is to import and initialize the `SmartAi` class with appropriate options for the AI services you plan to use:
 
 ```typescript
 import { SmartAi } from '@push.rocks/smartai';
 
 const smartAi = new SmartAi({
-  openaiToken: 'your-openai-access-token',
-  anthropicToken: 'your-anthropic-access-token'
+  openaiToken: 'your-openai-token',
+  anthropicToken: 'your-anthropic-token',
+  perplexityToken: 'your-perplexity-token',
+  groqToken: 'your-groq-token',
+  ollama: {
+    baseUrl: 'http://localhost:11434',
+    model: 'llama2'
+  }
 });
 
 await smartAi.start();
 ```
 
-### Creating Conversations with AI
+### Chat Interactions
 
-`SmartAi` provides a flexible interface to create and manage conversations with different AI providers. You can create a conversation with any supported AI provider like OpenAI or Anthropic by specifying the provider you want to use:
+#### Synchronous Chat
+
+For simple question-answer interactions:
 
 ```typescript
-const openAiConversation = await smartAi.createConversation('openai');
-const anthropicConversation = await smartAi.createConversation('anthropic');
-```
-
-### Chatting with AI
-
-Once you have a conversation instance, you can start sending messages to the AI and receive responses. Each conversation object provides methods to interact in a synchronous or asynchronous manner, depending on your use case.
-
-#### Synchronous Chat Example
-
-Here's how you can have a synchronous chat with OpenAI:
-
-```typescript
-const response = await openAiConversation.chat({
-  systemMessage: 'This is a greeting from the system.',
-  userMessage: 'Hello, AI! How are you today?',
+const response = await smartAi.openaiProvider.chat({
+  systemMessage: 'You are a helpful assistant.',
+  userMessage: 'What is the capital of France?',
   messageHistory: [] // Previous messages in the conversation
 });
 
-console.log(response.message); // Log the response from AI
+console.log(response.message);
 ```
 
-#### Streaming Chat Example
+#### Streaming Chat
 
-For real-time, streaming interactions, you can utilize the streaming capabilities provided by the conversation object. This enables a continuous exchange of messages between your application and the AI:
+For real-time, streaming interactions:
 
 ```typescript
-const inputStreamWriter = openAiConversation.getInputStreamWriter();
-const outputStream = openAiConversation.getOutputStream();
+const textEncoder = new TextEncoder();
+const textDecoder = new TextDecoder();
 
-inputStreamWriter.write('Hello, AI! Can you stream responses?');
+// Create input and output streams
+const { writable, readable } = new TransformStream();
+const writer = writable.getWriter();
 
-const reader = outputStream.getReader();
-reader.read().then(function processText({done, value}) {
-  if (done) {
-    console.log('Stream finished.');
-    return;
-  }
-  console.log('AI says:', value);
-  reader.read().then(processText); // Continue reading messages
+// Send a message
+const message = {
+  role: 'user',
+  content: 'Tell me a story about a brave knight'
+};
+
+writer.write(textEncoder.encode(JSON.stringify(message) + '\n'));
+
+// Process the response stream
+const stream = await smartAi.openaiProvider.chatStream(readable);
+const reader = stream.getReader();
+
+while (true) {
+  const { done, value } = await reader.read();
+  if (done) break;
+  console.log('AI:', value); // Process each chunk of the response
+}
+```
+
+### Audio Generation
+
+For providers that support audio generation (currently OpenAI):
+
+```typescript
+const audioStream = await smartAi.openaiProvider.audio({
+  message: 'Hello, this is a test of text-to-speech'
+});
+
+// Handle the audio stream (e.g., save to file or play)
+```
+
+### Document Processing
+
+For providers that support document processing (OpenAI, Ollama, and Anthropic):
+
+```typescript
+// Using OpenAI
+const result = await smartAi.openaiProvider.document({
+  systemMessage: 'Classify the document type',
+  userMessage: 'What type of document is this?',
+  messageHistory: [],
+  pdfDocuments: [pdfBuffer] // Uint8Array of PDF content
+});
+
+// Using Ollama with llava
+const analysis = await smartAi.ollamaProvider.document({
+  systemMessage: 'You are a document analysis assistant',
+  userMessage: 'Extract the key information from this document',
+  messageHistory: [],
+  pdfDocuments: [pdfBuffer] // Uint8Array of PDF content
+});
+
+// Using Anthropic with Claude 3
+const anthropicAnalysis = await smartAi.anthropicProvider.document({
+  systemMessage: 'You are a document analysis assistant',
+  userMessage: 'Please analyze this document and extract key information',
+  messageHistory: [],
+  pdfDocuments: [pdfBuffer] // Uint8Array of PDF content
 });
 ```
 
-### Extending Conversations
+Both providers will:
+1. Convert PDF documents to images
+2. Process each page using their vision models
+3. Return a comprehensive analysis based on the system message and user query
 
-The modular design of `@push.rocks/smartai` allows you to extend conversations with additional features, such as handling audio responses or integrating other AI-powered functionalities. Utilize the provided AI providers' APIs to explore and implement a wide range of AI interactions within your conversations.
+### Vision Processing
 
-### Conclusion
+For providers that support vision tasks (OpenAI, Ollama, and Anthropic):
 
-With `@push.rocks/smartai`, integrating AI functionalities into your applications becomes streamlined and efficient. By leveraging the standardized interface provided by the package, you can easily converse with multiple AI models, expanding the capabilities of your applications with cutting-edge AI features. Whether you're implementing simple chat interactions or complex, real-time communication flows, `@push.rocks/smartai` offers the tools and flexibility needed to create engaging, AI-enhanced experiences.
+```typescript
+// Using OpenAI's GPT-4 Vision
+const description = await smartAi.openaiProvider.vision({
+  image: imageBuffer, // Buffer containing the image data
+  prompt: 'What do you see in this image?'
+});
+
+// Using Ollama's Llava model
+const analysis = await smartAi.ollamaProvider.vision({
+  image: imageBuffer,
+  prompt: 'Analyze this image in detail'
+});
+
+// Using Anthropic's Claude 3
+const anthropicAnalysis = await smartAi.anthropicProvider.vision({
+  image: imageBuffer,
+  prompt: 'Please analyze this image and describe what you see'
+});
+```
+
+## Error Handling
+
+All providers implement proper error handling. It's recommended to wrap API calls in try-catch blocks:
+
+```typescript
+try {
+  const response = await smartAi.openaiProvider.chat({
+    systemMessage: 'You are a helpful assistant.',
+    userMessage: 'Hello!',
+    messageHistory: []
+  });
+} catch (error) {
+  console.error('AI provider error:', error.message);
+}
+```
 
 ## License and Legal Information
 

test/test.ts

@@ -25,14 +25,14 @@ tap.test('should create chat response with openai', async () => {
     ],
   });
   console.log(`userMessage: ${userMessage}`);
-  console.log(response.message.content);
+  console.log(response.message);
 });
 
 tap.test('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 result = await testSmartai.openaiProvider.document({
-    systemMessage: 'Classify the document. Only the following answers are allowed: "invoice", "bank account statement", "contract", "other"',
+    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],

ts/00_commitinfo_data.ts

@@ -1,8 +1,8 @@
 /**
- * autocreated commitinfo by @pushrocks/commitinfo
+ * autocreated commitinfo by @push.rocks/commitinfo
  */
 export const commitinfo = {
   name: '@push.rocks/smartai',
-  version: '0.0.13',
+  version: '0.2.0',
   description: 'A TypeScript library for integrating and interacting with multiple AI models, offering capabilities for chat and potentially audio responses.'
 }

ts/abstract.classes.multimodal.ts

@@ -1,32 +1,86 @@
+/**
+ * Message format for chat interactions
+ */
+export interface ChatMessage {
+  role: 'assistant' | 'user' | 'system';
+  content: string;
+}
+
+/**
+ * Options for chat interactions
+ */
+export interface ChatOptions {
+  systemMessage: string;
+  userMessage: string;
+  messageHistory: ChatMessage[];
+}
+
+/**
+ * Response format for chat interactions
+ */
+export interface ChatResponse {
+  role: 'assistant';
+  message: string;
+}
+
+/**
+ * Abstract base class for multi-modal AI models.
+ * Provides a common interface for different AI providers (OpenAI, Anthropic, Perplexity, Ollama)
+ */
 export abstract class MultiModalModel {
   /**
-   * starts the model
+   * Initializes the model and any necessary resources
+   * Should be called before using any other methods
    */
   abstract start(): Promise<void>;
   
   /**
-   * stops the model
+   * Cleans up any resources used by the model
+   * Should be called when the model is no longer needed
    */
   abstract stop(): Promise<void>;
 
-  public abstract chat(optionsArg: {
-    systemMessage: string,
-    userMessage: string,
-    messageHistory: {
-      role: 'assistant' | 'user';
-      content: string;
-    }[]
-  }): Promise<{
-    role: 'assistant';
-    message: string;
-  }>
+  /**
+   * Synchronous chat interaction with the model
+   * @param optionsArg Options containing system message, user message, and message history
+   * @returns Promise resolving to the assistant's response
+   */
+  public abstract chat(optionsArg: ChatOptions): Promise<ChatResponse>;
   
   /**
-   * Defines a streaming interface for chat interactions.
-   * The implementation will vary based on the specific AI model.
-   * @param input
+   * Streaming interface for chat interactions
+   * Allows for real-time responses from the model
+   * @param input Stream of user messages
+   * @returns Stream of model responses
    */
-  public abstract chatStream(input: ReadableStream<string>): Promise<ReadableStream<string>>;
+  public abstract chatStream(input: ReadableStream<Uint8Array>): Promise<ReadableStream<string>>;
 
+  /**
+   * Text-to-speech conversion
+   * @param optionsArg Options containing the message to convert to speech
+   * @returns Promise resolving to a readable stream of audio data
+   * @throws Error if the provider doesn't support audio generation
+   */
+  public abstract audio(optionsArg: { message: string }): Promise<NodeJS.ReadableStream>;
 
+  /**
+   * Vision-language processing
+   * @param optionsArg Options containing the image and prompt for analysis
+   * @returns Promise resolving to the model's description or analysis of the image
+   * @throws Error if the provider doesn't support vision tasks
+   */
+  public abstract vision(optionsArg: { image: Buffer; prompt: string }): Promise<string>;
+
+  /**
+   * Document analysis and processing
+   * @param optionsArg Options containing system message, user message, PDF documents, and message history
+   * @returns Promise resolving to the model's analysis of the documents
+   * @throws Error if the provider doesn't support document processing
+   */
+  public abstract document(optionsArg: {
+    systemMessage: string;
+    userMessage: string;
+    pdfDocuments: Uint8Array[];
+    messageHistory: ChatMessage[];
+  }): Promise<{ message: any }>;
 }

ts/provider.anthropic.ts

@@ -1,6 +1,10 @@
 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 { ImageBlockParam, TextBlockParam } from '@anthropic-ai/sdk/resources/messages';
+
+type ContentBlock = ImageBlockParam | TextBlockParam;
 
 export interface IAnthropicProviderOptions {
   anthropicToken: string;
@@ -23,40 +27,214 @@ export class AnthropicProvider extends MultiModalModel {
 
   async stop() {}
 
-  public async chatStream(input: ReadableStream<string>): Promise<ReadableStream<string>> {
-    // TODO: implement for OpenAI
+  public async chatStream(input: ReadableStream<Uint8Array>): Promise<ReadableStream<string>> {
+    // Create a TextDecoder to handle incoming chunks
+    const decoder = new TextDecoder();
+    let buffer = '';
+    let currentMessage: { role: string; content: string; } | null = null;
 
-    const returnStream = new ReadableStream();
-    return returnStream;
+    // Create a TransformStream to process the input
+    const transform = new TransformStream<Uint8Array, string>({
+      async transform(chunk, controller) {
+        buffer += decoder.decode(chunk, { stream: true });
+
+        // Try to parse complete JSON messages from the buffer
+        while (true) {
+          const newlineIndex = buffer.indexOf('\n');
+          if (newlineIndex === -1) break;
+
+          const line = buffer.slice(0, newlineIndex);
+          buffer = buffer.slice(newlineIndex + 1);
+
+          if (line.trim()) {
+            try {
+              const message = JSON.parse(line);
+              currentMessage = {
+                role: message.role || 'user',
+                content: message.content || '',
+              };
+            } catch (e) {
+              console.error('Failed to parse message:', e);
+            }
+          }
+        }
+
+        // If we have a complete message, send it to Anthropic
+        if (currentMessage) {
+          const stream = await this.anthropicApiClient.messages.create({
+            model: 'claude-3-opus-20240229',
+            messages: [{ role: currentMessage.role, content: currentMessage.content }],
+            system: '',
+            stream: true,
+            max_tokens: 4000,
+          });
+
+          // Process each chunk from Anthropic
+          for await (const chunk of stream) {
+            const content = chunk.delta?.text;
+            if (content) {
+              controller.enqueue(content);
+            }
+          }
+
+          currentMessage = null;
+        }
+      },
+
+      flush(controller) {
+        if (buffer) {
+          try {
+            const message = JSON.parse(buffer);
+            controller.enqueue(message.content || '');
+          } catch (e) {
+            console.error('Failed to parse remaining buffer:', e);
+          }
+        }
+      }
+    });
+
+    // Connect the input to our transform stream
+    return input.pipeThrough(transform);
   }
 
   // Implementing the synchronous chat interaction
-  public async chat(optionsArg: {
-    systemMessage: string;
-    userMessage: string;
-    messageHistory: {
-      role: 'assistant' | 'user';
-      content: string;
-    }[];
-  }) {
+  public async chat(optionsArg: ChatOptions): Promise<ChatResponse> {
+    // Convert message history to Anthropic format
+    const messages = optionsArg.messageHistory.map(msg => ({
+      role: msg.role === 'assistant' ? 'assistant' as const : 'user' as const,
+      content: msg.content
+    }));
+
     const result = await this.anthropicApiClient.messages.create({
       model: 'claude-3-opus-20240229',
       system: optionsArg.systemMessage,
       messages: [
-        ...optionsArg.messageHistory,
-        { role: 'user', content: optionsArg.userMessage },
+        ...messages,
+        { role: 'user' as const, content: optionsArg.userMessage }
       ],
       max_tokens: 4000,
     });
 
+    // Extract text content from the response
+    let message = '';
+    for (const block of result.content) {
+      if ('text' in block) {
+        message += block.text;
+      }
+    }
+
     return {
-      role: result.role as 'assistant',
-      message: result.content.join('\n'),
+      role: 'assistant' as const,
+      message,
     };
   }
 
-  private async audio(messageArg: string) {
+  public async audio(optionsArg: { message: string }): Promise<NodeJS.ReadableStream> {
     // Anthropic does not provide an audio API, so this method is not implemented.
     throw new Error('Audio generation is not yet supported by Anthropic.');
   }
+
+  public async vision(optionsArg: { image: Buffer; prompt: string }): Promise<string> {
+    const base64Image = optionsArg.image.toString('base64');
+    
+    const content: ContentBlock[] = [
+      {
+        type: 'text',
+        text: optionsArg.prompt
+      },
+      {
+        type: 'image',
+        source: {
+          type: 'base64',
+          media_type: 'image/jpeg',
+          data: base64Image
+        }
+      }
+    ];
+
+    const result = await this.anthropicApiClient.messages.create({
+      model: 'claude-3-opus-20240229',
+      messages: [{
+        role: 'user',
+        content
+      }],
+      max_tokens: 1024
+    });
+
+    // Extract text content from the response
+    let message = '';
+    for (const block of result.content) {
+      if ('text' in block) {
+        message += block.text;
+      }
+    }
+    return message;
+  }
+
+  public async document(optionsArg: {
+    systemMessage: string;
+    userMessage: string;
+    pdfDocuments: Uint8Array[];
+    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);
+      documentImageBytesArray = documentImageBytesArray.concat(documentImageArray);
+    }
+
+    // Convert message history to Anthropic format
+    const messages = optionsArg.messageHistory.map(msg => ({
+      role: msg.role === 'assistant' ? 'assistant' as const : 'user' as const,
+      content: msg.content
+    }));
+
+    // Create content array with text and images
+    const content: ContentBlock[] = [
+      {
+        type: 'text',
+        text: optionsArg.userMessage
+      }
+    ];
+
+    // Add each document page as an image
+    for (const imageBytes of documentImageBytesArray) {
+      content.push({
+        type: 'image',
+        source: {
+          type: 'base64',
+          media_type: 'image/jpeg',
+          data: Buffer.from(imageBytes).toString('base64')
+        }
+      });
+    }
+
+    const result = await this.anthropicApiClient.messages.create({
+      model: 'claude-3-opus-20240229',
+      system: optionsArg.systemMessage,
+      messages: [
+        ...messages,
+        { role: 'user', content }
+      ],
+      max_tokens: 4096
+    });
+
+    // Extract text content from the response
+    let message = '';
+    for (const block of result.content) {
+      if ('text' in block) {
+        message += block.text;
+      }
+    }
+
+    return {
+      message: {
+        role: 'assistant',
+        content: message
+      }
+    };
+  }
 }

ts/provider.groq.ts

@@ -0,0 +1,192 @@
+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';
+
+export interface IGroqProviderOptions {
+  groqToken: string;
+  model?: string;
+}
+
+export class GroqProvider extends MultiModalModel {
+  private options: IGroqProviderOptions;
+  private baseUrl = 'https://api.groq.com/v1';
+
+  constructor(optionsArg: IGroqProviderOptions) {
+    super();
+    this.options = {
+      ...optionsArg,
+      model: optionsArg.model || 'llama-3.3-70b-versatile', // Default model
+    };
+  }
+
+  async start() {}
+
+  async stop() {}
+
+  public async chatStream(input: ReadableStream<Uint8Array>): Promise<ReadableStream<string>> {
+    // Create a TextDecoder to handle incoming chunks
+    const decoder = new TextDecoder();
+    let buffer = '';
+    let currentMessage: { role: string; content: string; } | null = null;
+
+    // Create a TransformStream to process the input
+    const transform = new TransformStream<Uint8Array, string>({
+      async transform(chunk, controller) {
+        buffer += decoder.decode(chunk, { stream: true });
+
+        // Try to parse complete JSON messages from the buffer
+        while (true) {
+          const newlineIndex = buffer.indexOf('\n');
+          if (newlineIndex === -1) break;
+
+          const line = buffer.slice(0, newlineIndex);
+          buffer = buffer.slice(newlineIndex + 1);
+
+          if (line.trim()) {
+            try {
+              const message = JSON.parse(line);
+              currentMessage = {
+                role: message.role || 'user',
+                content: message.content || '',
+              };
+            } catch (e) {
+              console.error('Failed to parse message:', e);
+            }
+          }
+        }
+
+        // If we have a complete message, send it to Groq
+        if (currentMessage) {
+          const response = await fetch(`${this.baseUrl}/chat/completions`, {
+            method: 'POST',
+            headers: {
+              'Authorization': `Bearer ${this.options.groqToken}`,
+              'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+              model: this.options.model,
+              messages: [{ role: currentMessage.role, content: currentMessage.content }],
+              stream: true,
+            }),
+          });
+
+          // Process each chunk from Groq
+          const reader = response.body?.getReader();
+          if (reader) {
+            try {
+              while (true) {
+                const { done, value } = await reader.read();
+                if (done) break;
+
+                const chunk = new TextDecoder().decode(value);
+                const lines = chunk.split('\n');
+
+                for (const line of lines) {
+                  if (line.startsWith('data: ')) {
+                    const data = line.slice(6);
+                    if (data === '[DONE]') break;
+
+                    try {
+                      const parsed = JSON.parse(data);
+                      const content = parsed.choices[0]?.delta?.content;
+                      if (content) {
+                        controller.enqueue(content);
+                      }
+                    } catch (e) {
+                      console.error('Failed to parse SSE data:', e);
+                    }
+                  }
+                }
+              }
+            } finally {
+              reader.releaseLock();
+            }
+          }
+
+          currentMessage = null;
+        }
+      },
+
+      flush(controller) {
+        if (buffer) {
+          try {
+            const message = JSON.parse(buffer);
+            controller.enqueue(message.content || '');
+          } catch (e) {
+            console.error('Failed to parse remaining buffer:', e);
+          }
+        }
+      }
+    });
+
+    // Connect the input to our transform stream
+    return input.pipeThrough(transform);
+  }
+
+  // Implementing the synchronous chat interaction
+  public async chat(optionsArg: ChatOptions): Promise<ChatResponse> {
+    const messages = [
+      // System message
+      {
+        role: 'system',
+        content: optionsArg.systemMessage,
+      },
+      // Message history
+      ...optionsArg.messageHistory.map(msg => ({
+        role: msg.role,
+        content: msg.content,
+      })),
+      // User message
+      {
+        role: 'user',
+        content: optionsArg.userMessage,
+      },
+    ];
+
+    const response = await fetch(`${this.baseUrl}/chat/completions`, {
+      method: 'POST',
+      headers: {
+        'Authorization': `Bearer ${this.options.groqToken}`,
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({
+        model: this.options.model,
+        messages,
+        temperature: 0.7,
+        max_completion_tokens: 1024,
+        stream: false,
+      }),
+    });
+
+    if (!response.ok) {
+      const error = await response.json();
+      throw new Error(`Groq API error: ${error.message || response.statusText}`);
+    }
+
+    const result = await response.json();
+
+    return {
+      role: 'assistant',
+      message: result.choices[0].message.content,
+    };
+  }
+
+  public async audio(optionsArg: { message: string }): Promise<NodeJS.ReadableStream> {
+    // Groq does not provide an audio API, so this method is not implemented.
+    throw new Error('Audio generation is not yet supported by Groq.');
+  }
+
+  public async vision(optionsArg: { image: Buffer; prompt: string }): Promise<string> {
+    throw new Error('Vision tasks are not yet supported by Groq.');
+  }
+
+  public async document(optionsArg: {
+    systemMessage: string;
+    userMessage: string;
+    pdfDocuments: Uint8Array[];
+    messageHistory: ChatMessage[];
+  }): Promise<{ message: any }> {
+    throw new Error('Document processing is not yet supported by Groq.');
+  }
+}

ts/provider.ollama.ts

@@ -1,3 +1,252 @@
 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';
 
-export class OllamaProvider {}
+export interface IOllamaProviderOptions {
+  baseUrl?: string;
+  model?: string;
+  visionModel?: string; // Model to use for vision tasks (e.g. 'llava')
+}
+
+export class OllamaProvider extends MultiModalModel {
+  private options: IOllamaProviderOptions;
+  private baseUrl: string;
+  private model: string;
+  private visionModel: string;
+
+  constructor(optionsArg: IOllamaProviderOptions = {}) {
+    super();
+    this.options = optionsArg;
+    this.baseUrl = optionsArg.baseUrl || 'http://localhost:11434';
+    this.model = optionsArg.model || 'llama2';
+    this.visionModel = optionsArg.visionModel || 'llava';
+  }
+
+  async start() {
+    // Verify Ollama is running
+    try {
+      const response = await fetch(`${this.baseUrl}/api/tags`);
+      if (!response.ok) {
+        throw new Error('Failed to connect to Ollama server');
+      }
+    } catch (error) {
+      throw new Error(`Failed to connect to Ollama server at ${this.baseUrl}: ${error.message}`);
+    }
+  }
+
+  async stop() {}
+
+  public async chatStream(input: ReadableStream<Uint8Array>): Promise<ReadableStream<string>> {
+    // Create a TextDecoder to handle incoming chunks
+    const decoder = new TextDecoder();
+    let buffer = '';
+    let currentMessage: { role: string; content: string; } | null = null;
+
+    // Create a TransformStream to process the input
+    const transform = new TransformStream<Uint8Array, string>({
+      async transform(chunk, controller) {
+        buffer += decoder.decode(chunk, { stream: true });
+
+        // Try to parse complete JSON messages from the buffer
+        while (true) {
+          const newlineIndex = buffer.indexOf('\n');
+          if (newlineIndex === -1) break;
+
+          const line = buffer.slice(0, newlineIndex);
+          buffer = buffer.slice(newlineIndex + 1);
+
+          if (line.trim()) {
+            try {
+              const message = JSON.parse(line);
+              currentMessage = {
+                role: message.role || 'user',
+                content: message.content || '',
+              };
+            } catch (e) {
+              console.error('Failed to parse message:', e);
+            }
+          }
+        }
+
+        // If we have a complete message, send it to Ollama
+        if (currentMessage) {
+          const response = await fetch(`${this.baseUrl}/api/chat`, {
+            method: 'POST',
+            headers: {
+              'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+              model: this.model,
+              messages: [{ role: currentMessage.role, content: currentMessage.content }],
+              stream: true,
+            }),
+          });
+
+          // Process each chunk from Ollama
+          const reader = response.body?.getReader();
+          if (reader) {
+            try {
+              while (true) {
+                const { done, value } = await reader.read();
+                if (done) break;
+
+                const chunk = new TextDecoder().decode(value);
+                const lines = chunk.split('\n');
+
+                for (const line of lines) {
+                  if (line.trim()) {
+                    try {
+                      const parsed = JSON.parse(line);
+                      const content = parsed.message?.content;
+                      if (content) {
+                        controller.enqueue(content);
+                      }
+                    } catch (e) {
+                      console.error('Failed to parse Ollama response:', e);
+                    }
+                  }
+                }
+              }
+            } finally {
+              reader.releaseLock();
+            }
+          }
+
+          currentMessage = null;
+        }
+      },
+
+      flush(controller) {
+        if (buffer) {
+          try {
+            const message = JSON.parse(buffer);
+            controller.enqueue(message.content || '');
+          } catch (e) {
+            console.error('Failed to parse remaining buffer:', e);
+          }
+        }
+      }
+    });
+
+    // Connect the input to our transform stream
+    return input.pipeThrough(transform);
+  }
+
+  // Implementing the synchronous chat interaction
+  public async chat(optionsArg: ChatOptions): Promise<ChatResponse> {
+    // Format messages for Ollama
+    const messages = [
+      { role: 'system', content: optionsArg.systemMessage },
+      ...optionsArg.messageHistory,
+      { role: 'user', content: optionsArg.userMessage }
+    ];
+
+    // Make API call to Ollama
+    const response = await fetch(`${this.baseUrl}/api/chat`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({
+        model: this.model,
+        messages: messages,
+        stream: false
+      }),
+    });
+
+    if (!response.ok) {
+      throw new Error(`Ollama API error: ${response.statusText}`);
+    }
+
+    const result = await response.json();
+    
+    return {
+      role: 'assistant' as const,
+      message: result.message.content,
+    };
+  }
+
+  public async audio(optionsArg: { message: string }): Promise<NodeJS.ReadableStream> {
+    throw new Error('Audio generation is not supported by Ollama.');
+  }
+
+  public async vision(optionsArg: { image: Buffer; prompt: string }): Promise<string> {
+    const base64Image = optionsArg.image.toString('base64');
+    
+    const response = await fetch(`${this.baseUrl}/api/chat`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({
+        model: this.visionModel,
+        messages: [{
+          role: 'user',
+          content: optionsArg.prompt,
+          images: [base64Image]
+        }],
+        stream: false
+      }),
+    });
+
+    if (!response.ok) {
+      throw new Error(`Ollama API error: ${response.statusText}`);
+    }
+
+    const result = await response.json();
+    return result.message.content;
+  }
+
+  public async document(optionsArg: {
+    systemMessage: string;
+    userMessage: string;
+    pdfDocuments: Uint8Array[];
+    messageHistory: ChatMessage[];
+  }): Promise<{ message: any }> {
+    // Convert PDF documents to images using SmartPDF
+    const smartpdfInstance = new plugins.smartpdf.SmartPdf();
+    let documentImageBytesArray: Uint8Array[] = [];
+
+    for (const pdfDocument of optionsArg.pdfDocuments) {
+      const documentImageArray = await smartpdfInstance.convertPDFToPngBytes(pdfDocument);
+      documentImageBytesArray = documentImageBytesArray.concat(documentImageArray);
+    }
+
+    // Convert images to base64
+    const base64Images = documentImageBytesArray.map(bytes => Buffer.from(bytes).toString('base64'));
+
+    // Send request to Ollama with images
+    const response = await fetch(`${this.baseUrl}/api/chat`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({
+        model: this.visionModel,
+        messages: [
+          { role: 'system', content: optionsArg.systemMessage },
+          ...optionsArg.messageHistory,
+          {
+            role: 'user',
+            content: optionsArg.userMessage,
+            images: base64Images
+          }
+        ],
+        stream: false
+      }),
+    });
+
+    if (!response.ok) {
+      throw new Error(`Ollama API error: ${response.statusText}`);
+    }
+
+    const result = await response.json();
+    return {
+      message: {
+        role: 'assistant',
+        content: result.message.content
+      }
+    };
+  }
+}

ts/provider.openai.ts

@@ -27,11 +27,72 @@ export class OpenAiProvider extends MultiModalModel {
 
   public async stop() {}
 
-  public async chatStream(input: ReadableStream<string>): Promise<ReadableStream<string>> {
-    // TODO: implement for OpenAI
+  public async chatStream(input: ReadableStream<Uint8Array>): Promise<ReadableStream<string>> {
+    // Create a TextDecoder to handle incoming chunks
+    const decoder = new TextDecoder();
+    let buffer = '';
+    let currentMessage: { role: string; content: string; } | null = null;
 
-    const returnStream = new ReadableStream();
-    return returnStream;
+    // Create a TransformStream to process the input
+    const transform = new TransformStream<Uint8Array, string>({
+      async transform(chunk, controller) {
+        buffer += decoder.decode(chunk, { stream: true });
+
+        // Try to parse complete JSON messages from the buffer
+        while (true) {
+          const newlineIndex = buffer.indexOf('\n');
+          if (newlineIndex === -1) break;
+
+          const line = buffer.slice(0, newlineIndex);
+          buffer = buffer.slice(newlineIndex + 1);
+
+          if (line.trim()) {
+            try {
+              const message = JSON.parse(line);
+              currentMessage = {
+                role: message.role || 'user',
+                content: message.content || '',
+              };
+            } catch (e) {
+              console.error('Failed to parse message:', e);
+            }
+          }
+        }
+
+        // If we have a complete message, send it to OpenAI
+        if (currentMessage) {
+          const stream = await this.openAiApiClient.chat.completions.create({
+            model: 'gpt-4',
+            messages: [{ role: currentMessage.role, content: currentMessage.content }],
+            stream: true,
+          });
+
+          // Process each chunk from OpenAI
+          for await (const chunk of stream) {
+            const content = chunk.choices[0]?.delta?.content;
+            if (content) {
+              controller.enqueue(content);
+            }
+          }
+
+          currentMessage = null;
+        }
+      },
+
+      flush(controller) {
+        if (buffer) {
+          try {
+            const message = JSON.parse(buffer);
+            controller.enqueue(message.content || '');
+          } catch (e) {
+            console.error('Failed to parse remaining buffer:', e);
+          }
+        }
+      }
+    });
+
+    // Connect the input to our transform stream
+    return input.pipeThrough(transform);
   }
 
   // Implementing the synchronous chat interaction
@@ -44,7 +105,7 @@ export class OpenAiProvider extends MultiModalModel {
     }[];
   }) {
     const result = await this.openAiApiClient.chat.completions.create({
-      model: 'gpt-4-turbo-preview',
+      model: 'gpt-4o',
 
       messages: [
         { role: 'system', content: optionsArg.systemMessage },
@@ -102,7 +163,7 @@ export class OpenAiProvider extends MultiModalModel {
     );
 
     const result = await this.openAiApiClient.chat.completions.create({
-      model: 'gpt-4-vision-preview',
+      model: 'gpt-4o',
       // response_format: { type: "json_object" }, // not supported for now
       messages: [
         { role: 'system', content: optionsArg.systemMessage },
@@ -131,4 +192,27 @@ export class OpenAiProvider extends MultiModalModel {
       message: result.choices[0].message,
     };
   }
+
+  public async vision(optionsArg: { image: Buffer; prompt: string }): Promise<string> {
+    const result = await this.openAiApiClient.chat.completions.create({
+      model: 'gpt-4-vision-preview',
+      messages: [
+        {
+          role: 'user',
+          content: [
+            { type: 'text', text: optionsArg.prompt },
+            {
+              type: 'image_url',
+              image_url: {
+                url: `data:image/jpeg;base64,${optionsArg.image.toString('base64')}`
+              }
+            }
+          ]
+        }
+      ],
+      max_tokens: 300
+    });
+
+    return result.choices[0].message.content || '';
+  }
 }

ts/provider.perplexity.ts

@@ -1,3 +1,171 @@
 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';
 
-export class PerplexityProvider {}
+export interface IPerplexityProviderOptions {
+  perplexityToken: string;
+}
+
+export class PerplexityProvider extends MultiModalModel {
+  private options: IPerplexityProviderOptions;
+
+  constructor(optionsArg: IPerplexityProviderOptions) {
+    super();
+    this.options = optionsArg;
+  }
+
+  async start() {
+    // Initialize any necessary clients or resources
+  }
+
+  async stop() {}
+
+  public async chatStream(input: ReadableStream<Uint8Array>): Promise<ReadableStream<string>> {
+    // Create a TextDecoder to handle incoming chunks
+    const decoder = new TextDecoder();
+    let buffer = '';
+    let currentMessage: { role: string; content: string; } | null = null;
+
+    // Create a TransformStream to process the input
+    const transform = new TransformStream<Uint8Array, string>({
+      async transform(chunk, controller) {
+        buffer += decoder.decode(chunk, { stream: true });
+
+        // Try to parse complete JSON messages from the buffer
+        while (true) {
+          const newlineIndex = buffer.indexOf('\n');
+          if (newlineIndex === -1) break;
+
+          const line = buffer.slice(0, newlineIndex);
+          buffer = buffer.slice(newlineIndex + 1);
+
+          if (line.trim()) {
+            try {
+              const message = JSON.parse(line);
+              currentMessage = {
+                role: message.role || 'user',
+                content: message.content || '',
+              };
+            } catch (e) {
+              console.error('Failed to parse message:', e);
+            }
+          }
+        }
+
+        // If we have a complete message, send it to Perplexity
+        if (currentMessage) {
+          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: 'mixtral-8x7b-instruct',
+              messages: [{ role: currentMessage.role, content: currentMessage.content }],
+              stream: true,
+            }),
+          });
+
+          // Process each chunk from Perplexity
+          const reader = response.body?.getReader();
+          if (reader) {
+            try {
+              while (true) {
+                const { done, value } = await reader.read();
+                if (done) break;
+
+                const chunk = new TextDecoder().decode(value);
+                const lines = chunk.split('\n');
+
+                for (const line of lines) {
+                  if (line.startsWith('data: ')) {
+                    const data = line.slice(6);
+                    if (data === '[DONE]') break;
+
+                    try {
+                      const parsed = JSON.parse(data);
+                      const content = parsed.choices[0]?.delta?.content;
+                      if (content) {
+                        controller.enqueue(content);
+                      }
+                    } catch (e) {
+                      console.error('Failed to parse SSE data:', e);
+                    }
+                  }
+                }
+              }
+            } finally {
+              reader.releaseLock();
+            }
+          }
+
+          currentMessage = null;
+        }
+      },
+
+      flush(controller) {
+        if (buffer) {
+          try {
+            const message = JSON.parse(buffer);
+            controller.enqueue(message.content || '');
+          } catch (e) {
+            console.error('Failed to parse remaining buffer:', e);
+          }
+        }
+      }
+    });
+
+    // Connect the input to our transform stream
+    return input.pipeThrough(transform);
+  }
+
+  // Implementing the synchronous chat interaction
+  public async chat(optionsArg: ChatOptions): Promise<ChatResponse> {
+    // Make API call to Perplexity
+    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: 'mixtral-8x7b-instruct',  // Using Mixtral model
+        messages: [
+          { role: 'system', content: optionsArg.systemMessage },
+          ...optionsArg.messageHistory,
+          { role: 'user', content: optionsArg.userMessage }
+        ],
+      }),
+    });
+
+    if (!response.ok) {
+      throw new Error(`Perplexity API error: ${response.statusText}`);
+    }
+
+    const result = await response.json();
+    
+    return {
+      role: 'assistant' as const,
+      message: result.choices[0].message.content,
+    };
+  }
+
+  public async audio(optionsArg: { message: string }): Promise<NodeJS.ReadableStream> {
+    throw new Error('Audio generation is not supported by Perplexity.');
+  }
+
+  public async vision(optionsArg: { image: Buffer; prompt: string }): Promise<string> {
+    throw new Error('Vision tasks are not supported by Perplexity.');
+  }
+
+  public async document(optionsArg: {
+    systemMessage: string;
+    userMessage: string;
+    pdfDocuments: Uint8Array[];
+    messageHistory: ChatMessage[];
+  }): Promise<{ message: any }> {
+    throw new Error('Document processing is not supported by Perplexity.');
+  }
+}