Ollama SDK Hands-On Workshop

This workshop provides a comprehensive, hands-on introduction to the @tekimax/ollama-sdk. By the end of this workshop, you’ll have practical experience using both the command-line interface and the programmatic API for working with large language models through Ollama.

Prerequisites

Before starting this workshop, ensure you have:

Node.js (v16 or later) installed
Ollama installed and running locally
Basic familiarity with JavaScript/TypeScript and command-line operations
A code editor (VS Code recommended)
At least 8GB RAM (16GB+ recommended for larger models)
Terminal or command prompt access

Part 1: CLI Basics

This section focuses on using the command-line interface to interact with Ollama models.

Setting Up

Install the Ollama SDK globally:

npm install -g @tekimax/ollama-sdk

Verify installation:

ollama-sdk help

You should see the help output with available commands and options.

Exploring Available Models

Let’s start by exploring what models are available in your Ollama installation:

ollama-sdk list

This command displays all models currently downloaded to your system, along with their sizes.

Workshop Activity #1:

Run the list command
Note which models are available
If you don’t have any models yet, the list will be empty

Basic Text Generation

Generate your first text using a model:

ollama-sdk generate -m llama2 -p "Explain quantum computing to a 5-year old"

If you don’t have the llama2 model, replace it with any model from your list, or pull llama2 first:

ollama-sdk pull -m llama2

Workshop Activity #2:

Generate text with a simple prompt
Try different prompts and observe the responses
Note the response time and quality

Working with Parameters

The CLI supports several parameters to control text generation:

# With streaming enabled
ollama-sdk generate -m llama2 -p "Write a poem about space" -s

# With temperature control (0.0-1.0)
ollama-sdk generate -m llama2 -p "Write a story" -t 0.7

# With top-p sampling
ollama-sdk generate -m llama2 -p "Brainstorm business ideas" --top-p 0.9

Parameter Explanation:

-m, --model: The model to use (e.g., llama2, mistral)
-p, --prompt: The input text prompt
-s, --stream: Stream the response token-by-token instead of waiting for the complete response
-t, --temperature: Controls randomness (0.0 = deterministic, 1.0 = maximum creativity)
--top-p: Controls diversity through nucleus sampling (0.0-1.0)

Workshop Activity #3:

Compare output with different temperature settings:
- Low temperature (0.2): More focused, deterministic responses
- Medium temperature (0.5): Balanced randomness
- High temperature (0.8): More creative, varied responses
Document the differences in outputs

Creating Embeddings

Embeddings are vector representations of text that capture semantic meaning:

ollama-sdk embed -m nomic-embed-text -p "Artificial intelligence is transforming industries"

The output will be a high-dimensional vector (usually displayed as the first few values).

Workshop Activity #4:

Create embeddings for several related sentences
Create embeddings for unrelated sentences
Note the dimension of the embeddings (will vary based on model)

Managing Models

You can download (pull), view details, and remove models:

# Pull a new model
ollama-sdk pull -m mistral

# Show details of a model
ollama-sdk show -m llama2

# Remove a model you no longer need
ollama-sdk remove -m unused-model

Workshop Activity #5:

Pull a small model like orca-mini (if not already present)
View its details with the show command
Compare the license and size information with another model

CLI Exercises

Exercise 1: Text Comparison

Generate descriptions of the same topic with three different temperature settings
Compare how the outputs differ in style and content

Exercise 2: Model Comparison

Run the same prompt through 2-3 different models
Compare response quality, speed, and style

Exercise 3: Parameter Experimentation

Create a simple prompt
Generate responses with different combinations of temperature and top-p
Document which settings give the best results for your use case

Part 2: API Fundamentals

This section covers using the Ollama SDK programmatically in JavaScript/TypeScript applications.

SDK Installation

Create a new project directory and initialize:

mkdir ollama-workshop
cd ollama-workshop
npm init -y
npm install @tekimax/ollama-sdk

Create a basic test file:

touch test.js

Basic Client Setup

Open test.js in your editor and add the following code:

// test.js
const { OllamaKit } = require('@tekimax/ollama-sdk');

async function main() {
  // Initialize the client
  const ollama = new OllamaKit({
    // Default host is http://localhost:11434
  });
  
  console.log('Ollama client initialized');
  
  // List models
  const models = await ollama.listModels();
  console.log('Available models:');
  for (const model of models.models) {
    console.log(`- ${model.name} (${(model.size / 1024 / 1024 / 1024).toFixed(2)} GB)`);
  }
}

main().catch(console.error);

Run the script:

node test.js

Workshop Activity #6:

Run the script to list available models
Compare the output with the CLI list command result

Text Generation API

Create a new file for text generation:

touch generate.js

Add code to generate text:

// generate.js
const { OllamaKit } = require('@tekimax/ollama-sdk');

async function generateText() {
  const ollama = new OllamaKit();
  
  const result = await ollama.generate({
    model: 'llama2',
    prompt: 'Explain the basics of machine learning in simple terms',
    temperature: 0.7,
    top_p: 0.9,
    top_k: 40,
    num_predict: 200,
  });
  
  console.log('Generated Text:');
  console.log('---------------');
  console.log(result.response);
  console.log('---------------');
  console.log(`Total tokens: ${result.eval_count || 'N/A'}`);
  console.log(`Generation time: ${result.total_duration ? (result.total_duration / 1000000).toFixed(2) + 's' : 'N/A'}`);
}

generateText().catch(console.error);

Run the script:

node generate.js

Parameter Reference:

{
  model: string,          // Model name
  prompt: string,         // Input prompt
  system?: string,        // System message to guide model behavior
  template?: string,      // Custom prompt template
  context?: number[],     // Previous token context
  raw?: boolean,          // Return raw model output
  format?: string,        // Request specific output format (e.g. 'json')
  temperature?: number,   // Randomness (0.0-1.0)
  top_p?: number,         // Nucleus sampling (0.0-1.0)
  top_k?: number,         // Top-k sampling (1-100)
  num_predict?: number,   // Maximum number of tokens to generate
  stop?: string[],        // Strings that will stop generation when produced
}

Workshop Activity #7:

Modify the generate.js script to use different parameters
Experiment with the system parameter to guide model behavior
Try using stop sequences to end generation at specific points

Streaming Responses

For longer responses or better user experience, streaming is recommended:

touch stream.js

Add the following code:

// stream.js
const { OllamaKit } = require('@tekimax/ollama-sdk');

async function streamText() {
  const ollama = new OllamaKit();
  
  console.log('Starting stream generation...');
  
  const stream = await ollama.generateStream({
    model: 'llama2',
    prompt: 'Write a short story about an AI learning to be creative',
    temperature: 0.8,
  });
  
  let fullText = '';
  
  // Process the stream
  for await (const chunk of stream) {
    process.stdout.write(chunk.response);
    fullText += chunk.response;
    
    // If this is the final chunk
    if (chunk.done) {
      console.log('\n\nGeneration complete!');
      console.log(`Total length: ${fullText.length} characters`);
    }
  }
}

streamText().catch(console.error);

Run the script:

node stream.js

Workshop Activity #8:

Run the streaming example
Modify it to include a progress indicator (e.g., token count)
Implement a way to save the streamed output to a file

Working with Embeddings

Create a file for working with embeddings:

touch embeddings.js

Add code to create and compare embeddings:

// embeddings.js
const { OllamaKit } = require('@tekimax/ollama-sdk');

async function workWithEmbeddings() {
  const ollama = new OllamaKit();
  
  // Define some example texts
  const texts = [
    "Artificial intelligence is transforming how we work",
    "Machine learning models require significant training data",
    "Today's weather is sunny and warm",
    "Climate change is affecting global weather patterns",
  ];
  
  // Create embeddings for all texts
  console.log('Creating embeddings...');
  const embeddings = [];
  
  for (const text of texts) {
    const result = await ollama.createEmbedding({
      model: 'nomic-embed-text', // Or another embedding model
      prompt: text,
    });
    
    embeddings.push({
      text,
      embedding: result.embedding,
    });
    
    console.log(`Created embedding for: "${text.substring(0, 30)}..."`);
  }
  
  // Calculate similarity between embeddings
  console.log('\nCalculating similarities:');
  
  for (let i = 0; i < embeddings.length; i++) {
    for (let j = i + 1; j < embeddings.length; j++) {
      const similarity = calculateCosineSimilarity(
        embeddings[i].embedding,
        embeddings[j].embedding
      );
      
      console.log(`Similarity between "${embeddings[i].text.substring(0, 20)}..." and "${embeddings[j].text.substring(0, 20)}...": ${similarity.toFixed(4)}`);
    }
  }
}

// Helper function to calculate cosine similarity
function calculateCosineSimilarity(vec1, vec2) {
  // Dot product
  let dotProduct = 0;
  for (let i = 0; i < vec1.length; i++) {
    dotProduct += vec1[i] * vec2[i];
  }
  
  // Magnitudes
  const mag1 = Math.sqrt(vec1.reduce((sum, val) => sum + val * val, 0));
  const mag2 = Math.sqrt(vec2.reduce((sum, val) => sum + val * val, 0));
  
  // Cosine similarity
  return dotProduct / (mag1 * mag2);
}

workWithEmbeddings().catch(console.error);

Run the script:

node embeddings.js

Workshop Activity #9:

Add more example texts to the embeddings script
Observe which texts have higher similarity scores
Create a simple function to find the most similar text to a query

API Exercises

Exercise 1: Create a Simple Q&A System

Create a script that:
- Takes a user question as input
- Generates a response using the Ollama API
- Formats and displays the answer

Exercise 2: Compare Embedding Models

Create embeddings for the same sentences using different models
Compare the dimensionality and similarity results

Exercise 3: Build a Simple Chat Application

Create a script that maintains conversation history
Send the conversation history with each new message
Implement a simple CLI chat interface

Part 3: Advanced Topics

This section covers more advanced usage of the Ollama SDK.

Tool Calling with Models

Many advanced LLMs now support tool or function calling capabilities, which allow the model to request the execution of external functions to access data or perform actions. The Ollama SDK provides support for this functionality.

Create a new file:

touch tool-calling.js

Add the following code:

// tool-calling.js
const { OllamaKit } = require('@tekimax/ollama-sdk');

async function demonstrateToolCalling() {
  // Initialize the client
  const ollama = new OllamaKit();
  
  // Define tools (functions the model can call)
  const tools = [
    {
      type: "function",
      name: "get_weather",
      description: "Get the current weather for a location",
      parameters: {
        type: "object",
        required: ["location"],
        properties: {
          location: {
            type: "string",
            description: "The city name, e.g., 'San Francisco, CA'"
          },
          unit: {
            type: "string",
            enum: ["celsius", "fahrenheit"],
            description: "The unit of temperature"
          }
        }
      }
    }
  ];
  
  console.log('Calling model with tools...');
  
  try {
    // Call the model with tools
    const response = await ollama.tools.callWithTools({
      model: 'llama3', // Use a model that supports tool calling
      prompt: 'What is the weather like in New York?',
      tools: tools,
      temperature: 0.7
    });
    
    console.log('Model response:', response.message.content);
    
    // Check if the model wants to use tools
    if (response.message.tool_calls && response.message.tool_calls.length > 0) {
      console.log('The model requested to use tools:');
      
      const toolCalls = response.message.tool_calls;
      // In a real application, you would execute the tools here
      
      for (const toolCall of toolCalls) {
        console.log(`- Tool: ${toolCall.name}`);
        console.log(`  Input: ${JSON.stringify(toolCall.input, null, 2)}`);
        
        // Mock tool execution
        const toolResult = { 
          temperature: 72, 
          unit: "fahrenheit", 
          condition: "sunny" 
        };
        
        console.log(`  Result: ${JSON.stringify(toolResult, null, 2)}`);
        
        // Send tool results back to the model
        const followUpResponse = await ollama.tools.executeToolCalls(
          {
            model: 'llama3',
            prompt: 'What is the weather like in New York?',
            tools: tools
          },
          [toolCall],
          [{
            tool_call_id: toolCall.id,
            role: "tool",
            name: toolCall.name,
            content: JSON.stringify(toolResult)
          }]
        );
        
        console.log('Final response with tool results:', followUpResponse.message.content);
      }
    } else {
      console.log('No tools were called by the model.');
    }
  } catch (error) {
    console.error('Error:', error.message);
  }
}

demonstrateToolCalling().catch(console.error);

Run the script:

node tool-calling.js

Workshop Activity #14:

Expand the tools with additional functions (e.g., search, calculator, etc.)
Create a streaming version of the tool calling example
Implement actual tool execution (e.g., using a weather API or other services)

Tool Calling with the CLI

You can also use tool calling via the CLI:

# First, create a JSON file with tool definitions
echo '[
  {
    "type": "function",
    "name": "get_weather",
    "description": "Get the current weather for a location",
    "parameters": {
      "type": "object",
      "required": ["location"],
      "properties": {
        "location": {
          "type": "string",
          "description": "The city name, e.g., San Francisco, CA"
        }
      }
    }
  }
]' > weather-tools.json

# Then call the CLI with tools
ollama-sdk tools -m llama3 -p "What's the weather like in Tokyo?" --tools-file weather-tools.json

Workshop Activity #15:

Create a more complex tools JSON file with multiple functions
Experiment with different prompts to see when tools get called
Compare how different models use tool calling capability

OpenAI Compatibility Layer

The Ollama SDK includes an OpenAI compatibility layer that allows you to use Ollama models with code designed for the OpenAI API.

Create a new file:

touch openai-compat.js

Add the following code:

// openai-compat.js
const { OpenAICompatManager } = require('@tekimax/ollama-sdk/openai');

async function testOpenAICompat() {
  // Initialize the OpenAI-compatible client
  const openai = new OpenAICompatManager({
    host: 'http://localhost:11434', // Ollama server
    defaultModel: 'llama2',
  });
  
  console.log('OpenAI compatibility layer initialized');
  
  // List available models (OpenAI format)
  const models = await openai.models.list();
  console.log('Available models:');
  console.log(models.data.map(model => model.id));
  
  // Generate chat completion (OpenAI format)
  console.log('\nGenerating chat completion...');
  const completion = await openai.chat.completions.create({
    model: 'llama2', // Can use Ollama model names directly
    messages: [
      { role: 'system', content: 'You are a helpful assistant that provides concise answers.' },
      { role: 'user', content: 'What is the difference between supervised and unsupervised learning?' }
    ],
    temperature: 0.7,
  });
  
  console.log('\nResponse:');
  console.log(completion.choices[0].message.content);
  
  // Create embeddings (OpenAI format)
  console.log('\nCreating embedding...');
  const embedding = await openai.embeddings.create({
    model: 'nomic-embed-text',
    input: 'The quick brown fox jumps over the lazy dog',
  });
  
  console.log(`Created embedding with ${embedding.data[0].embedding.length} dimensions`);
  console.log(`First 5 values: [${embedding.data[0].embedding.slice(0, 5).join(', ')}]`);
}

testOpenAICompat().catch(console.error);

Run the script:

node openai-compat.js

Workshop Activity #10:

Run the OpenAI compatibility example
Compare the response format with regular Ollama API responses
If you have existing OpenAI code, try adapting it to use the compatibility layer

Error Handling and Retries

Create a file to demonstrate robust error handling:

touch error-handling.js

Add the following code:

// error-handling.js
const { OllamaKit } = require('@tekimax/ollama-sdk');

async function robustGeneration() {
  const ollama = new OllamaKit();
  
  try {
    // Attempt to use a model that doesn't exist
    const result = await generateWithRetry(ollama, {
      model: 'non-existent-model',
      prompt: 'Hello, world!',
    });
    
    console.log('Generated text:', result.response);
  } catch (error) {
    console.error('Final error after retries:', error.message);
  }
}

async function generateWithRetry(client, options, maxRetries = 3) {
  let attempt = 0;
  const fallbackModels = ['llama2', 'mistral', 'orca-mini'];
  
  while (attempt < maxRetries) {
    try {
      console.log(`Attempt ${attempt + 1}/${maxRetries} with model: ${options.model}`);
      return await client.generate(options);
    } catch (error) {
      attempt++;
      console.log(`Error: ${error.message}`);
      
      if (attempt >= maxRetries) {
        throw new Error(`Failed after ${maxRetries} attempts: ${error.message}`);
      }
      
      if (error.message.includes('model') && error.message.includes('not found')) {
        // Try a fallback model
        const fallbackIndex = attempt - 1;
        if (fallbackIndex < fallbackModels.length) {
          console.log(`Trying fallback model: ${fallbackModels[fallbackIndex]}`);
          options.model = fallbackModels[fallbackIndex];
          continue;
        }
      }
      
      // Add exponential backoff
      const delay = Math.pow(2, attempt) * 1000;
      console.log(`Retrying in ${delay}ms...`);
      await new Promise(resolve => setTimeout(resolve, delay));
    }
  }
}

robustGeneration().catch(console.error);

Run the script:

node error-handling.js

Workshop Activity #11:

Run the error handling example
Modify the fallback models list to match models you have installed
Add additional error handling for different types of errors

Performance Optimization

Create a file to demonstrate performance optimizations:

touch performance.js

Add the following code:

// performance.js
const { OllamaKit } = require('@tekimax/ollama-sdk');

async function performanceDemo() {
  console.log('Performance Optimization Demo');
  
  // Create a single client instance to reuse
  const ollama = new OllamaKit();
  
  // 1. Reusing context for continued conversations
  console.log('\n1. Context Reuse:');
  const contextDemo = await contextReuseDemo(ollama);
  
  // 2. Parallel processing for independent requests
  console.log('\n2. Parallel Processing:');
  await parallelProcessingDemo(ollama);
  
  // 3. Efficient streaming for large responses
  console.log('\n3. Efficient Streaming:');
  await efficientStreamingDemo(ollama);
}

async function contextReuseDemo(client) {
  console.log('Starting conversation with context reuse...');
  
  // First message
  const response1 = await client.generate({
    model: 'llama2',
    prompt: 'My name is Alice and I love hiking.',
    system: 'You are a helpful assistant that remembers user information.',
  });
  
  console.log('Response 1:', response1.response);
  console.log('Context size:', response1.context?.length || 'N/A');
  
  // Second message using the context from first response
  const response2 = await client.generate({
    model: 'llama2',
    prompt: 'What is my name and what do I enjoy doing?',
    context: response1.context, // Reuse context to maintain conversation
    system: 'You are a helpful assistant that remembers user information.',
  });
  
  console.log('Response 2:', response2.response);
  console.log('Context size:', response2.context?.length || 'N/A');
  
  return response2.context;
}

async function parallelProcessingDemo(client) {
  console.log('Processing multiple independent requests in parallel...');
  const startTime = Date.now();
  
  const prompts = [
    'Explain what is machine learning',
    'Write a haiku about programming',
    'What are the benefits of exercise',
    'Describe quantum computing briefly',
  ];
  
  // Process all prompts in parallel
  const results = await Promise.all(
    prompts.map(prompt => 
      client.generate({
        model: 'llama2',
        prompt,
        temperature: 0.7,
      })
    )
  );
  
  const endTime = Date.now();
  console.log(`Processed ${prompts.length} requests in ${(endTime - startTime) / 1000}s`);
  
  for (let i = 0; i < results.length; i++) {
    console.log(`\nPrompt ${i + 1}: ${prompts[i]}`);
    console.log(`Response ${i + 1} (first 50 chars): ${results[i].response.substring(0, 50)}...`);
  }
}

async function efficientStreamingDemo(client) {
  console.log('Demonstrating efficient streaming processing...');
  
  const stream = await client.generateStream({
    model: 'llama2',
    prompt: 'Write a paragraph about the importance of clean code',
    temperature: 0.7,
  });
  
  let buffer = '';
  const chunkSize = 20; // Process in chunks
  const processingDelay = 100; // ms
  let lastProcess = Date.now();
  let totalTokens = 0;
  
  console.log('Streaming response with efficient processing:');
  
  for await (const chunk of stream) {
    buffer += chunk.response;
    totalTokens++;
    
    // Process the buffer in chunks
    const now = Date.now();
    if (now - lastProcess > processingDelay || chunk.done) {
      process.stdout.write(buffer);
      buffer = '';
      lastProcess = now;
      
      // Simulate processing time
      await new Promise(resolve => setTimeout(resolve, 5));
    }
  }
  
  console.log(`\n\nTotal tokens processed: ${totalTokens}`);
}

performanceDemo().catch(console.error);

Run the script:

node performance.js

Workshop Activity #12:

Run the performance optimization examples
Measure the execution time of parallel vs. sequential requests
Experiment with different buffer sizes in the streaming example

Integrating with Web Applications

Set up a basic Express.js server that uses the Ollama SDK:

npm install express cors
touch server.js

Add the following code to server.js:

// server.js
const express = require('express');
const cors = require('cors');
const { OllamaKit } = require('@tekimax/ollama-sdk');

const app = express();
const port = 3000;

// Middleware
app.use(cors());
app.use(express.json());
app.use(express.static('public'));

// Initialize Ollama client
const ollama = new OllamaKit();

// Routes
app.get('/api/models', async (req, res) => {
  try {
    const models = await ollama.listModels();
    res.json(models);
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

app.post('/api/generate', async (req, res) => {
  try {
    const { model, prompt, temperature } = req.body;
    
    if (!model || !prompt) {
      return res.status(400).json({ error: 'Model and prompt are required' });
    }
    
    const result = await ollama.generate({
      model,
      prompt,
      temperature: temperature || 0.7,
    });
    
    res.json(result);
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

app.post('/api/embed', async (req, res) => {
  try {
    const { model, prompt } = req.body;
    
    if (!model || !prompt) {
      return res.status(400).json({ error: 'Model and prompt are required' });
    }
    
    const result = await ollama.createEmbedding({
      model,
      prompt,
    });
    
    res.json(result);
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

// Start server
app.listen(port, () => {
  console.log(`Ollama API server running at http://localhost:${port}`);
});

Create a basic frontend:

mkdir -p public
touch public/index.html

Add the following HTML:

<!-- public/index.html -->
<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>Ollama Web Interface</title>
  <style>
    body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; }
    textarea { width: 100%; height: 100px; margin: 10px 0; padding: 10px; }
    select, button { padding: 8px 12px; margin: 5px; }
    #result { white-space: pre-wrap; background: #f5f5f5; padding: 15px; border-radius: 5px; margin-top: 20px; }
    .loader { display: inline-block; width: 20px; height: 20px; border: 3px solid rgba(0,0,0,.2); border-radius: 50%; border-top-color: #333; animation: spin 1s linear infinite; }
    @keyframes spin { to { transform: rotate(360deg); } }
  </style>
</head>
<body>
  <h1>Ollama Web Interface</h1>
  
  <div>
    <label for="model-select">Select Model:</label>
    <select id="model-select">
      <option value="">Loading models...</option>
    </select>
  </div>
  
  <div>
    <label for="temperature">Temperature:</label>
    <input type="range" id="temperature" min="0" max="1" step="0.1" value="0.7">
    <span id="temp-value">0.7</span>
  </div>
  
  <div>
    <textarea id="prompt" placeholder="Enter your prompt here..."></textarea>
  </div>
  
  <div>
    <button id="generate-btn">Generate</button>
    <button id="embed-btn">Create Embedding</button>
    <span id="loading" style="display:none;"><span class="loader"></span> Processing...</span>
  </div>
  
  <div id="result"></div>

  <script>
    // Load models when page loads
    window.addEventListener('DOMContentLoaded', async () => {
      try {
        const response = await fetch('/api/models');
        const data = await response.json();
        
        const modelSelect = document.getElementById('model-select');
        modelSelect.innerHTML = '';
        
        data.models.forEach(model => {
          const option = document.createElement('option');
          option.value = model.name;
          option.textContent = model.name;
          modelSelect.appendChild(option);
        });
      } catch (error) {
        console.error('Error loading models:', error);
      }
    });
    
    // Update temperature display
    document.getElementById('temperature').addEventListener('input', (e) => {
      document.getElementById('temp-value').textContent = e.target.value;
    });
    
    // Generate text
    document.getElementById('generate-btn').addEventListener('click', async () => {
      const model = document.getElementById('model-select').value;
      const prompt = document.getElementById('prompt').value;
      const temperature = parseFloat(document.getElementById('temperature').value);
      const resultDiv = document.getElementById('result');
      const loadingSpan = document.getElementById('loading');
      
      if (!model || !prompt) {
        resultDiv.textContent = 'Please select a model and enter a prompt';
        return;
      }
      
      try {
        resultDiv.textContent = '';
        loadingSpan.style.display = 'inline-block';
        
        const response = await fetch('/api/generate', {
          method: 'POST',
          headers: { 'Content-Type': 'application/json' },
          body: JSON.stringify({ model, prompt, temperature }),
        });
        
        const data = await response.json();
        
        if (response.ok) {
          resultDiv.textContent = data.response;
        } else {
          resultDiv.textContent = `Error: ${data.error}`;
        }
      } catch (error) {
        resultDiv.textContent = `Error: ${error.message}`;
      } finally {
        loadingSpan.style.display = 'none';
      }
    });
    
    // Create embedding
    document.getElementById('embed-btn').addEventListener('click', async () => {
      const model = document.getElementById('model-select').value;
      const prompt = document.getElementById('prompt').value;
      const resultDiv = document.getElementById('result');
      const loadingSpan = document.getElementById('loading');
      
      if (!model || !prompt) {
        resultDiv.textContent = 'Please select a model and enter a text to embed';
        return;
      }
      
      try {
        resultDiv.textContent = '';
        loadingSpan.style.display = 'inline-block';
        
        const response = await fetch('/api/embed', {
          method: 'POST',
          headers: { 'Content-Type': 'application/json' },
          body: JSON.stringify({ model, prompt }),
        });
        
        const data = await response.json();
        
        if (response.ok) {
          resultDiv.textContent = `Embedding created with ${data.embedding.length} dimensions\n\n`;
          resultDiv.textContent += `First 10 values: [${data.embedding.slice(0, 10).map(v => v.toFixed(6)).join(', ')}]`;
        } else {
          resultDiv.textContent = `Error: ${data.error}`;
        }
      } catch (error) {
        resultDiv.textContent = `Error: ${error.message}`;
      } finally {
        loadingSpan.style.display = 'none';
      }
    });
  </script>
</body>
</html>

Run the server:

node server.js

Access the web interface at http://localhost:3000

Workshop Activity #13:

Run the web server and access the interface
Generate text responses using different models and temperatures
Create embeddings and observe the vectors
Modify the server to add a streaming endpoint

Advanced Exercises

Exercise 1: Create a Chat Interface with History

Extend the web application to maintain conversation history
Display a chat-like interface with user and assistant messages
Implement streaming for a better user experience

Exercise 2: Build a Model Management Dashboard

Add functionality to pull and remove models from the interface
Display detailed model information
Show model status and download progress

Exercise 3: Implement a Vector Database

Store embeddings in a simple database (e.g., JSON file)
Add a search function to find similar entries
Create a simple knowledge base backed by embeddings

Part 4: Real-World Projects

This section contains larger project ideas to demonstrate practical applications of the Ollama SDK.

Building a Chat Application

Create a full-featured chat application:

Multiple conversation management
Chat history persistence
System prompt customization
Model switching
Streaming responses

Creating a Document Q&A System

Build a system that can answer questions based on provided documents:

Process documents to create chunks
Generate embeddings for each chunk
Store embeddings with text in a simple database
For each query, find relevant chunks using embedding similarity
Use relevant chunks as context for the model to generate answers

Semantic Search Implementation

Implement a semantic search system:

Create a corpus of text documents
Generate embeddings for all documents or paragraphs
Create a search interface that converts queries to embeddings
Return and highlight the most similar results

Final Project

Choose one of the project ideas above or create your own. The final project should demonstrate:

Proper SDK usage
Efficient handling of responses
Good user experience
Error handling
Performance considerations

Conclusion

This workshop has covered the fundamentals of using the @tekimax/ollama-sdk from basic CLI usage to advanced API integrations. You now have the skills to:

Use the CLI for quick model interactions
Incorporate the SDK into JavaScript/TypeScript applications
Work with text generation and embeddings
Build real-world applications using LLMs

Remember that large language models are a rapidly evolving field. Stay updated with the latest SDK features and model capabilities to make the most of your projects.

Getting Started

API Reference

Guides

Workshops & Tutorials

Workshop

Ollama SDK Hands-On Workshop

Table of Contents

Prerequisites

Part 1: CLI Basics

Setting Up

Exploring Available Models

Basic Text Generation

Working with Parameters

Creating Embeddings

Managing Models

CLI Exercises

Part 2: API Fundamentals

SDK Installation

Basic Client Setup

Text Generation API

Streaming Responses

Working with Embeddings

API Exercises

Part 3: Advanced Topics

Tool Calling with Models

Tool Calling with the CLI

OpenAI Compatibility Layer

Error Handling and Retries

Performance Optimization

Integrating with Web Applications

Advanced Exercises

Part 4: Real-World Projects

Building a Chat Application

Creating a Document Q&A System

Semantic Search Implementation

Final Project

Conclusion

Additional Resources

Getting Started

API Reference

Guides

Workshops & Tutorials

​Ollama SDK Hands-On Workshop

​Table of Contents

​Prerequisites

​Part 1: CLI Basics

​Setting Up

​Exploring Available Models

​Basic Text Generation

​Working with Parameters

​Creating Embeddings

​Managing Models

​CLI Exercises

​Part 2: API Fundamentals

​SDK Installation

​Basic Client Setup

​Text Generation API

​Streaming Responses

​Working with Embeddings

​API Exercises

​Part 3: Advanced Topics

​Tool Calling with Models

​Tool Calling with the CLI

​OpenAI Compatibility Layer

​Error Handling and Retries

​Performance Optimization

​Integrating with Web Applications

​Advanced Exercises

​Part 4: Real-World Projects

​Building a Chat Application

​Creating a Document Q&A System

​Semantic Search Implementation

​Final Project

​Conclusion

​Additional Resources

Ollama SDK Hands-On Workshop

Table of Contents

Prerequisites

Part 1: CLI Basics

Setting Up

Exploring Available Models

Basic Text Generation

Working with Parameters

Creating Embeddings

Managing Models

CLI Exercises

Part 2: API Fundamentals

SDK Installation

Basic Client Setup

Text Generation API

Streaming Responses

Working with Embeddings

API Exercises

Part 3: Advanced Topics

Tool Calling with Models

Tool Calling with the CLI

OpenAI Compatibility Layer

Error Handling and Retries

Performance Optimization

Integrating with Web Applications

Advanced Exercises

Part 4: Real-World Projects

Building a Chat Application

Creating a Document Q&A System

Semantic Search Implementation

Final Project

Conclusion

Additional Resources