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Readme

@agentic-kit/ollama

A JavaScript/TypeScript client for the Ollama LLM server, supporting model listing, text generation, streaming responses, embeddings, and model management.

Installation

npm install @agentic-kit/ollama

Usage

import OllamaClient, { GenerateInput } from '@agentic-kit/ollama';

// Create a client (default port 11434)
const client = new OllamaClient('http://localhost:11434');

// List available models
const models = await client.listModels();
console.log('Available models:', models);

// Non-streaming text generation
const output = await client.generate({ model: 'mistral', prompt: 'Hello, Ollama!' });
console.log(output);

// Streaming generation
await client.generate(
  { model: 'mistral', prompt: 'Hello, streaming!', stream: true },
  (chunk) => {
    console.log('Received chunk:', chunk);
  }
);

// Pull a model to local cache
await client.pullModel('mistral');

// Generate embeddings
const embedding = await client.generateEmbedding('Compute embeddings');
console.log('Embedding vector length:', embedding.length);

// Generate a conversational response with context
const response = await client.generateResponse(
  'What is the capital of France?',
  'Geography trivia'
);
console.log(response);

// Delete a pulled model when done
await client.deleteModel('mistral');

API Reference

new OllamaClient(baseUrl?: string) – defaults to http://localhost:11434
.listModels(): Promise<string[]>
.generate(input: GenerateInput, onChunk?: (chunk: string) => void): Promise<string | void>
.generateStreamingResponse(prompt: string, onChunk: (chunk: string) => void, context?: string): Promise<void>
.generateEmbedding(text: string): Promise<number[]>
.generateResponse(prompt: string, context?: string): Promise<string>
.pullModel(model: string): Promise<void>
.deleteModel(model: string): Promise<void>

GenerateInput type

interface GenerateInput {
  model: string;
  prompt: string;
  stream?: boolean;
}

Contributing

Please open issues or pull requests on GitHub.