@epi/ollama

Simple interface to the Ollama API

Run Chat Example

deno run -A https://esm.sh/jsr/@epi/ollama/examples/chat.ts

Example

import { Chat } from "@epi/ollama";

const messages = [ { role: "user", content: "What is the meaning of life?" } ];

await Chat({messages}); // "The meaning of life is 42."

Streaming

import { Chat } from "@epi/ollama";

const messages = [
    {
        role: "system",
        content: "You are a helpful assistant."
    }
];

while (true) {
    const reply = await prompt(">>>");

    messages.push({ role: "user", content: reply });

    const updater = (_text: string, token: string) => {

        Deno.writeAll(Deno.stdout, new TextEncoder().encode(token));

        return true;
    }

    await Chat({ messages, model: "llama3" }, updater);

    console.log();
}

Options

interface Options {
    // API Endpoint for Ollama. Default: http://localhost:11434/api/chat
    API_URL: string;

    // Ollama model to run
    model: string;

    // List of chat messages
    messages: {role: 'user' | 'assistant' | 'system', content: string};

    // Whether or not to stream the response. On by default
    stream: boolean;

    // Model configuration below
    options: ModelConfig;

    // Time to keep model loaded after response. Default "3m"
    keep_alive: string;

    // Force the model to respond in JSON
    format?: "json";
}

interface ModelConfig {
    // Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)
    mirostat?: number,

    // Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)
    mirostat_eta?: number,

    // Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)
    mirostat_tau?: number,

    // Sets the size of the context window used to generate the next token. (Default: 2048)
    num_ctx?: number,

    // Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)
    repeat_last_n?: number,

    // Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)
    repeat_penalty?: number,

    // The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)
    temperature?: number,

    // Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0)
    seed?: number,

    // Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate stop parameters in a modelfile.
    stop?: string,

    // Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)
    tfs_z?: number,

    // Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)
    num_predict?: number,

    // Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)
    top_k?: number,

    // Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)
    top_p?: number,
}