Ollama

OverviewCopy Anchor Link

Ollama runs open models on your own machine, behind an OpenAI-compatible API.

• OLLamaLLMService: an OpenAILLMService subclass with Ollama's base URL and default model.

InstallationCopy Anchor Link

bun add @pipedog/ollama

It depends on @pipedog/openai, which is installed with it.

import { OLLamaLLMService } from "@pipedog/ollama/llm";

PrerequisitesCopy Anchor Link

• Ollama running locally, with a model pulled (ollama pull llama2). No API key is needed; apiKey defaults to "ollama", which a local Ollama ignores.

OLLamaLLMServiceCopy Anchor Link

ConfigurationCopy Anchor Link

The other options (organization, project, defaultHeaders, serviceTier, retryOnTimeout, retryTimeoutSecs, function calling) are OpenAILLMService's. See OpenAI.

SettingsCopy Anchor Link

Runtime-updatable settings, passed as settings: new OLLamaLLMService.Settings({...}) (OllamaLLMSettings). They are OpenAI's settings, with these defaults:

Every other setting keeps OpenAI's default. Unset sampling settings are left out of the request, so the provider's own defaults apply. extra carries provider-specific request fields.

UsageCopy Anchor Link

import { LLMUpdateSettingsFrame } from "@pipedog/core/frames/frames";
import { OLLamaLLMService } from "@pipedog/ollama/llm";

const llm = new OLLamaLLMService({
  settings: new OLLamaLLMService.Settings({
    model: "llama2",
    systemInstruction: "You are a helpful assistant in a voice conversation.",
    temperature: 0.7,
  }),
});

An Ollama server on another host, behind a proxy that checks a key:

const remote = new OLLamaLLMService({
  baseUrl: "https://ollama.example.com/v1",
  apiKey: process.env.OLLAMA_PROXY_KEY ?? "",
  settings: new OLLamaLLMService.Settings({ model: "mistral" }),
});

Change settings mid-call with an LLMUpdateSettingsFrame. The next completion uses them:

// Queue it on the worker, or push it from a processor upstream of the service.
const update = new LLMUpdateSettingsFrame({
  delta: new OLLamaLLMService.Settings({ temperature: 0.2, maxTokens: 300 }),
});

NotesCopy Anchor Link

• Runs a completion for each LLMContextFrame, between an LLMFullResponseStartFrame and an LLMFullResponseEndFrame, and streams the reply as LLMTextFrames, as OpenAILLMService does. Function calls, token usage metrics and runInference work the same way.
• "developer" messages are sent as "user" messages, since the API doesn't support that role (supportsDeveloperRole is false).
• OLLamaLLMService doesn't require an API key, unlike the other OpenAI-compatible services, and never falls back to OPENAI_API_KEY.
• Ollama seems to ignore "developer" messages, so they are sent as "user" messages.

Runtime notesCopy Anchor Link

Generated from pipecat.runtimes and pipecat.compat in packages/ollama/package.json: ✅ means the compat smoke test passes on that runtime.

• The client is the official openai JS SDK (through @pipedog/openai), which talks through fetch. It works on Bun, Node, Deno and Cloudflare Workers.
• The deprecated model constructor option is not ported. Use settings.
• Tested against a local OpenAI-compatible mock server, not against Ollama itself.