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feat(ollama): add native /api/chat provider for streaming + tool calling (#11853)
Merged via /review-pr -> /prepare-pr -> /merge-pr.
Prepared head SHA: 0a723f98e6
Co-authored-by: BrokenFinger98 <115936166+BrokenFinger98@users.noreply.github.com>
Co-authored-by: steipete <58493+steipete@users.noreply.github.com>
Reviewed-by: @steipete
This commit is contained in:
Sunwoo Yu
419
src/agents/ollama-stream.ts
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419
src/agents/ollama-stream.ts
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@@ -0,0 +1,419 @@
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import type { StreamFn } from "@mariozechner/pi-agent-core";
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import type {
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AssistantMessage,
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StopReason,
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TextContent,
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ToolCall,
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Tool,
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Usage,
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} from "@mariozechner/pi-ai";
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import { createAssistantMessageEventStream } from "@mariozechner/pi-ai";
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import { randomUUID } from "node:crypto";
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export const OLLAMA_NATIVE_BASE_URL = "http://127.0.0.1:11434";
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// ── Ollama /api/chat request types ──────────────────────────────────────────
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interface OllamaChatRequest {
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model: string;
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messages: OllamaChatMessage[];
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stream: boolean;
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tools?: OllamaTool[];
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options?: Record<string, unknown>;
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}
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interface OllamaChatMessage {
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role: "system" | "user" | "assistant" | "tool";
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content: string;
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images?: string[];
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tool_calls?: OllamaToolCall[];
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tool_name?: string;
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}
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interface OllamaTool {
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type: "function";
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function: {
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name: string;
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description: string;
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parameters: Record<string, unknown>;
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};
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}
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interface OllamaToolCall {
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function: {
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name: string;
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arguments: Record<string, unknown>;
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};
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}
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// ── Ollama /api/chat response types ─────────────────────────────────────────
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interface OllamaChatResponse {
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model: string;
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created_at: string;
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message: {
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role: "assistant";
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content: string;
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tool_calls?: OllamaToolCall[];
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};
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done: boolean;
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done_reason?: string;
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total_duration?: number;
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load_duration?: number;
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prompt_eval_count?: number;
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prompt_eval_duration?: number;
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eval_count?: number;
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eval_duration?: number;
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}
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// ── Message conversion ──────────────────────────────────────────────────────
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type InputContentPart =
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| { type: "text"; text: string }
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| { type: "image"; data: string }
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| { type: "toolCall"; id: string; name: string; arguments: Record<string, unknown> }
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| { type: "tool_use"; id: string; name: string; input: Record<string, unknown> };
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function extractTextContent(content: unknown): string {
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if (typeof content === "string") {
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return content;
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}
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if (!Array.isArray(content)) {
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return "";
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}
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return (content as InputContentPart[])
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.filter((part): part is { type: "text"; text: string } => part.type === "text")
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.map((part) => part.text)
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.join("");
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}
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function extractOllamaImages(content: unknown): string[] {
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if (!Array.isArray(content)) {
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return [];
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}
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return (content as InputContentPart[])
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.filter((part): part is { type: "image"; data: string } => part.type === "image")
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.map((part) => part.data);
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}
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function extractToolCalls(content: unknown): OllamaToolCall[] {
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if (!Array.isArray(content)) {
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return [];
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}
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const parts = content as InputContentPart[];
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const result: OllamaToolCall[] = [];
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for (const part of parts) {
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if (part.type === "toolCall") {
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result.push({ function: { name: part.name, arguments: part.arguments } });
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} else if (part.type === "tool_use") {
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result.push({ function: { name: part.name, arguments: part.input } });
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}
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}
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return result;
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}
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export function convertToOllamaMessages(
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messages: Array<{ role: string; content: unknown }>,
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system?: string,
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): OllamaChatMessage[] {
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const result: OllamaChatMessage[] = [];
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if (system) {
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result.push({ role: "system", content: system });
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}
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for (const msg of messages) {
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const { role } = msg;
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if (role === "user") {
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const text = extractTextContent(msg.content);
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const images = extractOllamaImages(msg.content);
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result.push({
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role: "user",
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content: text,
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...(images.length > 0 ? { images } : {}),
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});
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} else if (role === "assistant") {
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const text = extractTextContent(msg.content);
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const toolCalls = extractToolCalls(msg.content);
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result.push({
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role: "assistant",
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content: text,
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...(toolCalls.length > 0 ? { tool_calls: toolCalls } : {}),
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});
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} else if (role === "tool" || role === "toolResult") {
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// SDK uses "toolResult" (camelCase) for tool result messages.
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// Ollama API expects "tool" role with tool_name per the native spec.
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const text = extractTextContent(msg.content);
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const toolName =
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typeof (msg as { toolName?: unknown }).toolName === "string"
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? (msg as { toolName?: string }).toolName
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: undefined;
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result.push({
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role: "tool",
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content: text,
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...(toolName ? { tool_name: toolName } : {}),
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});
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}
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}
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return result;
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}
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// ── Tool extraction ─────────────────────────────────────────────────────────
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function extractOllamaTools(tools: Tool[] | undefined): OllamaTool[] {
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if (!tools || !Array.isArray(tools)) {
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return [];
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}
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const result: OllamaTool[] = [];
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for (const tool of tools) {
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if (typeof tool.name !== "string" || !tool.name) {
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continue;
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}
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result.push({
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type: "function",
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function: {
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name: tool.name,
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description: typeof tool.description === "string" ? tool.description : "",
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parameters: (tool.parameters ?? {}) as Record<string, unknown>,
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},
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});
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}
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return result;
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}
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// ── Response conversion ─────────────────────────────────────────────────────
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export function buildAssistantMessage(
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response: OllamaChatResponse,
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modelInfo: { api: string; provider: string; id: string },
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): AssistantMessage {
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const content: (TextContent | ToolCall)[] = [];
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if (response.message.content) {
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content.push({ type: "text", text: response.message.content });
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}
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const toolCalls = response.message.tool_calls;
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if (toolCalls && toolCalls.length > 0) {
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for (const tc of toolCalls) {
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content.push({
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type: "toolCall",
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id: `ollama_call_${randomUUID()}`,
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name: tc.function.name,
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arguments: tc.function.arguments,
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});
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}
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}
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const hasToolCalls = toolCalls && toolCalls.length > 0;
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const stopReason: StopReason = hasToolCalls ? "toolUse" : "stop";
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const usage: Usage = {
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input: response.prompt_eval_count ?? 0,
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output: response.eval_count ?? 0,
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cacheRead: 0,
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cacheWrite: 0,
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totalTokens: (response.prompt_eval_count ?? 0) + (response.eval_count ?? 0),
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cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
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};
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return {
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role: "assistant",
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content,
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stopReason,
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api: modelInfo.api,
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provider: modelInfo.provider,
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model: modelInfo.id,
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usage,
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timestamp: Date.now(),
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};
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}
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// ── NDJSON streaming parser ─────────────────────────────────────────────────
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export async function* parseNdjsonStream(
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reader: ReadableStreamDefaultReader<Uint8Array>,
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): AsyncGenerator<OllamaChatResponse> {
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const decoder = new TextDecoder();
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let buffer = "";
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while (true) {
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const { done, value } = await reader.read();
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if (done) {
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break;
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}
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buffer += decoder.decode(value, { stream: true });
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const lines = buffer.split("\n");
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buffer = lines.pop() ?? "";
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for (const line of lines) {
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const trimmed = line.trim();
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if (!trimmed) {
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continue;
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}
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try {
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yield JSON.parse(trimmed) as OllamaChatResponse;
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} catch {
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console.warn("[ollama-stream] Skipping malformed NDJSON line:", trimmed.slice(0, 120));
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}
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}
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}
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if (buffer.trim()) {
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try {
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yield JSON.parse(buffer.trim()) as OllamaChatResponse;
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} catch {
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console.warn(
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"[ollama-stream] Skipping malformed trailing data:",
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buffer.trim().slice(0, 120),
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);
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}
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}
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}
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// ── Main StreamFn factory ───────────────────────────────────────────────────
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function resolveOllamaChatUrl(baseUrl: string): string {
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const trimmed = baseUrl.trim().replace(/\/+$/, "");
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const normalizedBase = trimmed.replace(/\/v1$/i, "");
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const apiBase = normalizedBase || OLLAMA_NATIVE_BASE_URL;
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return `${apiBase}/api/chat`;
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}
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export function createOllamaStreamFn(baseUrl: string): StreamFn {
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const chatUrl = resolveOllamaChatUrl(baseUrl);
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return (model, context, options) => {
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const stream = createAssistantMessageEventStream();
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const run = async () => {
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try {
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const ollamaMessages = convertToOllamaMessages(
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context.messages ?? [],
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context.systemPrompt,
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);
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const ollamaTools = extractOllamaTools(context.tools);
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// Ollama defaults to num_ctx=4096 which is too small for large
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// system prompts + many tool definitions. Use model's contextWindow.
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const ollamaOptions: Record<string, unknown> = { num_ctx: model.contextWindow ?? 65536 };
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if (typeof options?.temperature === "number") {
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ollamaOptions.temperature = options.temperature;
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}
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if (typeof options?.maxTokens === "number") {
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ollamaOptions.num_predict = options.maxTokens;
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}
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const body: OllamaChatRequest = {
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model: model.id,
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messages: ollamaMessages,
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stream: true,
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...(ollamaTools.length > 0 ? { tools: ollamaTools } : {}),
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options: ollamaOptions,
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};
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const headers: Record<string, string> = {
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"Content-Type": "application/json",
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...options?.headers,
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};
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if (options?.apiKey) {
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headers.Authorization = `Bearer ${options.apiKey}`;
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}
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const response = await fetch(chatUrl, {
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method: "POST",
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headers,
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body: JSON.stringify(body),
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signal: options?.signal,
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});
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if (!response.ok) {
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const errorText = await response.text().catch(() => "unknown error");
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throw new Error(`Ollama API error ${response.status}: ${errorText}`);
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}
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if (!response.body) {
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throw new Error("Ollama API returned empty response body");
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}
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const reader = response.body.getReader();
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let accumulatedContent = "";
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const accumulatedToolCalls: OllamaToolCall[] = [];
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let finalResponse: OllamaChatResponse | undefined;
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for await (const chunk of parseNdjsonStream(reader)) {
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if (chunk.message?.content) {
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accumulatedContent += chunk.message.content;
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}
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// Ollama sends tool_calls in intermediate (done:false) chunks,
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// NOT in the final done:true chunk. Collect from all chunks.
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if (chunk.message?.tool_calls) {
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accumulatedToolCalls.push(...chunk.message.tool_calls);
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}
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if (chunk.done) {
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finalResponse = chunk;
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break;
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}
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}
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if (!finalResponse) {
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throw new Error("Ollama API stream ended without a final response");
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}
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finalResponse.message.content = accumulatedContent;
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if (accumulatedToolCalls.length > 0) {
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finalResponse.message.tool_calls = accumulatedToolCalls;
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}
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const assistantMessage = buildAssistantMessage(finalResponse, {
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api: model.api,
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provider: model.provider,
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id: model.id,
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});
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const reason: Extract<StopReason, "stop" | "length" | "toolUse"> =
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assistantMessage.stopReason === "toolUse" ? "toolUse" : "stop";
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stream.push({
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type: "done",
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reason,
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message: assistantMessage,
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});
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} catch (err) {
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const errorMessage = err instanceof Error ? err.message : String(err);
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stream.push({
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type: "error",
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reason: "error",
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error: {
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role: "assistant" as const,
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content: [],
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stopReason: "error" as StopReason,
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errorMessage,
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api: model.api,
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provider: model.provider,
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model: model.id,
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usage: {
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input: 0,
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output: 0,
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cacheRead: 0,
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cacheWrite: 0,
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totalTokens: 0,
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cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
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},
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timestamp: Date.now(),
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},
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});
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} finally {
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stream.end();
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}
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};
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queueMicrotask(() => void run());
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return stream;
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};
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}
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