mirror of
https://github.com/run-llama/LlamaIndexTS.git
synced 2026-07-19 18:43:34 -04:00
feat: support ollama tool call (#1461)
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@@ -0,0 +1,8 @@
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---
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"llamaindex": patch
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"@llamaindex/ollama": patch
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---
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feat: support ollama tool call
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Note that `OllamaEmbedding` now is not the subclass of `Ollama`.
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@@ -0,0 +1,3 @@
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import { OpenAI } from "./openai.js";
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export class Ollama extends OpenAI {}
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@@ -0,0 +1,35 @@
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import { Ollama } from "@llamaindex/ollama";
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import assert from "node:assert";
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import { test } from "node:test";
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import { getWeatherTool } from "./fixtures/tools.js";
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import { mockLLMEvent } from "./utils.js";
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await test("ollama", async (t) => {
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await mockLLMEvent(t, "ollama");
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await t.test("ollama function call", async (t) => {
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const llm = new Ollama({
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model: "llama3.2",
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});
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const chatResponse = await llm.chat({
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messages: [
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{
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role: "user",
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content: "What is the weather in Paris?",
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},
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],
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tools: [getWeatherTool],
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});
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if (
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chatResponse.message.options &&
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"toolCall" in chatResponse.message.options
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) {
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assert.equal(chatResponse.message.options.toolCall.length, 1);
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assert.equal(
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chatResponse.message.options.toolCall[0]!.name,
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getWeatherTool.metadata.name,
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);
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} else {
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throw new Error("Expected tool calls in response");
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}
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});
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});
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@@ -0,0 +1,5 @@
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{
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"llmEventStart": [],
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"llmEventEnd": [],
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"llmEventStream": []
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}
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@@ -1,5 +1,4 @@
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import { streamConverter } from "../utils";
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import { extractText } from "../utils/llms";
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import { extractText, streamConverter } from "../utils";
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import type {
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ChatResponse,
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ChatResponseChunk,
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@@ -1,7 +1 @@
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import type { BaseEmbedding } from "@llamaindex/core/embeddings";
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import { Ollama } from "@llamaindex/ollama";
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/**
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* OllamaEmbedding is an alias for Ollama that implements the BaseEmbedding interface.
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*/
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export class OllamaEmbedding extends Ollama implements BaseEmbedding {}
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export { OllamaEmbedding } from "@llamaindex/ollama";
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@@ -1,16 +1,20 @@
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import { BaseEmbedding } from "@llamaindex/core/embeddings";
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import type {
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ChatResponse,
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ChatResponseChunk,
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CompletionResponse,
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LLM,
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LLMChatParamsNonStreaming,
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LLMChatParamsStreaming,
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LLMCompletionParamsNonStreaming,
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LLMCompletionParamsStreaming,
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LLMMetadata,
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import {
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ToolCallLLM,
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type BaseTool,
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type ChatResponse,
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type ChatResponseChunk,
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type CompletionResponse,
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type LLMChatParamsNonStreaming,
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type LLMChatParamsStreaming,
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type LLMCompletionParamsNonStreaming,
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type LLMCompletionParamsStreaming,
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type LLMMetadata,
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type ToolCallLLMMessageOptions,
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} from "@llamaindex/core/llms";
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import { extractText, streamConverter } from "@llamaindex/core/utils";
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import { randomUUID } from "@llamaindex/env";
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import type { ChatRequest, GenerateRequest, Tool } from "ollama";
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import {
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Ollama as OllamaBase,
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type Config,
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@@ -38,7 +42,8 @@ export type OllamaParams = {
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options?: Partial<Options>;
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};
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export class Ollama extends BaseEmbedding implements LLM {
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export class Ollama extends ToolCallLLM {
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supportToolCall: boolean = true;
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public readonly ollama: OllamaBase;
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// https://ollama.ai/library
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@@ -78,12 +83,16 @@ export class Ollama extends BaseEmbedding implements LLM {
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chat(
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params: LLMChatParamsStreaming,
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): Promise<AsyncIterable<ChatResponseChunk>>;
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chat(params: LLMChatParamsNonStreaming): Promise<ChatResponse>;
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chat(
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params: LLMChatParamsNonStreaming,
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): Promise<ChatResponse<ToolCallLLMMessageOptions>>;
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async chat(
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params: LLMChatParamsNonStreaming | LLMChatParamsStreaming,
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): Promise<ChatResponse | AsyncIterable<ChatResponseChunk>> {
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const { messages, stream } = params;
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const payload = {
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): Promise<
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ChatResponse<ToolCallLLMMessageOptions> | AsyncIterable<ChatResponseChunk>
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> {
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const { messages, stream, tools } = params;
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const payload: ChatRequest = {
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model: this.model,
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messages: messages.map((message) => ({
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role: message.role,
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@@ -94,11 +103,30 @@ export class Ollama extends BaseEmbedding implements LLM {
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...this.options,
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},
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};
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if (tools) {
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payload.tools = tools.map((tool) => Ollama.toTool(tool));
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}
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if (!stream) {
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const chatResponse = await this.ollama.chat({
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...payload,
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stream: false,
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});
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if (chatResponse.message.tool_calls) {
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return {
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message: {
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role: "assistant",
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content: chatResponse.message.content,
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options: {
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toolCall: chatResponse.message.tool_calls.map((toolCall) => ({
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name: toolCall.function.name,
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input: toolCall.function.arguments,
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id: randomUUID(),
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})),
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},
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},
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raw: chatResponse,
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};
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}
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return {
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message: {
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@@ -126,7 +154,7 @@ export class Ollama extends BaseEmbedding implements LLM {
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params: LLMCompletionParamsStreaming | LLMCompletionParamsNonStreaming,
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): Promise<CompletionResponse | AsyncIterable<CompletionResponse>> {
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const { prompt, stream } = params;
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const payload = {
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const payload: GenerateRequest = {
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model: this.model,
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prompt: extractText(prompt),
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stream: !!stream,
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@@ -152,15 +180,39 @@ export class Ollama extends BaseEmbedding implements LLM {
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}
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}
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private async getEmbedding(prompt: string): Promise<number[]> {
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const payload = {
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model: this.model,
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prompt,
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options: {
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...this.options,
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static toTool(tool: BaseTool): Tool {
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return {
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type: "function",
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function: {
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name: tool.metadata.name,
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description: tool.metadata.description,
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parameters: {
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type: tool.metadata.parameters?.type,
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required: tool.metadata.parameters?.required,
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properties: tool.metadata.parameters?.properties,
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},
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},
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};
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const response = await this.ollama.embeddings({
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}
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}
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export class OllamaEmbedding extends BaseEmbedding {
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private readonly llm: Ollama;
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constructor(params: OllamaParams) {
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super();
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this.llm = new Ollama(params);
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}
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private async getEmbedding(prompt: string): Promise<number[]> {
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const payload = {
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model: this.llm.model,
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prompt,
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options: {
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...this.llm.options,
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},
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};
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const response = await this.llm.ollama.embeddings({
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...payload,
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});
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return response.embedding;
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