fix: Refactor image interface and update to move llama cloud -> together provider

agent/src/index.ts

@@ -197,6 +197,7 @@ export function getTokenForProvider(
                 settings.ETERNALAI_API_KEY
             );
         case ModelProviderName.LLAMACLOUD:
+        case ModelProviderName.TOGETHER:
             return (
                 character.settings?.secrets?.LLAMACLOUD_API_KEY ||
                 settings.LLAMACLOUD_API_KEY ||

packages/core/src/generation.ts

@@ -78,17 +78,25 @@ export async function generateText({
 
     // if runtime.getSetting("LLAMACLOUD_MODEL_LARGE") is true and modelProvider is LLAMACLOUD, then use the large model
     if (
-        runtime.getSetting("LLAMACLOUD_MODEL_LARGE") &&
-        provider === ModelProviderName.LLAMACLOUD
+        (runtime.getSetting("LLAMACLOUD_MODEL_LARGE") &&
+            provider === ModelProviderName.LLAMACLOUD) ||
+        (runtime.getSetting("TOGETHER_MODEL_LARGE") &&
+            provider === ModelProviderName.TOGETHER)
     ) {
-        model = runtime.getSetting("LLAMACLOUD_MODEL_LARGE");
+        model =
+            runtime.getSetting("LLAMACLOUD_MODEL_LARGE") ||
+            runtime.getSetting("TOGETHER_MODEL_LARGE");
     }
 
     if (
-        runtime.getSetting("LLAMACLOUD_MODEL_SMALL") &&
-        provider === ModelProviderName.LLAMACLOUD
+        (runtime.getSetting("LLAMACLOUD_MODEL_SMALL") &&
+            provider === ModelProviderName.LLAMACLOUD) ||
+        (runtime.getSetting("TOGETHER_MODEL_SMALL") &&
+            provider === ModelProviderName.TOGETHER)
     ) {
-        model = runtime.getSetting("LLAMACLOUD_MODEL_SMALL");
+        model =
+            runtime.getSetting("LLAMACLOUD_MODEL_SMALL") ||
+            runtime.getSetting("TOGETHER_MODEL_SMALL");
     }
 
     elizaLogger.info("Selected model:", model);
@@ -120,7 +128,8 @@ export async function generateText({
             case ModelProviderName.ETERNALAI:
             case ModelProviderName.ALI_BAILIAN:
             case ModelProviderName.VOLENGINE:
-            case ModelProviderName.LLAMACLOUD: {
+            case ModelProviderName.LLAMACLOUD:
+            case ModelProviderName.TOGETHER: {
                 elizaLogger.debug("Initializing OpenAI model.");
                 const openai = createOpenAI({ apiKey, baseURL: endpoint });
 
@@ -806,12 +815,6 @@ export const generateImage = async (
     data?: string[];
     error?: any;
 }> => {
-    const { prompt, width, height } = data;
-    let { count } = data;
-    if (!count) {
-        count = 1;
-    }
-
     const model = getModel(runtime.imageModelProvider, ModelClass.IMAGE);
     const modelSettings = models[runtime.imageModelProvider].imageSettings;
 
@@ -866,16 +869,19 @@ export const generateImage = async (
             const imageURL = await response.json();
             return { success: true, data: [imageURL] };
         } else if (
+            runtime.imageModelProvider === ModelProviderName.TOGETHER ||
+            // for backwards compat
             runtime.imageModelProvider === ModelProviderName.LLAMACLOUD
         ) {
             const together = new Together({ apiKey: apiKey as string });
+            // Fix: steps 4 is for schnell; 28 is for dev.
             const response = await together.images.create({
                 model: "black-forest-labs/FLUX.1-schnell",
-                prompt,
-                width,
-                height,
+                prompt: data.prompt,
+                width: data.width,
+                height: data.height,
                 steps: modelSettings?.steps ?? 4,
-                n: count,
+                n: data.count,
             });
             const urls: string[] = [];
             for (let i = 0; i < response.data.length; i++) {
@@ -902,11 +908,11 @@ export const generateImage = async (
 
             // Prepare the input parameters according to their schema
             const input = {
-                prompt: prompt,
+                prompt: data.prompt,
                 image_size: "square" as const,
                 num_inference_steps: modelSettings?.steps ?? 50,
-                guidance_scale: 3.5,
-                num_images: count,
+                guidance_scale: data.guidanceScale || 3.5,
+                num_images: data.count,
                 enable_safety_checker: true,
                 output_format: "png" as const,
                 seed: data.seed ?? 6252023,
@@ -945,7 +951,7 @@ export const generateImage = async (
             const base64s = await Promise.all(base64Promises);
             return { success: true, data: base64s };
         } else {
-            let targetSize = `${width}x${height}`;
+            let targetSize = `${data.width}x${data.height}`;
             if (
                 targetSize !== "1024x1024" &&
                 targetSize !== "1792x1024" &&
@@ -956,9 +962,9 @@ export const generateImage = async (
             const openai = new OpenAI({ apiKey: apiKey as string });
             const response = await openai.images.generate({
                 model,
-                prompt,
+                prompt: data.prompt,
                 size: targetSize as "1024x1024" | "1792x1024" | "1024x1792",
-                n: count,
+                n: data.count,
                 response_format: "b64_json",
             });
             const base64s = response.data.map(
@@ -1157,6 +1163,7 @@ export async function handleProvider(
         case ModelProviderName.ALI_BAILIAN:
         case ModelProviderName.VOLENGINE:
         case ModelProviderName.LLAMACLOUD:
+        case ModelProviderName.TOGETHER:
             return await handleOpenAI(options);
         case ModelProviderName.ANTHROPIC:
             return await handleAnthropic(options);

packages/core/src/models.ts

@@ -128,6 +128,27 @@ export const models: Models = {
             [ModelClass.IMAGE]: "black-forest-labs/FLUX.1-schnell",
         },
     },
+    [ModelProviderName.TOGETHER]: {
+        settings: {
+            stop: [],
+            maxInputTokens: 128000,
+            maxOutputTokens: 8192,
+            repetition_penalty: 0.4,
+            temperature: 0.7,
+        },
+        imageSettings: {
+            steps: 4,
+        },
+        endpoint: "https://api.together.ai/v1",
+        model: {
+            [ModelClass.SMALL]: "meta-llama/Llama-3.2-3B-Instruct-Turbo",
+            [ModelClass.MEDIUM]: "meta-llama-3.1-8b-instruct",
+            [ModelClass.LARGE]: "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
+            [ModelClass.EMBEDDING]:
+                "togethercomputer/m2-bert-80M-32k-retrieval",
+            [ModelClass.IMAGE]: "black-forest-labs/FLUX.1-schnell",
+        },
+    },
     [ModelProviderName.LLAMALOCAL]: {
         settings: {
             stop: ["<|eot_id|>", "<|eom_id|>"],

packages/core/src/tests/generation.test.ts

@@ -1,7 +1,12 @@
 import { describe, expect, it, vi, beforeEach } from "vitest";
 import { ModelProviderName, IAgentRuntime } from "../types";
 import { models } from "../models";
-import { generateText, generateTrueOrFalse, splitChunks, trimTokens } from "../generation";
+import {
+    generateText,
+    generateTrueOrFalse,
+    splitChunks,
+    trimTokens,
+} from "../generation";
 import type { TiktokenModel } from "js-tiktoken";
 
 // Mock the elizaLogger
@@ -42,6 +47,8 @@ describe("Generation", () => {
             getSetting: vi.fn().mockImplementation((key: string) => {
                 if (key === "LLAMACLOUD_MODEL_LARGE") return false;
                 if (key === "LLAMACLOUD_MODEL_SMALL") return false;
+                if (key === "TOGETHER_MODEL_LARGE") return false;
+                if (key === "TOGETHER_MODEL_SMALL") return false;
                 return undefined;
             }),
         } as unknown as IAgentRuntime;
@@ -122,53 +129,56 @@ describe("Generation", () => {
         });
     });
 
-	describe("trimTokens", () => {
-		const model = "gpt-4" as TiktokenModel;
-
-		it("should return empty string for empty input", () => {
-			const result = trimTokens("", 100, model);
-			expect(result).toBe("");
-		});
-
-		it("should throw error for negative maxTokens", () => {
-			expect(() => trimTokens("test", -1, model)).toThrow("maxTokens must be positive");
-		});
-
-		it("should return unchanged text if within token limit", () => {
-			const shortText = "This is a short text";
-			const result = trimTokens(shortText, 10, model);
-			expect(result).toBe(shortText);
-		});
-
-		it("should truncate text to specified token limit", () => {
-			// Using a longer text that we know will exceed the token limit
-			const longText = "This is a much longer text that will definitely exceed our very small token limit and need to be truncated to fit within the specified constraints."
-			const result = trimTokens(longText, 5, model);
-
-			// The exact result will depend on the tokenizer, but we can verify:
-			// 1. Result is shorter than original
-			expect(result.length).toBeLessThan(longText.length);
-			// 2. Result is not empty
-			expect(result.length).toBeGreaterThan(0);
-			// 3. Result is a proper substring of the original text
-			expect(longText.includes(result)).toBe(true);
-		});
-
-		it("should handle non-ASCII characters", () => {
-			const unicodeText = "Hello 👋 World 🌍";
-			const result = trimTokens(unicodeText, 5, model);
-			expect(result.length).toBeGreaterThan(0);
-		});
-
-		it("should handle multiline text", () => {
-			const multilineText = `Line 1
+    describe("trimTokens", () => {
+        const model = "gpt-4" as TiktokenModel;
+
+        it("should return empty string for empty input", () => {
+            const result = trimTokens("", 100, model);
+            expect(result).toBe("");
+        });
+
+        it("should throw error for negative maxTokens", () => {
+            expect(() => trimTokens("test", -1, model)).toThrow(
+                "maxTokens must be positive"
+            );
+        });
+
+        it("should return unchanged text if within token limit", () => {
+            const shortText = "This is a short text";
+            const result = trimTokens(shortText, 10, model);
+            expect(result).toBe(shortText);
+        });
+
+        it("should truncate text to specified token limit", () => {
+            // Using a longer text that we know will exceed the token limit
+            const longText =
+                "This is a much longer text that will definitely exceed our very small token limit and need to be truncated to fit within the specified constraints.";
+            const result = trimTokens(longText, 5, model);
+
+            // The exact result will depend on the tokenizer, but we can verify:
+            // 1. Result is shorter than original
+            expect(result.length).toBeLessThan(longText.length);
+            // 2. Result is not empty
+            expect(result.length).toBeGreaterThan(0);
+            // 3. Result is a proper substring of the original text
+            expect(longText.includes(result)).toBe(true);
+        });
+
+        it("should handle non-ASCII characters", () => {
+            const unicodeText = "Hello 👋 World 🌍";
+            const result = trimTokens(unicodeText, 5, model);
+            expect(result.length).toBeGreaterThan(0);
+        });
+
+        it("should handle multiline text", () => {
+            const multilineText = `Line 1
 	Line 2
 	Line 3
 	Line 4
 	Line 5`;
-			const result = trimTokens(multilineText, 5, model);
-			expect(result.length).toBeGreaterThan(0);
-			expect(result.length).toBeLessThan(multilineText.length);
-		});
-	});
+            const result = trimTokens(multilineText, 5, model);
+            expect(result.length).toBeGreaterThan(0);
+            expect(result.length).toBeLessThan(multilineText.length);
+        });
+    });
 });

packages/core/src/types.ts

@@ -192,6 +192,7 @@ export type Models = {
     [ModelProviderName.GROK]: Model;
     [ModelProviderName.GROQ]: Model;
     [ModelProviderName.LLAMACLOUD]: Model;
+    [ModelProviderName.TOGETHER]: Model;
     [ModelProviderName.LLAMALOCAL]: Model;
     [ModelProviderName.GOOGLE]: Model;
     [ModelProviderName.CLAUDE_VERTEX]: Model;
@@ -216,6 +217,7 @@ export enum ModelProviderName {
     GROK = "grok",
     GROQ = "groq",
     LLAMACLOUD = "llama_cloud",
+    TOGETHER = "together",
     LLAMALOCAL = "llama_local",
     GOOGLE = "google",
     CLAUDE_VERTEX = "claude_vertex",

packages/plugin-image-generation/src/index.ts

@@ -11,7 +11,7 @@ import { generateImage } from "@ai16z/eliza";
 
 import fs from "fs";
 import path from "path";
-import { validateImageGenConfig } from "./enviroment";
+import { validateImageGenConfig } from "./environment";
 
 export function saveBase64Image(base64Data: string, filename: string): string {
     // Create generatedImages directory if it doesn't exist
@@ -97,7 +97,17 @@ const imageGeneration: Action = {
         runtime: IAgentRuntime,
         message: Memory,
         state: State,
-        options: any,
+        options: {
+            width?: number;
+            height?: number;
+            count?: number;
+            negativePrompt?: string;
+            numIterations?: number;
+            guidanceScale?: number;
+            seed?: number;
+            modelId?: string;
+            jobId?: string;
+        },
         callback: HandlerCallback
     ) => {
         elizaLogger.log("Composing state for message:", message);
@@ -116,9 +126,23 @@ const imageGeneration: Action = {
         const images = await generateImage(
             {
                 prompt: imagePrompt,
-                width: 1024,
-                height: 1024,
-                count: 1,
+                width: options.width || 1024,
+                height: options.height || 1024,
+                ...(options.count != null ? { count: options.count || 1 } : {}),
+                ...(options.negativePrompt != null
+                    ? { negativePrompt: options.negativePrompt }
+                    : {}),
+                ...(options.numIterations != null
+                    ? { numIterations: options.numIterations }
+                    : {}),
+                ...(options.guidanceScale != null
+                    ? { guidanceScale: options.guidanceScale }
+                    : {}),
+                ...(options.seed != null ? { seed: options.seed } : {}),
+                ...(options.modelId != null
+                    ? { modelId: options.modelId }
+                    : {}),
+                ...(options.jobId != null ? { jobId: options.jobId } : {}),
             },
             runtime
         );