openai is working

packages/agent/package.json

@@ -20,12 +20,19 @@
         "@elizaos/plugin-bootstrap": "workspace:*",
         "@elizaos/plugin-openai": "workspace:*",
         "@elizaos/core": "workspace:*",
+        "@types/body-parser": "1.19.5",
+        "@types/cors": "2.8.17",
+        "@types/express": "5.0.0",
+        "cors": "2.8.5",
+        "express": "4.21.1",
+        "multer": "1.4.5-lts.1",
         "readline": "1.3.0",
         "ws": "8.18.0",
         "yargs": "17.7.2",
         "minipass": "7.1.2"
     },
     "devDependencies": {
+        "@types/multer": "^1.4.12",
         "ts-node": "10.9.2",
         "tsup": "8.3.5"
     }

packages/agent/src/defaultCharacter.ts

@@ -1,11 +1,11 @@
 import { type Character } from "@elizaos/core";
-import { OpenAIPlugin } from "@elizaos/plugin-openai";
+import { openaiPlugin } from "@elizaos/plugin-openai";
 
 export const defaultCharacter: Character = {
     name: "Eliza",
     username: "eliza",
     plugins: [
-        OpenAIPlugin
+        openaiPlugin
     ],
     settings: {
         secrets: {},

packages/agent/src/index.ts

@@ -46,7 +46,7 @@ export function parseArguments(): {
     characters?: string;
 } {
     try {
-        return yargs(process.argv.slice(3))
+        return yargs(process.argv.slice(2))
             .option("character", {
                 type: "string",
                 description: "Path to the character JSON file",
@@ -184,6 +184,7 @@ async function loadCharacterTryPath(characterPath: string): Promise<Character> {
     // Try different path resolutions in order
     const pathsToTry = [
         characterPath, // exact path as specified
+        path.resolve(process.cwd(), '..', '..', characterPath), // relative to root directory
         path.resolve(process.cwd(), characterPath), // relative to cwd
         path.resolve(process.cwd(), "agent", characterPath), // Add this
         path.resolve(__dirname, characterPath), // relative to current script

packages/agent/src/server.ts

@@ -166,7 +166,7 @@ export class CharacterServer {
                     return;
                 }
 
-                const transcription = await runtime.getModelProviderManager().call(ModelType.AUDIO_TRANSCRIPTION, {
+                const transcription = await runtime.call(ModelType.TRANSCRIPTION, {
                     file: fs.createReadStream(audioFile.path),
                     model: "whisper-1",
                 });
@@ -286,9 +286,7 @@ export class CharacterServer {
                     return;
                 }
 
-                const zeroVector = runtime.getModelProviderManager().call(ModelType.EMBEDDING, {
-                    text: null,
-                });
+                const zeroVector = await runtime.call(ModelType.TEXT_EMBEDDING, text);
 
                 // save response to memory
                 const responseMessage: Memory = {
@@ -828,7 +826,7 @@ export class CharacterServer {
                 // Get the text to convert to speech
                 const textToSpeak = response.text;
 
-                const speechResponse = await runtime.getModelProviderManager().call(ModelType.AUDIO_TRANSCRIPTION, {
+                const speechResponse = await runtime.call(ModelType.TRANSCRIPTION, {
                     text: textToSpeak,
                     runtime,
                 });

packages/core/src/generation.ts

@@ -80,7 +80,8 @@ export async function generateText({
 }): Promise<string> {
   logFunctionCall("generateText", runtime);
 
-  const { text } = await runtime.call(modelType, {
+  const text = await runtime.call(modelType, {
+    runtime,
     context,
     stopSequences,
   });
@@ -228,6 +229,7 @@ export const generateObject = async ({
   }
 
   const { object } = await runtime.call(modelType, {
+    runtime,
     context,
     stop: stopSequences,
   });
@@ -285,7 +287,8 @@ export async function generateMessageResponse({
   logger.debug("Context:", context);
 
   return await withRetry(async () => {
-    const { text } = await runtime.call(modelType, {
+    const text = await runtime.call(modelType, {
+    runtime,
       context,
       stop: stopSequences,
     });

packages/core/src/knowledge.ts

@@ -31,9 +31,7 @@ async function get(
         return [];
     }
 
-    const embedding = await runtime.call(ModelType.TEXT_EMBEDDING, {
-        text: processed,
-    });
+    const embedding = await runtime.call(ModelType.TEXT_EMBEDDING, processed);
     const fragments = await runtime.knowledgeManager.searchMemoriesByEmbedding(
         embedding,
         {
@@ -71,9 +69,7 @@ async function set(
     chunkSize = 512,
     bleed = 20
 ) {
-    const embedding = await runtime.call(ModelType.TEXT_EMBEDDING, {
-        text: null,
-    });
+    const embedding = await runtime.call(ModelType.TEXT_EMBEDDING, null);
     await runtime.documentsManager.createMemory({
         id: item.id,
         agentId: runtime.agentId,
@@ -88,9 +84,7 @@ async function set(
     const fragments = await splitChunks(preprocessed, chunkSize, bleed);
 
     for (const fragment of fragments) {
-        const embedding = await runtime.call(ModelType.TEXT_EMBEDDING, {
-            text: fragment,
-        });
+        const embedding = await runtime.call(ModelType.TEXT_EMBEDDING, fragment);
         await runtime.knowledgeManager.createMemory({
             // We namespace the knowledge base uuid to avoid id
             // collision with the document above.

packages/core/src/memory.ts

@@ -66,15 +66,11 @@ export class MemoryManager implements IMemoryManager {
 
         try {
             // Generate embedding from text content
-            memory.embedding = await this.runtime.call(ModelType.TEXT_EMBEDDING, {
-                text: memoryText,
-            });
+            memory.embedding = await this.runtime.call(ModelType.TEXT_EMBEDDING, memoryText);
         } catch (error) {
             logger.error("Failed to generate embedding:", error);
             // Fallback to zero vector if embedding fails
-            memory.embedding = await this.runtime.call(ModelType.TEXT_EMBEDDING, {
-                text: null,
-            });
+            memory.embedding = await this.runtime.call(ModelType.TEXT_EMBEDDING, null);
         }
 
         return memory;

packages/core/src/parsing.ts

@@ -85,7 +85,7 @@ export function parseJsonArrayFromText(text: string) {
     let jsonData = null;
 
     // First try to parse with the original JSON format
-    const jsonBlockMatch = text.match(jsonBlockPattern);
+    const jsonBlockMatch = text?.match(jsonBlockPattern);
 
     if (jsonBlockMatch) {
         try {
@@ -144,6 +144,8 @@ export function parseJSONObjectFromText(
     let jsonData = null;
     const jsonBlockMatch = text.match(jsonBlockPattern);
 
+    console.log("parseJsonArrayFromText", text);
+
     try {
         if (jsonBlockMatch) {
             // Parse the JSON from inside the code block

packages/core/src/runtime.ts

@@ -567,6 +567,8 @@ export class AgentRuntime implements IAgentRuntime {
             modelType: ModelType.TEXT_SMALL,
         });
 
+        console.log("***** result", result);
+
         const evaluators = parseJsonArrayFromText(
             result,
         ) as unknown as string[];

packages/core/tsup.config.ts

@@ -7,7 +7,7 @@ export default defineConfig({
     clean: true,
     format: ["esm"], // Ensure you're targeting CommonJS
     platform: "node",
-    target: "node18",
+    target: "node23",
     bundle: true,
     splitting: true, // Add this for better code splitting
     dts: true, // Generate declaration files

packages/plugin-bootstrap/src/providers/facts.ts

@@ -16,9 +16,7 @@ const factsProvider: Provider = {
             actors: state?.actorsData,
         });
 
-        const embedding = await runtime.call(ModelType.TEXT_EMBEDDING, {
-            text: recentMessages,
-        });
+        const embedding = await runtime.call(ModelType.TEXT_EMBEDDING, recentMessages);
 
         const memoryManager = new MemoryManager({
             runtime,

packages/plugin-openai/src/index.ts

@@ -13,6 +13,8 @@ export const openaiPlugin: Plugin = {
           return new Array(1536).fill(0);
         }
 
+        console.log("text", text)
+
         const baseURL = process.env.OPENAI_BASE_URL ?? "https://api.openai.com/v1";
 
         // use fetch to call embedding endpoint
@@ -29,13 +31,14 @@ export const openaiPlugin: Plugin = {
         });
 
         const data = await response.json();
+        console.log("data", data);
         return data.data[0].embedding;
       },
       [ModelType.TEXT_LARGE]: async ({
         runtime,
         context,
         modelType,
-        stopSequences = ["\n"],
+        stopSequences = [],
       }: GenerateTextParams
     ) => {
         // TODO: pull variables from json
@@ -51,7 +54,6 @@ export const openaiPlugin: Plugin = {
         const openai = createOpenAI({
           apiKey: process.env.OPENAI_API_KEY,
           baseURL,
-          fetch: runtime.fetch,
         });
 
         const smallModel = process.env.OPENAI_SMALL_MODEL ?? process.env.SMALL_MODEL ?? "gpt-4o-mini";

turbo.json

@@ -11,7 +11,8 @@
       },
       "dev": {
         "persistent": true,
-        "dependsOn": ["build"]
+        "dependsOn": ["build"],
+        "cache": false
       }
     }
   }