fixing compile errors

Author: mike dupont

package.json

@@ -6,9 +6,9 @@
         "build-docker": "turbo run build",
         "cleanstart": "if [ -f agent/data/db.sqlite ]; then rm agent/data/db.sqlite; fi && pnpm --filter \"@elizaos/agent\" start --isRoot",
         "cleanstart:debug": "if [ -f agent/data/db.sqlite ]; then rm agent/data/db.sqlite; fi && cross-env NODE_ENV=development VERBOSE=true DEBUG=eliza:* pnpm --filter \"@elizaos/agent\" start --isRoot",
-        "start": "pnpm --filter \"@elizaos/agent\" start --isRoot",
+        "start": "pnpm --filter \"@elizaos/agent\" start --isRoot --characters=characters/eliza.character.json",
         "start:client": "pnpm --dir client dev",
-        "start:debug": "cross-env NODE_ENV=development VERBOSE=true DEBUG=eliza:* pnpm --filter \"@elizaos/agent\" start --isRoot",
+        "start:debug": "cross-env NODE_ENV=development VERBOSE=true DEBUG=eliza:* pnpm --filter \"@elizaos/agent\" start --isRoot --characters=characters/eliza.character.json",
         "dev": "bash ./scripts/dev.sh",
         "lint": "bash ./scripts/lint.sh",
         "prettier-check": "npx prettier --check --cache .",

packages/core/generation.ts

@@ -14,7 +14,7 @@ import { Buffer } from "buffer";
 import { createOllama } from "ollama-ai-provider";
 import OpenAI from "openai";
 import { encodingForModel, TiktokenModel } from "js-tiktoken";
-import { AutoTokenizer } from "@huggingface/transformers";
+//import { AutoTokenizer } from "@huggingface/transformers";
 import Together from "together-ai";
 import { ZodSchema } from "zod";
 import { elizaLogger } from "./index.ts";
@@ -100,13 +100,13 @@ export async function trimTokens(
     return truncateTiktoken("gpt-4o", context, maxTokens);
 }
 
-async function truncateAuto(
+/*async function truncateAuto(
     modelPath: string,
     context: string,
     maxTokens: number
 ) {
     try {
-        const tokenizer = await AutoTokenizer.from_pretrained(modelPath);
+        //const tokenizer = await AutoTokenizer.from_pretrained(modelPath);
         const tokens = tokenizer.encode(context);
 
         // If already within limits, return unchanged
@@ -124,7 +124,7 @@ async function truncateAuto(
         // Return truncated string if tokenization fails
         return context.slice(-maxTokens * 4); // Rough estimate of 4 chars per token
     }
-}
+}*/
 
 async function truncateTiktoken(
     model: TiktokenModel,

packages/core/src/generation.ts

@@ -507,30 +507,33 @@ export async function generateText({
 
 	  case ModelProviderName.BEDROCK: {
                 elizaLogger.debug("Initializing Bedrock model.");
-
-                const { text: bedrockResponse } = await aiGenerateText({
-                    model: bedrock(model),
-                    prompt: context,
-                    system:
+	    try {
+              const { text: bedrockResponse } = await aiGenerateText({
+                model: bedrock(model),
+                prompt: context,
+                system:
                         runtime.character.system ??
-                        settings.SYSTEM_PROMPT ??
+                          settings.SYSTEM_PROMPT ??
                         undefined,
-                    tools: tools,
-                    onStepFinish: onStepFinish,
-                    maxSteps: maxSteps,
-                    temperature: temperature,
-                    maxTokens: max_response_length,
-                    frequencyPenalty: frequency_penalty,
-                    presencePenalty: presence_penalty,
-                    experimental_telemetry: experimental_telemetry,
+                tools: tools,
+                onStepFinish: onStepFinish,
+                maxSteps: maxSteps,
+                temperature: temperature,
+                maxTokens: max_response_length,
+                frequencyPenalty: frequency_penalty,
+                presencePenalty: presence_penalty,
+                experimental_telemetry: experimental_telemetry,
                 });
-
-                response = bedrockResponse;
-                elizaLogger.debug("Received response from bedrock model.");
-                break;
-            }
-
-            case ModelProviderName.CLAUDE_VERTEX: {
+              response = bedrockResponse;
+              elizaLogger.debug("Received response from bedrock model.");
+	    } catch (error) {
+              elizaLogger.error("Error in bedrock:", error);
+              throw error;
+	    }	  
+            break;
+	  }
+      
+      case ModelProviderName.CLAUDE_VERTEX: {
                 elizaLogger.debug("Initializing Claude Vertex model.");
 
                 const anthropic = createAnthropic({

packages/core/src/localembeddingManager.ts

@@ -1,11 +1,11 @@
 import path from "node:path";
 import { fileURLToPath } from "url";
-import { FlagEmbedding, EmbeddingModel } from "fastembed";
+//import { FlagEmbedding, EmbeddingModel } from "fastembed";
 import elizaLogger from "./logger";
 
 class LocalEmbeddingModelManager {
     private static instance: LocalEmbeddingModelManager | null;
-    private model: FlagEmbedding | null = null;
+    private model: null = null;
     private initPromise: Promise<void> | null = null;
     private initializationLock = false;
 
@@ -79,11 +79,11 @@ class LocalEmbeddingModelManager {
 
             elizaLogger.debug("Initializing BGE embedding model...");
 
-            this.model = await FlagEmbedding.init({
-                cacheDir: cacheDir,
-                model: EmbeddingModel.BGESmallENV15,
-                maxLength: 512,
-            });
+            // this.model = await FlagEmbedding.init({
+            //     cacheDir: cacheDir,
+            //     model: EmbeddingModel.BGESmallENV15,
+            //     maxLength: 512,
+            // });
 
             elizaLogger.debug("BGE model initialized successfully");
         } catch (error) {

packages/core/src/ragknowledge.ts

@@ -367,7 +367,7 @@ export class RAGKnowledgeManager implements IRAGKnowledgeManager {
         };
 
         const startTime = Date.now();
-        let content = file.content;
+        const content = file.content;
 
         try {
             const fileSizeKB = new TextEncoder().encode(content).length / 1024;