diff --git a/examples/lightrag_bedrock_demo.py b/examples/lightrag_bedrock_demo.py
index 36ec3857..c515922e 100644
--- a/examples/lightrag_bedrock_demo.py
+++ b/examples/lightrag_bedrock_demo.py
@@ -3,46 +3,39 @@ LightRAG meets Amazon Bedrock ⛰️
 """
 
 import os
+import logging
 
 from lightrag import LightRAG, QueryParam
 from lightrag.llm import bedrock_complete, bedrock_embedding
 from lightrag.utils import EmbeddingFunc
 
-WORKING_DIR = "./dickens"
+logging.getLogger("aiobotocore").setLevel(logging.WARNING)
 
+WORKING_DIR = "./dickens"
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
 rag = LightRAG(
     working_dir=WORKING_DIR,
     llm_model_func=bedrock_complete,
-    llm_model_name="anthropic.claude-3-haiku-20240307-v1:0",
-    node2vec_params = {
-        'dimensions': 1024,
-        'num_walks': 10,
-        'walk_length': 40,
-        'window_size': 2,
-        'iterations': 3,
-        'random_seed': 3
-    },
+    llm_model_name="Anthropic Claude 3 Haiku // Amazon Bedrock",
     embedding_func=EmbeddingFunc(
         embedding_dim=1024,
         max_token_size=8192,
-        func=lambda texts: bedrock_embedding(texts)
+        func=bedrock_embedding
     )
 )
 
-with open("./book.txt") as f:
+with open("./book.txt", 'r', encoding='utf-8') as f:
     rag.insert(f.read())
 
-# Naive search
-print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))
-
-# Local search
-print(rag.query("What are the top themes in this story?", param=QueryParam(mode="local")))
-
-# Global search
-print(rag.query("What are the top themes in this story?", param=QueryParam(mode="global")))
-
-# Hybrid search
-print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")))
+for mode in ["naive", "local", "global", "hybrid"]:
+    print("\n+-" + "-" * len(mode) + "-+")
+    print(f"| {mode.capitalize()} |")
+    print("+-" + "-" * len(mode) + "-+\n")
+    print(
+        rag.query(
+            "What are the top themes in this story?",
+            param=QueryParam(mode=mode)
+        )
+    )
diff --git a/lightrag/llm.py b/lightrag/llm.py
index 8fc0da2e..48defb4d 100644
--- a/lightrag/llm.py
+++ b/lightrag/llm.py
@@ -1,6 +1,9 @@
 import os
+import copy
 import json
+import botocore
 import aioboto3
+import botocore.errorfactory
 import numpy as np
 import ollama
 from openai import AsyncOpenAI, APIConnectionError, RateLimitError, Timeout
@@ -50,43 +53,70 @@ async def openai_complete_if_cache(
         )
     return response.choices[0].message.content
 
+
+class BedrockError(Exception):
+    """Generic error for issues related to Amazon Bedrock"""
+
+
 @retry(
-    stop=stop_after_attempt(3),
-    wait=wait_exponential(multiplier=1, min=4, max=10),
-    retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
+    stop=stop_after_attempt(5),
+    wait=wait_exponential(multiplier=1, max=60),
+    retry=retry_if_exception_type((BedrockError)),
 )
 async def bedrock_complete_if_cache(
-    model, prompt, system_prompt=None, history_messages=[], base_url=None,
+    model, prompt, system_prompt=None, history_messages=[],
     aws_access_key_id=None, aws_secret_access_key=None, aws_session_token=None, **kwargs
 ) -> str:
     os.environ['AWS_ACCESS_KEY_ID'] = os.environ.get('AWS_ACCESS_KEY_ID', aws_access_key_id)
     os.environ['AWS_SECRET_ACCESS_KEY'] = os.environ.get('AWS_SECRET_ACCESS_KEY', aws_secret_access_key)
     os.environ['AWS_SESSION_TOKEN'] = os.environ.get('AWS_SESSION_TOKEN', aws_session_token)
 
-    hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
-
+    # Fix message history format
     messages = []
-    messages.extend(history_messages)
+    for history_message in history_messages:
+        message = copy.copy(history_message)
+        message['content'] = [{'text': message['content']}]
+        messages.append(message)
+
+    # Add user prompt
     messages.append({'role': "user", 'content': [{'text': prompt}]})
 
+    # Initialize Converse API arguments
     args = {
         'modelId': model,
         'messages': messages
     }
 
+    # Define system prompt
     if system_prompt:
         args['system'] = [{'text': system_prompt}]
 
+    # Map and set up inference parameters
+    inference_params_map = {
+        'max_tokens': "maxTokens",
+        'top_p': "topP",
+        'stop_sequences': "stopSequences"
+    }
+    if (inference_params := list(set(kwargs) & set(['max_tokens', 'temperature', 'top_p', 'stop_sequences']))):
+        args['inferenceConfig'] = {}
+        for param in inference_params:
+            args['inferenceConfig'][inference_params_map.get(param, param)] = kwargs.pop(param)
+
+    hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
     if hashing_kv is not None:
         args_hash = compute_args_hash(model, messages)
         if_cache_return = await hashing_kv.get_by_id(args_hash)
         if if_cache_return is not None:
             return if_cache_return["return"]
 
+    # Call model via Converse API
     session = aioboto3.Session()
     async with session.client("bedrock-runtime") as bedrock_async_client:
 
-        response = await bedrock_async_client.converse(**args, **kwargs)
+        try:
+            response = await bedrock_async_client.converse(**args, **kwargs)
+        except Exception as e:
+            raise BedrockError(e)
 
         if hashing_kv is not None:
             await hashing_kv.upsert({
@@ -200,7 +230,7 @@ async def bedrock_complete(
     prompt, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
     return await bedrock_complete_if_cache(
-        "anthropic.claude-3-sonnet-20240229-v1:0",
+        "anthropic.claude-3-haiku-20240307-v1:0",
         prompt,
         system_prompt=system_prompt,
         history_messages=history_messages,