Fix AWS Bedrock Boto3 Timeout Errors

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Fix AWS Bedrock Boto3 Timeout Errors

Hey everyone, let's talk about a super common headache when you're diving deep into AWS Bedrock, especially when you're working with those massive language models like Claude Sonnet. We're talking about those frustrating Read timeout errors that pop up because the default boto3 read timeout just isn't cutting it. You know, the one that’s usually around 60 seconds? Yeah, that’s often way too short when you're trying to process large chunks of memory or have a really long conversation history. The good news is, we've got a solution, and it’s all about adding some much-needed timeout configuration support to the AWSBedrockLLM class.

Understanding the Timeout Problem

So, what exactly is going on here? When you're using AWS Bedrock through boto3, the underlying library called botocore has a default read timeout. Think of this timeout as a timer that waits for a response from the AWS servers. If the server doesn't send back a response within that time, your program throws a Read timeout error. For many simple requests, this 60-second default is totally fine. But when you're dealing with generative AI, especially with models that need to crunch a lot of data – like extracting key information from a long chat log or generating complex text – that 60 seconds can vanish in a blink. This is precisely what we're seeing with AWS Bedrock. The AWSBedrockLLM class, as it's currently set up, doesn't give us a way to tell boto3 to wait longer. This leads to errors like:

Failed to generate response: Read timeout on endpoint URL: "https://bedrock-runtime.us-west-2.amazonaws.com/model/us.anthropic.claude-sonnet-4-20250514-v1%3A0/converse"

The root cause is pretty straightforward. If you peek into the mem0/llms/aws_bedrock.py file, you'll see how the boto3 client is initialized. Right now, it looks something like this:

def _initialize_aws_client(self):
    aws_config = self.config.get_aws_config()
    self.client = boto3.client("bedrock-runtime", **aws_config)

Notice what's missing? There's no explicit mention of timeouts. This means it's just falling back to the default botocore settings, which, as we've established, are often too restrictive for the demanding tasks mem0 is designed to handle with AWS Bedrock. This default is especially problematic for:

  • Large conversation contexts: The more history the model needs to consider, the longer it will take.
  • Complex memory extraction operations: Pulling out nuanced information requires more processing power and time.
  • Models with longer inherent processing times: Some models are just slower than others, and the default timeout doesn't account for this variability.

So, the core issue is that the flexibility to adjust how long boto3 waits for a response from AWS Bedrock is missing. This feature is crucial for building robust applications that rely on these powerful AI models for more than just quick, simple queries.

Implementing the Timeout Solution

Alright, guys, let's get down to fixing this! The proposed solution is pretty elegant and involves adding timeout configuration directly into the AWSBedrockConfig class. This way, you get fine-grained control over how long your boto3 client waits for responses from AWS Bedrock. It’s all about making mem0 more robust and user-friendly when tackling complex AI tasks.

First things first, we need to update the AWSBedrockConfig class. This class is where we define all the configuration settings for our AWS Bedrock integration. We're going to add two new parameters: read_timeout and connect_timeout. The read_timeout is the one that addresses the immediate problem – how long to wait for data after a connection is established. The connect_timeout is also important; it defines how long boto3 will try to establish a connection to the AWS endpoint in the first place. You can see the proposed changes in mem0/configs/llms/aws_bedrock.py:

from botocore.config import Config

class AWSBedrockConfig(BaseLlmConfig):
    def __init__(
        self,
        # ... existing parameters ...
        read_timeout: int = 1000,  # Default increased to 1000 seconds
        connect_timeout: int = 60,   # Default remains 60 seconds
        **kwargs,
    ):
        # ... existing init code ...
        self.read_timeout = read_timeout
        self.connect_timeout = connect_timeout
    
    def get_boto_config(self) -> Config:
        """Get boto3 Config object with timeout settings."""
        return Config(
            read_timeout=self.read_timeout,
            connect_timeout=self.connect_timeout
        )

Notice that we've set a new default for read_timeout to 1000 seconds. This is a significant increase from the default 60 seconds and is much more likely to accommodate longer processing times. The connect_timeout stays at 60 seconds, which is usually sufficient for establishing a connection.

Crucially, we've added a new method, get_boto_config(), which returns a botocore.config.Config object. This object encapsulates our desired timeout settings. This is the standard way boto3 allows you to pass custom configurations.

Now, the second part of the solution involves updating the AWSBedrockLLM class itself, specifically in mem0/llms/aws_bedrock.py. This is where we’ll actually use the configuration we just set up. Here’s how the _initialize_aws_client method should look:

def _initialize_aws_client(self):
    try:
        aws_config = self.config.get_aws_config()
        boto_config = self.config.get_boto_config() # Get our custom config
        
        self.client = boto3.client(
            "bedrock-runtime", 
            config=boto_config,  # Pass the timeout config here!
            **aws_config
        )
        
        self._test_connection() # Good practice to keep this
    except Exception as e:
        # Handle potential exceptions during client initialization
        print(f"Error initializing AWS Bedrock client: {e}")
        raise

See that? We now fetch the boto_config object from our updated AWSBedrockConfig and pass it directly to the boto3.client call using the config parameter. This tells boto3 to use our specified read_timeout and connect_timeout instead of the defaults. By making these changes, we empower users to prevent those frustrating timeout errors and ensure their applications can reliably interact with AWS Bedrock, even for the most demanding tasks. It's a simple yet powerful enhancement!

Why This Matters: The Benefits You Get

Making these changes to support timeout configurations for the AWS Bedrock client isn't just about squashing a bug; it's about unlocking the full potential of mem0 when paired with powerful cloud AI services. We're talking about a significant improvement in reliability and usability, especially for the core use cases that mem0 is built for. Let’s break down exactly why this is such a big deal, guys.

Handling Large Conversations and Memory Extraction

One of mem0’s main selling points is its ability to manage and extract meaningful information from conversations. Think about a customer support chat that spans hundreds, even thousands, of messages. To accurately summarize this, identify key issues, or extract specific data points, the underlying LLM needs time to process all that context. The default 60-second timeout is simply insufficient for this. By allowing us to set a much longer read_timeout – say, 600 seconds or even 3600 seconds as AWS recommends – we ensure that mem0 can reliably perform these complex memory extraction tasks without being prematurely cut off. This directly translates to more accurate insights and a better user experience because the tool actually works for the scenarios it’s designed for.

Supporting Complex Prompt Processing

Beyond just conversation history, mem0 often uses complex system prompts and instructions to guide the LLM's behavior. Crafting these prompts can involve multiple steps, detailed explanations, and specific formatting. When the LLM has to interpret and act upon these elaborate instructions, especially in conjunction with large contexts, the processing time can increase considerably. Our new timeout configuration means that these sophisticated prompt-processing operations are less likely to fail due to simple time constraints. The LLM gets the time it needs to