Fixing Bedrock Prompt Caching Error: Extraneous Key Issue
Introduction
In the realm of AI development, efficient prompt caching is crucial for optimizing performance and reducing costs. However, developers sometimes encounter issues that can disrupt this process. This article delves into a specific bug encountered with LiteLLM when interacting with AWS Bedrock, focusing on an extraneous key error (cachePoint) that leads to prompt caching failure. We will explore the problem, its cause, and provide a detailed explanation along with a practical example to help you understand and resolve this issue effectively.
Understanding Prompt Caching and Its Importance
Before diving into the specifics of the bug, it's essential to grasp the concept of prompt caching. Prompt caching is a technique used to store the results of previous model requests, allowing the system to quickly retrieve and reuse these results when the same or similar prompts are encountered again. This not only reduces latency but also minimizes the computational load on the AI model, resulting in significant cost savings.
Effective prompt caching is a cornerstone of efficient AI application development. By avoiding redundant computations, it speeds up response times, enhances user experience, and lowers operational costs. However, when caching mechanisms fail due to errors like the cachePoint issue, it can lead to performance bottlenecks and increased expenses. Therefore, understanding and resolving these issues is critical for maintaining an optimized AI infrastructure.
The Bedrock Prompt Caching Failure: Extraneous Key 'cachePoint' Issue
The core of the problem lies in how LiteLLM, a library designed to simplify interactions with various AI models, handles caching for AWS Bedrock. Specifically, the issue arises when LiteLLM incorrectly applies cache_control_injection_points to the AWS Bedrock request. This leads to the inclusion of an extraneous key, cachePoint, in the request payload, which Bedrock's API does not recognize, resulting in a BadRequestError.
The error message, "extraneous key [cachePoint] is not permitted", clearly indicates that the Bedrock API is rejecting the request due to an unexpected field. This typically happens when the request structure deviates from the expected schema. In this case, the cachePoint key is being injected into the message content, which is not a valid part of the Bedrock API's message format. This injection is triggered by LiteLLM's caching mechanism, which, in this instance, is misconfigured for Bedrock.
Analyzing the Root Cause
To understand the root cause, it's crucial to examine the structure of the erroneous request payload. The following JSON snippet illustrates the problematic part of the request:
...
{
"role": "user",
"content": [
{
"toolResult": {
"content": [
{
"text": "OMITTED"
}
],
"toolUseId": "tooluse_TaYxhY4KRZm0KEXwq2HE6w"
}
},
{
"cachePoint": { // seems invalid
"type": "default"
}
}
]
}
As you can see, the cachePoint object is included within the content array of a message. This placement is incorrect because the Bedrock API does not expect a cachePoint key within the message content. The presence of this extraneous key causes the API to reject the request, leading to the BadRequestError. The issue stems from how LiteLLM's cache_control_injection_points are being applied, which is not compatible with Bedrock's API structure.
Reproducing the Problem: A Step-by-Step Guide
To better illustrate the problem and facilitate debugging, let's walk through a practical example. The following Python code snippet demonstrates how to reproduce the error using LiteLLM:
from dotenv import load_dotenv
from litellm import completion
response = completion(
model="bedrock/arn:aws:bedrock:us-east-1:622032586528:inference-profile/us.amazon.nova-pro-v1:0",
max_retries=0,
temperature=0.0,
stream=False,
tools=[
{
"type": "function",
"function": {
"name": "think",
"description": "Use when you want to think through a problem, clarify your assumptions, or break down complex steps before acting or responding.",
"parameters": {
"properties": {
"thoughts": {
"description": "Precisely describe what you are thinking about.",
"title": "Thoughts",
"type": "string",
}
},
"required": ["thoughts"],
"title": "ThinkSchema",
"type": "object",
"additionalProperties": False,
},
"strict": True,
},
},
{
"type": "function",
"function": {
"name": "OpenMeteoTool",
"description": "Retrieve current, past, or future weather forecasts for a location.",
"parameters": {
"properties": {
"location_name": {
"description": "The name of the location to retrieve weather information.",
"title": "Location Name",
"type": "string",
},
"country": {
"anyOf": [{
"type": "string"
}, {
"type": "null"
}],
"description": "Country name.",
"title": "Country",
},
"start_date": {
"anyOf": [{
"format": "date",
"type": "string"
}, {
"type": "null"
}],
"description": "Start date for the weather forecast in the format YYYY-MM-DD (UTC)",
"title": "Start Date",
},
"end_date": {
"anyOf": [{
"format": "date",
"type": "string"
}, {
"type": "null"
}],
"description": "End date for the weather forecast in the format YYYY-MM-DD (UTC)",
"title": "End Date",
},
"temperature_unit": {
"default": "celsius",
"description": "The unit to express temperature",
"enum": ["celsius", "fahrenheit"],
"title": "Temperature Unit",
"type": "string",
},
},
"required": ["location_name", "country", "start_date", "end_date", "temperature_unit"],
"title": "OpenMeteoToolInput",
"type": "object",
"additionalProperties": False,
},
"strict": True,
},
},
{
"type": "function",
"function": {
"name": "DuckDuckGo",
"description": "Search for online trends, news, current events, real-time information, or research topics.",
"parameters": {
"properties": {
"query": {
"description": "The search query.",
"title": "Query",
"type": "string"
}
},
"required": ["query"],
"title": "DuckDuckGoSearchToolInput",
"type": "object",
"additionalProperties": False,
},
"strict": True,
},
},
{
"type": "function",
"function": {
"name": "final_answer",
"description": "Sends the final answer to the user",
"parameters": {
"properties": {
"response": {
"description": "The final answer to the user",
"title": "Response",
"type": "string"
}
},
"required": ["response"],
"title": "FinalAnswerToolSchema",
"type": "object",
"additionalProperties": False,
},
"strict": True,
},
},
],
tool_choice="required",
cache_control_injection_points=[{
"location": "message",
"index": 0
}, {
"location": "message",
"index": 3
}],
messages=[
{
"role": "system",
"content": "# Role\nAssume the role of a helpful AI assistant.\n\n# Instructions\nPlan activities for a given destination based on current weather and events.\nWhen the user sends a message, figure out a solution and provide a final answer to the user by calling the 'final_answer' tool.\n\nIMPORTANT: The facts mentioned in the final answer must be backed by evidence provided by relevant tool outputs.\n\n# Tools\nYou must use a tool to retrieve factual or historical information.\nNever use the tool twice with the same input if not stated otherwise.\n\nName: think\nDescription: Use when you want to think through a problem, clarify your assumptions, or break down complex steps before acting or responding.\nAllowed: True\n\nName: OpenMeteoTool\nDescription: Retrieve current, past, or future weather forecasts for a location.\nAllowed: True\n\nName: DuckDuckGo\nDescription: Search for online trends, news, current events, real-time information, or research topics.\nAllowed: True\n\nName: final_answer\nDescription: Sends the final answer to the user\nAllowed: True\n\n\n# Notes\n- Use markdown syntax to format code snippets, links, JSON, tables, images, and files.\n- If the provided task is unclear, ask the user for clarification.\n- Do not refer to tools or tool outputs by name when responding.\n- Always take it one step at a time. Don't try to do multiple things at once.\n- When the tool doesn't give you what you were asking for, you must either use another tool or a different tool input.\n- You should always try a few different approaches before declaring the problem unsolvable.\n- If you can't fully answer the user's question, answer partially and describe what you couldn't achieve.\n- You cannot do complex calculations, computations, or data manipulations without using tools.\n- The current date and time is: 2025-12-04\n",
},
{
"role": "user",
"content": [{
"type": "text",
"text": "What to do in Boston today?"
}]
},
{
"role": "assistant",
"tool_calls": [
{
"id": "tooluse_TaYxhY4KRZm0KEXwq2HE6w",
"type": "function",
"function": {
"arguments": '{"end_date": "2025-12-04", "country": "USA", "location_name": "Boston", "temperature_unit": "celsius", "start_date": "2025-12-04"}',
"name": "OpenMeteoTool",
},
}
],
},
{
"tool_call_id": "tooluse_TaYxhY4KRZm0KEXwq2HE6w",
"role": "tool",
"name": "OpenMeteoTool",
"content": '{"latitude": 42.365166, "longitude": -71.0618, "generationtime_ms": 23.885011672973633, "utc_offset_seconds": 0, "timezone": "GMT", "timezone_abbreviation": "GMT", "elevation": 19.0, "current_units": {"time": "iso8601", "interval": "seconds", "temperature_2m": "°C", "rain": "mm", "relative_humidity_2m": "%", "wind_speed_10m": "km/h"}, "current": {"time": "2025-12-04T12:00", "interval": 900, "temperature_2m": -0.0, "rain": 0.0, "relative_humidity_2m": 84, "wind_speed_10m": 6.9}, "daily_units": {"time": "iso8601", "temperature_2m_max": "°C", "temperature_2m_min": "°C", "rain_sum": "mm"}, "daily": {"time": ["2025-12-04"], "temperature_2m_max": [3.5], "temperature_2m_min": [-1.1], "rain_sum": [0.0]}}',
},
],
)
print(response)
By running this code, you will encounter the following error:
litellm.exceptions.BadRequestError: litellm.BadRequestError: BedrockException - {"message":"The model returned the following errors: Malformed input request: #/messages/2/content/0: extraneous key [cachePoint] is not permitted, please reformat your input and try again."}
This error confirms that the cachePoint key is indeed causing the issue, and the Bedrock API is rejecting the request due to the malformed input.
Resolving the Issue: Practical Solutions
Now that we have a clear understanding of the problem, let's explore potential solutions. The primary goal is to prevent the cachePoint key from being injected into the Bedrock request when it is not expected.
1. Adjusting cache_control_injection_points
The most direct solution is to adjust the cache_control_injection_points configuration in LiteLLM. This involves modifying the points where caching metadata is injected into the request. For Bedrock, it's essential to ensure that cachePoint is not added to the message content.
One approach is to conditionally apply caching based on the model being used. If the model is Bedrock, you might need to disable or modify the cache_control_injection_points to avoid the error. This can be done by checking the model name and adjusting the caching configuration accordingly.
2. Updating LiteLLM Version
If the issue is due to a bug in an older version of LiteLLM, updating to the latest version may resolve the problem. Newer versions often include bug fixes and improvements that address compatibility issues with various AI model providers, including AWS Bedrock. To update LiteLLM, you can use pip:
pip install --upgrade litellm
3. Customizing Request Transformation
Another solution involves customizing the request transformation logic within LiteLLM. This allows you to intercept the request before it is sent to Bedrock and modify it to remove the cachePoint key. This approach requires a deeper understanding of LiteLLM's internal workings but provides a flexible way to handle compatibility issues.
4. Contacting LiteLLM Support
If you've tried the above solutions and are still facing issues, reaching out to LiteLLM support or community forums can provide additional assistance. The developers and community members may have encountered similar problems and can offer specific guidance tailored to your situation.
Best Practices for Prompt Caching
While resolving the cachePoint issue is crucial, it's also important to adopt best practices for prompt caching to ensure long-term efficiency and reliability. Here are some key practices to consider:
- Implement Conditional Caching: As mentioned earlier, apply caching strategies conditionally based on the model provider. Different providers may have different API requirements, and a one-size-fits-all approach can lead to errors.
- Monitor Cache Performance: Regularly monitor the performance of your caching mechanism. Track metrics such as cache hit rate, latency, and error rates to identify potential issues and optimize caching configurations.
- Use Cache Invalidation Strategies: Implement cache invalidation strategies to ensure that the cache remains up-to-date. This may involve setting expiration times for cached responses or invalidating the cache when the underlying data changes.
- Test Caching Thoroughly: Thoroughly test your caching implementation to identify and resolve issues before deploying to production. This includes testing with different types of prompts, model providers, and caching configurations.
Conclusion
Encountering errors like the extraneous key [cachePoint] issue can be frustrating, but understanding the underlying cause and implementing the right solutions can help you overcome these challenges. By carefully configuring your caching mechanisms, staying up-to-date with library versions, and adopting best practices, you can ensure efficient and reliable prompt caching for your AI applications.
By addressing this issue and optimizing your caching strategy, you'll not only improve the performance of your AI applications but also reduce costs and enhance the overall user experience. Remember to consult the official LiteLLM documentation for the most up-to-date information and guidance on using its features effectively.
