Fixing Gemini's Empty Text Issue In Pydantic AI

by Alex Johnson 48 views

Are you encountering the frustrating "Unable to submit request because it must include at least one parts field" error when working with the Gemini model in Pydantic AI? This issue arises when the Gemini model unexpectedly returns an empty text response, causing your agent to crash. Let's dive deep into this problem, explore its root causes, and find effective solutions.

The Core Problem: Empty Responses and Pydantic AI

At the heart of the issue lies the Gemini model's occasional tendency to generate empty text responses. While this might seem harmless at first glance, it can wreak havoc on your Pydantic AI workflow. When the model provides an empty response, Pydantic AI appears to misinterpret this, leading to an HTTP 400 error.

The error message, "Unable to submit request because it must include at least one parts field," is quite explicit. It means the request sent to the Gemini backend lacks the necessary parts field, which is crucial for defining the prompt input. This omission typically happens when Pydantic AI tries to incorporate the previous empty response into the next model request. The result is an empty parts array, causing the Gemini backend to reject the request.

Imagine this scenario: you're building a sophisticated AI agent using Pydantic AI and Gemini. Everything works perfectly until, unexpectedly, the Gemini model decides to answer with nothing. The subsequent request, meant to build upon the previous interaction, then fails. This leads to a breakdown in your AI agent's functionality. This is a common and often unpredictable situation that we'll investigate thoroughly.

Detailed Breakdown of the Issue

Let's break down the typical sequence of events that lead to this error, so we can better understand the nuances of the problem.

  1. Initial Prompt: Your AI agent sends a request to the Gemini model with a clear prompt. This prompt is well-formatted and includes all the necessary components.
  2. Empty Response: The Gemini model, for reasons that are not always clear, returns an empty text response. This response is valid in terms of the API; it doesn't violate any technical constraints. However, it's problematic in the context of subsequent requests.
  3. Pydantic AI's Handling: Pydantic AI receives this empty response. It proceeds to incorporate this response into the context for the next interaction.
  4. Malformed Request: When the next request is generated, the empty response from Gemini is included. However, something goes wrong, and the parts field, which should contain the empty text, is omitted. The resulting request contains an empty parts array.
  5. HTTP 400 Error: Because the request now has an empty parts field, the Gemini backend correctly throws an HTTP 400 error, indicating an invalid request.
  6. Agent Failure: Your AI agent is unable to recover from this error, and the entire process crashes. The user experience is disrupted, and the functionality of your application is compromised.

This sequence of events highlights that the core issue lies in how Pydantic AI handles the Gemini model's empty responses. It's a critical bug that needs to be addressed for the stability and reliability of AI applications that use these tools.

Diagnosing the Root Cause: Where Does the Problem Lie?

The problem seems to originate from a combination of the Gemini model's behavior and how Pydantic AI interprets and processes empty responses. To find a solution, we must consider both factors.

Potential Culprits

  • Gemini Model: The Gemini model's tendency to produce empty text responses is the initial trigger. While the model should ideally avoid this, it's essential to understand why it happens and whether it can be mitigated.
  • Pydantic AI: Pydantic AI is responsible for parsing the model's responses and formatting the next request. This is where the bug seems to reside. The software may not correctly handle empty text responses, leading to an incorrect parts field in subsequent requests.
  • Underlying Libraries: The Google GenAI library could be involved. It might improperly handle the empty text within the framework of Pydantic AI.

Investigating the Problem

To thoroughly diagnose the issue, you could engage in the following investigation steps:

  • Logging: Implement comprehensive logging of all requests and responses. Pay close attention to the content of the parts field to see how it changes during empty responses.
  • Debugging: Use a debugger to step through Pydantic AI's code, focusing on how it handles empty responses. See how the empty text is incorporated into the next request.
  • Testing: Create a test case that specifically prompts the Gemini model to generate an empty response. This will help you identify the exact point where the error occurs.

By following these steps, you can pinpoint the exact source of the bug and find the best way to resolve it.

Implementing Solutions and Workarounds

Addressing this issue requires a multi-pronged approach. The core strategy should focus on preventing the malformed requests from reaching the Gemini backend.

Potential Solutions

Here are some possible solutions:

  1. Pydantic AI Update: The most elegant solution is to update Pydantic AI to correctly handle the empty text responses from Gemini. This might involve changing how the response is parsed or how the next request is formatted.
  2. Input Validation: Add input validation to filter out or transform empty text responses before they're sent to Gemini. This would prevent the malformed request in the first place.
  3. Error Handling: Implement robust error handling. If an HTTP 400 error occurs, your agent should be able to recover gracefully, perhaps by retrying the request or trying an alternative approach.
  4. Post-Processing: After receiving a response from the Gemini model, add a step to check if the text is empty. If it is, regenerate the prompt or try a different approach.

Workarounds

If you can't wait for a fix or need an immediate solution, consider these workarounds:

  • Pre-processing: Before sending the request, add a check to make sure the prompt isn't likely to produce an empty response. You might do this by rephrasing the question or using a different prompt strategy.
  • Filtering: Add a filter to remove any empty text responses. This may involve editing the responses before they are used in subsequent requests.
  • Response Analysis: After receiving a response, check to see if the content is empty. If it is, ask the model again or try a different approach.

Implementing these solutions and workarounds can prevent or mitigate the impact of the empty text responses from the Gemini model. This can help stabilize your AI agent and improve the user experience.

Example Code and Implementation

Here's an example of how you can implement a workaround using Python and Pydantic AI. This code snippet shows how to check for an empty response and re-prompt the Gemini model.

from pydantic_ai import OpenAI
from pydantic import BaseModel, Field

# Assuming you have set up your API key correctly

# Define your model
class MyResponse(BaseModel):
    text: str = Field(..., description="The response text.")

# Initialize the LLM client (replace with your setup)
llm = OpenAI(model="gemini-3-pro-preview")

# Your prompt
prompt = "Write a short poem about the ocean."

# Retry mechanism
max_retries = 3
retries = 0

while retries < max_retries:
    try:
        response: MyResponse = llm.generate(MyResponse, prompt=prompt)
        if response.text.strip():  # Check if the text is not empty
            print(f"Response: {response.text}")
            break # Exit the loop if the response is not empty
        else:
            print("Received empty response. Re-prompting...")
            prompt = "Please try again to write a short poem about the ocean."
            retries += 1
    except Exception as e:
        print(f"An error occurred: {e}")
        retries += 1
        if retries < max_retries:
            print("Retrying...")
        else:
            print("Max retries reached. Exiting.")

Explanation

This Python code snippet includes a retry mechanism. This helps you handle situations where the Gemini model provides an empty text response. This is a practical example of a workaround to deal with the bug.

  1. Model Definition: A Pydantic model (MyResponse) is defined to represent the expected response.
  2. LLM Initialization: It initializes your LLM client (replace OpenAI with your specific setup).
  3. Prompt: It sets your initial prompt.
  4. Retry Loop: The code uses a while loop with a maximum number of retries.
  5. Generation: Inside the loop, it attempts to generate a response using llm.generate().
  6. Empty Response Check: After generating the response, it checks if response.text contains any text. If the response is empty, it re-prompts the model and increases the retry counter.
  7. Error Handling: It includes an except block to catch potential errors, providing a safety net in case of problems.
  8. Retry Logic: If an error occurs or an empty response is received, the code tries to re-prompt or retry a certain number of times before giving up. The idea is to make sure your agent is resilient against errors.

Conclusion: Improving the Reliability of Your AI Agent

Encountering the "Unable to submit request" error from the Gemini model when working with Pydantic AI can be a significant challenge. However, by understanding the root cause, diagnosing the specific issues, and implementing the solutions and workarounds discussed in this guide, you can significantly enhance the stability and reliability of your AI applications.

The key takeaway is to handle the empty text responses gracefully. Whether through proactive input validation, error handling, or response post-processing, your primary focus should be to prevent malformed requests from reaching the Gemini backend.

As the technologies and libraries evolve, stay updated with the latest versions and monitor any fixes or updates related to this specific problem. With the right strategies, you can keep your AI agents robust and reliable.

For more in-depth information about Pydantic AI, you can explore the official Pydantic AI documentation. Also, consider looking at the Google Gemini API documentation for a deeper understanding of the model's behavior. These resources are invaluable when diagnosing and resolving issues.

External Links: