It sounds like you’re encountering a “400 Bad Request” error with a [GoogleGenerativeAI Error] message, specifically “The input token count (2993755) exceeds the maximum number of tokens allowed (1048576).” This clearly indicates that the conversation history or the input you’re sending to the Gemini API is too large.
You’re right that it’s likely trying to send your “entire thread” (the full conversation history) with each request, and this has suddenly grown too large for the model’s context window.
Let’s break down why this happens and what to do, specifically addressing your concerns about “removing code” and “JSON files” without clear instructions.
Why it’s Happening: Context Window Limits
AI models like Gemini have a limited “context window.” This is the amount of text (measured in “tokens,” which are like words or parts of words) they can process at any given time. When you’re having a continuous conversation with an AI, the API typically sends the previous turns of the conversation along with your new input so the AI remembers the context.
If your conversation gets very long, the combined length of all those turns exceeds the model’s context window, leading to the error you’re seeing.
The “Blaze Plan” (Google Cloud Billing):
You mentioned a “Blaze Plan.” While a Blaze Plan on Google Cloud (which enables billing) is necessary to use certain APIs and higher quotas, it does not increase the fundamental context window size of a specific AI model. The token limit you’re hitting is a model-specific architectural constraint, not a billing or quota limit.
Specific Help: What to Do
Since you’ve been working exclusively with AI and might not be comfortable with direct code manipulation without guidance, I’ll give you very specific, actionable steps. The core solution is to manage the conversation history that you send to the API.
Understanding Your Setup (Crucial First Step):
Before I can give you the most precise advice, I need to make an educated guess about how you’re interacting with the Gemini API.
Are you using:
- Google Colab, Jupyter Notebook, or a similar Python environment? (This is common for building AI prototypes.)
- A web application framework (like Flask, Django, Node.js, etc.)?
- A specific library or SDK (e.g., google-generativeai Python library)?
- Something else entirely?
Knowing this helps me pinpoint where you’re storing and sending the conversation history.
General Solutions (Likely Applicable to Your Situation):
Regardless of your exact setup, the principle is the same: You need to reduce the number of tokens in your API request.
Here are the most common ways to do this, ranging from simplest to more involved:
Solution 1: Start a New Chat/Conversation (Simplest, but temporary)
You mentioned “I have tried everything to start a new chat.” This is the most direct way to solve the immediate problem because it clears the context. If you’re using a UI or an SDK that has a “new chat” button or function, use it.
How to do it (If you’re writing code):
If your code is explicitly managing a list of messages (e.g., messages = ), you need to clear that list.
Example (Python using google-generativeai library):
Let’s say your code looks something like this (simplified):
import google.generativeai as genai
… (API key setup) …
model = genai.GenerativeModel(‘gemini-pro’)
chat = model.start_chat(history=) # Initialize with an empty history
def send_message_to_gemini(prompt):
response = chat.send_message(prompt)
return response.text
… later in your code when the error occurs …
You need to effectively do this to clear the history for a new “chat”
chat = model.start_chat(history=) # Re-initialize the chat object
print(“Chat history cleared. Starting a new conversation.”)
What you need to find in your code: Look for where you are initializing your chat object or where you are storing the history of the conversation. Resetting or re-initializing that object/variable is how you “start a new chat” programmatically.
Solution 2: Implement “Conversation Summarization” or “Windowing” (More Robust)
This is the long-term solution. Instead of sending the entire conversation history, you only send a portion of it, or you summarize older parts.
Option A: “Sliding Window” (Recommended for simplicity)
Keep only the most recent ‘N’ turns of the conversation. This is often the easiest to implement first.
How to do it (Conceptual):
- Maintain a list or array of past messages (your history).
- Before sending to the API, check the total token count of your history + your new prompt.
- If it exceeds a certain threshold (e.g., 70-80% of the model’s context window, or a fixed number of turns), remove the oldest messages from history until it’s within limits.
Example (Python using google-generativeai library):
import google.generativeai as genai
… (API key setup) …
Model configuration
model_name = ‘gemini-pro’ # Or whatever model you’re using
model = genai.GenerativeModel(model_name)
This will store the actual history we pass to the model
In a real application, you might persist this.
conversation_history =
Define a reasonable max number of recent turns to keep
You’ll need to experiment with this based on your typical message length
MAX_TURNS_TO_KEEP = 10 # Example: Keep last 10 turns (5 user, 5 AI)
def send_message_with_history_management(user_message):
global conversation_history # Modify the global history
# Add the user's message to the history
conversation_history.append({'role': 'user', 'parts': [user_message]})
# --- History Management Logic ---
# This is a simple sliding window based on number of turns.
# A more advanced version would check token count.
if len(conversation_history) > MAX_TURNS_TO_KEEP:
# Keep only the last MAX_TURNS_TO_KEEP entries
conversation_history = conversation_history[-MAX_TURNS_TO_KEEP:]
print(f"DEBUG: Trimmed history to {len(conversation_history)} turns.")
# Start a new chat with the (potentially trimmed) history
# Each call to model.start_chat creates a new context,
# so we pass the current state of conversation_history
chat = model.start_chat(history=conversation_history)
try:
response = chat.send_message(user_message) # Send the user's message
ai_response = response.text
# Add the AI's response to the history
conversation_history.append({'role': 'model', 'parts': [ai_response]})
return ai_response
except Exception as e:
print(f"Error sending message: {e}")
# Optionally, clear history on severe error if you can't recover
# conversation_history.clear()
return "An error occurred. Please try again."
Example Usage:
print(“Starting conversation…”)
print(f"AI: {send_message_with_history_management(‘Hello!’)}“)
print(f"AI: {send_message_with_history_management(‘How are you doing today?’)}”)
… continue sending messages. After MAX_TURNS_TO_KEEP, older ones will be dropped.
print(f"AI: {send_message_with_history_management(‘Can you tell me more about AI models?’)}")
… simulate a very long conversation to trigger trimming …
for i in range(20):
print(f"User: What is fact {i}?“)
print(f"AI: {send_message_with_history_management(f’Fact {i} is that the sky is blue and it is currently {i} degrees.')}”)
Where to look in your code for history:
You are likely constructing a list of dictionaries, where each dictionary represents a turn in the conversation and has role (e.g., ‘user’, ‘model’) and parts keys. This list is then passed to the API. Find where this list is built and modify it before it’s sent.
Option B: Summarization (More Complex, but effective for very long contexts)
For extremely long conversations where even a sliding window isn’t enough, you can periodically summarize the older parts of the conversation using the AI model itself.
How it works:
- When the history gets too long, take the oldest X turns.
- Send those X turns to the AI with a prompt like “Summarize the following conversation history concisely:”
- Replace the X turns in your history with the AI’s summary. This saves many tokens.
This is more advanced and might be something to explore once you have the sliding window working.
Regarding “JSON files”
When people say “remove JSON files,” they’re likely referring to situations where:
- You’re saving your conversation history to a file: If your application is persisting the full conversation history to a JSON file (e.g., conversation_log.json) to load it back up later, and that file has become excessively large, then yes, deleting or truncating that file would effectively “clear” your saved history.
- Action: Look for any code that writes to or reads from files with a .json extension that might contain your chat history.
- You’re dealing with API request/response logs: Some debugging tools or frameworks might log every API request and response, potentially as JSON. If you’re trying to clear those logs to reduce disk space, that’s a different issue and won’t directly solve the API token limit error.
How to “remove JSON files” (if applicable):
If your code explicitly loads or saves history from a JSON file, you’d either:
- Manually delete the file: (e.g., using your operating system’s file explorer). Be careful if you have other important data in it.
- Modify your code to clear the history before saving: Or, modify your code to save only a limited portion of the history.
Crucial Advice for Debugging:
- Print the len(conversation_history) and the content of conversation_history: Before you send a request to the Gemini API, add print(f"Sending {len(conversation_history)} turns to API.") and print(conversation_history) to your code. This will immediately show you what’s being sent and how long it is.
- Check Token Count: If you want to be precise, you can estimate token count. The google-generativeai library might have utility functions for this, or you can use a simple rule of thumb (e.g., 1 word is roughly 1-1.5 tokens).
- Isolate the problem: If you’re building a complex application, try to create a minimal reproducible example – a very small script that just tries to send a long history to the Gemini API. This helps you confirm that the problem is indeed the history length and not something else.
Summary of Action Plan:
- Identify where your conversation history is stored and managed in your code. It will likely be a Python list of dictionaries.
- Implement a “sliding window” approach (Solution 2A): Modify your code to keep only the MAX_TURNS_TO_KEEP most recent messages in that history list before you pass it to model.start_chat(history=…) or chat.send_message(). Start with a small MAX_TURNS_TO_KEEP (e.g., 5-10) and increase it if your responses are losing too much context.
- If you’re persisting history to a file: Locate any JSON files that store your conversation history and either delete them or modify your code to only save a truncated version of the history.
You’ve got this! The error message is very specific about the problem, and managing conversation history is a common challenge in building AI applications. Start with the sliding window; it’s the most effective and straightforward solution for this particular error.