Hi everyone, I was working on a long-term project with ChatGPT until I found that it was having trouble remembering old conversations. I am trying to switch to Gemini and use a pay as you go plan. I want AI to be able to read and understand the entire chat hx seamlessly with each message that goes back and forth I exported my conversation from ChatGPT and am trying to build an app in AI studio where it can upload and read the file for understanding. It seems to be getting stuck when it tries to read the file. Sometimes I get a basic error message. Other times it says my quota has been reached (which isn’t actually the case).
The raw ChatGPT JSON export contains nested metadata that inflates token size and can trigger Tokens-Per-Minute (TPM) rate limits. Convert your export into a clean Markdown or plain-text file containing only the conversation dialogue to reduce payload size. For large chat histories, use Context Caching so the history is stored once rather than re-sent with every prompt. Please try uploading the cleaned file and let us know if you continue encountering any errors.
I followed your advice and started building a persistent Gemini workspace in Google AI Studio Build mode. I specifically instructed it to use the Gemini Interactions API with previous_interaction_id, persistent project memory, and a database, and to eventually handle a cleaned Markdown/plain-text version of my ChatGPT export rather than the raw JSON.
During initial testing, I found some problems:
The first simple stateful Gemini test took almost 2 minutes. AI Studio diagnosed a Vite/file-watcher/server-restart problem caused by database/log writes, plus synchronous Project Memory processing and unnecessary background/polling behavior. It changed those things.
A subsequent 2 + 2 test took ~35 seconds. Instrumentation showed:
Gemini API response: ~1.14 seconds
Gemini response received → HTTP response sent: ~33.73 seconds
Browser/rendering after that: <30 ms
So the Gemini API itself was responding quickly; the server was holding the HTTP response open for ~34 seconds. AI Studio diagnosed an unclosed HTTP response/event-loop issue and changed the response lifecycle.
After that, one of my diagnostic prompts returned:
However, I have verified that this application is using my Tier 1 Prepay Gemini API key, not my free-tier key.
I then accidentally sent another diagnostic prompt through the application and got:
400 Request contains an invalid argument
So I stopped testing.
The application did successfully maintain the stateful conversation: I asked it to remember BLUE BANANA 7429, and a later prompt correctly retrieved that phrase. However, the response took ~1 minute 42 seconds.
I don’t want to manually inspect the application’s code. I want AI Studio to build this correctly and automatically.
What do you recommend I do next?
Specifically, I need guidance on:
whether the Interactions API is the right architecture for this long-term project;
how to eliminate the remaining latency;
why a Tier 1 Prepay API key is producing a generate_content_free_tier_requests 429;
whether the app is making multiple Gemini requests per user prompt because of Project Memory extraction/indexing;
and, most importantly, how you recommend handling my very large historical ChatGPT conversation using cleaned Markdown/plain text, deterministic chunking, persistent storage, and caching/context management without repeatedly sending the entire history or running into TPM limits.
I want the historical conversation to remain completely searchable and chronologically intact, and I need the AI to be able to continue the project without me manually supplying the previous conversation after every prompt.