Handling Large Input Text in OpenAI's GPT-3 with FastAPI and Pydantic
I'm sure I'm missing something obvious here, but I've been banging my head against this for hours. I'm working on a FastAPI application that integrates OpenAI's GPT-3 for text generation. The scenario arises when I attempt to send a large input text (over 2048 tokens) to GPT-3. The API call fails with the behavior message `"Exceeded maximum context length"`, which suggests that the input is too lengthy. I have the following setup using Pydantic for model validation: ```python from fastapi import FastAPI, HTTPException from pydantic import BaseModel import openai app = FastAPI() class TextInput(BaseModel): text: str @app.post("/generate") async def generate_text(input: TextInput): if len(input.text.split()) > 2048: # naive check for token length raise HTTPException(status_code=400, detail="Input text is too long.") response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": input.text}] ) return response["choices"][0]["message"]["content"] ``` I attempted to implement a token count check before making the API call, but it seems that splitting by whitespace is not a reliable way to measure token length, leading to false negatives. I also considered using `tiktoken` from OpenAI to count tokens accurately, but I'm not sure how to integrate it into my current validation logic. Here’s what I tried: 1. I added a simple check with `len(input.text.split())` to limit the text input, but it's not effective for token counting. 2. I read about using `tiktoken` and ran this code snippet: ```python import tiktoken encoding = tiktoken.encoding_for_model("gpt-3.5-turbo") token_count = len(encoding.encode(input.text)) ``` However, I don't know how to incorporate this token count check in the FastAPI route without causing blocking due to the synchronous nature of the tokenization. How should I modify my FastAPI endpoint to handle larger texts appropriately and avoid exceeding the token limit? For context: I'm using Python on macOS. I've been using Python for about a year now. Could this be a known issue? The project is a desktop app built with Python. Any suggestions would be helpful.