Yes, you can train an OpenAI Custom GPT with thousands of small PDF files. However, there are some considerations and steps involved:

Considerations:

File Size: OpenAI has limitations on the total file size you can upload at once. You might need to split your PDFs into smaller chunks or upload them in batches. Data Preparation: PDF files need to be converted into text format before training. This can be done using various tools like OCR (Optical Character Recognition) or libraries like PyPDF2. Model Size: The number of PDFs and their total size will influence the required model size. Larger datasets might necessitate larger models, which can be more computationally expensive to train and run. Fine-tuning vs. Embedding: You can either fine-tune a pre-trained GPT model on your PDF data or use embeddings to create a vector database for semantic search. Fine-tuning is more powerful but requires more computational resources and expertise. Embeddings are simpler but might be less accurate for complex queries. Steps:

Data Preparation:

Convert PDFs to text using OCR or libraries. Clean and preprocess the text data (remove noise, normalize, etc.). Split the data into training and validation sets. Model Selection:

Choose a pre-trained GPT model (e.g., GPT-3) suitable for your task. Consider the model size and computational resources required. Training:

Use OpenAI's API or a compatible framework (e.g., Hugging Face) to fine-tune the model on your prepared data. Experiment with different hyperparameters (learning rate, batch size, etc.) to optimize performance. Deployment:

Deploy the trained model to an API or integrate it into your application. Use the model to generate text, answer questions, or perform other tasks based on the PDF content. Additional Tips:

Data Quality: Ensure the quality of the extracted text from the PDFs. Data Quantity: More data generally leads to better model performance. Model Architecture: Experiment with different model architectures (e.g., GPT-3, GPT-4) to find the best fit for your task. Evaluation: Continuously evaluate the model's performance on a validation set and make adjustments as needed. By following these steps and considering the factors mentioned above, you can effectively train a Custom GPT model on your thousands of small PDF files.