Hi,
How to add custom data (ex. CSVLoader) into code with model 3.5-turbo, for example into ChatCompletion call?
Thanks,
Pavel
Hi,
How to add custom data (ex. CSVLoader) into code with model 3.5-turbo, for example into ChatCompletion call?
Thanks,
Pavel
Great topic, subscribing. I thought about the same problem and the thoughts are:
Yes, I did exactly as you wrote, it’s more or less ok, but in this case I can’t:
Then there is option 3: Finetuning.
Kindly share your experience here if You’d try that in your project.
P.S. However, you can only fine-tune InstructGPT (Ada, Davinci etc) models and GPT-3 models, not GPT-3.5 models, as stated in the official OpenAI documentation
Also, as option 4, it is possible to create embeddings vectors from custom data organized as multiple text documents. For searching the information for a user, the GPT API call retrieves the most relevant document using just these embeddings. Then with another prompt, GPT extracts the matching answer from the most relevant document.
Finetuning is not a option for such task. It’s good for text, but not for concrete numbers. Numbers can not be probability, they must be equal. You can have answer “it seems we have iPhone on the stock”. The same issuew with converting concrete numbwes/dates/facts into embeddings and search by relevant.
For GPT-3 we can add customer data source, it works with bugs, but ok.
For 3.5 - I can’t find and asked.
For GPT-3 we can add customer data source, it works with bugs, but ok.
Do you mean the data sources such as CSV, not text embeddings? Could you share more?
Yes, exactly. Langchain allows to add it easy.