3 Tips for Working With the OpenAI API
Working with OpenAI's API comes with its own challenges. Therefore, in this video, I'll share with you three practical tips for effectively working with a tool like OpenAI's API.
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👀 Code reviewers:
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🎥 Video edited by Mark Bacskai: / bacskaimark
🔖 Chapters:
0:00 Intro
0:56 Tip 1: Take token limits into account
3:03 Estimating token size
5:06 Tip 2: Make sure you don’t hit a rate limit
6:17 Tip 3: Use an appropriate model
7:30 Summary
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Пікірлер: 12
Working with OpenAI's API comes with its own challenges. Therefore, in this video, I'll share with you three practical tips for effectively working with a tool like OpenAI's API.
For rate limiting, you can have a look at tenacity
Please do the Specification pattern! I love the pattern videos
This was fantastic and lined up with a project I'm currently working on. Given your JSON example - how would you suggest handling that situation (where you can't chunk the prompt)? Got a new subscriber! 👍
We use tenacity for rate limiting. Seems good so far
Hello any change you bring ClickHouse database integration with python. Like a small project with all steps, from installation to ingestion of real time feed data and on the fly data manipulation example. Ideally with integration with FastAPI and applied for example for tickdata price or IoT sensor data. If possible do some benchmarks in comparison with another DB for timeseries like: InfluxDB, TimeScaleDB or DuckDB. I feel that is missing something like this in KZread. Thanks in advance.
Altijd goede videos!
Why not just use langchain?
How to use claude3 in pycharm?
Can i intern with you. Been leaning from your youtube. Maybe some grunt work just to work with a legend is a dream.
i cannot understand what your mean,even i watched half video here,what are you talking about?? your are working with chatgpt api,but why and how? and what are you going to do? just add some code？you should show us what do you want to do and do it ,if it was IT,so make the tites as IT