▲ 1 r/keyofsuccess
Always keep going
Because when you have no-money, no-job, no-project is running, every rejection feels like a verdict instead of a data point.
Look at this like a data, not a final word for your life.
u/yrjdev — 8 hours ago
Now get your Week of Social Media content in one click.
I launched a product 1 month ago with all the necessary updates.
Product is creatorjot.com
It extract content from YouTube link and then convert that into Social Media post.
Main feature is not converting post, but to write them specifically for the specific platform like Twitter, LinkedIn and Reddit.
After make the creatorjot.com live, a user suggest and ask for a feature.
Seo content Writer, so I also added a option, now you can generation seo content:
- 3 Titles (for comparison)
- Description
- Tags
- Keywords
- Most important "Chapters" (that wastes your hours to write)
How Creatorjot.com works?
it first extract the transcript, compress the text, analyze the script with timestamp, create a rich context to use in generations.
Have feedback for creatorjot.com helps a lot to me to shape the creatorjot.com.
Free to use, no credit card required
u/yrjdev — 9 hours ago
Built a Better version of RAG to get the exact chunk that needed
A few times ago, when I started to learn about the RAG models, I very disappointed about it response, I tried almost all the possible tricks and method to make it better, but still in some place, it's response got bad.
Then I searched about a lot of RAG model optimization, that method also not worked well, then finally I try to make a better version of RAG model.
Why I need a better version? I'm a building SupportGPT.
>
For that I need a better RAG architecture than Standard RAG, then I built QIndex RAG.
>Q-Index: Ingest generates questions per chunk, embeds questions (not chunks), stores chunk as payload. Query matches question-to-question.
Why this is better than Standard RAG?
- This stores the hypothetical questions that a user can ask and a particular chunk contains that means. In return it increases the amount of accuracy and similarity, because now db also have the words or actual sentence a user can ask, because docs not built to answer the single question, instead docs focus to give you understanding and uses method for the product.
- This increase the similarity scores from ~ 0.60 to ~ 0.90 - with the exact chunk that I need to answer the user question
- Qindex uses Context aware or Semantic Chunking to keep the context across the chunks.
Cons (that I want to highlight and still working on that):
- As most of the common questions contains the same starting like "How I ..", "What is ..", "Should ...", "How do I ...", etc. So these types of questions sometime attract the arbitrary chunks.
- User ask directly question instead of making it a proper question, instead of asking "How can I reset password?" they ask "reset password", so to tackle this - I'm building a question optimizer that add useful phrase based on the user question.
I published this on GitHub and you can use this in your local with Ollama or your Live API keys.
Easy to use and implement, built in Python.
Link: https://github.com/ameghcoder/qindex-rag
For any recommendation or suggestion you can raise an Issue in GitHub repo or comment to this post.
Thanks
u/yrjdev — 3 days ago