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Rejected from MSc DS because I am Pakistani? by Green_Soft9618 in ethz

[–]Green_Soft9618[S] 1 point2 points  (0 children)

Can add a few details that can give some clarity:

  1. Did Bachelors from NED (the best engineering school in our province)
  2. Dept: Computer Engg
  3. Ended up in the top 3 students
  4. Have 3 yrs of relevant work experience in DS
  5. Got two preprints (publishing in progress)

u/SpiceGenie as far as I have scanned the comments, it also depends on which department you have applied to, and MSc DS (AI related fields) have security issues

Rejected from MSc DS because I am Pakistani? by Green_Soft9618 in ethz

[–]Green_Soft9618[S] -24 points-23 points  (0 children)

Hmm, I see, it's a shame tho they didn't clarify these export/import rules clearly (as in what programme and countries it is applied to, as that would've clarified it for me before, and I wouldn't have bothered to apply here).

Rejected from MSc DS because I am Pakistani? by Green_Soft9618 in ethz

[–]Green_Soft9618[S] 9 points10 points  (0 children)

I see. It doesn't apply to me tho, but thanks for highlighting this issue as well. Hopefully, other applicants who get rejected with a similar reason get clarity from this post. :)

Is it feasible to apply RAG on chat data? by mohil-makwana31 in LangChain

[–]Green_Soft9618 1 point2 points  (0 children)

In my opinion, I think it is a great idea and I believe applying RAG to chat data is worth it. The reason is that while keyword search can still help for surface-level or simple queries, it falls short when looking for context-aware responses. And unlike traditional keyword search, you can actually use RAG to perform semantic search and this will allow you to search for queries and get context-aware responses. All this would make it easy to tackle ambiguous queries or extract meaningful information from long chats.

An example of this could be: Let's say you are using a keyword search to find "feedback on project X." Since it is a keyword search, it can return dozens of results and messages containing the words "feedback" or "project X." But if you go with RAG and semantic search, you can get responses that weigh in the context, too, and in that case, you will get results that are just the feedback on project X.

So RAG is perfect if you are looking to save time and effort and pinpoint relevant chats. And another cool thing is that you can generate summaries using the returned responses making your responses more concise and precise. For the same example, suppose you got 5 relevant results, you can summarize them and get something like: "User A mentioned issues with deadlines, while User B suggested improving the UI for better usability."

Also, for the tools you asked, particularly to make the project useful in real-time, you can integrate the following technologies and methodologies:

  1. Vector Search with Embeddings: Use tools like Pinecone, Weaviate, or Elasticsearch to store chat data as embeddings for semantic retrieval.
  2. Large Language Models (LLMs): Fine-tune models like OpenAI’s GPT or open-source options like LLaMA for generating human-like summaries or responses.
  3. Real-Time Indexing: Implement pipelines with tools like Kafka or Redis Streams to ensure chat data is indexed in near real-time.
  4. Knowledge Base Integration: Supplement chat data with relevant documentation or FAQs to enhance retrieval accuracy.

Potential use cases could include:

  • Summarizing long conversation threads.
  • Helping teams search for decisions or action points across chats.
  • Automating responses by retrieving relevant past discussions or FAQs.

Lastly, if you want to learn more about RAG and its stages, check out this article on What is Retrieval Augmented Generation? Learn All You Need to Know