Untitled

Update on my capstone project in the #llmzoomcamp DataTalksClub:

Peer Review 3:

Update on my capstone project in the #llmzoomcamp DataTalksClub:

Peer Review 2:

Update on my capstone project in the #llmzoomcamp DataTalksClub:

Peer Review 1:

Reviewed an amazing project which incorporates: 1. Design Pattern structured modularity; very-well architectured project 2. Use of Object Relational Mapping decoupling choice of varied DB implementation. 3. The crafty decouplement of request/serving with from the ingestion pipeline with task queue enabling asynchronous processing and better scalability.

Update on my capstone project in the #llmzoomcamp @DataTalksClub:

Completed of my RAG pipeline using:

BAAI/bge-base-en-v1.5 for embedding with FastEmbed

Cloud Qdrant as the vector database

OpenAI gpt-4o-mini for LLM (augment)

Now would use FastAPI for serving the service from a Streamlit app hosted at Streamlit Cloud.

Update on my capstone project in the #llmzoomcamp @DataTalksClub:

So got to know the Temperature param is really important to be set different for different functions in a RAG system.

For the main prompt to the llm with context(s) from Qdrant along with user query, it should not be more than 0.2. Well, that is what I got the best results with.

Update on my capstone project in the #llmzoomcamp @DataTalksClub:

For any RAG system worth its salt, a follow-up question should not return a 'NOT IN CONTEXT' to the user.

For this I plan a mechanism to determine whether a question is a follow-up question. If it is, we should execute Query Re-writing with a sperate call to the LLM with a. the user's last question b. LLM's last response c. user's current follow-up query

Update on my capstone project in the #llmzoomcamp @DataTalksClub

Single YouTube Chapter transcript chunking ground logic-->>

1. If chapter less than 1000 words do nothing

2. if the chapter is more than 1000 words (1001-1500) , it would be broken into 2 parts with 200 words overlap between chunks.

3. if the chapter is more than 1500 words (1501-2000) , it would be broken into 3 parts with 200 words overlap between chunks.

Update on my capstone project in the #llmzoomcamp @DataTalksClub.

Chunking YouTube transcripts by Chapters for 'upsertion' to vector database is a good strategy. But what if there is only one chapter? What worked for me is chunking that single chapter with words overlap and naming each created chapter with a numbered part, e.g { ......., "chapter_title" = "How to survive a Tsunami - Part 1", ........... },

Started working on the RAG pipeline for my capstone project in the #llmzoomcamp @DataTalksClub.

In this phase, I’ll be building the knowledge base for my RAG system using YouTube transcript data collected through the ingestion pipeline.

Could come back to #llmzoomcamp after a hiatus , engaging with #mlopszoomcamp and its associated capstone project @DataTalksClub. Took on the dlt-Cognee workshop & its homework. Learnt about:

Ingesting, indexing and querying with dlt &Cognee

Knowledge graphs with Cognee

For my #capstoneproject at #mlopszoomcamp with @DataTalksClub further completed:

Increased model efficiency

Deployment of model service with & without containerization.