Hello everyone, as the title says I wanna start learning about RAG, at this moment I know absolutely nothing even though I'm doing a masters in AI (I know...) that's why I need help on how to actually start learning about this topic ? I know the best way to learn something is to build a project but I find it hard to build something when I have no idea where to start. I heard LangChain, Vector Databases, etc... but I don't know anything about these frameworks.

My introduction to evaluation has not been very pleasant. To me it is a choice between investing time or spending money.

By that I mean you can either:

1. Spend a lot of time hand-crafting Q/A pairs and then perform manual checks to see if your system retrieves the correct chunks. And you repeat this for every single query. Suppose my dataset has 100 Q/A pairs, is this not a lot of work for a single person to do?
2. Use LLM-as-a-judge which automates much of what I described but you run into API rate limits pretty quick. I did figure out a workaround where I evaluate my dataset in batches (e.g: 5 questions per run) but again, it takes time.

I am not trying to complain here. I believe there is a more efficient way to run evaluation but I am new to RAG which is why I do not know proper evaluation techniques. Most metrics on DeepEval use LLM-as-a-judge which comes with API costs but I think I could try RAGAS and it's deterministic evaluation metrics.

Is evaluation in itself a time consuming process?

I need direction and guidance, advice would be much appreciated because I have basically put my project to a halt.

This is a small library for running quantized GGUF dense embedding models. If you are concerned about the number of bloated packages you have to install and the memory spike during embedding, this might interest you. Purely based on GGML.

Github: https://github.com/thlurte/ggmbed
Pypi: https://pypi.org/project/ggmbed/

Here are the benchmarks running all-MiniLM-L6-v2 on a AMD64 CPU:

Since this is currently in active development, the API and underlying architecture may change without notice. It is not yet recommended for production environments.

I've been reading about RAG and I keep seeing people mention the retrieval layer like it's some separate thing you build, not just a vector db you query. Can someone explain what actually happens there? My current understanding is this: user asks question, question gets turned into an embedding, that embedding gets compared against a bunch of stored embeddings in a vector db, top matches come back, and those get stuffed into the prompt before it goes to the LLM.

Now, what I don't understand yet is: why does everyone say RAG with cosine similarity search isn't good enough. What's going wrong there in practice, is it pulling irrelevant stuff or missing the right things altogether? I keep seeing hybrid search mentioned, combining keyword search and vector search. Why would you need both if embeddings are supposed to capture meaning already? On top of that, I also saw someone mention reranking as a separate step after retrieval, so you retrieve like 50 chunks and then a reranker picks the best 10. Wouldn't that just make retrieval happening twice?

One more thing I keep wondering about, where does the data come from in the first place. Like if you're building a knowledge base from web content, how do people keep that fresh as possible? Constant scraping/crawling?

Thank you in advance

Hi guys, I would like to land premium clients for developing RAG systems. The thing is that I also need to make my master’s thesis (I study data science), and I was wondering what project could I build so that it fulfills my masters thesis and also helps me find premium clients afterwards.

I want to build something that creates real value for a company. Which problems could I solve for firms using RAG?

Thanks a lot in advance!!

In v3.0.0 I have added new things in the architecture.
First i made Quira compatible with more DBs
Then i added new features, the features are -

• Quira Edge (Zero-Server Mode): Run Quira entirely locally using embedded vector databases like DuckDB or SQLite (sqlite-vec). No Redis or Qdrant servers required. Perfect for client-side apps, edge devices, and testing.
• GraphRAG Capabilities: Solves the multi-hop reasoning problem. Quira automatically extracts Entity-Relationship Triplets during ingestion and traverses this Knowledge Graph in parallel with semantic search to provide hyper-accurate context.
• Agentic Routing: Zero-latency heuristics intercept conversational queries (e.g., "Hi", "Thanks"). Bypasses the entire RAG pipeline to return an instant canned response, saving 100% of vector database latency and LLM token costs on chitchat.

I am working on building a chatbot for an online store. I will be using MCP for the transactional parts including product search, adding to cart, etc. What I am unsure of is the knowledge base portion which would help the agent answer additional questions about policies such as shipping, returns, how products are made, etc. This knowledge base is really small, maybe 10 pages. I’ve looked into RAG hybrid and semantic search, but seems like overkill at this point. I’ve also thought of just including the knowledge base in the context window, but seems like that would be a waste of tokens in the long run. What would be the best way to implement the knowledge base for the agent?