chris_5f

Interesting Read!

I did something similar with f5bot but this one is more structured and cleaner.

In this example, we’ll build a basic Agentic RAG system using:

VectorStore: Retrieves relevant information from a pre-indexed document database. WebSearch: Fetches up-to-date data from the web when VectorStore lacks the required information. The AI agent dynamically selects the appropriate tool based on the query, showcasing the adaptability and efficiency of agentic RAG.

LLM: gemini-2.0-flash-exp Embedding Model: BAAI/bge-small-en-v1.5

Try it out!

Rewrite-Retrieve-Read is a three-step framework for tasks that involve retrieval augmentation, such as open-domain question answering. It focuses on improving the quality of retrieved information and generating accurate outputs by refining the input query.

I am curious if there is a full team that does the research?

Do they use any AI Tools? The biggest question is do we have ant tools which helps in this? I think BBC or Discovery along with some archeological team should make a tool like this and leverage the tech power. maybe there are some patterns that we couldn't decode and AI can. Would be a fun thing to follow on.

Good Post. But I struggled to understand it a first. Would be really helpful if you add a short description explaining it.

Very Sad to see Claude Sonnet not included. Deepseek is now a good competitor too. Hoping to see something similar for both of them. Is there anything available for it already?

Good Game. Was it created using some AI Model?

This is an awesome resource. This guy Shubham posts great stuff on twitter and linkedin too. Check out in case you haven't.

I have got a lot of my LLM resources through his page. He is a more of a LLM curator and less of a creator but highly recommend.

This is just amazing. I tried out with my startup's repo and it was a blast. Shared with my community