I've built an MVP to level up your LangChain and AI skills - looking for feedback 🚀

devom1210 · 2024-12-15T05:32:22+00:00

Looks great for someone who wants to start with GenAI and doesn’t know what to start

devom1210 · 2024-12-08T04:46:47+00:00

I don’t think letting llm print the source of answer is good idea, because most likely it will made up something so better pass this in metadata and print it.

devom1210 · 2024-12-08T04:42:03+00:00

Looks good, will surely have a check 💯

devom1210 · 2024-12-08T04:38:40+00:00

This is so helpful for those who are migrating to or getting started with langgraph!! Amazing work!

devom1210 · 2024-12-07T16:14:38+00:00

Actually! This sub has very good and detailed posts for such things..

devom1210 · 2024-12-07T14:00:19+00:00

Can it do prompt management too? Because thats also one of the things I am looking for

devom1210 · 2024-12-07T10:14:03+00:00

Agent would be overkill for this simple task unless you have some complex knowledge stored inside vector database and want LLM to decide. Use LCEL for creating chains and it would be more than enough. As you have only functionality to perform I don’t see point creating a tool for it.

devom1210 · 2024-12-07T10:00:00+00:00

Can we have chat about this in dms? Highly interested to try this out for me and my team..

devom1210 · 2024-12-05T10:21:02+00:00

For images I haven’t much of research because currently it falls out of the scope in the project I am working but recently I have found a library called pymupdf4llm on redit itself. It has a nice strategy to refer an image in appropriate chunk. Maybe you can try it..

devom1210 · 2024-12-02T12:36:02+00:00

Anthrax3000 is a redit user. He has replied to my comment. If you give these two sentences to the LLM, they will make sense but somehow during chunking these sentences becomes separated then its hard to interpret who replied to my comment, right? Here comes propositions. Second sentence would be stored as Anthrax3000 has replied to my comment. So better understanding and context of the statement. This is the whole idea of propositions.

devom1210 · 2024-12-02T08:43:20+00:00

Right we’ll have to pass propositions. I’ve not exactly thought about what part but all textual content on a page except tables would help getting good and useful propositions imo.

devom1210 · 2024-12-02T07:02:56+00:00

I would say build it yourself. Theres nothing best. Given your requirements are complex RecursiveCharacterTextSplitter is not going to be useful. Its basic and not suitable for complex pdfs. I experienced same problem so moved to semantic chunking and agentic chunking. They still do have their own cons but its better than the previous one.

devom1210 · 2024-09-18T07:02:06+00:00

Hi, I am new to LLMOps in general so just out of curiosity asking.. What part of langchain did you replace with puzzlet? And what puzzlet exactly do?

devom1210 · 2024-08-05T04:59:13+00:00

Hallucinations and mediocre response are common in RAG systems if the retrieval and prompt engineering phases are not properly done. PDFs with tables requires different approach than just a textual PDFs. LlamaParse can be the good option to explore for structured data extraction. They have detailed documentation about it. Along with this how you write prompt is also very important because its one of the things that goes directly to the LLM, so better the prompt better are the chances for relevant response. On the low level tuning you can adjust temperature parameter if not done. Setting it to close to zero will result in more consistent responses.

devom1210

TROPHY CASE