For each page:

- Extract text as usual.

- Capture the whole page as an image (~200 DPI).

- Optionally extract images/graphs within the page and include them in the same LLM call.

- Optionally add a bit of context from neighboring pages.

Then wrap everything with a clear prompt (structured output + how you want graphs handled), and you’re set.

At this point, models like GPT-5-nano/mini or Gemini 2.5 Flash are cheap and strong enough to make this practical.

Yeah, it’s a bit like using a rocket launcher on a mosquito, but this is actually very easy to implement and quite flexible and powerfuL. works across almost any format, Markdown is both AI and human friendly, and surprisingly maintainable.

one of the main reasons i build these ai search tools from scratch is that i can fully control the depth and breadth (and also customize loader to whatever data/sites). and currently the web search isn't very transparent on what sites they do not have full text or just use snippets.

having computer use + websearch is definitely something very powerful (openai's deep research essentially)

Haven’t looked into this space for few months , but iirc, previously SOTA was like GPT VITS or something ?

They're not the silver bullet many initially hoped for, they're not a complete replacement for simpler methods like BM25. They only have very limited "semantic understanding" (and as people throw increasingly large chunks into embedding models, the meanings can get even fuzzier)

Overly high expectations lets people believe that embeddings will retrieve exactly what they mean, and With larger top-k values and LLMs that are exceptionally good at rationalizing responses, it can be difficult to notice mismatches unless you examine the results closely.

• LLMs, at least GPT models, tend to overstate their confidence. • A frequency-based approach appears to achieve calibration closer to the ideal.

This kinda passes my vibe test. That said, I wonder—rather than running 100 trials, could we approximate this by using something like a log-probability ratio? This would especially apply in cases where answers are yes or no, assuming the output spans more than one token.

tried https://github.com/unum-cloud/uform which i do like, especially they also support languages other than English. Any recommendations on other alternatives?

I must say, though, that they are doing a commendable job integrating sources like YouTube and Reddit. These platforms benefit from special preprocessing and indeed add value.

price and speed benchmark: https://wow.groq.com/

But the "completely novel silicon architecture" and the "self-developed LPU" (claiming not to use GPUs)... makes me bit skeptical. After all, pure speed might be achievable through stacking computational power and model quantization. Shouldn't innovation at the GPU level be quite challenging, especially to achieve such groundbreaking speeds?

various small, open sourced, and verticle LLMs vs one large GPT models would be quite interesting.

When I build stuff with GPT-3, especially in the earlier days, I get the strong impression that it's like we are doing machine learning without Numpy and Pandas.

with LangChain, many of the systems I have built can be done in just one or two lines, making life much easier for rest of us. I also believe that LangChain's Agent framework is underappreciated as it was pretty ahead of its time until the official ChatGPT plugins were released. (contributed to LangChain a bit too.)

Unfortunately, the documentation is lacking indeed. While I understand the need to move quickly, it is not good that some crucial concepts like Customized LLM have inadequate documentation. (Perhaps having some LLM builds on top of the repo would be more effective than documentation at this point.)

did a rough implementation myself, works well for articles even 20k tokens. but kind slow because all the additional overlapping runs required. (and more costly)

while I like the "AI" part (the large language model), think it would be more interesting and productive to use same backend for full text semantic search & question answering or summarizations.

But it is cool to see Notion trying this way, kinda curious to see the results when so many people have access to this type of generative model.