Talixi
you can toggle web search and upload your PDFs or images, so that whatever material you generate will be up-to-date and context aware with even your custom prompt parameter per lesson level
so this is the stuff we introduced lately. i think it's pretty cool
Talixi
you can toggle web search and upload your PDFs
So.. the technical implementation no one asked for, but for the few subsets of nerds here
Unlike traditional "inaccurate" PDF parsers that often lose formatting or table structures, Talixi treats your files as multimodal inputs. We upload the raw PDF buffer directly to Gemini. The model sees the document layout, diagrams, and tables exactly as a human would (aka utilizing RAG). Then the AI uses its internal vision and document understanding to extract key topics, notable facts, and page-specific highlights as structured data (using zod for validation). So this just eliminates the need for external OCR libraries or complex text-extraction scripts.
Also the web search isn't just a simple link scraper; it uses native Google Search Grounding using built-in gemini tool calling capability (`google_search` tool). When the search grounding context is toggled on, the AI generates a specific search query based on your lesson topic, executes it via Google's live search engine, and retrieves "Grounding Metadata." Then the system extracts structured citations (URLs and snippet previews) directly from the Google Search results, which are then displayed in the sources section of your lesson.
With that said, you can now literally build an efficient multimodal AI workflow by following this approach.
Unlike traditional "inaccurate" PDF parsers that often lose formatting or table structures, Talixi treats your files as multimodal inputs. We upload the raw PDF buffer directly to Gemini. The model sees the document layout, diagrams, and tables exactly as a human would (aka utilizing RAG). Then the AI uses its internal vision and document understanding to extract key topics, notable facts, and page-specific highlights as structured data (using zod for validation). So this just eliminates the need for external OCR libraries or complex text-extraction scripts.
Also the web search isn't just a simple link scraper; it uses native Google Search Grounding using built-in gemini tool calling capability (`google_search` tool). When the search grounding context is toggled on, the AI generates a specific search query based on your lesson topic, executes it via Google's live search engine, and retrieves "Grounding Metadata." Then the system extracts structured citations (URLs and snippet previews) directly from the Google Search results, which are then displayed in the sources section of your lesson.
With that said, you can now literally build an efficient multimodal AI workflow by following this approach.
❤6
Kirakos
in nutshell, Koye Feche >>>>> Bole
"alcohol won't hurt my kid"
the kid 21 years later:
the kid 21 years later:
🤣18😁2😭1
"i am gonna make this AI wrapper and get rich"
ironically, i am doing the same thing too. but i do believe healthcare and education aren't totally a shot in the dark. and the mainstream (almost 70% based on some blog i read) doesn't target the consumer grade. the productivity and software automation tools are deeply tied with the enterprise. so i feel like making an affordable product for the consumer in unexploited field is still doable and profitable
ironically, i am doing the same thing too. but i do believe healthcare and education aren't totally a shot in the dark. and the mainstream (almost 70% based on some blog i read) doesn't target the consumer grade. the productivity and software automation tools are deeply tied with the enterprise. so i feel like making an affordable product for the consumer in unexploited field is still doable and profitable
🔥7❤1