Laajte bu Indexu Llama

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. Luñu njëkka sos LlamaIndex ngir def?
   • Keppal LLMs ci say done ngir gëna am doole
   • Taggat xeetu fondasioŋ yu bees
   • Defar ay nataal ak wideo
   • Saytu ay cluster GPU ci cloud bi
2. Lan lañu njëkka woowe LlamaIndex bi ñu koy dugal?
   • Caden de données
   • Index GPT
   • Base vecteur
   • LamaHub
3. Lan moo tax ñu soxla seet ludul tënk lépp ci laaj bi?
   • LLMs mënu lire fichier text
   • dencukaay yi jagul yoon
   • Modèle yi dañu am palanteer yu gàtt te xamu ñu ay done yu nëbbu
   • Retrieval dafay tax model yi di gëna gaaw ci tàggat yaram
4. Ci biir pipeline LlamaIndex, lan lañuy sos ci bépp paccu këyit ngir mëna seetlu semantik?
   • Fichier ZIP buñ komprime
   • ab liiñ tablo SQL
   • Nataalu ekraŋ
   • Vecteur buy dugal
5. Lan lañu tabax LlamaParse ngir liggéey?
   • Jàngat PDF yu jaffe, tablo ak këyitu ingestion
   • Xayma kodu cosaan bi ci garabi sintaks
   • Xayma audio ci transkripsioŋ
   • jàngat paketu reso bi
6. Where do LlamaIndex node postprocessors run in the documented query pipeline?
   • Before documents are parsed or indexed
   • After retrieval and before response synthesis
   • Only after a final answer is returned
   • Between model pretraining and tokenizer training
7. What does LlamaIndex’s SubQuestionQueryEngine do with a complex multi-source question?
   • Converts every document into a single unsearchable string
   • Runs only a fixed keyword lookup with no query planning
   • Creates subquestions for relevant data sources and synthesizes the intermediate responses
   • Fine-tunes the language model on the documents
8. In a LlamaIndex query pipeline, what is the response synthesizer responsible for?
   • Choosing the original PDF files before ingestion
   • Training the embedding model from scratch
   • Replacing the retriever’s index with a file system
   • Combining the query and retrieved Nodes to produce a response