Dolphin-llama3 & Groq-llama3

thnx a lot :D

thnx :)

HyDE, Hypothetical Document Embeddings. Works very well and easy to implement. Similarity search on a vector database using a hallucinated answer to the question instead of the question usually gives better similarity

yes this is nice, thnx :)

RAG is a bit too of an exact match because it is based on concepts and similar concepts. Therefore no match, no return. HyDE makes the search a bit more fuzzy by expanding the query and introducing more concepts. It would be good to have an evaluator to check on the faithfuness of retrieval and the relevance of the ouputs to the original query.

interesting, will look into

@@AllAboutAIllms such as those built on transformer architectures, are fundamentally stateless, meaning they do not inherently maintain information about previous inputs across separate input sequences like recurrent neural networks. however; they can emulate state-like behavior through the use of positional and specialized embeddings that incorporate contextual information within a given sequence, processing data in a stateless manner, the autoregressive nature of many llms allows them to generate text by sequentially predicting the next token based on the accumualted outputs, mimicking a form of statefulness. allowing them to handle extensive and complex sequences effectively, tho each processing step inherently lacks a continuous internal state beyond its immediate inputs.

interesting