[HN Gopher] Knowledge retrieval architectures for LLMs (2023)
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       Knowledge retrieval architectures for LLMs (2023)
        
       Author : burakemir
       Score  : 36 points
       Date   : 2023-04-27 21:13 UTC (1 hours ago)
        
 (HTM) web link (mattboegner.com)
 (TXT) w3m dump (mattboegner.com)
        
       | triyambakam wrote:
       | This is super helpful. I'm building a document question-answering
       | service over a custom data corpus (related to Saivism, a sect of
       | Hinduism). So far the first pass has been to manually chunk the
       | text (based on headings, chapters etc.) and then I've used
       | OpenAI's embedding service and storing the embeddings in
       | Pinecone. All stiched together using LangChain. To ask a
       | question, the question is again embedded, then searched against
       | the vector store, then the related documents are provided as
       | context to the LLM along with the question.
       | 
       | So far it was really easy to set up the prototype, but the
       | results weren't as great as I had hoped, so I'm excited to see
       | how I could improve it.
       | 
       | Edit: wow, I didn't see this before. LangChain implements one of
       | the featured article's suggestions (HyDE) -
       | https://python.langchain.com/en/latest/modules/chains/index_...
        
       | vectoral wrote:
       | This is one of the areas of LLMs that I find most interesting. So
       | far, I've found simple question-answering over vectorstores to be
       | a lackluster experience. In particular, the more information you
       | embed and stick into the vectorstore, the less useful the system
       | becomes as you are less likely to get the information you're
       | looking for (especially if the users don't understand their
       | queries need to look like the docs the want to ask about.
       | 
       | I haven't had a chance to try out hypothetical embedded docs yet,
       | but I expect they only provide a marginal improvement (especially
       | if QAing over proprietary data or information).
       | 
       | I'd love to see any other interesting, more up-to-date resources
       | anyone has found on this topic. I found this recent paper
       | interesting: https://arxiv.org/abs/2304.11062
        
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       (page generated 2023-04-27 23:00 UTC)