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LLM Pipelines, Embeddings & Retrieval
Building something on top of embeddings, retrieval, or multiple LLM agents: real applied pipelines over medical literature and campaign data, the vector-search (FAISS) errors that show up once an index grows past a toy size, and the LangChain/LlamaIndex import errors that block getting started at all.
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ModuleNotFoundError: No module named 'langchain_community'Fix LangChain ModuleNotFoundError & LlamaIndex Errors →ModuleNotFoundError: No module named 'faiss'Fix No module named 'faiss' and FAISS ntotal != k Errors →RuntimeError: Error: '!(d % M == 0)' failed: The dimension of the vector (d) should be a multiple of the number of subquantizers (M)FAISS nprobe, IndexIVFPQ and GPU Memory Errors Fixed →
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Building LLM Pipelines over Medical Literature: Embedding, Clustering, and What You Actually LearnMulti-Agent Document Analysis: Architecture & TradeoffsRecommender Systems That Automate Campaign Execution: NLP and Collaborative Filtering in PracticeFix LangChain ModuleNotFoundError & LlamaIndex ErrorsFix No module named 'faiss' and FAISS ntotal != k ErrorsFAISS nprobe, IndexIVFPQ and GPU Memory Errors Fixed