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Generative AI with LangChain : Build Large Language Model (LLM) Apps with Python, ChatGPT, and Other LLMs. by Auffarth, Ben

Table of Contents: “…Vector indexing -- Vector libraries -- Vector databases -- Loading and retrieving in LangChain -- Document loaders -- Retrievers in LangChain -- kNN retriever -- PubMed retriever -- Custom retrievers -- Implementing a chatbot -- Document loader -- Vector storage -- Memory -- Conversation buffers -- Remembering conversation summaries -- Storing knowledge graphs -- Combining several memory mechanisms -- Long-term persistence -- Moderating responses -- Summary -- Questions -- Chapter 6: Developing Software with Generative AI -- Software development and AI -- Code LLMs -- Writing code with LLMs -- StarCoder -- StarChat -- Llama 2 -- Small local model -- Automating software development -- Summary -- Questions -- Chapter 7: LLMs for Data Science -- The impact of generative models on data science -- Automated data science -- Data collection -- Visualization and EDA -- Preprocessing and feature extraction -- AutoML -- Using agents to answer data science questions -- Data exploration with LLMs -- Summary -- Questions -- Chapter 8: Customizing LLMs and Their Output -- Conditioning LLMs -- Methods for conditioning -- Reinforcement learning with human feedback -- Low-rank adaptation -- Inference-time conditioning -- Fine-tuning -- Setup for fine-tuning -- Open-source models -- Commercial models -- Prompt engineering -- Prompt techniques -- Zero-shot prompting -- Few-shot learning -- Chain-of-thought prompting -- Self-consistency -- Tree-of-thought -- Summary -- Questions -- Chapter 9: Generative AI in Production -- How to get LLM apps ready for production -- Terminology -- How to evaluate LLM apps -- Comparing two outputs -- Comparing against criteria -- String and semantic comparisons -- Running evaluations against datasets -- How to deploy LLM apps -- FastAPI web server -- Ray -- How to observe LLM apps -- Tracking responses -- Observability tools.…”
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