04056nam a22004695i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001600172072001600188072002300204072001500227100007900242245014800321250001800469264005400487300004300541336002600584337002600610338003600636347002400672505062700696520177501323650004703098650003603145650005703181650001903238650003603257650003303293710003403326773002603360776003603386776003603422776003603458856004603494912001403540912001503554999001703569979-8-8688-0882-1DE-He21320251030114643.0cr nn 008mamaa241226s2024    xxu|    s    |||| 0|eng d  a97988688088219979-8-8688-0882-17 a10.1007/979-8-8688-0882-12doi 4aQA76.73.P98 7aUMX2bicssc 7aCOM0513602bisacsh 7aUMX2thema1 aJay, Rabi.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut98965710aGenerative AI Apps with LangChain and Pythonh[electronic resource] :bA Project-Based Approach to Building Real-World LLM Apps /cby Rabi Jay.  a1st ed. 2024. 1aBerkeley, CA :bApress :bImprint: Apress,c2024.  aXX, 513 p. 58 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aChapter 1: Introduction to LangChain and LLMs -- Chapter 2: Integrating LLM APIs with LangChain -- Chapter 3: Building Q&A and Chatbot Apps -- Chapter 4: Exploring LLMs -- Chapter 5: Mastering Prompts for Creative Content -- Chapter 6: Building Chatbots and Automated Analysis Systems Using Chains -- Chapter 7: Building Advanced Q&A and Search applications Using Retrieval-Augmented Generation (RAG) -- Chapter 8: Your First Agent App -- Chapter 9: Building Different Types of Agents -- Chapter 10: Projects: Building Agent Apps for Common Use Cases. - Chapter 11: Building & Deploying a ChatGPT Like App Using Streamlit.  aFuture-proof your programming career through practical projects designed to grasp the intricacies of LangChain’s components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level. Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you’ll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries. Developing LangChain-based Generative AI LLM Apps with Python employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you’ll learn-by-be doing, enhancing your career possibilities in today’s rapidly evolving landscape. You will: Understand different types of LLMs and how to select the right ones for responsible AI. Structure effective prompts. Master LangChain concepts, such as chains, models, memory, and agents. Apply embeddings effectively for search, content comparison, and understanding similarity. Setup and integrate Pinecone vector database for indexing, structuring data, and search. Build Q & A applications for multiple doc formats. Develop multi-step AI workflow apps using LangChain agents. 0aPython (Computer program language).964608 0aArtificial intelligence.958441 0aProgramming languages (Electronic computers).96620614aPython.94755324aArtificial Intelligence.95844124aProgramming Language.9662082 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z979886880881408iPrinted edition:z979886880883808iPrinted edition:z979886881357340uhttps://doi.org/10.1007/979-8-8688-0882-1  aZDB-2-CWD  aZDB-2-SXPC  c66082d66082