國立高雄大學圖資館 |

Language: English

Back

Generative AI apps with LangChain an...

Jay, Rabi.

Generative AI apps with LangChain and Pythona project-based approach to building real-world LLM apps /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Generative AI apps with LangChain and Pythonby Rabi Jay.
Reminder of title:	a project-based approach to building real-world LLM apps /
Author:	Jay, Rabi.
Published:	Berkeley, CA :Apress :2024.
Description:	xx, 513 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Application softwareDevelopment.
Online resource:	https://doi.org/10.1007/979-8-8688-0882-1
ISBN:	9798868808821$q(electronic bk.)

Generative AI apps with LangChain and Pythona project-based approach to building real-world LLM apps /
Jay, Rabi.

Generative AI apps with LangChain and Pythona project-based approach to building real-world LLM apps /[electronic resource] :by Rabi Jay. - Berkeley, CA :Apress :2024. - xx, 513 p. :ill., digital ;24 cm.

Chapter 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.

Future-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.

ISBN: 9798868808821$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-0882-1doiSubjects--Topical Terms:

189413
Application software
--Development.

LC Class. No.: QA76.76.A65

Dewey Class. No.: 005.3

Generative AI apps with LangChain and Pythona project-based approach to building real-world LLM apps /
LDR:03421nmm a2200325 a 4500 001 673739
003 DE-He213
005 20241226115227.0
006 m d
007 cr nn 008maaau
008 250422s2024 cau s 0 eng d
020 $a 9798868808821$q(electronic bk.)
020 $a 9798868808814$q(paper)
024 7 $a 10.1007/979-8-8688-0882-1 $2 doi
035 $a 979-8-8688-0882-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.76.A65
072 7 $a UMX $2 bicssc
072 7 $a COM051360 $2 bisacsh
072 7 $a UMX $2 thema
082 0 4 $a 005.3 $2 23
090 $a QA76.76.A65 $b J42 2024
100 1 $a Jay, Rabi. $3 987331
245 1 0 $a Generative AI apps with LangChain and Python $h [electronic resource] : $b a project-based approach to building real-world LLM apps / $c by Rabi Jay.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2024.
300 $a xx, 513 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 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.
520 $a Future-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.
650 0 $a Application software $x Development. $3 189413
650 0 $a Artificial intelligence. $3 194058
650 0 $a LangChain (Computer program language) $3 987332
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Python. $3 763308
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Programming Language. $3 913494
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-0882-1
950 $a Professional and Applied Computing (SpringerNature-12059)