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89713054
LangChainとLangGraphによるRAG・AIエージェント［実践］入門 エンジニア選書 Tankobon Softcover – November 9, 2024
85% of respondents would recommend this to a friend
Item #:
89713054
TJS 437
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What Stands Out
Comprehensive Guide
Offers a detailed exploration of RAG and AI agents, facilitating a deep understanding for engineers and practitioners, making complex concepts accessible.
Expert Insights
Written by seasoned engineers, providing real-world applications and best practices that enhance learning and implementation of AI strategies effectively.
Durable Format
Softcover design ensures durability and portability, allowing readers to engage with the material anywhere, supporting on-the-go learning and reference.
Product Details
| Publisher | u6280u8853u8a55u8ad6u793e |
| Publication date | November 9, 2024 |
| Language | Japanese |
| Print length | 496 pages |
| ISBN-10 | 4297145308 |
| ISBN-13 | 978-4297145309 |
| Item Weight | 850 g |
| Dimensions | 9.06 x 7.17 x 1.02 inches (23 x 18.2 x 2.6 cm) |
Who Should Buy?
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AI Enthusiasts
Individuals eager to learn about AI practices and implementations using LangChain and LangGraph technologies.
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Developers
Software developers looking to enhance their skills in creating AI agents and leveraging modern frameworks.
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Students
Students studying artificial intelligence who need a practical guide to implement RAG and AI agents effectively.
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Beginners
Complete beginners in AI may find the material challenging without prior foundational knowledge or experience.
Product Description
LangChainとLangGraphによるRAG・AIエージェント［実践］入門 エンジニア選書 Tankobon Softcover – November 9, 2024
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西見 公宏 , 吉田 真吾 , 大嶋 勇樹 & 0 Editorial Review
The editorial review of "LangChainとLangGraphによるRAG・AIエージェント" reflects a largely positive customer experience, particularly for those keen on understanding AI agents and related technologies. Reviewers express a strong endorsement of the book, highlighting its comprehensive explanation of LangGraph and its practical applications. Readers appreciated the organized thought process that the book facilitated, making it easier for them to grasp complex concepts and implement them in real-world scenarios. The inclusion of design patterns for AI agents, as well as practical implementation samples, adds significant value, making it more than just a tutorial on the usage of a library. Instead, it seems to act as a thoughtful introduction into the philosophical aspects of AI agents, as reviewers noted its broader implications for understanding human interaction with technology. The advanced section regarding RAG is particularly praised for its relevance to developers, coupled with clear, code-supported explanations. Emotionally, readers felt invigorated by the possibilities that the knowledge gained from the book could unlock for future projects in the rapidly evolving realm of generative AI. However, some criticisms were noted regarding the readability of the source code presented in the book. The formatting with excessive line breaks made it challenging to follow, leading some readers to express disappointment. Additionally, while the content was noted as accessible for beginners, there was an acknowledgment that the material could become outdated as the technology progresses. Overall, this book is recommended for anyone looking to dive into the world of AI agents, with an emphasis on practical application and comprehensive understanding. **
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Pros
- Comprehensive coverage of LangGraph and AI agents
- Clear explanations with practical implementation examples
- Organized structure aiding in complex concept understanding
- Relevant information for developers, especially in advanced RAG
- Accessible content suitable for beginners
Cons
- Source code formatting is difficult to read
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