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OEM TOYOTA 1657716030 RADIATOR PIPE 16577-16030Brand TOYOTA Product Name RADIATOR PIPE Product Number 16577 16030 Compatible Car Levin Ceres Trueno Marino (LVN CRE TRN MRN) AE101 Compatible Engine 4AGZE 4AGZE 4AGZE 4AGZE Product Description With respect to a radiator pipe Levin Ceres Trueno Marino Compatible Models: Levin Ceres Trueno Marino (LVN CRE TRN MRN) Chassis Models: AE101 Compatible Year Models: 1991 06 1995 05 Notes Images are for illustration purpose only. Actual product may vary.
| Brand | TOYOTA |
| Product Name | RADIATOR PIPE |
| Product Number | 16577-16030 |
| Compatible Car | Levin / Ceres / Trueno / Marino (LVN / CRE / TRN / MRN) AE101 |
| Compatible Engine | 4AGZE 4AGZE 4AGZE 4AGZE |
| Product Description | With respect to a radiator pipe Levin / Ceres / Trueno / Marino Compatible Models: Levin / Ceres / Trueno / Marino (LVN / CRE / TRN / MRN) Chassis Models: AE101 Compatible Year Models: 1991 / 06-1995 / 05 - |
| Notes | Images are for illustration purpose only. Actual product may vary. |
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4.7 ★★★★★
Based on 11 reviews
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Product Reviews
★★★★★ 5
Great book
Format: Paperback
Love the fact you put examples in python and javascript.
Great book.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on June 10, 2025
★★★★★ 5
proper documentation from langchain
Format: Paperback
Liked the book. But Still missing Human in the loop.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 19, 2025
★★★★★ 3
Already outdated
Format: Paperback
Concepts are sound but the code in this book is already obsolete
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Reviewed in the United States on August 5, 2025
★★★★★ 5
Unlocking Practical AI: A Developer’s Guide to Building with LLMs and LangChain
Format: Paperback
If you're a developer eager to move beyond LLM experimentation and build robust, context-aware AI applications, this book offers both inspiration and practical guidance. The authors open with a clear passion for the transformative potential of large language models (LLMs) and LangChain, framing these technologies as not just enhancements to the developer’s toolkit, but as gateways to new kinds of “thing-building” superpowers. This sense of possibility is grounded in step-by-step instruction, making the book approachable for those with Python or JavaScript backgrounds who may be new to the world of production-grade AI agents.
What stands out is the book’s careful scaffolding: starting with foundational concepts like prompt-based programming and progressing to advanced capabilities such as retrieval-augmented generation, agent planning, and tool integration. Each stage is contextualized with real-world use cases, like customizing chatbots to interact with your own documents, personalizing user experiences through memory, and deploying to production with reliability and security in mind. The focus on chain-of-thought reasoning and LangGraph’s agent architecture demonstrates the authors’ awareness of the current state of AI, where context and planning are just as important as raw language ability.
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Reviewed in the United States on July 17, 2025
★★★★★ 5
Excellent Handbook
Format: Paperback
What got my interest in this book is the author. He's well-known as a leader in the LangChain world. And his book did not disappoint. It goes through the whole life cycle of development and there is a lot useful insights. If you are looking to build real-world genAI/agentic apps, then this book is certainly a great choice.
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Reviewed in the United States on April 11, 2025
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