Building AI Intensive Python Applications: create intelligent apps with LLMs and vector databases
Cover -- FM -- Table of Contents -- Preface -- Chapter 1: Getting Started with Generative AI -- Technical requirements -- Defining the terminology -- The generative AI stack -- Python and GenAI -- OpenAI API -- MongoDB with Vector Search -- Important features of generative AI -- Why use generative A...
Gespeichert in:
| Hauptverfasser: | , , , , , , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing, Limited
2024
|
| Ausgabe: | First edition |
| Online-Zugang: | DE-1102 |
| Zusammenfassung: | Cover -- FM -- Table of Contents -- Preface -- Chapter 1: Getting Started with Generative AI -- Technical requirements -- Defining the terminology -- The generative AI stack -- Python and GenAI -- OpenAI API -- MongoDB with Vector Search -- Important features of generative AI -- Why use generative AI? -- The ethics and risks of GenAI -- Summary -- Chapter 2: Building Blocks of Intelligent Applications -- Technical requirements -- Defining intelligent applications -- The building blocks of intelligent applications -- LLMs - reasoning engines for intelligent apps -- Use cases for LLM reasoning engines -- Diverse capabilities of LLMs -- Multi-modal language models -- A paradigm shift in AI development -- Embedding models and vector databases - semantic long-term memory -- Embedding models -- Vector databases -- Model hosting -- Your (soon-to-be) intelligent app -- Sample application - RAG chatbot -- Implications of intelligent applications for software engineering -- Summary -- Part 1 -- Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design -- Chapter 3: Large Language Models -- Technical requirements -- Probabilistic framework -- n-gram language models -- Machine learning for language modelling -- Artificial neural networks -- Training an artificial neural network -- ANNs for natural language processing -- Tokenization -- Embedding -- Predicting probability distributions -- Dealing with sequential data -- Recurrent neural networks -- Transformer architecture -- LLMs in practice -- The evolving field of LLMs -- Prompting, fine-tuning, and RAG -- Summary -- Chapter 4: Embedding Models -- Technical requirements -- What is an embedding model? -- How do embedding models differ from LLMs? -- When to use embedding models versus LLMs -- Types of embedding models -- Choosing embedding models -- Task requirements. |
| Beschreibung: | 1 Online-Ressource (xviii, 273 Seiten) Illustrationen |
| ISBN: | 9781836207245 |
Internformat
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| 245 | 1 | 0 | |a Building AI Intensive Python Applications |b create intelligent apps with LLMs and vector databases |c Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan |
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| 520 | 3 | |a Cover -- FM -- Table of Contents -- Preface -- Chapter 1: Getting Started with Generative AI -- Technical requirements -- Defining the terminology -- The generative AI stack -- Python and GenAI -- OpenAI API -- MongoDB with Vector Search -- Important features of generative AI -- Why use generative AI? -- The ethics and risks of GenAI -- Summary -- Chapter 2: Building Blocks of Intelligent Applications -- Technical requirements -- Defining intelligent applications -- The building blocks of intelligent applications -- LLMs - reasoning engines for intelligent apps -- Use cases for LLM reasoning engines -- Diverse capabilities of LLMs -- Multi-modal language models -- A paradigm shift in AI development -- Embedding models and vector databases - semantic long-term memory -- Embedding models -- Vector databases -- Model hosting -- Your (soon-to-be) intelligent app -- Sample application - RAG chatbot -- Implications of intelligent applications for software engineering -- Summary -- Part 1 -- Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design -- Chapter 3: Large Language Models -- Technical requirements -- Probabilistic framework -- n-gram language models -- Machine learning for language modelling -- Artificial neural networks -- Training an artificial neural network -- ANNs for natural language processing -- Tokenization -- Embedding -- Predicting probability distributions -- Dealing with sequential data -- Recurrent neural networks -- Transformer architecture -- LLMs in practice -- The evolving field of LLMs -- Prompting, fine-tuning, and RAG -- Summary -- Chapter 4: Embedding Models -- Technical requirements -- What is an embedding model? -- How do embedding models differ from LLMs? -- When to use embedding models versus LLMs -- Types of embedding models -- Choosing embedding models -- Task requirements. | |
| 700 | 1 | |a Perlmutter, Ben |e Verfasser |4 aut | |
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| 700 | 1 | |a Narváez, Sigfrido |e Verfasser |4 aut | |
| 700 | 1 | |a Rueckstiess, Thomas |e Verfasser |4 aut | |
| 700 | 1 | |a Weller, Henry |e Verfasser |4 aut | |
| 700 | 1 | |a Alake, Richmond |e Verfasser |4 aut | |
| 700 | 1 | |a Ranjan, Shubham |e Verfasser |4 aut | |
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Datensatz im Suchindex
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| author | Palmer, Rachelle Perlmutter, Ben Gangadhar, Ashwin Larew, Nicholas Narváez, Sigfrido Rueckstiess, Thomas Weller, Henry Alake, Richmond Ranjan, Shubham |
| author_facet | Palmer, Rachelle Perlmutter, Ben Gangadhar, Ashwin Larew, Nicholas Narváez, Sigfrido Rueckstiess, Thomas Weller, Henry Alake, Richmond Ranjan, Shubham |
| author_role | aut aut aut aut aut aut aut aut aut |
| author_sort | Palmer, Rachelle |
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| ctrlnum | (ZDB-30-PQE)31653000 (OCoLC)1466914857 (DE-599)KEP107524996 |
| edition | First edition |
| format | Electronic eBook |
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| id | DE-604.BV049891417 |
| illustrated | Illustrated |
| indexdate | 2024-12-06T13:16:03Z |
| institution | BVB |
| isbn | 9781836207245 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035230563 |
| oclc_num | 1466914857 |
| open_access_boolean | |
| owner | DE-1102 |
| owner_facet | DE-1102 |
| physical | 1 Online-Ressource (xviii, 273 Seiten) Illustrationen |
| psigel | ZDB-30-PQE ZDB-30-PQE FAN_Einzelkauf_2024 |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Packt Publishing, Limited |
| record_format | marc |
| spelling | Palmer, Rachelle Verfasser aut Building AI Intensive Python Applications create intelligent apps with LLMs and vector databases Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan First edition Birmingham Packt Publishing, Limited 2024 1 Online-Ressource (xviii, 273 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Cover -- FM -- Table of Contents -- Preface -- Chapter 1: Getting Started with Generative AI -- Technical requirements -- Defining the terminology -- The generative AI stack -- Python and GenAI -- OpenAI API -- MongoDB with Vector Search -- Important features of generative AI -- Why use generative AI? -- The ethics and risks of GenAI -- Summary -- Chapter 2: Building Blocks of Intelligent Applications -- Technical requirements -- Defining intelligent applications -- The building blocks of intelligent applications -- LLMs - reasoning engines for intelligent apps -- Use cases for LLM reasoning engines -- Diverse capabilities of LLMs -- Multi-modal language models -- A paradigm shift in AI development -- Embedding models and vector databases - semantic long-term memory -- Embedding models -- Vector databases -- Model hosting -- Your (soon-to-be) intelligent app -- Sample application - RAG chatbot -- Implications of intelligent applications for software engineering -- Summary -- Part 1 -- Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design -- Chapter 3: Large Language Models -- Technical requirements -- Probabilistic framework -- n-gram language models -- Machine learning for language modelling -- Artificial neural networks -- Training an artificial neural network -- ANNs for natural language processing -- Tokenization -- Embedding -- Predicting probability distributions -- Dealing with sequential data -- Recurrent neural networks -- Transformer architecture -- LLMs in practice -- The evolving field of LLMs -- Prompting, fine-tuning, and RAG -- Summary -- Chapter 4: Embedding Models -- Technical requirements -- What is an embedding model? -- How do embedding models differ from LLMs? -- When to use embedding models versus LLMs -- Types of embedding models -- Choosing embedding models -- Task requirements. Perlmutter, Ben Verfasser aut Gangadhar, Ashwin Verfasser aut Larew, Nicholas Verfasser aut Narváez, Sigfrido Verfasser aut Rueckstiess, Thomas Verfasser aut Weller, Henry Verfasser aut Alake, Richmond Verfasser aut Ranjan, Shubham Verfasser aut Erscheint auch als Druck-Ausgabe 978-1-83620-725-2 |
| spellingShingle | Palmer, Rachelle Perlmutter, Ben Gangadhar, Ashwin Larew, Nicholas Narváez, Sigfrido Rueckstiess, Thomas Weller, Henry Alake, Richmond Ranjan, Shubham Building AI Intensive Python Applications create intelligent apps with LLMs and vector databases |
| title | Building AI Intensive Python Applications create intelligent apps with LLMs and vector databases |
| title_auth | Building AI Intensive Python Applications create intelligent apps with LLMs and vector databases |
| title_exact_search | Building AI Intensive Python Applications create intelligent apps with LLMs and vector databases |
| title_full | Building AI Intensive Python Applications create intelligent apps with LLMs and vector databases Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan |
| title_fullStr | Building AI Intensive Python Applications create intelligent apps with LLMs and vector databases Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan |
| title_full_unstemmed | Building AI Intensive Python Applications create intelligent apps with LLMs and vector databases Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan |
| title_short | Building AI Intensive Python Applications |
| title_sort | building ai intensive python applications create intelligent apps with llms and vector databases |
| title_sub | create intelligent apps with LLMs and vector databases |
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