RAG with Python Cookbook. Practical Recipes from Data Preprocessing to LLM Agents Dominik Polzer

RAG with Python Cookbook. Practical Recipes from Data Preprocessing to LLM Agents Dominik Polzer - okladka książki

Autor:: Dominik Polzer
Wydawnictwo:: O'Reilly Media (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 378
Dostępne formaty:: ePub

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As businesses race to unlock the full potential of large language models (LLMs), a critical challenge has emerged: How do you connect these tools to real-time, external data to solve real-world problems? Retrieval-augmented generation (RAG) is the answer. By combining LLMs with information retrieval, RAG empowers you to build everything from intelligent chatbots to autonomous, task-solving agents.

Packed with over 70 practical recipes, this go-to guide tackles a wide range of GenAI applications through structured hands-on learning. Author Dominik Polzer provides the tools you need to design, implement, and optimize RAG systems for your unique use cases. Whether you're working with simple data retrieval or designing cutting-edge autonomous agents, this cookbook will help you stay ahead of the curve.

Learn core RAG components including embedding, retrieval, and generation techniques
Understand advanced workflows like semantic-aware chunking and multi-query prompting
Build custom solutions such as chatbots and autonomous agents for specific data challenges
Continuously evaluate and optimize systems for accuracy, relevance, and performance

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Szczegóły książki

ISBN Ebooka:: 979-83-416-0053-9, 9798341600539
Data wydania ebooka :: 2026-04-28 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
Język publikacji:: angielski
Rozmiar pliku ePub:: 14.7MB
Rozmiar pliku Mobi:: 14.7MB

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Spis treści książki

Preface
- Who This Book Is For
- What Youll Learn and How the Book Is Organized
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
1. Getting Started with RAG
- 1.1. Identifying High-Value RAG Use Cases for Your Organization
- 1.2. Choosing Your IDE and Coding Agent Setup
- 1.3. Getting Started with Jupyter Notebooks in VS Code
- 1.4. Storing Secrets and API Keys with .env Files
- 1.5. Building Your First RAG App
- 1.6. Choosing the Frameworks and Libraries for Your RAG Applications
- 1.7. Running the Code Examples in the Book Repository
2. Foundation Models
- 2.1. Defining a Suitable Prompt Template
- 2.2. Selecting the Right Language Model for Your Task
- 2.3. Generating Content with the OpenAI API
- 2.4. Generating Content with Googles Gemini Models
- 2.5. Generating Content with the Anthropic API
- 2.6. Running Open Source Models Locally with Ollama
- 2.7. Creating Structured Outputs with the OpenAI SDK and Pydantic
3. Loading Data
- 3.1. Loading Word Files in Python
- 3.2. Loading PDF Files
- 3.3. Loading and Handling Tabular Data from Excel and CSV Files
- 3.4. Loading Structured Data from a PostgreSQL Database
- 3.5. Loading Audio Files via Speech-to-Text Models
- 3.6. Extracting Text from Images and PDFs via Tesseract OCR
- 3.7. Extracting Text from Images via Multimodal Models
- 3.8. Generating Text Description for Images via Multimodal Models
- 3.9. Generating Text Summaries for Embedded Tables via Multimodal Models
- 3.10. Parsing PDFs with Multimodal Content
- 3.11. Loading Videos via Speech-to-Text and Multimodal Models
4. Data Preparation
- 4.1. Adding Metadata to Enable Metadata Filtering
- 4.2. Enhancing Data Quality by Replacing Abbreviations and Technical Terms
- 4.3. Improving Search Accuracy by Creating Hypothetical Questions for Text Chunks
- 4.4. Splitting Documents via Character Splitting
- 4.5. Splitting Documents with Recursive Text Splitters
- 4.6. Chunking Documents with Document-Aware Splitting
- 4.7. Splitting Text with Semantic-Aware Chunkers
- 4.8. Splitting Text with Agentic Chunkers
5. Embeddings
- 5.1. Mapping the Linguistic Meaning of Text Chunks to a Numerical Representation
- 5.2. Visualizing Semantic Relationships Between Text Chunks via Dimensionality Reduction Techniques
- 5.3. Calculating the Distance Between Embeddings
- 5.4. Choosing the Right Embedding Model
- 5.5. Generating Embeddings for Images and Text with CLIP
- 5.6. Performing Text Classification with Embeddings
- 5.7. Improving Search Results with a Hybrid Search Approach
6. Vector Databases and Similarity Searches
- 6.1. Choosing the Right Vector Database
- 6.2. Storing and Searching Embeddings with FAISS
- 6.3. Storing and Working with Embeddings in a Chroma Vector Database
- 6.4. Storing Embeddings in PostgreSQL with the pgvector Extension
- 6.5. Performing Similarity Search in PostgreSQL
- 6.6. Accelerating Vector Searches in PostgreSQL with Indexing Techniques
- 6.7. Combining Keyword and Similarity Search to Improve Retrieval Accuracy with PostgreSQL
7. Retrieval
- 7.1. Optimizing Query Results via Metadata Filtering in PostgreSQL
- 7.2. Enhancing Retrieval Accuracy with HyDE
- 7.3. Improving Search Results with Multiquery Retrieval
- 7.4. Addressing Complex Requests by Designing a Query Routing System
- 7.5. Enhancing Retrieved Documents by Designing an Auto-Merging Retriever
- 7.6. Retrieving More Complete Text Chunks with a Sentence Window Retriever
- 7.7. Improving Retrieval Relevancy with Reranking Methods
- 7.8. Decomposing Complex Queries into Multiple Subqueries
8. Agentic RAG
- 8.1. Designing a Custom Tool in Python
- 8.2. Using Workflow Patterns in Multiagent Systems
- 8.3. Choosing an Agentic Framework
- 8.4. Building an Agentic System via Function Calling
- 8.5. Accelerating Agents with asyncio
- 8.6. Building a Sales Negotiation Agent with OpenAIs Agents SDK and Chroma
- 8.7. Enriching Your Agents Capabilities with MCP Tools
- 8.8. Building an Agentic System with LangGraph
9. Graph RAG
- 9.1. Creating Your First Neo4j Knowledge Graph and Feeding It with Text from Documents
- 9.2. Extending the Knowledge Graph with Structured Data
- 9.3. Building Your First Cypher Query
- 9.4. Enabling Semantic Search on a Neo4j Knowledge Graph
- 9.5. Optimizing the Knowledge Graph for RAG Systems
10. Evaluating RAG Systems
- 10.1. Choosing the Right Evaluation Metrics for RAG Systems
- 10.2. Evaluating RAG Systems by Humans
- 10.3. Creating Synthetic Data for Automated Testing
- 10.4. Evaluating the Retriever Step by Calculating Context Precision@k
- 10.5. Evaluating Faithfulness During Generation with LLM-as-a-Judge
- 10.6. Evaluating the Response Relevancy of Your RAG System
11. RAG Web Apps
- 11.1. Building Your First Streamlit App
- 11.2. Building a Chatbot App with Streamlit
- 11.3. Adding PDF Analyzer Functionality to Your Chatbot
- 11.4. Connecting Your RAG App to a SQL Database
- 11.5. Deploying Your Streamlit App with Docker and AWS
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