RAG-Driven Generative AI: Build Custom Retrieval Augmented Generation Pipelines with LlamaIndex, Deep Lake, and Pinecone
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham
Packt Publishing, Limited
2024
|
| Ausgabe: | 1st ed. |
| Links: | https://ebookcentral.proquest.com/lib/hm-bib/detail.action?docID=31691374 https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=31691374 |
| Abstract: | Cover -- Copyright Page -- Contributors -- Table of Contents -- Preface -- Chapter 1: Why Retrieval Augmented Generation? -- What is RAG? -- Naïve, advanced, and modular RAG configurations -- RAG versus fine-tuning -- The RAG ecosystem -- The retriever (D) -- Collect (D1) -- Process (D2) -- Storage (D3) -- Retrieval query (D4) -- The generator (G) -- Input (G1) -- Augmented input with HF (G2) -- Prompt engineering (G3) -- Generation and output (G4) -- The evaluator (E) -- Metrics (E1) -- Human feedback (E2) -- The trainer (T) -- Naïve, advanced, and modular RAG in code -- Part 1: Foundations and basic implementation -- 1. Environment -- 2. The generator -- 3. The Data -- 4.The query -- Part 2: Advanced techniques and evaluation -- 1. Retrieval metrics -- 2. Naïve RAG -- 3. Advanced RAG -- 4. Modular RAG -- Summary -- Questions -- References -- Further reading -- Chapter 2: RAG Embedding Vector Stores with Deep Lake and OpenAI -- From raw data to embeddings in vector stores -- Organizing RAG in a pipeline -- A RAG-driven generative AI pipeline -- Building a RAG pipeline -- Setting up the environment -- The installation packages and libraries -- The components involved in the installation process -- 1. Data collection and preparation -- Collecting the data -- Preparing the data -- 2. Data embedding and storage -- Retrieving a batch of prepared documents -- Verifying if the vector store exists and creating it if not -- The embedding function -- Adding data to the vector store -- Vector store information -- 3. Augmented input generation -- Input and query retrieval -- Augmented input -- Evaluating the output with cosine similarity -- Summary -- Questions -- References -- Further reading -- Chapter 3: Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI -- Why use index-based RAG? -- Architecture. |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Umfang: | 1 online resource (335 pages) |
| ISBN: | 9781836200901 |
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| id | DE-604.BV049893494 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-11T14:00:44Z |
| institution | BVB |
| isbn | 9781836200901 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035232598 |
| oclc_num | 1466909629 |
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| owner | DE-M347 |
| owner_facet | DE-M347 |
| physical | 1 online resource (335 pages) |
| psigel | ZDB-30-PQE ZDB-30-PQE FHM_Einzelkauf |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Packt Publishing, Limited |
| record_format | marc |
| spelling | Rothman, Denis Verfasser aut RAG-Driven Generative AI Build Custom Retrieval Augmented Generation Pipelines with LlamaIndex, Deep Lake, and Pinecone 1st ed. Birmingham Packt Publishing, Limited 2024 1 online resource (335 pages) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Cover -- Copyright Page -- Contributors -- Table of Contents -- Preface -- Chapter 1: Why Retrieval Augmented Generation? -- What is RAG? -- Naïve, advanced, and modular RAG configurations -- RAG versus fine-tuning -- The RAG ecosystem -- The retriever (D) -- Collect (D1) -- Process (D2) -- Storage (D3) -- Retrieval query (D4) -- The generator (G) -- Input (G1) -- Augmented input with HF (G2) -- Prompt engineering (G3) -- Generation and output (G4) -- The evaluator (E) -- Metrics (E1) -- Human feedback (E2) -- The trainer (T) -- Naïve, advanced, and modular RAG in code -- Part 1: Foundations and basic implementation -- 1. Environment -- 2. The generator -- 3. The Data -- 4.The query -- Part 2: Advanced techniques and evaluation -- 1. Retrieval metrics -- 2. Naïve RAG -- 3. Advanced RAG -- 4. Modular RAG -- Summary -- Questions -- References -- Further reading -- Chapter 2: RAG Embedding Vector Stores with Deep Lake and OpenAI -- From raw data to embeddings in vector stores -- Organizing RAG in a pipeline -- A RAG-driven generative AI pipeline -- Building a RAG pipeline -- Setting up the environment -- The installation packages and libraries -- The components involved in the installation process -- 1. Data collection and preparation -- Collecting the data -- Preparing the data -- 2. Data embedding and storage -- Retrieving a batch of prepared documents -- Verifying if the vector store exists and creating it if not -- The embedding function -- Adding data to the vector store -- Vector store information -- 3. Augmented input generation -- Input and query retrieval -- Augmented input -- Evaluating the output with cosine similarity -- Summary -- Questions -- References -- Further reading -- Chapter 3: Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI -- Why use index-based RAG? -- Architecture. X:EBC https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=31691374 Aggregator |
| spellingShingle | Rothman, Denis RAG-Driven Generative AI Build Custom Retrieval Augmented Generation Pipelines with LlamaIndex, Deep Lake, and Pinecone |
| title | RAG-Driven Generative AI Build Custom Retrieval Augmented Generation Pipelines with LlamaIndex, Deep Lake, and Pinecone |
| title_auth | RAG-Driven Generative AI Build Custom Retrieval Augmented Generation Pipelines with LlamaIndex, Deep Lake, and Pinecone |
| title_exact_search | RAG-Driven Generative AI Build Custom Retrieval Augmented Generation Pipelines with LlamaIndex, Deep Lake, and Pinecone |
| title_full | RAG-Driven Generative AI Build Custom Retrieval Augmented Generation Pipelines with LlamaIndex, Deep Lake, and Pinecone |
| title_fullStr | RAG-Driven Generative AI Build Custom Retrieval Augmented Generation Pipelines with LlamaIndex, Deep Lake, and Pinecone |
| title_full_unstemmed | RAG-Driven Generative AI Build Custom Retrieval Augmented Generation Pipelines with LlamaIndex, Deep Lake, and Pinecone |
| title_short | RAG-Driven Generative AI |
| title_sort | rag driven generative ai build custom retrieval augmented generation pipelines with llamaindex deep lake and pinecone |
| title_sub | Build Custom Retrieval Augmented Generation Pipelines with LlamaIndex, Deep Lake, and Pinecone |
| url | https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=31691374 |
| work_keys_str_mv | AT rothmandenis ragdrivengenerativeaibuildcustomretrievalaugmentedgenerationpipelineswithllamaindexdeeplakeandpinecone |