Udemy - Query Your Custom Documents using LlamaIndex

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[ DevCourseWeb.com ] Udemy - Query Your Custom Documents using LlamaIndex
  • Get Bonus Downloads Here.url (0.2 KB)
  • ~Get Your Files Here !
    • 1. Introduction to Large Language Model.mp4 (10.7 MB)
    • 10. What is In-Context learning .mp4 (20.9 MB)
    • 11. Difference between In-Context Learning and Fine-Tuning .mp4 (20.6 MB)
    • 12. Pricing.mp4 (46.0 MB)
    • 13. How does LlamaIndex applications work internally .mp4 (40.7 MB)
    • 14. Why is Indexing required in LLM application .mp4 (14.9 MB)
    • 15. (Hands-on) Base Program to read Text Files.mp4 (43.1 MB)
    • 16. Notes VectorStoreIndex.html (0.4 KB)
    • 17. Concept of SimpleDirectoryReader and VectorStoreIndex.mp4 (15.8 MB)
    • 18. (Hands-on) Base Program to read Docs.mp4 (25.8 MB)
    • 19. (Hands-on) How to verify sources of the response .mp4 (66.1 MB)
    • 2. How to connect to external Data .mp4 (3.4 MB)
    • 20. (Hands-on) Base program to read PDF.mp4 (30.2 MB)
    • 21. (Hands-on) How to persist indexes .mp4 (55.7 MB)
    • 22. The concept of Custom LLM and Customization.mp4 (6.1 MB)
    • 23. (Hands-on) Changing underlying LLM model.mp4 (46.7 MB)
    • 24. (Hands-on) Custom LLM Model max_tokens.mp4 (21.1 MB)
    • 25. (Hands-on) Custom LLM model Control over more parameters.mp4 (31.5 MB)
    • 26. (Hands-on) Expose API Integration with endpoint part 1.mp4 (15.4 MB)
    • 27. (Hands-on) Expose API Integration with endpoint part 2.mp4 (65.4 MB)
    • 28. (Hands-on) Expose API Integration with endpoint part 3.mp4 (47.6 MB)
    • 29. (Hands-on) Option of Recursive files processing from Directories and Sub Dir.mp4 (56.0 MB)
    • 3. What is LlamaIndex .mp4 (12.1 MB)
    • 30. What is VectorDB .mp4 (53.3 MB)
    • 31. (Hands-On) Build LLM apps using ChromaDB.mp4 (91.8 MB)
    • 32. High Level Overview Token Predictor and Cost Analysis.mp4 (10.6 MB)
    • 33. (Hands- On) Identify tokens using Token Predictor.mp4 (49.2 MB)
    • 4. GPT 3 Models and Environment Setup.mp4 (9.6 MB)
    • 5. LlamaIndex and OpenAI Package Installation.mp4 (12.3 MB)
    • 6. How to get and update OpenAI API key.mp4 (20.8 MB)
    • 7. Overview of required steps to build apps using LlamaIndex.mp4 (13.4 MB)
    • 8. (Hands-On) Methods of creating Document objects.mp4 (37.9 MB)
    • 9. Notes [Update] Document object output.html (1.3 KB)
    • Bonus Resources.txt (0.4 KB)
    • base program for pdf file
      • basic_chat_pdf.py (0.4 KB)
      • pdf
        • LLM.pdf (7.8 MB)
        base program to read docs
        • basic_chat_doc.py (0.4 KB)
        • doc
          • SDTerms.docx (74.5 KB)
          basic program for text file
          • basic_chat_text.py (0.3 KB)
          • data
            • sample.txt (73.4 KB)
            changing underlying llm
            • basic_LLM_custom.py (0.7 KB)
            • input text
              • sample.txt (73.4 KB)
              create document object
              • create_document.py (0.4 KB)
              • data
                • sample.txt.txt (0.0 KB)
                custom llm models control over parameters
                • basic_LLM_custom parameterized 2.py (0.8 KB)
                • input text
                  • sample.txt (73.4 KB)
                  custom llm models max tokens
                  • basic_LLM_custom parameterized.py (0.7 KB)
                  • input text
                    • sample.txt (73.4 KB)
                    how to persist indexes
                    • basic_chat_storage.py (0.9 KB)
                    • basic_main_storage.py (0.1 KB)
                    • input text
                      • sample.txt (73.4 KB)
                      integration with endpoint 3
                      • basic_index.py (1.6 KB)
                      • config.ini (0.0 KB)
                      • input text
                        • sample.txt (73.4 KB)
                        integration with endpoint part 1
                        • basic_setup.py (0.2 KB)
                        integration with endpoint part 2
                        • basic_index.py (1.2 KB)
                        • config.ini (0.0 KB)
                        • input text
                          • sample.txt (73.4 KB)
                          recursive files processing input doc
                          • SDTerms.docx (74.5 KB)
                          multi
                          • file1.txt (0.0 KB)
                          • file2.txt (0.0 KB)
                          • file3.txt (0.0 KB)
                          • file4.txt (0.0 KB)
                          pdf
                          • LLM.pdf (7.8 MB)
                          text
                          • sample.txt (73.4 KB)
                          • sample2.txt (0.0 KB)
                          • sample3.txt (0.0 KB)
                          • recursive_files.py (0.6 KB)
                          • sample.txt (73.4 KB)
                          • vector db - chroma
                            • basic_chroma_db.py (1.1 KB)
                            • config.ini (0.0 KB)
                            • input text
                              • sample.txt (73.4 KB)
                              verify sources of the response
                              • basic_chat.py (0.6 KB)
                              • data
                                • sample.txt (73.4 KB)

Description

Query Your Custom Documents using LlamaIndex



https://DevCourseWeb.com

Published 6/2023
Created by Praveen Singh
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 33 Lectures ( 1h 48m ) | Size: 1 GB

Learn to build LLM applications using LlamaIndex from scratch in Python

What you'll learn
Become proficient in LlamaIndex
Learn to query your custom documents using LlamaIndex
Get the understanding of different aspects of LlamaIndex
Get the understanding of different concepts of Large Language Models

Requirements
Basic Python programming experience is required.



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Udemy - Query Your Custom Documents using LlamaIndex


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1011.2 MB
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Udemy - Query Your Custom Documents using LlamaIndex


Torrent hash: 43E3B52198785B1B59477DD420593A940A988486