Udemy - Master Vector Databases

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[ DevCourseWeb.com ] Udemy - Master Vector Databases
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction
    • 1. Introduction to Vector Database.mp4 (86.3 MB)
    • 2. Vectors and Embeddings.mp4 (40.7 MB)
    • 3. Explain vector database like I'm 5.mp4 (29.9 MB)
    • 4. How vector database store data.mp4 (32.1 MB)
    • 5. How do vector database works.mp4 (26.8 MB)
    • 6. Vectors in 2D.mp4 (37.0 MB)
    2. The power of embeddings
    • 1. Create embeddings using OpenAI.mp4 (92.4 MB)
    • 1.1 VD_Vector_Embedding_OpenAI.ipynb (47.5 KB)
    • 2. Sentence Embedding Models.html (3.5 KB)
    3. Using SQLite as vector storage
    • 1. Setup and basic operations.mp4 (55.3 MB)
    • 2. Creating, storing and retrieving vector data.mp4 (142.8 MB)
    • 2.1 VD_SQLite_Vector_search.ipynb (16.8 KB)
    • 3. Finding nearest vector.mp4 (48.3 MB)
    • 4. Vector search using sqlite-vss extension.mp4 (149.2 MB)
    • 4.1 VD_SQLite_VSS.ipynb (12.0 KB)
    4. ChromaDB
    • 1. Introduction to ChromaDB.mp4 (125.4 MB)
    • 2. Revolutionizing the Data access with Vector Database.html (5.2 KB)
    • 3. Methods on collections.mp4 (60.6 MB)
    • 3.1 Vector_Database_ChromaDB.ipynb (2.5 KB)
    • 4. Storing The Matrix collections.mp4 (103.9 MB)
    • 4.1 Vector_Database_ChromaDB__The_Matrix_.ipynb (25.2 KB)
    • 5. Adding document associated embeddings.mp4 (96.0 MB)
    • 6. Query data with 'where' filter.mp4 (96.0 MB)
    • 7. ChromaDB + Langchain - QA on multiple documents - Part 1.mp4 (133.3 MB)
    • 7.1 VD_ChromaDB_+_Langchain_QA_Multiple_documents.ipynb (27.2 KB)
    • 8. ChromaDB + Langchain - QA on multiple documents - Part 2.mp4 (92.1 MB)
    5. Facebook AI Similarity Search (FAISS)
    • 1. Introduction to FAISS.mp4 (127.6 MB)
    • 2. Using similarity search for nearest neighbours.mp4 (67.0 MB)
    6. Pinecone
    • 1. Introduction to Pinecone.mp4 (113.0 MB)
    • 10. Vector IDs must be string.mp4 (51.1 MB)
    • 11. Sentence transformer embeddings.mp4 (85.8 MB)
    • 12. Semantic search with metadata filtering - news articles.mp4 (252.7 MB)
    • 2. Setup account, create an index, dashboard review.mp4 (97.0 MB)
    • 3. Understanding index creation configuration.mp4 (82.9 MB)
    • 4. Index management.mp4 (114.9 MB)
    • 5. Insert vector data to an index.mp4 (104.7 MB)
    • 6. Query vector data.mp4 (94.3 MB)
    • 7. Upsert vector data in batches.mp4 (100.3 MB)
    • 8. Upsert batches in parallel.mp4 (17.7 MB)
    • 9. Upsert with metadata.html (0.4 KB)
    7. Qdrant
    • 1. Introduction to Qdrant vector database.mp4 (82.2 MB)
    • 2. Connect with APIs.mp4 (59.8 MB)
    • 3. Create a qdrant python client.mp4 (37.0 MB)
    • 4. Create a collection.mp4 (53.3 MB)
    • 5. Create a vector store.mp4 (45.3 MB)
    • 6. Add document to vector store on the cloud.mp4 (118.7 MB)
    • 7. Query the document.mp4 (86.0 MB)
    • 8. Create a streamlit QA app.html (0.9 KB)
    8. Congratulations and Thank You!
    • 1. Your feedback is very valuable!.html (0.7 KB)
    • Bonus Resources.txt (0.4 KB)

Description

Master Vector Databases

https://DevCourseWeb.com

Published 11/2023
Created by Adnan Waheed
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 43 Lectures ( 7h 16m ) | Size: 3.16 GB

Master Vector Database using Python, Embeddings, ChromaDB, Facebook FAISS, Qdrant, LangChain, Open AI

What you'll learn
Master Vector Database, Embeddings, ChromaDB, FAISS, Qdrant and much more
Learn integration Vector databases with LangChain, Open AI
Master Embeddings
Transformer Models for vector embedding, Generative AI, Open AI API Usage
Understand the fundamentals of vector databases and their role in AI, generative AI, and LLM (Language Model Models).
Implement code along exercises to build and optimize vector indexing systems for real-world applications.

Requirements
Basic python knowledge



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Udemy - Master Vector Databases
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