Vector Databases for AI and RAG Applications 7 Sections27 LessonsLifetime Access
Curriculum
- 7 Sections
- 27 Lessons
- Lifetime Access
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- Vector Databases Deep Dive - Fundamentals3 Items
- Building Vector Databases - Hands-on - Chroma Vector Database8 Items
- 2.1Setting Up the Development Environment
- 2.2Setting Up VS Code, Python and the OpenAI API Key
- 2.3Following the Chroma Database Workflow
- 2.4Creating a Chroma Vector Database, Adding Documents and Querying Them
- 2.5Looping Through the Results and Showing Similarity Search Results
- 2.6Creating OpenAI Embeddings Raw Without Chroma
- 2.7Understanding Vector Databases Metrics and Data Structures
- 2.8Summarizing the Section
- Common Measures of Vector Similarity4 Items
- Pinecone Vector Database9 Items
- 4.1Taking a Deep Dive into Pinecone
- 4.2Creating a Pinecone Account and Getting a Dashboard Overview
- 4.3Building Our Pinecone Index in Code
- 4.4Upserting and Querying Our Pinecone Index in Code
- 4.5Querying Pinecone Manually Using the Dashboard
- 4.6Using the LangChain Pinecone Wrapper: Create Index, Upsert and Similarity Search
- 4.7Cleaning Up: Delete the Pinecone Index
- 4.8Exploring Other Vector Databases: Challenge
- 4.9Summarizing the Section
- Resources1 Item
- Practice Lab1 Item
- Claim Your Certificate1 Item
Vector Databases for AI and RAG Applications Practice Lab
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