Vector Databases with Python ChromaDB Pinecone RAG


Vector Databases with Python ChromaDB Pinecone RAG
3.59 GB | 1h 1min 8s | mp4 | 1920X1080 | 16:9

Files Included :

FileName :1 Welcome and Course Overview.mp4 | Size: (68.37 MB)
FileName :2 Understanding Embeddings and Vector Databases.mp4 | Size: (86.54 MB)
FileName :3 Setting Up the Development Environment.mp4 | Size: (144.32 MB)
FileName :4 Creating Embeddings with the OpenAI API.mp4 | Size: (260.83 MB)
FileName :5 Measuring Semantic Similarity.mp4 | Size: (380.08 MB)
FileName :6 Chunking Documents for AI.mp4 | Size: (103.85 MB)
FileName :8 Searching with ChromaDB.mp4 | Size: (163.33 MB)
FileName :9 Loading and Indexing PDF Documents.mp4 | Size: (177.69 MB)
FileName :10 Building the Semantic Search Engine.mp4 | Size: (436.93 MB)
FileName :11 Understanding Retrieval-Augmented Generation (RAG).mp4 | Size: (196.55 MB)
FileName :12 Building the RAG Backend with LangChain.mp4 | Size: (241.09 MB)
FileName :13 Building the Chat Interface.mp4 | Size: (195.35 MB)
FileName :14 Improving the RAG Chatbot.mp4 | Size: (218.53 MB)
FileName :16 Migrating from ChromaDB to Pinecone.mp4 | Size: (213.7 MB)
FileName :17 Building Better Vector Search Systems.mp4 | Size: (183.88 MB)
FileName :18 Course Summary and Next Steps.mp4 | Size: (112.61 MB)

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