Storing, Querying, and Managing Vector Data with Qdrant

Overview

This course focuses entirely on **Qdrant**, an open-source and cloud-native vector database. It covers setting up a Qdrant collection, storing embeddings, querying efficiently, configuring indexing, and managing large-scale vector data.

Syllabus

Unit 1: Getting Started with Qdrant

Customizing Qdrant Collection Name
Retrieve and Print Qdrant Collection Configuration
Creating and Managing Multiple Qdrant Collections
Managing Qdrant Collections by Deleting Unused Resources

Unit 2: Working with Embeddings

Generating Text Embeddings with SentenceTransformer
Creating and Managing Qdrant Collections for Embeddings
Verifying Stored Vector Count in Qdrant

Unit 3: Querying Data with Qdrant

Creating and Querying Vector Embeddings in Qdrant
Modifying Qdrant Search Queries and Limits
Adding Metadata Filters to Qdrant Queries
Enhancing Qdrant Query with Vector Retrieval

Unit 4: Vector Data Management

Modifying Vector Metadata with Qdrant
Remove Vector from Qdrant Collection
Ensuring Data Consistency in Qdrant with Update and Deletion Verification

Unit 5: Handling Large Vector Data

Batching Vector Data for Efficient Upsert Operations
Verifying Batched Vector Insertion in Qdrant

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