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CodeSignal

Storing, Indexing, and Managing Vector Data with ChromaDB

via CodeSignal

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Overview

This course focuses entirely on ChromaDB, a lightweight open-source vector database. It covers setting up, storing embeddings, searching efficiently, handling indexing, and managing large-scale vector data.

Syllabus

  • Unit 1: Setting Up and Initializing ChromaDB
    • Customizing Your ChromaDB Collection Name
    • Loading ChromaDB Collection
    • Collection Status Check in ChromaDB
    • Managing Multiple Collections in ChromaDB
    • Cleaning Up Collections in ChromaDB
  • Unit 2: Inserting and Storing Embeddings in ChromaDB
    • Loading a Pre-trained Model
    • Embedding Function and Collection Setup
    • Inserting Documents into ChromaDB
    • Verify Stored Documents in ChromaDB
    • Retrieve and Verify Embedding Vectors
  • Unit 3: Querying and Searching in ChromaDB
    • Exploring ChromaDB Query Results
    • Multi-Query Search in ChromaDB
    • Exploring ChromaDB Query Results
    • Filtering Search Results in ChromaDB
  • Unit 4: Indexing and Optimizing Search Performance with ChromaDB
    • Access and Update Collection Metadata
    • Optimizing ChromaDB Collection Metadata
    • Enhance Search with Metadata Changes
    • Experiment with Indexing Strategies
  • Unit 5: Handling Large-Scale Vector Data in ChromaDB
    • Generating Random Vectors with NumPy
    • Managing Vector Data in ChromaDB
    • Querying Vector Data in ChromaDB

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