Software Engineering
281 learners
Scaling up RAG with Vector Databases
Discover how to scale RAG systems using vector databases. Learn to preprocess documents, store embeddings in ChromaDB, retrieve relevant chunks, and construct prompts. Manage updates and large-scale ingestion with batch strategies for efficient retrieval.
ChromaDB
Python
See path
4 lessons
17 practices
1 hour
Badge for RAG Systems and Vector Databases,
RAG Systems and Vector Databases
Lessons and practices
Lesson 1: Chunking Text for Retrieval-Augmented Generation Systems
Mastering Document Chunking Basics
Preserve Sentence Boundaries in Chunks
Enhance Text Chunking Skills
Organize Chunks with Metadata
Enhance Text with Keyword Detection
Lesson 2: Storing and Managing Text Chunks in Vector Databases
Loading and chunking text
Building a ChromaDB Collection
Dynamic Chunk Management in ChromaDB
Dynamic Document Management in ChromaDB
Lesson 3: Retrieving and Prompt Building in RAG Systems
Enhance Your Retrieval Function
Crafting Context-Rich LLM Prompts
Metadata-Based Retrieval Enhancement
Refine Retrieval with Distance Threshold
Lesson 4: Metadata-Based Filtering in RAG Systems
Where Clauses
Crafting Metadata Enhanced Search
Enhance Metadata Filtering Skills
Enhance Search with Fallback Mechanism
Meet Cosmo:
The smartest AI guide in the universe
Our built-in AI guide and tutor, Cosmo, prompts you with challenges that are built just for you and unblocks you when you get stuck.
Sign up
Join the 1M+ learners on CodeSignal
Be a part of our community of 1M+ users who develop and demonstrate their skills on CodeSignal