Part 4: Knowledge Management & RAG

Overview

Vector databases and retrieval systems for Retrieval-Augmented Generation (RAG) pipelines.

Tool	Purpose	Best For	Complexity
Chroma	Simple vector DB	Prototyping	Low
Weaviate	Semantic search at scale	Production RAG	Medium
Milvus	Large-scale embeddings	Enterprise deployments	High
Lancedb	AI-native design	Modern applications	Low

Performance Comparison

Database	Storage (1M vectors)	Query Latency	Max Scale
Chroma	4GB	50-200ms	10M
Weaviate	6GB	20-100ms	100M+
Milvus	3GB	10-50ms	1B+
LanceDB	2GB	10-30ms	100M+

Decision Tree

graph TD
    A["Need vector DB?"] -->|Quick prototype| B["Chroma"]
    A -->|Production RAG| C["Weaviate"]
    A -->|Enterprise scale| D["Milvus"]
    A -->|Modern stack| E["LanceDB"]

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Next: Choose a vector database for your RAG pipeline!