ChromaDB and Weaviate – Vector Databases for RAG

1. What is a Vector Database?

A vector database stores embeddings and allows us to search for data based on semantic similarity.

In a RAG system:

Documents → Embeddings → Vector Database → Retrieval → LLM

Two popular choices are ChromaDB and Weaviate.

2. ChromaDB

ChromaDB is an open-source vector database that is simple and easy to use.

It can store documents, embeddings, and metadata, and can automatically generate embeddings when an embedding function is configured.

collection.add(
    documents=["This is a document."],
    ids=["1"]
)

It is a great choice for learning, prototypes, and small RAG applications.

3. Weaviate

Weaviate is also an open-source vector database. It stores objects together with their properties, metadata, and vectors.

It supports different types of search, including:

• Semantic search with near_text
• Hybrid search with hybrid
• Similarity search with near_object

This makes Weaviate useful for applications such as RAG, recommendations, and semantic search.

4. ChromaDB vs Weaviate

5. Which One Should You Choose?

For beginners and small RAG projects, ChromaDB is a simple choice.

For applications that need more advanced search capabilities and scalability, Weaviate is worth considering.

The important thing is to understand the overall RAG flow first:

Documents
    ↓
Chunking
    ↓
Embeddings
    ↓
ChromaDB / Weaviate
    ↓
Retrieve relevant chunks
    ↓
LLM
    ↓
Answer