Weaviate — Open-Source Vector Database at Scale

What it is

Weaviate is an open-source vector database designed for semantic search at scale. It stores data objects alongside their vector embeddings and supports hybrid search that combines vector similarity with BM25 keyword matching. Built-in modules handle RAG (retrieval-augmented generation), reranking, and multi-tenancy.

Weaviate targets teams building AI-powered search, recommendation engines, and RAG pipelines who need a database that understands meaning rather than just keywords. It scales horizontally and supports multi-tenant architectures for SaaS applications.

Why it saves time or tokens

Traditional keyword search misses semantically related results. Weaviate's vector search retrieves relevant documents even when the exact words differ, which means fewer retrieval misses and fewer follow-up queries. For RAG pipelines, better retrieval means the LLM receives more relevant context, producing accurate answers with fewer tokens wasted on irrelevant passages.

The built-in RAG module eliminates the need to build retrieval-then-generate pipelines manually. You send a query, Weaviate retrieves relevant objects, and a configured LLM generates an answer in one API call.

How to use

1. Start Weaviate with Docker: docker compose up -d using the official compose file
2. Define a schema (collection) with your data properties and vectorizer module
3. Import data objects and query with nearText, nearVector, or hybrid search operators

Example

import weaviate

client = weaviate.connect_to_local()

collection = client.collections.get('Article')

results = collection.query.hybrid(
    query='machine learning optimization',
    limit=5,
    alpha=0.75  # weight toward vector search
)

for obj in results.objects:
    print(obj.properties['title'])

This hybrid query blends vector similarity (75% weight) with BM25 keyword matching (25% weight) to find the most relevant articles.

Related on TokRepo

• AI tools for RAG — retrieval-augmented generation tools and frameworks
• AI tools for database — database tools for AI applications curated on TokRepo

Common pitfalls

• Choosing the wrong vectorizer module at schema creation time locks you into that embedding model; plan your vectorizer before importing data
• Hybrid search alpha parameter needs tuning per use case; 0.75 favors vectors, 0.25 favors keywords
• Multi-tenancy requires planning upfront; migrating a single-tenant Weaviate instance to multi-tenant is non-trivial