Official Resources

Documentation Python Client Roadmap TypeScript Client Java Client Recipes Blog v1.29 Release v1.30 Release v1.31 Release NVIDIA Partnership

Key Features

  • Hybrid Search Architecture: Single API call with alpha blend of BM25 (BlockMax WAND) + vector similarity for comprehensive search capabilities.
  • Multi-vector & MUVERA: Late-interaction ColBERT/ColPali support with MUVERA compression flattening to fixed 768-dim vectors (configurable).
  • GPU Acceleration: NVIDIA cuVS HNSW build 4-5× faster with auto-conversion to CPU-serve format for cost optimization.
  • Modular Ecosystem: 25+ runtime modules including text2vec-openai, multi2vec-google, reranker-cohere, generative-openai integrations.
  • Enterprise Security: TLS 1.3, RBAC, SSO (OIDC/SAML), SOC 2 Type II, HIPAA-ready with regional isolation capabilities.
  • Flexible Deployment: Self-hosted Docker/K8s Helm, embedded (Go/Java), Weaviate Cloud (serverless & dedicated) options.
  • Advanced Core: Rust + Go micro-services, HNSW ANN, async WAL, pluggable storage (RocksDB, in-memory) architecture.

Code Examples

Local Docker Setup

bash 
docker run -p 8080:8080 -v $(pwd)/weaviate_data:/var/lib/weaviate \
  semitechnologies/weaviate:1.31

Python Hybrid Search

python 
import weaviate
client = weaviate.connect_to_local()

collection = client.collections.get("SupportTickets")
response = collection.query.hybrid(
    query="login issues after OS upgrade",
    alpha=0.75,
    limit=5
)

GPU Index Build (cuVS)

yaml 
# docker-compose.yml
services:
  weaviate:
    image: semitechnologies/weaviate:1.31
    environment:
      ENABLE_GPU: "true"
      GPU_DEVICE: "0"

SDK Matrix Overview

text 
# Multi-language SDK support:
# Python: weaviate-client v3.26.2
# TypeScript: weaviate-ts-client v2.7.0
# Java: io.weaviate:client v5.3.0
# Go: github.com/weaviate/weaviate-go-client v4.11.0

# Architecture highlights:
# - Rust + Go micro-services
# - HNSW ANN with async WAL
# - Pluggable storage (RocksDB, in-memory)
# - 25+ runtime modules for model integrations

Use Cases

  • RAG systems - LangChain, LlamaIndex, CrewAI using hybrid retrieval + reranker
  • Agentic workflows - Query Agent, Transformation Agent, Personalization Agent (GA)
  • Multimodal applications - Text + image + video in one collection via multi2vec-google
  • Enterprise search - HIPAA, SOC 2, regional latency <50ms with Edge & dedicated clusters
  • Hybrid search applications - Single API combining semantic and keyword search

Pros & Cons

Advantages

  • Open-source with hybrid search out-of-the-box capabilities
  • MUVERA compression significantly reduces multi-vector storage requirements
  • 25+ model integrations at ingest and query time
  • Enterprise-grade security & compliance (SOC 2, HIPAA-ready)
  • GPU acceleration with automatic CPU fallback for cost optimization

Disadvantages

  • GPU build requires CUDA 12+ drivers and setup complexity
  • Multi-vector tuning complexity for optimal performance
  • Multi-node clustering still requires DIY implementation
  • Free Cloud tier limited to 1M objects / 1GB RAM

Future Outlook & Integrations

  • Multi-node Clustering [Target v1.32]: Raft consensus + shard replication targeting v1.32
  • Hybrid GPU→CPU Tiering [In Development]: Auto-fallback to cut infrastructure costs by 40%
  • Domain-specific Agents [Roadmap]: Finance, healthcare, e-commerce agent blueprints
  • 4-bit Quantization [Future]: int4 PQ for 2× memory reduction optimization