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Multi-Modal Engine Specs

Weaviate Multi-Modal Vector Engine

Reviewed by Umar Abbas • CTO & Principal AI Architect

Weaviate is an open-source, multi-modal vector database designed for high-scale AI applications. It combines vector search with a GraphQL API interface, modular ML models (like CLIP and text2vec), and hybrid dense-sparse BM25 keyword reranking.

Engine LanguageGo Native
API InterfaceGraphQL & REST
Search SLASub-18ms Hybrid
Multi-ModalCLIP Text + Image
Hybrid Retrieval

Weaviate Hybrid Dense-Sparse Search Pipeline

Weaviate Hybrid Fusion Search Pipeline

Interactive Flow Diagram
Weaviate Hybrid Fusion Search Pipeline
Stage 1:

Text alternative for screen readers & search engines
Step Stage Name Function & Detail Metrics / SLA
1 N/A
2 N/A
3 N/A
4 N/A
Python Implementation

Weaviate Hybrid Search Python Client

import weaviate

client = weaviate.connect_to_local()
sop_collection = client.collections.get("SopDocument")

# Execute hybrid dense-sparse search with alpha parameter
response = sop_collection.query.hybrid(
  query="quarterly audit compliance rules",
  alpha=0.75, # 75% vector score + 25% BM25 keyword score
  limit=5
)

for obj in response.objects:
  print(obj.properties["title"], obj.metadata.score)
Engine Architecture

Four-Layer Weaviate Stack

Weaviate Vector Database Layers

Layered Stack Architecture
L4
GraphQL & gRPC API Gateway
(Core System Layer)

Structured GraphQL query layer and high-speed gRPC data streaming

L3
Vectorizer Modules
(Core System Layer)

text2vec-openai, multi2vec-clip, and custom ONNX transformer modules

L2
HNSW & Inverted Index
(Core System Layer)

Dual-engine indexing combining vector HNSW with BM25 inverted keyword index

L1
LSM Storage Engine
(Core System Layer)

Log-Structured Merge-tree storage layer for high write performance

Architectural Layer Stack
Text alternative for screen readers & search engines
  • Layer 4: GraphQL & gRPC API Gateway (Core System Layer) — Structured GraphQL query layer and high-speed gRPC data streaming
  • Layer 3: Vectorizer Modules (Core System Layer) — text2vec-openai, multi2vec-clip, and custom ONNX transformer modules
  • Layer 2: HNSW & Inverted Index (Core System Layer) — Dual-engine indexing combining vector HNSW with BM25 inverted keyword index
  • Layer 1: LSM Storage Engine (Core System Layer) — Log-Structured Merge-tree storage layer for high write performance
Production Telemetry

60M Vector Telemetry Benchmark

Evaluated ParameterMeasured Telemetry
Indexed Multi-Modal Vectors60,000,000
Hybrid Search SLA17.2ms
Cross-Modal Recall Precision98.6%
Buyer FAQ

Frequently Asked Questions

What is the primary advantage of Weaviate's GraphQL API?↓

GraphQL allows applications to fetch vector objects, metadata fields, and cross-referenced class relationships in a single network round-trip.

Does Weaviate support multi-modal search across images and text?↓

Yes. Using multi2vec-clip modules, Weaviate embeds images and text into a shared vector space for cross-modal similarity search.

How does hybrid search work in Weaviate?↓

Weaviate blends HNSW dense vector cosine distance with BM25 sparse keyword scores using alpha-weighted Reciprocal Rank Fusion (RRF).

Who owns the Weaviate cluster deployment and schema?↓

Your organization holds 100% legal ownership of all Weaviate schema definitions, vector indexes, and cluster code.

Deploy Multi-Modal Weaviate Architecture

Consult with CTO Umar Abbas to configure production Weaviate vector databases.

Request Weaviate Discovery