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Most vector databases store pre-computed vectors - you embed content yourself and hand the vector to the DB. Weaviate goes further: it ships built-in vectorizer modules that embed content automatically at ingest time. Define your schema once with a vectorizer configured, and Weaviate handles the rest. This is especially powerful for multimodal apps where you want to embed both text and images in the same collection.

Schema and Data Classes

Weaviate uses a schema of "data classes" (analogous to collections):

python

import weaviate

client = weaviate.connect_to_local()

client.collections.create(
    name="Article",
    vectorizer_config=weaviate.classes.config.Configure.Vectorizer.text2vec_openai(),
    properties=[
        weaviate.classes.config.Property(name="title", data_type=weaviate.classes.config.DataType.TEXT),
        weaviate.classes.config.Property(name="body", data_type=weaviate.classes.config.DataType.TEXT),
    ],
)

With text2vec_openai configured, inserting a document triggers automatic embedding via the OpenAI embeddings API.

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Built-In Vectorizer Modules

Module	Input	Notes
`text2vec-openai`	Text	Calls OpenAI embeddings API
`text2vec-cohere`	Text	Cohere embed API
`text2vec-transformers`	Text	Local HuggingFace model
`multi2vec-clip`	Text + Images	CLIP embeddings, multimodal
`img2vec-neural`	Images	ResNet-50, self-hosted

Batch Import

python

articles = client.collections.get("Article")

with articles.batch.dynamic() as batch:
    for item in my_data:
        batch.add_object({"title": item["title"], "body": item["body"]})

Weaviate handles batching, rate limiting, and retries automatically.

Hybrid Search (BM25 + Vector)

python

results = articles.query.hybrid(
    query="LLM memory management techniques",
    alpha=0.5,  # 0 = pure BM25, 1 = pure vector
    limit=5,
)
for r in results.objects:
    print(r.properties["title"])

alpha=0.5 gives equal weight to keyword and semantic similarity. Tune based on your dataset.

GraphQL Query API

Weaviate also exposes a GraphQL API for complex queries:

bash

{
  Get {
    Article(
      nearText: {concepts: ["memory management"]}
      limit: 3
    ) {
      title
      body
      _additional { certainty }
    }
  }
}

Docker Self-Host

bash

version: "3.8"
services:
  weaviate:
    image: semitechnologies/weaviate:latest
    ports:
      - "8080:8080"
    environment:
      QUERY_DEFAULTS_LIMIT: 20
      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: "true"
      DEFAULT_VECTORIZER_MODULE: text2vec-openai
      ENABLE_MODULES: text2vec-openai,multi2vec-clip
      OPENAI_APIKEY: "${OPENAI_API_KEY}"

Weaviate Cloud

Weaviate Cloud offers a managed service with a free sandbox tier. Connect with:

python

client = weaviate.connect_to_weaviate_cloud(
    cluster_url="https://your-cluster.weaviate.network",
    auth_credentials=weaviate.classes.init.Auth.api_key("YOUR_KEY"),
)

Full documentation at weaviate.io/developers/weaviate.

#weaviate#multimodal#vector-search#graphql#hybrid-search

Mahmudul Haque Qudrati

CEO & ML Engineer

Visionary technologist, software engineer, and machine learning specialist. Founder and CEO of Pristren, directing engineering teams that ship production-grade AI/ML pipelines, mission-critical full-stack applications, and developer tooling. Creator of Zlyqor, the unified team workspace platform. Author of 540+ technical guides and benchmark research reports on large language models, agentic workflows, Model Context Protocol (MCP), and modern web stacks.