Weaviate: Multimodal Vector Search With Built-In Vectorizers
Weaviate vectorizes text and images automatically with pluggable vectorizer modules, and combines BM25 keyword search with vector search in a single hybrid query.
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):
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)
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:
{
Get {
Article(
nearText: {concepts: ["memory management"]}
limit: 3
) {
title
body
_additional { certainty }
}
}
}
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// written byFIG. AUTH-01
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Mahmudul Haque Qudrati
CEO & ML Engineer
CEO and ML Engineer at Pristren. Builds AI-powered software for teams and writes about machine learning, LLMs, developer tools, and practical AI applications.
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