Weaviate

Overview

Weaviate is an open-source, cloud-native vector database designed to store, index, and search high-dimensional vector embeddings. Founded in 2019, it positions itself as a core component for building AI-native applications, particularly those requiring semantic understanding and retrieval capabilities. Unlike traditional databases that rely on exact matches or keyword searches, Weaviate stores data objects alongside their vector representations, which are numerical arrays that capture the semantic meaning of data points. This allows for similarity searches based on semantic proximity, rather than just keyword presence.

Developers use Weaviate to enable various AI-powered functionalities, including semantic search, where users can query using natural language and receive results based on meaning, even if specific keywords aren't present. It is also central to Retrieval Augmented Generation (RAG) architectures, where a large language model (LLM) retrieves relevant information from a knowledge base (stored in Weaviate) before generating a response, thereby reducing hallucinations and grounding responses in specific data. Other use cases include recommendation systems that suggest items based on user preferences and item similarity, as well as anomaly detection by identifying data points that are semantically distant from the norm.

Weaviate supports various data types, including text, images, and custom data, by converting them into vectors using integrated or external machine learning models. It offers a GraphQL-like API for interaction and provides client libraries for multiple programming languages, including Python and TypeScript/JavaScript, which are often cited as primary examples for new users. The platform allows for straightforward local development using Docker, and its managed cloud service, Weaviate Cloud (WCD), offers a scalable deployment option. Weaviate's architecture is designed for scalability and performance, utilizing techniques like Hierarchical Navigable Small Worlds (HNSW) for efficient nearest neighbor searches, as detailed in its vector index documentation.

The system's adaptability for different use cases, from question answering to content moderation, stems from its ability to handle diverse data modalities and integrate with various embedding models and large language models. This flexibility positions Weaviate as a foundational layer for developers building intelligent applications that require a semantic understanding of their data.

Key features

• Vector Storage and Indexing: Stores data objects and their high-dimensional vector embeddings, optimizing them for similarity search.
• Semantic Search: Enables queries based on meaning and context rather than keywords, retrieving results semantically similar to the query.
• Retrieval Augmented Generation (RAG): Facilitates grounding large language models (LLMs) by providing relevant contextual information from a knowledge base, reducing model hallucinations.
• Multi-modal Data Support: Handles various data types including text, images, and custom data by converting them into vectors.
• Integrated Vectorization: Supports modules for automatically generating vectors from data using pre-trained models or integrating with external embedding services.
• Scalability and Performance: Designed for high-throughput and low-latency vector searches, utilizing efficient indexing algorithms like HNSW.
• GraphQL API and Client Libraries: Offers a GraphQL-like API for flexible data interaction and provides SDKs for Python, TypeScript/JavaScript, Go, Java, and Ruby.
• Real-time Data Import: Supports real-time data ingestion, allowing applications to work with up-to-date information.
• Filtering and Aggregation: Provides capabilities to filter search results based on object properties and perform aggregations on vector data.
• Hybrid Search: Combines vector search with keyword-based search for comprehensive retrieval, as outlined in the Weaviate hybrid search guide.

Pricing

Weaviate offers both a managed cloud service (Weaviate Cloud, WCD) and an open-source version. The WCD provides a free sandbox for evaluation and a consumption-based pricing model for paid tiers. As of June 2026, the WCD Launch tier starts at a fixed hourly rate.

For detailed and up-to-date pricing, refer to the official Weaviate pricing page.

Common integrations

• Generative AI Models: Integrates with large language models (LLMs) from providers like OpenAI, Cohere, and Hugging Face for vectorization and RAG applications, enabling capabilities like those described in Hugging Face's BERT documentation.
• Embedding Models: Supports various embedding models (e.g., Transformer models, sentence-transformers) to convert raw data into vector embeddings.
• Apache Kafka: Can be integrated with Apache Kafka for real-time data ingestion and stream processing before vectorization.
• Docker: Used for local development and deployment of the open-source Weaviate instance, as described in the Weaviate Docker Compose guide.
• Kubernetes: Deployable on Kubernetes for scalable and production-grade self-managed deployments, often using tools like Rancher for Kubernetes management.
• Data Ingestion Pipelines: Connects with various data sources and ETL tools to populate the vector database with embeddings.

Alternatives

• Pinecone: A managed vector database service focused on large-scale AI applications, offering a serverless architecture.
• Qdrant: An open-source vector similarity search engine and database, providing both a self-hosted option and a managed cloud service.
• Milvus: An open-source vector database built for AI applications, supporting vector similarity search across various data types.
• Redis Stack: Includes RedisGears and RedisJSON modules which can be combined with vector embeddings for similarity search, offering persistence and real-time capabilities.
• PostgreSQL with pgvector: An extension for PostgreSQL that adds vector similarity search capabilities, leveraging existing relational database infrastructure.

Getting started

To get started with Weaviate, you can use the Python client to create a schema, add data, and perform a vector search. First, ensure you have Weaviate running, either locally via Docker or through a Weaviate Cloud instance.

import weaviate
import json

# Replace with your Weaviate instance URL
client = weaviate.Client("http://localhost:8080") # For local instance
# client = weaviate.Client(
#     url = "YOUR_WEAVIATE_CLOUD_URL",
#     auth_client_secret=weaviate.AuthApiKey("YOUR_API_KEY")
# )

# 1. Define a schema for your data
class_obj = {
    "class": "Question",
    "vectorizer": "text2vec-transformers", # Or other module like 'text2vec-openai'
    "properties": [
        {
            "name": "answer",
            "dataType": ["text"],
            "description": "The answer to the question"
        },
        {
            "name": "question",
            "dataType": ["text"],
            "description": "The question itself"
        }
    ]
}

# Add the class to Weaviate
client.schema.create_class(class_obj)
print("Schema created successfully.")

# 2. Add data
data = [
    {"question": "What is the capital of France?", "answer": "Paris"},
    {"question": "What is the largest ocean on Earth?", "answer": "Pacific Ocean"},
    {"question": "Who wrote 'Hamlet'?", "answer": "William Shakespeare"}
]

with client.batch as batch:
    for item in data:
        batch.add_data_object(item, "Question")

print("Data added successfully.")

# 3. Perform a semantic search
search_results = client.query
    .get("Question", ["question", "answer"])
    .with_near_text({"concepts": ["French cities"]})
    .with_limit(1)
    .do()

print("\nSemantic Search Results:")
for result in search_results["data"]["Get"]["Question"]:
    print(f"Question: {result['question']}, Answer: {result['answer']}")

# Expected Output (may vary slightly based on vectorizer):
# Semantic Search Results:
# Question: What is the capital of France?, Answer: Paris

# Optional: Clean up (uncomment to delete the class)
# client.schema.delete_class("Question")
# print("Schema deleted.")

This Python example demonstrates how to define a data schema, ingest data, and execute a semantic search using Weaviate's client library. For more detailed instructions and advanced features, consult the official Weaviate Quickstart Guide.