Weaviate

Weaviate Tutorial

Weaviate is an open-source vector database built for AI applications. It lets you store, index, and query embeddings (numerical representations of text, images, or audio) so your app can understand similarity, meaning, and context. Used in semantic search, recommendation engines, chatbots, and RAG pipelines, Weaviate is a backbone technology for scaling AI.

Make Money With This 💰

• Offer Weaviate setup services for startups building AI apps.

• Create RAG-powered chatbots and monetise SaaS subscriptions.

Use Cases

• AI search engines: semantic retrieval beyond keywords.

• Chatbots & assistants: grounding answers in your own data.

• Recommender systems: content suggestions based on similarity.

• Enterprise knowledge base: private AI search across company docs.

Key Features

• Vector search: retrieve items by meaning, not keywords.

• Hybrid search: combine keyword + semantic queries.

• Multi-modal support: text, images, audio embeddings.

• Scalable & cloud-native with sharding and replication.

• Rich APIs: GraphQL, REST, and client libraries in multiple languages.

Getting Started

Step 1: Go to Weaviate.io

Step 2: Sign up for Weaviate Cloud Service (WCS) or download open-source version via Docker.

Step 3: Connect to an embedding model (e.g. OpenAI, Hugging Face).

Step 4: Ingest data (documents, images, audio).

Step 5: Query using semantic search or GraphQL API.

Example Prompt (for a chatbot built with Weaviate)

Type: “What’s the difference between GPT-3 and GPT-4?”

What you’ll see: AI retrieves accurate comparisons from stored documents instead of hallucinating.

Tool Snapshot: Pros & Cautions

Best if: you’re building apps with retrieval-augmented generation (RAG) or semantic search.

Not ideal if: you only need a simple keyword search (SQL or Elasticsearch may suffice).

Pricing Snapshot

• Open-source: free self-hosted.

• Weaviate Cloud Service (WCS): starts with free tier, then usage-based.

• Enterprise: custom SLAs and scaling options.

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