# Weaviate Features and Practical Usage 🚀 Ever wished you could delete all the embedding-API plumbing from your app code? Weaviate's model provider integrations let you wire up vectorization, generation, and reranking just by writing it into your collection config. 📌 Title and Feature URL Title: Model provider integrations URL: 📝 Overview Weaviate integrates with 20+ model providers including OpenAI, Cohere, Google, AWS, Azure OpenAI, Mistral, Anthropic, Hugging Face, and Ollama. You can plug them into automatic embedding at import, automatic embedding of query text, generation for RAG, and reranking of search results. The big win is that your application no longer needs code to call an embedding API and pass vectors in. 🔧 How It Works Integrations fall into three roles: - Vectorizer (embeddings): text or multimodal vectorization. - Generative (LLM): text generation for RAG pipelines. - Reranker: result refinement (offered by Cohere, Jina AI, NVIDIA, Voyage AI). There are also two delivery forms. API-based providers (OpenAI, Google, Cohere, AWS Bedrock, etc.) call external services, while locally hosted options (Ollama, Hugging Face Transformers, Model2vec) run on your own infrastructure. API-based modules are enabled by default in v1.33+. 🛠 Practical Usage - Specify an embedding provider with Configure.Vectors at collection creation, and Weaviate vectorizes automatically at both import and query time. - Configure generation with Configure.Generative to run RAG over your search results. - Configure a reranker with Configure.Reranker. - Automatic vectorization targets text / text[] properties. Weaviate sorts property names alphabetically, concatenates them, optionally prepends the collection name, and sends the string to the model (you can also exclude properties per-field). 🎯 Use Cases - Internal document search: auto-vectorize body text at import, and auto-vectorize the query with the same model so they stay consistent. - Model swapping: change vendor or model by editing collection config only. - Closed-network requirements: use a locally hosted option like Ollama to keep data in-house through embedding generation. - RAG chat: combine retrieval and generation within the same configuration, minimizing external orchestration. ⚠️ Caveats - API-based providers require API keys and incur usage charges. - Rate limits follow each provider's policy; watch out during bulk imports. - For versions before v1.27, the concatenated string is lowercased before being sent to the model. - For versions before v1.33, set ENABLE_API_BASED_MODULES to use API-based modules. #Weaviate# #Embeddings#