mention weaviate as an option (langchain-ai#1770)

docs/docs/modules/indexes/vector_stores/index.mdx

@@ -87,6 +87,7 @@ Here's a quick guide to help you pick the right vector store for your use case:
 - If you're looking for something that can run in-memory in browser-like environments, then go for [MemoryVectorStore](./integrations/memory)
 - If you come from Python and you were looking for something similar to FAISS, pick [HNSWLib](./integrations/hnswlib) or [Faiss](./integrations/faiss)
 - If you're looking for an open-source full-featured vector database that you can run locally in a docker container, then go for [Chroma](./integrations/chroma)
+- If you're looking for an open-source production-ready vector database that you can run locally (in a docker container) or hosted in the cloud, then go for [Weaviate](./integrations/weaviate).
 - If you're using Supabase already then look at the [Supabase](./integrations/supabase) vector store to use the same Postgres database for your embeddings too
 - If you're looking for a production-ready vector store you don't have to worry about hosting yourself, then go for [Pinecone](./integrations/pinecone)
 - If you are already utilizing SingleStore, or if you find yourself in need of a distributed, high-performance database, you might want to consider the [SingleStore](./integrations/singlestore) vectore store.