# Embedding stores To use RAG, you need a place to store the embeddings (the vectors). These places are called Vector Stores (or EmbeddingStore in the Langchain4J library). The agent SDK includes support out of the box for two stores: 1. **A simple embedding store that keeps things in memory.** This is the default store. It is not particularly sophisticated with how it looks things up, but is a good option when loading simple documents from the classpath. No other configuration is required to activate this store. 2. **Pinecone.** [Pinecone.io](https://pinecone.io/) is a much more advanced vector database for processing and searching your documents. They include a free version in addition to paid versions. ### Configuring Pinecone To enable Pinecone support, add the following to `application.properties`. You'll receive your key after you create your first instance in Pinecone. The built-in retrievers for Organization Uploads and Personal Uploads will create the necessary indices you need along with namespaces as needed. If you create other retrievers, you'll need to manage that piece yourself. ```properties peoplelogic.agent.rag.store.type=pinecone peoplelogic.agent.rag.store.key=pcsk_xxxx peoplelogic.agent.rag.store.host=https://host.svc.aped-4627-b74a.pinecone.io ``` ### Connecting other stores In addition to the built-in stores, you can pull in support for other embedding stores through Langchain4J. We are happy to help you get this setup - just Slack us or email . We'll show where to set this up when you create Content Retrievers later in the documentation. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.peoplelogic.dev/guides/getting-started-with-the-talent-agent-sdk/working-with-documents/embedding-stores.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.