> For the complete documentation index, see [llms.txt](https://docs.envector.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.envector.io/1.4.x/core-concepts/index.md). # Index An encrypted index organizes vectors for secure search. Index data is always encrypted; the server computes similarity without accessing plaintext vectors. ## Properties * Encryption: Cipher-only for index storage. * Query Modes: Currently, only Plaintext queries are supported to prioritize the protection of the DB itself. * Similarity: Inner Product (IP). * Dimension: 32-4096 (max 4096). ## Mental Model Think of an index like a table in an RDBMS: * Each "row" is a vector, and it can have associated metadata (e.g., ID, title, URI). * All vectors in the same index share the same fixed dimension (analogous to a fixed schema). * You can insert rows, query for similar rows, and remove the whole table when done. ## Lifecycle Inserted vectors flow through three logical stages: **ingest** (durable persistence), **indexing** (organizing the data into search-optimized form), and **search-ready** (reflected in queries). `Index.insert()` runs the full sequence by default, so most users do not need to think about the stages explicitly. For high-throughput pipelines that want to decouple ingest from indexing, see [Index Operation Lifecycle](/1.4.x/sdk-user-guide/advanced-user-guide/index-operation-lifecycle.md).
Index Lifecycle
## Guidance * Choose the dimension to match your embedding model. * Currently, queries are supported only in plaintext format to focus on protecting the DB itself. * Unload rarely used indexes to conserve memory. * Implement robust error handling for production. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## 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, and the optional `goal` query parameter: ``` GET https://docs.envector.io/1.4.x/core-concepts/index.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.