Vector Index

AI model performance is positively correlated with the amount of data fed to it, and enterprises must continuously input business details to obtain more accurate AI outputs

When revoking permissions, enterprises often only delete the access credentials to the source documents while leaving the vector index in the database; decommissioning and settlement requires assessing whether to remove the corresponding vectors from the index and trigger an index rebuild.

Decades of technical debt, fragile code dependencies and rigid relational databases leave legacy systems unable to support real-time data ingestion, vector indexing and low-latency API interactions.

Manually deleting model weights or vector indexes will cause the tool to re-download or rebuild them on next startup, which is time-consuming and may incur traffic costs.

Agent knowledge retrieval usually relies on vector indexes or keyword indexes, and the quality of the indexed text directly determines the recall results.

In a RAG assistant, a vector index may bypass the original system's permission checks, causing unauthorized users to silently obtain sensitive documents through semantic retrieval, and if personal data is involved, this may also constitute a compliance issue.

A vector index is essentially a copy of the documents, must be kept in sync with the data source, requires rebuilding the entire index when the embedding model is changed, and must enforce the same access permission controls as the original system.

As an independent storage system, a vector index has access control separate from the original system, making it prone to silent leaks where the original application has already restricted access but the assistant can still recall the documents; fixes include post-retrieval permission checks or synchronously writing permission metadata into the vector store and enforcing filtering.

Introducing a vector index adds maintenance burden: the copy must be kept in sync with the source data; the entire index must be rebuilt when the embedding model is changed; and the copy must enforce exactly the same access permissions as the original system.

The vector index itself is not the source of truth but a derived view; the original records must be kept in auditable storage so they can be rebuilt if the index is corrupted or migrated.

Most enterprise RAG systems bake access control into the vector index at the embedding stage, treating permissions like content freshness, so that revoked access rights can still return results through retrieval, forming a security lag window.

Denormalizing permissions into the vector index inherits all the weaknesses of cached authorization data: correct until it is no longer correct, with no mechanism to tell you when it starts to fail.

In practice, this requires a clear split in the pipeline from enterprise data lake to RAG: the vector index stays ignorant of identity, a thin, fast, synchronous authorization layer is retained to query the governance system directly on each retrieval request, scoped to the candidate documents returned by vector search, and monitored and alerted separately.

If a vector index cannot be incrementally updated, it will quickly fall out of sync with a continuously changing codebase, causing search results to become outdated.

Local vector indexing technology converts text into semantic vectors via an embedding model and stores them in a local database, enabling semantic retrieval to be completed entirely on the local machine without sending the original text to the cloud.

向量数据库通过嵌入模型将非结构化数据转化为高维向量，并通过近似最近邻算法（如HNSW、IVF）实现语义相似度检索