VecForge: A Local-First Hybrid Vector Database with Persistent Agent Memory and Order-Preserving Grover-Inspired Reranking

Retrieval-augmented applications and AI assistants need a vector store that runs inside the user's own process, keeps data encrypted on disk, combines semantic and keyword retrieval, and remembers information across conversations. This paper presents VecForge 2.0, an embedded Python vector database built for these needs. One SQLite file, optionally encrypted with SQLCipher, holds documents, vectors, metadata, access keys, agent sessions and index snapshots. Queries fuse FAISS dense search with an incremental BM25 index, and namespace and deletion constraints apply inside both indexes. A memory layer, served over the Model Context Protocol, ranks memories by relevance, importance and recency. The paper also proves that the Grover-inspired reranker of the previous release applies only the diffusion half of Grover's algorithm, so it can only keep or reverse a ranking, and replaces it with an O(N) closed form of the full algorithm that preserves order. Eighteen experiments evaluate every feature. On the SciFact benchmark, hybrid fusion reaches an nDCG@10 of 0.707, against 0.645 for dense search, 0.665 for BM25 and 0.681 for reciprocal rank fusion. Compared with the previous release, in-index constraints raise recall in small namespaces from 0.5% to 100%, incremental indexing makes insertion 183 times faster, and snapshots make reopening 9 to 17 times faster, while encryption and access control add little cost. The evaluation also reports where the design falls short, including slow, highly selective filters and memory deduplication that confuses paraphrases with changed facts. A single local file can therefore serve both retrieval and long-term memory for AI agents.