npx shadcn add @memcity/memory-searchVector search, knowledge graph, episodic memory, and a production-grade 16-step RAG pipeline. One component. One line to install.
npx shadcn add @memcity/memory-searchimport { Memory } from "memcity";
const memory = new Memory(components.memcity, {
tier: "pro",
ai: {
gateway: "openrouter",
model: "google/gemini-2.0-flash-001",
},
});
// Ingest a document
await memory.ingest({
text: documentContent,
knowledgeBaseId: "kb_engineering",
source: "design-doc.md",
});
// Search with the full 16-step pipeline
const results = await memory.search({
query: "How does authentication work?",
knowledgeBaseId: "kb_engineering",
});How it works
From raw documents to intelligent search results. See what happens under the hood.
PDF, Markdown, images, audio, video — 25+ file types. Text is chunked with configurable overlap and heading preservation.
Each chunk gets embedded (Jina v4, 1024d), indexed for BM25, and optionally analyzed for entities and relationships.
Query routing, HyDE, hybrid search, RRF fusion, reranking, chunk expansion, citations, and caching. All configurable.
Architecture
Vector search, knowledge graph, episodic memory, and RAG pipeline — all connected through Convex's real-time database.
Hybrid vector search with weighted RRF fusion for best retrieval quality.
Automatic NER with BFS, best-first, and hybrid graph traversal strategies.
Episodic memory with temporal decay and consolidation over conversations.
Every step is a toggle. Disable what you don't need, tune what you do.
Capabilities
From basic vector search to enterprise-grade knowledge management, all in a single Convex component.
From query routing to cached results, every search passes through a production-grade retrieval pipeline you can configure per-query.
Automatic entity extraction with relationship tracking. Your documents become a navigable graph of connected concepts.
PDF, DOCX, images (OCR), audio transcription, video, spreadsheets, and more. All processed automatically.
Per-user memory with decay and consolidation. Your AI remembers conversations over time.
proRecursive abstractive processing for tree-organized retrieval. High-level answers from large corpora.
proSemantic embeddings plus BM25 keyword search with weighted RRF fusion.
Fine-grained document-level permissions with principal hierarchies for users, roles, and groups.
teamImmutable audit trail for every search, ingest, and deletion. Full compliance support.
teamPer-organization document limits, storage caps, and API rate limiting with automatic enforcement.
teamUnder the hood
Every query passes through a production-grade retrieval pipeline. Here's what happens when you call memory.search()
$ memcity pipeline --verbose
[01] quota_check -- 2ms PASS
[02] cache_lookup -- 1ms MISS
[03] query_routing -- 15ms complexity=high
[04] decomposition -- 45ms sub_queries=3
[05] query_expansion -- 32ms variations=4
[06] hyde_generation -- 120ms hypothetical_docs=2
[07] embedding -- 28ms dimensions=1024
[08] hybrid_search -- 85ms semantic=50 bm25=50
[09] rrf_fusion -- 3ms candidates=42
[10] acl_filter -- 5ms permitted=38
[11] dedup+graphrag -- 95ms entities=12 edges=28
[12] reranking -- 110ms jina_v3
[13] chunk_expansion -- 45ms expanded=8
[14] citations -- 12ms breadcrumbs=8
[15] raptor_summary -- 65ms summaries=3
[16] format+cache -- 4ms cached=true
total: 667ms | tokens: 3,420 | cost: $0.0028Pricing
One-time payment. Lifetime updates. No subscriptions.
Perfect for prototyping and small projects.
Full pipeline for production applications.
Enterprise controls for multi-tenant apps.
Feedback themes
“The one-command install plus sane defaults got us to production faster than wiring a custom RAG stack from scratch.”
Integration Team
Common feedback theme @ Early adopter interviews
“Hybrid search plus reranking materially improved answer relevance on noisy internal docs.”
Platform Team
Common feedback theme @ Early adopter interviews
“Document-level ACLs and audit trails were mandatory for enterprise evaluations.”
Security Team
Common feedback theme @ Early adopter interviews
“Large file support (PDF, images, audio, and video) reduced ingestion edge cases and support burden.”
Data Ops Team
Common feedback theme @ Early adopter interviews
“Usage quotas and tier controls gave us predictable cost boundaries before broad rollout.”
Product Team
Common feedback theme @ Early adopter interviews
“Graph-based retrieval helped surface relationships keyword search kept missing.”
Applied AI Team
Common feedback theme @ Early adopter interviews
Start with the free community tier. Upgrade when you need knowledge graphs, file processing, or enterprise controls.