Files

T

Igor Lins e Silva f20a1a30fe docs(website): align mempalaceofficial.com with honest benchmarks

Part of #875. Bring the VitePress site into line with the new README
and the reproducibility scorecard: drop category-error comparisons,
drop retracted claims, retain only metrics and caveats that survive
audit.

website/index.md
 - New tagline matches README (local-first, verbatim, pluggable backend,
   96.6% R@5 raw, zero API calls).
 - Replace the "MemPalace hybrid 100% / Supermemory ~99% / Mastra
   94.87% / Mem0 ~85%" comparison table with a single honest table
   showing MemPalace's own retrieval-recall numbers (raw 96.6%,
   hybrid v4 held-out 98.4%). Add an explicit sentence explaining why
   we no longer publish a cross-system table on the landing page
   (retrieval recall vs QA accuracy are different metrics).
 - Soften the "ChromaDB-powered vector search" feature blurb to be
   backend-agnostic, since the retrieval layer is pluggable.

website/reference/benchmarks.md
 - Full rewrite of the retrieval-recall tables. No more "100%"
   headline; honest held-out 98.4% R@5 replaces it. Added the
   model-agnostic rerank result (99.2% R@5 / 100% R@10 with
   minimax-m2.7 via Ollama) to show the pipeline is not Haiku-specific.
 - Drop the LoCoMo "Hybrid v5 + Sonnet rerank (top-50) 100%" row.
   With per-conversation session counts of 19-32 and top_k=50, the
   retrieval stage returns every session by construction — the number
   measures an LLM's reading comprehension, not retrieval.
 - Drop the cross-system comparison tables. Link out to each project's
   own research page (Mastra, Mem0, Supermemory) for their published
   numbers and metric definitions.
 - Rewrite reproduction commands to use the correct repository and
   demonstrate the new --llm-backend ollama flag.

website/concepts/the-palace.md
 - Remove the "+34%" row / paragraph. Wing/room filtering is standard
   metadata filtering in the vector store, not a novel retrieval
   mechanism — the April-7 note already retracted that framing; this
   finishes the retraction on the website where it had remained.

website/guide/searching.md
 - Same treatment for "34% retrieval improvement". Reframe as
   operational scoping, not a novel boost.

website/reference/contributing.md
 - Update the "palace structure matters" bullet to reflect the same
   framing: scoping-not-magic.

website/concepts/knowledge-graph.md
 - Replace the MemPalace-vs-Zep feature matrix with a short "related
   work" note that links to Zep's own documentation for authoritative
   details on their deployment model. Avoids claims we cannot verify
   at source.

2026-04-14 21:37:45 -03:00

2.7 KiB

Raw Blame History

Knowledge Graph

MemPalace includes a temporal entity-relationship graph — like Zep's Graphiti, but SQLite instead of Neo4j. Local and free.

What It Stores

Entity-relationship triples with temporal validity:

Subject → Predicate → Object [valid_from → valid_to]

Facts have time windows. When something stops being true, you invalidate it — and historical queries still find it.

Usage

Python API

from mempalace.knowledge_graph import KnowledgeGraph

kg = KnowledgeGraph()

# Add facts
kg.add_triple("Kai", "works_on", "Orion", valid_from="2025-06-01")
kg.add_triple("Maya", "assigned_to", "auth-migration", valid_from="2026-01-15")
kg.add_triple("Maya", "completed", "auth-migration", valid_from="2026-02-01")

# Query: everything about Kai
kg.query_entity("Kai")
# → [Kai → works_on → Orion (current), Kai → recommended → Clerk (2026-01)]

# Query: what was true in January?
kg.query_entity("Maya", as_of="2026-01-20")
# → [Maya → assigned_to → auth-migration (active)]

# Timeline
kg.timeline("Orion")
# → chronological story of the project

Invalidating Facts

When something stops being true:

kg.invalidate("Kai", "works_on", "Orion", ended="2026-03-01")

Now queries for Kai's current work won't return Orion. Historical queries still will.

MCP Tools

Through the MCP server, the knowledge graph is available as tools:

Tool	Description
`mempalace_kg_query`	Query entity relationships with time filtering
`mempalace_kg_add`	Add facts
`mempalace_kg_invalidate`	Mark facts as ended
`mempalace_kg_timeline`	Chronological entity story
`mempalace_kg_stats`	Graph overview

Storage

The knowledge graph uses SQLite with two tables:

entities — people, projects, tools, concepts:

id — lowercase normalized name
name — display name
type — person, project, tool, concept, etc.
properties — JSON blob for extra metadata

triples — relationships between entities:

subject → predicate → object
valid_from — when this became true
valid_to — when it stopped being true (NULL = still current)
confidence — 0.0 to 1.0
source_closet — link back to the verbatim memory

Database location: ~/.mempalace/knowledge_graph.sqlite3

Temporal entity-relationship graphs are a familiar pattern — Zep's Graphiti, for example, also exposes a bi-temporal model. MemPalace's knowledge graph is local-first (SQLite, everything on disk) and free; Zep is a managed service backed by Neo4j with its own pricing, SLAs, and compliance surface. See Zep's own documentation for authoritative details on their deployment model.

2.7 KiB Raw Blame History