synaptic-memory 0.14.3

Knowledge graph + MCP tool server for LLM agents — 3rd-gen retrieval, graph-aware multi-turn exploration, structured data tools, DB→ontology one-liner.

Synaptic Memory

Knowledge graph + MCP tool server for LLM agents.

Any data in, structured graph out. LLM agents explore it with 29 atomic tools.

한국어 README

---

2 Lines to Start

from synaptic import SynapticGraph

# Any data → knowledge graph (CSV, JSONL, directory)
graph = await SynapticGraph.from_data("./my_data/")

# Or directly from a database — SQLite / PostgreSQL / MySQL / Oracle / MSSQL
graph = await SynapticGraph.from_database(
    "postgresql://user:pass@host:5432/dbname"
)

# Live database? Use CDC mode and only re-read what changed.
graph = await SynapticGraph.from_database(
    "postgresql://user:pass@host:5432/dbname",
    db="knowledge.db",
    mode="cdc",       # deterministic node IDs + sync state recorded
)
result = await graph.sync_from_database(
    "postgresql://user:pass@host:5432/dbname"
)
print(result.added, result.updated, result.deleted)

# Or bring your own chunker (LangChain, Unstructured, custom OCR, ...)
chunks = my_parser.split("manual.pdf")
graph = await SynapticGraph.from_chunks(chunks)

# Search
result = await graph.search("my question")

That's it. Auto-detects file format or DB schema, generates ontology profile, ingests, indexes, builds FK edges.

Live database sync (CDC) — mode="cdc" enables incremental updates: tables with an updated_at-style column are read with a watermark filter, others fall back to per-row content hashing. Deletes are detected via a TEMP TABLE LEFT JOIN; FK rewires re-link the corresponding RELATED edges. Search results are identical to a full reload (locked in by a regression test). Supports SQLite, PostgreSQL, MySQL/MariaDB.

Office files (PDF/DOCX/PPTX/XLSX/HWP) are supported through the optional xgen-doc2chunk package. Install with pip install synaptic-memory[docs] or use from_chunks() with your own parser.

---

What it does

Your data (CSV, JSONL, PDF/DOCX/PPTX/XLSX/HWP, SQL database)
  ↓  auto-detect format / auto-discover DB schema + FKs
  ↓  DocumentIngester (text) / TableIngester / DbIngester
  ↓
Knowledge Graph
  ├─ Documents: Category → Document → Chunk
  └─ Structured: table rows as ENTITY nodes + RELATED edges (FKs)
  ↓
29 MCP tools → LLM agent explores via graph-aware multi-turn tool use

Two jobs, nothing else:

1. Build the graph well — cheap extraction, no LLM at index time
2. Give the LLM good tools — the agent decides what to search

---

Install

pip install synaptic-memory                # Core (zero deps)
pip install synaptic-memory[sqlite]        # + SQLite FTS5 backend
pip install synaptic-memory[korean]        # + Kiwi morphological analyzer
pip install synaptic-memory[vector]        # + usearch HNSW index
pip install synaptic-memory[mcp]           # + MCP server for Claude
pip install synaptic-memory[all]           # Everything

---

Quick Start

Option A: Two lines (easiest)

from synaptic import SynapticGraph

# CSV file
graph = await SynapticGraph.from_data("products.csv")

# JSONL documents
graph = await SynapticGraph.from_data("documents.jsonl")

# Entire directory (scans all CSV/JSONL)
graph = await SynapticGraph.from_data("./my_corpus/")

# With embedding (optional, improves semantic search)
graph = await SynapticGraph.from_data(
    "./my_corpus/",
    embed_url="http://localhost:11434/v1",
)

# Search
result = await graph.search("my question")

Option B: MCP server (Claude Desktop / Code)

synaptic-mcp --db my_graph.db
synaptic-mcp --db my_graph.db --embed-url http://localhost:11434/v1

Claude can now call 29 tools to explore your graph.

Option C: Full control

from synaptic.backends.sqlite_graph import SqliteGraphBackend
from synaptic.extensions.domain_profile import DomainProfile
from synaptic.extensions.document_ingester import DocumentIngester, JsonlDocumentSource

profile = DomainProfile.load("my_profile.toml")
backend = SqliteGraphBackend("graph.db")
await backend.connect()

source = JsonlDocumentSource("docs.jsonl", "chunks.jsonl")
ingester = DocumentIngester(profile=profile, backend=backend)
await ingester.ingest(source)

---

3rd-Generation Retrieval

Generation	Approach	LLM cost at indexing
1st (GraphRAG)	LLM extracts entities + relations + summaries	High
2nd (LightRAG)	Delays LLM to query time	Medium
3rd (this)	Relation-free graph, hybrid retrieval	Zero

No LLM at indexing. The graph is a search index, not a knowledge base.

---

Agent Tools (29 total)

Text search tools

Tool	Purpose
deep_search	Recommended. Search → expand → read documents in ONE call
compare_search	Auto-decompose multi-topic queries, search in parallel
search	FTS + vector hybrid search
expand	1-hop graph neighbours
get_document	Full document with query-relevant chunks
search_exact	Literal substring match for IDs/codes
follow	Walk a specific edge type

Structured data tools

Tool	Purpose
filter_nodes	Property filter (>=, <=, contains) — returns {total, showing} for accurate counting
aggregate_nodes	GROUP BY + COUNT/SUM/AVG/MAX/MIN with optional WHERE pre-filter
join_related	FK-based related record lookup — walks RELATED edges (O(degree))

Navigation tools

Tool	Purpose
list_categories	Category list with document counts
count	Structural count by kind/category/year
session_info	Multi-turn session state

All tools return { data, hints, session }. The SearchSession tracks seen nodes across turns so the agent never re-reads the same chunk.

---

Retrieval Pipeline

Query
  ↓  Kiwi morphological analysis (Korean) or regex (other)
  ↓  BM25 FTS + title 3x boost + substring fallback
  ↓  Vector search (usearch HNSW, optional)
  ↓  Vector PRF (pseudo relevance feedback, 2-pass)
  ↓  PPR graph discovery (personalized pagerank)
  ↓  GraphExpander (1-hop: category siblings, chunk-next, entity mentions)
  ↓  HybridReranker (lexical + semantic + graph + structural + authority + temporal)
  ↓  MaxP document aggregation (coverage bonus)
  ↓  Cross-encoder reranker (bge-reranker-v2-m3 via TEI, optional)
  ↓  EvidenceAggregator (MMR diversity + per-doc cap + category coverage)
Result

---

Benchmarks

Single-shot retrieval (EvidenceSearch + embed + reranker)

Dataset	Type	Nodes	MRR	Hit
KRRA Easy	Korean documents	19,720	0.967	20/20
KRRA Hard	Korean documents	19,720	1.000	15/15
X2BEE Easy	PostgreSQL (e-commerce)	19,843	1.000	20/20
assort Easy	Fashion CSV	13,909	0.867	13/15
HotPotQA-24	English multi-hop	226	0.964	24/24
Allganize RAG-ko	Korean enterprise	200	0.905	—
Allganize RAG-Eval	Finance/medical/legal KO	300	0.874	—
PublicHealthQA	Korean public health	77	0.600	56/77

Multi-turn agent (GPT-4o-mini, 5 turns max)

Dataset	Result
KRRA Hard agent	10-13/15 (67-87%)
X2BEE Hard agent	17/19 (89%)
assort Hard agent	12/15 (80%)

Structured data queries (filter / aggregate / FK join / count) work end-to-end through graph-aware tools.

---

Architecture

SynapticGraph.from_data("./data/")          ← Easy API
  ↓
Auto-detect → DomainProfile → Ingest → Index
  ↓
StorageBackend (Protocol)
  ├── MemoryBackend        (testing)
  ├── SqliteGraphBackend   (recommended, FTS5 + HNSW)
  ├── KuzuBackend          (embedded Cypher)
  ├── PostgreSQLBackend    (pgvector)
  └── CompositeBackend     (mix backends)
  ↓
Retrieval pipeline (BM25 + vector + PRF + PPR + reranker + MMR)
  ↓
Agent tools (29) → MCP server → LLM agent

---

Backends

Backend	Vector Search	Scale	Use Case
MemoryBackend	cosine	~10K	Testing
SqliteGraphBackend	usearch HNSW	~100K	Default
KuzuBackend	HNSW	~10M	Graph-heavy
PostgreSQLBackend	pgvector	~1M	Production
CompositeBackend	Qdrant	Unlimited	Scale-out

---

Optional Extras

Extra	What it adds
korean	Kiwi morphological analyzer for Korean FTS
vector	usearch HNSW index (100x faster vector search)
embedding	aiohttp for embedding API calls
mcp	MCP server for Claude Desktop/Code
sqlite	aiosqlite backend
docs	xgen-doc2chunk for PDF/DOCX/PPTX/XLSX/HWP loading

---

Documentation

Doc	What it is
docs/GUIDE.md	Friendly intro — what/why/how, zero jargon
docs/TUTORIAL.md	30-minute hands-on walkthrough
docs/CONCEPTS.md	3rd-gen GraphRAG + pipeline internals
docs/ARCHITECTURE.md	Original neural-inspired design
docs/COMPARISON.md	vs GraphRAG / LightRAG / LazyGraphRAG
docs/ROADMAP.md	Future plans

Dev

uv sync --extra dev --extra sqlite --extra mcp
uv run pytest tests/ -q                   # 687+ tests
uv run ruff check --fix

License

MIT