默认路径：索引时零API调用。零基础设施。零锁定。 一个面向LLM代理的知识图谱+MCP工具服务器，支持混合检索， 基于CDC的实时数据库同步，以及内置的韩国FTS。

韩国어 README

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5分钟开始

该命令构建一个微小的SQLite支持图，并运行三次搜索——全部 在索引时不调用任何LLM。省略内存中的--db， 零依赖烟雾测试。扩展示例的完整来源： examples/quickstart.py。

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为什么不直接叫RAG？

纯RAG通常从独立的块中回答。Synaptic先构建一个图， 因此，代理可以搜索、跟踪关系、检查结构化行并记忆 这些证据起了作用。

它不是向量数据库的替代品。它是图形和工具图层

Synaptic Memory

Default path: zero API calls at index time. Zero infra. Zero lock-in. A knowledge graph + MCP tool server for LLM agents, with hybrid retrieval, CDC-based live database sync, and Korean FTS built in.

한국어 README

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5-minute start

pip install "synaptic-memory[sqlite,korean,vector]"
synaptic-quickstart --db quickstart.db

That command builds a tiny SQLite-backed graph and runs three searches — all without calling any LLM at indexing time. Omit --db for an in-memory, zero-dependency smoke test. Full source for the expanded example: examples/quickstart.py.

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Why not just RAG?

Plain RAG usually answers from independent chunks. Synaptic builds a graph first, so an agent can search, follow relations, inspect structured rows, and remember which evidence helped.

It is not a vector database replacement. It is the graph and tool layer around your existing documents, SQL data, embedding endpoint, and agent runtime.

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Build and search

import asyncio
from synaptic import SynapticGraph

async def main():
    # Any data → knowledge graph (CSV, JSONL, directory)
    graph = await SynapticGraph.from_data("./my_data/", preset="rag")
    try:
        result = await graph.search("my question")
        print(result.nodes[0].node.title if result.nodes else "no result")
    finally:
        await graph.close()

asyncio.run(main())

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

Presets keep the common knobs compact:

# local: deterministic, no external services (default)
graph = await SynapticGraph.from_chunks(chunks, preset="local")

# rag: reads SYNAPTIC_EMBED_URL / SYNAPTIC_RERANK_URL if set
graph = await SynapticGraph.from_data("./docs/", preset="rag")

# agent: rag + deterministic component bridging for multi-turn exploration
graph = await SynapticGraph.from_data("./docs/", preset="agent")

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