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spikuit-core

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Core engine + lightweight RAG client for Spikuit — a knowledge base that gets smarter the more you use it.

⚠️ Pre-1.0 / under active development. Expect frequent breaking changes until v1.0.0.

spikuit-core ships in two profiles:

Install Pulls in Use case
pip install spikuit-core httpx, numpy Read-only RAG client over an exported brain.db bundle
pip install spikuit-core[engine] + fsrs, networkx, aiosqlite, sqlite-vec, msgspec Full Brain engine: FSRS scheduling, knowledge graph, spreading activation, STDP

The minimal install is what you deploy to a server — the heavy engine deps stay on the authoring machine.

Quick start (RAG client)

Author a brain locally with the full engine (or via the spkt CLI from spikuit-cli), export it once, then retrieve from anywhere with just spikuit-core:

import asyncio
import os

from spikuit_core import QABot

os.environ["SPIKUIT_EMBEDDER_BASE_URL"] = "http://localhost:1234/v1"

brain = QABot.load("brain.db")

async def main() -> None:
    hits = await brain.retrieve("What is a monad?", limit=5, domain="math")
    for h in hits:
        print(f"{h.score:.3f}  {h.content[:80]}")

asyncio.run(main())

QABot.load resolves the embedder endpoint in this order:

  1. SPIKUIT_EMBEDDER_BASE_URL / SPIKUIT_EMBEDDER_API_KEY env vars
  2. base_url= / api_key= keyword arguments
  3. The hint stored in the bundle

If the bundle was exported with provider="none", QABot runs in keyword-only mode and never calls an embedding API.

Quick start (full engine)

import asyncio
from spikuit_core import Circuit, Neuron

async def main() -> None:
    c = Circuit(db_path="brain.db")
    await c.connect()
    await c.add_neuron(
        Neuron.create("---\ntype: concept\ndomain: math\n---\n# Monad\n\n型の文脈化を表す抽象")
    )
    await c.close()

asyncio.run(main())

Engine symbols (Circuit, Neuron, Session, etc.) are loaded lazily via PEP 562 __getattr__ — importing them without the [engine] extras raises a friendly ImportError pointing at the install command.

License

Apache-2.0

Metadata

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