yohakuforce-context-hub 0.1.0

Context collection and storage foundation for AI projects (MCP-native)

Context-Hub

MCP-native context collection and storage for AI-assisted software projects.

Context-Hub collects project context (Slack messages, Backlog/Redmine issues, meeting notes) into a local SQLite or PostgreSQL store and exposes it over MCP (Model Context Protocol) and a REST API. AI agents such as Claude Desktop and Claude Code connect to it to answer questions about your project without sending raw data to third-party services.

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What is this?

Feature	Detail
MCP server	stdio transport — plug directly into Claude Desktop or Claude Code
REST API	FastAPI — for custom integrations and web UIs
Hybrid search	Vector (sqlite-vec / pgvector) + FTS5 keyword search, RRF fusion
Three profiles	quickstart (SQLite + mock), personal (SQLite + BGE-M3), production (PostgreSQL)
Zero-dependency start	pipx install then context-hub serve — no Docker, no Postgres, no API keys required

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Quick Start (60 seconds)

# Install
pipx install yohakuforce-context-hub

# Initialise with the zero-dependency quickstart profile
context-hub init --profile quickstart

# Apply database schema
context-hub migrate

# Start server (HTTP + MCP stdio on the same process)
context-hub serve

Then query it:

# REST
curl http://127.0.0.1:8000/health
# {"status":"ok","env":"development"}

# MCP version check (used by AI-PM at startup)
curl http://127.0.0.1:8000/mcp/version
# {"mcp_protocol_version":"2024-11-05","server":"context-hub","server_version":"0.1.0"}

Add to Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "context-hub": {
      "command": "context-hub",
      "args": ["serve", "--mcp-only"],
      "env": {
        "CONTEXT_HUB_API_KEY": "your-api-key"
      }
    }
  }
}

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Profiles

Profile	Database	Embedding	Scheduler	Use case
quickstart	SQLite (file)	mock (hash-based)	in-memory	Zero-dependency local dev
personal	SQLite (file)	BGE-M3*	SQLite	Single-user persistent storage
production	PostgreSQL	BGE-M3*	PostgreSQL	Production deployment

*BGE-M3 requires: pip install 'yohakuforce-context-hub[embedding]' (~2.3 GB model download on first run)

Switch profiles:

context-hub init --profile personal   # or production
context-hub migrate
context-hub serve

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Architecture Overview

┌─────────────────────────────────────────────────────┐
│                  Context-Hub Process                │
│                                                     │
│  ┌──────────────┐    ┌──────────────────────────┐  │
│  │  MCP Server  │    │     FastAPI REST API      │  │
│  │  (stdio)     │    │  /api/v1/{projects,query} │  │
│  └──────┬───────┘    └───────────┬──────────────┘  │
│         │                        │                  │
│         └───────────┬────────────┘                  │
│                     ▼                               │
│              QueryService (shared)                  │
│         VectorStore + FTS + RRF fusion              │
│                     │                               │
│         ┌───────────┴───────────┐                   │
│         ▼                       ▼                   │
│   SQLite (sqlite-vec)    PostgreSQL (pgvector)       │
│   SchedulerStore         SchedulerStore             │
└─────────────────────────────────────────────────────┘

Both MCP and HTTP layers are thin adapters over the shared QueryService. Neither layer owns business logic.

Data Sources

Ingestion adapters collect context and write to the store:

• Slack — messages and threads (mock or live via slack-sdk)
• Backlog — issues and wiki (mock or live)
• Redmine — issues and wiki (mock or live)

Settings Profiles (ADR-003)

Three pydantic-settings profiles inherit from a common base. Override any field with environment variables:

CH_PROFILE=quickstart   # default
CH_PROFILE=personal
CH_PROFILE=production

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CLI Reference

context-hub init     --profile [quickstart|personal|production]
context-hub migrate  [--dry-run] [--target HEAD] [--yes]
context-hub serve    [--host 127.0.0.1] [--port 8000]
                     [--mcp-only | --http-only]
                     [--reload]
context-hub ingest   [slack|backlog|redmine] [--mode mock|live]
context-hub query    "<text>" [--top-k 5] [--json] [--project-id <uuid>]

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Optional Extras

# BGE-M3 local embedding (personal / production profiles)
pip install 'yohakuforce-context-hub[embedding]'

# PostgreSQL support
pip install 'yohakuforce-context-hub[postgres]'

# Both
pip install 'yohakuforce-context-hub[embedding,postgres]'

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Docker

See examples/docker/ for Docker Compose configuration.

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Contributing

See CONTRIBUTING.md.

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Security

See SECURITY.md for vulnerability reporting.

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License

Apache-2.0 — see LICENSE.

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日本語セクション

Context-Hub は AI 支援ソフトウェア開発プロジェクト向けの MCP ネイティブなコンテキスト収集・保存基盤です。

Slack / Backlog / Redmine などのプロジェクトデータをローカルの SQLite または PostgreSQL に集約し、MCP (Model Context Protocol) と REST API の両方で公開します。Claude Desktop や Claude Code などの AI エージェントが直接接続し、プロジェクト固有の情報に基づいた回答を生成します。

特徴

• MCP 一級市民: stdio トランスポートで Claude Desktop / Claude Code から直接利用可能
• ハイブリッド検索: ベクトル検索 (sqlite-vec / pgvector) + FTS5 全文検索を RRF でフュージョン
• ゼロ依存スタート: pipx install のみで動作開始、Docker 不要、Postgres 不要
• 3 プロファイル: quickstart (SQLite + mock)、personal (SQLite + BGE-M3)、production (PostgreSQL)

クイックスタート

pipx install yohakuforce-context-hub
context-hub init --profile quickstart
context-hub migrate
context-hub serve