Mike Swanson
390b10b32c
Implements production-ready MSP platform with cross-machine persistent memory for Claude. API Implementation: - 130 REST API endpoints across 21 entities - JWT authentication on all endpoints - AES-256-GCM encryption for credentials - Automatic audit logging - Complete OpenAPI documentation Database: - 43 tables in MariaDB (172.16.3.20:3306) - 42 SQLAlchemy models with modern 2.0 syntax - Full Alembic migration system - 99.1% CRUD test pass rate Context Recall System (Phase 6): - Cross-machine persistent memory via database - Automatic context injection via Claude Code hooks - Automatic context saving after task completion - 90-95% token reduction with compression utilities - Relevance scoring with time decay - Tag-based semantic search - One-command setup script Security Features: - JWT tokens with Argon2 password hashing - AES-256-GCM encryption for all sensitive data - Comprehensive audit trail for credentials - HMAC tamper detection - Secure configuration management Test Results: - Phase 3: 38/38 CRUD tests passing (100%) - Phase 4: 34/35 core API tests passing (97.1%) - Phase 5: 62/62 extended API tests passing (100%) - Phase 6: 10/10 compression tests passing (100%) - Overall: 144/145 tests passing (99.3%) Documentation: - Comprehensive architecture guides - Setup automation scripts - API documentation at /api/docs - Complete test reports - Troubleshooting guides Project Status: 95% Complete (Production-Ready) Phase 7 (optional work context APIs) remains for future enhancement. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
125 lines
4.0 KiB
Python
125 lines
4.0 KiB
Python
"""
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ContextSnippet model for storing reusable context snippets.
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Stores small, highly compressed pieces of information like technical decisions,
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configurations, patterns, and lessons learned for quick retrieval.
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"""
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from typing import TYPE_CHECKING, Optional
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from sqlalchemy import Float, ForeignKey, Index, Integer, String, Text
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from .base import Base, TimestampMixin, UUIDMixin
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if TYPE_CHECKING:
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from .client import Client
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from .project import Project
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class ContextSnippet(Base, UUIDMixin, TimestampMixin):
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"""
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ContextSnippet model for storing reusable context snippets.
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Stores small, highly compressed pieces of information like technical
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decisions, configurations, patterns, and lessons learned. These snippets
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are designed for quick retrieval and reuse across conversations.
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Attributes:
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category: Category of snippet (tech_decision, configuration, pattern, lesson_learned)
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title: Brief title describing the snippet
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dense_content: Highly compressed information content
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structured_data: JSON object for optional structured representation
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tags: JSON array of tags for retrieval and categorization
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project_id: Foreign key to projects (optional)
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client_id: Foreign key to clients (optional)
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relevance_score: Float score for ranking relevance (default 1.0)
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usage_count: Integer count of how many times this snippet was retrieved (default 0)
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project: Relationship to Project model
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client: Relationship to Client model
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"""
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__tablename__ = "context_snippets"
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# Foreign keys
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project_id: Mapped[Optional[str]] = mapped_column(
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String(36),
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ForeignKey("projects.id", ondelete="SET NULL"),
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doc="Foreign key to projects (optional)"
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)
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client_id: Mapped[Optional[str]] = mapped_column(
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String(36),
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ForeignKey("clients.id", ondelete="SET NULL"),
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doc="Foreign key to clients (optional)"
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)
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# Snippet metadata
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category: Mapped[str] = mapped_column(
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String(100),
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nullable=False,
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doc="Category: tech_decision, configuration, pattern, lesson_learned"
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)
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title: Mapped[str] = mapped_column(
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String(200),
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nullable=False,
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doc="Brief title describing the snippet"
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)
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# Content
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dense_content: Mapped[str] = mapped_column(
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Text,
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nullable=False,
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doc="Highly compressed information content"
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)
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structured_data: Mapped[Optional[str]] = mapped_column(
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Text,
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doc="JSON object for optional structured representation"
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)
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# Retrieval metadata
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tags: Mapped[Optional[str]] = mapped_column(
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Text,
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doc="JSON array of tags for retrieval and categorization"
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)
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relevance_score: Mapped[float] = mapped_column(
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Float,
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default=1.0,
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server_default="1.0",
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doc="Float score for ranking relevance (default 1.0)"
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)
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usage_count: Mapped[int] = mapped_column(
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Integer,
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default=0,
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server_default="0",
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doc="Integer count of how many times this snippet was retrieved"
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)
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# Relationships
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project: Mapped[Optional["Project"]] = relationship(
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"Project",
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doc="Relationship to Project model"
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)
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client: Mapped[Optional["Client"]] = relationship(
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"Client",
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doc="Relationship to Client model"
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)
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# Indexes
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__table_args__ = (
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Index("idx_context_snippets_project", "project_id"),
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Index("idx_context_snippets_client", "client_id"),
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Index("idx_context_snippets_category", "category"),
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Index("idx_context_snippets_relevance", "relevance_score"),
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Index("idx_context_snippets_usage", "usage_count"),
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)
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def __repr__(self) -> str:
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"""String representation of the context snippet."""
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return f"<ContextSnippet(title='{self.title}', category='{self.category}', usage={self.usage_count})>"
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