Does this skill support async Python development?

Yes, the skill enforces the Semantik protocol where all I/O methods, such as 'load_documents', must be implemented as asynchronous iterators or methods.

What is a Semantik plugin?

A Semantik plugin is a Python-based extension that adds capabilities like document loading, text chunking, or embedding to the Semantik semantic search platform.

What types of plugins can I build with this skill?

This skill supports six plugin types: connectors (sources), embeddings (vectors), chunking (segmentation), rerankers (ordering), extractors (metadata), and agents (LLM logic).

Do I need to install the core Semantik application to develop a plugin?

No, you can use the recommended protocol-based approach which uses structural typing (duck typing), allowing you to develop plugins with zero dependencies on Semantik.

How do I handle sensitive API keys in my plugin?

You can define fields in the 'get_secret_fields' method to ensure they are encrypted at rest, or use the '_env' suffix pattern to resolve values from environment variables.

Semantik Plugin Development

Name: Semantik Plugin Development
Author: jbmiller10

byjbmiller10

Data Science & ML

Builds custom plugins for the Semantik search engine to handle document ingestion, embeddings, and AI-powered reranking.

About

This skill provides comprehensive guidance for developing plugins for Semantik, a self-hosted semantic search platform. It facilitates the creation of connectors, embedding providers, chunking strategies, rerankers, and autonomous agents through a protocol-based Python architecture. By utilizing this skill, developers can extend Semantik's core capabilities, implement custom data sources, and integrate specific LLM behaviors while following established best practices for security, testing, and async implementation.

Key Features

Template generation for 6 specific plugin types including connectors and agents
Automated configuration and secret field schema definitions for the Semantik UI
Detailed data format references for IngestedDocuments, ChunkDicts, and RerankResults
Protocol-based development guidance ensuring zero-dependency integration
Standardized testing patterns using protocol validation and mock fixtures
0 GitHub stars

Use Cases

Building a custom data connector to ingest proprietary company documents into a semantic search index
Implementing a specialized embedding plugin to use internal or niche vector models
Creating an LLM-powered agent that executes complex workflows based on retrieved search results

About

Key Features

Template generation for 6 specific plugin types including connectors and agents
Automated configuration and secret field schema definitions for the Semantik UI
Detailed data format references for IngestedDocuments, ChunkDicts, and RerankResults
Protocol-based development guidance ensuring zero-dependency integration
Standardized testing patterns using protocol validation and mock fixtures
0 GitHub stars

Use Cases

Building a custom data connector to ingest proprietary company documents into a semantic search index
Implementing a specialized embedding plugin to use internal or niche vector models
Creating an LLM-powered agent that executes complex workflows based on retrieved search results