kotaemon: Self-Hosted Document QA with Citations in One Command
Plus replace MCP tools with modular skill files
Grab your coffee. Here are this week’s highlights.
📅 Today’s Picks
kotaemon: Self-Hosted Document QA with Citations in One Command
Problem
Building a RAG app for document Q&A usually means assembling a parser, vector database, retrieval pipeline, and UI from scratch.
Each piece has its own setup, and getting everything to work together can take hours of debugging.
Solution
kotaemon packages the entire RAG stack into a single Docker image, letting you skip the setup and go straight to asking questions.
Key features:
Citations linked to exact PDF pages for verifiable answers
Question answering across multiple documents with figures and tables
Works with local models or cloud APIs like OpenAI, Azure, and Groq
Extensible Gradio-based UI with multi-user document management
gws: Replace Bulky MCP Tools with 100+ Modular Skill Files
Problem
Connecting AI agents to Google Workspace through MCP often means injecting every tool definition into each request, even if only a couple are needed.
That overhead quickly eats into the token budget, leaving less room for reasoning and task execution.
Solution
gws solves this by replacing bulky tool definitions with 100+ modular SKILL.md files.
Agents load only the skills they need, keeping the context lean and efficient.
Key features:
Works with Claude Code, Cursor, Gemini CLI, and other AI agents out of the box
100+ skill files covering Google Docs, Sheets, Drive, Calendar, and more
Agents load only relevant skills instead of full tool definitions
☕️ Weekly Finds
unstructured [RAG] - Turn any document into clean, structured data ready for RAG pipelines and LLM applications.
json_repair [LLM] - Repair malformed JSON from LLMs, APIs, and logs. A drop-in replacement for json.loads() that auto-fixes broken output.
MindsDB [AI Agents] - Query AI models directly from your database using SQL. Connect 200+ data sources to LLMs, ML, and vector operations.
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