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onlyoffice-rag-pipeline

Extract, chunk, embed, and index OnlyOffice documents for RAG systems.

Updated Jun 19, 2025
Search Tags:#document processing#knowledge base#RAG#embedding#OnlyOffice
One-click install
npx skills add https://github.com/gatovillano/KognitoAI --skill onlyoffice-rag-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: onlyoffice-rag-pipeline
Source: https://github.com/gatovillano/KognitoAI/tree/main/skills/onlyoffice_rag_pipeline
Command: npx skills add https://github.com/gatovillano/KognitoAI --skill onlyoffice-rag-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sqlalchemy, pydantic, asyncio, langchain_core, uuid, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the integration of OnlyOffice documents with the RAG (Retrieval-Augmented Generation) system, streamlining document processing and knowledge base augmentation.

Core Features & Use Cases

  • Document Processing: Extracts text from OnlyOffice documents and prepares it for indexing.
  • Embedding Generation: Creates embeddings for text chunks to enable efficient searching.
  • Indexing: Stores documents and their embeddings in the RAG system for fast retrieval.
  • Use Case: For organizations looking to automate the process of indexing their OnlyOffice documents into a RAG system for quick access to information.

Quick Start

Use the onlyoffice-rag-pipeline skill to process documents from the OnlyOffice platform and integrate them with the KAI system using the command: ProcessOnlyOfficeDocumentTool run --document_id uuid-del-documento

Frequently Asked Questions about onlyoffice-rag-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate OnlyOffice document processing for a RAG knowledge base?▼

Automating OnlyOffice document processing for a RAG knowledge base involves extracting text, chunking it, generating embeddings, and indexing the data. This pipeline uses Python scripts with sqlalchemy and pydantic to streamline integration.

What is the best way to extract text from OnlyOffice documents for retrieval systems?▼

The best way to extract text from OnlyOffice documents for retrieval systems is through an automated pipeline that chunks the text and generates embeddings. This ensures the extracted data is properly formatted for fast knowledge base retrieval.

Can I use sqlalchemy and asyncio to manage my document indexing pipeline?▼

Yes, you can use sqlalchemy and asyncio to manage your document indexing pipeline. These libraries handle asynchronous database operations and data validation during the text extraction and embedding generation phases for the RAG system.

Does langchain_core work with OnlyOffice document embedding generation?▼

Langchain_core works with OnlyOffice document embedding generation by providing the framework to process text chunks. It integrates with the pipeline to create embeddings that enable efficient searching within the RAG knowledge base.

How do I start indexing OnlyOffice documents into the RAG system?▼

To start indexing OnlyOffice documents into the RAG system, run the processing tool with a specific document UUID. This triggers the automated pipeline to extract text, generate embeddings, and store the data for retrieval.

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