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  1. APPS
  2. Knowledge
  3. LLM Knowledge Integration for Document Pages v 16.0
  4. Sales Conditions FAQ

LLM Knowledge Integration for Document Pages

by Apexive Solutions LLC https://github.com/apexive/odoo-llm
Odoo
v 16.0 Third Party 11
Download for v 16.0 Deploy on Odoo.sh
Apps purchases are linked to your Odoo account, please sign in or sign up first.
Availability
Odoo Online
Odoo.sh
On Premise
Odoo Apps Dependencies Discuss (mail)
Community Apps Dependencies Show
• Document Page
• LLM Knowledge
• Documents Knowledge
• LLM Integration Base
• LLM Vector Store Base
Lines of code 5885
Technical Name llm_document_page
LicenseAGPL-3
Websitehttps://github.com/apexive/odoo-llm
Versions 16.0 18.0
You bought this module and need support? Click here!
Availability
Odoo Online
Odoo.sh
On Premise
Odoo Apps Dependencies Discuss (mail)
Community Apps Dependencies Show
• Document Page
• LLM Knowledge
• Documents Knowledge
• LLM Integration Base
• LLM Vector Store Base
Lines of code 5885
Technical Name llm_document_page
LicenseAGPL-3
Websitehttps://github.com/apexive/odoo-llm
Versions 16.0 18.0

LLM RAG Integration for Document Pages

Connect your knowledge base documents to AI-powered search and generation

What is this module? This module connects your Document Page knowledge base with the LLM RAG (Retrieval Augmented Generation) system, making your internal documentation searchable and usable by large language models.

Key Features

Knowledge Base Integration

Seamlessly bring your document pages into the AI-powered RAG system:

  • Convert page content to AI-friendly markdown
  • Preserve document page hierarchy
  • Include related documents and categories
  • Maintain formatting and links

Metadata Preservation

Keep important context with your documents:

  • Document contributors
  • Last update timestamps
  • Category information
  • Document relationships

Easy Integration

Simple tools to connect your knowledge base:

  • One-click document creation from UI
  • Batch processing of multiple pages
  • Programmatic integration via API
  • Automatic document organization

How It Works

The module creates a seamless integration between the Document Page knowledge base and the LLM RAG system:

Select Documents

Choose document pages from your knowledge base

Create RAG Docs

Convert pages to LLM-optimized format

Add to Collection

Organize in LLM RAG collections

Process & Embed

Process through the RAG pipeline

Usage Guide

Creating RAG Documents from the UI

Step 1: Select Document Pages

Navigate to Knowledge → Document Pages and select one or more documents you want to make available to the LLM system.

Step 2: Use the Create RAG Document Action

Click on the "Create RAG Document" action button in the list or form view.

Step 3: Configure Document Creation

In the wizard, you can:

  • Customize the document name template
  • Choose to process documents immediately

Step 4: Process Documents

The RAG documents will be created, referencing your original document pages. If you selected "Process Immediately," they will be processed through the full RAG pipeline.

[RAG Document Creation Workflow Image]

The Create RAG Document wizard makes it easy to add document pages to your LLM knowledge base

Processing Entire Categories

You can also add entire categories of documents to your LLM RAG system programmatically:

from odoo import api, SUPERUSER_ID

def add_knowledge_base_to_rag(env, category_id, collection_id):
    # Get all pages in this category
    pages = env['document.page'].search([
        ('parent_id', '=', category_id),
        ('type', '=', 'content')
    ])

    # Get model ID for document.page
    model_id = env['ir.model'].search([('model', '=', 'document.page')], limit=1).id

    # Create documents
    created_docs = env['llm.document']
    for page in pages:
        # Create document with reference to the page
        document = env['llm.document'].create({
            'name': f"{page.name} - Knowledge Base",
            'model_id': model_id,
            'res_id': page.id,
            'collection_ids': [(4, collection_id)]
        })

        # Process through RAG pipeline
        document.process_document()
        created_docs |= document

    return created_docs

# Example usage
env = api.Environment(cr, SUPERUSER_ID, {})
category_id = 1  # ID of your document.page category
collection_id = 1  # ID of your llm.document.collection

docs = add_knowledge_base_to_rag(env, category_id, collection_id)

Technical Implementation

The module works by implementing a custom rag_parse method on the document.page model. This method is called by the LLM RAG module during document processing.

How documents are converted:
  • Page title becomes the main heading
  • Metadata section includes contributor and timestamp information
  • Main content is preserved in its original format
  • Related pages are linked at the end of the document

Sample Output

Here's an example of how a document page is converted to the RAG format:

# Product Return Policy

## Metadata
**Last Contributor:** Administrator
**Last Updated:** 2023-05-15 14:30:22
**Category:** Policies

## Content
<p>Our product return policy allows customers to return items within 30 days of purchase.</p>
<p>To qualify for a return, items must be:</p>
<ul>
  <li>In original condition</li>
  <li>With original packaging</li>
  <li>Accompanied by a receipt or proof of purchase</li>
</ul>

## Related Pages
- [Return Process](/web#id=12&model=document.page&view_type=form)
- [Refund Policy](/web#id=15&model=document.page&view_type=form)

Benefits for Your Organization

  • Unified Knowledge Base: Make your existing documentation available to your AI systems
  • Improved AI Responses: LLMs can answer questions based on your internal knowledge
  • Reduced Duplication: Maintain documentation in one place and use it everywhere
  • Up-to-date Information: Changes to your knowledge base can be reflected in the LLM system

Installation

  1. Ensure both document_page and llm_rag modules are installed
  2. Install this module (llm_document_page)
  3. No additional configuration is needed - the integration is automatic

Developed by Apexive Solutions LLC | Licensed under LGPL-3

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