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LLM RAG LlamaIndex

by
Odoo
v 18.0 Third Party 26
Download for v 18.0 Deploy on Odoo.sh
Availability
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Odoo.sh
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Odoo Apps Dependencies Discuss (mail)
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Lines of code 4804
Technical Name llm_knowledge_llama
LicenseLGPL-3
Websitehttps://github.com/apexive/odoo-llm
Versions 16.0 18.0
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Availability
Odoo Online
Odoo.sh
On Premise
Odoo Apps Dependencies Discuss (mail)
Community Apps Dependencies
Lines of code 4804
Technical Name llm_knowledge_llama
LicenseLGPL-3
Websitehttps://github.com/apexive/odoo-llm
Versions 16.0 18.0
Advanced RAG Chunking

Smart Chunking for
Better AI Answers

Upgrade your RAG pipeline with LlamaIndex's advanced chunking. Parse markdown while preserving structure, create semantic chunks, and get more accurate AI retrieval.

See How It Works
Markdown
Structure Preserved
Semantic
Chunking
Better
Retrieval

Why Basic Chunking Fails

Basic Chunking

  • Splits at arbitrary character limits (500, 1000 chars)
  • Breaks in the middle of sentences or code blocks
  • Loses markdown structure (headers, lists, formatting)
  • Creates chunks without semantic meaning
  • AI retrieves incomplete or broken context
  • Poor accuracy for technical documentation

LlamaIndex Chunking

  • Understands markdown structure (headers, lists, code)
  • Splits at natural semantic boundaries
  • Preserves complete sections with their context
  • Creates meaningful, self-contained chunks
  • AI retrieves accurate, complete information
  • Optimized for technical and structured content

How It Works

LlamaIndex analyzes your markdown documents and creates intelligent chunks that preserve structure and semantic meaning

1

Parse Markdown

LlamaIndex analyzes document structure: headers, lists, code blocks, paragraphs

2

Identify Boundaries

Find natural semantic boundaries: section breaks, topic changes, complete thoughts

3

Create Chunks

Split into self-contained chunks that preserve context and structure

4

Better Retrieval

AI finds complete, accurate context leading to better answers

See The Difference

Same markdown document, two different chunking approaches

Basic Chunking

# Installation Guide
To install the package, run:
pip install mypacka← BROKEN
Problem: Split mid-sentence at 500 char limit. Lost context and broke code example.

LlamaIndex Chunking

# Installation Guide
To install the package, run:
pip install mypackage
This will install all dependencies.
Success: Complete section preserved. Code block intact. Full context maintained.

Key Features

Markdown Parsing

Understands markdown structure: headers, lists, code blocks, tables. Preserves formatting and hierarchy.

Semantic Chunking

Splits at natural boundaries, not arbitrary character limits. Each chunk is a complete, meaningful unit.

Better Accuracy

AI retrieves complete context with full sentences, code examples, and relevant sections intact.

LlamaIndex Integration

Powered by LlamaIndex's proven node parsing algorithms used by thousands of RAG applications.

Documentation Optimized

Designed for technical docs, API references, knowledge bases, and structured content.

Drop-in Upgrade

Works with your existing LLM Knowledge setup. Install and your chunking automatically improves.

Perfect For

Technical Documentation

API docs, developer guides, installation instructions. Preserve code examples, command snippets, and configuration blocks.

Example: "How do I install the SDK?" → AI returns complete installation section with all commands intact

Knowledge Bases

Company wikis, training materials, process documentation. Maintain hierarchical structure and complete procedures.

Example: "What's our deployment process?" → AI retrieves entire step-by-step procedure with all sub-steps

Educational Content

Tutorials, courses, learning materials. Keep examples and explanations together for better comprehension.

Example: "Explain recursion" → AI shows complete concept with code example and explanation in one chunk

Structured Reports

Research papers, specifications, long-form content. Preserve section organization and maintain context across subsections.

Example: "What were the Q3 findings?" → AI retrieves complete quarterly section with all data points

Get Started

1

Install Python Dependencies

pip install llama_index nltk
2

Install Module in Odoo

Apps → Search "LLM Knowledge LlamaIndex" → Install

3

Automatic Upgrade

Your existing knowledge collections now use LlamaIndex chunking. Process new resources to see improved accuracy.

Requirements

Requires LLM Knowledge module. Works with any vector store (pgvector, Qdrant, Chroma).

LLM Knowledge LlamaIndex

Advanced RAG chunking for better AI retrieval accuracy

Need Help?

Email: info@apexive.com

GitHub: github.com/apexive/odoo-llm

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