| Availability |
Odoo Online
Odoo.sh
On Premise
|
| Odoo Apps Dependencies |
Discuss (mail)
|
| Community Apps Dependencies | Show |
| Lines of code | 4778 |
| Technical Name |
llm_knowledge_llama |
| License | LGPL-3 |
| Website | https://github.com/apexive/odoo-llm |
| Versions | 16.0 18.0 |
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.
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
Parse Markdown
LlamaIndex analyzes document structure: headers, lists, code blocks, paragraphs
Identify Boundaries
Find natural semantic boundaries: section breaks, topic changes, complete thoughts
Create Chunks
Split into self-contained chunks that preserve context and structure
Better Retrieval
AI finds complete, accurate context leading to better answers
See The Difference
Same markdown document, two different chunking approaches
Basic Chunking
LlamaIndex Chunking
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
Install Python Dependencies
pip install llama_index nltk
Install Module in Odoo
Apps → Search "LLM Knowledge LlamaIndex" → Install
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
Please log in to comment on this module