RAG for Odoo
by Kitworks Systems
800,00 €
| Odoo Apps Dependencies | base, base_automation, bus, http_routing, mail |
| Community Apps Dependencies | Show |
| Technical Name | kw_ai_rag |
| License | OPL-1 |
| Website | https://kitworks.systems/ |
| Versions | 17.0 18.0 19.0 |
AI Connector — Feature
AI RAG — Knowledge Base
Index your documents and Odoo records — let AI answer questions from your data with source citations.
Sources
PDF, DOCX, URL, Odoo
Backends
4 vector stores
Citations
Always included
Key Features
- Multiple Source Types. Index PDFs, DOCX files, web pages (URL), and Odoo record sets — all in the same knowledge base.
- Configurable Chunking. Set chunk size, overlap, and splitting strategy per knowledge source to optimize retrieval quality.
- Similarity Search. Choose cosine, dot product, or Euclidean distance — configurable per backend and use case.
- Source Citations. Every AI answer includes references to the source documents and chunks used — verifiable and auditable.
- Pluggable Vector Backends. Choose pgvector, Qdrant, FAISS, or ChromaDB — swap backends without changing indexing logic.
- Automatic Re-Indexing. Odoo record sources update their embeddings when records change — knowledge stays current.
How It Works
1
Add Sources
Upload PDFs, add URLs, or select an Odoo model to create a knowledge source
2
Index Content
Content is chunked, embedded, and stored in the vector backend automatically
3
AI Retrieves & Cites
Agents and fields query the knowledge base and return answers with source citations
Requirements
- ✓ kw_ai (AI Connector Core)
- ✓ At least one vector backend: kw_ai_rag_pgvector (recommended), kw_ai_rag_qdrant, kw_ai_rag_faiss, or kw_ai_rag_chroma
- ✓ At least one embedding module: kw_ai_embed_openai, kw_ai_embed_google, or kw_ai_embed_ollama
- ✓ Odoo 18.0
- ✓ OPL-1 license
Get Started
Book a demo — we will index your documents and run live Q&A against your data during the session.
Book a DemoPart of Ultimate AI Connector Suite · OPL-1 · Odoo 18.0