LLM Qdrant Integration

Qdrant vector database integration for high-performance semantic search at scale.

Module Type: 🗄️ Vector Store (High Performance)

Architecture

┌───────────────────────────────────────────────────────────────┐
│                    Used By (RAG Modules)                      │
│        ┌───────────────┐           ┌───────────────┐         │
│        │ llm_knowledge │           │llm_assistant  │         │
│        │   (RAG)       │           │  (with RAG)   │         │
│        └───────┬───────┘           └───────┬───────┘         │
└────────────────┼───────────────────────────┼─────────────────┘
                 └─────────────┬─────────────┘
                               ▼
              ┌───────────────────────────────────────────┐
              │             llm_store                     │
              │        (Vector Store API)                 │
              └─────────────────────┬─────────────────────┘
                                    │
                                    ▼
              ┌───────────────────────────────────────────┐
              │       ★ llm_qdrant (This Module) ★        │
              │          Qdrant Implementation            │
              │  🔷 High Performance │ Scalable │ Fast    │
              └─────────────────────┬─────────────────────┘
                                    │
                                    ▼
              ┌───────────────────────────────────────────┐
              │              Qdrant Server                │
              │           (localhost:6333)                │
              └───────────────────────────────────────────┘

Installation

What to Install

For high-performance RAG:

# 1. Start Qdrant server
docker run -p 6333:6333 qdrant/qdrant

# 2. Install the Odoo module
odoo-bin -d your_db -i llm_knowledge,llm_qdrant

Auto-Installed Dependencies

llm (core infrastructure)
llm_store (vector store abstraction)

Why Choose Qdrant?

Feature	Qdrant
Performance	⚡ Very fast search
Scale	📈 Handles millions of vectors
Filtering	🔍 Advanced payload filtering
Production	✅ Built for production

Vector Store Comparison

Feature	llm_pgvector	llm_qdrant	llm_chroma
Server	🐘 PostgreSQL	🔷 Qdrant server	🌈 Chroma server
Setup	Easy	Moderate	Moderate
Scale	Medium	High	Medium
Best For	Simple RAG	Large scale	Development

Common Setups

I want to...	Install
High-performance RAG	`llm_knowledge` + `llm_qdrant`
Chat + scalable RAG	`llm_assistant` + `llm_openai` + `llm_knowledge` + `llm_qdrant`

Features

Qdrant vector storage
High-performance similarity search
Scalable vector operations
Advanced filtered search support
Collection management

Configuration

Set up Qdrant server connection in LLM > Configuration > Vector Stores:

Host: Qdrant server hostname (e.g., localhost)
Port: Qdrant port (default: 6333)
API Key: Authentication key (if required)
Collection Name: Default collection name

Requirements

Odoo 18.0+
Python package: qdrant-client
Qdrant server instance (Docker or standalone)

License

LGPL-3