Qodo Embed

Qodo Embed is a lightweight embedding solution that converts text and code into dense semantic vectors using state-of-the-art transformer models. It enables fast similarity search, code / text retrieval, clustering, and AI-powered recommendations — all with a simple Python or CLI interface. Qodo Embed is designed for easy integration into applications, research projects, or internal tools that need semantic search or embedding-based features without heavy ML infrastructure.

Features of Qodo Embed:

• Generates high-quality semantic embeddings for text and code using Sentence-Transformers models.
• Fast similarity search and nearest-neighbor retrieval for code snippets, documentation, or free text.
• Simple Python API and shell-first workflow for quick experiments and production use.
• Supports common embedding models such as all-MiniLM-L6-v2 and can be extended to others.
• Minimal resource and dependency footprint — suitable for lightweight servers and dev machines.
• Works offline once models are downloaded and cached locally (no continual internet calls required for inference).
• Designed for easy integration with existing search stacks and vector databases.
• Extensible: supports custom preprocessing, batching, and similarity scoring strategies.

Usage Instructions:

To check the working of Qodo Embed, run these commands in your shell:

• sudo su
• sudo apt update
• cd /opt/qodo-embed-lite
• source venv/bin/activate
• python -c 'from sentence_transformers import SentenceTransformer; m = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2"); print("✅ Model loaded successfully!")'
• pip show sentence-transformers

Disclaimer: Qodo Embed is provided by its developers/community and may be distributed under the project's chosen license. It is not affiliated with, endorsed by, or sponsored by any third-party vendor by default. The software is provided "as is," without any warranty, express or implied. Users assume full responsibility for their usage; the developers and contributors are not liable for any damages, losses, or consequences resulting from using Qodo Embed. Review and comply with the project's license and any applicable regulations before use.