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Codex enables you to seamlessly leverage knowledge from Subject Matter Experts (SMEs) to improve your RAG/Agentic applications.

Thecleanlab-codex library provides a simple interface to integrate Codex's capabilities into your RAG application.See immediate impact with just a few lines of code!

Install the package:

pip install cleanlab-codex

Integrating Codex into your RAG application is as simple as:

fromcleanlab_codeximportProjectproject=Project.from_access_key(...)# Your existing RAG code:context=rag_retrieve_context(user_query)prompt=rag_form_prompt(user_query,retrieved_context)response=rag_generate_response(prompt)# Detect bad responses and remediate with Cleanlabresults=project.validate(query=query,context=context,response=response,messages=[...,prompt])final_response= (results["expert_answer"]# Codex's answerifresults["expert_answer"]isnotNoneelseresponse# Your RAG system's initial response)

• Detect Knowledge Gaps and Hallucinations: Codex identifies knowledge gaps and incorrect/untrustworthy responses in your AI application, to help you know which questions require expert input.
• Save SME time: Codex ensures that SMEs see the most critical knowledge gaps first.
• Easy Integration: Integrate Codex into any RAG/Agentic application with just a few lines of code.
• Immediate Impact: SME answers instantly improve your AI, without any additional Engineering/technical work.

Comprehensive documentation along with tutorials and examples can be foundhere.

cleanlab-codex is distributed under the terms of theMIT license.