Codex enables you to seamlessly leverage knowledge from Subject Matter Experts (SMEs) to improve your RAG/Agentic applications.
The cleanlab-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-codexIntegrating Codex into your RAG application is as simple as:
from cleanlab_codex import Validator
validator = Validator(codex_access_key=...) # optional configurations can improve accuracy/latency
# 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 Cleanlab
results = validator.validate(query=query, context=context, response=response,
form_prompt=rag_form_prompt)
final_response = (
results["expert_answer"] # Codex's answer
if results["is_bad_response"] and results["expert_answer"]
else response # 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 found here.
cleanlab-codex is distributed under the terms of the MIT license.