OPEN SOURCE
Evaluate Your
RAG-Powered Chatbots
With ContextCheck, a toolset designed to empower developers and researchers to assess their RAG-powered systems and chatbots, you will enhance their performance and ensure they cover every use case.
What is ContextCheck?
ContextCheck provides a comprehensive suite of features to detect regressions, perform penetration tests, and assess hallucinations, ensuring your AI performs at the highest level. Configurable via YAML and seamlessly integrated into continuous integration (CI) pipelines, ContextCheck streamlines your development process and guarantees the robustness and reliability of your AI systems.
Key Features
Flexible Endpoint Integration
YAML-Driven Setup
Dynamic Test Creation
In-Depth RAG Analysis
Edge Case Detection
How It Works?
1. Endpoint Interaction
Seamlessly connect with your chatbot’s endpoint to ask queries and evaluate responses in real-time.
4. LLM & RAG Evaluation
Assess the performance of both Language Models and RAG systems, including challenging edge cases.
2. Low-Code Configuration
Utilize our intuitive YAML-based setup to quickly configure and customize your evaluation parameters.
5. Comparative Analysis
Evaluate multiple LLMs to determine which one best suits your specific use case.
3. Automatic Test Generation
Generate relevant test sets based on your current application, ensuring thorough coverage of potential scenarios.
6. RAG Performance Insights
Identify scenarios where the RAG process excels and where it needs improvement.
ContextCheck Architecture
Configuration Layer
YAML Configuration: Defines evaluation parameters, test scenarios, and reference answers/questions.
Structure
The Evaluation Engine runs tests from the YAML configuration, the Regression Detection module monitors and identifies regressions, and the Penetration Testing module simulates adversarial scenarios.
Hallucination Detection
Implements models to identify and flag hallucinations in LLM responses.
Structure
CI Integration: Hooks into CI pipelines to automate testing. Local Execution: Allows for local testing and validation.
Data Access Layer
The Vector DB Interface connects to vector databases for retrieval, and the Document DB Interface connects to document databases for context-aware testing.
Test Generation & Validation
Secondary LLM Integration generates questions and answers using a powerful LLM, while the User Validation Interface lets users validate the test sets.
Reporting & Analytics
Implements models to identify and flag hallucinations in LLM responses.
Who Benefits from ContextCheck?
Developers
Streamlined debugging: Quickly identify and resolve issues in your chatbot’s performance.
Continuous improvement: Iterate and enhance your chatbot with data-driven insights.
Businesses
Quality assurance: Ensure your AI-powered Chatbots meet the highest standards.
Risk mitigation: Identify potential issues before they impact your users.
Researchers
Benchmark creation: Establish standardized tests for comparing different RAG systems.
Edge case exploration: Dive deep into the nuances of language model behavior in complex scenarios.
Use Cases & Workflows
LLM Validation
Create a YAML file with custom Q&A. ContextCheck uses it to validate LLM performance (e.g., Amazon Bedrock) and benchmark accuracy and relevance against your criteria.
Natural Language Regex
Validate NLP correctness and reliability with regex-like natural language patterns. Set up advanced checks to ensure your AI handles complex linguistic structures accurately.
Endpoint Validation
Define an endpoint’s purpose (e.g., “Expertise in ancient Egypt”). A secondary LLM generates a test set of Q&A, which you validate. ContextCheck then uses it to rigorously evaluate the endpoint, ensuring your AI meets its intended purpose across diverse queries.
RAG System Evaluation
Tailored for RAG systems: use YAML files to test both retrieval and generation, ensuring your setup pulls relevant data and produces accurate responses.
Reference Document Assesment
Evaluate AI responses by comparing them to reference documents to ensure outputs align with authoritative sources, making it suitable for fact-critical applications.