Problem Statement:
The organization faced a challenge in managing a vast repository of over 10,000 articles within an open search. Users needed accurate and relevant information in response to their queries, which required an efficient retrieval system. The objective was to implement automation framework to find the accurate results from the articles based on the user queries
Solution:
Development and Implementation
Python Framework:
- Developed a Python Framework to automate the testing process.
- The script extracted queries from a file, interfacing with the RAG API to process each query.
Response Evaluation:
- Utilized semantic assertion to evaluate the relevance and accuracy of the responses.
- Established a scoring system where responses with a semantic score above 0.75 were deemed correct.
Database Integration:
- Saved responses in a database for further analysis and comparison.
- Enabled systematic tracking and evaluation of the responses.
Model Testing:
- Conducted tests using different language models: GPT-3.5, 4O, and 4O mini.
- Compared their performance based on semantic scores to identify the most effective model.
Analysis and Comparison:
Performance Metrics:
- Analysed the semantic scores across different models.
- Evaluated the accuracy and relevance of responses to determine model performance.
Results:
- Identified the model that consistently provided the highest accuracy in responses.
- Gather insights into the strengths and weaknesses of each model in handling complex queries.
Conclusion:
The implementation of the Q&A bot using RAG architecture proved effective in managing and retrieving information from a large corpus of articles. By leveraging semantic assertion scores, the organization was able to systematically evaluate the accuracy of the responses, leading to improved decision-making regarding model selection. This case study highlights the importance of integrating advanced language models with retrieval systems to enhance information accessibility and accuracy.
Future Recommendations:
Continuous Improvement:
- Regularly update the model based on new data and user feedback to maintain high accuracy levels.
Scalability:
- Explore scalability options to accommodate an expanding repository of articles and increased user queries.
User Feedback Integration:
Implement a mechanism to gather user feedback on response quality to further refine the system.
