RAG System with Langchain and OpenAI for Gas Emmision Complience
RAG System with Langchain and OpenAI for Gas Emmision Complience
Automated System For Validating Gas Emission Requests Against Regulatory Standards.
April 15, 2022
5 minutes read
SUMMARY
The client initially had an MVP platform designed to validate gas emission requests against regulatory documents. However, they faced significant challenges as the system was slow in processing requests and frequently yielded inaccurate results. The inefficiency and lack of reliability compromised the platform's effectiveness, leading to potential compliance issues and user dissatisfaction. Recognizing these critical shortcomings, the client sought to enhance the platform's capabilities to ensure rapid and precise compliance checks.
The client emphasized the importance of having an interactive system where users could directly query the bot and receive immediate responses specific to their cases. This functionality was crucial for users to understand precisely what aspects of their emission requests needed adjustments and what already complied with the applicable regulations. This real-time feedback mechanism aimed to streamline the compliance process, making it more user-friendly and efficient.
The client was also focused on scalability, particularly how the system would manage a growing number of regulatory documents. We aimed to ensure that our platform could handle increasing data volumes efficiently, using advanced data management strategies to maintain performance and accuracy.
TECH STACK
LangChain
GPT
DELIVERY TIMELINE
1 Week
Solution Architecture Design
2 Weeks
LangChain Agent Development & Customization
1 Week
Deployment & Testing
TECH CHALLENGE
In addressing the technical challenge, the need for exceptional accuracy in the agent's responses was critical given the regulatory context. To achieve this, our team explored various options, including AutoGen agents, in pursuit of the highest quality solution. However, after thorough testing and evaluation, we decided to proceed with LangChain. This choice was based on its superior performance in delivering precise and reliable answers, essential for handling sensitive regulatory information effectively.
SOLUTION
Our team chose to implement LangChain as the foundation for our solution. To handle the vector transformations and similarity searches essential for efficiently navigating through extensive regulatory documents, we integrated Faiss. This combination enabled our system to quickly retrieve the most relevant documents based on vector proximity, providing a robust backend for document handling and data retrieval.
To enhance the interaction and communication capabilities of the system, we employed GPT-3.5. By leveraging GPT-3.5, the system could interpret complex queries and provide clear, accurate, and compliant information in real-time, greatly aiding users in navigating the nuances of emission regulations.
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