Скачать или смотреть PS 25066: Development of an Al-driven ChatBOT for INGRES as a virtual assistant.

The rapid advancements in Artificial Intelligence and Natural Language Processing have revolutionized the way organizations interact with data and automate workflows. One of the most promising applications of AI lies in intelligent conversational agents, or chatbots, that act as intermediaries between humans and complex systems and has not only greatly reduced the work load but works as great assistant too.
The INGRES system, used as a knowledge and data repository for groundwater resources as well as other information, contains structured and unstructured data in the form of user manuals, documentation and historical reports. Extracting actionable insights from this diverse and vast data ecosystem is challenging and time consuming for users.
To address this challenge, our team, CodeXCrusaders, has taken up the problem statement under Smart India Hackathon 2025 (Problem Statement ID – 25066). The objective is to design and develop an AI-driven chatbot for INGRES that serves as a virtual assistant, capable of interacting in natural language, retrieving information seamlessly from both structured and unstructured sources, and providing accurate, context-aware responses.
This project falls under the Smart Automation theme, aligning with the broader vision of increasing efficiency and accessibility in enterprise systems through the power of AI.

Proposed Solution:
India as a country, has people using more than 120 languages thus, creating a major issue of language barrier. We’ve developed a chatbot that provides not just multilingual access but the utilisation of vector database to incorporate the historical reports with their retrieval via LangChain RAG pipelines allows it to deliver accurate and contextual responses. Usage of the Machine Learning model allows for predicting and estimating various variables such as groundwater recharge for the near future, thus instantiating our model’s usefulness. This, alongwith the usage of redis powered caching that provides fast information access and reduces high database cost as well as interactive visualizations that helps in presenting the same numeric data graphically allow for a more comprehensive build. How this helps in the problem solving is via simplifying access to data by bringing the historical and current data together. This not only helps in decision making but also in enhancing the user’s experience and overall performance.

Objectives of the Project-
The primary objective of the project is to create an intelligent conversational assistant that can bridge the gap between human queries and complex datasets stored in INGRES. The specific objectives include:
1. Natural Language Understanding (NLU): Enable users to query INGRES in natural language, without needing technical knowledge of SQL or database structures.
2. Dual Data Source Handling: Efficiently process and retrieve information from both structured data (Postgres/INGRES databases) and unstructured data (user manuals, documentation).
3. Retrieval Augmented Generation (RAG): Integrate embeddings and advanced retrieval mechanisms to provide accurate and contextually relevant responses.
4. Error Handling & Robustness: Design the system to gracefully manage missing data, erroneous queries, and large dataset constraints.
5. Scalability & Viability: Ensure the solution can handle large-scale queries, support multiple concurrent users, and adapt to organizational needs.
6. User-Centric Design: Provide an intuitive conversational interface that minimizes friction and promotes adoption.
7. Enhanced Knowledge Access: Reduce dependency on technical staff by democratizing access to INGRES data for all stakeholders.
Through these objectives, the chatbot will act not just as a query tool, but as a virtual assistant capable of guiding, supporting, and enabling data-driven decision-making.