Archive-Bot

Inspiration

The inspiration for this project came from the need to efficiently access outdated website information. Searching through old archives can be tedious, so we wanted to create a chatbot that leverages Retrieval-Augmented Generation (RAG) to make this process simpler and more intuitive.

What it does

Archive-Bot allows users to ask questions and retrieve historical data from websites by interacting with a chatbot. It simplifies the process of finding information hidden in large, outdated archives by providing accurate responses in real-time.

How we built it

We built the chatbot using a combination of RAG for knowledge retrieval and fine-tuned natural language processing models to adapt the bot to domain-specific website history. The system pulls data from archived sources and generates responses that align with user queries.

Challenges we ran into

One major challenge was training the model to accurately retrieve and synthesize relevant historical data without being overwhelmed by irrelevant information. Additionally, optimizing the retrieval process for large-scale website archives proved complex.

Accomplishments that we're proud of

We successfully built a chatbot that reduces the effort required to retrieve historical data. The integration of RAG allows for real-time, accurate responses, making it a unique solution for users dealing with vast amounts of archival content.

What we learned

We learned a great deal about optimizing retrieval processes for large datasets, fine-tuning language models for domain-specific applications, and the intricacies of handling website history data.

What's next for Archive-Bot

In the future, we plan to expand Archive-Bot’s capabilities to cover more domains and data sources. We also aim to improve the chatbot’s conversational abilities and incorporate more advanced features like multilingual support and sentiment analysis.

Built With

• langchain
• meta-llama-3-70b-instruct
• python
• rag
• streamlit