ARIAS U.S. RAG Chatbot
A specialized AI chatbot built to search and answer over ARIAS U.S. Quarterly archives from 1995 to present, with context-aware and author-based querying
ARIAS U.S. RAG Chatbot Demo Access
This project's code and demo are protected for client confidentiality. I'd be happy to showcase similar solutions tailored to your specific needs.
Project Overview
This AI chatbot is tailored to ARIAS•U.S. content: it indexes the full archive of Quarterly articles dating back to the mid-1990s, enabling users to search by topic, context, or author. Built using LangChain and Pinecone vector embeddings over document chunks, the chatbot returns precise quotes, article references, and summaries drawn from the ARIAS archives.
Development Challenges
Key challenges included ingesting decades of PDF/HTML archival formats, segmenting articles for semantic embedding, ensuring author-metadata linking, and balancing recall vs precision in context search. Also caching common queries and managing embedding updates as new Quarterly issues publish.
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Technologies Used
- Next.js
- React
- Node.js
- LangChain
- Pinecone
- OpenAI API
- Vector Embeddings
- PDF / HTML parsing
- Metadata linking
Outcomes & Results
The ARIAS U.S.–centered chatbot enabled fast, accurate retrieval from the Quarterly archive, reducing manual search effort, increasing member engagement, and supporting research and arbitrator reference tasks.
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ARIAS U.S. RAG Chatbot Demo Access
This ai is protected due to client confidentiality and intellectual property considerations.
This project was developed for a client with specific confidentiality requirements. The code repository and live demo are kept private to protect:
- Proprietary algorithms and business logic
- Custom implementation strategies
- Client data and specific requirements
- Competitive advantages and unique features
While I can't provide direct access to this specific project, I'm happy to discuss how I've implemented similar solutions and how we could adapt this approach for your needs.
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