Archilles: RAG for researchers with citations and LLM access via MCP

Archilles: RAG for Researchers with Citations and LLM Access via MCP

Archilles is a specialized Retrieval Augmented Generation (RAG) system designed for researchers. It allows users to query their personal research libraries, retrieve relevant information with precise page-level citations, and leverage Large Language Models (LLMs) through the MCP platform. This tool streamlines the research process by providing direct access to knowledge and facilitating the integration of AI capabilities into academic workflows.

What Archilles Does

Archilles enables researchers to build a knowledge base from their collected documents, such as research papers, books, and articles. When a query is submitted, Archilles retrieves the most relevant passages from these documents. Crucially, it provides exact page numbers for each retrieved piece of information, ensuring academic integrity and allowing for easy verification. Furthermore, it integrates with MCP, a platform that provides access to various LLMs, allowing users to generate summaries, answer complex questions, or perform other text-based tasks based on the retrieved research content.

Key Features

• Page-Level Citations: Every piece of retrieved information is linked to its specific page number within the source document, enhancing accuracy and traceability.
• LLM Integration via MCP: Seamlessly connect to and utilize powerful LLMs for advanced text analysis, generation, and summarization.
• Personal Library Querying: Build and query a private, curated knowledge base tailored to your specific research needs.
• RAG Architecture: Leverages Retrieval Augmented Generation to combine information retrieval with LLM capabilities for more informed and contextually relevant outputs.

Who Archilles is For

Archilles is an essential tool for academics, researchers, PhD students, and anyone engaged in in-depth literature review . It is particularly beneficial for individuals working with large volumes of research material who require precise citation management and wish to integrate advanced AI functionalities into their study. Developers looking to build similar RAG systems or integrate citation-aware LLM capabilities into their applications will also find Archilles a valuable reference and starting point.