My solution for the "Agentic Arena Community Contest" (RAG, Qdrant, Mistral OCR)

🤖📈 This workflow is my personal solution for the Agentic Arena Community Contest, where the goal is to build a Retrieval-Augmented Generation (RAG) AI agent capable of answering questions based on a provided PDF knowledge base.

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Key Advantages

✅ End-to-End RAG Implementation Fully automates the ingestion, processing, and retrieval of knowledge from PDFs into a vector database.

✅ Accuracy through Multi-Layered Retrieval Combines embeddings, Qdrant search, and Cohere reranking to ensure the agent retrieves the most relevant policy information.

✅ Robust Evaluation System Includes an automated correctness evaluation pipeline powered by GPT-4.1 as a judge, ensuring transparent scoring and continuous improvement.

✅ Citation-Driven Compliance The AI agent is instructed to provide citations for every answer, making it suitable for high-stakes use cases like policy compliance.

✅ Scalability and Modularity Can easily integrate with different data sources (Google Drive, APIs, other storage systems) and be extended to new use cases.