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.

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.

• ✅ Seamless Collaboration with Google Sheets
  Both the evaluation set and the results are integrated with Google Sheets, enabling easy monitoring, iteration, and reporting.

• ✅ Cloud and Self-Hosted Flexibility
  Works with self-hosted Qdrant on Hetzner, Mistral Cloud for OCR, and OpenAI/Cohere APIs, combining local control with powerful cloud AI services.

How it Works

1. Knowledge Base Ingestion (The "Setup" Execution):

   • When started manually, the workflow first clears an existing Qdrant vector database collection.
   • It then searches a specified Google Drive folder for PDF files. For each PDF found, it performs the following steps:
     • Uploads the file to the Mistral AI API.
     • Processes the PDF using Mistral's OCR service to extract text and convert it into a structured markdown format.
     • Splits the text into manageable chunks.
     • Generates embeddings for each text chunk using OpenAI's model.
     • Stores the embeddings in the Qdrant vector store, creating a searchable knowledge base.

2. Agent Evaluation (The "Testing" Execution):

   • The workflow is triggered by an evaluation Google Sheet containing questions and correct answers.
   • For each question, the core AI Agent is activated. This agent:
     • Uses the RAG tool to search the pre-populated Qdrant vector store for relevant information from the PDFs.
     • Employs a Cohere reranker to refine the search results for the highest quality context.
     • Leverages a GPT-4.1 model to generate an answer based strictly on the retrieved context.
   • The agent's answer is then passed to an "LLM as a Judge" (another GPT-4.1 instance), which compares it to the ground truth answer from the evaluation sheet.
   • The judge provides a detailed score (1-5) based on factual correctness and citation accuracy.
   • Finally, both the agent's answer and the correctness score are saved back to a Google Sheet for review.

Set up Steps

To implement this solution, you need to configure the following components and credentials:

1. Configure Core AI Services:

   • OpenAI API Credentials: Required for the main AI agent, the judge LLM, and generating embeddings.
   • Mistral AI API Credentials: Necessary for the OCR service that processes PDF files.
   • Cohere API Credentials: Used for the reranker node that improves retrieval quality.
   • Google Service Accounts: Set up OAuth for Google Sheets (to read questions and save results) and Google Drive (to access the PDF source files).

2. Set up the Vector Database (Qdrant):

   • This workflow uses a self-hosted Qdrant instance. You must deploy and configure your own Qdrant server.
   • Update the Qdrant Vector Store and RAG nodes with the correct API endpoint URL and credentials for your Qdrant instance.
   • Ensure the collection name (agentic-arena) is created or matches your setup.

3. Connect Data Sources:

   • PDF Source: In the "Search PDFs" node, update the folderId parameter to point to your own Google Drive folder containing the contest PDFs.
   • Evaluation Sheet: In the "Eval Set" node, update the documentId to point to your own copy of the evaluation Google Sheet containing the test questions and answers.
   • Results Sheet: In the "Save Eval" node, update the documentId to point to the Google Sheet where you want to save the evaluation results.

Need help customizing?

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