{
  "workflow": {
    "id": 8944,
    "name": "My solution for the \"Agentic Arena Community Contest\" (RAG, Qdrant, Mistral OCR)",
    "views": 681,
    "recentViews": 1,
    "totalViews": 681,
    "createdAt": "2025-09-26T09:12:39.109Z",
    "description": "🤖📈 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.\n\n\n---\n\n### Key Advantages\n\n* ✅ **End-to-End RAG Implementation**\n  Fully automates the ingestion, processing, and retrieval of knowledge from PDFs into a vector database.\n\n* ✅ **Accuracy through Multi-Layered Retrieval**\n  Combines embeddings, Qdrant search, and Cohere reranking to ensure the agent retrieves the most relevant policy information.\n\n* ✅ **Robust Evaluation System**\n  Includes an automated correctness evaluation pipeline powered by GPT-4.1 as a judge, ensuring transparent scoring and continuous improvement.\n\n* ✅ **Citation-Driven Compliance**\n  The AI agent is instructed to provide **citations for every answer**, making it suitable for high-stakes use cases like policy compliance.\n\n* ✅ **Scalability and Modularity**\n  Can easily integrate with different data sources (Google Drive, APIs, other storage systems) and be extended to new use cases.\n\n* ✅ **Seamless Collaboration with Google Sheets**\n  Both the evaluation set and the results are integrated with Google Sheets, enabling easy monitoring, iteration, and reporting.\n\n* ✅ **Cloud and Self-Hosted Flexibility**\n  Works with self-hosted **Qdrant** on Hetzner, Mistral Cloud for OCR, and OpenAI/Cohere APIs, combining local control with powerful cloud AI services.\n\n---\n### **How it Works**\n\n1.  **Knowledge Base Ingestion (The \"Setup\" Execution):**\n    *   When started manually, the workflow first clears an existing Qdrant vector database collection.\n    *   It then searches a specified Google Drive folder for PDF files. For each PDF found, it performs the following steps:\n        *   **Uploads the file** to the Mistral AI API.\n        *   **Processes the PDF** using Mistral's OCR service to extract text and convert it into a structured markdown format.\n        *   **Splits the text** into manageable chunks.\n        *   **Generates embeddings** for each text chunk using OpenAI's model.\n        *   **Stores the embeddings** in the Qdrant vector store, creating a searchable knowledge base.\n\n2.  **Agent Evaluation (The \"Testing\" Execution):**\n    *   The workflow is triggered by an evaluation Google Sheet containing questions and correct answers.\n    *   For each question, the core **AI Agent** is activated. This agent:\n        *   Uses the **RAG tool** to search the pre-populated Qdrant vector store for relevant information from the PDFs.\n        *   Employs a **Cohere reranker** to refine the search results for the highest quality context.\n        *   Leverages a **GPT-4.1 model** to generate an answer based strictly on the retrieved context.\n    *   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.\n    *   The judge provides a detailed score (1-5) based on factual correctness and citation accuracy.\n    *   Finally, both the agent's answer and the correctness score are saved back to a Google Sheet for review.\n\n---\n\n### **Set up Steps**\n\nTo implement this solution, you need to configure the following components and credentials:\n\n1.  **Configure Core AI Services:**\n    *   **OpenAI API Credentials:** Required for the main AI agent, the judge LLM, and generating embeddings.\n    *   **Mistral AI API Credentials:** Necessary for the OCR service that processes PDF files.\n    *   **Cohere API Credentials:** Used for the reranker node that improves retrieval quality.\n    *   **Google Service Accounts:** Set up OAuth for Google Sheets (to read questions and save results) and Google Drive (to access the PDF source files).\n\n2.  **Set up the Vector Database (Qdrant):**\n    *   This workflow uses a self-hosted Qdrant instance. You must deploy and configure your own Qdrant server.\n    *   Update the **Qdrant Vector Store** and **RAG** nodes with the correct API endpoint URL and credentials for your Qdrant instance.\n    *   Ensure the collection name (`agentic-arena`) is created or matches your setup.\n\n3.  **Connect Data Sources:**\n    *   **PDF Source:** In the **\"Search PDFs\"** node, update the `folderId` parameter to point to your own Google Drive folder containing the contest PDFs.\n    *   **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.\n    *   **Results Sheet:** In the **\"Save Eval\"** node, update the `documentId` to point to the Google Sheet where you want to save the evaluation results.\n\n---\n\n### **Need help customizing?**  \n[Contact me](mailto:info@n3w.it) for consulting and support or add me on [Linkedin](https://www.linkedin.com/in/davideboizza/).",
    "workflow": {
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      },
      "name": "My Agentic Arena Community Contest",
      "tags": [],
      "nodes": [
        {
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          "name": "Only if we are evaluating",
          "type": "n8n-nodes-base.evaluation",
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              "assignments": [
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                  "type": "string",
                  "value": "={{ $json.question }}"
                },
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            "filtersUI": {
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          "position": [
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          ],
          "parameters": {
            "color": 4,
            "width": 1024,
            "height": 560,
            "content": "## Eval for Correctness"
          },
          "typeVersion": 1
        },
        {
          "id": "aadf0db5-676b-47c0-99ef-12f9b14aaffe",
          "name": "Sticky Note4",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            -1648,
            208
          ],
          "parameters": {
            "color": 4,
            "width": 656,
            "height": 288,
            "content": "## Eval Input\n\nCorrect the _Set-Node_ directly to your Agent, once done"
          },
          "typeVersion": 1
        },
        {
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          "name": "Respond to Chat",
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            "message": "={{ $json.output }}",
            "options": {}
          },
          "typeVersion": 1
        },
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          "id": "adf0d271-cae7-4aa7-b8fd-e58a3d4fd4af",
          "name": "Filter Empty Rows",
          "type": "n8n-nodes-base.filter",
          "position": [
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          ],
          "parameters": {
            "options": {},
            "conditions": {
              "options": {
                "version": 2,
                "leftValue": "",
                "caseSensitive": true,
                "typeValidation": "strict"
              },
              "combinator": "and",
              "conditions": [
                {
                  "id": "9eab8192-200a-4520-afe9-b13c14cd000c",
                  "operator": {
                    "type": "string",
                    "operation": "notEmpty",
                    "singleValue": true
                  },
                  "leftValue": "={{ $json.chatInput }}",
                  "rightValue": ""
                }
              ]
            }
          },
          "typeVersion": 2.2
        },
        {
          "id": "ac63265e-e361-4df3-9a32-4f3aca4d504f",
          "name": "Save Eval",
          "type": "n8n-nodes-base.evaluation",
          "position": [
            560,
            272
          ],
          "parameters": {
            "outputs": {
              "values": [
                {
                  "outputName": "correctness",
                  "outputValue": "={{ $json.Correctness }}"
                },
                {
                  "outputName": "agent answer",
                  "outputValue": "={{ $('AI Agent').item.json.output }}"
                }
              ]
            },
            "sheetName": {
              "__rl": true,
              "mode": "list",
              "value": "gid=0",
              "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1D1P_B70KYuzLXVhSFGd6t19Q2u7msEenvhUqajwLw7k/edit#gid=0",
              "cachedResultName": "Sheet1"
            },
            "documentId": {
              "__rl": true,
              "mode": "list",
              "value": "1ed0c-Nt4BTETlbwpCA3oTdp7Z_-h1om2tJGasOpPvbo",
              "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1ed0c-Nt4BTETlbwpCA3oTdp7Z_-h1om2tJGasOpPvbo/edit?usp=drivesdk",
              "cachedResultName": "Public Agentic Arena Evaluation Set"
            }
          },
          "credentials": {
            "googleSheetsOAuth2Api": {
              "id": "credential-id",
              "name": "googleSheetsOAuth2Api Credential"
            }
          },
          "typeVersion": 4.7
        },
        {
          "id": "baac5f6a-46ab-4207-94c0-cc2445c3a0aa",
          "name": "Run Evaluation",
          "type": "n8n-nodes-base.evaluation",
          "position": [
            208,
            272
          ],
          "parameters": {
            "prompt": "You are an expert factual evaluator assessing the accuracy of answers compared to established ground truths.\n\nEvaluate the factual correctness of a given output compared to the provided ground truth on a scale from 1 to 5. Use detailed reasoning to thoroughly analyze all claims before determining the final score.\n\n# Scoring Criteria\n\n- 5: Highly similar - The output and ground truth are nearly identical, with only minor, insignificant differences.\n- 4: Somewhat similar - The output is largely similar to the ground truth but has few noticeable differences.\n- 3: Moderately similar - There are some evident differences, but the core essence is captured in the output.\n- 2: Slightly similar - The output only captures a few elements of the ground truth and contains several differences.\n- 1: Not similar - The output is significantly different from the ground truth, with few or no matching elements.\n- 0: Not similar at all – The outpus is completely different from the ground truth or not provided. Like nothings is matching.\n\nEvery correct Citation (ideally exact) inside of the Output that matches the provided ground truth is a strong boost for a good score. Also important: A Citation DOES NOT require to be in square brackets. Correct Text is perfectly fine.\n\n# Evaluation Steps\n\n1. Identify and list the key elements present in both the output and the ground truth.\n2. Compare these key elements to evaluate their similarities and differences, considering both content and structure.\n3. Analyze the semantic meaning conveyed by both the output and the ground truth, noting any significant deviations.\n4. Consider factual accuracy of specific details, including names, dates, numbers, and relationships.\n5. Assess whether the output maintains the factual integrity of the ground truth, even if phrased differently.\n6. Determine the overall level of similarity and accuracy according to the defined criteria.\n7. Check if the Citation of Source is provided and matches the original source of the ground truth.\n\n# Output Format\n\nProvide:\n- A detailed analysis of the comparison (extended reasoning)\n- A one-sentence summary highlighting key differences (not similarities)\n- The final similarity score as an integer (0, 1, 2, 3, 4, or 5)\n\nAlways follow the JSON format below and return nothing else:\n{\n  \"extended_reasoning\": \"<detailed step-by-step analysis of factual accuracy and similarity>\",\n  \"reasoning_summary\": \"<one sentence summary focusing on key differences>\",\n  \"score\": <number: integer from 1 to 5>,\n  \"expected_citation\": \"<exact correct citation of the source like in expected answer>\",\n}\n\n# Examples\n\n**Example 1:**\n\nInput:\n- Output: \"The cat sat on the mat.\"\n- Ground Truth: \"The feline is sitting on the rug.\"\n\nExpected Output:\n{\n  \"extended_reasoning\": \"I need to compare 'The cat sat on the mat' with 'The feline is sitting on the rug.' First, let me identify the key elements: both describe an animal ('cat' vs 'feline') in a position ('sat' vs 'sitting') on a surface ('mat' vs 'rug'). The subject is semantically identical - 'cat' and 'feline' refer to the same animal. The action is also semantically equivalent - 'sat' and 'sitting' both describe the same position, though one is past tense and one is present continuous. The location differs in specific wording ('mat' vs 'rug') but both refer to floor coverings that serve the same function. The basic structure and meaning of both sentences are preserved, though they use different vocabulary and slightly different tense. The core information being conveyed is the same, but there are noticeable wording differences.\",\n  \"reasoning_summary\": \"The sentences differ in vocabulary choice ('cat' vs 'feline', 'mat' vs 'rug') and verb tense ('sat' vs 'is sitting').\",\n  \"score\": 3\n}\n\n**Example 2:**\n\nInput:\n- Output: \"The quick brown fox jumps over the lazy dog.\"\n- Ground Truth: \"A fast brown animal leaps over a sleeping canine.\"\n\nExpected Output:\n{\n  \"extended_reasoning\": \"I need to compare 'The quick brown fox jumps over the lazy dog' with 'A fast brown animal leaps over a sleeping canine.' Starting with the subjects: 'quick brown fox' vs 'fast brown animal'. Both describe the same entity (a fox is a type of animal) with the same attributes (quick/fast and brown). The action is described as 'jumps' vs 'leaps', which are synonymous verbs describing the same motion. The object in both sentences is a dog, described as 'lazy' in one and 'sleeping' in the other, which are related concepts (a sleeping dog could be perceived as lazy). The structure follows the same pattern: subject + action + over + object. The sentences convey the same scene with slightly different word choices that maintain the core meaning. The level of specificity differs slightly ('fox' vs 'animal', 'dog' vs 'canine'), but the underlying information and imagery remain very similar.\",\n  \"reasoning_summary\": \"The sentences use different but synonymous terminology ('quick' vs 'fast', 'jumps' vs 'leaps', 'lazy' vs 'sleeping') and varying levels of specificity ('fox' vs 'animal', 'dog' vs 'canine').\",\n  \"score\": 4\n}\n\n# Notes\n\n- Focus primarily on factual accuracy and semantic similarity, not writing style or phrasing differences.\n- Identify specific differences rather than making general assessments.\n- Pay special attention to dates, numbers, names, locations, and causal relationships when present.\n- Consider the significance of each difference in the context of the overall information.\n- Be consistent in your scoring approach across different evaluations.\n- Value the Citation if correct. False Citation is a negative factor. A missing Citation is strong negative factor.",
            "options": {},
            "operation": "setMetrics",
            "actualAnswer": "={{ $json.output || \"No output provided.\" }}",
            "expectedAnswer": "={{ $('Eval Set').item.json.answer }}"
          },
          "typeVersion": 4.7
        },
        {
          "id": "f9b69d7f-87c5-412e-844f-b198aa7b64d0",
          "name": "Sticky Note",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            144,
            160
          ],
          "parameters": {
            "content": "## Hook up your own GSheet for saving Outputs"
          },
          "typeVersion": 1
        },
        {
          "id": "56ade4de-8554-4a50-9a9c-e0a9f4ad30b0",
          "name": "LLM as a Judge",
          "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
          "position": [
            576,
            496
          ],
          "parameters": {
            "model": {
              "__rl": true,
              "mode": "list",
              "value": "gpt-4.1",
              "cachedResultName": "gpt-4.1"
            },
            "options": {
              "temperature": 0.1,
              "responseFormat": "json_object"
            }
          },
          "credentials": {
            "openAiApi": {
              "id": "credential-id",
              "name": "openAiApi Credential"
            }
          },
          "typeVersion": 1.2
        },
        {
          "id": "787c3ae3-8d48-4cb3-8380-0f00a69962ed",
          "name": "Sticky Note1",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            144,
            432
          ],
          "parameters": {
            "color": 3,
            "height": 224,
            "content": "## Do not touch this!\n\n![I see you](https://cloud.let-the-work-flow.com/workflow-data/eval-emoji-72.png)\nSincerely,\n_Pure Eval_"
          },
          "typeVersion": 1
        },
        {
          "id": "c050a45d-7708-4981-8f87-09f97589e2e6",
          "name": "When clicking ‘Execute workflow’",
          "type": "n8n-nodes-base.manualTrigger",
          "position": [
            -1568,
            -1216
          ],
          "parameters": {},
          "typeVersion": 1
        },
        {
          "id": "81da391f-e1a8-4bec-8020-ba452132c46f",
          "name": "Mistral Upload",
          "type": "n8n-nodes-base.httpRequest",
          "position": [
            -960,
            -912
          ],
          "parameters": {
            "url": "https://api.mistral.ai/v1/files",
            "method": "POST",
            "options": {},
            "sendBody": true,
            "contentType": "multipart-form-data",
            "authentication": "predefinedCredentialType",
            "bodyParameters": {
              "parameters": [
                {
                  "name": "purpose",
                  "value": "ocr"
                },
                {
                  "name": "file",
                  "parameterType": "formBinaryData",
                  "inputDataFieldName": "data"
                }
              ]
            },
            "nodeCredentialType": "mistralCloudApi"
          },
          "credentials": {
            "mistralCloudApi": {
              "id": "credential-id",
              "name": "mistralCloudApi Credential"
            }
          },
          "typeVersion": 4.2
        },
        {
          "id": "1c48b0d2-8256-406f-86cc-c05f3b762117",
          "name": "Mistral Signed URL",
          "type": "n8n-nodes-base.httpRequest",
          "position": [
            -640,
            -912
          ],
          "parameters": {
            "url": "=https://api.mistral.ai/v1/files/{{ $json.id }}/url",
            "options": {},
            "sendQuery": true,
            "sendHeaders": true,
            "authentication": "predefinedCredentialType",
            "queryParameters": {
              "parameters": [
                {
                  "name": "expiry",
                  "value": "24"
                }
              ]
            },
            "headerParameters": {
              "parameters": [
                {
                  "name": "Accept",
                  "value": "application/json"
                }
              ]
            },
            "nodeCredentialType": "mistralCloudApi"
          },
          "credentials": {
            "mistralCloudApi": {
              "id": "credential-id",
              "name": "mistralCloudApi Credential"
            }
          },
          "typeVersion": 4.2
        },
        {
          "id": "1868dc23-f486-4ec7-85e4-41ca7a677c7a",
          "name": "Mistral DOC OCR",
          "type": "n8n-nodes-base.httpRequest",
          "position": [
            -320,
            -912
          ],
          "parameters": {
            "url": "https://api.mistral.ai/v1/ocr",
            "method": "POST",
            "options": {},
            "jsonBody": "={\n  \"model\": \"mistral-ocr-latest\",\n  \"document\": {\n    \"type\": \"document_url\",\n    \"document_url\": \"{{ $json.url }}\"\n  },\n  \"include_image_base64\": true\n}",
            "sendBody": true,
            "specifyBody": "json",
            "authentication": "predefinedCredentialType",
            "nodeCredentialType": "mistralCloudApi"
          },
          "credentials": {
            "mistralCloudApi": {
              "id": "credential-id",
              "name": "mistralCloudApi Credential"
            }
          },
          "typeVersion": 4.2
        },
        {
          "id": "1795d977-cce8-42aa-a74e-9d8ac80bf5c5",
          "name": "Loop Over Items",
          "type": "n8n-nodes-base.splitInBatches",
          "position": [
            -1552,
            -640
          ],
          "parameters": {
            "options": {}
          },
          "typeVersion": 3
        },
        {
          "id": "1bc2beaf-c0da-41ec-95c4-740ae35cd55d",
          "name": "Refresh collection",
          "type": "n8n-nodes-base.httpRequest",
          "position": [
            -1248,
            -1216
          ],
          "parameters": {
            "url": "http://XX.XX.XX:6333/collections/agentic-arena/points/delete",
            "method": "POST",
            "options": {},
            "jsonBody": "{\n  \"filter\": {}\n}",
            "sendBody": true,
            "sendHeaders": true,
            "specifyBody": "json",
            "authentication": "genericCredentialType",
            "genericAuthType": "httpHeaderAuth",
            "headerParameters": {
              "parameters": [
                {
                  "name": "Content-Type",
                  "value": "application/json"
                }
              ]
            }
          },
          "credentials": {
            "httpHeaderAuth": {
              "id": "credential-id",
              "name": "httpHeaderAuth Credential"
            }
          },
          "typeVersion": 4.2
        },
        {
          "id": "83d76a59-2184-4d36-bb00-eb882c6882eb",
          "name": "Embeddings OpenAI",
          "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
          "position": [
            -768,
            -368
          ],
          "parameters": {
            "options": {
              "stripNewLines": false
            }
          },
          "credentials": {
            "openAiApi": {
              "id": "credential-id",
              "name": "openAiApi Credential"
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          },
          "typeVersion": 1.1
        },
        {
          "id": "f2170147-3b23-460c-b9d4-8d5554a8d90c",
          "name": "Default Data Loader",
          "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
          "position": [
            -624,
            -400
          ],
          "parameters": {
            "options": {
              "metadata": {
                "metadataValues": [
                  {
                    "name": "document",
                    "value": "={{$('Get PDF').item.binary.data.fileName}}"
                  }
                ]
              }
            }
          },
          "typeVersion": 1
        },
        {
          "id": "8c819fbd-daa2-4390-af6e-10665073e0e7",
          "name": "Code",
          "type": "n8n-nodes-base.code",
          "position": [
            0,
            -912
          ],
          "parameters": {
            "jsCode": "const data = $json.pages;\n\nreturn data.map(entry => ({\n  json: {\n    markdown: entry.markdown\n  }\n}));"
          },
          "typeVersion": 2
        },
        {
          "id": "2f4108c9-9ac2-49e9-b9d1-52db9d50d518",
          "name": "Wait",
          "type": "n8n-nodes-base.wait",
          "position": [
            -224,
            -624
          ],
          "webhookId": "1000b40d-5dc5-4795-9dd2-8a23653c2b49",
          "parameters": {},
          "typeVersion": 1.1
        },
        {
          "id": "994ae38c-8f8d-4dfb-9441-a72de0d32d3c",
          "name": "Qdrant Vector Store",
          "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
          "position": [
            -704,
            -624
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            "text": "={{ $json.chatInput }}",
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              "systemMessage": "=You are a Ministry of Finance (MoF) policy compliance assistant specialized in permit applications and regulatory analysis. Your role is to evaluate permit applications against established MoF policies and provide accurate determinations.\n\n**CORE INSTRUCTIONS:**\n1. **Always use the RAG tool** for each request to retrieve relevant policy information\n2. **Never invent or fabricate information** - only use verified policy data\n3. **Respond in English** with clear and professional language\n4. **Provide definitive determinations** - start with \"Yes\" or \"No\" followed by specific reasoning\n\n**RESPONSE STRUCTURE:**\n- **Opening determination:** Clear Yes/No or specific outcome statement\n- **Reasoning:** Concise explanation referencing specific policy requirements\n- **Citations:** Always include citations in format: Citation: [MoF Policy ###: Policy Name]\n\n**KEY ANALYSIS AREAS:**\n- Zone requirements and submission timelines\n- Payment timing (minimum 7 days before activation)\n- Entity type eligibility (nonprofit vs. for-profit restrictions)\n- Officer conflict of interest rules\n- Emergency processing classifications\n- Seasonal adjustment requirements\n- Fee calculations and waiver eligibility\n\n**EXAMPLE RESPONSE FORMAT:**\n```\nYes/No—[specific violation]. [Timeline/requirement details], [consequences].\n\nCitation: [MoF Policy ###: Policy Name, MoF Policy ###: Policy Name]\n```\n\n**QUALITY STANDARDS:**\n- Be precise with dates, timelines, and calculations\n- Identify ALL applicable violations in complex cases\n- Reference multiple policies when relevant\n- Maintain professional, authoritative tone\n- Ensure legal accuracy and compliance\n\nAlways cross-reference multiple policies when evaluating complex scenarios involving multiple potential violations."
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            "content": "# Agentic Arena Community Contest\n\n## Overview\n\nThis competition challenges you to build a Retrieval-Augmented Generation (RAG) AI agent in n8n that can accurately answer questions based on a provided PDF knowledge base. \n\n## What You're Building\n\nYou'll create an n8n workflow that:\n\n- Ingests PDF documents into a vector database or knowledge retrieval system\n- Processes natural language questions about the content\n- Returns accurate answers based solely on the provided documentation with proper citations\n- Demonstrates high accuracy when tested against our evaluation questions\n\n\n"
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          "name": "Sticky Note6",
          "type": "n8n-nodes-base.stickyNote",
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          "parameters": {
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            "content": "\n## Rules\n\n### 1. PDF Knowledge Base\n- Find a folder with the [PDF files here](https://drive.google.com/drive/folders/1FqVwbNrAPn2dHhIwSEtlu5kl3z2jEC0U?usp=sharing).\n- Copy or download them for use from your own system\n\n### 2. Evaluation Set\n- Find the Google Sheet with the [evaluation set here](https://docs.google.com/spreadsheets/d/1cgZzr0-D5Kpd6HrKowyoN_fI2dY0dkP9Ljljxix0AlI/edit?usp=sharing).\n- Copy the Sheet to use it in your workflow\n\n### 3. Starter Workflow\n- [Download this starter workflow](https://file.notion.so/f/f/d147bfac-0bab-4c58-884f-f45c5f5a13e8/7f8419b8-7ac3-4c55-8c5c-e0874aa7a46f/AgenticArena_Challenge1_StarterWorkflow.json?table=block&id=2685b6e0-c94f-809b-905c-cf39f05e0e0f&spaceId=d147bfac-0bab-4c58-884f-f45c5f5a13e8&expirationTimestamp=1758902400000&signature=C3ERsAdCUDcvYkODZBR5LZLxx9q4_2Nk1XEqjgOQNOY&downloadName=AgenticArena_Challenge1_StarterWorkflow.json) and import into your n8n instance "
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          "position": [
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          "parameters": {
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            "content": "## My Solution\n\n1. **Create a collection on Qdrant** (Self-hosted) - Set up a new vector collection in Qdrant for storing embeddings\n\n2. **Retrieve PDF files from Google Drive** - Download or access the PDF documents from Google Drive\n\n3. **Convert PDF documents using Mistral OCR** - Process the PDF files through Mistral's OCR (Optical Character Recognition) system to extract text\n\n4. **Embed documents in Qdrant Vector Store** - Generate embeddings for the extracted content and store them in the Qdrant vector database\n\n5. **Set up Evaluation Workflow with AI Agent** - Configure an evaluation workflow that includes:\n   - AI Agent integration\n   - Connection to Qdrant vector database\n   - Cohere reranker implementation\n   - GPT-4.1 model (or GPT-4o/GPT-4 Turbo, depending on the actual model being used)\n\n6. **Save output to Google Sheets** - Export and store the results in a [Google Sheets document](https://docs.google.com/spreadsheets/d/1ed0c-Nt4BTETlbwpCA3oTdp7Z_-h1om2tJGasOpPvbo/edit?usp=sharing)\n"
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                {
                  "id": "ca7c30f2-444d-4551-988d-0f513e5ee4b1",
                  "name": "file_id",
                  "type": "string",
                  "value": "={{ $json.id }}"
                }
              ]
            }
          },
          "typeVersion": 3.4
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      "active": false,
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      "versionId": "d7715ff3-8833-4b10-862e-0eb02f65c2ff",
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    "status": "published",
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      "name": "Davide",
      "username": "n3witalia",
      "bio": "Full-stack Web Developer based in Italy specialising in Marketing & AI-powered automations. For business enquiries, send me an email at info@n3w.it or add me on Linkedin.com/in/davideboizza",
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          "fileBuffer": "data:image/svg+xml;base64,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"
        },
        "displayName": "Google Drive",
        "typeVersion": 3,
        "nodeCategories": [
          {
            "id": 3,
            "name": "Data & Storage"
          }
        ]
      },
      {
        "id": 111,
        "icon": "fa:sign-in-alt",
        "name": "n8n-nodes-base.executeWorkflow",
        "codex": {
          "data": {
            "alias": [
              "n8n",
              "call",
              "sub",
              "workflow",
              "sub-workflow",
              "subworkflow"
            ],
            "details": "The Execute Workflow node can be used when you want your workflow to treat another workflow as a step in your flow. It allows you to modularize your workflows and have a single source of truth for series of actions you perform often. ",
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.executeworkflow/"
                }
              ]
            },
            "categories": [
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Helpers",
                "Flow"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Execute Workflow",
          "color": "#ff6d5a"
        },
        "iconData": {
          "icon": "sign-in-alt",
          "type": "icon"
        },
        "displayName": "Execute Sub-workflow",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 514,
        "icon": "fa:pause-circle",
        "name": "n8n-nodes-base.wait",
        "codex": {
          "data": {
            "alias": [
              "pause",
              "sleep",
              "delay",
              "timeout"
            ],
            "resources": {
              "generic": [
                {
                  "url": "https://n8n.io/blog/how-to-get-started-with-crm-automation-and-no-code-workflow-ideas/",
                  "icon": "👥",
                  "label": "How to get started with CRM automation (with 3 no-code workflow ideas"
                },
                {
                  "url": "https://n8n.io/blog/aws-workflow-automation/",
                  "label": "7 no-code workflow automations for Amazon Web Services"
                }
              ],
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.wait/"
                }
              ]
            },
            "categories": [
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Helpers",
                "Flow"
              ]
            }
          }
        },
        "group": "[\"organization\"]",
        "defaults": {
          "name": "Wait",
          "color": "#804050"
        },
        "iconData": {
          "icon": "pause-circle",
          "type": "icon"
        },
        "displayName": "Wait",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 565,
        "icon": "fa:sticky-note",
        "name": "n8n-nodes-base.stickyNote",
        "codex": {
          "data": {
            "alias": [
              "Comments",
              "Notes",
              "Sticky"
            ],
            "categories": [
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Helpers"
              ]
            }
          }
        },
        "group": "[\"input\"]",
        "defaults": {
          "name": "Sticky Note",
          "color": "#FFD233"
        },
        "iconData": {
          "icon": "sticky-note",
          "type": "icon"
        },
        "displayName": "Sticky Note",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 834,
        "icon": "file:code.svg",
        "name": "n8n-nodes-base.code",
        "codex": {
          "data": {
            "alias": [
              "cpde",
              "Javascript",
              "JS",
              "Python",
              "Script",
              "Custom Code",
              "Function"
            ],
            "details": "The Code node allows you to execute JavaScript in your workflow.",
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.code/"
                }
              ]
            },
            "categories": [
              "Development",
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Helpers",
                "Data Transformation"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Code"
        },
        "iconData": {
          "type": "file",
          "fileBuffer": "data:image/svg+xml;base64,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"
        },
        "displayName": "Code",
        "typeVersion": 2,
        "nodeCategories": [
          {
            "id": 5,
            "name": "Development"
          },
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 837,
        "icon": "fa:sign-out-alt",
        "name": "n8n-nodes-base.executeWorkflowTrigger",
        "codex": {
          "data": {
            "resources": {
              "generic": [],
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.executeworkflowtrigger/"
                }
              ]
            },
            "categories": [
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Helpers"
              ]
            }
          }
        },
        "group": "[\"trigger\"]",
        "defaults": {
          "name": "When Executed by Another Workflow",
          "color": "#ff6d5a"
        },
        "iconData": {
          "icon": "sign-out-alt",
          "type": "icon"
        },
        "displayName": "Execute Workflow Trigger",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 838,
        "icon": "fa:mouse-pointer",
        "name": "n8n-nodes-base.manualTrigger",
        "codex": {
          "data": {
            "resources": {
              "generic": [],
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.manualworkflowtrigger/"
                }
              ]
            },
            "categories": [
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0"
          }
        },
        "group": "[\"trigger\"]",
        "defaults": {
          "name": "When clicking ‘Execute workflow’",
          "color": "#909298"
        },
        "iconData": {
          "icon": "mouse-pointer",
          "type": "icon"
        },
        "displayName": "Manual Trigger",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 844,
        "icon": "fa:filter",
        "name": "n8n-nodes-base.filter",
        "codex": {
          "data": {
            "alias": [
              "Router",
              "Filter",
              "Condition",
              "Logic",
              "Boolean",
              "Branch"
            ],
            "details": "The Filter node can be used to filter items based on a condition. If the condition is met, the item will be passed on to the next node. If the condition is not met, the item will be omitted. Conditions can be combined together by AND(meet all conditions), or OR(meet at least one condition).",
            "resources": {
              "generic": [],
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.filter/"
                }
              ]
            },
            "categories": [
              "Core Nodes"
            ],
            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Flow",
                "Data Transformation"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Filter",
          "color": "#229eff"
        },
        "iconData": {
          "icon": "filter",
          "type": "icon"
        },
        "displayName": "Filter",
        "typeVersion": 2,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          }
        ]
      },
      {
        "id": 1119,
        "icon": "fa:robot",
        "name": "@n8n/n8n-nodes-langchain.agent",
        "codex": {
          "data": {
            "alias": [
              "LangChain",
              "Chat",
              "Conversational",
              "Plan and Execute",
              "ReAct",
              "Tools"
            ],
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Agents",
                "Root Nodes"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "AI Agent",
          "color": "#404040"
        },
        "iconData": {
          "icon": "robot",
          "type": "icon"
        },
        "displayName": "AI Agent",
        "typeVersion": 3,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
        "id": 1141,
        "icon": "file:openAiLight.svg",
        "name": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
        "codex": {
          "data": {
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.embeddingsopenai/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Embeddings"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Embeddings OpenAI"
        },
        "iconData": {
          "type": "file",
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