Make Openai Citation For File Retrieval Rag

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A recipe for Make Openai Citation For File Retrieval Rag

Recipe Template

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    "name": "Make OpenAI Citation for File Retrieval RAG",
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            "createdAt": "2024-09-13T02:43:13.014Z",
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                "content": "## Within N8N, there will be a chat button to test"
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            "parameters": {
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                "content": "## Make OpenAI Citation for File Retrieval RAG\n\n## Use case\n\nIn this example, we will ensure that all texts from the OpenAI assistant search for citations and sources in the vector store files. We can also format the output for Markdown or HTML tags.\n\nThis is necessary because the assistant sometimes generates strange characters, and we can also use dynamic references such as citations 1, 2, 3, for example.\n\n## What this workflow does\n\nIn this workflow, we will use an OpenAI assistant created within their interface, equipped with a vector store containing some files for file retrieval.\n\nThe assistant will perform the file search within the OpenAI infrastructure and will return the content with citations.\n\n- We will make an HTTP request to retrieve all the details we need to format the text output.\n\n## Setup\n\nInsert an OpenAI Key\n\n## How to adjust it to your needs\n\nAt the end of the workflow, we have a block of code that will format the output, and there we can add Markdown tags to create links. Optionally, we can transform the Markdown formatting into HTML.\n\n\nby Davi Saranszky Mesquita\nhttps:\/\/www.linkedin.com\/in\/mesquitadavi\/"
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            "notes": "https:\/\/www.npmjs.com\/package\/@n8n\/chat",
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            "position": [
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            "parameters": {
                "options": {
                    "preserveOriginalTools": false
                },
                "resource": "assistant",
                "assistantId": {
                    "__rl": true,
                    "mode": "list",
                    "value": "asst_QAfdobVCVCMJz8LmaEC7nlId",
                    "cachedResultName": "Teste"
                }
            },
            "credentials": {
                "openAiApi": {
                    "id": "UfNrqPCRlD8FD9mk",
                    "name": "OpenAi Lourival"
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                "content": "## Setup\n\n- Configure OpenAI Key\n\n### In this step, we will use an assistant created within the OpenAI platform that contains a vector store a.k.a file retrieval"
            },
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        },
        {
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            "name": "Get ALL Thread Content",
            "type": "n8n-nodes-base.httpRequest",
            "position": [
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            ],
            "parameters": {
                "url": "=https:\/\/api.openai.com\/v1\/threads\/{{ $json.threadId }}\/messages",
                "options": [],
                "sendHeaders": true,
                "authentication": "predefinedCredentialType",
                "headerParameters": {
                    "parameters": [
                        {
                            "name": "OpenAI-Beta",
                            "value": "assistants=v2"
                        }
                    ]
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            "credentials": {
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            "position": [
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            "parameters": {
                "content": "### Retrieving all thread content is necessary because the OpenAI tool does not retrieve all citations upon request."
            },
            "typeVersion": 1
        },
        {
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            "name": "Split all message iterations from a thread",
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                "fieldToSplitOut": "data"
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            "name": "Split all citations from a single message",
            "type": "n8n-nodes-base.splitOut",
            "position": [
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            "parameters": {
                "options": [],
                "fieldToSplitOut": "text.annotations"
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            "alwaysOutputData": true
        },
        {
            "id": "98af62f5-adb0-4e07-a146-fc2f13b851ce",
            "name": "Retrieve file name from a file ID",
            "type": "n8n-nodes-base.httpRequest",
            "onError": "continueRegularOutput",
            "position": [
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            ],
            "parameters": {
                "url": "=https:\/\/api.openai.com\/v1\/files\/{{ $json.file_citation.file_id }}",
                "options": [],
                "sendQuery": true,
                "authentication": "predefinedCredentialType",
                "queryParameters": {
                    "parameters": [
                        {
                            "name": "limit",
                            "value": "1"
                        }
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                            "name": "filename",
                            "type": "string",
                            "value": "={{ $json.filename }}"
                        },
                        {
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                            "name": "text",
                            "type": "string",
                            "value": "={{ $('Split all citations from a single message').item.json.text }}"
                        }
                    ]
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            },
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            "position": [
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            "parameters": {
                "width": 200,
                "height": 220,
                "content": "### A file retrieval request contains a lot of information, and we want only the text that will be substituted and the file name.\n\n- id\n- filename\n- text\n"
            },
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            "id": "53c79a6c-7543-435f-b40e-966dff0904d4",
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            "position": [
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            "parameters": {
                "width": 200,
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                "content": "### With the last three splits, we may have many citations and texts to substitute. By doing an aggregation, it will be possible to handle everything as a single request."
            },
            "typeVersion": 1
        },
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            "type": "n8n-nodes-base.stickyNote",
            "position": [
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            "parameters": {
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                "content": "### This simple code will take all the previous files and citations and alter the original text, formatting the output. In this way, we can use Markdown tags to create links, or if you prefer, we can add an HTML transformation node."
            },
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            "type": "n8n-nodes-base.code",
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                "jsCode": "let saida = $('OpenAI Assistant with Vector Store').item.json.output;\n\nfor (let i of $input.item.json.data) {\n saida = saida.replaceAll(i.text, \" _(\"+ i.filename+\")_ \");\n}\n\n$input.item.json.output = saida;\nreturn $input.item;"
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                        "node": "Get ALL Thread Content",
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        "Split all citations from a single message": {
            "main": [
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                        "node": "Retrieve file name from a file ID",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "Split all message iterations from a thread": {
            "main": [
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                        "node": "Split all content from a single message",
                        "type": "main",
                        "index": 0
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                ]
            ]
        },
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}

How to Use an n8n Template

1

Create a New Workflow

Click "New Workflow" in your n8n dashboard to get started.

2

Copy & Paste Template

First, copy this template: Click here to copy the JSON .
Then, in n8n, click the three dots (···) → "Import from file" and paste the JSON code.

3

Customize the Nodes

Go through each node in the workflow to update inputs like spreadsheet IDs, email addresses, or message content. Adjust field mappings to match your data.

4

Grant Access

For nodes that connect to external apps (like Google Sheets or Slack), you'll need to grant access. Connect your accounts using OAuth or an API key and save the credentials in the node.

5

Test It

Run the workflow by clicking "Execute Node" for each step or "Run Once" for the whole thing. Check the right sidebar to inspect data and debug any errors (they'll show up in red).

6

Activate Workflow

Once everything works as expected, click the "Activate" toggle to turn your workflow on. You're all set!