Merge pull request #19 from enescingoz/dev_json

Change file format to .json

AI_Research_RAG_and_Data_Analysis/Vector Database as a Big Data Analysis Tool for AI Agents [2_2 KNN].txt

@@ -1,544 +0,0 @@
-{
-"id": "itzURpN5wbUNOXOw",
-"meta": {
-"instanceId": "205b3bc06c96f2dc835b4f00e1cbf9a937a74eeb3b47c99d0c30b0586dbf85aa"
-},
-"name": "[2/2] KNN classifier (lands dataset)",
-"tags": [
-{
-"id": "QN7etptCmdcGIpkS",
-"name": "classifier",
-"createdAt": "2024-12-08T22:08:15.968Z",
-"updatedAt": "2024-12-09T19:25:04.113Z"
-}
-],
-"nodes": [
-{
-"id": "33373ccb-164e-431c-8a9a-d68668fc70be",
-"name": "Embed image",
-"type": "n8n-nodes-base.httpRequest",
-"position": [
--140,
--240
-],
-"parameters": {
-"url": "https://api.voyageai.com/v1/multimodalembeddings",
-"method": "POST",
-"options": {},
-"jsonBody": "={{\n{\n \"inputs\": [\n {\n \"content\": [\n {\n \"type\": \"image_url\",\n \"image_url\": $json.imageURL\n }\n ]\n }\n ],\n \"model\": \"voyage-multimodal-3\",\n \"input_type\": \"document\"\n}\n}}",
-"sendBody": true,
-"specifyBody": "json",
-"authentication": "genericCredentialType",
-"genericAuthType": "httpHeaderAuth"
-},
-"credentials": {
-"httpHeaderAuth": {
-"id": "Vb0RNVDnIHmgnZOP",
-"name": "Voyage API"
-}
-},
-"typeVersion": 4.2
-},
-{
-"id": "58adecfa-45c7-4928-b850-053ea6f3b1c5",
-"name": "Query Qdrant",
-"type": "n8n-nodes-base.httpRequest",
-"position": [
-440,
--240
-],
-"parameters": {
-"url": "={{ $json.qdrantCloudURL }}/collections/{{ $json.collectionName }}/points/query",
-"method": "POST",
-"options": {},
-"jsonBody": "={{\n{\n \"query\": $json.ImageEmbedding,\n \"using\": \"voyage\",\n \"limit\": $json.limitKNN,\n \"with_payload\": true\n}\n}}",
-"sendBody": true,
-"specifyBody": "json",
-"authentication": "predefinedCredentialType",
-"nodeCredentialType": "qdrantApi"
-},
-"credentials": {
-"qdrantApi": {
-"id": "it3j3hP9FICqhgX6",
-"name": "QdrantApi account"
-}
-},
-"typeVersion": 4.2
-},
-{
-"id": "258026b7-2dda-4165-bfe1-c4163b9caf78",
-"name": "Majority Vote",
-"type": "n8n-nodes-base.code",
-"position": [
-840,
--240
-],
-"parameters": {
-"language": "python",
-"pythonCode": "from collections import Counter\n\ninput_json = _input.all()[0]\npoints = input_json['json']['result']['points']\nmajority_vote_two_most_common = Counter([point[\"payload\"][\"landscape_name\"] for point in points]).most_common(2)\n\nreturn [{\n \"json\": {\n \"result\": majority_vote_two_most_common \n }\n}]\n"
-},
-"typeVersion": 2
-},
-{
-"id": "e83e7a0c-cb36-46d0-8908-86ee1bddf638",
-"name": "Increase limitKNN",
-"type": "n8n-nodes-base.set",
-"position": [
-1240,
--240
-],
-"parameters": {
-"options": {},
-"assignments": {
-"assignments": [
-{
-"id": "0b5d257b-1b27-48bc-bec2-78649bc844cc",
-"name": "limitKNN",
-"type": "number",
-"value": "={{ $('Propagate loop variables').item.json.limitKNN + 5}}"
-},
-{
-"id": "afee4bb3-f78b-4355-945d-3776e33337a4",
-"name": "ImageEmbedding",
-"type": "array",
-"value": "={{ $('Qdrant variables + embedding + KNN neigbours').first().json.ImageEmbedding }}"
-},
-{
-"id": "701ed7ba-d112-4699-a611-c0c134757a6c",
-"name": "qdrantCloudURL",
-"type": "string",
-"value": "={{ $('Qdrant variables + embedding + KNN neigbours').first().json.qdrantCloudURL }}"
-},
-{
-"id": "f5612f78-e7d8-4124-9c3a-27bd5870c9bf",
-"name": "collectionName",
-"type": "string",
-"value": "={{ $('Qdrant variables + embedding + KNN neigbours').first().json.collectionName }}"
-}
-]
-}
-},
-"typeVersion": 3.4
-},
-{
-"id": "8edbff53-cba6-4491-9d5e-bac7ad6db418",
-"name": "Propagate loop variables",
-"type": "n8n-nodes-base.set",
-"position": [
-640,
--240
-],
-"parameters": {
-"options": {},
-"assignments": {
-"assignments": [
-{
-"id": "880838bf-2be2-4f5f-9417-974b3cbee163",
-"name": "=limitKNN",
-"type": "number",
-"value": "={{ $json.result.points.length}}"
-},
-{
-"id": "5fff2bea-f644-4fd9-ad04-afbecd19a5bc",
-"name": "result",
-"type": "object",
-"value": "={{ $json.result }}"
-}
-]
-}
-},
-"typeVersion": 3.4
-},
-{
-"id": "6fad4cc0-f02c-429d-aa4e-0d69ebab9d65",
-"name": "Image Test URL",
-"type": "n8n-nodes-base.set",
-"position": [
--320,
--240
-],
-"parameters": {
-"options": {},
-"assignments": {
-"assignments": [
-{
-"id": "46ceba40-fb25-450c-8550-d43d8b8aa94c",
-"name": "imageURL",
-"type": "string",
-"value": "={{ $json.query.imageURL }}"
-}
-]
-}
-},
-"typeVersion": 3.4
-},
-{
-"id": "f02e79e2-32c8-4af0-8bf9-281119b23cc0",
-"name": "Return class",
-"type": "n8n-nodes-base.set",
-"position": [
-1240,
-0
-],
-"parameters": {
-"options": {},
-"assignments": {
-"assignments": [
-{
-"id": "bd8ca541-8758-4551-b667-1de373231364",
-"name": "class",
-"type": "string",
-"value": "={{ $json.result[0][0] }}"
-}
-]
-}
-},
-"typeVersion": 3.4
-},
-{
-"id": "83ca90fb-d5d5-45f4-8957-4363a4baf8ed",
-"name": "Check tie",
-"type": "n8n-nodes-base.if",
-"position": [
-1040,
--240
-],
-"parameters": {
-"options": {},
-"conditions": {
-"options": {
-"version": 2,
-"leftValue": "",
-"caseSensitive": true,
-"typeValidation": "strict"
-},
-"combinator": "and",
-"conditions": [
-{
-"id": "980663f6-9d7d-4e88-87b9-02030882472c",
-"operator": {
-"type": "number",
-"operation": "gt"
-},
-"leftValue": "={{ $json.result.length }}",
-"rightValue": 1
-},
-{
-"id": "9f46fdeb-0f89-4010-99af-624c1c429d6a",
-"operator": {
-"type": "number",
-"operation": "equals"
-},
-"leftValue": "={{ $json.result[0][1] }}",
-"rightValue": "={{ $json.result[1][1] }}"
-},
-{
-"id": "c59bc4fe-6821-4639-8595-fdaf4194c1e1",
-"operator": {
-"type": "number",
-"operation": "lte"
-},
-"leftValue": "={{ $('Propagate loop variables').item.json.limitKNN }}",
-"rightValue": 100
-}
-]
-}
-},
-"typeVersion": 2.2
-},
-{
-"id": "847ced21-4cfd-45d8-98fa-b578adc054d6",
-"name": "Qdrant variables + embedding + KNN neigbours",
-"type": "n8n-nodes-base.set",
-"position": [
-120,
--240
-],
-"parameters": {
-"options": {},
-"assignments": {
-"assignments": [
-{
-"id": "de66070d-5e74-414e-8af7-d094cbc26f62",
-"name": "ImageEmbedding",
-"type": "array",
-"value": "={{ $json.data[0].embedding }}"
-},
-{
-"id": "58b7384d-fd0c-44aa-9f8e-0306a99be431",
-"name": "qdrantCloudURL",
-"type": "string",
-"value": "=https://152bc6e2-832a-415c-a1aa-fb529f8baf8d.eu-central-1-0.aws.cloud.qdrant.io"
-},
-{
-"id": "e34c4d88-b102-43cc-a09e-e0553f2da23a",
-"name": "collectionName",
-"type": "string",
-"value": "=land-use"
-},
-{
-"id": "db37e18d-340b-4624-84f6-df993af866d6",
-"name": "limitKNN",
-"type": "number",
-"value": "=10"
-}
-]
-}
-},
-"typeVersion": 3.4
-},
-{
-"id": "d1bc4edc-37d2-43ac-8d8b-560453e68d1f",
-"name": "Sticky Note",
-"type": "n8n-nodes-base.stickyNote",
-"position": [
--940,
--120
-],
-"parameters": {
-"color": 6,
-"width": 320,
-"height": 540,
-"content": "Here we're classifying existing types of satellite imagery of land types:\n- 'agricultural',\n- 'airplane',\n- 'baseballdiamond',\n- 'beach',\n- 'buildings',\n- 'chaparral',\n- 'denseresidential',\n- 'forest',\n- 'freeway',\n- 'golfcourse',\n- 'harbor',\n- 'intersection',\n- 'mediumresidential',\n- 'mobilehomepark',\n- 'overpass',\n- 'parkinglot',\n- 'river',\n- 'runway',\n- 'sparseresidential',\n- 'storagetanks',\n- 'tenniscourt'\n"
-},
-"typeVersion": 1
-},
-{
-"id": "13560a31-3c72-43b8-9635-3f9ca11f23c9",
-"name": "Sticky Note1",
-"type": "n8n-nodes-base.stickyNote",
-"position": [
--520,
--460
-],
-"parameters": {
-"color": 6,
-"content": "I tested this KNN classifier on a whole `test` set of a dataset (it's not a part of the collection, only `validation` + `train` parts). Accuracy of classification on `test` is **93.24%**, no fine-tuning, no metric learning."
-},
-"typeVersion": 1
-},
-{
-"id": "8c9dcbcb-a1ad-430f-b7dd-e19b5645b0f6",
-"name": "Execute Workflow Trigger",
-"type": "n8n-nodes-base.executeWorkflowTrigger",
-"position": [
--520,
--240
-],
-"parameters": {},
-"typeVersion": 1
-},
-{
-"id": "b36fb270-2101-45e9-bb5c-06c4e07b769c",
-"name": "Sticky Note2",
-"type": "n8n-nodes-base.stickyNote",
-"position": [
--1080,
--520
-],
-"parameters": {
-"width": 460,
-"height": 380,
-"content": "## KNN classification workflow-tool\n### This n8n template takes an image URL (as anomaly detection tool does), and as output, it returns a class of the object on the image (out of land types list)\n\n* An image URL is received via the Execute Workflow Trigger, which is then sent to the Voyage.ai Multimodal Embeddings API to fetch its embedding.\n* The image's embedding vector is then used to query Qdrant, returning a set of X similar images with pre-labeled classes.\n* Majority voting is done for classes of neighbouring images.\n* A loop is used to resolve scenarios where there is a tie in Majority Voting (for example, we have 5 \"forest\" and 5 \"beach\"), and we increase the number of neighbours to retrieve.\n* When the loop finally resolves, the identified class is returned to the calling workflow."
-},
-"typeVersion": 1
-},
-{
-"id": "51ece7fc-fd85-4d20-ae26-4df2d3893251",
-"name": "Sticky Note3",
-"type": "n8n-nodes-base.stickyNote",
-"position": [
-120,
--40
-],
-"parameters": {
-"height": 200,
-"content": "Variables define another Qdrant's collection with landscapes (uploaded similarly as the crops collection, don't forget to switch it with your data) + amount of neighbours **limitKNN** in the database we'll use for an input image classification."
-},
-"typeVersion": 1
-},
-{
-"id": "7aad5904-eb0b-4389-9d47-cc91780737ba",
-"name": "Sticky Note4",
-"type": "n8n-nodes-base.stickyNote",
-"position": [
--180,
--60
-],
-"parameters": {
-"height": 80,
-"content": "Similarly to anomaly detection tool, we're embedding input image with the Voyage model"
-},
-"typeVersion": 1
-},
-{
-"id": "d3702707-ee4a-481f-82ca-d9386f5b7c8a",
-"name": "Sticky Note5",
-"type": "n8n-nodes-base.stickyNote",
-"position": [
-440,
--500
-],
-"parameters": {
-"width": 740,
-"height": 200,
-"content": "## Tie loop\nHere we're [querying](https://api.qdrant.tech/api-reference/search/query-points) Qdrant, getting **limitKNN** nearest neighbours to our image <*Query Qdrant node*>, parsing their classes from payloads (images were pre-labeled & uploaded with their labels to Qdrant) & calculating the most frequent class name <*Majority Vote node*>. If there is a tie <*check tie node*> in 2 most common classes, for example, we have 5 \"forest\" and 5 \"harbor\", we repeat the procedure with the number of neighbours increased by 5 <*propagate loop variables node* and *increase limitKNN node*>.\nIf there is no tie, or we have already checked 100 neighbours, we exit the loop <*check tie node*> and return the class-answer."
-},
-"typeVersion": 1
-},
-{
-"id": "d26911bb-0442-4adc-8511-7cec2d232393",
-"name": "Sticky Note6",
-"type": "n8n-nodes-base.stickyNote",
-"position": [
-1240,
-160
-],
-"parameters": {
-"height": 80,
-"content": "Here, we extract the name of the input image class decided by the Majority Vote\n"
-},
-"typeVersion": 1
-},
-{
-"id": "84ffc859-1d5c-4063-9051-3587f30a0017",
-"name": "Sticky Note10",
-"type": "n8n-nodes-base.stickyNote",
-"position": [
--520,
-80
-],
-"parameters": {
-"color": 4,
-"width": 540,
-"height": 260,
-"content": "### KNN (k nearest neighbours) classification\n1. The first pipeline is uploading (lands) dataset to Qdrant's collection.\n2. **This is the KNN classifier tool, which takes any image as input and classifies it based on queries to the Qdrant (lands) collection.**\n\n### To recreate it\nYou'll have to upload [lands](https://www.kaggle.com/datasets/apollo2506/landuse-scene-classification) dataset from Kaggle to your own Google Storage bucket, and re-create APIs/connections to [Qdrant Cloud](https://qdrant.tech/documentation/quickstart-cloud/) (you can use **Free Tier** cluster), Voyage AI API & Google Cloud Storage\n\n**In general, pipelines are adaptable to any dataset of images**\n"
-},
-"typeVersion": 1
-}
-],
-"active": false,
-"pinData": {
-"Execute Workflow Trigger": [
-{
-"json": {
-"query": {
-"imageURL": "https://storage.googleapis.com/n8n-qdrant-demo/land-use/images_train_test_val/test/buildings/buildings_000323.png"
-}
-}
-}
-]
-},
-"settings": {
-"executionOrder": "v1"
-},
-"versionId": "c8cfe732-fd78-4985-9540-ed8cb2de7ef3",
-"connections": {
-"Check tie": {
-"main": [
-[
-{
-"node": "Increase limitKNN",
-"type": "main",
-"index": 0
-}
-],
-[
-{
-"node": "Return class",
-"type": "main",
-"index": 0
-}
-]
-]
-},
-"Embed image": {
-"main": [
-[
-{
-"node": "Qdrant variables + embedding + KNN neigbours",
-"type": "main",
-"index": 0
-}
-]
-]
-},
-"Query Qdrant": {
-"main": [
-[
-{
-"node": "Propagate loop variables",
-"type": "main",
-"index": 0
-}
-]
-]
-},
-"Majority Vote": {
-"main": [
-[
-{
-"node": "Check tie",
-"type": "main",
-"index": 0
-}
-]
-]
-},
-"Image Test URL": {
-"main": [
-[
-{
-"node": "Embed image",
-"type": "main",
-"index": 0
-}
-]
-]
-},
-"Increase limitKNN": {
-"main": [
-[
-{
-"node": "Query Qdrant",
-"type": "main",
-"index": 0
-}
-]
-]
-},
-"Execute Workflow Trigger": {
-"main": [
-[
-{
-"node": "Image Test URL",
-"type": "main",
-"index": 0
-}
-]
-]
-},
-"Propagate loop variables": {
-"main": [
-[
-{
-"node": "Majority Vote",
-"type": "main",
-"index": 0
-}
-]
-]
-},
-"Qdrant variables + embedding + KNN neigbours": {
-"main": [
-[
-{
-"node": "Query Qdrant",
-"type": "main",
-"index": 0
-}
-]
-]
-}
-}
-}

Gmail_and_Email_Automation/Summarize your emails with A.I. (via Openrouter) and send to Line messenger.txt

@@ -1,177 +0,0 @@
-{
-"id": "QnVdtKiTf3nbrNkh",
-"meta": {
-"instanceId": "558d88703fb65b2d0e44613bc35916258b0f0bf983c5d4730c00c424b77ca36a",
-"templateCredsSetupCompleted": true
-},
-"name": "Summarize emails with A.I. then send to messenger",
-"tags": [],
-"nodes": [
-{
-"id": "50e12e63-df28-45ac-9208-48cbf5116d09",
-"name": "Read emails (IMAP)",
-"type": "n8n-nodes-base.emailReadImap",
-"position": [
-340,
-260
-],
-"parameters": {
-"options": {},
-"postProcessAction": "nothing"
-},
-"credentials": {
-"imap": {
-"id": "gXtdakU9M02LBQc3",
-"name": "IMAP account"
-}
-},
-"typeVersion": 2
-},
-{
-"id": "6565350b-2269-44e3-8f36-8797f32d3e09",
-"name": "Send email to A.I. to summarize",
-"type": "n8n-nodes-base.httpRequest",
-"position": [
-700,
-260
-],
-"parameters": {
-"url": "https://openrouter.ai/api/v1/chat/completions",
-"method": "POST",
-"options": {},
-"jsonBody": "={\n \"model\": \"meta-llama/llama-3.1-70b-instruct:free\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"I want you to read and summarize all the emails. If it's not rimportant, just give me a short summary with less than 10 words.\\n\\nHighlight as important if it is, add an emoji to indicate it is urgent:\\nFor the relevant content, find any action items and deadlines. Sometimes I need to sign up before a certain date or pay before a certain date, please highlight that in the summary for me.\\n\\nPut the deadline in BOLD at the top. If the email is not important, keep the summary short to 1 sentence only.\\n\\nHere's the email content for you to read:\\nSender email address: {{ encodeURIComponent($json.from) }}\\nSubject: {{ encodeURIComponent($json.subject) }}\\n{{ encodeURIComponent($json.textHtml) }}\"\n }\n ]\n}",
-"sendBody": true,
-"specifyBody": "json",
-"authentication": "genericCredentialType",
-"genericAuthType": "httpHeaderAuth"
-},
-"credentials": {
-"httpHeaderAuth": {
-"id": "WY7UkF14ksPKq3S8",
-"name": "Header Auth account 2"
-}
-},
-"typeVersion": 4.2,
-"alwaysOutputData": false
-},
-{
-"id": "d04c422a-c000-4e48-82d0-0bf44bcd9fff",
-"name": "Send summarized content to messenger",
-"type": "n8n-nodes-base.httpRequest",
-"position": [
-1100,
-260
-],
-"parameters": {
-"url": "https://api.line.me/v2/bot/message/push",
-"method": "POST",
-"options": {},
-"jsonBody": "={\n \"to\": \"U3ec262c49811f30cdc2d2f2b0a0df99a\",\n \"messages\": [\n {\n \"type\": \"text\",\n \"text\": \"{{ $json.choices[0].message.content.replace(/\\n/g, \"\\\\n\") }}\"\n }\n ]\n}\n\n\n ",
-"sendBody": true,
-"specifyBody": "json",
-"authentication": "genericCredentialType",
-"genericAuthType": "httpHeaderAuth"
-},
-"credentials": {
-"httpHeaderAuth": {
-"id": "SzcKjO9Nn9vZPL2H",
-"name": "Header Auth account 5"
-}
-},
-"typeVersion": 4.2
-},
-{
-"id": "57a1219c-4f40-407c-855b-86c4c7c468bb",
-"name": "Sticky Note",
-"type": "n8n-nodes-base.stickyNote",
-"position": [
-180,
-0
-],
-"parameters": {
-"width": 361,
-"height": 90,
-"content": "## Summarize emails with A.I.\nYou can find out more about the [use case](https://rumjahn.com/how-a-i-saved-my-kids-school-life-and-my-marriage/)"
-},
-"typeVersion": 1
-},
-{
-"id": "17686264-56ac-419e-a32b-dc5c75f15f1f",
-"name": "Sticky Note1",
-"type": "n8n-nodes-base.stickyNote",
-"position": [
-283,
-141
-],
-"parameters": {
-"color": 5,
-"width": 229,
-"height": 280,
-"content": "Find your email server's IMAP Settings. \n- Link for [gmail](https://www.getmailspring.com/setup/access-gmail-via-imap-smtp)"
-},
-"typeVersion": 1
-},
-{
-"id": "1862abd6-7dca-4c66-90d6-110d4fcf4d99",
-"name": "Sticky Note2",
-"type": "n8n-nodes-base.stickyNote",
-"position": [
-580,
-0
-],
-"parameters": {
-"color": 6,
-"width": 365,
-"height": 442,
-"content": "For the A.I. you can use Openrouter.ai. \n- Set up a free account\n- The A.I. model selected is FREE to use.\n## Credentials\n- Use header auth\n- Username: Authorization\n- Password: Bearer {insert your API key}.\n- The password is \"Bearer\" space plus your API key."
-},
-"typeVersion": 1
-},
-{
-"id": "c4a3a76f-539d-4bbf-8f95-d7aaebf39a55",
-"name": "Sticky Note3",
-"type": "n8n-nodes-base.stickyNote",
-"position": [
-1000,
-0
-],
-"parameters": {
-"color": 4,
-"width": 307,
-"height": 439,
-"content": "Don't use the official Line node. It's outdated.\n## Credentials\n- Use header auth\n- Username: Authorization\n- Password: Bearer {channel access token}\n\nYou can find your channel access token at the [Line API console](https://developers.line.biz/console/). Go to Messaging API and scroll to the bottom."
-},
-"typeVersion": 1
-}
-],
-"active": false,
-"pinData": {},
-"settings": {
-"executionOrder": "v1"
-},
-"versionId": "81216e6a-2bd8-4215-8a96-376ee520469d",
-"connections": {
-"Read emails (IMAP)": {
-"main": [
-[
-{
-"node": "Send email to A.I. to summarize",
-"type": "main",
-"index": 0
-}
-]
-]
-},
-"Send email to A.I. to summarize": {
-"main": [
-[
-{
-"node": "Send summarized content to messenger",
-"type": "main",
-"index": 0
-}
-]
-]
-}
-}
-}