366 lines
11 KiB
JSON
366 lines
11 KiB
JSON
{
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"id": "iGAzT789R7Q1fOOE",
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"meta": {
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"instanceId": "7a1e9dd164c758cbdeb7cf88274e567a937a36ed99d4d22ff24b645841097c48",
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"templateId": "3577",
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"templateCredsSetupCompleted": true
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},
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"name": "Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI",
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"tags": [],
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"nodes": [
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{
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"id": "0f361616-a552-43ed-9754-794780113955",
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"name": "When chat message received",
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"type": "@n8n/n8n-nodes-langchain.chatTrigger",
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"position": [
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380,
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240
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],
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"webhookId": "c22b2240-ff07-44e5-a1aa-63584150a1cb",
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"parameters": {
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"options": {}
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},
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"typeVersion": 1.1
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},
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{
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"id": "e8b9815d-0fe5-4e7c-a20b-1602384580cd",
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"name": "Google Gemini Chat Model",
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"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
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"position": [
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560,
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480
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],
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"parameters": {
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"options": {},
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"modelName": "models/gemini-2.0-flash"
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},
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"typeVersion": 1
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},
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{
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"id": "a4b15997-de4d-4c78-b623-e936442134af",
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"name": "Sticky Note",
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"type": "n8n-nodes-base.stickyNote",
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"position": [
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1260,
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280
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],
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"parameters": {
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"color": 3,
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"width": 800,
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"height": 500,
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"content": "## AI Travel Agent Powered by Couchbase.\n\n### You will need to:\n1. Setup your Google API Credentials for the Gemini LLM\n2. Setup your OpenAI Credentials for the OpenAI embedding nodes.\n3. Create a Couchbase cluster (using [Couchbase Capella](https://cloud.couchbase.com/) in the cloud, or Couchbase Server)\n4. Add [Database credentials](https://docs.couchbase.com/cloud/clusters/manage-database-users.html#create-database-credentials) with appropriate permissions for the operations you want to perform\n5. Configure [Allowed IP addresses](https://docs.couchbase.com/cloud/clusters/allow-ip-address.html) for your n8n instance. Use `0.0.0.0/0` for easier testing.\n6. Create a bucket, scope, and collection. We recommend the following:\n - Bucket: `travel-agent`\n - Scope: `vectors`\n - Collection: `points-of-interest`\n7. Navigate to the Data Tools, click the Search tab, and click Import Search Index. Upload the following JSON file found [here](https://gist.github.com/ejscribner/6f16343d4b44b1af31e8f344557814b0).\n\n\nOnce all of that is configured you will need to send the loading webhook with some data points (see example).\n\nThis should create vectorized data in `points-of-interest` collection.\n\nOnce you have data points there try to ask the Agent questions about the data points and test the response. Eg. \"Where should I go for a romantic getaway?\""
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},
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"typeVersion": 1
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},
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{
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"id": "34866f8e-00b0-4706-82d7-491b9531a8b6",
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"name": "Webhook",
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"type": "n8n-nodes-base.webhook",
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"position": [
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800,
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1000
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],
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"webhookId": "3ca6fbdd-a157-4e9d-9042-237048da85b6",
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"parameters": {
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"path": "3ca6fbdd-a157-4e9d-9042-237048da85b6",
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"options": {
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"rawBody": true
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},
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"httpMethod": "POST"
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},
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"typeVersion": 2
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},
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{
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"id": "26d4e62a-42b0-4e09-8585-827e5bcc9fff",
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"name": "Default Data Loader",
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"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
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"position": [
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1180,
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1360
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],
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"parameters": {
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"options": {},
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"jsonData": "={{ $json.body.raw_body.point_of_interest.title }} - {{ $json.body.raw_body.point_of_interest.description }}",
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"jsonMode": "expressionData"
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},
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"typeVersion": 1
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},
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{
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"id": "63fc308f-4d1c-4d24-9b20-68d7e6c2dbba",
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"name": "Recursive Character Text Splitter",
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"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
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"position": [
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1280,
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1540
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],
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"parameters": {
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"options": {}
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},
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"typeVersion": 1
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},
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{
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"id": "84f8c32b-8e0c-457c-aaec-17827042674d",
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"name": "Sticky Note1",
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"type": "n8n-nodes-base.stickyNote",
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"position": [
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-60,
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1060
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],
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"parameters": {
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"width": 720,
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"height": 460,
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"content": "## CURL Command to Ingest Data.\n\nHere is an example of how you can load data into your webhook once its active and ready to get requests.\n\n```\ncurl -X POST \"webhook url\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"raw_body\": {\n \"point_of_interest\": {\n \"title\": \"Eiffel Tower\",\n \"description\": \"Iconic iron lattice tower located on the Champ de Mars in Paris, France.\"\n }\n }\n }'\n```\n\n(replace webhook url with the URL listed in the webhook node)\n\nA shell script to bulk insert six data points can be found [here](https://gist.github.com/ejscribner/355a46a0a383a4878e65e2230b92c6b5). Be sure to activate the workflow and use the production Webhook URL when running the script."
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},
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"typeVersion": 1
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},
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{
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"id": "b2cf8788-849c-4420-b448-bd49caa4941e",
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"name": "Simple Memory",
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"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
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"position": [
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720,
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480
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],
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"parameters": {},
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"typeVersion": 1.3
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},
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{
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"id": "0bf7fef9-f999-42a8-a6a8-ab111fe9a084",
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"name": "AI Travel Agent",
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"type": "@n8n/n8n-nodes-langchain.agent",
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"position": [
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600,
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240
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],
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"parameters": {
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"options": {
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"maxIterations": 10,
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"systemMessage": "You are a helpful assistant for a trip planner. You have a vector search capability to locate points of interest, Use it and don't invent much."
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}
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},
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"typeVersion": 1.8
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},
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{
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"id": "3af3c8ce-582b-407c-847a-8063f9ad2e1a",
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"name": "Retrieve docs with Couchbase Search Vector",
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"type": "n8n-nodes-couchbase.vectorStoreCouchbaseSearch",
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"position": [
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860,
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500
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],
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"parameters": {
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"mode": "retrieve-as-tool",
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"topK": 10,
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"options": {},
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"toolName": "PointofinterestKB",
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"embedding": "embedding",
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"textFieldKey": "description",
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"couchbaseScope": {
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"__rl": true,
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"mode": "list",
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"value": "",
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"cachedResultUrl": "",
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"cachedResultName": ""
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},
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"couchbaseBucket": {
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"__rl": true,
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"mode": "list",
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"value": ""
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},
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"toolDescription": "The list of Points of Interest from the database.",
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"vectorIndexName": {
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"__rl": true,
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"mode": "list",
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"value": "",
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"cachedResultUrl": "",
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"cachedResultName": ""
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},
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"couchbaseCollection": {
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"__rl": true,
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"mode": "list",
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"value": "",
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"cachedResultUrl": "",
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"cachedResultName": ""
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}
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},
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"typeVersion": 1.1
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},
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{
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"id": "77a4e857-607a-4bbc-a28d-8a715f9415d5",
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"name": "Insert docs with Couchbase Search Vector",
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"type": "n8n-nodes-couchbase.vectorStoreCouchbaseSearch",
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"position": [
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1100,
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1120
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],
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"parameters": {
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"mode": "insert",
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"options": {},
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"embedding": "embedding",
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"textFieldKey": "description",
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"couchbaseScope": {
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"__rl": true,
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"mode": "list",
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"value": "",
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"cachedResultUrl": "",
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"cachedResultName": ""
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},
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"couchbaseBucket": {
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"__rl": true,
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"mode": "list",
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"value": ""
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},
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"vectorIndexName": {
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"__rl": true,
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"mode": "list",
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"value": "",
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"cachedResultUrl": "",
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"cachedResultName": ""
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},
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"embeddingBatchSize": 1,
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"couchbaseCollection": {
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"__rl": true,
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"mode": "list",
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"value": "",
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"cachedResultUrl": "",
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"cachedResultName": ""
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}
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},
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"typeVersion": 1.1
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},
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{
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"id": "4c0274c3-6647-4f45-b7d4-d63cfe2102ea",
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"name": "Generate OpenAI Embeddings using text-embedding-3-small",
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"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
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"position": [
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960,
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740
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],
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"parameters": {
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"options": {}
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},
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"typeVersion": 1.2
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},
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{
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"id": "83f864fa-a298-4738-a102-ca2d283377de",
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"name": "Generate OpenAI Embeddings using text-embedding-3-small1",
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"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
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"position": [
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1000,
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1340
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],
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"parameters": {
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"options": {}
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},
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"typeVersion": 1.2
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}
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],
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"active": true,
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"pinData": {},
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"settings": {
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"callerPolicy": "workflowsFromSameOwner",
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"executionOrder": "v1"
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},
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"versionId": "80e40e5a-35a3-4fa4-b90e-ac9d76897bbd",
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"connections": {
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"Webhook": {
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"main": [
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[
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{
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"node": "Insert docs with Couchbase Search Vector",
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"type": "main",
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"index": 0
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}
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]
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]
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},
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"Simple Memory": {
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"ai_memory": [
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[
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{
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"node": "AI Travel Agent",
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"type": "ai_memory",
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"index": 0
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}
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]
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]
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},
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"Default Data Loader": {
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"ai_document": [
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[
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{
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"node": "Insert docs with Couchbase Search Vector",
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"type": "ai_document",
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"index": 0
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}
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]
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]
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},
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"Google Gemini Chat Model": {
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"ai_languageModel": [
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[
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{
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"node": "AI Travel Agent",
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"type": "ai_languageModel",
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"index": 0
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}
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]
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]
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},
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"When chat message received": {
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"main": [
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[
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{
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"node": "AI Travel Agent",
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"type": "main",
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"index": 0
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}
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]
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]
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},
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"Recursive Character Text Splitter": {
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"ai_textSplitter": [
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[
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{
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"node": "Default Data Loader",
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"type": "ai_textSplitter",
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"index": 0
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}
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]
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]
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},
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"Retrieve docs with Couchbase Search Vector": {
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"ai_tool": [
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[
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{
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"node": "AI Travel Agent",
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"type": "ai_tool",
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"index": 0
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}
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]
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]
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},
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"Generate OpenAI Embeddings using text-embedding-3-small": {
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"ai_embedding": [
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[
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{
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"node": "Retrieve docs with Couchbase Search Vector",
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"type": "ai_embedding",
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"index": 0
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}
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]
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]
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},
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"Generate OpenAI Embeddings using text-embedding-3-small1": {
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"ai_embedding": [
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[
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{
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"node": "Insert docs with Couchbase Search Vector",
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"type": "ai_embedding",
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"index": 0
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}
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]
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]
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}
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}
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} |