Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI

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  "name": "Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI",
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        "content": "## AI Travel Agent Powered by Couchbase.

### You will need to:
1. Setup your Google API Credentials for the Gemini LLM
2. Setup your OpenAI Credentials for the OpenAI embedding nodes.
3. Create a Couchbase cluster (using [Couchbase Capella](https://cloud.couchbase.com/) in the cloud, or Couchbase Server)
4. 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
5. 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.
6. Create a bucket, scope, and collection. We recommend the following:
   - Bucket: `travel-agent`
   - Scope: `vectors`
   - Collection: `points-of-interest`
7. 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).


Once all of that is configured you will need to send the loading webhook with some data points (see example).

This should create vectorized data in  `points-of-interest` collection.

Once 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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        "path": "3ca6fbdd-a157-4e9d-9042-237048da85b6",
        "options": {
          "rawBody": true
        },
        "httpMethod": "POST"
      },
      "typeVersion": 2
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      "name": "Default Data Loader",
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      "position": [
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      "parameters": {
        "options": {},
        "jsonData": "={{ $json.body.raw_body.point_of_interest.title }} - {{ $json.body.raw_body.point_of_interest.description }}",
        "jsonMode": "expressionData"
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      "typeVersion": 1
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        "content": "## CURL Command to Ingest Data.

Here is an example of how you can load data into your webhook once its active and ready to get requests.

```
curl -X POST \"webhook url\" \
  -H \"Content-Type: application/json\" \
  -d '{
    \"raw_body\": {
      \"point_of_interest\": {
        \"title\": \"Eiffel Tower\",
        \"description\": \"Iconic iron lattice tower located on the Champ de Mars in Paris, France.\"
      }
    }
  }'
```

(replace webhook url with the URL listed in the webhook node)

A 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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          "maxIterations": 10,
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      "parameters": {
        "mode": "retrieve-as-tool",
        "topK": 10,
        "options": {},
        "toolName": "PointofinterestKB",
        "embedding": "embedding",
        "textFieldKey": "description",
        "couchbaseScope": {
          "__rl": true,
          "mode": "list",
          "value": "",
          "cachedResultUrl": "",
          "cachedResultName": ""
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        "couchbaseBucket": {
          "__rl": true,
          "mode": "list",
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