Make Integration

Make (formerly Integromat) is a visual automation platform that lets you connect FLTR to hundreds of apps with a drag-and-drop interface.

Why Make?

Visual Workflow Builder

Design complex automations with an intuitive visual interface

Advanced Logic

Routers, filters, aggregators, and iterators for complex workflows

Error Handling

Built-in error handlers and retry mechanisms

Real-time Execution

Instant triggers and webhooks for immediate processing

Prerequisites

Make account (make.com)
FLTR API key from www.tryfltr.com
Dataset ID (e.g., ds_abc123)

Quick Start: Search Knowledge Base

Step 1: Create a New Scenario

Log in to Make
Click Create a new scenario
Name it “FLTR Knowledge Search”

Step 2: Add HTTP Module

Click + to add a module
Search for HTTP
Select Make a request

Step 3: Configure FLTR Request

URL:

https://api.fltr.com/v1/mcp/query

Method: POST Headers:

Key	Value
`Authorization`	`Bearer YOUR_API_KEY`
`Content-Type`	`application/json`

Body type: Raw Request content:

{
  "query": "{{1.query}}",
  "dataset_id": "ds_abc123",
  "limit": 5
}

Step 4: Test the Module

Click Run once
The module will execute and show results
Click on the module to view the response

Step 5: Process Results

Add another module to use the search results:

Click + after the HTTP module
Choose your destination (Slack, Email, etc.)
Map the results using:
- {{2.data.results[].content}}
- {{2.data.results[].metadata.title}}
- {{2.data.results[].score}}

Common Scenarios

1. Gmail to FLTR Search

Modules:

Gmail → Watch emails
HTTP → FLTR Query
Gmail → Send reply

FLTR Query Body:

{
  "query": "{{1.subject}} {{1.text}}",
  "dataset_id": "ds_support_kb",
  "limit": 3,
  "rerank": true
}

Gmail Reply:

Hi {{1.from.name}},

Here are some resources that might help:

{{2.data.results[1].metadata.title}}
{{2.data.results[1].content}}

{{2.data.results[2].metadata.title}}
{{2.data.results[2].content}}

Best regards

2. Slack Q&A Bot

Modules:

Slack → Watch messages (Instant)
Router → Split based on message content
HTTP → FLTR Query
Slack → Reply in thread

Router Filter:

{{1.text}} contains "?"

FLTR Query:

{
  "query": "{{1.text}}",
  "dataset_id": "ds_company_kb",
  "limit": 1
}

Slack Reply:

📚 {{2.data.results[1].metadata.title}}

{{2.data.results[1].content}}

_Relevance: {{formatNumber(2.data.results[1].score; 2)}}%_

3. Document Indexer

Modules:

Google Drive → Watch files
Google Drive → Download a file
HTTP → FLTR Upload Document
Slack → Send notification

FLTR Upload: URL: https://api.fltr.com/v1/datasets/ds_abc123/documents Method: POST Body:

{
  "content": "{{2.data}}",
  "metadata": {
    "title": "{{1.name}}",
    "source": "Google Drive",
    "drive_id": "{{1.id}}",
    "created_at": "{{1.createdTime}}"
  }
}

4. Customer Support Automation

Modules:

Webhook → Custom webhook
HTTP → FLTR Query
Filter → High relevance only
Zendesk → Create ticket with context

Filter Condition:

{{2.data.results[1].score}} >= 0.7

Zendesk Ticket:

Subject: {{1.customer_email}} - Support Request

Description:
{{1.message}}

---
Related Documentation:

{{join(map(2.data.results; "- " + metadata.title + "\n" + content); "\n\n")}}

Advanced Techniques

Using Iterators

Process each search result individually:

Add Iterator module after FLTR query
Connect it to {{http.data.results}}
Each result becomes a separate operation

Example: Send each result to different Slack channel

Iterator input: {{2.data.results}}

Slack message:
Channel: #{{iterator.metadata.category}}
Message: {{iterator.content}}

Using Aggregators

Combine multiple results into one output:

Add Array aggregator after iterator
Aggregate field: {{iterator.content}}
Join results with separator

Example: Create single email with all results

{{join(map(2.data.results; "• " + metadata.title); "\n")}}

Routers for Conditional Logic

Split workflow based on result quality:

Add Router after FLTR query
Route 1 filter: {{2.data.results[1].score}} >= 0.8
Route 2 filter: {{2.data.results[1].score}} < 0.8
Different actions for each route

Error Handlers

Add error handling to HTTP modules:

Right-click HTTP module
Select Add error handler
Choose Error handler route
Add actions for error cases

Example error notification:

⚠️ FLTR Query Failed

Error: {{error.message}}
Query: {{1.query}}
Time: {{now}}

Data Transformation

Use Tools modules to transform data: Set Variable:

Name: formattedResults
Value: {{map(2.data.results; metadata.title + ": " + substring(content; 0; 100))}}

Text Parser: Parse FLTR response for specific fields:

Pattern: "title": "([^"]+)"
Text: {{2.data}}

All FLTR Endpoints for Make

Query Dataset

Module: HTTP - Make a request Configuration:

URL: https://api.fltr.com/v1/mcp/query
Method: POST

Headers:
Authorization: Bearer YOUR_API_KEY
Content-Type: application/json

Body:
{
  "query": "{{input}}",
  "dataset_id": "ds_abc123",
  "limit": 5,
  "rerank": false
}

Batch Query

Search multiple queries at once:

URL: https://api.fltr.com/v1/mcp/batch-query
Method: POST

Body:
{
  "queries": {{array(1.question1; 1.question2; 1.question3)}},
  "dataset_id": "ds_abc123",
  "limit": 3
}

Upload Document

URL: https://api.fltr.com/v1/datasets/DATASET_ID/documents
Method: POST

Body:
{
  "content": "{{1.text}}",
  "metadata": {
    "title": "{{1.title}}",
    "source": "Make",
    "created_at": "{{now}}"
  }
}

Create Dataset

URL: https://api.fltr.com/v1/datasets
Method: POST

Body:
{
  "name": "{{1.name}}",
  "description": "{{1.description}}",
  "is_public": false
}

List Datasets

URL: https://api.fltr.com/v1/datasets
Method: GET

Headers:
Authorization: Bearer YOUR_API_KEY

Working with Make Variables

Accessing Array Items

FLTR returns results as an array. Access items:

First result: {{2.data.results[1]}}
Second result: {{2.data.results[2]}}
All results: {{2.data.results[]}}

Mapping Arrays

Transform all results:

{{map(2.data.results; metadata.title)}}
{{map(2.data.results; content)}}
{{map(2.data.results; formatNumber(score * 100; 0) + "%")}}

Filtering Results

Filter by score:

{{filter(2.data.results; score > 0.7)}}

Filter by metadata:

{{filter(2.data.results; metadata.category = "tutorial")}}

Joining Results

Combine multiple results:

{{join(map(2.data.results; "• " + metadata.title); "\n")}}

Rate Limiting in Make

Make respects FLTR’s rate limits (1,000 req/hour for API keys).

Handling Rate Limits

Add Sleep Module:
- Between iterations, add Tools → Sleep
- Duration: 1-2 seconds

Error Handler:

Filter: {{2.statusCode}} = 429
Action: Tools → Sleep (3600 seconds)
Then: Resume execution

Scheduling:
- Reduce scenario frequency
- Schedule runs during off-peak hours

Monitoring Usage

Add a module to track requests:

Tools → Set Variable
Name: request_count
Value: {{request_count + 1}}
Add filter when count > 900

Webhook Triggers

Use Make’s instant webhooks for real-time scenarios:

Step 1: Create Webhook

Add Webhooks → Custom webhook
Click Add to create new webhook
Copy the webhook URL

Step 2: Configure Trigger

Send data to webhook from external source:

curl -X POST https://hook.make.com/abc123... \
  -H "Content-Type: application/json" \
  -d '{"query": "How do I reset my password?"}'

Step 3: Process with FLTR

Add HTTP module after webhook
Use {{1.query}} in FLTR request
Return response to webhook caller

Enable webhook response:

Webhook Response → Status: 200
Body: {{2.data}}

Security Best Practices

Storing API Keys

Never hard-code API keys in scenarios:

Go to Scenario settings
Add Environment variable
Name: FLTR_API_KEY
Value: Your API key
Use in headers: Bearer {{env.FLTR_API_KEY}}

Webhook Security

Validate webhook requests:

Add Router after webhook
Filter: {{1.secret}} = "your_secret_key"
Invalid requests go to error handler

Error Logging

Log errors for debugging:

Add Tools → Set variable in error handler
Send error details to logging service
Include: timestamp, error message, input data

Complete Example: Support Ticket Automation

Here’s a full scenario configuration: Module 1: Webhook

Type: Custom webhook
Instant trigger

Module 2: HTTP - FLTR Query

URL: https://api.fltr.com/v1/mcp/query
Method: POST

Headers:
Authorization: Bearer {{env.FLTR_API_KEY}}
Content-Type: application/json

Body:
{
  "query": "{{1.subject}} {{1.description}}",
  "dataset_id": "ds_support_docs",
  "limit": 5,
  "rerank": true
}

Module 3: Router Route 1 (High confidence):

Filter: {{2.data.results[1].score}} >= 0.8
Action: Auto-respond with answer

Route 2 (Medium confidence):

Filter: {{2.data.results[1].score}} >= 0.5
Action: Create ticket with suggested docs

Route 3 (Low confidence):

Filter: {{2.data.results[1].score}} < 0.5
Action: Escalate to human agent

Module 4a: Email - Send auto-response

To: {{1.email}}
Subject: Re: {{1.subject}}

Hi,

Based on your inquiry, here's what might help:

{{2.data.results[1].metadata.title}}
{{2.data.results[1].content}}

If this doesn't resolve your issue, we'll get back to you soon.

Module 4b: Zendesk - Create ticket

Subject: {{1.subject}}
Requester: {{1.email}}
Comment:
{{1.description}}

---
Suggested resources:
{{join(map(2.data.results; "• " + metadata.title); "\n")}}

Module 4c: Slack - Alert team

⚠️ Low-confidence ticket needs attention

From: {{1.email}}
Subject: {{1.subject}}

Best match: {{formatNumber(2.data.results[1].score * 100; 0)}}%

Resources

Make Academy

Learn Make fundamentals

FLTR API Reference

Complete API documentation

Zapier Integration

Alternative: Use Zapier

API Keys Guide

Manage your API keys

Next Steps

Try n8n integration for self-hosted workflows
Set up webhooks for event notifications
Explore Zapier integration for simpler workflows

​Make Integration

​Why Make?

Visual Workflow Builder

Advanced Logic

Error Handling

Real-time Execution

​Prerequisites

​Quick Start: Search Knowledge Base

​Step 1: Create a New Scenario

​Step 2: Add HTTP Module

​Step 3: Configure FLTR Request

​Step 4: Test the Module

​Step 5: Process Results

​Common Scenarios

​1. Gmail to FLTR Search

​2. Slack Q&A Bot

​3. Document Indexer

​4. Customer Support Automation

​Advanced Techniques

​Using Iterators

​Using Aggregators

​Routers for Conditional Logic

​Error Handlers

​Data Transformation

​All FLTR Endpoints for Make

​Query Dataset

​Batch Query

​Upload Document

​Create Dataset

​List Datasets

​Working with Make Variables

​Accessing Array Items

​Mapping Arrays

​Filtering Results

​Joining Results

​Rate Limiting in Make

​Handling Rate Limits

​Monitoring Usage

​Webhook Triggers

​Step 1: Create Webhook

​Step 2: Configure Trigger

​Step 3: Process with FLTR

​Security Best Practices

​Storing API Keys

​Webhook Security

​Error Logging

​Complete Example: Support Ticket Automation

​Resources

Make Academy

FLTR API Reference

Zapier Integration

API Keys Guide

​Next Steps

Make Integration

Why Make?

Prerequisites

Quick Start: Search Knowledge Base

Step 1: Create a New Scenario

Step 2: Add HTTP Module

Step 3: Configure FLTR Request

Step 4: Test the Module

Step 5: Process Results

Common Scenarios

1. Gmail to FLTR Search

2. Slack Q&A Bot

3. Document Indexer

4. Customer Support Automation

Advanced Techniques

Using Iterators

Using Aggregators

Routers for Conditional Logic

Error Handlers

Data Transformation

All FLTR Endpoints for Make

Query Dataset

Batch Query

Upload Document

Create Dataset

List Datasets

Working with Make Variables

Accessing Array Items

Mapping Arrays

Filtering Results

Joining Results

Rate Limiting in Make

Handling Rate Limits

Monitoring Usage

Webhook Triggers

Step 1: Create Webhook

Step 2: Configure Trigger

Step 3: Process with FLTR

Security Best Practices

Storing API Keys

Webhook Security

Error Logging

Complete Example: Support Ticket Automation

Resources

Next Steps