ReSynap

ReSynap: Knowledge Graph AI Engine for MindMap Generation and Automation

Welcome to ReSynap—an advanced Knowledge Graph AI Engine designed to generate dynamic mind maps from text, transform text chats into visual knowledge graphs, and automate a range of tasks that integrate with customer data platforms (CDPs) and more.

Features

1. Dynamic Mind Map Generation: Easily convert text into comprehensive mind maps that visually organize information and relationships.

2. Text-to-Knowledge Graphs: Transform unstructured text chats into interactive knowledge graphs, uncovering insights and making connections intuitive.

3. Customer Profile Classification: Automatically classify customer profiles within CDPs, allowing for personalized segmentation and enhanced targeting.

4. Data Reports and Insights: Generate detailed data reports with ease, helping teams stay informed with up-to-date insights.

5. Customer Segmentation: Segment customer data effectively based on behavior, demographics, or any custom metric that fits your business needs.

6. Real-Time Product Recommendations: Get tailored product recommendations for specific user profiles, enabling targeted marketing and enhancing user experience.

7. Automated Tasks:

   1. Send personalized emails at scale.
   2. Trigger Zalo notifications to keep customers engaged.
   3. Automate various customer-related workflows with seamless integration.

Use Cases

• Marketing Automation: Automate segmentation and customer communications.
• Customer Data Analysis: Deepen your insights into customer behavior and preferences.
• Product Recommendations: Serve real-time, relevant recommendations to improve engagement and conversion.
• Knowledge Management: Use knowledge graphs for easy data retrieval and management.
• Workflow Automation: Set up triggers for automating tasks across multiple platforms.

Getting Started

1. Installation: [TODO]
2. Usage: [TODO]
3. API Integration: [TODO]

Contributions

Contributions to ReSynap are welcome! Please follow the [contribution guidelines](link to contribution guidelines) and submit your pull requests.

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With ReSynap, transform data and text into actionable, visual insights and automate your workflows seamlessly!

System requirements

• Python 3.10+
• FastAPI
• Pydantic
• Qdrant Vector Database. Refer to the Qdrant documentation for instructions: https://qdrant.tech/documentation/quick-start/
• Qdrant client library (install with pip install qdrant-client)
• ArangoDB for graph database
• Redis or Apache Kafka for data processing on queue
• Large language models (LLMs) likes Google Gemini or OpenAI

Installation

1. Clone the repository:

   git clone https://github.com/trieu/resynap

2. Create env

    python -m venv env
    source env/bin/activate

3. Install the dependencies:

   pip install -r requirements.txt

4. Start the Qdrant server:

   ./start_qdrant.sh

5. Next, create a .env file by coping the file sample.env or create your own file with content:

    QDRANT_HOST=localhost
    QDRANT_PORT=6333

    QDRANT_CLOUD_HOST=
    QDRANT_CLOUD_API_KEY=

    API_HOST=0.0.0.0
    API_PORT=8000

    REDIS_HOST=
    REDIS_PORT=0
    DEFAULT_AUTHORIZATION_KEY=personalization_test

Running the API

uvicorn main:api_personalization --reload 

This will start the API server. You can access the API documentation at http://localhost:8000/docs

API Endpoints

All API Endpoints must be called with the header: Authorization = [your_api_key]
The [your_api_key] must be in Redis. E.g: 127.0.0.1:6480> set personalization_test true

Profiles

• POST /add-profile/
  • Adds a single user profile to the database.
  • Request body: ProfileRequest object (see below)
  • Response: {"status": "Profile added successfully"}
• POST /add-profiles/
  • Adds multiple user profiles to the database in bulk.
  • Request body: List of ProfileRequest objects
  • Response: {"status": "All profiles added successfully"}
• POST /check-profile-for-recommendation/
  • Add or update a profile, then get real-time recommendations for the profile
  • Request body: ProfileRequest object
  • Response: List of recommended products (see example below)

Products

The product management API

• POST /add-product/
  • Adds a single product to the database.
  • Request body: ProductRequest object (see below)
  • Response: {"status": "Product added successfully"}
• POST /add-products/
  • Adds multiple products to the database in bulk.
  • Request body: List of ProductRequest objects
  • Response: {"status": "All products added successfully"}

Contents

The content management API

• POST /add-content/
  • Adds a single content item to the database.
  • Request body: ContentRequest object (see below)
  • Response: {"status": "Content item added successfully"}
• POST /add-contents/
  • Adds multiple content items to the database in bulk.
  • Request body: List of ContentRequest objects
  • Response: {"status": "All content items added successfully"}

Recommendations

The recommendation API to personalize product, contents and both.

• GET /recommend/{profile_id}
  • Gets personalized product recommendations for a given profile ID.
  • Path parameters:
    • profile_id: The ID of the user profile.
    • item_type: The item type to get recommendation: product, content, or both.
  • Query parameters:
    • top_n (optional): The maximum number of recommendations to return (default: 8).
    • except_product_ids (optional): Comma-separated string of product IDs to exclude from
    • except_content_ids (optional): Comma-separated string of content IDs to exclude from recommendations (e.g., "item_1,item_3").
  • Response: List of recommended product / content IDs (see example below)

Data Models

ProfileRequest:

{
    "profile_id": "crm_11",
    "page_view_keywords": [
        "car",
        "bike",
        "accessories"
    ],
    "purchase_keywords": [
        "helmet",
        "gloves"
    ],
    "interest_keywords": [
        "travel",
        "photography",
        "outdoors"
    ],
    "additional_info": {
        "age": 28,
        "location": "Germany"
    }
}

ProductRequest:

{
    "product_id": "item_1",
    "product_name": "Macbook Pro",
    "product_category": "Electronics",
    "product_keywords": ["tech", "work", "portable"],
    "additional_info": {"brand": "Apple", "price": 1200}
}

ContentRequest:

{
    "content_id": "item_1",
    "title": "Macbook Pro",
    "content_category": "Electronics",
    "content_keywords": ["tech", "work", "portable"]
}

Example Usage

Adding a Profile

curl -X POST \
  http://localhost:8000/add-profile/ \
  -H 'Content-Type: application/json' \
  -H 'Authorization: personalization_test' \
  -d '{
    "profile_id": "crm_15",
    "page_view_keywords": [
      "car",
      "bike",
      "accessories"
    ],
    "purchase_keywords": [
      "helmet",
      "gloves"
    ],
    "interest_keywords": [
      "travel",
      "photography",
      "outdoors"
    ],
    "additional_info": {
      "age": 28,
      "location": "Germany"
    }
  }'

Getting Recommendations

curl -X GET \
  -H 'Authorization: personalization_test' \
  "http://localhost:8000/recommend/crm_15?top_n=5&except_product_ids=item_1,item_6" 

Future Improvements

• Implement more sophisticated vectorization techniques for user profiles and product data.
• Add support for more advanced filtering and ranking options for recommendations.
• Integrate with a user interface for managing profiles and products.