ReSynap

Open Source AI-first Customer 360 Framework

🔑 Core Features of ReSynap Framework

• Unified Customer Profile: Combines customer data from all sources into one clear profile.
• Smart Identity Matching: Uses AI to merge duplicate profiles and link anonymous users to known ones.
• Real-Time Data Handling: Tracks and processes customer actions instantly.
• Built-in AI/ML Tools: Supports predictive models like churn risk, lifetime value, and customer segments.
• Customer Knowledge Graph: Maps relationships between people, products, and behaviors for deeper insights.
• Generative AI Integration: Connects with ChatGPT, Gemini, and others to power chatbots, summaries, and content.
• Modular & Open Source: Easy to customize, extend, and plug in different tools or databases.
• Scalable Infrastructure: Handles big data and AI workloads with distributed storage and compute.
• APIs for Activation: Offers APIs to pull insights and trigger actions in tools like CRMs and marketing platforms.
• Data Privacy & Security: Built-in features for consent, data protection, and compliance (e.g., GDPR, CCPA).

🚀 Real-World Use Cases

• Personalization at Scale: Tailors content and offers across all channels in real-time.
• Smarter Customer Support: Gives agents full context and predicts issues before they happen.
• Dynamic Segmentation: Builds live customer groups based on behavior and predictions.
• Churn Prevention: Spots customers likely to leave and auto-triggers retention offers.
• CLV Growth: Predicts customer value and helps focus on high-potential relationships.
• AI Chatbots: Delivers personalized, context-aware conversations powered by C360 data.
• Journey Automation: Builds smart customer flows that adapt in real-time.
• Next Best Action: Recommends the most relevant content, offer, or step for each user.
• Fraud Detection: Flags unusual behavior using AI and behavior analytics.
• Voice of Customer Insights: Analyzes feedback with AI to uncover trends and sentiments.

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)
• PostgreSQL for customer 360 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:

   python -m pip install --upgrade pip
   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:

    # Customer 360 configuration

    # Chatbot configuration
    CHATBOT_NAME=AI Buddy
    CHATBOT_DEV_MODE=true
    CHATBOT_HOSTNAME=chatbot.example.com
    DEFAULT_AUTHORIZATION_KEY=personalization_test
    EMBEDDING_MODEL_NAME=intfloat/multilingual-e5-small

    # GOOGLE cloud configuration
    GOOGLE_APPLICATION_CREDENTIALS=
    GOOGLE_SERVICE_ACCOUNT_JSON=

    # GEMINI model configuration
    GEMINI_TEXT_MODEL_ID=gemini-2.0-flash-lite
    GEMINI_IMAGE_MODEL_ID=imagen-3.0-generate-002
    GEMINI_API_KEY=

    # Google 
    GOOGLE_SEARCH_ENGINE_ID=
    GOOGLE_SEARCH_API_KEY=

    # AWS configuration
    AWS_ACCESS_KEY_ID=
    AWS_SECRET_ACCESS_KEY=
    AWS_REGION=
    AWS_S3_BUCKET_PUBLIC=

    # Qdrant dev configuration
    QDRANT_HOST=localhost
    QDRANT_PORT=6333

    # Qdrant cloud configuration
    QDRANT_CLOUD_HOST=
    QDRANT_CLOUD_API_KEY=

    # API configuration
    API_HOST=0.0.0.0
    API_PORT=8000

    # Redis configuration
    REDIS_HOST=localhost
    REDIS_PORT=6380

    # PostgreSQL configuration
    PG_HOST=localhost 
    PG_PORT=5433 
    PG_DATABASE=customer360
    PG_USER=
    PG_PASSWORD=

    # Airflow configuration
    AIRFLOW_BASE_URL=http://localhost:8080/api/v1
    AIRFLOW_USERNAME=
    AIRFLOW_PASSWORD=

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.