Aiklyra 1.0.1

A Python client library for Aiklyra API

Aiklyra Python Package

Aiklyra is a Python client library that provides a simple interface to your FastAPI-powered conversation analysis API. It allows developers to easily submit conversation data for clustering, analysis, and graph processing using an asynchronous workflow.

---

Table of Contents

• Features
• Installation
• Usage
• Example
• Graph Processing and Visualization
• Testing
• Development
• License
• Contributing
• Contact

---

Features

• Asynchronous Analysis: Submit conversation data for analysis and later retrieve detailed results via job status.
• Graph Processing: Construct, filter, and visualize directed graphs representing conversation flows.
• Customizable Filters: Apply graph filtering strategies like thresholding, top-K filtering, and advanced reconnecting filters.
• Custom Exceptions: Detailed exception classes (InvalidAPIKeyError, InsufficientCreditsError, etc.) for better error handling.
• Pydantic Models: Uses Pydantic for data validation and serialization.
• Easy Integration: Designed to integrate seamlessly with existing Python codebases.

---

Installation

1. Clone or Download the Repository:

   git clone https://github.com/AiklyraData/aiklyra.git
   cd Aiklyra
   

2. Install via setup.py:

   pip install .
   

   or

3. Editable Installation (recommended for development):

   pip install -e .
   

Requirements

• Python 3.8+
• requests
• pydantic
• networkx
• pyvis

---

Usage

Basic Setup

1. Configure the Client
   The client now uses asynchronous endpoints and does not require an API key. Simply specify the API's base URL.

2. Import the Client

   from aiklyra.client import AiklyraClient
   

3. Initialize the Client

   client = AiklyraClient(base_url="http://your-api-base-url")
   

4. Submit Analysis & Retrieve Results
   The analysis workflow now occurs in two steps:

   • Submit your conversation data for analysis. This call returns a job ID.
   • Check the job status using the returned job ID to obtain the full analysis results.

   # Submit conversation data for analysis
   submission = client.submit_analysis(
       conversation_data=your_conversation_data,
       min_clusters=5,
       max_clusters=10,
       top_k_nearest_to_centroid=10
   )
   
   # Later, check the job status to retrieve the analysis result:
   job_status = client.check_job_status(submission.job_id)
   # The actual analysis is available in the "result" field.
   analysis = job_status.result
   

---

Example

Below is an example script that demonstrates the complete workflow:

from aiklyra.client import AiklyraClient
from aiklyra.exceptions import AnalysisError, AiklyraAPIError
import time

def main():
    # Replace with your API's base URL
    base_url = "http://your-api-base-url"

    # Initialize the client (no API key required)
    client = AiklyraClient(base_url=base_url)

    # Example conversation data
    conversation_data = {
        "conversation_1": [
            {"role": "user", "content": "Hi, I need help with my account."},
            {"role": "assistant", "content": "Sure, please provide your account ID."},
            {"role": "user", "content": "It's 12345."}
        ],
        "conversation_2": [
            {"role": "user", "content": "Can I change my subscription plan?"},
            {"role": "assistant", "content": "Yes, you can change it from your settings."},
            {"role": "user", "content": "Great, thank you!"}
        ]
    }

    try:
        # Submit analysis job
        submission = client.submit_analysis(
            conversation_data=conversation_data,
            min_clusters=5,
            max_clusters=10,
            top_k_nearest_to_centroid=10
        )
        print(f"Job submitted. Job ID: {submission.job_id}")

        # Optionally, wait before checking job status (or poll periodically)
        time.sleep(2)

        # Check job status and retrieve analysis result
        job_status = client.check_job_status(submission.job_id)
        if job_status.status == "completed":
            analysis = job_status.result
            print("Analysis Result:")
            print("Transition Matrix:", analysis.transition_matrix)
            print("Intent by Cluster:", analysis.intent_by_cluster)
        else:
            print(f"Job status: {job_status.status}")

    except AnalysisError as e:
        print(f"Analysis failed: {e}")
    except AiklyraAPIError as e:
        print(f"API Error: {e}")

if __name__ == "__main__":
    main()

Run the script:

python example_usage.py

---

Graph Processing and Visualization

Graph Construction

After obtaining the analysis result (a ConversationFlowAnalysisResponse object), you can process it into a directed graph:

from aiklyra.graph.processor import GraphProcessor

# Assuming 'analysis' is your ConversationFlowAnalysisResponse instance
graph_processor = GraphProcessor(analysis)
graph = graph_processor.graph  # This is a NetworkX graph

Graph Filtering

Apply filters to refine the graph. For example, you can use various filters to remove low-weight edges or retain only the top-K connections:

from aiklyra.graph.filters import ThresholdFilter, TopKFilter

# Apply a threshold filter to remove edges below a certain weight.
threshold_filter = ThresholdFilter(threshold=0.3)
filtered_graph = graph_processor.filter_graph(threshold_filter)

# Or apply a top-K filter.
topk_filter = TopKFilter(top_k=3)
filtered_graph = graph_processor.filter_graph(topk_filter)

Visualization

Visualize the graph using PyVis or NetworkX:

# PyVis visualization (creates an HTML file)
graph_processor.plot_graph_html(file_name="conversation_flow.html")

# Alternatively, visualize using NetworkX's built-in methods
graph_processor.visualize_graph()

---

Testing

1. Install Development Dependencies:

   pip install -r requirements.txt
   

2. Run Tests:

   python -m unittest discover tests
   

   or

   pytest
   

---

Development

• Branching: Use feature branches for new features or bug fixes.
• Pull Requests: Open a PR with a clear description and pass all tests before merging.
• Coding Standards: Follow PEP 8 style guidelines.

---

License

This project is licensed under the Apache License 2.0.
You are free to use, distribute, and modify the library under the terms of this license.

---

Contributing

We welcome contributions! To get started:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Commit your changes (git commit -m "Add my feature")
4. Push to the branch (git push origin feature/my-feature)
5. Open a Pull Request on GitHub

Please ensure your contributions include tests, documentation, and follow the coding standards described above.

---

Contact

• Author: Your Name ([e-mail])
• GitHub: @achrefbenammar404

If you have questions, suggestions, or issues, feel free to open an issue on the GitHub repository or reach out by email!

---

Thank you for using Aiklyra! We look forward to seeing how you integrate it into your projects.