Using Semantic Kernel connectors onnx and kernel memory for rag

[Barshan-Mandal]

Description
This issue involves integrating Semantic Kernel connectors with ONNX models and Kernel Memory to implement Retrieval Augmented Generation (RAG). The goal is to enhance data-driven features in applications by leveraging advanced embeddings and large language models (LLMs).

Objectives
Integrate ONNX Models: (lets say phi 3 vision 128 k)
Utilize ONNX models for efficient and scalable inference.
Ensure compatibility with Semantic Kernel connectors for seamless integration.
Implement Kernel Memory:
Use Kernel Memory for efficient indexing and querying of datasets.
Support continuous data hybrid pipelines for RAG, synthetic memory, and prompt engineering.
Enhance RAG Capabilities:
Enable Natural Language querying to obtain answers from indexed data.
Provide citations and links to original sources for transparency and reliability.
Tasks
Setup Environment:
Install necessary dependencies (LibVLCSharp, ONNX Runtime, etc.).
Configure Semantic Kernel and Kernel Memory.
Develop Integration:
Implement connectors to integrate ONNX models with Semantic Kernel.
Develop methods to import and index documents using Kernel Memory.
Testing and Validation:
Test the integration with various datasets.
Validate the accuracy and performance of the RAG system.
Documentation:
Document the setup, integration process, and usage instructions.
Provide examples and best practices for using the system.
Expected Outcome
A robust system that combines Semantic Kernel connectors, ONNX models, and Kernel Memory to enhance RAG capabilities, enabling efficient and accurate data retrieval and generation.