AI Planet Internship Projects

This repository contains all the projects I worked on during my internship at AI Planet. Each project showcases the skills and knowledge I have gained in various domains of AI and machine learning.

Projects

1. Hybrid Search RAG
2. Product Documentation
3. Advanced RAG Cross Encoder Reranking
4. Advanced RAG Fine-tune Embeddings
5. ChromaDB Vector Store using LlamaIndex
6. Customer Feedback Agent
7. Evaluating and Optimizing RAG Pipelines with BeyondLLM
8. GenAI Stack API
9. Llama Index Project
10. Medical Heart Q&A
11. Notebook Autogen Project
12. Notebook CrewaI Project
13. Notebook Hybrid Search RAG
14. RAG Bootcamp Module 4 Using BeyondLLM

Skills Acquired

During my internship at AI Planet, I have developed and refined the following skills:

Retrieval-Augmented Generation (RAG)

• Hybrid Retrieval: Combining different retrieval methods to enhance the accuracy and relevance of information.
• Cross-Encoder Reranking: Implementing cross-encoders to rerank search results for improved performance.
• Embedding Fine-tuning: Adjusting embeddings to better fit specific tasks and datasets.

AI Model Fine-tuning

• Model Customization: Tailoring pre-trained models to specific tasks through fine-tuning techniques.
• Performance Optimization: Techniques to optimize the efficiency and accuracy of AI models.

Embedding Optimization

• Vector Embeddings: Understanding and manipulating vector embeddings for various applications.
• Semantic Search: Using optimized embeddings to improve semantic search capabilities.

Vector Storage Solutions

• ChromaDB Integration: Implementing ChromaDB for efficient vector storage and retrieval.
• LlamaIndex Utilization: Leveraging LlamaIndex for managing large-scale vector data.

Customer Feedback Analysis

• Sentiment Analysis: Analyzing customer feedback to determine sentiment and insights.
• Automated Feedback Agents: Creating AI agents that automatically process and respond to customer feedback.

Generative AI Stack API Development

• API Design: Designing and implementing APIs for generative AI models.
• Stack Integration: Integrating various AI tools and models into cohesive stacks for streamlined usage.

Efficient Indexing with LlamaIndex

• Index Management: Creating and managing indexes for large datasets.
• Search Optimization: Enhancing search capabilities through efficient indexing techniques.

Medical AI Applications

• Domain-Specific Q&A Systems: Developing question and answer systems tailored to medical domains.
• Healthcare Data Processing: Techniques for processing and analyzing healthcare-related data.

Automated Documentation and Notebook Generation

• Documentation Automation: Using AI to automatically generate product documentation.
• Notebook Automation: Creating systems that auto-generate Jupyter notebooks for various tasks and analyses.

Large Language Models (LLMs)

• LLM Utilization: Leveraging large language models for various NLP tasks.
• Fine-tuning LLMs: Customizing large language models for domain-specific applications.
• Prompt Engineering: Crafting effective prompts to guide LLMs in generating desired outputs.
• Model Evaluation: Assessing the performance of LLMs and implementing improvements.

Multimodal AI

• Audio-Visual Integration: Combining audio and visual data for enhanced AI applications.
• Multimodal Embeddings: Developing embeddings that capture information across multiple modalities.
• Cross-Modal Retrieval: Implementing retrieval systems that leverage data from different modalities.