Natural Language Query Agent

Step 1: Data Preparation

Lecture Notes and Table of LLM Architectures: Collect the provided lecture notes and table data.

Data Structuring: Organize the data into a format suitable for processing. You can use a combination of text files, CSVs, or JSON formats.

Embedding Representation: Convert the text data into embeddings using pre-trained models like BERT, RoBERTa, or Sentence Transformers.

Step 2: Setup and Tools

Python Environment: Set up a Python environment with necessary libraries. You can use virtualenv or conda for environment management.

Dependencies: Install required libraries such as transformers, sentence-transformers, langchain, llama-index, faiss, and flask for serving the model.

GitHub Repository: Create a repository to track your code and documentation.

Step 3: Data Indexing and Storage

Vector Indexing: Use FAISS (Facebook AI Similarity Search) to create a vector index of the embeddings for efficient similarity search.

Storage: Store the indexed data in a format that can be easily accessed during query processing. You can use a simple file system storage or a lightweight database like SQLite.

Step 4: Query Processing

Input Handling: Create a function to handle natural language queries.

Search and Retrieval: Use the vector index to find relevant embeddings and retrieve corresponding text snippets.

Response Generation: Use a pre-trained LLM (like GPT-3 or an open-source equivalent) to generate a conversational response based on the retrieved snippets.

Step 5: Conversational Features

Contextual Awareness: Implement a mechanism to keep track of the conversation context for follow-up questions.

Citing References: Include references to the source text used in the response to enhance credibility and avoid hallucination.

Summary Generation: Develop a feature to summarize the conversation session into key points or flashcards.

Step 6: Documentation and Presentation

README File: Write a comprehensive README file explaining your approach, including setup instructions, usage examples, and a description of your design choices.

**Documentation: **Document your code with comments and create additional documentation if necessary, such as a detailed design document or a system architecture diagram.

Live Demonstration: Ensure your implementation can be demonstrated live. You can use a simple web interface or command-line tool for this purpose.

Step 7: Future Work and Scalability

Scaling: Outline a plan for scaling the system to handle more lectures and papers, focusing on efficient indexing and retrieval. Deployment: Describe a deployment plan, considering factors like hosting, API endpoints, and load balancing.