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.