Smart Teacher Assistant (Chat with Your Document)

[therehmanujjan]

Smart Teacher Assistant (Chat with Your Document)

Package Installations

The notebook installs several libraries to enable its functionality:

• pypdf and PyPDF2: Libraries for reading and manipulating PDF files.
• scikit-learn: Used for machine learning tasks.
• langchain and related modules: A framework for building applications powered by large language models.
• langchain_chroma: For integrating the Chroma database with LangChain.
• cohere: To use Cohere’s embedding and generative AI services.
• google-api-python-client, google-auth modules: For working with Google APIs.
• langgraph: A library for managing data flow and graph-based structures.

Key Libraries and Features

• LangChain: Framework for building AI-powered applications using prompts and embeddings.
• Google Generative AI: Provides integration with Google’s advanced AI tools.
• Chroma and Cohere: Enable document embeddings and memory storage for AI applications.
• PyPDF2: Facilitates PDF processing and text extraction.

Code Structure

Import Statements

The notebook imports libraries for:

• PDF reading and processing (e.g., PyPDF2).
• Text splitting for document processing (e.g., RecursiveCharacterTextSplitter from LangChain).
• Database operations (e.g., SQLite3).
• Managing embeddings and AI memory (e.g., Chroma, CohereEmbeddings).

Potential Use Cases

1. Document Analysis: Reading, splitting, and processing PDFs.
2. Embedding Generation: Using Cohere to create embeddings for documents.
3. Generative AI Applications: Leveraging Google’s Generative AI for conversational or text-based outputs.
4. Data Storage: Managing embeddings and AI knowledge with tools like Chroma and LangGraph.

Notable Features

• UUID generation: Ensures unique identification for documents or data points.
• Pretty Printing: For better readability of output using the pprint library.

Intended Purpose

The notebook seems designed for creating an intelligent agent capable of:

• Processing and analyzing PDF documents.
• Generating and storing embeddings for downstream tasks.
• Using generative AI tools for advanced applications.

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How to Use

1. Run the installation cells to set up the environment.
2. Import the necessary libraries.
3. Follow the remaining cells for specific functionalities like document embedding, AI interactions, or data storage.