The project focuses on implementing and evaluating advanced Natural Language Processing (NLP) techniques using state-of-the-art libraries and models.

Overview The notebook explores various aspects of NLP, leveraging powerful frameworks such as:

LangChain and its extensions for language model orchestration OpenAI API for GPT-based model interaction ChromaDB for vector database management Hugging Face for model integration Datasets for handling structured and unstructured data Features Extensive experiments with multiple NLP models and datasets. Well-documented methodology with clear markdown explanations. Demonstrates real-world applications of language models using LangChain and ChromaDB. Covers tasks such as document processing, embedding generation, and query-answering. Getting Started Clone the repository. Open the Jupyter notebook in your preferred environment (Jupyter or Google Colab). Follow the instructions and run each section step-by-step. Ensure you have Python and the required dependencies installed (handled by the initial cells). Dependencies The project relies on the following libraries:

langchain, langchain-openai, langchain-community, langchain-experimental openai, chromadb, datasets, unstructured huggingface, langchain-huggingface