MCP from Scratch

Overview

The mcp.ipynb file is a core component of this project. It demonstrates the process of working with documents, including loading, processing, and creating a vector store for further use in machine learning or natural language processing tasks.

Key Steps in mcp.ipynb

1. Loading Documents:

   • The load_langgraph_docs() function is used to load documents and calculate tokens per document.

2. Saving Documents:

   • The save_llms_full(documents) function saves the loaded documents to a file for persistence.

3. Splitting Documents:

   • The split_documents(documents) function splits the documents into smaller chunks for easier processing.

4. Creating a Vector Store:

   • The create_vectorstore(split_docs) function creates a vector store from the split documents, which can be used for tasks like similarity search or embedding-based queries.

Dependencies

Ensure the following dependencies are installed:

• Python 3.x
• Required libraries (e.g., OpenAI, LangChain, or any other libraries used in the project)

Environment Setup

The project may require an OpenAI API key for certain operations. Ensure the API key is set up as an environment variable (OPENAI_API_KEY) or loaded through a configuration file.

Usage

1. Open the mcp.ipynb file in Jupyter Notebook or VS Code.
2. Run the cells sequentially to execute the document processing pipeline.
3. Use the generated vector store for downstream tasks.

Notes

• The implementation of the functions (load_langgraph_docs, save_llms_full, split_documents, and create_vectorstore) is modular, making it easy to adapt or extend for other use cases.
• Ensure proper handling of sensitive information like API keys.