Documentation for CodeExpertH563_langchain

Overview

The CodeExpertH563_langchain.ipynb notebook is designed to serve as an AI assistant for code-related queries. It leverages various natural language processing (NLP) and machine learning (ML) techniques to understand user queries, search for relevant information within a corpus of documents, and generate responses accordingly.

Dependencies

Before running the notebook, ensure that the following Python packages are installed:

• bitsandbytes
• accelerate
• langchain
• arxiv
• fitz
• sentence_transformers
• faiss-gpu
• pymongo

Ensure to reset the environment after installing all the dependencies to import in the notebook. Additionally, the notebook relies on models and tools from the Hugging Face Transformers library, so make sure it's installed as well.

Functionality

Initialization

The notebook starts by importing necessary libraries and installing required packages via pip. It then initializes various components such as tokenizers, models, and databases.

Main Functions

unzip_file(zip_file_path)

This function unzips a given zip file into a specified directory.

read_files_in_directory(directory)

This function reads all files in a given directory and returns their contents as a list of documents.

generate(files_content, query)

This function orchestrates the process of generating a response to a given query. It involves splitting the documents' content into chunks, creating a vector database for similarity search, executing a retrieval question-answer (QA) pipeline, and generating a response based on the retrieved information.

Continuous Processing Loop

The notebook enters a continuous loop where it checks for new documents in a MongoDB GridFS collection. For each document found, it downloads associated files, processes them, generates a response to the provided prompt/question, updates the document in the database with the response, and repeats the process.

Components

Text Splitter

Uses a recursive character-based text splitter to divide documents into manageable chunks for processing.

Embeddings

Utilizes a pre-trained Hugging Face embeddings model to convert text data into numerical embeddings, which are then used for similarity search.

Vector Store

Creates a vector database using FAISS (Facebook AI Similarity Search) for efficient similarity search operations.

Retrieval QA

Implements a question-answer pipeline that retrieves relevant information from the vector database and generates responses to user queries.

Memory

Maintains a conversation buffer memory to keep track of past interactions and prevent repetitive responses.

Pipeline

Defines a text generation pipeline using Hugging Face Transformers for generating textual responses.

Usage

To use the notebook:

1. Ensure all dependencies are installed.
2. Run the notebook.
3. Provide prompts/questions either manually or through the MongoDB GridFS collection.
4. Retrieve responses from the notebook's output or the updated documents in the database.

Conclusion

The CodeExpertH563_langchain.ipynb notebook provides a scalable and efficient solution for handling code-related queries through an AI assistant. By leveraging NLP and ML techniques, it aims to assist developers in finding relevant information and generating responses tailored to their needs.