In this Pull Request, I have enhanced the documentation and project explanation for the "Custom Knowledge Chat using HuggingFace with LangChain." These improvements include detailed information on why LangChain was chosen for context-based question answering and how to run the code both in Google Colab and locally.
Changes Made:
Choosing LangChain for Context-Based Question Answering:
Added a section explaining the decision to use LangChain for the project.
Highlighted the key capabilities of LangChain, such as PDF processing, text chunking, text embeddings, and vector database management.
HuggingFace Models for Answer Retrieval:
Provided information on the HuggingFace models used in the project, particularly the Language Model (LLM) from the "google/flan-t5-large" repository.
Included details about LLM configuration parameters.
How to Run the Code in Google Colab:
Added a step-by-step guide on how to run the code in Google Colab, including uploading the notebook, executing code cells, and interacting with the chatbot.
How to Run the Code Locally:
Included instructions for running the code on a local machine.
Detailed steps for modifying the Amakrusai.py file, installing dependencies, and executing the Python script.
Screenshot and Result:
Mentioned the inclusion of screenshots to showcase the project's output.
Purpose:
The purpose of these enhancements is to provide clear and comprehensive documentation for the project. Users can now better understand the rationale behind LangChain's selection, how to run the code in different environments, and what to expect from the project's output.
This updated documentation will help users navigate the project effectively, make informed decisions about its usage, and contribute to further improvements.
Reviewers:
Please review these enhancements to the project's documentation and provide feedback as needed. Your input is valuable in ensuring the clarity and usability of the documentation.
Additional Notes:
issue : #150
Screenshots and other visual elements will be included to enhance the understanding of the project.
All changes have been thoroughly reviewed and tested for accuracy.
Feel free to reach out if you have any questions or require further clarification regarding these updates.
Description:
In this Pull Request, I have enhanced the documentation and project explanation for the "Custom Knowledge Chat using HuggingFace with LangChain." These improvements include detailed information on why LangChain was chosen for context-based question answering and how to run the code both in Google Colab and locally.
Changes Made:
Choosing LangChain for Context-Based Question Answering:
Added a section explaining the decision to use LangChain for the project. Highlighted the key capabilities of LangChain, such as PDF processing, text chunking, text embeddings, and vector database management. HuggingFace Models for Answer Retrieval:
Provided information on the HuggingFace models used in the project, particularly the Language Model (LLM) from the "google/flan-t5-large" repository. Included details about LLM configuration parameters. How to Run the Code in Google Colab:
Added a step-by-step guide on how to run the code in Google Colab, including uploading the notebook, executing code cells, and interacting with the chatbot. How to Run the Code Locally:
Included instructions for running the code on a local machine. Detailed steps for modifying the Amakrusai.py file, installing dependencies, and executing the Python script. Screenshot and Result:
Mentioned the inclusion of screenshots to showcase the project's output. Purpose:
The purpose of these enhancements is to provide clear and comprehensive documentation for the project. Users can now better understand the rationale behind LangChain's selection, how to run the code in different environments, and what to expect from the project's output.
This updated documentation will help users navigate the project effectively, make informed decisions about its usage, and contribute to further improvements.
Reviewers:
Please review these enhancements to the project's documentation and provide feedback as needed. Your input is valuable in ensuring the clarity and usability of the documentation.
Additional Notes: issue : #150 Screenshots and other visual elements will be included to enhance the understanding of the project. All changes have been thoroughly reviewed and tested for accuracy. Feel free to reach out if you have any questions or require further clarification regarding these updates.