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Documentation

File Structure:

  • docs/ - Documentation files
  • code/ - Code files
  • storage/ - Storage files
  • vectorstores/ - Vector Databases
  • .env - Environment Variables
  • Dockerfile - Dockerfile for Hugging Face
  • .chainlit - Chainlit Configuration
  • chainlit.md - Chainlit README
  • README.md - Repository README
  • .gitignore - Gitignore file
  • requirements.txt - Python Requirements
  • .gitattributes - Gitattributes file

Code Structure

  • code/main.py - Main Chainlit App
  • code/config.yaml - Configuration File to set Embedding related, Vector Database related, and Chat Model related parameters.
  • code/modules/vector_db.py - Vector Database Creation
  • code/modules/chat_model_loader.py - Chat Model Loader (Creates the Chat Model)
  • code/modules/constants.py - Constants (Loads the Environment Variables, Prompts, Model Paths, etc.)
  • code/modules/data_loader.py - Loads and Chunks the Data
  • code/modules/embedding_model.py - Creates the Embedding Model to Embed the Data
  • code/modules/llm_tutor.py - Creates the RAG LLM Tutor
    • The Function qa_bot() loads the vector database and the chat model, and sets the prompt to pass to the chat model.
  • code/modules/helpers.py - Helper Functions

Storage and Vectorstores

  • storage/data/ - Data Storage (Put your pdf files under this directory, and urls in the urls.txt file)

  • storage/models/ - Model Storage (Put your local LLMs under this directory)

  • vectorstores/ - Vector Databases (Stores the Vector Databases generated from code/modules/vector_db.py)

Useful Configurations

set these in code/config.yaml:

  • ["embedding_options"]["embedd_files"] - If set to True, embeds the files from the storage directory everytime you run the chainlit command. If set to False, uses the stored vector database.
  • ["embedding_options"]["expand_urls"] - If set to True, gets and reads the data from all the links under the url provided. If set to False, only reads the data in the url provided.
  • ["embedding_options"]["search_top_k"] - Number of sources that the retriever returns
  • ["llm_params]["use_history"] - Whether to use history in the prompt or not
  • ["llm_params]["memory_window"] - Number of interactions to keep a track of in the history

LlamaCpp

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