OllaLab-Lean - Interactive Web Apps are interactive web applications designed to help both novice and experienced developers with Research and Development tasks. The shared common components of the applications are:
- Python and related packages found in the requirement files.
- Ollama for managing local openweight Large Language Models (LLMs).
- LangChain for orchestrating LLM pipelines, allowing users to seamlessly connect, manage, and optimize their workflows.
- Streamlit server to locally host dynamic LLM-based web applications.
- Neo4J vector database supporting retrieval-augmented generation (RAG) tasks.
- [2024/10/30] 🚀 "OllaLab-Lean - Interactive Web Apps" section is officially initialized.
OllaLab-Lean - Interactive Web Apps will be automatically installed following the main OllaLab-Lean Installation
If you have issues with the main installation or simply want to install just the OllaLab-Lean - Interactive Web Apps, you can follow the steps below.
- Use git clone or http download to download OllaLab Lean
- Get into FedRAMP-OllaLab-Lean/streamlit_app/app
There are several ways to install Python. You may find the official guide in Official Python Downloads.
If you have Visual Studio Code installed, you may also follow Getting Started with Python in VS Code
On windows, you may also Install Python from Microsoft App Store
A virtual environment is created on top of an existing Python installation, known as the virtual environment’s “base” Python, and may optionally be isolated from the packages in the base environment, so only those explicitly installed in the virtual environment are available. More details are in Creation of virtual environment
Virtual environments are created by executing the venv module:
python -m venv ./.venv
If successfuly, a folder ".venv" will be created in /path/to/new/virtual/environment. You will then need to invoke the virtual environment. Assuming you are at the folder containing the ".venv" folder for the virtual environment you've just set up. You can launch the virtual environment by:
- On windows
.venv/Scripts/activate.bat //In CMD
.venv/Scripts/Activate.ps1 //In Powershel
- On Linux or Mac
source .venv/bin/activate
In this step, you need to get to the folder containing the OllaLab-Lean - Interactive Web Apps where you can see this README file, the requirements.txt file, and the "app" folder.
First, you need to update pip which is a package manager program included in your Python installation.
python3 -m pip install --upgrade pip
python3 -m pip --version
Then, you install the packages listed in the requirements.txt file.
python3 -m pip install -r requirements.txt
Note
Some packages were disabled in the requirements.txt because they may cause the process to stop. You may install those packages individually later. You may also disable additional packages that you may not need. Individual packages can be installed by "pip install "
Feel free to check out Additional info on installing packages within Python Virtual Environment
Ollama is an AI tool that allows users to run large language models (LLMs) locally on their computer. Installation files of Ollama for Mac, Linux, and Windows can be found at Official Ollama Installation Files
Command to install Ollama on Linux:
curl -fsSL https://ollama.com/install.sh | sh
To verify your Ollama installation, you may go to localhost:11434 or 127.0.0.1:11434. If the installation went well, you should see "Ollama is running".
Next, you need to pull LLMs from Ollama Library. Your network may block the downloading. In such case, you may need to prepare the models locally which is a big topic by itself and will be supported by another documentation. Pull model is easy
ollama pull <model name>:[tag]
For example
ollama pull llama3.1:8b
ollama pull codellama:13b
Important
Check the model size in Ollama Library before you pull and use a model. The size of a model should not exceeed 75% of your available ram.
Use the following command to check whether the model(s) was pulled successfully
ollama list
Neo4j is a graph database management system that stores and retrieves connected data including vectorized data for Retrieval Augmented Generation feature. The Official Neo4J Installation Guide contains the steps needed to install Neo4J on Linux, Mac, and Windows.
On Mac, you can also install Neo4J from Homebrew using "brew install neo4j"
Once Neo4J was installed and started, you should be able to view http://127.0.0.1:7474/browser/
You may then follow Neo4J Create, Start, and Stop Databases to create your first database.
To run the app, you need to CD into the streamlit_app folder (this folder), assuming you are already in a terminal.
Make sure the previous steps were completed successfully
Launch the welcome page by this command:
streamlit run main.py
A browser window will open and you will see the welcome page. You need to keep the terminal window open.
To stop the app, close the terminal window.
To restart Streamlit, press "CTRL+C" in the terminal to exit out of Streamlit and launch the welcome page again.
Simple Chat with Prompt Variables
- Allow Folder Chat app to run independently from the stack
- Add more prompt variable files for more use cases
- Develop advanced prompt engineering and prompt operation features
- Develop advanced RAG features
We welcome contributions to OllaLab-Lean especially on the Planned Features and Applications!
Please see our Contributing Guide for more information on how to get started.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project and/or cloning the project, you agree to abide by its terms.