This is a chat application powered by LlamaIndex, a Python library for building applications using large language models (LLMs). The application allows users to ask questions about LlamaIndex and receive responses from the assistant.

Feel free to try the app here: llamadocschat

• Python 3.9 or higher installed
• OpenAI account and an OpenAI api key
• Pinecone account and a Pinecount api key

1. Clone the Project Repository

   • Use Git to clone the repository to your local machine.

2. Install Dependencies with Pipenv

   • In the project's root directory, run the following command to set up a virtual environment with Python 3.10 and install the required packages:

     pipenv --python 3.10 install

3. Set Up Environment Variables

   • Populate the .env file with your OpenAI API key:

     OPENAI_API_KEY=your_api_key_here
     PINECONE_API_KEY=your_pinecone_api_key
     PINECONE_INDEX_HOST=your_pinecone_index_host
     

Follow these steps to run the LlamaIndex Document Helper:

1. Activate the Virtual Environment

   • Before running the script, activate the pipenv shell to ensure you're using the project's virtual environment:

     pipenv shell

2. Run the Script

   • Start the main script via the command line:

     streamlit run main.py

   • Open your web browser and navigate to http://localhost:8501 to access the chat interface.
   • Ask questions about LlamaIndex and interact with the assistant.

This is a chat interface powered by LlamaIndex, designed to provide responses to user queries regarding LlamaIndex. Below is an overview of the functionality and structure of the code:

1. Importing Necessary Libraries: The code imports required libraries and modules, including dotenv, llama_index, and streamlit.

2. Setting Page Configuration: The set_page_config function configures the page layout for the web app using Streamlit.

3. Setting Sidebar Content: The set_sidebar function sets up the sidebar content, which includes information about the developer and links to GitHub and LinkedIn.

4. Retrieving Vector Index: The get_index function retrieves the vector index from Pinecone, a vector similarity search service.

5. Retrieving Response from Chat Engine: The retrieve_augmented_generation_response function retrieves the response from the chat engine, powered by LlamaIndex. It also sets up postprocessors for sentence embedding optimization and duplicate removal.

6. Initializing Chat Messages: The initialize_chat_messages function initializes the chat messages, setting an initial message from the assistant.

7. Getting User Input Prompt: The get_user_prompt function obtains the user input prompt from the chat input.

8. Displaying Chat Messages: The display_messages_on_feed function displays the chat messages on the feed, including any references/sources provided by the assistant.

9. Storing Messages with References: The store_messages_with_references function stores the user's message and retrieves the assistant's response using the chat engine. It also retrieves and displays any references/sources provided by the assistant.

1. Main Method: The main method orchestrates the functionality of the chat interface. It starts by retrieving the vector index using the get_index function, sets up the page configuration and sidebar, initializes chat messages, retrieves augmented generation responses, obtains user prompts, displays messages on the feed, and stores messages with references.

2. Interacting with the Interface: Users can interact with the chat interface by inputting queries about LlamaIndex. The assistant, powered by LlamaIndex and RAG (Retrieval-Augmented Generation), provides relevant and informative responses leveraging the vector index and chat engine provided by LlamaIndex.

• Chat with the LlamaIndex assistant to get answers about LlamaIndex.
• Relevant responses powered by LlamaIndex's vector index and chat engine.
• References and sources provided by the assistant for further reading.