https://github.com/liteobject/demo-llamaindex
https://github.com/liteobject/demo-llamaindex
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/liteobject/demo-llamaindex
- Owner: LiteObject
- Created: 2024-07-17T15:41:51.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-20T16:46:50.000Z (over 1 year ago)
- Last Synced: 2024-12-29T18:21:14.835Z (about 1 year ago)
- Language: Python
- Size: 22.5 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# Demo llamaIndex
## What is Llama Index?
>LlamaIndex (formerly known as GPT Index) is an advanced data framework designed to facilitate the connection between large language models (LLMs) and external data sources. It serves as a central interface, enabling the integration of custom data with LLMs to create powerful, data-aware applications.
## Project
This project demonstrates the use of LlamaIndex for document indexing and querying using AI models.
## Description
This application uses LlamaIndex to create a vector store index from documents in a `data` directory. It then uses this index to answer queries about the content of these documents. The project showcases the integration of Hugging Face embeddings and the Ollama LLM.
## Features
- Document indexing using LlamaIndex
- Vector store querying
- Integration with Hugging Face embeddings (BAAI/bge-base-en-v1.5)
- Use of Ollama LLM (llama3 model)
## Setup
1. Ensure you have Python installed on your system.
2. Install the required dependencies (you may want to use a virtual environment):
```
pip install llama-index huggingface_hub ollama
```
3. Make sure you have the Ollama service running with the llama3 model available.
## Usage
1. Place your documents in the `data` directory. The example uses a file named `story.txt`.
2. Run the application:
```
python app.py
```
3. The application will index the documents and then perform a sample query: "Who is Byte?"
## File Structure
- `app.py`: Main application file
- `data/`: Directory containing documents to be indexed
- `story.txt`: Sample story about Byte, an AI robot
- `.pylintrc`: Pylint configuration file
## Configuration
The project uses the following configurations:
- Embedding model: BAAI/bge-base-en-v1.5
- LLM: Ollama (llama3 model)
- LLM request timeout: 360.0 seconds
## Links
- [Starter Example (local)](https://docs.llamaindex.ai/en/stable/getting_started/starter_example_local/)
- [Starter Example (OpenAI)](https://docs.llamaindex.ai/en/stable/getting_started/starter_example/)
- [GitHub/Llama-Index](https://github.com/run-llama/llama_index)