https://github.com/lzytitan494/simplest-rag

A Retrieval-Augmented Generation (RAG) pipeline for laptop-related issues using Langchain and Chroma DB
https://github.com/lzytitan494/simplest-rag

chromadb langchain llama3 nomic-embed-text ollama python rag

Last synced: about 1 month ago
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A Retrieval-Augmented Generation (RAG) pipeline for laptop-related issues using Langchain and Chroma DB

• Host: GitHub
• URL: https://github.com/lzytitan494/simplest-rag
• Owner: lzytitan494
• Created: 2024-07-27T16:49:37.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-08-06T06:03:37.000Z (10 months ago)
• Last Synced: 2025-04-08T15:17:12.506Z (about 1 month ago)
• Topics: chromadb, langchain, llama3, nomic-embed-text, ollama, python, rag
• Language: Python
• Homepage:
• Size: 390 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## LaptopWiki - RAG Pipeline with Ollama and ChromaDB

This project implements a Retrieval Augmented Generation (RAG) pipeline for answering questions about laptop issues. It leverages the Laptop Wiki community as a knowledge base and uses powerful open-source language models from Ollama for information extraction and question answering.







### How it Works

1. **Data Extraction and Refinement:**

    * Data is extracted from the Laptop Wiki community.

    * The extracted data is rephrased and refined using the `llama3:8b` LLM from Ollama to ensure high-quality language and consistency.

2. **Vector Database Creation:**

    * The refined data is split into chunks and embedded using the `nomic-embed-text` model from Ollama.

    * These embeddings are stored in a ChromaDB vector database for efficient similarity search.

3. **Question Answering:**

    * User queries are embedded using the same `nomic-embed-text` model.

    * The ChromaDB database is queried for the most relevant chunks based on similarity to the query embedding.

    * The retrieved chunks, along with the original query, are fed into the `llama3:8b` LLM with a specific prompt to generate a comprehensive and helpful answer.

### Files

* **`create_database.py`:**  This script handles the entire pipeline for creating the vector database:

    * Downloads the required Ollama models (`llama3:8b`, `nomic-embed-text`).

    * Loads data from text files in the `sample_data` directory.

    * Rephrases the loaded data using the `llama3:8b` LLM.

    * Splits the data into chunks, embeds them, and stores them in the ChromaDB database at the specified path.

* **`query_database.py`:**  This script loads the created database and answers user queries:

    * Downloads the required Ollama models.

    * Loads the ChromaDB database from the specified path.

    * Takes a user query as input, retrieves relevant information from the database, and feeds it to the `llama3:8b` LLM to generate a response. 

### Instructions for Running

1. **Install Dependencies:**

   ```bash

   pip install -r requirements.txt

   ```

   

2. **Download Ollama Models:**

   ```bash

   ollama pull llama3:8b

   ollama pull nomic-embed-text 

   ```

3. **Prepare your data:**

   * Place your Laptop Wiki data in text files within the `sample_data` directory. 

4. **Create the Database:**

   ```bash

   python create_database.py

   ```

5. **Run the Question Answering System:**

   ```bash

   python query_database.py

   ```

   You can then input your laptop-related queries.

### Notes:

* Ensure that the paths to your data (`DATA_PATH`) and database (`CHROMA_PATH`) are correctly set in the scripts.

* ChromaDB persistency can sometimes be finicky. If you encounter issues, try deleting the existing database directory and recreating it.

* This project is a starting point, and you can further customize it by:

    * Adding more data sources.

    * Fine-tuning the LLMs for your specific use case. 

    * Experimenting with different embedding models and prompt engineering for better results.