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https://github.com/udit-rawat/quest

Quest is a on device RAG that helps you quickly find solutions to coding problems. It searches through a database of answers and gives you clear, easy-to-understand solutions tailored to your question. Whether you're looking for explanations or code examples, Quest helps you find the right answer fast.
https://github.com/udit-rawat/quest

deepseek-r1 hnsw leetcode ollama ollama-api qwen2 rag sentence-transformers

Last synced: 8 months ago
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Quest is a on device RAG that helps you quickly find solutions to coding problems. It searches through a database of answers and gives you clear, easy-to-understand solutions tailored to your question. Whether you're looking for explanations or code examples, Quest helps you find the right answer fast.

Awesome Lists containing this project

README 

          
# Quest



**Quest** is a Retrieval-Augmented Generation (RAG) engine designed to assist with solving and explaining Data Structures and Algorithms (DSA) problems. It leverages a combination of retrieval-based methods and generative models to provide accurate and context-aware solutions to coding problems, explanations of concepts, and metadata about LeetCode-style questions.



![Quest](templates/Quest.png)



## Features



- **Exact Matching Retrieval**: Quickly retrieves solutions for exact matches of problem titles from a curated dataset.

- **Context-Aware Generation**: Generates detailed explanations and solutions for general queries using a generative model.

- **Metadata Integration**: Provides metadata such as problem difficulty, related topics, and edge cases for retrieved solutions.

- **Efficient Search**: Uses FAISS and HNSW for fast and accurate similarity search in high-dimensional spaces.

- **Customizable**: Supports different models and configurations for retrieval and generation.

- **Pre-Generated Solutions**: Includes **1800+ solutions in JSON format**, created using the `qwen2.5-coder:1.5b` model on a local machine.

- **Optimized Inference Speed**: Reduced query retrieval time from 1 minute to 15 seconds and reasoning model time from 20 minutes to under 4 minutes.

- **Dynamic Prompt Switching**: Implements dynamic prompt switching for Qwen and Deepseek models to prevent infinite reasoning loops.

- **Unknown Query Handling**: Switches to model’s own inference for queries with confidence below 0.6 for better accuracy.

- **HNSW Indexing**: Fine-tuned HNSW parameters for faster and more relevant query retrieval.

- **Memory Buffer**: Retains past interactions for more contextually accurate responses over multiple queries.

- **Dataset Generation**: Created an on-device dataset of over 1850 coding questions JSON, enriched with metadata.

- **Reasoning Model Integration**: Integrated Deepseekr1 7B for complex problem-solving with optimized prompt templates.

- **Frontend Interface**: Built with Flask and Bootstrap, featuring a clean and responsive design with interactive elements.

- **Search and Query Optimization**: Optimized query engine with efficient indexing and metadata-based retrieval.

- **Code Quality**: Well-structured code, easy to understand, extend, and deploy, hosted on GitHub for public access.



---



## Installation



1. Clone the repository:



   ```bash

   git clone https://github.com/udit-rawat/Quest.git

   cd Quest

   ```



2. Install the required dependencies:



   ```bash

   pip install -r requirements.txt

   ```



3. Unzip the `leetcode_solutions.zip` file to access the solutions in JSON format:

   ```bash

   unzip leetcode_solutions.zip -d src/DSAAssistant/components/

   ```



---



## Setting Up Ollama



To use the generative models (`qwen2.5-coder:1.5b` and `deepseek-r1:1.5b`), you need to install Ollama and pull the required models.



### Install Ollama



- **macOS**:

  Download and install Ollama from the official website:  

  [Ollama for macOS](https://ollama.ai/download/mac)



- **Windows**:

  Download and install Ollama from the official website:  

  [Ollama for Windows](https://ollama.ai/download/windows)



### Pull the Models



Once Ollama is installed, pull the required models using the following commands:



```bash

# Pull qwen2.5-coder:1.5b

ollama pull qwen2.5-coder:1.5b



# Pull deepseek-r1:1.5b

ollama pull deepseek-r1:1.5b

```



---



## Usage



### Running the RAG Engine



1. Start the RAG engine:



   ```bash

   python app.py

   ```



2. Use the engine to query solutions or explanations. Example:

   ```bash

   Query: "Explain the concept of dynamic programming."

   ```



---



## Project Structure



```

Quest/

├── app.py                          # Main application entry point

├── requirements.txt                # List of dependencies

├── .gitignore                      # Files and directories to ignore

├── README.md                       # Project documentation

├── src/

│   ├── DSAAssistant/               # Core components of the RAG engine

│   │   ├── components/

│   │   │   ├── retriever2.py       # LeetCode retriever implementation

│   │   │   ├── rag_engine.py       # RAG engine implementation

│   │   │   ├── leetcode_hnsw.index # Pre-built HNSW index

│   │   │   ├── leetcode_metadata.pkl # Metadata for LeetCode problems

│   │   │   ├── leetcode_solutions.zip # Zipped JSON solutions

│   │   │   └── ...                 # Other components

│   │   └── ...                     # Additional modules

│   └── ...                         # Other source files

└── ...                             # Configuration and other files

```



---



## Configuration



The RAG engine can be configured using the following parameters:



- **Retriever**:



  - `index_path`: Path to the HNSW index file.

  - `metadata_path`: Path to the metadata file.

  - `model_name`: Name of the sentence transformer model for encoding.



- **Generation**:

  - `ollama_url`: URL for the Ollama API (if using a remote model).

  - `model_name`: Name of the generative model.

  - `temperature`: Controls the randomness of the generated output.

  - `top_p`: Controls the diversity of the generated output.



Example configuration:



```python

retriever = LeetCodeRetriever(

    index_path="path/to/leetcode_hnsw.index",

    metadata_path="path/to/leetcode_metadata.pkl",

    model_name="all-MiniLM-L6-v2"

)



rag_engine = RAGEngine(

    retriever=retriever,

    ollama_url="http://localhost:11434/api/generate",

    model_name="qwen2.5-coder:1.5b",

    temperature=0.4,

    top_p=0.9

)

```



---



## Contributing



Contributions are welcome! If you'd like to contribute to **Quest**, please follow these steps:



1. Fork the repository.

2. Create a new branch for your feature or bugfix:

   ```bash

   git checkout -b feature/your-feature-name

   ```

3. Commit your changes:

   ```bash

   git commit -m "Add your feature or fix"

   ```

4. Push your branch:

   ```bash

   git push origin feature/your-feature-name

   ```

5. Open a pull request and describe your changes.



---



## Acknowledgments



- **FAISS** and **HNSW** for efficient similarity search.

- **Sentence Transformers** for encoding text into embeddings.

- **Ollama** for providing the generative model API.



---



## Contact



For questions or feedback, feel free to reach out:



- **Udit Rawat**

- GitHub: [udit-rawat](https://github.com/udit-rawat)

- Email: [uditrawat1905@gmail.com](mailto:uditrawat1905@gmail.com)



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