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https://github.com/armanjscript/hybrid-rag-chatbot

A powerful web-based application designed to answer questions based on the content of uploaded PDF documents. This project leverages a Hybrid Retrieval-Augmented Generation (RAG) approach, combining the strengths of vector-based semantic search and keyword-based search to deliver accurate and relevant responses
https://github.com/armanjscript/hybrid-rag-chatbot

bm25 chroma chromadb ensemble-retriever hybrid-rag langchain langchain-ollama ollama ollama-embeddings pypdf qwen2-5 rag rag-chatbot streamlit

Last synced: 29 days ago
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A powerful web-based application designed to answer questions based on the content of uploaded PDF documents. This project leverages a Hybrid Retrieval-Augmented Generation (RAG) approach, combining the strengths of vector-based semantic search and keyword-based search to deliver accurate and relevant responses

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# Hybrid RAG Chatbot with Streamlit, LangChain, Chroma, and Ollama



[![GitHub Stars](https://img.shields.io/github/stars/yourusername/hybrid-rag-chatbot?style=social)](https://github.com/armanjscript/Hybrid-RAG-chatbot)

[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)



## Description



Welcome to the **Hybrid RAG Chatbot**, a powerful web-based application designed to answer questions based on the content of uploaded PDF documents. This project leverages a **Hybrid Retrieval-Augmented Generation (RAG)** approach, combining the strengths of vector-based semantic search and keyword-based search to deliver accurate and relevant responses. Built with cutting-edge technologies like **Streamlit**, **LangChain**, **Chroma**, and **Ollama**, this chatbot is ideal for researchers, students, or professionals who need to extract insights from documents efficiently.



The application features a user-friendly interface where you can upload PDFs, adjust settings, and interact with the chatbot in a conversational manner. Responses are generated in real-time, with citations to the source documents for transparency and verifiability.



## Features



| Feature | Description |

|---------|-------------|

| **PDF Upload & Processing** | Upload multiple PDF files, which are automatically split into chunks and indexed for querying. |

| **Conversational Interface** | Ask questions in a chat-like interface and receive detailed answers based on document content. |

| **Hybrid RAG Technique** | Combines vector search (Chroma) and keyword search (BM25) for enhanced retrieval accuracy. |

| **Adjustable Parameters** | Tune the **Temperature** (response creativity) and **Hybrid Search Ratio** (balance between vector and keyword search). |

| **Real-Time Responses** | Answers are streamed in real-time for a seamless user experience. |

| **Error Handling** | Robust error handling and fallback mechanisms ensure reliable operation. |

| **Source Citations** | Responses include references to the source documents, enhancing trust and verifiability. |



## How It Works



The Hybrid RAG Chatbot uses a sophisticated pipeline to process documents and generate answers. Here’s a detailed look at the process:



### Document Processing

- **PDF Loading**: Uploaded PDFs are loaded using `PyPDFLoader` from LangChain.

- **Text Splitting**: Documents are split into manageable chunks (1000 characters, 200-character overlap) using `RecursiveCharacterTextSplitter`.

- **Metadata Tagging**: Each chunk is tagged with metadata, such as the source file name, for traceability.



### Retrieval

The chatbot employs a **Hybrid RAG** approach, integrating two retrieval methods:

- **Vector Search**: Documents are embedded into a high-dimensional vector space using `OllamaEmbeddings` (model: `nomic-embed-text:latest`) and stored in a Chroma vector store. This enables semantic similarity searches, retrieving the top 5 most relevant chunks (`k=5`).

- **Keyword Search**: A BM25 retriever ranks documents based on keyword matching, also retrieving the top 5 chunks (`k=5`).

- **Hybrid Search**: An `EnsembleRetriever` combines the results of both methods, with a configurable **Hybrid Search Ratio** (e.g., 0.5 for equal weighting, 0.7 for 70% vector search). If hybrid search fails, it falls back to vector search for robustness.



### Augmentation

- Retrieved document chunks are formatted into a context string, including the source file name and content, using a custom `format_docs` function.

- The context is combined with the user’s query in a `ChatPromptTemplate` to provide the language model with all necessary information.



### Generation

- The formatted context and query are passed to an `OllamaLLM` (model: `qwen2.5:latest`) to generate a detailed response.

- The response is parsed using `StrOutputParser` and streamed to the user interface in real-time using Streamlit’s `st.write_stream`.



### Diagram of the RAG Pipeline

The following diagram illustrates the flow of the Hybrid RAG pipeline:



```mermaid

flowchart LR

    A(["User Query"])

    A --> B["Hybrid Retriever"]

    B --> C["Vector Search (Chroma)"]

    C --> D["Embeddings (OllamaEmbeddings)"]

    B --> E["Keyword Search (BM25)"]

    C --> F["Retrieved Documents"]

    E --> F

    F --> G["Formatter"]

    G --> H["Prompt Template"]

    H --> I["LLM"]

    I --> J["Output Parser"]

    J --> K["Response"]

```



This diagram can be rendered in GitHub to visualize the pipeline from query to response.



## Environment Setup



To run the Hybrid RAG Chatbot, you’ll need to set up the following:



- **Python 3.8 or later**: Ensure Python is installed on your system. Download from [python.org](https://www.python.org/downloads/).

- **Ollama**: A tool for running large language models locally. Install it based on your operating system:

  - **Windows**: Download the installer from [Ollama Download](https://ollama.com/download) and run it.

  - **macOS**: Download the installer from [Ollama Download](https://ollama.com/download), unzip it, and drag the `Ollama.app` to your Applications folder.

  - **Linux**: Run the installation script as per the [Ollama GitHub repository](https://github.com/ollama/ollama).

- **Python Libraries**: Install the required dependencies listed in `requirements.txt`.



## Installation



Follow these steps to set up the project:



1. **Clone the Repository**:

   ```bash

   git clone https://github.com/armanjscript/Hybrid-RAG-chatbot.git

   ```

2. **Navigate to the Project Directory**:

   ```bash

   cd Hybrid-RAG-chatbot

   ```

3. **Install Dependencies**:

   ```bash

   pip install -r requirements.txt

   ```

   The `requirements.txt` file includes dependencies like `streamlit`, `langchain`, `chromadb`, `langchain-ollama`, and others.



## Usage



1. **Start Ollama**: Ensure the Ollama service is running on your system. Follow the instructions from the [Ollama GitHub repository](https://github.com/ollama/ollama) to start the service.

2. **Run the Streamlit App**:

   ```bash

   streamlit run hybrid_rag.py

   ```

   This will launch the app in your default web browser.

3. **Upload PDFs**:

   - In the sidebar, use the file uploader to select one or more PDF files.

   - The files are saved locally in the `uploaded_pdfs` directory and indexed in a Chroma database (`chroma_db`).

4. **Adjust Settings**:

   - Use the sliders in the sidebar to set the **Temperature** (0.0 to 1.0) and **Hybrid Search Ratio** (0.0 to 1.0).

   - Optionally, clear all documents to reset the application.

5. **Ask Questions**:

   - Enter your query in the chat input field in the main interface.

   - The chatbot will retrieve relevant document chunks, generate a response, and stream it in real-time.

   - Responses include citations to the source documents for reference.



## Configuration



The application allows you to fine-tune its behavior through two key parameters:



| Parameter | Description | Range |

|-----------|-------------|-------|

| **Temperature** | Controls the randomness of the LLM’s responses. Lower values (e.g., 0.0) produce more deterministic answers, while higher values (e.g., 1.0) increase creativity. | 0.0 to 1.0 |

| **Hybrid Search Ratio** | Balances the contribution of vector search (semantic) and keyword search (BM25). A value of 0.5 gives equal weight, while 0.7 prioritizes vector search. | 0.0 to 1.0 |



## Contributing



We welcome contributions to enhance the Hybrid RAG Chatbot! To contribute:

- Fork the repository.

- Make your changes in a new branch.

- Submit a pull request with a clear description of your changes.



For detailed guidelines, refer to the [CONTRIBUTING.md](CONTRIBUTING.md) file.



## License



This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.



## Contact



For questions, feedback, or collaboration opportunities, please reach out:

- **Email**: [armannew73@gmail.com]

- **GitHub Issues**: Open an issue on this repository for bug reports or feature requests.



## Acknowledgments



This project builds on the following open-source technologies:

- [Streamlit](https://streamlit.io/) for the web interface

- [LangChain](https://www.langchain.com/) for document processing and RAG pipeline

- [Chroma](https://www.trychroma.com/) for vector storage

- [Ollama](https://ollama.com/) for local language models and embeddings



Thank you for exploring the Hybrid RAG Chatbot! We hope it simplifies your document analysis tasks.