https://github.com/mrigank005/qdrant
https://github.com/mrigank005/qdrant
Last synced: 11 months ago
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- Host: GitHub
- URL: https://github.com/mrigank005/qdrant
- Owner: Mrigank005
- License: mit
- Created: 2025-08-15T04:37:33.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-08-15T04:51:06.000Z (11 months ago)
- Last Synced: 2025-08-15T06:22:42.294Z (11 months ago)
- Language: Python
- Size: 29.3 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Qdrant RAG Pipeline
A complete implementation of a Retrieval-Augmented Generation (RAG) pipeline using Qdrant vector database, featuring document injection and semantic search capabilities.
## Features
- **Document Processing**: Supports multiple file formats (PDF, DOCX, CSV, JSON, etc.)
- **Vector Embeddings**: Uses state-of-the-art sentence transformers
- **Hybrid Search**: Combines semantic and keyword search
- **Analytics Dashboard**: Track search performance and patterns
- **Security**: Input validation and sanitization
- **Health Monitoring**: System resource tracking
## Components
1. **Data-Injection.py**: Ingests and processes documents into Qdrant collections
2. **Data-Retrieval.py**: Interactive search interface with analytics
3. **config.yaml**: Centralized configuration for all components
## Installation
```bash
# Clone the repository
git clone https://github.com/yourusername/qdrant-rag-pipeline.git
cd qdrant-rag-pipeline
# Install dependencies
pip install -r requirements.txt
# Download spaCy model
python -m spacy download en_core_web_sm
```
## Configuration
Edit `config.yaml` to customize:
```yaml
qdrant:
host: "localhost" # Qdrant server address
port: 6333 # Qdrant port
model:
name: "all-MiniLM-L6-v2" # Embedding model
device: "cpu" # cpu/cuda/mps
processing:
chunk_size: 500 # Text chunk size
supported_extensions: # File types to process
- ".pdf"
- ".docx"
- ".txt"
Input Directory
input_dir: "file\\path\\docs" # Custom path to your documents folder
```
## Usage
### 1. Inject Documents
```bash
python Data-Injection.py
```
### 2. Search Interface
```bash
python Data-Retrieval.py
```
## System Architecture
```mermaid
graph TD
A[Documents] --> B[Data Injection]
B --> C[Qdrant Vector DB]
C --> D[Data Retrieval]
D --> E[User Interface]
```
## License
[MIT](LICENSE)