Achievements
- Developed and deployed a pipeline leveraging Retrieval-Augmented Generation (RAG) and Large Language Models (LLM) to extract critical information from public procurement documents.
- Implemented the solution on Google Cloud Platform (GCP) for scalability and performance.
- Streamlined the analysis process to enhance decision-making in public procurement.
- Designed a robust architecture enabling efficient text extraction, storage, and querying.
Context
This project aimed to streamline the analysis and decision-making process for public procurement documents. By employing Retrieval-Augmented Generation (RAG) and Large Language Models (LLM), the solution effectively extracts and contextualizes information from unstructured text.
The pipeline consists of the following key components:
- Text Extraction: Processes unstructured text from public procurement documents for analysis.
- Information Retrieval: Utilizes RAG to identify and extract relevant data points.
- Scalability: Deployed on GCP, ensuring high availability and performance for large-scale processing.
Technologies Used
- Google Cloud Platform (GCP): For scalable and reliable deployment.
- Retrieval-Augmented Generation (RAG): To enhance the accuracy and relevance of information retrieval.
- Large Language Models (LLM): For intelligent text processing and querying.