Cross-Language Information Retrieval Solution
Introduction
We developed the Cross-Language Information Retrieval Solution to address the challenge of multilingual data retrieval without relying on translation APIs. By leveraging a state-of-the-art BGE-M3 multilingual embedding model, this solution enables seamless cross-language search, allowing users to retrieve data in various languages with high accuracy.
Problem/Opportunity
Organizations working with multilingual data need help in retrieving information across different languages. Traditional methods often rely on translation APIs, resulting in inaccuracies and slow processing times. The need for a fast, scalable solution that allows real-time cross-language search, while maintaining the original context, became paramount.
Solution
Our solution eliminates the need for translation by implementing a vector-based retrieval system. Key features include:
●BGE-M3 Embedding Model: Used for cross-language vectorization, ensuring accurate multilingual search capabilities.●Vector Database (Qdrant): Allows fast querying of both structured and unstructured data.●LangChain for Data Processing: Handles large datasets by chunking and indexing data like JSON, PDFs, CSVs, images, and audio.●Cloud Deployment: The system is built on AWS, utilizing EC2, S3, and Lambda for efficient computation and storage, but also supports Azure, Google Cloud, and on-premise deployments.
Implementation Process
The solution integrates into the client’s AI HUB, a private SaaS platform, allowing rapid prototyping and deployment in both classified and unclassified environments. The vectorized search queries are processed in real time, supporting large-scale data ingestion and retrieval. This architecture is future-proof and adaptable to evolving client requirements.
Technology used: Python, FastAPI, LangChain, Vectodb, OpenSource Embedding model (BGE-M3), AWS etc.
Results
●Performance: The system can process and vectorize 1 million documents in under 4 hours, achieving an accuracy rate of 90-95% for cross-language searches.●Scalability: Efficiently handles datasets up to terabytes in size, with costs scaling based on data volume.
Conclusion
Our solution provides a groundbreaking approach to multilingual data retrieval, combining speed, accuracy, and scalability without the need for traditional translation methods. This innovative solution is ideal for organizations dealing with complex, mission-critical data analysis across multiple languages.
