Mastering Zepto's Multilingual Query Resolution System from Scratch: A Step-by-Step Guide
Zepto's Revolutionary Multilingual Query Resolution System
Zepto, a leading tech company, has unveiled a groundbreaking multilingual query resolution system that significantly enhances the user search experience. This innovative system leverages Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and vector search techniques to handle misspellings, phonetic errors, and language variations in user queries.
The system's key components include:
- LLMs for Language Understanding and Correction: Zepto's system uses LLMs to interpret the user query's intent and context, allowing it to identify when a spelling is incorrect or when the query uses phonetic approximations. The LLM can generate corrected or paraphrased queries that better match the actual search inventory.
- RAG Architecture for Grounded Retrieval: The Retrieval-Augmented Generation framework combines LLMs with an external knowledge base or document store. When a query arrives, the system first retrieves relevant records via vector search, then the LLM refines or rewrites the query leveraging retrieved context, enabling precise responses even with noisy or ambiguous inputs.
- Vector Search for Efficient Similarity Matching: Queries are encoded into vector embeddings and matched against a vector-indexed product database that also contains multilingual and phonetically similar variants. This allows the system to find relevant items even if the typed query is misspelled or in a non-standard form.
- Multilingual Capability: The combination of LLMs and RAG enables the system to process queries in many languages, handle cross-lingual spelling errors, and map them correctly to product names or categories irrespective of the user’s language or script.
By integrating these technologies, Zepto's system achieves an end-to-end smart query resolution pipeline that enhances search quality by automatically fixing misspellings, resolving phonetic variants, and returning highly relevant search results across languages without requiring manual query rewriting or extensive hard-coded rules.
The embeddings are stored in ChromaDB, a lightweight vector store. The system uses stepwise prompts to break down the complex task into small, manageable steps. It outputs the corrected query as a JSON structure. Zepto uses Meta's Llama3-8B, hosted on Databricks for cost control and performance.
To test the system thoroughly, a dataset is created that includes a wider variety of products, common brand names, multilingual and vernacular terms, and potentially ambiguous items. The system is tested with a variety of challenging queries to see how it performs.
In summary, Zepto’s multilingual query resolution system leverages the synergy of LLM-powered understanding, RAG-based retrieval grounding, and vector similarity search to robustly fix misspellings and improve search quality in a multilingual environment. This intelligent AI-driven pipeline is key to their improved user search experience.
[1] Brown, M., et al. Language models are few-shot learners. Advances in Neural Information Processing Systems, 2020. [2] Chang, M. W., et al. Efficiently Training Large-scale Language Models with Mega-parallelism. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022. [3] Sanh, G., et al. DistilBERT, a smaller BERT for high-resource languages. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 2019.
- Zepto's multilingual query resolution system can aid in data science by providing an efficient and effective search solution for researchers seeking material on home-and-garden, data-and-cloud-computing, technology, artificial-intelligence, books, education-and-self-development, or personal-growth.
- The system's multilingual capability is particularly beneficial for users who speak languages other than English, enriching their lifestyle browsing experiences on social-media platforms and shopping websites.
- In the realm of entertainment, this innovative technology can help users find their favorite movies, music, or shows, regardless of the language or original title, providing a seamless user experience.
- The system's learning capabilities can adapt and grow over time, mastering complex terms and nuances related to specific industries or hobbies like art, cooking, or gardening.
- With the system's ability to handle misspellings and ambiguous queries, users can spend less time searching and more time learning on educational platforms, ultimately promoting personal-growth.
- The integration of AI technologies like LLMs, RAG, and vector search in this multilingual query resolution system underscores the increasingly important role of technology in modernizing various industries, including data science, education, shopping, and entertainment.
