Finding the right car parts in the vast landscape of e-commerce can be a daunting and time-consuming task for customers. Smart Assist by ENSO AI is here to simplify this process using advanced AI, specifically Large Language Models (LLMs), to streamline the search experience.
Vectorization with LLMs
Understanding Vectorization
Vectorization transforms data into numerical vectors that machine learning algorithms can process. For LLMs, textual data is converted into dense vector representations, enabling AI to effectively understand and manipulate text.
Techniques Employed
Word Embeddings: Models like Word2Vec and GloVe create word embeddings by mapping words into a continuous vector space where semantically similar words are closer together. This captures contextual similarity but is limited by a fixed representation for each word.
Contextual Embeddings: Advanced models like BERT and GPT generate dynamic representations of words based on their context within a sentence, allowing for a deeper understanding of meaning and nuance.
Implementation in Smart Assist
Smart Assist leverages contextual embeddings generated by LLMs to process customer queries. For example, when a customer asks for “a brake pad for a 2015 Honda Civic,” the LLM converts the query into a vector that encapsulates the request’s context and intent, facilitating accurate search results.
Further Examples
- Example Query: “Looking for an oil filter for a 2018 Ford F-150.”
- Process: The LLM interprets the query, extracting key components like “oil filter,” “2018,” “Ford,” and “F-150.” It then generates a vector that represents the entire context.
- Outcome: The vectorized query is matched against the indexed database, returning precise results for compatible oil filters.
- Example Query: “Need spark plugs that fit a 2020 Toyota Corolla.”
- Process: The LLM parses the phrase, understanding the need for “spark plugs” and the vehicle details “2020 Toyota Corolla.” It produces a vector outlining these requirements.
- Outcome: Using this vector, Smart Assist retrieves relevant product listings for spark plugs suitable for the 2020 Toyota Corolla.
Structuring Information with LLMs
Data Structuring Challenges
E-commerce platforms often deal with a mix of structured and unstructured data, including product descriptions, specifications, and customer reviews. Structuring this data is essential for efficient retrieval and analysis.
LLM Approaches
Named Entity Recognition (NER): LLMs identify and classify key entities within unstructured text, extracting information such as “brake pad,” “2015,” “Honda,” and “Civic.”
Relation Extraction: LLMs discern relationships between entities, understanding how different pieces of information are connected, such as linking “brake pad” to “2015 Honda Civic.”
Semantic Parsing: LLMs break down sentences into their constituent parts and understand their roles and relationships, parsing sentences to grasp their syntactic structure and semantic meaning.
Application in Smart Assist
In Smart Assist, the structuring process begins with raw data ingestion from various sources, like text documents and PDFs. LLMs perform entity recognition and relation extraction to convert this data into a structured format.
Further Examples
- Example Raw Text: “Durable air filter suitable for 2017 Jeep Wrangler. Enhances engine performance and longevity.”
- Structured Data:
- Product: Air Filter
- Model Year: 2017
- Brand: Jeep
- Model: Wrangler
- Features: Enhances engine performance, Longevity
- Structured Data:
- Example Raw Text: “Premium wiper blades for 2019 Subaru Outback. Offers streak-free wiping in all weather conditions.”
- Structured Data:
- Product: Wiper Blades
- Model Year: 2019
- Brand: Subaru
- Model: Outback
- Features: Streak-free wiping, All-weather conditions
- Structured Data:
Indexing and Retrieval
Once structured, data is indexed using techniques like inverted indices and B-trees for efficient retrieval. When a customer query is received, the LLM-generated vector is used to perform a similarity search within the indexed data, ensuring relevant results are retrieved quickly and accurately.
Real-Time Solutions: Speed and Accuracy
Smart Assist is engineered to provide rapid and precise search results through real-time processing capabilities. For example, a query like “I need a brake pad for a 2015 Honda Civic” utilizes a combination of search algorithms, such as BM25 and semantic search techniques, to identify the exact component and propose compatible alternatives. The system’s latency is minimized by employing in-memory databases and optimizing query execution plans.
Natural Language Processing (NLP)
A core component of Smart Assist’s functionality is its advanced NLP capabilities. Using deep learning models like BERT and GPT, the assistant comprehends and processes customer queries, understanding context, handling synonyms, and interpreting colloquial language for accurate and relevant search results.
Further Examples
- Synonym Handling: A customer might query “car battery” or “automobile battery.” The LLM’s NLP capabilities ensure both queries are understood as referring to the same product type.
- Outcome: The system retrieves relevant battery listings for both queries, ensuring consistent and accurate results.
- Colloquial Language Interpretation: A query like “I need new tires for my ride” is understood by the LLM to mean “I need new tires for my car.”
- Outcome: The assistant processes the colloquial term “ride” and accurately returns tire options for the vehicle specified in the user’s profile or subsequent query details.
Enhancing Operational Efficiency
Human errors in data entry and search queries can be detrimental to business operations. Smart Assist minimizes these errors through precise data handling and robust validation mechanisms. Employing data validation techniques, such as schema validation and anomaly detection, ensures the accuracy and integrity of the data, reducing returns and exchanges, and resulting in higher customer satisfaction and lower operational costs.
Ready to see how Smart Assist can optimize your car parts e-commerce platform? Schedule a call with our experts from ENSO AI and discover how we can help you streamline operations and boost customer satisfaction. Let’s revolutionize your e-commerce experience together!
