Building a Sentiment-Aware Product Review Search with RAG and LLM in Python

This tutorial illustrates how to prototype advanced AI systems locally using Hugging Face Transformers, FAISS, and Python, creating a structured framework for building, testing, and iterating on solutions that integrate retrieval-augmented generation (RAG) and sentiment analysis capabilities. By shifting to local processing, this approach significantly reduces costs, ensures privacy, and removes reliance on external APIs. Hugging Face's open-source models enable Data Distiller users to overcome complex implementation challenges and develop functional prototypes efficiently, all while keeping sensitive data within their infrastructure. This approach is particularly valuable for privacy-conscious organizations and cost-sensitive projects.

By leveraging Hugging Face’s modular tools and pretrained models, you can refine specific components of the system, such as document retrieval accuracy or sentiment-aware response generation, without starting from scratch. This accelerates the validation process, enabling iterative improvements and rapid feedback loops. Local prototyping with Hugging Face not only reduces reliance on external APIs, which often incur ongoing costs, but also provides greater control over data flow, ensuring compliance with privacy regulations.

The sentiment-aware RAG tutorial showcases how Python’s ecosystem and Hugging Face Transformers enable seamless integration of sentiment metadata into retrieval and response pipelines. This local-first solution fosters innovative applications across domains, from financial sentiment analysis to product reviews and customer feedback categorization. Hugging Face’s pretrained models make it easy to extend this framework to specific industries, unlocking new possibilities without significant investment in computational resources. With Hugging Face’s accessible tools and Python’s versatility, businesses can rapidly visualize, test, and deploy solutions that provide actionable insights while maintaining cost efficiency and data security.

Case Study

In the e-commerce industry, providing an intuitive and engaging product search experience is critical for customer satisfaction and conversion rates. Customers often rely on product reviews to make informed purchasing decisions but are overwhelmed by the volume of unstructured feedback. This case study demonstrates how a sentiment-aware Retrieval-Augmented Generation (RAG) system can transform the product search experience by enabling conversational, sentiment-driven insights directly on the website.

Customers exploring a product catalog often have specific questions that require dynamic and detailed answers. Traditional search solutions, like keyword-based search bars, fail to provide nuanced responses and leave users frustrated. For example:

• A customer might ask, "What do customers think about the durability of this product?" but only receive a list of generic reviews without context.
• Another user searching for negative reviews about battery life may struggle to filter out irrelevant or overly positive results.
• Beginners looking for summarized feedback might find the sheer number of reviews overwhelming.

To address these pain points, we need a solution that can:

1. Retrieve relevant reviews quickly and efficiently.
2. Analyze and incorporate sentiment to prioritize or filter feedback.
3. Provide conversational, natural language responses that summarize customer insights.

RAG Setup and Architecture

Setup Phase (Steps 1-4): Preparing the Data

1. Generate Embeddings for Reviews: The reviews (text data) are passed through a pre-trained embedding model, such as all-MiniLM-L6-v2. This model converts the reviews into numerical vector representations, known as embeddings. These embeddings capture the meaning of the reviews in a way that enables comparison and similarity detection.
2. Store Embeddings in a FAISS (Facebook AI Similarity Search) Vector Database:: The generated embeddings are stored in a FAISS vector database. FAISS indexes these embeddings to enable efficient similarity searches. Each embedding represents a review and is indexed by its unique ID.
3. Include Metadata for Reviews: Metadata, such as sentiment or an ID for each review, is paired with the review content to form documents. These documents are stored in an in memory data store. This step ensures that each embedding in the FAISS database is linked to the corresponding review details.
4. Set Up a Link Between Embeddings and Metadata: A mapping is created between the FAISS vector index and the document store, ensuring that the vector representation (embeddings) can be matched with the original review content and metadata. This mapping enables retrieval of relevant context during a search.

RAG Phase (Steps 5-9): Processing a Query

1. Generate Embeddings for the Query: When a question (query) is asked, it is converted into an embedding using the same model (all-MiniLM-L6-v2). This step ensures the query is represented in the same vector space as the reviews, enabling effective comparison.
2. Find Similar Reviews: The query embedding is compared against the embeddings in the FAISS vector database. FAISS uses Euclidean distance to identify the most similar reviews. This step narrows down the search to the most relevant matches.
3. Retrieve Review Content: The IDs of the top matches from FAISS are used to fetch the corresponding documents (review content and metadata) from the InMemoryDocstore. This step ensures that the retrieved results include both the vectorized data and the human-readable review content.
4. Use an LLM to Generate an Answer: The retrieved reviews are passed to a language model (LLM) for contextual understanding. The LLM processes these documents, understands their content, and generates a response based on the question.
5. Deliver the Final Answer: The LLM outputs the final answer to the query. This answer is grounded in the context of the retrieved reviews, ensuring it is relevant and informed.

Now try out the tutorial in depth here