Vicent Perez

Hey! ⁤⁤In today's post, I'll show you a recent project I worked on that demonstrates how #RAG applications can help companies avoid fines ⛔ 💸 ⁤In the supplements industry (proteins, creatine, vitamins…), compliance with regulations is critical. ⁤⁤There are strict rules about what companies can and cannot say in product descriptions in their #webshops. ⁤⁤As an example, let's focus on a creatine product description: ⁤ ⁤✅ Allowed: Creatine helps to improve performance during explosive strength efforts. ⁤❌ Not allowed: Creatine stimulates/supports hormone production. ⁤Failing to adhere to these regulations can lead to significant #fines. ⁤⁤So, how can #LLMs help businesses stay compliant and avoid costly mistakes? ⁤⁤Here's a simplified example showing how, using #Node.js, #Supabase, #LangChain, and #OpenAI we can achieve this. ⁤1️⃣ First, we need to gather the EU public regulatory data. ⁤⁤As there is no API, we can do this by scraping using tools like Cheerio.js, a popular lib for web scraping in Node.js. ⁤2️⃣ Once the data is collected, we need to process and structure it to create the embeddings. ⁤⁤These embeddings can then be stored in a vector database, such as Supabase with the pgvector extension. ⁤⁤If you are not familiar with those terms: ⁤ ⁤📌 An #embedding is basically a numerical representation of the data that captures its meaning for machine learning tasks. ⁤📌 A #vectorstore is a specialized type of database designed for natural language queries. ⁤⁤When a user inputs a query, the system searches the vector store for embeddings that closely match the input. ⁤⁤We can use a method called cosine similarity, which measures the similarity between two vectors. ⁤⁤After that, the system returns the most relevant documents for further processing. ⁤ ⁤3️⃣ Now that we have the claims data in Supabase, we can start creating the chain that is going to interact with the LLM. ⁤⁤For this, we are going to use LangChain, a framework designed to build applications on top of LLMs. ⁤ ⁤4️⃣ This chain will have, as an input, a product description. ⁤⁤The first step will be the embedding generation of this text to be able to query the vector database and retrieve related regulatory documents that align with the product description. ⁤ ⁤5️⃣ The retrieved documents are passed to the LLM as context, enabling it to determine if the description is compliant or if any changes are needed. ⁤ ⁤6️⃣ To ensure consistent and accurate results, we need to use a prompt template. ⁤⁤This template will guide the LLM by clearly defining its role (as a regulatory compliance classifier), the desired outcome (result in JSON format), and any other relevant instructions. ⁤ ⁤✅ With this, we can automate the process of regulatory compliance checking or at least flag products that may require a manual double-check. ⁤ ⁤Hope you learned something new today ⁤Stay tuned for more content, and if you need any help just drop me a message. ⁤ 📩 ⁤Have a lovely day! ⁤⁤ ⁤