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Category: python

  • k-NN (k-Nearest Neighbors) search in OpenSearch

    To perform a k-NN (k-Nearest Neighbors) search in OpenSearch after loading your manuals (or any documents) as vector embeddings, you’ll use the knn query within the OpenSearch search API. Here’s how you can do it: Understanding the knn Query The knn query in OpenSearch allows you to find the k most similar vectors to a Read more

  • Loading manuals into a vector database

    Here’s a breakdown of how to load manuals into a vector database, focusing on the key steps and considerations: 1. Choose a Vector Database: Several vector databases are available, each with its own strengths and weaknesses.1 Some popular options include: Consider factors like scalability, ease of use, cost, integration with your existing stack, and specific Read more

  • Building a Product Manual Chatbot with Amazon OpenSearch and Open-Source LLMs

    This article guides you through building an intelligent chatbot that can answer questions based on your product manuals, leveraging the power of Amazon OpenSearch for semantic search and open-source Large Language Models (LLMs) for generating informative responses. This approach provides a cost-effective and customizable solution without relying on Amazon Bedrock. The Challenge: Navigating through lengthy Read more

  • Integrating Documentum with an Amazon Bedrock Chatbot API for Product Manuals

    This article outlines the process of building a product manual chatbot API using Amazon Bedrock, with a specific focus on integrating content sourced from a Documentum repository. By leveraging the power of vector embeddings and Large Language Models (LLMs) within Bedrock, we can create an intelligent and accessible way for users to find information within Read more

  • Spring AI and Langchain Comparison

    A Comparative Look for AI Application DevelopmentThe landscape of building applications powered by Large Language Models (LLMs) is rapidly evolving. Two prominent frameworks that have emerged to simplify this process are Spring AI and Langchain. While both aim to make LLM integration more accessible to developers, they approach the problem from different ecosystems and with Read more

  • Automating Customer Communication: Building a Production-Ready LangChain Agent for Order Notifications

    In the fast-paced world of e-commerce, proactive and timely communication with customers is paramount for fostering trust and ensuring a seamless post-purchase experience. Manually tracking new orders and sending confirmation emails can be a significant drain on resources and prone to delays. This article presents a comprehensive guide to building a production-ready LangChain agent designed Read more

  • Intelligent Order Monitoring Langchain LLM tools

    Building Intelligent Order Monitoring: A LangChain Agent for Database ChecksIn today’s fast-paced e-commerce landscape, staying on top of new orders is crucial for efficient operations and timely fulfillment. While traditional monitoring systems often rely on static dashboards and manual checks, the power of Large Language Models (LLMs) and agentic frameworks like LangChain offers a more Read more

  • RAG to with sample FAQ and LLM

    Code Explanation: RAG with FAQ and OpenAI This Python code implements a Retrieval Augmented Generation (RAG) system specifically designed to answer questions from an FAQ dataset using OpenAI’s language models. Here’s a step-by-step explanation of the code: 1. Import Libraries: 2. load_faq_data(data_path): 3. chunk_faq_data(faq_data): 4. create_embeddings(chunks): 5. create_vector_store(chunks, embeddings): 6. create_rag_chain(vector_store, llm): 7. rag_query(rag_chain, query): Read more

  • RAG with locally running LLM

    Sample code to enable running the LLM locally. This will involve using a local LLM instead of OpenAI. Key Changes: To run this code with a local LLM: Important Considerations: Read more

  • Apache Spark

    Let’s illustrate Apache Spark with a classic “word count” example using PySpark (the Python API for Spark). This example demonstrates the fundamental concepts of distributed data processing with Spark. Scenario: You have a large text file (or multiple files) and you want to count the occurrences of each unique word in the file(s). Steps: from Read more