LangChain and DataStax Astra DB Power Esynergy’s Retrieve-Augment Architecture for Sales Copilot

esynergy developed Sales Copilot, an AI-powered conversational assistant for sales teams using Langchain and DataStax Astra DB. The application ingests customer data from SharePoint, encodes it into vectors, indexes in Astra DB for low-latency search, retrieves relevant chunks for a query, and passes them to Claude2 to generate responses. This retrieval-augmented setup allows generating contextual responses by conditioning on relevant data. The application seamlessly integrates with an organization's SharePoint instance, allowing it to ingest customer profiles, communication records, and case studies. Langchain plays a crucial role in the preprocessing stage, enabling efficient splitting of files into manageable chunks for processing. AWS Bedrock suite is used for text embedding models, transforming chunks into dense vector representations. The retrieval-augmented architecture ensures relevant data points are retrieved from Astra DB and used to generate informed responses. Using Langchain accelerated the development of a production-grade conversational assistant, offering benefits such as minimizing repetitive tasks and fostering more meaningful conversations with customers.