Building a Customer Service RAG AI Agent with LangFlow3 min readJust now--The title itself conveys the essence of the topic. Retrieval-Augmented Generation (RAG) enhances the output of Large Language Models (LLMs) by incorporating external knowledge retrieval. The data used for training can be either structured or unstructured. Essentially, LLMs are a class of neural network-based models trained on vast amounts of data with billions of parameters. Before diving into the implementation, its essential to familiarize ourselves with key technologies such as LangChain, LangFlow, LangFlow commands, ChromaDB, LLAMA, Ollama (including large language models and embedding models), and AWS.AI agent where it extracts the required information from the documents and responds to the user query.LangFlowLangFlow is an open-source, UI-based tool designed for building and experimenting with LangChain applications. LangChain, on the other hand, is an open-source framework in Python that enables the development of various applications using Large Language Models (LLMs). To install LangFlow, you need Python version 3.8 or higher, as the library relies on LangChain and FastAPI. Currently, I am using version 1.1.4.Ollamaollama is a CLI tool to access and download the models locally. There are different types of models with different applications. Like Tool-Supporting, Instruct, Text, Code, Embedding models.Instruct Models - Designed for chat interfaces, these models are trained to respond to user queries in a conversational manner.Embedding Models - These models generate vector embeddings for text, facilitating tasks like search and retrieval augmented generation (RAG).Devloping AI agentFirst, we begin with data collection, cleaning, and alignment. This process can be done manually or with powerful libraries such as Pandas, NumPy, and web scraping tools like BeautifulSoup. Once the data is cleaned, it should be structured in formats such as .pdf, .csv, .txt, or .json.The next essential step is familiarizing yourself with the Python programming language. Once comfortable with Python, you can build a workflow in LangFlow as outlined below:LangFlowInstead of using the LLAMA model from AWS Bedrock, you can utilize the Ollama LLAMA model, which runs locally on your machine. I recommend ensuring that your system has suitable resources to support the application efficiently.Steps to Build a Customer Support Agent:Install all required LangFlow dependencies:pip (version 24.0)langflow (version 1.1.4)Ollama (latest version)Python (version 3.8+)3. Follow the LangFlow workflow diagram to structure the application process.4. Set up and execute the necessary workflows to vectorize the input training data.5. Instead of using the AWS Bedrock API, configure Ollamas local host URL for both embedding and instruction models.6. Retrieve the Python API from LangFlow and integrate it according to your applications requirements.Note: This article focuses on using LangFlow to build a RAG-based agent. However, for a fully functional system, youll need to integrate the API with a database, fine-tune the model (optional), set up a web interface, handle networking, and implement data storage, among other considerations.ConclusionThis is Part 1 of the series, focusing on understanding the background and how the workflow operates. In the next article, I will cover deploying the RAG agent, including training and fine-tuning the model using open-source application data from data collection to AWS services. Stay tuned for a deep dive into the deployment process!