Step-By-Step Guide to Build a DIY RAG Stack & Top 10 Considerations

Take control of your data visualization with a DIY RAG stack. This guide walks you through the process and highlights key considerations.

May 12, 2024

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Step-By-Step Guide to Build a DIY RAG Stack & Top 10 Considerations

Table of Contents

Do not index

RAG Stack, an acronym for Retrieval Augmented Generation, is a revolutionary approach that blends information retrieval and text generation. This cutting-edge technology enhances the outcomes for natural language processing by combining the best of both worlds. Imagine a system that not only generates realistic text but also understands the context of what it's generating. This blog post will delve into the purposes and benefits of RAG Stack and how it can revolutionize the way we interact with technology. Let's jump in!

What Is Retrieval Augmented Generation (RAG)?

RAG is an innovative AI framework designed to enhance the quality of Large Language Models (LLMs) by grounding them in external sources of knowledge. This grounding is achieved through two primary phases: retrieval and content generation. In the retrieval phase, algorithms search for and extract information relevant to a user's query from an external knowledge base. In the content generation phase, the LLM incorporates this retrieved information to produce a more accurate and informative response.

RAG has several key benefits.

The ability to minimize the chances of LLMs leaking sensitive data or generating incorrect information.

RAG reduces the need for continuous model retraining and updates, thereby lowering the computational and financial costs associated with running LLM-powered chatbots in an enterprise setting.

By enabling LLMs to go beyond their training data and access external knowledge sources, RAG enhances the model's ability to provide personalized and verifiable responses to user queries.

Examples of RAG Use Cases

RAG can be applied in various real-world scenarios to improve the accuracy and reliability of AI-powered systems. For instance, organizations can use RAG to enhance Customer Care chatbots by grounding them in verifiable content. In this scenario, the RAG framework enables the chatbot to provide employees with personalized answers based on relevant information extracted from internal documents and policies.

Enhancing Response Accuracy with RAG

RAG can help LLMs recognize questions they cannot answer by training them to pause and acknowledge when they lack the necessary information. In a challenging scenario where an employee seeks details about maternity leave policies, a chatbot leveraging RAG would refrain from providing inaccurate responses and instead admit to not having the specific information.

Leveraging External Knowledge for Personalized Responses

RAG serves as a powerful tool for enhancing the capabilities of LLMs and ensuring that they deliver accurate, reliable, and verifiable information to users. By leveraging external knowledge sources, LLMs can provide more personalized responses and adapt to ever-changing contexts effectively.

How Does Rag Work

Rag Llm

Rag Pipeline

Rag Rating

Rag Workflow

10 Essential Considerations When Constructing a RAG Stack

1. Data Access and Life Cycle Management

Managing the cycle from data acquisition to deletion is crucial. Start by connecting to various data sources and collecting data swiftly and accurately. Ensure every piece of data is processed, enriched, available, and archived or deleted when needed. Constant monitoring, updates, and maintenance are key for data integrity, security, business needs, and compliance standards.

2. Data Indexing & Hybrid Search

Creating searchable data representations and maintaining a large-scale index over time is complex. A hybrid search combines different methodologies for more relevant results, requiring continuous updates and refinement for accurate and current data retrieval.

3. Enterprise Security & Access Control at Scale

Security and Access Control Lists are vital for data management. Proper implementation of ACLs ensures user access to permitted resources based on roles. Regularly updating and maintaining ACLs in a dynamic index is essential for evolving organizational structures and roles.

4. Chat User Interface

Building an adaptable chat interface is a simple task; integrating it with value-added services is a challenge. Recommendations, next-best-action tasks, and autonomous automation need thoughtful integration for value addition.

5. Comprehensive System Interaction

Developing a system integrating indices, Large Language Models, and entailment checks is intricate. It requires accounting for diverse data types and sources to enhance response quality and relevance significantly.

6. Prompt Engineering

Creating clear and contextually rich prompts for accurate responses from Large Language Models is essential. Design prompts considering model capabilities, focusing on specificity to avoid imprecise answers, and integrating adaptive mechanisms for real-time feedback.

7. Chain of Reasoning

Moving beyond basic interactions, a chain of reasoning allows systems to engage in meaningful dialogue. It involves connecting multiple pieces of information logically to provide nuanced, contextual responses, making conversations more insightful.

8. Enterprise Integration

Integrating a RAG into an existing setup requires thorough interoperability assessments. SDKs and meticulous planning are essential for seamless interactions within the technological ecosystem, avoiding overwhelming users with more dashboards.

9. Continuous Operation

Continuous updates, upgrades, and enhancements are necessary for optimal performance and to adapt to evolving needs. Ongoing refinement is vital for sustained operational excellence, considering potential attrition risks among skilled developers.

10. Cost Considerations

Cost management is crucial for scaling technologies like LLMs within companies. Balancing operational costs, maintenance, updates, employee training, and support is necessary for long-term viability of the system.

Try our Serverless LLM Platform today to 10x your internal operations. Get started for free, no credit card required — sign in with Google and embark on your journey with us today!

What Is RAG LLM

RAG LLM Meaning

Rag Model Llm

Rag Use Case

Llm Tech Stack

Rag Fine Tuning

Rag Nlp

Rag Api

Rag Systems

Rag Evaluation

Rag Service

Rag Use Cases

Rag Software

RAG Architecture LLM

LLM Rag Meaning

RAG LLM Example

Step-By-Step Guide to Build a DIY RAG Stack

Installing Libraries for RAG Stack Development

To begin building a Retrieval-Augmented Generation (RAG) stack from scratch, the first step involves installing several essential libraries that simplify the process of accessing large language models, reranking models, and database connection methods. These libraries streamline the complexities associated with creating an extensive codebase to achieve desired results. The following libraries are pivotal for setting up a RAG stack

1. LlamaIndex

This library forms the core of the LLM/data framework, providing functionalities to connect various data sources like files, PDFs, and websites to both open-source (e.g., Llama) and closed (like OpenAI or Cohere) large language models. LlamaIndex abstracts complexities linked to data ingestion, RAG pipeline implementation, and the development of large language model (LLM) applications

2. PyMongo

This Python driver for MongoDB enables functionalities to connect to a MongoDB database, allowing the querying of data stored as documented in the database using different methods provided by the library

3. Data sets

This Hugging Face library offers access to a suite of data collections through specifying their path on the Hugging Face platform.

4. Pandas

This library facilitates the creation of data structures that enhance efficient data processing and modification in Python environments.

To install these libraries, you can use pip, a popular package manager for Python, with the following commands:


!pip install llama-index

!pip install llama-index-vector-stores-mongodb

!pip install llama-index-embeddings-openai

!pip install pymongo

!pip install datasets

!pip install pandas

Data Loading and OpenAI Key SetUp

Once you have the necessary libraries installed, the next step is to load the data and set up your OpenAI API key. This key is crucial to enable access to features like LLM models provided by OpenAI. Here's how you can set up your API key within the development environment:


Python

import os

os.environ["OPENAI_API_KEY"] = ""

Then, load the data within the development environment. In this example, we're using data sourced from the Hugging Face platform, specifically the Airbnb dataset available via MongoDB. This dataset contains Airbnb listings with property descriptions, reviews, and metadata alongside text and image embeddings for property descriptions and listing photos. After loading the data, you can convert it to a pandas DataFrame for further processing:


Python

from datasets import load_dataset

import pandas as pd

dataset = load_dataset("MongoDB/airbnb_embeddings")

dataset_df = pd.DataFrame(dataset['train'])

dataset_df.head(5)

Removing Text Embeddings

For the sake of demonstration, text-embedding fields must be removed from the original dataset to create a new embedding field tailored to the requirements specified by the LlamaIndex document configuration. To remove the 'text-embeddings' attribute from every data point in the dataset, you can use the following code snippet:


Python

dataset_df = dataset_df.drop(columns=['text_embeddings'])

These initial steps lay the groundwork for building a comprehensive RAG stack for various applications, like chatbot-like systems for responding to user inquiries.

Use ChatBees’ Serverless LLM to 10x Internal Operations

ChatBees focuses on optimizing RAG Stack for internal operations such as customer support, employee support, etc., providing the most accurate responses and seamlessly integrating into workflows in a low-code, no-code manner. Our agentic framework within ChatBees automatically selects the best strategy to enhance response quality for these specific use cases. This improvement in predictability and accuracy empowers operations teams to efficiently handle a higher volume of queries.

Key Features of ChatBees

Serverless RAG

Offering simplified, secure, and high-performing APIs to link various data sources (PDFs/CSVs, Websites, GDrive, Notion, Confluence) for immediate knowledge base search/chat/summarization. This does not require DevOps for deployment and maintenance of the service

Use Cases

ChatBees can be leveraged for various applications such as onboarding – granting quick access to onboarding materials and resources for customers or internal employees like support, sales, or research teams. It is beneficial for sales enablement to find product information and customer data quickly, customer support for swift and accurate responses to customer inquiries, and product & engineering for easy access to project data, bug reports, discussions, and resources, encouraging efficient collaboration.

Interested in optimizing your internal operations? Try our Serverless LLM Platform now to witness a 10x improvement in your operational efficiency. Get started for free without the need for a credit card – simply sign in with Google and embark on your journey with us today!