Skip to main content

End-to-End RAG Solution with AWS Bedrock and LangChain

 In this blog, we will dive deep into the Retrieval–Augmented Generation (RAG) concept and explore how it can be used to enhance the capabilities of language models. We will also build an end–to–end application using these concepts. Let’s understand about RAG is, its use cases, and its benefits. Retrieval–augmented generation (RAG) is a process of optimizing the output of a large language model so it references an authoritative knowledge base outside of its training data source before generating a response. In–shot RAG is a technique for enhancing the accuracy and reliability of generating an AI model with facts fetched from external sources. I will explain how to create a RAG application to query your own PDF. For this, we will leverage aws bedrock Llama 3 8B Instruct model, LangChain framework and streamlit.

Key Technologies

1. Streamlit:a. Interactive front–end for the application.b. Simple yet powerful framework for building Python webapps.

2. LangChain:a. Framework for creating LLM–powered workflows.b. Provides seamless integration with AWS Bedrock.

3. AWS Bedrock:a. State–of–the–art LLM platform.b. Powered by the highly efficient Llama 3 8B Instruct model.

Let’s get started. The implementation of this application involves three

components.


1. Create a vector store

Load–>Transform–>Embed.

We will use the FAISS vector database, to efficiently handle queries,

the text is tokenized and embedded into a vector store using FAISS

(Facebook AI Similarity Search).


2. Query vector store “Retrieve most similar”

The way to handle this at query time, embed the unstructured

query and retrieve the embedding vector that is most similar to the embedded query. A vector stores embed data and performs a

vector search for you.


[ Good Read: AI in Healthcare ]


3. Response generation using LLM:

Imports and Setup

  1. os: Used for handling file paths and checking if files exist on disk.

  2. pickle: A Python library for serializing and deserializing Python objects to store/retrieve embeddings.

  3. boto3: AWS SDK for Python; used to interact with Amazon Bedrock services.

  4. streamlit: A library for creating web apps for data science and machine learning projects.

  5. Bedrock: Used to interact with Amazon Bedrock for deploying large language models (LLMs).

  6. Bedrock Embeddings: To generate embeddings using Bedrock models.

  7. FAISS: A library for efficient similarity search and clustering of dense vectors.

  8. Recursive Character Text Splitter: Splits large text into manageable chunks for embedding generation.

  9. Pdf Reader: From PyPDF2, used to extract text from PDF files.

  10. Prompt Template: Defines the structure of the prompt for the LLM.

  11. Retrieval QA: Combines a retriever and a chain to create a question–answering system.

  12. Stuff Documents Chain: Combines multiple documents into a single context for answering questions.

  13. LLM Chain: A chain that interacts with a language model using a defined prompt.

  14. Initialize Bedrock and Embedding Models

  15. Initializes an Amazon Bedrock client using boto3 to interact with Bedrock services.

Initializes the bedrock titan embedding model amazon.titan–embed–text–v1 for generating vector embeddings of text.


Location: United States

Comments

Post a Comment

Popular posts from this blog

Cloud Data Warehouses vs. Data Lakes: Choosing the Right Solution for Your Data Strategy

In today’s data-driven world, companies rely on vast amounts of data to fuel business intelligence, predictive analytics, and decision-making processes. As businesses grow, so do their data storage needs. Two popular storage solutions are cloud data warehouses  and data lakes . While they may seem similar, these technologies serve distinct purposes, each with unique advantages and challenges. Here’s a closer look at the key differences, advantages, and considerations to help you decide which one aligns best with your data strategy. What Are Cloud Data Warehouses? Cloud data warehouses are designed for structured data and are optimized for analytics. They allow businesses to perform fast, complex queries on large volumes of data and produce meaningful insights. Popular cloud data warehouses include solutions like Amazon Redshift, Google BigQuery , and Snowflake. These tools enable companies to store, query, and analyze structured data, often in real-time, which can be incredibly use...
Post a Comment
Read more

Cloud Security Posture Management – How to Stay Compliant

  Cloud computing has become the backbone of modern business operations. Organizations are increasingly migrating their workloads, applications, and data to the cloud to leverage its scalability, flexibility, and cost-efficiency. However, with this shift comes a new set of challenges, particularly in ensuring cloud data protection, security, and compliance of cloud environments. This is where Cloud Security Posture Management (CSPM) comes into play.   CSPM is a critical component of cloud security that helps organizations identify and remediate risks, enforce compliance, and maintain a strong security posture in their cloud infrastructure. In this blog, we’ll explore what CSPM is, why it’s essential, and how organizations can use it to stay compliant with industry regulations and standards.   What is Cloud Security Posture Management (CSPM)? Cloud Security Posture Management (CSPM) refers to a set of tools, processes, and practices designed to continuously monitor, assess...
Post a Comment
Read more

Optimizing Cloud Spending: The Synergy Of DevOps And FinOps

In the rapidly growing field of cloud computing, managing expenses continues to be a challenge for businesses of all sizes. As organizations increasingly engage with cloud services, efficient management of cloud spend becomes an even more important responsibility. In this blog, we will explore how collaboration between DevOps and FinOps practices can lead to significant cost savings and increased operational efficiency. The Rise of Cloud Computing One of the major technological innovations that have changed the way organizations operate over recent years is the rise of cloud computing. Cloud computing has brought about a complete transformation in the way businesses operate making rapid scaling, high flexibility and maintaining cost-effectiveness for them, unlike traditional on-premises solutions that struggle to keep up with the growing demand. On the other hand, the cloud services billing model has its drawbacks. For example, it can lead to uncontrolled costs if the users don’t handl...
Post a Comment
Read more