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.

You can check more info about: RAG Solution with AWS Bedrock.

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