Retrieval-Augmented Generation (RAG) Application using AWS Bedrock, Titan Model, and the FastAPI

Imagine having a system that understands your questions in context, retrieves relevant information, and then uses that knowledge to craft insightful responses. This is the power of Retrieval-Augmented Generation (RAG) applications, and building one can be a fascinating journey that delves into the world of Artificial Intelligence (AI) and Large Language Models (LLMs). This article will guide you through the exciting process of creating an RAG application from scratch, focusing on the core concepts and functionalities. We’ll delve into the theoretical aspects of RAG, and explore its implementation using Python’s FastAPI framework, AWS Bedrock for knowledge base management, and the Titan model for text embedding.

Understanding Retrieval-Augmented Generation (RAG)

RAG is a two-stage approach that combines the efficiency of information retrieval with the creativity of language models. Here’s a breakdown of the process:

1. Retrieval:

• The system receives a user query or input.
• An?embedding model?converts the query and stored documents (articles, products, etc.) into dense numerical vectors. These vectors capture the semantic meaning of the text.
• A?similarity search?algorithm like cosine similarity then identifies documents with vector representations closest to the query vector. This retrieves a set of potentially relevant documents.

2. Augmented Generation:

• The retrieved documents are fed into a?large language model (LLM)?like Titan.
• The LLM analyzes the retrieved documents and user queries to understand the context.
• Based on this understanding, the LLM generates a new text response, such as a product recommendation, a summary of relevant information, or a continuation of a story.

Benefits of RAG:

• Improved Relevance:?By combining retrieval and generation, RAG offers more relevant recommendations compared to pure retrieval systems.
• Factual Accuracy:?The retrieved documents provide a factual foundation for the generated text, enhancing accuracy.
• Novelty and Creativity:?LLMs can generate creative and novel responses that go beyond simply regurgitating retrieved information.

Building a RAG Application with FastAPI, AWS Bedrock, and Titan

Here’s a step-by-step walkthrough of building a recommendation engine using the chosen technologies:

1. Data Preparation:

• Gather a corpus of text data relevant to your recommendation domain (e.g., product descriptions, articles).
• Preprocess the data by cleaning, tokenizing, and normalizing the text.

2. Embedding Model Selection:

• Choose a pre-trained embedding model like Amazon Titan Embeddings G1 — Text. These models map text to dense vectors that capture semantic similarity.

3. Knowledge Base Setup with AWS Bedrock:

• Utilize AWS Bedrock, a managed service for building and managing large-scale knowledge bases.
• Index your preprocessed text data into the Bedrock knowledge base. This allows for efficient retrieval based on semantic similarity.

4. FastAPI Framework:

• Employ FastAPI, a Python framework for building high-performance APIs.
• Develop API endpoints that accept user queries and return recommendations.

5. Cosine Similarity Search:

• Implement cosine similarity search within the FastAPI application.
• Calculate the cosine similarity between the query vector and document vectors in the Bedrock knowledge base during a query.
• Retrieve a set of top-ranked documents with the highest cosine similarity scores.

6. Titan LLM Integration:

• Integrate the Titan LLM into your FastAPI application. Titan is a powerful generative pre-trained transformer model capable of text summarization, question answering, and creative writing.
• Pass the retrieved documents and the user query to Titan for context understanding.

7. Recommendation Generation:

• Based on the provided context, Titan generates the final recommendation text.
• This could be a product suggestion with justifications based on retrieved documents, a concise summary of relevant information, or a continuation of a story that aligns with the user’s query and retrieved content.

8. Deployment:

• Deploy your FastAPI application to a cloud platform for scalability and accessibility.

Cosine Similarity and Retrieval

Cosine similarity is a mathematical concept used to measure the similarity between two vectors. In the context of RAG, cosine similarity is employed during the retrieval stage.

Here’s how it works:

1. User Query Embedding:?The user’s query is preprocessed and converted into a vector using the Titan model.
2. Knowledge Base Embeddings:?Each document within the knowledge base already has a corresponding vector representation generated during the embedding generation stage.
3. Cosine Similarity Calculation:?The cosine similarity between the user query vector and each document vector in the knowledge base is calculated. This value ranges from 0 to 1, where 1 indicates perfect similarity.
4. Retrieval Based on Similarity:?Documents with the highest cosine similarity scores are considered the most relevant to the user’s query and are retrieved for further processing by the LLM.

AI and LLM in Action

The power of RAG lies in the interplay of two key AI concepts:

• Embedding Models:?These models bridge the gap between text and numerical representations. Converting text into vectors enables efficient similarity calculations between documents and queries. In our example, The AWS Titan Embeddings G1 encodes the user query and documents from the Bedrock knowledge base into vectors. This allows us to find documents semantically close to the user’s interest.
• Large Language Models (LLMs):?LLMs like Titan are trained on massive datasets of text and code, enabling them to understand and generate human-quality text. In the RAG context, Titan leverages the retrieved documents from Bedrock to grasp the relevant domain and user intent. This understanding guides Titan in crafting an informative recommendation response.

Conclusion

As AI technology continues to evolve, the potential applications of RAG are vast and varied. From personalized product recommendations to sophisticated virtual assistants, RAG can revolutionize how we interact with information and machines. By following the outlined steps and leveraging these cutting-edge tools, developers can create systems that understand user intent and provide insightful and creative responses that meet and exceed expectations.

Written By SajilKumar