What is AWS Bedrock?
AWS Bedrock is Amazon's fully managed service that provides access to foundation models from leading AI companies through a single API. It enables developers to build generative AI applications without managing infrastructure.
Key Features
- Multiple foundation models
- No infrastructure management
- Enterprise security
- Pay-per-use pricing
Available Models
- Claude (Anthropic)
- Llama 2 (Meta)
- Stable Diffusion (Stability AI)
- Amazon Titan
How to Build a RAG System on Bedrock
RAG (Retrieval Augmented Generation) combines the power of LLMs with your custom data. Here's a step-by-step guide to building a RAG system with AWS Bedrock:
1Prepare Your Data
Start by organizing your documents (PDFs, text files, etc.) and cleaning the data for optimal processing.
# Example: Load and prepare documents
from langchain.document_loaders import DirectoryLoader
loader = DirectoryLoader('./documents')
documents = loader.load()2Create Embeddings
Use Bedrock's embedding models to convert your text into vector representations.
# Create embeddings using Bedrock
bedrock = boto3.client('bedrock-runtime')
embeddings = BedrockEmbeddings(client=bedrock)
vectors = embeddings.embed_documents(texts)3Store in Vector Database
Store embeddings in a vector database like Amazon OpenSearch or Pinecone for efficient retrieval.
# Store in vector database
from langchain.vectorstores import OpenSearchVectorSearch
vectorstore = OpenSearchVectorSearch.from_documents(
  documents, embeddings, opensearch_url
)4Query and Generate
Use Bedrock's LLMs to generate responses based on retrieved context.
# Query and generate response
llm = Bedrock(model_id="anthropic.claude-v2")
qa_chain = RetrievalQA.from_chain_type(
  llm=llm, retriever=vectorstore.as_retriever()
)
response = qa_chain.run(query)AWS Bedrock Use Cases
Customer Support
Build intelligent chatbots that understand context and provide accurate responses using your knowledge base.
Code Generation
Generate code snippets, documentation, and technical explanations based on your codebase.
Content Creation
Create marketing content, reports, and summaries tailored to your brand voice and style.
AWS Bedrock vs OpenAI
| Feature | AWS Bedrock | OpenAI |
|---|---|---|
| Model Variety | Multiple providers | GPT models only |
| Enterprise Features | ✓ VPC, IAM, compliance | Limited |
| Pricing | Pay-per-use | Subscription + usage |
| Data Privacy | Your data stays private | Opt-out required |
| Integration | Native AWS services | API only |
Written by Soleyman Shahir
#1 Cloud Engineering Educator
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