semantic-search

Semantic Search Example

This demonstrates a simple embeddings search application with Amazon Titan Embeddings, LangChain, and Streamlit.

The example matches a user’s query to the closest entries in an in-memory vector database. We then display those matches directly in the user interface. This can be useful if you want to troubleshoot a RAG application, or directly evaluate an embeddings model.

For simplicity, we use the in-memory FAISS database to store and search for embeddings vectors. In a real-world scenario at scale, you will likely want to use a persistent data store like the vector engine for Amazon OpenSearch Serverless or the pgvector extension for PostgreSQL.

Contents

The example consists of four files: A Streamlit application in Python, a supporting file to make calls to Bedrock, a requirements file, and a data file to search against.

Setup

From the command line, run the following in the code folder:

pip3 install -r requirements.txt -U

Running

From the command line, run the following in the code folder:

streamlit run search_app.py

You should now be able to access the Streamlit web application from your browser.

Try a few prompts from the web application:

• How can I monitor my usage?
• How can I customize models?
• Which programming languages can I use?
• Comment mes données sont-elles sécurisées ?
• 私のデータはどのように保護されていますか？
• Quais fornecedores de modelos estão disponíveis por meio do Bedrock?
• In welchen Regionen ist Amazon Bedrock verfügbar?
• 有哪些级别的支持？

Note that even though the source material was in English, the queries in other languages were matched with relevant entries.