This repo shows how to leverage Elastic search capabilities (both text and vector ones) togheter with Google Cloud's GenerativeAI model (Gemini-pro) and VertexAI features to create a new retail experience. With this repo you will:
- Create a python streamlit app with an intelligent search bar
- Integrate with Gemini models and VertexAI APIs
- Configure an Elastic cluster as a private data source to build context for LLMs
- Ingest data from multiple data sources (Web Crawler, files, BigQuery)
- Use Elastic's text_embeddings and vector search for finding relevant content
- and more...
too see details around how to configure all the components have a look at this repo here. It refers to the Palm2 version of this demo, but it's still valid because only a few lines regarding LLM model call/init change.
Try queries like:
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"List the 3 top paint primers in the product catalog, specify also the sales price for each product and product key features. Then explain in bullet points how to use a paint primer". You can also try asking for related urls and availability --> leveraging private product catalog + public knowledge
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"could you please list the available IKEA stores in UK" --> --> it will likely use (crawled docs)
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"Which are the ways to contact IEKA customer support in the UK? What is the webpage url for customer support?" --> it will likely use crawled docs
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Please provide the social media accounts info from the company --> it will likely use crawled docs
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Please provide the full address of the Manchester store in UK --> it will likely use crawled docs
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are you offering a free parcel delivery? --> it will likely use crawled docs
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Could you please list my past orders? Please specify price for each product --> it will search into BigQuery order dataset
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Which product are available in the product catalog for the Bathroom category? Give a short description of each product
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List all the items I have bought in my order history in bullet points
After uploading the image (find some in the samples/images folder) try queries like this one:
- What's in the image? Do you have similar products in your catalog? If yes list them with descriptions and prices