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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -137,7 +137,7 @@ It defines an index flow like this:
| [Docs to Knowledge Graph](examples/docs_to_knowledge_graph) | Extract relationships from Markdown documents and build a knowledge graph |
| [Embeddings to Qdrant](examples/text_embedding_qdrant) | Index documents in a Qdrant collection for semantic search |
| [FastAPI Server with Docker](examples/fastapi_server_docker) | Run the semantic search server in a Dockerized FastAPI setup |
| [Product_Taxonomy_Knowledge_Graph](examples/product_taxonomy_knowledge_graph) | Build knowledge graph for product recommendations |
| [Product Recommendation](examples/product_recommendation) | Build real-time product recommendations with LLM and graph database|
| [Image Search with Vision API](examples/image_search_example) | Generates detailed captions for images using a vision model, embeds them, enables live-updating semantic search via FastAPI and served on a React frontend|

More coming and stay tuned 👀!
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# Build Real-Time Product Recommendation based on LLM Taxonomy Extraction and Knowledge Graph
# Build Real-Time Recommendation Engine with LLM and Graph Database

We will process a list of products and use LLM to extract the taxonomy and complimentary taxonomy for each product.
We will build a real-time product recommendation engine with LLM and graph database. In particular, we will use LLM to understand the category (taxonomy) of a product. In addition, we will use LLM to enumerate the complementary products - users are likely to buy together with the current product (pencil and notebook).

We will use Graph to explore the relationships between products that can be further used for product recommendations or labeling.

Please drop [CocoIndex on Github](https://github.com/cocoindex-io/cocoindex) a star to support us and stay tuned for more updates. Thank you so much 🥥🤗. [![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)

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