Merge pull request #563 from superlinked/fm1320-patch-1

Update custom_retriever_with_llamaindex.md

Author: fm1320

docs/articles/custom_retriever_with_llamaindex.md

@@ -1,19 +1,21 @@
 # How We Approached Building a Custom Steam Games Retriever with Superlinked and LlamaIndex
 
-The goal was simple: take Superlinked's core strengths in handling complex, multi-modal retrieval scenarios and package them in a way that developers can easily adopt and extend in their own RAG systems.
-
 ## Why Superlinked + LlamaIndex?
 
+The goal was simple: take Superlinked's core strengths in handling complex, multi-modal retrieval scenarios and package them in a way that developers can easily adopt and extend in their own RAG systems.
+
 Superlinked excels at creating sophisticated vector spaces through its mixture of encoders approach, allowing you to combine multiple embedding models, apply custom weighting schemes, and handle complex multi-modal data with ease. LlamaIndex, on the other hand, provides the robust infrastructure for RAG applications, from document processing and node management to query engines and response synthesis.
+
 As retrieval-augmented generation (RAG) systems continue to evolve, the need for **custom, domain-specific retrievers** is becoming more and more obvious. Sure, traditional vector databases are great for basic similarity search but the moment you throw in more complex, context-heavy queries, they start to fall short. Especially when you're working with real-world data that needs richer filtering or semantic understanding.
-In this guide, we'll show you our approach for building a custom LlamaIndex retriever that leverages Superlinked's mixture of encoders architecture. We've refined this approach through numerous production deployments, and now we're making it available for the broader developer community.
 
-You can follow allong this guide in colab: 
+You can follow allong this guide in a colab notebook: 
 - [Google Colab of this guide](https://colab.research.google.com/github/superlinked/VectorHub/blob/main/docs/assets/use_cases/custom_retriever_with_llamaindex/superlinked_custom_retriever_with_llamaindex.ipynb)
 
 If you prefer to start using Superlinked's retriever right away you can have a look at the full implementation with Llamaindex:
 - [Link to full offical integration on Llamahub](https://links.superlinked.com/llama_hub_in_article)
 
+In this guide, we'll show you our approach for building a custom LlamaIndex retriever that leverages Superlinked's mixture of encoders architecture. We've refined this approach through numerous production deployments, and now we're making it available for the broader developer community.
+
 ## Why Custom Retrievers Matter
 1. **Tuned for Your Domain** – Generic retrievers are fine for general use, but they tend to miss the subtle stuff. Think about jargon, shorthand, or domain-specific phrasing—those don’t usually get picked up unless your retriever knows what to look for. That’s where custom ones shine: you can hardwire in that context.
 2. **Works Beyond Just Text** – Most real-world data isn’t just plain text. You’ll often have metadata and tags too. For example, in a game recommendation system, we don’t just care about the game description—we also want to factor in genres, tags, user ratings, and more. Think about this logic: someone searching for a “strategy co-op game with sci-fi elements” won’t get far with text-only matching.