The Good Tech Companies - Speedrun Your RAG: Build an AI Recommender for your Steam Library

Episode Date: October 1, 2025

This story was originally published on HackerNoon at: https://hackernoon.com/speedrun-your-rag-build-an-ai-recommender-for-your-steam-library. Build a Steam game retriever with Superlinked and LlamaIndex. Use the official SuperlinkedRetriever to add fast, accurate RAG search to your app. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai, #artificial-intelligence, #gaming, #rag, #machine-learning, #hackernoon-top-story, #good-company, #ai-recommender-steam-library, and more. This story was written by: @superlinked. Learn more about this writer by checking @superlinked's about page, and for more stories, please visit hackernoon.com. Custom retrievers give you control over domain context, metadata, and ranking logic. They outperform generic similarity search when queries are messy or jargon heavy. Superlinked combines multiple text fields into one semantic space and runs queries in memory for snappy results. LlamaIndex provides the clean retriever interface and plugs straight into query engines and response synthesis. There is an official Superlinked retriever integration for LlamaIndex that you can import and use.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. Speed run your rag. Build an AI recommender for your Steam library. By Superlinked, a smarter, faster way to search your Steam library. You know the feeling. You search for a game that is strategic, co-op, maybe with a sci-fi theme. You get a wall of titles that sort of match. What you wanted was a shortlist that really captures the vibe behind your words. In this guide, we show how to build exactly that by pairing superlinked with llama index. The result is a custom steam game retriever that understands genre plus description plus tags and serves answers in milliseconds. Want to see this on your data with real

queries and latency numbers? Get in touch. TLDR custom retrievers give you control over domain context, metadata, and ranking logic. They outperform generic similarity search when queries are messy or jargon heavy. Superlink combines multiple text fields into one semantic space and runs queries in memory for snappy results. Lama Index provides the clean retriever interface and plugs straight into query engines and response synthesis. There is an official superlink retriever integration for Lama Index that you can import and use. See below. Official Superlink integrates with Lama Index through the official listed on Lama Hub, so you can add superlink to your existing Lama Index stack with a simple install and then plug it into A.

Learn more on the official integration page. The class and constructor parameters are documented in the Lama Index API reference. Prefer to build it by hand or customize the logic further? Read on why superlink plus Lama Index? The goal is simple. Take superlinked strengths for multi-field retrieval and package them so developers can adopt and extend in real rag systems. Superlinkhead helps you define expressive vector spaces and queries that mix fields like name, description, and genre into a single semantic view. Lama index brings the retrieval abstraction, query engines, and response synthesis that slot into app sand agents with minimal glue. You can also follow along in Google CoLab using the same building blocks from the superlinked notebooks.

Why custom retrievers matter. One, tune 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 the 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 science. Wi-Fi elements won't get far with text-only matching. 3. Custom filtering and ranking logic. Sometimes you want to apply your own rules to how things are scored or filtered.

Maybe you want to prioritize newer content or penalize results that don't meet certain quality thresholds. I mean, having that kind of control is like giving your retriever an actual brain. It can reason through relevance instead of just relying on vector distances. 4. Performance gains. Let's be real. General purpose solutions are built to work okay for everyone, not great for you. If you know your data and your access patterns, you can fine-tune your retriever to run faster,

rank better, and reduce unnecessary noise in the results. Implementation Breakdown. Part 1. Core dependencies and imports the import structure reveals our hybrid approach. Lama Index Core provides the retrieval abstraction layer. Super-length framework handles vector computation and semantic search. PANDAS Manages data pre-processing in manipulation. Part 2. Understanding L-L-L-A-M-A-I-N-D-E-X custom retrievers before diving into our superlinked implementation, it's crucial to understand how LAM-Dex's custom retriever architecture works and why it's so powerful for building domain-specific rag applications. B-A-S-E-R-E-R-E-R abstraction Lama Index provides an abstract class that serves as the foundation

for all-retrieval operations. The beauty of this design lies in its summary. simplicity, any custom retriever only needs to implement one core method. The mode here is the presence of the retrieval protocol from the Lama Index. Ask this, Retrieval Protocol, makes it easy to plug in different backends or strategies without having to touch the rest of your system. Let's break it down in what's exactly as going on. One input. This is the query object passed into your retriever.

At minimum, it contains the user's raw query string, eG, sci-fi strategy games. But it can also include extra metadata like filters, embeddings, or user preferences. Basically, anything that might help shape a more relevant response. 2. Output. The Retriever returns a list of nodes. These are your chunks of content, documents, or data entries, each paired with a relevant score. The higher the score, the more relevant the node is to the query.

This list is what gets passed downstream to the LLM or other post-processing steps. As in our case, we are plugging on the 3. Processing Backend agnostic here's the cool part. How you get from query to result is totally up to you. You can use a vector database, a traditional search engine, a Rest API, or even something handcrafted for your specific use case. This decouples logic and gives you full control over the retrieval stack. Why this matters, this abstraction is clean and powerful. It means you can combine multiple strategies, use dense vector search and keyword filter

together if needed. Run A, B tests easily. Compare different retrievers to see what gives better results for your users. Plug into any agent or tool. Whether you're building a chatbot, a search UI, or a full-blown agent system, this retriever interface slots in easily. Think of the retrieval protocol as the API contract between your retrieval brain and everything else. Once you follow it, you're free to innovate however you want behind the scenes. Plugging S-U-P-E-R-L-I-N-K-E. into L-L-L-A-M-A-I-N-D-E-X all right. So the class is basically our go-to tool for giving smart, semantic game recommendations. We'll start with a quick look at how it's put together, and then later dive deeper into

each part to really see what makes this thing tick. First up is the schema definition, think of it as the foundation. It's what keeps everything organized and reliable. Using superlinkids, we lay out key details like, and this keeps all the game info clean and consistent, and it plugs right into superlinked's pipelines so everything flows smoothly. Next up is the text similarity space. This is where the magic of semantic search happens. It uses the model to turn a bunch of game info, like the name, description, genre, etc., into dense vector representations. Basically, it smushes all that text together into something the model

can understand. The cool part? This lets the retriever understand what a user means, not just what words they type. So if someone searches for something like Open World Adventure, it can find games that actually fit that vibe, not just ones with those exact words. The combined text field is where things really start to click. It takes different bits of info, like the game's name, description, genre, and more and S mashes them into one big chunk of text. This gives the model a fuller picture of each game when turning it into vectors. The result? Way better recommendations, since it's pulling in a bunch of different details all at once instead of just looking at one thing in isolation. And finally, in memory execution is what makes everything super snappy. Thanks to

superlinkids, the retriever can handle queries in real time, no delays, just instant results. That means whether someone's hunting for a specific genry or just browsing for something new to play, they get fast and accurate recommendations without waiting around. Put all these pieces together, and you've got the, a solid setup for delivering game recommendations that actually make sense for the user. It's fast, smart, and personal. Here's what the full thing looks like in action. Integration architecture deep dive part three. S-U-P-R-L-I-N-K-E-D schema definition and setup now is the time when we go a bid deep dive on certain thing. Starting with schema design, now in superlinked, the schema isn't just about defining data types, it's more like a formal definition

between our data and the underlying vector compute engine. This schema determines how our data gets parsed, indexed, and queried, so getting it right is crucial. In R, the schema is defined like this. Let's break down what some of these elements actually does. Right pointing arrow. Think of this as our primary key. It gives each game a unique identity and allows superlink to index and retrieve items efficiently. I mean basically it's about how we are telling the superlink to segregate the unique identify of the games, and by the way it's especially important when you're dealing with thousands of records. And now these aren't just type hints. They enable Superlink to optimize operations differently depending on the field. For instance, fields can be embedded and compared semantically, while fields

can support numeric filtering or sorting. This is the semantic anchor of our retriever. It's a synthetic field where we can catenate the game name, description, genre, and other relevant attributes into a single block of text. This lets us build a single text similarity space using sentence transformer embeddings. Why do this? Because users don't just search by genre our name, they describe what they're looking for. By embedding all the important signals into, we can better match fuzzy, natural language queries with the most relevant games. Part 4. Vector space configuration to power the semantic search over our Steam Games dataset. I made two intentional design choices that balance performance, and flexibility. First, for the embedding model, I selected from the sentence transformers library.

This model produces 768 dimensional embeddings that strike a solid middle ground. They're expressive enough to capture rich semantic meaning, yet lightweight enough to be fast in production. I mean it's a reliable general purpose model, known to perform well across diverse text types, which matters a lot when your data ranges from short genre tags to long form game descriptions. In our case, I know I needed a model that wouldn't choke on either end of that spectrum, and handled it cleanly. Next, although superlinked supports multi-space indexing, where you can combine multiple fields or even modalities, like text plus images. I deliberately kept things simple with a single. I would have included the in here too, but I don't have the information on the release date for the games.

But just to put this out there, if we have the release date information, I could plug in the recency space here, and I can even sort the games with the along with the recency of the games. Cool. Part 5. Data Pipeline and Executor Setupad the heart of our retrieval system is a streamlined pipeline built for both clarity and speed. I start with the, which serves as our ETL layer. It ensures that each field in the dataset is correctly typed and consistently mapped to our Schema, essentially acting as the contract between our raw CSV data and the superlink indexing layer. Once the data is structured, I feed it into an, which is ideal for datasets that comfortably fit in memory. This approach keeps everything lightning fast without introducing storage overhead or network latency. Finally, the Queriesaire handled by N, which is optimized for sub-millimeter latency. This is what makes SuperLink suitable for real-time applications like interactive recommendation systems, where speed directly impacts user experience. Part 6. The Retrieval Engine, one of the things that makes Superlink genuinely enjoyable to work with

is its fluent style query builder. If you've used libraries like SQL Alchemy or Django ORM, the pattern will feel familiar. Each method in the chain adds clarity instead of clutter. In our case, the query starts by selecting the relevant index and defining the similarity search using the method, which computes cosine similarity in the embedding space. This is what allows us to retrieve smantically close games based on the user's natural language query. Another thoughtful design decision I made was to explicitly select the fields I care about in the result set, rather than doing something like. This might sound minor, but it keeps the data lean,

reduces processing overhead, and ensures we're not passing around unnecessary payload during post-processing. Think of it as precision over bulk, especially important when you're moving data between components in a latency-sensitive pipeline. Part 7. Result processing in node creation now once we receive the results from superlinked, I transformed them into a format that plays well with Lama Index. First, I construct a human-readable text string by combining the game's name with its short description. This becomes the content of each node, making it easier for the language model to reason about. It's a small touch, but it really improves how relevant and understandable the retrieved data is when passed to the LLM. Next, I make sure that all original fields from the dataset, including things

like genre, pricing, and game details, are retained in the metadata. This ice crucial because downstream processes might want to filter, display, or rank results based on this information. I don't want to lose any useful context once we start working with the retrieved nodes. Finally, I apply a lightweight score normalization strategy. Instead of relying on raw similarity scores, we assign scores based on the position of the result in the ranked list. This keeps things simple and consistent. The top result all ways has the highest score, and the rest follow in descending order. It's not fancy, but it gives us a stable and interpretable scoring system that works well across different queries. Showtime,

executing the pipeline. Now that all components are in place, it's time to bring our retrieval augmented generation, RAG, system to life. Below is the end-to-end integration of superlinked and Lama index in action. This setup combines our custom semantic retriever with an LLM-powered response generator. Queries move smoothly through the pipeline, and instead of just spitting out raw data, It returns a thoughtful suggestion on what kind of game the user might actually want to play based on what they asked. Takeaways, custom retrievers let you bake domain rules and jargon into the system. Combining multiple text fields into one index improves query understanding. In Lama Index you only need to implement underscore retrieve for a custom back end.

Superlinked in memory executor gives real-time latency on moderate datasets. Schema choice matters for clean parsing and mapping. Simple position-based scoring is a stable default when you want predictable ranks. If you want a quick chat about where mixture of encoders or multi-field retrieval fits in your pipeline, talk to one of our engineers. Integration references. Superlinked retriever package on Pippi and Lama Index docs for custom retrievers. Contributors.

Vipel Maheshwary, author, Philip McRagulai, editor. Thank you for listening to this Hackernoon story, read by artificial intelligence. Visit hackernoon.com to read, write, learn and publish.