Retrieval and ranking

Once data has been represented as vectors and we can measure similarity between them, we gain a powerful new capability. Instead of matching exact values or keywords, we can retrieve information based on meaning. This lesson exists to show how similarity scores become a practical tool for finding, ranking, and preparing relevant data, which is a core idea behind retrieval-augmented generation (RAG) systems.

Using similarity scores to find relevant data

Similarity scores allow us to ask a simple question: which stored items are closest to this query? We compare a query vector against a collection of vectors and look for the highest scores. Those scores act as signals of relevance rather than exact matches.

In practice, this means we can take a piece of text, compute its vector, and then identify which existing texts are most related. The result is a short list of candidates that are likely to be useful.

similarities = [
    (doc_id, cosine_similarity(query_vector, doc_vector))
    for doc_id, doc_vector in vectors.items()
]

Ranking items based on similarity

Once similarity scores are computed, ranking is a natural next step. We sort items by their scores so that the most relevant entries appear first. This ordering is often more important than the absolute score values themselves.

Ranking turns a large collection into an ordered list that can be processed from best to worst. Programs typically focus on the top results rather than everything.

ranked = sorted(similarities, key=lambda item: item[1], reverse=True)
top_matches = ranked[:3]

Retrieving text or records using vector similarity

After ranking, we retrieve the underlying data associated with the highest-scoring vectors. The vectors themselves are rarely the final output. Instead, they act as pointers to text, records, or other structured information.

This retrieval step bridges numerical computation and meaningful content. It is where vector math turns back into something a program can reason about or present.

retrieved_texts = [documents[doc_id] for doc_id, _ in top_matches]

Preparing retrieved data for further processing

Retrieved data is usually not used as-is. It is often cleaned, truncated, or combined before being passed downstream. The goal is to make the retrieved content easy for the next stage to consume.

In AI systems, this preparation step often shapes how effective later reasoning or generation will be.

context = "\n\n".join(retrieved_texts)

Understanding similarity-based retrieval as a building block for RAG

Similarity-based retrieval is not a complete system on its own. It is a building block that feeds relevant information into other components. In RAG systems, retrieved content is provided as context to a language model so it can produce grounded, informed outputs.

At this stage, we are not building RAG itself. We are establishing the mental model that similarity search plus retrieval is the foundation it rests on.

Conclusion

We have seen how similarity scores move from abstract numbers to practical retrieval and ranking. By using vectors to find relevant data, ordering results by similarity, and preparing retrieved content for further use, we now have a clear picture of how similarity underpins retrieval-based workflows. This orientation is enough to recognize and reason about RAG systems when they appear later.