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docs: fix extra colon in documentation (#1653)

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qdrant-landing/content/documentation/search-precision/reranking-semantic-search.md
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@@ -158,7 +158,7 @@ co = cohere.Client("<API-KEY>")
### Creating a Collection
A collection is basically a named group of points (vectors with data) that you can search through. All the vectors in a collection need to have the same size and be compared using one distance metric. Here’s how to create one::
A collection is basically a named group of points (vectors with data) that you can search through. All the vectors in a collection need to have the same size and be compared using one distance metric. Here’s how to create one:
```jsx
client.create_collection(
@@ -371,4 +371,4 @@ As you can see, the reranking did its job. Positions for documents 10 and 1 got
Reranking is a powerful way to boost the relevance and precision of search results in RAG systems. By combining Qdrant’s vector search capabilities with tools like Cohere’s Rerank model or ColBERT, you can refine search outputs, ensuring the most relevant information rises to the top.
This guide demonstrated how reranking enhances precision without sacrificing recall, delivering sharper, context-rich results. With these tools, you’re equipped to create search systems that provide meaningful and impactful user experiences. Start implementing reranking to take your applications to the next level!
This guide demonstrated how reranking enhances precision without sacrificing recall, delivering sharper, context-rich results. With these tools, you’re equipped to create search systems that provide meaningful and impactful user experiences. Start implementing reranking to take your applications to the next level!