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fix all course links

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kanungle
2026-03-16 23:04:33 -07:00
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qdrant-landing/content/course/essentials/day-1/chunking-strategies.md
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@@ -450,7 +450,7 @@ In Qdrant, this metadata lives in the **payload** - a JSON object attached to ea
### What Metadata Enables
**Disclaimer**: For performance reasons, filterable fields must be indexed using the [Payload Index](https://qdrant.tech/documentation/concepts/indexing/#payload-index).
**Disclaimer**: For performance reasons, filterable fields must be indexed using the [Payload Index](/documentation/concepts/indexing/#payload-index).
**1. Filtered Search (Exact Match)**
You can filter results based on exact metadata values, which is perfect for categorical data.
qdrant-landing/content/course/essentials/day-1/distance-metrics.md
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@@ -165,4 +165,4 @@ If you are training your own model or designing custom features, use these guide
* **Dot product** accounts for magnitude and direction.
4. **Experiment:** Qdrant allows you to set distance metrics per named vector, making it easy to A/B test different metrics on your specific data.
Reference: [Distance Metrics in Qdrant Documentation](https://qdrant.tech/documentation/concepts/search/#metrics)
Reference: [Distance Metrics in Qdrant Documentation](/documentation/concepts/search/#metrics)
qdrant-landing/content/course/essentials/day-1/embedding-models.md
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@@ -170,7 +170,7 @@ Choosing the right embedding source is a critical decision that balances cost, p
### 1. On-Premise, Optimized: FastEmbed by Qdrant
[FastEmbed](https://qdrant.tech/documentation/fastembed/) is Qdrant's optimized embedding solution designed for on-premise, high-speed generation with minimal dependencies. It delivers low-latency, CPU-friendly embedding generation using quantized model weights and ONNX Runtime, making it up to 50% faster than traditional PyTorch-based models while maintaining competitive accuracy.
[FastEmbed](/documentation/fastembed/) is Qdrant's optimized embedding solution designed for on-premise, high-speed generation with minimal dependencies. It delivers low-latency, CPU-friendly embedding generation using quantized model weights and ONNX Runtime, making it up to 50% faster than traditional PyTorch-based models while maintaining competitive accuracy.
The default model for standalone use, [`BAAI/bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5), is lightweight at ~67MB compared to 300MB+ for many Hugging Face models. While the `qdrant-client` integration allows you to specify any compatible model, using the default is a great way to get started quickly.
@@ -233,7 +233,7 @@ While vectors capture the essence of data, payloads hold structured metadata for
Payloads can store textual data (descriptions, tags, categories), numerical values (dates, prices, ratings), and complex structures (nested objects, arrays). When searching for dog images, for example, the vector finds visually similar images while payload filters narrow results to images taken within the last year, tagged with "vacation," or meeting specific rating criteria.
Learn more: [Payload Documentation](https://qdrant.tech/documentation/concepts/payload/)
Learn more: [Payload Documentation](/documentation/concepts/payload/)
### Payload Types
@@ -303,7 +303,7 @@ Here are some of the most common condition types:
<aside role="alert"> This list covers the most common conditions available at the time of this course. Qdrant is constantly evolving, and new filtering capabilities may have been added.
For the complete, most up-to-date list of all available filtering conditions, please refer to the **[official Filtering documentation](https://qdrant.tech/documentation/concepts/filtering/#filtering-conditions)**.</aside>
For the complete, most up-to-date list of all available filtering conditions, please refer to the **[official Filtering documentation](/documentation/concepts/filtering/#filtering-conditions)**.</aside>
### Filtering Capabilities Reference
@@ -381,7 +381,7 @@ client.create_payload_index(
When filters are highly selective, Qdrant's query planner may bypass vector indexing entirely and use payload indexes for faster results.
For comprehensive filtering examples and advanced usage patterns, see the [Filtering Documentation](https://qdrant.tech/documentation/concepts/filtering/) and [Complete Guide to Filtering in Vector Search](https://qdrant.tech/articles/vector-search-filtering/).
For comprehensive filtering examples and advanced usage patterns, see the [Filtering Documentation](/documentation/concepts/filtering/) and [Complete Guide to Filtering in Vector Search](/articles/vector-search-filtering/).
## Key Takeaways
qdrant-landing/content/course/essentials/day-1/pitstop-project.md
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@@ -349,4 +349,4 @@ encoder_large = SentenceTransformer("all-mpnet-base-v2") # Larger, potentially
encoder_fast = SentenceTransformer("all-MiniLM-L12-v2") # Different size/speed tradeoff
```
**Ready for Day 2?** Tomorrow you'll learn how Qdrant makes vector search lightning-fast through [HNSW](https://qdrant.tech/articles/filterable-hnsw/) indexing and how to optimize for production workloads.
**Ready for Day 2?** Tomorrow you'll learn how Qdrant makes vector search lightning-fast through [HNSW](/articles/filterable-hnsw/) indexing and how to optimize for production workloads.