krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-10-01 17:08:30 +02:00
Code Issues Packages Projects Releases Wiki Activity

fix: linkchecker include filter port mismatch (#2242)

Browse Source 
* initial commit; fixed anchor links on internal docs pages

* add back in absolute paths for links in code comments

* fix: update linkchecker include filter to match server port 1314

PR #1629 changed the Hugo server to port 1314 but forgot to update
the --include filter, which still matched port 1313. This caused all
links to be excluded, making the checker a no-op (0 checked, 82277 excluded).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Fix url rewrite regex so images are not impacted

* Fix links from non-documentation pages

* Fix broken links

* more broken links

* more broken links

* broken link

* Add srcset width descriptor to .lycheeignore

* Ignore URLs that contain a % character

* Anchor regex so it matches the entire URL

---------

Co-authored-by: kanungle <neil.kanungo@gmail.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>
This commit is contained in:
Andrey Vasnetsov
2026-03-30 17:21:32 +02:00
committed by GitHub
co-authored by Claude Opus 4.6 kanungle Abdon Pijpelink
parent dc0080fffa
commit 1a40961d62
272 changed files with 872 additions and 879 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/course/essentials/day-1/chunking-strategies.md
+7 -7
View File
@@ -55,7 +55,7 @@ This is where chunking comes in. The goal is to have chunks
By breaking a document into focused chunks, each chunk gets its own vector that accurately represents a specific idea. This allows the search to be far more precise.
**Example:** Consider a multi-page Document like the [Qdrant Collection Configuration Guide of Day 7](/course/essentials/day-7/collection-configuration-guide/) covering everything from HNSW to sharding and quantization.
**Example:** Consider a multi-page Document like the [Qdrant Collection Configuration Guide of Day 7](/course/essentials/day-7/) covering everything from HNSW to sharding and quantization.
If a user asks: *"What does the m parameter do?"*
@@ -420,7 +420,7 @@ The trade-off is computational cost. You're embedding the full document upfront
| **Recursive** | Flexible, handles messy input | Heuristic, sometimes brittle | Scraped web content, mixed sources |
| **Semantic** | High-quality, meaning-aware | Slower, resource-intensive | Legal, research, critical QA |
**Note**: Sometimes, it's necessary to keep the document intact. If chunking is too complicated, or the document is visually rich (diagrams, graphs etc.), you can use [VLMs](/documentation/advanced-tutorials/pdf-retrieval-at-scale/) to embed the whole page.
**Note**: Sometimes, it's necessary to keep the document intact. If chunking is too complicated, or the document is visually rich (diagrams, graphs etc.), you can use [VLMs](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) to embed the whole page.
## Adding Meaning with Metadata
@@ -439,7 +439,7 @@ In Qdrant, this metadata lives in the **payload** - a JSON object attached to ea
"section_title": "What Is a Vector",
"chunk_index": 7,
"chunk_count": 15,
"url": "https://qdrant.tech/documentation/concepts/collections/",
"url": "https://qdrant.tech/documentation/manage-data/collections/",
"tags": ["qdrant", "vector search", "point", "vector", "payload"],
"source_type": "documentation",
"created_at": "2025-01-15T10:00:00Z",
@@ -451,7 +451,7 @@ 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](/documentation/concepts/indexing/#payload-index).
**Disclaimer**: For performance reasons, filterable fields must be indexed using the [Payload Index](/documentation/manage-data/indexing/#payload-index).
**1. Filtered Search (Exact Match)**
You can filter results based on exact metadata values, which is perfect for categorical data.
@@ -469,7 +469,7 @@ filter = models.Filter(
```
**2. Hybrid Search with Text Filtering (Full-Text Search)**
For more powerful text-based filtering, you can combine vector search with traditional keyword search. This requires setting up a [full-text index](/documentation/concepts/indexing/#full-text-index) on a payload field.
For more powerful text-based filtering, you can combine vector search with traditional keyword search. This requires setting up a [full-text index](/documentation/manage-data/indexing/#full-text-index) on a payload field.
```python
# Find vectors that also contain the keyword "HNSW" in their content
filter = models.Filter(
@@ -487,13 +487,13 @@ filter = models.Filter(
# Top result per document - get the most relevant chunk from each source
group_by = "document_id"
```
You can read more about grouping [here](/documentation/concepts/hybrid-queries/?q=grouping#grouping).
You can read more about grouping [here](/documentation/search/hybrid-queries/?q=grouping#grouping).
**4. Rich Result Display**
- Original content with source attribution
- Section context for better understanding
- Direct links to full documents
- Creation timestamps for [freshness](/documentation/concepts/search-relevance/#time-based-score-boosting)
- Creation timestamps for [freshness](/documentation/search/search-relevance/#time-based-score-boosting)
**5. Permission Control**
```python