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vercel bot commented Jun 13, 2025

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pgvecto-rs-docs ✅ Ready (Inspect) Visit Preview 💬 Add feedback Jun 30, 2025 5:26am

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Pull Request Overview

This PR refactors documentation paths and the VitePress sidebar to introduce a /pgvecto_rs namespace, updating links accordingly across markdown files and configuration.

  • Standardizes relative links in FAQ and community guides
  • Aligns cloud docs’ Reference and Use Case URLs
  • Restructures sidebar entries and updates all links in .vitepress/config.mts

Reviewed Changes

Copilot reviewed 80 out of 80 changed files in this pull request and generated 1 comment.

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File Description
src/pgvecto_rs/faqs/benchmark.md Updated relative links to use-case and reference
src/pgvecto_rs/community/contributing.md Adjusted contributing guide links to ../ paths
src/cloud/monitoring/monitoring.md Changed Index Options link
src/cloud/getting-started/quick-start.md Updated Reference and Use Cases links
.vitepress/config.mts Prefixed sidebar links with /pgvecto_rs and restructured sections
Comments suppressed due to low confidence (3)

src/cloud/monitoring/monitoring.md:32

  • The Index Options link was redirected to the usage guide section instead of the reference doc. It should point to the reference page (e.g., ../../reference/indexing_options.md) or the updated reference path under vectorchord.
- Index Options: Details of the index options can be found in the [Index Options](../../reference/indexing_options.md)
+ Index Options: Details of the index options can be found in the [Index Options](../../vectorchord/usage/indexing#indexing-options)

src/cloud/getting-started/quick-start.md:99

  • The 'Reference' link now points to the indexing usage page instead of the reference section. It should link to the general reference path (e.g., ../../vectorchord/reference/) or the specific index reference page.
- In quick start, we will create a simple index. If you want to create a more advanced index, see [Reference](../../reference/). We provide rich user cases, see [Use Cases](../../use-case/).
+ In quick start, we will create a simple index. If you want to create a more advanced index, see [Reference](../../vectorchord/usage/indexing). We provide rich user cases, see [Use Cases](../../vectorchord/use-case/index).

.vitepress/config.mts:195

  • There is a syntax error due to a double comma at the end of this object literal. Remove the extra comma to avoid a parse error.
{ text: 'Cold Start Optimization', link: '/vectorchord/advanced-features/cold-start-optimization' },,

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Does the link to the original pgvecto.rs docs change, do I need to take references into account, also at the moment when I click on the pgvecto.rs docs on the preview it 404.

Comment on lines -36 to +86
| ----------------------- | ------------------- | -------------------- |
| L2 distance (`<->`) | `vector_l2_ops` | `halfvec_l2_ops` |
| inner product (`<#>`) | `vector_ip_ops` | `halfvec_ip_ops` |
| cosine distance (`<=>`) | `vector_cosine_ops` | `halfvec_cosine_ops` |
## Operators and Operator Classes

## Recommendations
### Vector Type

When dealing with large datasets (> $10^6$ vectors), please follow these guidelines for optimal performance:
#### Distance/Similarity Operators

1. First insert all vectors into the table before building the index
2. Select an appropriate number of lists (`build.internal.lists` parameter) based on your dataset size
3. The `lists` option should be configured based on the number of vectors. Below is a table to assist with your selection
4. Failure to follow these steps may result in significantly increased query latency
| Operator | Description | Operator Class |
| -------- | ------------------------------------------------------------ | ------------------- |
| `<->` | L2 distance | `vector_l2_ops` |
| `<#>` | Inner product | `vector_ip_ops` |
| `<=>` | Cosine distance | `vector_cosine_ops` |
| `@#` | Multi-vector [MaxSim](/vectorchord/usage/multi-vector-retrieval) distance | `vector_maxsim_ops` |

> [!NOTE]
> VectorChord's index leverages statistical properties of your dataset to optimize search performance. If you significantly update your vector data after building the index, the index efficiency may degrade. In such cases, rebuilding the index is recommended to restore optimal performance.
#### Comparison operators

| Operator | Description | Operator Class |
| -------- | ------------------------------------- | ------------------- |
| `<<->>` | Tests if L2 distance <= threshold | `vector_l2_ops` |
| `<<#>>` | Tests if inner product <= threshold | `vector_ip_ops` |
| `<<=>>` | Tests if cosine distance <= threshold | `vector_cosine_ops` |

See also: [Range Filter](search#range-filter)

### Halfvec Type

#### Distance/Similarity Operators

| Operator | Description | Operator Class |
| -------- | ------------------------------------------------------------ | -------------------- |
| `<->` | L2 distance | `halfvec_l2_ops` |
| `<#>` | Inner product | `halfvec_ip_ops` |
| `<=>` | Cosine distance | `halfvec_cosine_ops` |
| `@#` | Multi-vector [MaxSim](/vectorchord/usage/multi-vector-retrieval) distance | `vector_maxsim_ops` |

### Comparison operators

| Operator | Description | Operator Class |
| -------- | ------------------------------------- | -------------------- |
| `<<->>` | Tests if L2 distance <= threshold | `halfvec_l2_ops` |
| `<<#>>` | Tests if inner product <= threshold | `halfvec_ip_ops` |
| `<<=>>` | Tests if cosine distance <= threshold | `halfvec_cosine_ops` |

| vectors Range | List Calculation Formula | Example Result |
| ------------- | ------------------------------ | ---------------- |
| <128k | list = 1 | 1 |
| ≥128k and <2M | list = (2 * vectors) / 1000 | [256, 4000] |
| ≥2M and <100M | list ∈ [4√vectors, 8√vectors] | \[4000, 80000] |
| ≥100M | list ∈ [8√vectors, 16√vectors] | \[80000, 160000] |
See also: [Range Filter](search#range-filter)
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Cannot understand.

Comment on lines +42 to +46
| Operator | Description |
| -------- | ------------------------------------- |
| `<<->>` | Tests if L2 distance <= threshold |
| `<<#>>` | Tests if inner product <= threshold |
| `<<=>>` | Tests if cosine distance <= threshold |
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Please don’t write this kind of "good enough to understand if readers think twice" text. Readers should not play word games in the document.

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