Summarize all available vectors in the cookbook

To regroup within #5 if relevant - close if not

Issue : The cookbook is very useful to understand the different usages of vectors but a summary of vectors + usage might be useful

Proposal: md table with different vector types to put in the cookbook (draft below)

Here is a summary of the available vectors:

| Vector Type | Description | Best Used For |
|-------------|-------------|---------------|
| **Text Embedding Vectors** |
| `LitellmTextEmbVector` | Dense text embeddings using LiteLLM models | General text similarity, semantic search |
| `OpenAITextEmbVector` | Dense text embeddings using OpenAI models | General text similarity, semantic search when using OpenAI |
| **Text Processing Vectors** |
| `NGramVector` | Text similarity based on character n-grams | Fuzzy text matching, typo-tolerant search |
| `BagOfWordsVector` | Word-level text similarity using MinHash | Document similarity, keyword matching |
| `BiGramVector` | Character bigram-based text similarity | Language detection, fuzzy matching |
| **Categorical Vectors** |
| `CategoricalVector` | One-hot encoding with pitty factor | Exact category matching with tolerance |
| `VocabularyVector` | Weighted multi-category encoding | Tag systems, multi-label classification |
| `HashedVocabularyVector` | Hashed version of VocabularyVector | Large vocabulary spaces, memory-efficient tagging |
| **Numerical Vectors** |
| `IntegersSquareKernelVector` | Integer similarity with square kernel | Numerical range queries, year matching |
| **Geospatial Vectors** |
| `H3LocVector` | H3 geospatial hashing | Location-based search with Uber H3 |
| `GHLocVector` | Geohash-based location encoding | General purpose location search |
| **Hierarchical Vectors** |
| `HierarchicalVector` | Tree-structured data encoding | Category hierarchies, taxonomies |
| **Image Vectors** |
| `LitellmImageDescriptionVector` | Image embeddings via LLM descriptions | Image similarity via textual descriptions |
| `OpenAIImageDescriptionVector` | OpenAI-based image descriptions | High-quality image search via descriptions |
| `LitellmMultiModalEmbVector` | Multi-modal embeddings | Combined text and image search |
| **Special Purpose** |
| `ExistsVector` | Simple presence/absence encoding | Filtering, existence checks, flags |
| `SotaMinHashVector` | State-of-the-art MinHash implementation | Set similarity, efficient similarity search |
| `OneBitICWSMinHashVector` | One-bit ICWS MinHash | Memory-efficient set similarity |

rendered :

Vector Type	Description	Best Used For
Text Embedding Vectors
LitellmTextEmbVector	Dense text embeddings using LiteLLM models	General text similarity, semantic search
OpenAITextEmbVector	Dense text embeddings using OpenAI models	General text similarity, semantic search when using OpenAI
Text Processing Vectors
NGramVector	Text similarity based on character n-grams	Fuzzy text matching, typo-tolerant search
BagOfWordsVector	Word-level text similarity using MinHash	Document similarity, keyword matching
BiGramVector	Character bigram-based text similarity	Language detection, fuzzy matching
Categorical Vectors
CategoricalVector	One-hot encoding with pitty factor	Exact category matching with tolerance
VocabularyVector	Weighted multi-category encoding	Tag systems, multi-label classification
HashedVocabularyVector	Hashed version of VocabularyVector	Large vocabulary spaces, memory-efficient tagging
Numerical Vectors
IntegersSquareKernelVector	Integer similarity with square kernel	Numerical range queries, year matching
Geospatial Vectors
H3LocVector	H3 geospatial hashing	Location-based search with Uber H3
GHLocVector	Geohash-based location encoding	General purpose location search
Hierarchical Vectors
HierarchicalVector	Tree-structured data encoding	Category hierarchies, taxonomies
Image Vectors
LitellmImageDescriptionVector	Image embeddings via LLM descriptions	Image similarity via textual descriptions
OpenAIImageDescriptionVector	OpenAI-based image descriptions	High-quality image search via descriptions
LitellmMultiModalEmbVector	Multi-modal embeddings	Combined text and image search
Special Purpose
ExistsVector	Simple presence/absence encoding	Filtering, existence checks, flags
SotaMinHashVector	State-of-the-art MinHash implementation	Set similarity, efficient similarity search
OneBitICWSMinHashVector	One-bit ICWS MinHash	Memory-efficient set similarity