feat: remove the colber indexing & searching part

README.md

@@ -123,6 +123,9 @@ This new release adds new functionality and performance improvements:
 - [Matryoshka embeddings](https://huggingface.co/blog/matryoshka), which allow users to trade between efficiency and precision flexibly.
 - Superior retrieval performance when compared to the English-only `jina-colbert-v1-en`.
 
+[`jina-colbert-v1-en`](https://huggingface.co/jinaai/jina-colbert-v1-en)
+
+
 ## Usage
 
 ### Installation
@@ -157,52 +160,6 @@ results = RAG.search(query)
 ```
 
 ### Stanford ColBERT
-Typically, you would run the following code to index using the Stanford ColBERT library on a GPU machine. Check the reference at [Stanford ColBERT](https://github.com/stanford-futuredata/ColBERT?tab=readme-ov-file#installation) for more details.
-
-#### Indexing
-
-```python
-from colbert import Indexer
-from colbert.infra import ColBERTConfig
-
-if __name__ == "__main__":
-    config = ColBERTConfig(
-        doc_maxlen=512,
-        nbits=2
-    )
-    indexer = Indexer(
-        checkpoint="jinaai/jina-colbert-v2",
-        config=config,
-    )
-    docs = [
-        "ColBERT is a novel ranking model that adapts deep LMs for efficient retrieval.",
-        "Jina-ColBERT is a ColBERT-style model but based on JinaBERT so it can support both 8k context length, fast and accurate retrieval."
-    ]
-    indexer.index(name='demo', collection=docs)
-```
-
-#### Searching
-
-```python
-from colbert import Searcher
-from colbert.infra import ColBERTConfig
-
-k = 10
-
-if __name__ == "__main__":
-    config = ColBERTConfig(
-        query_maxlen=128
-    )
-    searcher = Searcher(
-        index='demo',
-        config=config
-    )
-    query = 'What does ColBERT do?'
-    results = searcher.search(query, k=k)
-
-``` 
-
-#### Creating vectors
 
 ```python
 from colbert.infra import ColBERTConfig
@@ -324,6 +281,8 @@ Additionally, we provide the following embedding models, you can also use them f
 - [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English bilingual model.
 - [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English bilingual model.
 - [`jina-embeddings-v2-base-es`](https://huggingface.co/jinaai/jina-embeddings-v2-base-es): 161 million parameters Spanish-English bilingual model.
+- [`jina-reranker-v2`](https://huggingface.co/jinaai/jina-reranker-v2-base-multilingual): multilingual reranker model.
+- [`jina-clip-v1`](https://huggingface.co/jinaai/jina-clip-v1): English multimodal (text-image) embedding model.
 
 ## Contact