docs: add FastembedColbertRanker to fastembed integration page (#442)

* docs: add FastembedColbertRanker to fastembed integration page

Updates the fastembed integration page to include the new
FastembedColbertRanker component from PR #3135, which adds
ColBERT late-interaction reranking support via fastembed.

- Added FastembedColbertRanker to the components list
- Added a usage example with ColBERT ranker in a pipeline

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* Update integrations/fastembed.md

Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com>

* Adopt new component name

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com>
Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>

Author: 4 people

integrations/fastembed.md

@@ -49,8 +49,9 @@ The `fastembed-haystack` integrations provides the following components:
 	- `FastembedDocumentEmbedder`: enriches documents with dense embeddings (used in indexing pipelines).
 	- `FastembedSparseTextEmbedder`: creates a sparse embedding for text (used in query/RAG pipelines).
 	- `FastembedSparseDocumentEmbedder`: enriches documents with sparse embeddings (used in indexing pipelines).
-- Ranker:
-	- `FastembedRanker`: ranks documents based on a query (used in query/RAG pipelines after the retrieval).
+- Rankers:
+	- `FastembedRanker`: ranks documents based on a query using cross-encoder models (used in query/RAG pipelines after the retrieval).
+	- `FastembedLateInteractionRanker`: ranks documents using ColBERT late-interaction (MaxSim) scoring (used in query/RAG pipelines after the retrieval).
 
 ### Example with dense embeddings
 
@@ -162,6 +163,44 @@ query_pipeline.connect("retriever.documents", "ranker.documents")
 result = query_pipeline.run({"text_embedder": {"text": query}, "ranker": { "query" : query }})
 ```
 
+### Example with Late Interaction ranker
+
+`FastembedLateInteractionRanker` uses ColBERT late-interaction scoring: the query and documents are encoded independently into token-level embeddings, and a MaxSim score is computed for each document. This offers stronger ranking quality than cross-encoders on many tasks while remaining efficient.
+
+> **Note:** ColBERT scores are unnormalized sums. They are meaningful for ranking within a single query but should not be compared across queries.
+
+```python
+from haystack import Document, Pipeline
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack_integrations.components.embedders.fastembed import FastembedDocumentEmbedder, FastembedTextEmbedder
+from haystack_integrations.components.rankers.fastembed import FastembedLateInteractionRanker
+
+document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
+
+query = "Who supports fastembed?"
+
+documents = [
+    Document(content="My name is Wolfgang and I live in Berlin"),
+    Document(content="I saw a black horse running"),
+    Document(content="Germany has many big cities"),
+    Document(content="fastembed is supported by and maintained by Qdrant."),
+]
+
+document_embedder = FastembedDocumentEmbedder()
+documents_with_embeddings = document_embedder.run(documents)["documents"]
+document_store.write_documents(documents_with_embeddings)
+
+query_pipeline = Pipeline()
+query_pipeline.add_component("text_embedder", FastembedTextEmbedder())
+query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store, top_k=4))
+query_pipeline.add_component("ranker", FastembedLateInteractionRanker(model_name="colbert-ir/colbertv2.0", top_k=2))
+query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+query_pipeline.connect("retriever.documents", "ranker.documents")
+
+result = query_pipeline.run({"text_embedder": {"text": query}, "ranker": {"query": query}})
+```
+
 ### License
 
 `fastembed-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.