fix lint: remove unused imports in integrations/fastembed

Signed-off-by: Arya Tayshete <avtayshete_b21@et.vjti.ac.in>

Author: xoaryaa

integrations/fastembed/haystack_integrations/components/rankers/fastembed/colbert_utils.py

@@ -1,4 +1,4 @@
 from __future__ import annotations
 
-# We’ll implement real MaxSim scoring utilities here in Step 3.
+# We will implement real MaxSim scoring utilities here in Step 3.
 # Keeping the file so imports are stable when we wire things up.

integrations/fastembed/src/haystack_integrations/components/rankers/fastembed/colbert_reranker.py

@@ -3,7 +3,7 @@
 
 from __future__ import annotations
 
-from typing import Any, Sequence, List, Optional
+from typing import Any, Sequence
 
 import numpy as np
 from haystack import Document, component
@@ -107,9 +107,7 @@ def warm_up(self):
             try:
                 self._encoder = LateInteractionTextEmbedding(**kwargs)
             except TypeError:
-                self._encoder = LateInteractionTextEmbedding(
-                    model_name=self.model, threads=self.threads
-                )
+                self._encoder = LateInteractionTextEmbedding(model_name=self.model, threads=self.threads)
 
             gen_q = self._encoder.query_embed(["warmup"])
             next(gen_q, None)
@@ -193,9 +191,7 @@ def run(
         doc_mats = self._encode_docs_batched(doc_texts)
 
         for d, d_mat in zip(docs_list, doc_mats):
-            d.score = _maxsim_score(
-                q_mat, d_mat, similarity=self.similarity, normalize=self.normalize
-            )
+            d.score = _maxsim_score(q_mat, d_mat, similarity=self.similarity, normalize=self.normalize)
 
         docs_list.sort(
             key=lambda d: (

integrations/fastembed/src/haystack_integrations/components/rankers/fastembed/ranker.py

@@ -131,21 +131,15 @@ def _prepare_fastembed_input_docs(self, documents: List[Document]) -> List[str]:
         concatenated_input_list = []
         for doc in documents:
             meta_values_to_embed = [
-                str(doc.meta[key])
-                for key in self.meta_fields_to_embed
-                if key in doc.meta and doc.meta.get(key)
+                str(doc.meta[key]) for key in self.meta_fields_to_embed if key in doc.meta and doc.meta.get(key)
             ]
-            concatenated_input = self.meta_data_separator.join(
-                [*meta_values_to_embed, doc.content or ""]
-            )
+            concatenated_input = self.meta_data_separator.join([*meta_values_to_embed, doc.content or ""])
             concatenated_input_list.append(concatenated_input)
 
         return concatenated_input_list
 
     @component.output_types(documents=List[Document])
-    def run(
-        self, query: str, documents: List[Document], top_k: Optional[int] = None
-    ) -> Dict[str, List[Document]]:
+    def run(self, query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict[str, List[Document]]:
         """
         Returns a list of documents ranked by their similarity to the given query, using FastEmbed.
 
@@ -162,9 +156,7 @@ def run(
 
         :raises ValueError: If `top_k` is not > 0.
         """
-        if not isinstance(documents, list) or (
-            documents and not isinstance(documents[0], Document)
-        ):
+        if not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
             msg = "FastembedRanker expects a list of Documents as input. "
             raise TypeError(msg)
         if query == "":