Feat: Support Ranking Method

llama_cpp/llama.py

@@ -997,12 +997,32 @@ def create_embedding(
             },
         }
 
+    def rank(
+            self,
+            query: str,
+            documents: List[str]
+    ) -> List[float]:
+        """Rank a query against a list of docs
+
+        Args:
+            query: The utf-8 encoded query string.
+            documents: The utf-8 encoded list of documents.
+
+        Returns:
+            A list of rank scores.
+        """
+        input = [f"{query}</s><s>{doc}" for doc in documents]
+        embeds = self.embed(input, special_tokenize=True)
+        rank_scores = [embed[0] for embed in embeds]
+        return rank_scores
+
     def embed(
         self,
         input: Union[str, List[str]],
         normalize: bool = False,
         truncate: bool = True,
         return_count: bool = False,
+        special_tokenize: bool = False,
     ):
         """Embed a string.
 
@@ -1074,7 +1094,7 @@ def decode_batch(seq_sizes: List[int]):
 
         # accumulate batches and encode
         for text in inputs:
-            tokens = self.tokenize(text.encode("utf-8"))
+            tokens = self.tokenize(text.encode("utf-8"), special=special_tokenize)
             if truncate:
                 tokens = tokens[:n_batch]