initial reranking implementation

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pytei-client"
-version = "0.1.0"
+version = "0.1.1"
 authors = [
     { name="Daniel Gomm", email="daniel.gomm@cwi.nl" },
 ]

src/pytei/client.py

@@ -137,12 +137,56 @@ def embed(self, inputs: Union[str, List[str]], normalize: bool = True, prompt_na
         else:
             raise AttributeError("text_input must be either a string or a list of strings.")
 
+    @staticmethod
+    def _build_rerank_request_body(query: str, texts: List[str], raw_scores: bool = False, return_text: bool = False,
+               truncate: bool = False, truncation_direction: Optional[Literal['left', 'right']] = None):
+        body = {
+            "query": query,
+            "texts": texts,
+            "raw_scores": raw_scores,
+            "return_text": return_text
+        }
+        if truncate is True:
+            body["truncate"] = True
+            if truncation_direction is None:
+                truncation_direction = "right"
+            body["truncation_direction"] = truncation_direction
+        return body
+
+    def _fetch_reranking_result(self, request_body: Dict) -> List[Rank]:
+        try:
+            response = requests.post(f"{self._endpoint}/rerank", json=request_body, headers={"Content-Type": "application/json"},
+                                     timeout=self._timeout)
+            response.raise_for_status()
+            raw_reranking_results = json.loads(response.text)
+            return [Rank(raw_result["index"], raw_result["score"], raw_result.get("text", None)) for raw_result in raw_reranking_results]
+        except (requests.RequestException, json.JSONDecodeError, IndexError, ValueError) as e:
+            raise RuntimeError(f"Failed to rerank texts: {e}")
 
-    def rerank(self, query: str, texts: List[str], raw_score: bool = False, return_text: bool = False,
-               truncate: bool = False, truncation_direction: Union[Literal['left', 'right'], None] = None) -> List[Rank]:
     def rerank(self, query: str, texts: List[str], raw_scores: bool = False, return_text: bool = False,
                truncate: bool = False, truncation_direction: Optional[Literal['left', 'right']] = None) -> List[Rank]:
-        raise NotImplementedError("Reranking is not yet implemented.")
+        """
+        Re-rank retrieval results.
+
+        :param query: Query text against which to re-rank the texts.
+        :type query: str
+        :param texts: Texts that should be re-ranked.
+        :type texts: List[str]
+        :param raw_scores: Whether to return raw scores. Default is False.
+        :type raw_scores: bool, optional
+        :param return_text: Whether to include the raw text in the ranking results. Default is False.
+        :type return_text: bool, optional
+        :param truncate: Whether to apply truncation. Default is False.
+        :type truncate: bool, optional
+        :param truncation_direction: Direction in which truncation is applied. Default is 'right'.
+        :type truncation_direction: str, optional
+        :return: A re-ranked list of the supplied texts.
+        :rtype: List[`Rank`]
+        """
+        body = self._build_rerank_request_body(query=query, texts=texts, raw_scores=raw_scores, return_text=return_text,
+                                               truncate=truncate,
+                                               truncation_direction=truncation_direction)
+        return self._fetch_reranking_result(body)
 
     def predict(self, inputs: Union[str, Tuple[str, str], List[Union[str, Tuple[str, str]]]], raw_scores: bool = False,
                 truncate: bool = False, truncation_direction: Optional[Literal['left', 'right']] = None) -> Union[PredictionResult, List[PredictionResult]]: