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Refine type hints #4

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Merged
daniel-gomm merged 1 commit into from
Jan 21, 2025
Merged

Refine type hints #4

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17 changes: 9 additions & 8 deletions src/pytei/client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@ class TEIClient:
using a specified datastore.
"""

def __init__(self, embedding_store: Union[EmbeddingStore, None] = None, url: str = "http://127.0.0.1:8080", timeout: int = 10):
def __init__(self, embedding_store: Optional[EmbeddingStore] = None, url: str = "http://127.0.0.1:8080", timeout: int = 10):
"""Constructor method

:param embedding_store: Data store used for cacheing. Defaults to in-memory caching.
Expand Down Expand Up @@ -47,7 +47,7 @@ def _fetch_embeddings(self, texts: List[str], body: Dict[str, Any]) -> List[np.n
"""Send a batched request to the embedding endpoint."""
body["inputs"] = texts
try:
response = requests.post(f"{self._endpoint}/embed", json={"inputs": texts}, headers={"Content-Type": "application/json"},
response = requests.post(f"{self._endpoint}/embed", json=body, headers={"Content-Type": "application/json"},
timeout=self._timeout)
response.raise_for_status() # Raise an HTTPError for non-200 responses
embeddings = json.loads(response.text)
Expand All @@ -56,9 +56,8 @@ def _fetch_embeddings(self, texts: List[str], body: Dict[str, Any]) -> List[np.n
raise RuntimeError(f"Failed to fetch embedding: {e}")

@staticmethod
def _build_embed_call_body(normalize: bool = True, prompt_name: Union[str, None] = None,
truncate: bool = False,
truncation_direction: Union[Literal['left', 'right'], None] = None) -> Dict[str, Any]:
def _build_embed_call_body(normalize: bool = True, prompt_name: Optional[str] = None, truncate: bool = False,
truncation_direction: Optional[Literal['left', 'right']] = None) -> Dict[str, Any]:
body = {"normalize": normalize}
if prompt_name is not None:
body["prompt_name"] = prompt_name
Expand All @@ -70,8 +69,8 @@ def _build_embed_call_body(normalize: bool = True, prompt_name: Union[str, None]
return body


def embed(self, inputs: Union[str, List[str]], normalize: bool = True, prompt_name: Union[str, None] = None,
truncate: bool = False, truncation_direction: Union[Literal['left', 'right'], None] = None,
def embed(self, inputs: Union[str, List[str]], normalize: bool = True, prompt_name: Optional[str] = None,
truncate: bool = False, truncation_direction: Optional[Literal['left', 'right']] = None,
skip_cache: bool = False) -> Union[np.ndarray, List[np.ndarray]]:
"""
Get the embedding for a single string or a batch of strings.
Expand Down Expand Up @@ -141,8 +140,10 @@ def embed(self, inputs: Union[str, List[str]], normalize: bool = True, prompt_na

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.")

def predict(self, inputs: Union[str, Tuple[str, str], List[Union[str, Tuple[str, str]]]], raw_scores: bool = False,
truncate: bool = False, truncation_direction: Union[Literal['left', 'right'], None] = None) -> Union[PredictionResult, List[PredictionResult]]:
truncate: bool = False, truncation_direction: Optional[Literal['left', 'right']] = None) -> Union[PredictionResult, List[PredictionResult]]:
raise NotImplementedError("Sequence classification is not yet implemented.")
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