Use dataclasses instead of namedtuples

Changelog.md

@@ -1,5 +1,11 @@
 # Changelog
 
+## 4.0.0
+
+- Turned all NamedTuples into Dataclasses. Even if this is technically a breaking change
+  probably not much source code is actually affected as constructing instances still behaves
+  the same.
+
 ## 3.5.1
 
 - Fix failing serialization of Prompt-based Documents in QA requests.

aleph_alpha_client/aleph_alpha_client.py

@@ -214,7 +214,7 @@ def _build_query_parameters(self) -> Mapping[str, str]:
     def _build_json_body(
         self, request: AnyRequest, model: Optional[str]
     ) -> Mapping[str, Any]:
-        json_body = request.to_json()
+        json_body = dict(request.to_json())
 
         if model is not None:
             json_body["model"] = model
@@ -721,7 +721,7 @@ def _build_query_parameters(self) -> Mapping[str, str]:
     def _build_json_body(
         self, request: AnyRequest, model: Optional[str]
     ) -> Mapping[str, Any]:
-        json_body = request.to_json()
+        json_body = dict(request.to_json())
 
         if model is not None:
             json_body["model"] = model

aleph_alpha_client/completion.py

@@ -1,9 +1,11 @@
-from typing import Any, Dict, List, Mapping, NamedTuple, Optional, Sequence
+from dataclasses import asdict, dataclass
+from typing import Any, Dict, List, Mapping, Optional, Sequence
 
 from aleph_alpha_client.prompt import Prompt
 
 
-class CompletionRequest(NamedTuple):
+@dataclass(frozen=True)
+class CompletionRequest:
     """
     Describes a completion request
 
@@ -212,13 +214,17 @@ class CompletionRequest(NamedTuple):
     repetition_penalties_include_completion: bool = True
     raw_completion: bool = False
 
-    def to_json(self) -> Dict[str, Any]:
+    def to_json(self) -> Mapping[str, Any]:
         payload = {k: v for k, v in self._asdict().items() if v is not None}
         payload["prompt"] = self.prompt.to_json()
         return payload
 
+    def _asdict(self) -> Mapping[str, Any]:
+        return asdict(self)
+
 
-class CompletionResult(NamedTuple):
+@dataclass(frozen=True)
+class CompletionResult:
     log_probs: Optional[Sequence[Mapping[str, Optional[float]]]] = None
     completion: Optional[str] = None
     completion_tokens: Optional[Sequence[str]] = None
@@ -235,8 +241,12 @@ def from_json(json: Dict[str, Any]) -> "CompletionResult":
             raw_completion=json.get("raw_completion"),
         )
 
+    def _asdict(self) -> Mapping[str, Any]:
+        return asdict(self)
 
-class CompletionResponse(NamedTuple):
+
+@dataclass(frozen=True)
+class CompletionResponse:
     model_version: str
     completions: Sequence[CompletionResult]
     optimized_prompt: Optional[Prompt] = None
@@ -259,3 +269,6 @@ def to_json(self) -> Mapping[str, Any]:
             **self._asdict(),
             "completions": [completion._asdict() for completion in self.completions],
         }
+
+    def _asdict(self) -> Mapping[str, Any]:
+        return asdict(self)

aleph_alpha_client/detokenization.py

@@ -1,7 +1,9 @@
-from typing import Any, Dict, List, NamedTuple, Optional, Sequence
+from dataclasses import dataclass, asdict
+from typing import Any, Dict, List, Mapping, Optional, Sequence
 
 
-class DetokenizationRequest(NamedTuple):
+@dataclass(frozen=True)
+class DetokenizationRequest:
     """Describes a detokenization request.
 
     Parameters
@@ -14,12 +16,16 @@ class DetokenizationRequest(NamedTuple):
 
     token_ids: Sequence[int]
 
-    def to_json(self) -> Dict[str, Any]:
+    def to_json(self) -> Mapping[str, Any]:
         payload = self._asdict()
         return payload
 
+    def _asdict(self) -> Mapping[str, Any]:
+        return asdict(self)
 
-class DetokenizationResponse(NamedTuple):
+
+@dataclass(frozen=True)
+class DetokenizationResponse:
     result: str
 
     @staticmethod

aleph_alpha_client/embedding.py

@@ -1,17 +1,19 @@
+from dataclasses import asdict, dataclass
 from enum import Enum
 from typing import (
     Any,
     Dict,
     List,
-    NamedTuple,
+    Mapping,
     Optional,
     Sequence,
     Tuple,
 )
 from aleph_alpha_client.prompt import Prompt
 
 
-class EmbeddingRequest(NamedTuple):
+@dataclass(frozen=True)
+class EmbeddingRequest:
     """
     Embeds a text and returns vectors that can be used for downstream tasks (e.g. semantic similarity) and models (e.g. classifiers).
 
@@ -73,13 +75,18 @@ class EmbeddingRequest(NamedTuple):
     contextual_control_threshold: Optional[float] = None
     control_log_additive: Optional[bool] = True
 
-    def to_json(self) -> Dict[str, Any]:
-        payload = self._asdict()
-        payload["prompt"] = self.prompt.to_json()
-        return payload
+    def to_json(self) -> Mapping[str, Any]:
+        return {
+            **self._asdict(),
+            "prompt": self.prompt.to_json(),
+        }
 
+    def _asdict(self) -> Mapping[str, Any]:
+        return asdict(self)
 
-class EmbeddingResponse(NamedTuple):
+
+@dataclass(frozen=True)
+class EmbeddingResponse:
     model_version: str
     embeddings: Optional[Dict[Tuple[str, str], List[float]]]
     tokens: Optional[List[str]]
@@ -120,7 +127,8 @@ class SemanticRepresentation(Enum):
     Query = "query"
 
 
-class SemanticEmbeddingRequest(NamedTuple):
+@dataclass(frozen=True)
+class SemanticEmbeddingRequest:
     """
     Embeds a text and returns vectors that can be used for downstream tasks (e.g. semantic similarity) and models (e.g. classifiers).
 
@@ -181,14 +189,19 @@ class SemanticEmbeddingRequest(NamedTuple):
     contextual_control_threshold: Optional[float] = None
     control_log_additive: Optional[bool] = True
 
-    def to_json(self) -> Dict[str, Any]:
-        payload = self._asdict()
-        payload["representation"] = self.representation.value
-        payload["prompt"] = self.prompt.to_json()
-        return payload
+    def to_json(self) -> Mapping[str, Any]:
+        return {
+            **self._asdict(),
+            "representation": self.representation.value,
+            "prompt": self.prompt.to_json(),
+        }
+
+    def _asdict(self) -> Mapping[str, Any]:
+        return asdict(self)
 
 
-class BatchSemanticEmbeddingRequest(NamedTuple):
+@dataclass(frozen=True)
+class BatchSemanticEmbeddingRequest:
     """
     Embeds multiple multi-modal prompts and returns their embeddings in the same order as they were supplied.
 
@@ -246,17 +259,22 @@ class BatchSemanticEmbeddingRequest(NamedTuple):
     contextual_control_threshold: Optional[float] = None
     control_log_additive: Optional[bool] = True
 
-    def to_json(self) -> Dict[str, Any]:
-        payload = self._asdict()
-        payload["representation"] = self.representation.value
-        payload["prompts"] = [prompt.to_json() for prompt in self.prompts]
-        return payload
+    def to_json(self) -> Mapping[str, Any]:
+        return {
+            **self._asdict(),
+            "representation": self.representation.value,
+            "prompts": [prompt.to_json() for prompt in self.prompts],
+        }
+
+    def _asdict(self) -> Mapping[str, Any]:
+        return asdict(self)
 
 
 EmbeddingVector = List[float]
 
 
-class SemanticEmbeddingResponse(NamedTuple):
+@dataclass(frozen=True)
+class SemanticEmbeddingResponse:
     """
     Response of a semantic embedding request
 
@@ -275,10 +293,15 @@ class SemanticEmbeddingResponse(NamedTuple):
 
     @staticmethod
     def from_json(json: Dict[str, Any]) -> "SemanticEmbeddingResponse":
-        return SemanticEmbeddingResponse(**json)
+        return SemanticEmbeddingResponse(
+            model_version=json["model_version"],
+            embedding=json["embedding"],
+            message=json.get("message"),
+        )
 
 
-class BatchSemanticEmbeddingResponse(NamedTuple):
+@dataclass(frozen=True)
+class BatchSemanticEmbeddingResponse:
     """
     Response of a batch semantic embedding request
 
@@ -294,7 +317,9 @@ class BatchSemanticEmbeddingResponse(NamedTuple):
 
     @staticmethod
     def from_json(json: Dict[str, Any]) -> "BatchSemanticEmbeddingResponse":
-        return BatchSemanticEmbeddingResponse(**json)
+        return BatchSemanticEmbeddingResponse(
+            model_version=json["model_version"], embeddings=json["embeddings"]
+        )
 
     @staticmethod
     def _from_model_version_and_embeddings(

aleph_alpha_client/evaluation.py

@@ -1,13 +1,15 @@
+from dataclasses import dataclass, asdict
 from typing import (
     Any,
     Dict,
-    NamedTuple,
+    Mapping,
     Optional,
 )
 from aleph_alpha_client.prompt import Prompt
 
 
-class EvaluationRequest(NamedTuple):
+@dataclass(frozen=True)
+class EvaluationRequest:
     """
     Evaluates the model's likelihood to produce a completion given a prompt.
 
@@ -39,13 +41,15 @@ class EvaluationRequest(NamedTuple):
     contextual_control_threshold: Optional[float] = None
     control_log_additive: Optional[bool] = True
 
-    def to_json(self) -> Dict[str, Any]:
-        payload = self._asdict()
-        payload["prompt"] = self.prompt.to_json()
-        return payload
+    def to_json(self) -> Mapping[str, Any]:
+        return {**self._asdict(), "prompt": self.prompt.to_json()}
 
+    def _asdict(self) -> Mapping[str, Any]:
+        return asdict(self)
 
-class EvaluationResponse(NamedTuple):
+
+@dataclass(frozen=True)
+class EvaluationResponse:
     model_version: str
     message: Optional[str]
     result: Dict[str, Any]