rm serde methods

Author: anakin87

integrations/vllm/src/haystack_integrations/components/embedders/vllm/document_embedder.py

@@ -5,7 +5,7 @@
 from dataclasses import replace
 from typing import Any
 
-from haystack import Document, component, default_from_dict, default_to_dict, logging
+from haystack import Document, component, logging
 from haystack.utils import Secret
 from more_itertools import batched
 from openai import APIError, AsyncOpenAI, OpenAI
@@ -146,36 +146,6 @@ def warm_up(self) -> None:
         )
         self._is_warmed_up = True
 
-    def to_dict(self) -> dict[str, Any]:
-        """
-        Serialize this component to a dictionary.
-
-        :returns: The serialized component as a dictionary.
-        """
-        return default_to_dict(
-            self,
-            model=self.model,
-            api_key=self.api_key,
-            api_base_url=self.api_base_url,
-            prefix=self.prefix,
-            suffix=self.suffix,
-            dimensions=self.dimensions,
-            batch_size=self.batch_size,
-            progress_bar=self.progress_bar,
-            meta_fields_to_embed=self.meta_fields_to_embed,
-            embedding_separator=self.embedding_separator,
-            timeout=self.timeout,
-            max_retries=self.max_retries,
-            http_client_kwargs=self.http_client_kwargs,
-            raise_on_failure=self.raise_on_failure,
-            extra_parameters=self.extra_parameters,
-        )
-
-    @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> "VLLMDocumentEmbedder":
-        """Deserialize this component from a dictionary."""
-        return default_from_dict(cls, data)
-
     def _prepare_texts_to_embed(self, documents: list[Document]) -> dict[str, str]:
         """Concatenate each Document's text with the selected meta fields."""
         texts_to_embed = {}

integrations/vllm/src/haystack_integrations/components/embedders/vllm/text_embedder.py

@@ -4,7 +4,7 @@
 
 from typing import Any
 
-from haystack import component, default_from_dict, default_to_dict
+from haystack import component
 from haystack.utils import Secret
 from openai import AsyncOpenAI, OpenAI
 from openai.types import CreateEmbeddingResponse
@@ -118,31 +118,6 @@ def warm_up(self) -> None:
         )
         self._is_warmed_up = True
 
-    def to_dict(self) -> dict[str, Any]:
-        """
-        Serialize this component to a dictionary.
-
-        :returns: The serialized component as a dictionary.
-        """
-        return default_to_dict(
-            self,
-            model=self.model,
-            api_key=self.api_key.to_dict() if self.api_key else None,
-            api_base_url=self.api_base_url,
-            prefix=self.prefix,
-            suffix=self.suffix,
-            dimensions=self.dimensions,
-            timeout=self.timeout,
-            max_retries=self.max_retries,
-            http_client_kwargs=self.http_client_kwargs,
-            extra_parameters=self.extra_parameters,
-        )
-
-    @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> "VLLMTextEmbedder":
-        """Deserialize this component from a dictionary."""
-        return default_from_dict(cls, data)
-
     def _prepare_input(self, text: str) -> dict[str, Any]:
         if not isinstance(text, str):
             msg = (

integrations/vllm/tests/test_document_embedder.py

@@ -6,6 +6,7 @@
 import numpy as np
 import pytest
 from haystack import Document
+from haystack.core.serialization import component_from_dict, component_to_dict
 from haystack.utils import Secret
 from openai import APIError
 from openai.types import CreateEmbeddingResponse, Embedding
@@ -96,7 +97,7 @@ def test_warm_up(self, monkeypatch):
     def test_to_dict(self, monkeypatch):
         monkeypatch.delenv("VLLM_API_KEY", raising=False)
 
-        component_dict = VLLMDocumentEmbedder(model=MODEL).to_dict()
+        component_dict = component_to_dict(VLLMDocumentEmbedder(model=MODEL), "embedder")
         assert component_dict == {
             "type": "haystack_integrations.components.embedders.vllm.document_embedder.VLLMDocumentEmbedder",
             "init_parameters": {
@@ -140,7 +141,7 @@ def test_from_dict(self, monkeypatch):
                 "extra_parameters": None,
             },
         }
-        embedder = VLLMDocumentEmbedder.from_dict(data)
+        embedder = component_from_dict(VLLMDocumentEmbedder, data, "embedder")
         assert embedder.api_key == Secret.from_env_var("VLLM_API_KEY", strict=False)
         assert embedder.model == MODEL
         assert embedder.api_base_url == "http://localhost:8000/v1"

integrations/vllm/tests/test_text_embedder.py

@@ -4,6 +4,7 @@
 from unittest.mock import AsyncMock, MagicMock
 
 import pytest
+from haystack.core.serialization import component_from_dict, component_to_dict
 from haystack.utils import Secret
 from openai.types import CreateEmbeddingResponse, Embedding
 from openai.types.create_embedding_response import Usage
@@ -84,7 +85,7 @@ def test_warm_up(self, monkeypatch):
     def test_to_dict(self, monkeypatch):
         monkeypatch.delenv("VLLM_API_KEY", raising=False)
 
-        component_dict = VLLMTextEmbedder(model=MODEL).to_dict()
+        component_dict = component_to_dict(VLLMTextEmbedder(model=MODEL), "embedder")
         assert component_dict == {
             "type": "haystack_integrations.components.embedders.vllm.text_embedder.VLLMTextEmbedder",
             "init_parameters": {
@@ -118,7 +119,7 @@ def test_from_dict(self, monkeypatch):
                 "extra_parameters": None,
             },
         }
-        embedder = VLLMTextEmbedder.from_dict(data)
+        embedder = component_from_dict(VLLMTextEmbedder, data, "embedder")
         assert embedder.api_key == Secret.from_env_var("VLLM_API_KEY", strict=False)
         assert embedder.model == MODEL
         assert embedder.api_base_url == "http://localhost:8000/v1"