refactor(sambanova): Drop instructor usage.

pyproject.toml

@@ -72,10 +72,6 @@ together = [
   "together",
 ]
 
-sambanova = [
-  "instructor",
-]
-
 ollama = [
   "ollama>=0.5.1"
 ]
@@ -94,19 +90,20 @@ sagemaker = [
 
 # These providers don't require any additional dependencies, but are included for completeness.
 azureopenai = []
-moonshot = []
-nebius = []
 databricks = []
 deepseek = []
 fireworks = []
 inception = []
-openai = []
-portkey = []
-openrouter = []
-lmstudio = []
 llama = []
-llamafile = []
 llamacpp = []
+llamafile = []
+lmstudio = []
+moonshot = []
+nebius = []
+openai = []
+openrouter = []
+portkey = []
+sambanova = []
 
 [project.urls]
 Documentation = "https://mozilla-ai.github.io/any-llm/"

src/any_llm/providers/sambanova/sambanova.py

@@ -1,18 +1,4 @@
-from collections.abc import AsyncIterator
-from typing import Any, cast
-
-from pydantic import BaseModel
-
 from any_llm.providers.openai.base import BaseOpenAIProvider
-from any_llm.types.completion import ChatCompletion, ChatCompletionChunk, CompletionParams
-from any_llm.utils.instructor import _convert_instructor_response
-
-MISSING_PACKAGES_ERROR = None
-try:
-    import instructor
-
-except ImportError as e:
-    MISSING_PACKAGES_ERROR = e
 
 
 class SambanovaProvider(BaseOpenAIProvider):
@@ -21,35 +7,4 @@ class SambanovaProvider(BaseOpenAIProvider):
     PROVIDER_NAME = "sambanova"
     PROVIDER_DOCUMENTATION_URL = "https://sambanova.ai/"
 
-    MISSING_PACKAGES_ERROR = MISSING_PACKAGES_ERROR
     SUPPORTS_COMPLETION_PDF = False
-
-    async def _acompletion(
-        self, params: CompletionParams, **kwargs: Any
-    ) -> ChatCompletion | AsyncIterator[ChatCompletionChunk]:
-        """Make the API call to SambaNova service with instructor for structured output."""
-
-        if params.reasoning_effort == "auto":
-            params.reasoning_effort = None
-
-        if params.response_format:
-            instructor_client = instructor.from_openai(self.client)
-            if not isinstance(params.response_format, type) or not issubclass(params.response_format, BaseModel):
-                msg = "response_format must be a pydantic model"
-                raise ValueError(msg)
-            response = await instructor_client.chat.completions.create(
-                model=params.model_id,
-                messages=cast("Any", params.messages),
-                response_model=params.response_format,
-                **params.model_dump(exclude_none=True, exclude={"model_id", "messages", "response_format"}),
-                **kwargs,
-            )
-            return _convert_instructor_response(response, params.model_id, self.PROVIDER_NAME)
-        return self._convert_completion_response_async(
-            await self.client.chat.completions.create(
-                model=params.model_id,
-                messages=cast("Any", params.messages),
-                **params.model_dump(exclude_none=True, exclude={"model_id", "messages"}),
-                **kwargs,
-            )
-        )

tests/unit/test_provider.py

@@ -166,7 +166,6 @@ def test_providers_raise_MissingApiKeyError(provider: LLMProvider) -> None:
         ("huggingface", "huggingface_hub"),
         ("mistral", "mistralai"),
         ("ollama", "ollama"),
-        ("sambanova", "instructor"),
         ("together", "together"),
         ("voyage", "voyageai"),
         ("watsonx", "ibm_watsonx_ai"),