structure saas with tools

.venv/lib/python3.10/site-packages/litellm/llms/databricks/embed/handler.py

@@ -0,0 +1,49 @@
+"""
+Calling logic for Databricks embeddings
+"""
+
+from typing import Optional
+
+from litellm.utils import EmbeddingResponse
+
+from ...openai_like.embedding.handler import OpenAILikeEmbeddingHandler
+from ..common_utils import DatabricksBase
+
+
+class DatabricksEmbeddingHandler(OpenAILikeEmbeddingHandler, DatabricksBase):
+    def embedding(
+        self,
+        model: str,
+        input: list,
+        timeout: float,
+        logging_obj,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        optional_params: dict,
+        model_response: Optional[EmbeddingResponse] = None,
+        client=None,
+        aembedding=None,
+        custom_endpoint: Optional[bool] = None,
+        headers: Optional[dict] = None,
+    ) -> EmbeddingResponse:
+        api_base, headers = self.databricks_validate_environment(
+            api_base=api_base,
+            api_key=api_key,
+            endpoint_type="embeddings",
+            custom_endpoint=custom_endpoint,
+            headers=headers,
+        )
+        return super().embedding(
+            model=model,
+            input=input,
+            timeout=timeout,
+            logging_obj=logging_obj,
+            api_key=api_key,
+            api_base=api_base,
+            optional_params=optional_params,
+            model_response=model_response,
+            client=client,
+            aembedding=aembedding,
+            custom_endpoint=True,
+            headers=headers,
+        )

.venv/lib/python3.10/site-packages/litellm/llms/databricks/embed/transformation.py

@@ -0,0 +1,48 @@
+"""
+Translates from OpenAI's `/v1/embeddings` to Databricks' `/embeddings`
+"""
+
+import types
+from typing import Optional
+
+
+class DatabricksEmbeddingConfig:
+    """
+    Reference: https://learn.microsoft.com/en-us/azure/databricks/machine-learning/foundation-models/api-reference#--embedding-task
+    """
+
+    instruction: Optional[
+        str
+    ] = None  # An optional instruction to pass to the embedding model. BGE Authors recommend 'Represent this sentence for searching relevant passages:' for retrieval queries
+
+    def __init__(self, instruction: Optional[str] = None) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(
+        self,
+    ):  # no optional openai embedding params supported
+        return []
+
+    def map_openai_params(self, non_default_params: dict, optional_params: dict):
+        return optional_params