UN-3412 [FEAT] Add Gemini embedding adapter for Google AI Studio

unstract/sdk1/src/unstract/sdk1/adapters/base1.py

@@ -1005,3 +1005,30 @@ def validate_model(adapter_metadata: dict[str, "Any"]) -> str:
             return model
         else:
             return f"ollama/{model}"
+
+
+class GeminiEmbeddingParameters(BaseEmbeddingParameters):
+    """See https://docs.litellm.ai/docs/providers/gemini."""
+
+    api_key: str
+    embed_batch_size: int | None = None
+
+    @staticmethod
+    def validate(adapter_metadata: dict[str, "Any"]) -> dict[str, "Any"]:
+        metadata_copy = {**adapter_metadata}
+        metadata_copy["model"] = GeminiEmbeddingParameters.validate_model(metadata_copy)
+
+        return GeminiEmbeddingParameters(**metadata_copy).model_dump()
+
+    @staticmethod
+    def validate_model(adapter_metadata: dict[str, "Any"]) -> str:
+        raw_model = adapter_metadata.get("model")
+        model = raw_model.strip() if isinstance(raw_model, str) else ""
+        if not model:
+            raise ValueError(
+                "The 'model' field is required for the Gemini embedding adapter. "
+                "Example: 'gemini/text-embedding-004'"
+            )
+        if not model.startswith("gemini/"):
+            model = f"gemini/{model}"
+        return model

unstract/sdk1/src/unstract/sdk1/adapters/embedding1/__init__.py

@@ -3,6 +3,7 @@
 from unstract.sdk1.adapters.base1 import register_adapters
 from unstract.sdk1.adapters.embedding1.azure_openai import AzureOpenAIEmbeddingAdapter
 from unstract.sdk1.adapters.embedding1.bedrock import AWSBedrockEmbeddingAdapter
+from unstract.sdk1.adapters.embedding1.gemini import GeminiEmbeddingAdapter
 from unstract.sdk1.adapters.embedding1.ollama import OllamaEmbeddingAdapter
 from unstract.sdk1.adapters.embedding1.openai import OpenAIEmbeddingAdapter
 from unstract.sdk1.adapters.embedding1.vertexai import VertexAIEmbeddingAdapter
@@ -16,6 +17,7 @@
     "adapters",
     "AzureOpenAIEmbeddingAdapter",
     "AWSBedrockEmbeddingAdapter",
+    "GeminiEmbeddingAdapter",
     "OpenAIEmbeddingAdapter",
     "VertexAIEmbeddingAdapter",
     "OllamaEmbeddingAdapter",

unstract/sdk1/src/unstract/sdk1/adapters/embedding1/gemini.py

@@ -0,0 +1,40 @@
+from typing import Any
+
+from unstract.sdk1.adapters.base1 import BaseAdapter, GeminiEmbeddingParameters
+from unstract.sdk1.adapters.enums import AdapterTypes
+
+
+class GeminiEmbeddingAdapter(GeminiEmbeddingParameters, BaseAdapter):
+    @staticmethod
+    def get_id() -> str:
+        return "gemini|5c2a36b8-0b8e-4f26-82c0-9f3b564cb066"
+
+    @staticmethod
+    def get_metadata() -> dict[str, Any]:
+        return {
+            "name": "Gemini",
+            "version": "1.0.0",
+            "adapter": GeminiEmbeddingAdapter,
+            "description": "Gemini embedding adapter",
+            "is_active": True,
+        }
+
+    @staticmethod
+    def get_name() -> str:
+        return "Gemini"
+
+    @staticmethod
+    def get_description() -> str:
+        return "Gemini embedding adapter"
+
+    @staticmethod
+    def get_provider() -> str:
+        return "gemini"
+
+    @staticmethod
+    def get_icon() -> str:
+        return "/icons/adapter-icons/Gemini.png"
+
+    @staticmethod
+    def get_adapter_type() -> AdapterTypes:
+        return AdapterTypes.EMBEDDING

unstract/sdk1/src/unstract/sdk1/adapters/embedding1/static/gemini.json

@@ -0,0 +1,44 @@
+{
+  "title": "Gemini Embedding",
+  "type": "object",
+  "required": [
+    "adapter_name",
+    "api_key",
+    "model"
+  ],
+  "properties": {
+    "adapter_name": {
+      "type": "string",
+      "title": "Name",
+      "default": "",
+      "description": "Provide a unique name for this adapter instance. Example: gemini-emb-1"
+    },
+    "model": {
+      "type": "string",
+      "title": "Model",
+      "default": "gemini/text-embedding-004",
+      "description": "Provide the name of the model. The gemini/ prefix will be added automatically if omitted. Recommended: gemini/text-embedding-004"
+    },
+    "api_key": {
+      "type": "string",
+      "title": "API Key",
+      "default": "",
+      "format": "password"
+    },
+    "embed_batch_size": {
+      "type": "number",
+      "minimum": 1,
+      "multipleOf": 1,
+      "title": "Embed Batch Size",
+      "description": "Number of texts to embed in a single batch. Leave empty to use the system default."
+    },
+    "timeout": {
+      "type": "number",
+      "minimum": 0,
+      "multipleOf": 1,
+      "title": "Timeout",
+      "default": 240,
+      "description": "Timeout in seconds"
+    }
+  }
+}

unstract/sdk1/src/unstract/sdk1/embedding.py

@@ -94,7 +94,7 @@
             self.platform_kwargs: dict[str, object] = kwargs
             self.kwargs: dict[str, object] = self.adapter.validate(self._adapter_metadata)
             self._cost_model: str | None = self.kwargs.pop("cost_model", None)
-        except ValidationError as e:
+        except (ValidationError, ValueError) as e:
             raise SdkError("Invalid embedding adapter metadata: " + str(e)) from e
 
         # Test connection - wrap in error handling

unstract/sdk1/tests/test_gemini_embedding.py

@@ -0,0 +1,152 @@
+import json
+
+import pytest
+from unstract.sdk1.adapters.embedding1.gemini import GeminiEmbeddingAdapter
+from unstract.sdk1.adapters.enums import AdapterTypes
+
+
+class TestGeminiEmbeddingAdapter:
+    def test_adapter_registration(self) -> None:
+        from unstract.sdk1.adapters.embedding1 import adapters
+
+        gemini_ids = [k for k in adapters if "gemini" in k.lower()]
+        assert len(gemini_ids) == 1
+
+    def test_get_id_format(self) -> None:
+        adapter_id = GeminiEmbeddingAdapter.get_id()
+        assert adapter_id.startswith("gemini|")
+        # Standard UUID-4 with hyphens is 36 characters
+        uuid_part = adapter_id.split("|")[1]
+        assert len(uuid_part) == 36
+
+    def test_get_adapter_type(self) -> None:
+        assert GeminiEmbeddingAdapter.get_adapter_type() == AdapterTypes.EMBEDDING
+
+    def test_get_name(self) -> None:
+        assert GeminiEmbeddingAdapter.get_name() == "Gemini"
+
+    def test_get_provider(self) -> None:
+        assert GeminiEmbeddingAdapter.get_provider() == "gemini"
+
+    def test_json_schema_loads(self) -> None:
+        schema = json.loads(GeminiEmbeddingAdapter.get_json_schema())
+        assert isinstance(schema, dict)
+        assert "title" in schema
+        assert "properties" in schema
+        assert schema["title"] == "Gemini Embedding"
+
+    def test_json_schema_required_fields(self) -> None:
+        schema = json.loads(GeminiEmbeddingAdapter.get_json_schema())
+        assert set(schema["required"]) == {"adapter_name", "api_key", "model"}
+
+    def test_json_schema_no_batch_size_default(self) -> None:
+        schema = json.loads(GeminiEmbeddingAdapter.get_json_schema())
+        assert "default" not in schema["properties"]["embed_batch_size"]
+
+    def test_json_schema_api_key_password_format(self) -> None:
+        schema = json.loads(GeminiEmbeddingAdapter.get_json_schema())
+        assert schema["properties"]["api_key"]["format"] == "password"
+
+    def test_json_schema_model_default(self) -> None:
+        schema = json.loads(GeminiEmbeddingAdapter.get_json_schema())
+        assert schema["properties"]["model"]["default"] == "gemini/text-embedding-004"
+
+    def test_validate_model_adds_prefix(self) -> None:
+        meta = {"model": "text-embedding-004", "api_key": "test"}
+        result = GeminiEmbeddingAdapter.validate_model(meta)
+        assert result == "gemini/text-embedding-004"
+
+    def test_validate_model_idempotent(self) -> None:
+        meta = {"model": "gemini/text-embedding-004", "api_key": "test"}
+        result = GeminiEmbeddingAdapter.validate_model(meta)
+        assert result == "gemini/text-embedding-004"
+
+    def test_validate_model_does_not_mutate_input(self) -> None:
+        meta = {"model": "text-embedding-004", "api_key": "test"}
+        GeminiEmbeddingAdapter.validate_model(meta)
+        assert meta["model"] == "text-embedding-004"
+
+    def test_validate_does_not_mutate_input(self) -> None:
+        meta = {"model": "text-embedding-004", "api_key": "test-key"}
+        original_model = meta["model"]
+        GeminiEmbeddingAdapter.validate(meta)
+        assert meta["model"] == original_model
+
+    def test_validate_model_empty_string_raises(self) -> None:
+        meta = {"model": "", "api_key": "test"}
+        with pytest.raises(ValueError, match="model.*required"):
+            GeminiEmbeddingAdapter.validate_model(meta)
+
+    def test_validate_model_whitespace_only_raises(self) -> None:
+        meta = {"model": "   ", "api_key": "test"}
+        with pytest.raises(ValueError, match="model.*required"):
+            GeminiEmbeddingAdapter.validate_model(meta)
+
+    def test_validate_model_none_raises(self) -> None:
+        meta = {"model": None, "api_key": "test"}
+        with pytest.raises(ValueError, match="model.*required"):
+            GeminiEmbeddingAdapter.validate_model(meta)
+
+    def test_validate_model_missing_key_raises(self) -> None:
+        meta = {"api_key": "test"}
+        with pytest.raises(ValueError, match="model.*required"):
+            GeminiEmbeddingAdapter.validate_model(meta)
+
+    def test_validate_empty_model_raises(self) -> None:
+        meta = {"model": "", "api_key": "test-key"}
+        with pytest.raises(ValueError, match="model.*required"):
+            GeminiEmbeddingAdapter.validate(meta)
+
+    def test_validate_none_model_raises(self) -> None:
+        meta = {"model": None, "api_key": "test-key"}
+        with pytest.raises(ValueError, match="model.*required"):
+            GeminiEmbeddingAdapter.validate(meta)
+
+    def test_validate_missing_api_key_raises(self) -> None:
+        from pydantic import ValidationError
+
+        meta = {"model": "gemini/text-embedding-004"}
+        with pytest.raises(ValidationError):
+            GeminiEmbeddingAdapter.validate(meta)
+
+    def test_validate_calls_validate_model(self) -> None:
+        meta = {"model": "text-embedding-004", "api_key": "test-key"}
+        validated = GeminiEmbeddingAdapter.validate(meta)
+        assert validated["model"] == "gemini/text-embedding-004"
+
+    def test_validate_embed_batch_size_none_by_default(self) -> None:
+        meta = {"model": "gemini/text-embedding-004", "api_key": "test-key"}
+        validated = GeminiEmbeddingAdapter.validate(meta)
+        assert validated["embed_batch_size"] is None
+
+    def test_validate_embed_batch_size_preserved(self) -> None:
+        meta = {
+            "model": "gemini/text-embedding-004",
+            "api_key": "test-key",
+            "embed_batch_size": 50,
+        }
+        validated = GeminiEmbeddingAdapter.validate(meta)
+        assert validated["embed_batch_size"] == 50
+
+    def test_validate_strips_extra_fields(self) -> None:
+        meta = {
+            "model": "gemini/text-embedding-004",
+            "api_key": "test-key",
+            "adapter_name": "my-adapter",
+            "unknown_field": "should_be_dropped",
+        }
+        validated = GeminiEmbeddingAdapter.validate(meta)
+        assert "adapter_name" not in validated
+        assert "unknown_field" not in validated
+
+    def test_validate_includes_base_fields(self) -> None:
+        meta = {"model": "gemini/text-embedding-004", "api_key": "test-key"}
+        validated = GeminiEmbeddingAdapter.validate(meta)
+        assert "timeout" in validated
+        assert "max_retries" in validated
+
+    def test_metadata(self) -> None:
+        metadata = GeminiEmbeddingAdapter.get_metadata()
+        assert metadata["name"] == "Gemini"
+        assert metadata["is_active"] is True
+        assert metadata["adapter"] is GeminiEmbeddingAdapter