feat(gemini): Support gemini-embedding-001 and fix models/ prefix in metadata keys (#3813)

# Add support for Google Gemini `gemini-embedding-001` embedding model
and correctly registers model type

MR message created with the assistance of Claude-4.5-sonnet

This resolves #3755

## What does this PR do?

This PR adds support for the `gemini-embedding-001` Google embedding
model to the llama-stack Gemini provider. This model provides
high-dimensional embeddings (3072 dimensions) compared to the existing
`text-embedding-004` model (768 dimensions). Old embeddings models (such
as text-embedding-004) will be deprecated soon according to Google
([Link](https://developers.googleblog.com/en/gemini-embedding-available-gemini-api/))

## Problem

The Gemini provider only supported the `text-embedding-004` embedding
model. The newer `gemini-embedding-001` model, which provides
higher-dimensional embeddings for improved semantic representation, was
not available through llama-stack.

## Solution

This PR consists of three commits that implement, fix the model
registration, and enable embedding generation:

### Commit 1: Initial addition of gemini-embedding-001

Added metadata for `gemini-embedding-001` to the
`embedding_model_metadata` dictionary:

```python
embedding_model_metadata: dict[str, dict[str, int]] = {
    "text-embedding-004": {"embedding_dimension": 768, "context_length": 2048},
    "gemini-embedding-001": {"embedding_dimension": 3072, "context_length": 2048},  # NEW
}
```

**Issue discovered:** The model was not being registered correctly
because the dictionary keys didn't match the model IDs returned by
Gemini's API.

### Commit 2: Fix model ID matching with `models/` prefix

Updated both dictionary keys to include the `models/` prefix to match
Gemini's OpenAI-compatible API response format:

```python
embedding_model_metadata: dict[str, dict[str, int]] = {
    "models/text-embedding-004": {"embedding_dimension": 768, "context_length": 2048},      # UPDATED
    "models/gemini-embedding-001": {"embedding_dimension": 3072, "context_length": 2048},  # UPDATED
}
```

**Root cause:** Gemini's OpenAI-compatible API returns model IDs with
the `models/` prefix (e.g., `models/text-embedding-004`). The
`OpenAIMixin.list_models()` method directly matches these IDs against
the `embedding_model_metadata` dictionary keys. Without the prefix, the
models were being registered as LLMs instead of embedding models.

### Commit 3: Fix embedding generation for providers without usage stats

Fixed a bug in `OpenAIMixin.openai_embeddings()` that prevented
embedding generation for providers (like Gemini) that don't return usage
statistics:

```python
# Before (Line 351-354):
usage = OpenAIEmbeddingUsage(
    prompt_tokens=response.usage.prompt_tokens,  # ← Crashed with AttributeError
    total_tokens=response.usage.total_tokens,
)

# After (Lines 351-362):
if response.usage:
    usage = OpenAIEmbeddingUsage(
        prompt_tokens=response.usage.prompt_tokens,
        total_tokens=response.usage.total_tokens,
    )
else:
    usage = OpenAIEmbeddingUsage(
        prompt_tokens=0,  # Default when not provided
        total_tokens=0,   # Default when not provided
    )
```

**Impact:** This fix enables embedding generation for **all** Gemini
embedding models, not just the newly added one.

## Changes

### Modified Files

**`llama_stack/providers/remote/inference/gemini/gemini.py`**
- Line 17: Updated `text-embedding-004` key to
`models/text-embedding-004`
- Line 18: Added `models/gemini-embedding-001` with correct metadata

**`llama_stack/providers/utils/inference/openai_mixin.py`**
- Lines 351-362: Added null check for `response.usage` to handle
providers without usage statistics

## Key Technical Details

### Model ID Matching Flow

1. `list_provider_model_ids()` calls Gemini's `/v1/models` endpoint
2. API returns model IDs like: `models/text-embedding-004`,
`models/gemini-embedding-001`
3. `OpenAIMixin.list_models()` (line 410) checks: `if metadata :=
self.embedding_model_metadata.get(provider_model_id)`
4. If matched, registers as `model_type: "embedding"` with metadata;
otherwise registers as `model_type: "llm"`

### Why Both Keys Needed the Prefix

The `text-embedding-004` model was already working because there was
likely separate configuration or manual registration handling it. For
auto-discovery to work correctly for **both** models, both keys must
match the API's model ID format exactly.

## How to test this PR

Verified the changes by:

1. **Model Auto-Discovery**: Started llama-stack server and confirmed
models are auto-discovered from Gemini API

2. **Model Registration**: Confirmed both embedding models are correctly
registered and visible
```bash
curl http://localhost:8325/v1/models | jq '.data[] | select(.provider_id == "gemini" and .model_type == "embedding")'
```

**Results:**
- ✅ `gemini/models/text-embedding-004` - 768 dimensions - `model_type:
"embedding"`
- ✅ `gemini/models/gemini-embedding-001` - 3072 dimensions -
`model_type: "embedding"`

3. **Before Fix (Commit 1)**: Models appeared as `model_type: "llm"`
without embedding metadata

4. **After Fix (Commit 2)**: Models correctly identified as `model_type:
"embedding"` with proper metadata

5. **Generate Embeddings**: Verified embedding generation works
```bash
curl -X POST http://localhost:8325/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"model": "gemini/models/gemini-embedding-001", "input": "test"}' | \
  jq '.data[0].embedding | length'
```

Author: jperezdealgaba

llama_stack/providers/remote/inference/gemini/gemini.py

@@ -4,6 +4,14 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from openai import NOT_GIVEN
+
+from llama_stack.apis.inference import (
+    OpenAIEmbeddingData,
+    OpenAIEmbeddingsRequestWithExtraBody,
+    OpenAIEmbeddingsResponse,
+    OpenAIEmbeddingUsage,
+)
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import GeminiConfig
@@ -14,8 +22,61 @@ class GeminiInferenceAdapter(OpenAIMixin):
 
     provider_data_api_key_field: str = "gemini_api_key"
     embedding_model_metadata: dict[str, dict[str, int]] = {
-        "text-embedding-004": {"embedding_dimension": 768, "context_length": 2048},
+        "models/text-embedding-004": {"embedding_dimension": 768, "context_length": 2048},
+        "models/gemini-embedding-001": {"embedding_dimension": 3072, "context_length": 2048},
     }
 
     def get_base_url(self):
         return "https://generativelanguage.googleapis.com/v1beta/openai/"
+
+    async def openai_embeddings(
+        self,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
+    ) -> OpenAIEmbeddingsResponse:
+        """
+        Override embeddings method to handle Gemini's missing usage statistics.
+        Gemini's embedding API doesn't return usage information, so we provide default values.
+        """
+        # Prepare request parameters
+        request_params = {
+            "model": await self._get_provider_model_id(params.model),
+            "input": params.input,
+            "encoding_format": params.encoding_format if params.encoding_format is not None else NOT_GIVEN,
+            "dimensions": params.dimensions if params.dimensions is not None else NOT_GIVEN,
+            "user": params.user if params.user is not None else NOT_GIVEN,
+        }
+
+        # Add extra_body if present
+        extra_body = params.model_extra
+        if extra_body:
+            request_params["extra_body"] = extra_body
+
+        # Call OpenAI embeddings API with properly typed parameters
+        response = await self.client.embeddings.create(**request_params)
+
+        data = []
+        for i, embedding_data in enumerate(response.data):
+            data.append(
+                OpenAIEmbeddingData(
+                    embedding=embedding_data.embedding,
+                    index=i,
+                )
+            )
+
+        # Gemini doesn't return usage statistics - use default values
+        if hasattr(response, "usage") and response.usage:
+            usage = OpenAIEmbeddingUsage(
+                prompt_tokens=response.usage.prompt_tokens,
+                total_tokens=response.usage.total_tokens,
+            )
+        else:
+            usage = OpenAIEmbeddingUsage(
+                prompt_tokens=0,
+                total_tokens=0,
+            )
+
+        return OpenAIEmbeddingsResponse(
+            data=data,
+            model=params.model,
+            usage=usage,
+        )