chore: bump Rust toolchain from 1.91.0 to 1.94.0 (#3257)

Bumps the Rust toolchain to 1.94.0 (latest installed) to unblock CI
failures caused by the AWS SDK's MSRV requirement. No lint fixes were
needed.

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>

.github/workflows/nodejs.yml

@@ -8,6 +8,7 @@ on:
     paths:
       - Cargo.toml
       - Cargo.lock
+      - rust-toolchain.toml
       - nodejs/**
       - rust/**
       - docs/src/js/**

.github/workflows/python.yml

@@ -8,6 +8,7 @@ on:
     paths:
       - Cargo.toml
       - Cargo.lock
+      - rust-toolchain.toml
       - python/**
       - rust/**
       - .github/workflows/python.yml

.github/workflows/rust.yml

@@ -8,6 +8,7 @@ on:
     paths:
       - Cargo.toml
       - Cargo.lock
+      - rust-toolchain.toml
       - rust/**
       - .github/workflows/rust.yml
 

python/pyproject.toml

@@ -83,7 +83,7 @@ embeddings = [
     "colpali-engine>=0.3.10",
     "huggingface_hub>=0.19.0",
     "InstructorEmbedding>=1.0.1",
-    "google.generativeai>=0.3.0",
+    "google-genai>=1.0.0",
     "boto3>=1.28.57",
     "awscli>=1.44.38",
     "botocore>=1.31.57",

python/python/lancedb/embeddings/gemini_text.py

@@ -19,10 +19,10 @@ from .utils import TEXT, api_key_not_found_help
 @register("gemini-text")
 class GeminiText(TextEmbeddingFunction):
     """
-    An embedding function that uses the Google's Gemini API. Requires GOOGLE_API_KEY to
+    An embedding function that uses Google's Gemini API. Requires GOOGLE_API_KEY to
     be set.
 
-    https://ai.google.dev/docs/embeddings_guide
+    https://ai.google.dev/gemini-api/docs/embeddings
 
     Supports various tasks types:
     | Task Type               | Description                                            |
@@ -46,9 +46,12 @@ class GeminiText(TextEmbeddingFunction):
 
     Parameters
     ----------
-    name: str, default "models/embedding-001"
-        The name of the model to use. See the Gemini documentation for a list of
-        available models.
+    name: str, default "gemini-embedding-001"
+        The name of the model to use. Supported models include:
+        - "gemini-embedding-001" (768 dimensions)
+
+        Note: The legacy "models/embedding-001" format is also supported but
+        "gemini-embedding-001" is recommended.
 
     query_task_type: str, default "retrieval_query"
         Sets the task type for the queries.
@@ -77,7 +80,7 @@ class GeminiText(TextEmbeddingFunction):
 
     """
 
-    name: str = "models/embedding-001"
+    name: str = "gemini-embedding-001"
     query_task_type: str = "retrieval_query"
     source_task_type: str = "retrieval_document"
 
@@ -114,23 +117,48 @@ class GeminiText(TextEmbeddingFunction):
         texts: list[str] or np.ndarray (of str)
             The texts to embed
         """
-        if (
-            kwargs.get("task_type") == "retrieval_document"
-        ):  # Provide a title to use existing API design
-            title = "Embedding of a document"
-            kwargs["title"] = title
+        from google.genai import types
 
-        return [
-            self.client.embed_content(model=self.name, content=text, **kwargs)[
-                "embedding"
-            ]
-            for text in texts
-        ]
+        task_type = kwargs.get("task_type")
+
+        # Build content objects for embed_content
+        contents = []
+        for text in texts:
+            if task_type == "retrieval_document":
+                # Provide a title for retrieval_document task
+                contents.append(
+                    {"parts": [{"text": "Embedding of a document"}, {"text": text}]}
+                )
+            else:
+                contents.append({"parts": [{"text": text}]})
+
+        # Build config
+        config_kwargs = {}
+        if task_type:
+            config_kwargs["task_type"] = task_type.upper()  # API expects uppercase
+
+        # Call embed_content for each content
+        embeddings = []
+        for content in contents:
+            config = (
+                types.EmbedContentConfig(**config_kwargs) if config_kwargs else None
+            )
+            response = self.client.models.embed_content(
+                model=self.name,
+                contents=content,
+                config=config,
+            )
+            embeddings.append(response.embeddings[0].values)
+
+        return embeddings
 
     @cached_property
     def client(self):
-        genai = attempt_import_or_raise("google.generativeai", "google.generativeai")
+        attempt_import_or_raise("google.genai", "google-genai")
 
         if not os.environ.get("GOOGLE_API_KEY"):
             api_key_not_found_help("google")
-        return genai
+
+        from google import genai as genai_module
+
+        return genai_module.Client(api_key=os.environ.get("GOOGLE_API_KEY"))

rust-toolchain.toml

@@ -1,2 +1,2 @@
 [toolchain]
-channel = "1.91.0"
+channel = "1.94.0"

rust/lancedb/src/embeddings/bedrock.rs

@@ -177,6 +177,7 @@ impl BedrockEmbeddingFunction {
                         ))
                         .send()
                         .await
+                        .map_err(Box::new)
                 })
             })
             .unwrap();