Fix api_base undefined bug in Gemini embeddings (#11393)

* Support Gemini "transport" configuration

Added Gemini transportation method configuration support.

* Sync updates in multi_modal_llms\gemini

* Updated Dashscope qwen llm defaults

Setting qwen default num_outputs and temperature

* cr

* support gemini embedding configuration

support configuring api_base, api_key, transport method

* fix gptrepo data connector encoding issue

reading a file in default encoding(GBK) will cause error characters problem. Added encoding configuration

* sync latest repo

* sync latest repo

* cr

* cr

* Fix api_base undefined bug in Gemini embeddings

* add comments

* fix linter test

* sync fix in integrations/embeddings

* fix unit test

---------

Co-authored-by: Haotian Zhang <socool.king@gmail.com>

llama-index-integrations/embeddings/llama-index-embeddings-gemini/llama_index/embeddings/gemini/base.py

 """Gemini embeddings file."""
 
+import os
 from typing import Any, List, Optional
 
 import google.generativeai as gemini
@@ -19,6 +20,8 @@ class GeminiEmbedding(BaseEmbedding):
             Defaults to "models/embedding-001".
 
         api_key (Optional[str]): API key to access the model. Defaults to None.
+        api_base (Optional[str]): API base to access the model. Defaults to Official Base.
+        transport (Optional[str]): Transport to access the model.
     """
 
     _model: Any = PrivateAttr()
@@ -36,12 +39,24 @@ class GeminiEmbedding(BaseEmbedding):
         model_name: str = "models/embedding-001",
         task_type: Optional[str] = "retrieval_document",
         api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        transport: Optional[str] = None,
         title: Optional[str] = None,
         embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
         callback_manager: Optional[CallbackManager] = None,
         **kwargs: Any,
     ):
-        gemini.configure(api_key=api_key)
+        # API keys are optional. The API can be authorised via OAuth (detected
+        # environmentally) or by the GOOGLE_API_KEY environment variable.
+        config_params: Dict[str, Any] = {
+            "api_key": api_key or os.getenv("GOOGLE_API_KEY"),
+        }
+        if api_base:
+            config_params["client_options"] = {"api_endpoint": api_base}
+        if transport:
+            config_params["transport"] = transport
+        # transport: A string, one of: [`rest`, `grpc`, `grpc_asyncio`].
+        gemini.configure(**config_params)
         self._model = gemini
 
         super().__init__(

llama-index-legacy/llama_index/legacy/embeddings/gemini.py

@@ -18,6 +18,8 @@ class GeminiEmbedding(BaseEmbedding):
             Defaults to "models/embedding-001".
 
         api_key (Optional[str]): API key to access the model. Defaults to None.
+        api_base (Optional[str]): API base to access the model. Defaults to Official Base.
+        transport (Optional[str]): Transport to access the model.
     """
 
     _model: Any = PrivateAttr()
@@ -35,6 +37,8 @@ class GeminiEmbedding(BaseEmbedding):
         model_name: str = "models/embedding-001",
         task_type: Optional[str] = "retrieval_document",
         api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        transport: Optional[str] = None,
         title: Optional[str] = None,
         embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
         callback_manager: Optional[CallbackManager] = None,