Refact: switch from google-generativeai to google-genai (#13140)

### What problem does this PR solve?

Refact: switch from oogle-generativeai to google-genai  #13132
Refact: commnet out unused pywencai.

### Type of change

- [x] Refactoring

rag/llm/embedding_model.py

@@ -20,7 +20,6 @@ from abc import ABC
 from urllib.parse import urljoin
 
 import dashscope
-import google.generativeai as genai
 import numpy as np
 import requests
 from ollama import Client
@@ -543,31 +542,87 @@ class BedrockEmbed(Base):
 class GeminiEmbed(Base):
     _FACTORY_NAME = "Gemini"
 
-    def __init__(self, key, model_name="models/text-embedding-004", **kwargs):
+    def __init__(self, key, model_name="gemini-embedding-001", **kwargs):
+        from google import genai
+        from google.genai import types
+
         self.key = key
-        self.model_name = "models/" + model_name
+        self.model_name = model_name[7:] if model_name.startswith("models/") else model_name
+        self.client = genai.Client(api_key=self.key)
+        self.types = types
+
+    @staticmethod
+    def _parse_embedding_vector(embedding):
+        if isinstance(embedding, dict):
+            values = embedding.get("values")
+            if values is None:
+                values = embedding.get("embedding")
+            if values is not None:
+                return values
+
+        values = getattr(embedding, "values", None)
+        if values is None:
+            values = getattr(embedding, "embedding", None)
+        if values is not None:
+            return values
+
+        raise TypeError(f"Unsupported embedding payload: {type(embedding)}")
+
+    @classmethod
+    def _parse_embedding_response(cls, response):
+        if response is None:
+            raise ValueError("Embedding response is empty")
+
+        embeddings = getattr(response, "embeddings", None)
+        if embeddings is None and isinstance(response, dict):
+            embeddings = response.get("embeddings")
+
+        if embeddings is None:
+            return [cls._parse_embedding_vector(response)]
+
+        return [cls._parse_embedding_vector(item) for item in embeddings]
+
+    def _build_embedding_config(self):
+        task_type = "RETRIEVAL_DOCUMENT"
+        if hasattr(self.types, "TaskType"):
+            task_type = getattr(self.types.TaskType, "RETRIEVAL_DOCUMENT", task_type)
+        try:
+            return self.types.EmbedContentConfig(task_type=task_type, title="Embedding of single string")
+        except TypeError:
+            # Compatible with SDK versions that do not accept title in embed config.
+            return self.types.EmbedContentConfig(task_type=task_type)
 
     def encode(self, texts: list):
         texts = [truncate(t, 2048) for t in texts]
         token_count = sum(num_tokens_from_string(text) for text in texts)
-        genai.configure(api_key=self.key)
+        config = self._build_embedding_config()
         batch_size = 16
         ress = []
         for i in range(0, len(texts), batch_size):
-            result = genai.embed_content(model=self.model_name, content=texts[i : i + batch_size], task_type="retrieval_document", title="Embedding of single string")
+            result = None
             try:
-                ress.extend(result["embedding"])
+                result = self.client.models.embed_content(
+                    model=self.model_name,
+                    contents=texts[i : i + batch_size],
+                    config=config,
+                )
+                ress.extend(self._parse_embedding_response(result))
             except Exception as _e:
                 log_exception(_e, result)
                 raise Exception(f"Error: {result}")
         return np.array(ress), token_count
 
     def encode_queries(self, text):
-        genai.configure(api_key=self.key)
-        result = genai.embed_content(model=self.model_name, content=truncate(text, 2048), task_type="retrieval_document", title="Embedding of single string")
+        config = self._build_embedding_config()
+        result = None
         token_count = num_tokens_from_string(text)
         try:
-            return np.array(result["embedding"]), token_count
+            result = self.client.models.embed_content(
+                model=self.model_name,
+                contents=[truncate(text, 2048)],
+                config=config,
+            )
+            return np.array(self._parse_embedding_response(result)[0]), token_count
         except Exception as _e:
             log_exception(_e, result)
             raise Exception(f"Error: {result}")