Improvement[Embeddings] Add dimension support to ZhipuAIEmbeddings (#25274)

- In the in ` embedding-3 ` and later models of Zhipu AI, it is
supported to specify the dimensions parameter of Embedding. Ref:
https://bigmodel.cn/dev/api#text_embedding-3 .
- Add test case for `embedding-3` model by assigning dimensions.

libs/community/langchain_community/embeddings/zhipuai.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
@@ -70,6 +70,11 @@ class ZhipuAIEmbeddings(BaseModel, Embeddings):
     """Model name"""
     api_key: str
     """Automatically inferred from env var `ZHIPU_API_KEY` if not provided."""
+    dimensions: Optional[int] = None
+    """The number of dimensions the resulting output embeddings should have.
+
+    Only supported in `embedding-3` and later models.
+    """
 
     @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
@@ -110,6 +115,13 @@ class ZhipuAIEmbeddings(BaseModel, Embeddings):
             A list of embeddings for each document in the input list.
             Each embedding is represented as a list of float values.
         """
+        if self.dimensions is not None:
+            resp = self.client.embeddings.create(
+                model=self.model,
+                input=texts,
+                dimensions=self.dimensions,
+            )
+        else:
             resp = self.client.embeddings.create(model=self.model, input=texts)
         embeddings = [r.embedding for r in resp.data]
         return embeddings

libs/community/tests/integration_tests/embeddings/test_zhipuai.py

@@ -18,3 +18,14 @@ def test_zhipuai_embedding_query() -> None:
     embedding = ZhipuAIEmbeddings()  # type: ignore[call-arg]
     res = embedding.embed_query(document)
     assert len(res) == 1024  # type: ignore[arg-type]
+
+
+def test_zhipuai_embedding_dimensions() -> None:
+    """Test ZhipuAI Text Embedding for query by assigning dimensions"""
+    document = "This is a test query."
+    embedding = ZhipuAIEmbeddings(
+        model="embedding-3",
+        dimensions=2048,
+    )  # type: ignore[call-arg]
+    res = embedding.embed_query(document)
+    assert len(res) == 2048  # type: ignore[arg-type]