community: Add Baichuan Embeddings batch size (#22942)

- **Support batch size** 
Baichuan updates the document, indicating that up to 16 documents can be
imported at a time

- **Standardized model init arg names**
    - baichuan_api_key -> api_key
    - model_name  -> model

libs/community/langchain_community/embeddings/baichuan.py

@@ -2,7 +2,7 @@ from typing import Any, Dict, List, Optional
 
 import requests
 from langchain_core.embeddings import Embeddings
-from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator
+from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
 from requests import RequestException
 
@@ -37,9 +37,16 @@ class BaichuanTextEmbeddings(BaseModel, Embeddings):
     """
 
     session: Any  #: :meta private:
-    model_name: str = "Baichuan-Text-Embedding"
-    baichuan_api_key: Optional[SecretStr] = None
+    model_name: str = Field(default="Baichuan-Text-Embedding", alias="model")
+    baichuan_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
     """Automatically inferred from env var `BAICHUAN_API_KEY` if not provided."""
+    chunk_size: int = 16
+    """Chunk size when multiple texts are input"""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        allow_population_by_field_name = True
 
     @root_validator(allow_reuse=True)
     def validate_environment(cls, values: Dict) -> Dict:
@@ -78,26 +85,35 @@ class BaichuanTextEmbeddings(BaseModel, Embeddings):
             A list of list of floats representing the embeddings, or None if an
             error occurs.
         """
-        response = self.session.post(
-            BAICHUAN_API_URL, json={"input": texts, "model": self.model_name}
-        )
-        # Raise exception if response status code from 400 to 600
-        response.raise_for_status()
-        # Check if the response status code indicates success
-        if response.status_code == 200:
-            resp = response.json()
-            embeddings = resp.get("data", [])
-            # Sort resulting embeddings by index
-            sorted_embeddings = sorted(embeddings, key=lambda e: e.get("index", 0))
-            # Return just the embeddings
-            return [result.get("embedding", []) for result in sorted_embeddings]
-        else:
-            # Log error or handle unsuccessful response appropriately
-            # Handle 100 <= status_code < 400, not include 200
-            raise RequestException(
-                f"Error: Received status code {response.status_code} from "
-                "`BaichuanEmbedding` API"
+        chunk_texts = [
+            texts[i : i + self.chunk_size]
+            for i in range(0, len(texts), self.chunk_size)
+        ]
+        embed_results = []
+        for chunk in chunk_texts:
+            response = self.session.post(
+                BAICHUAN_API_URL, json={"input": chunk, "model": self.model_name}
             )
+            # Raise exception if response status code from 400 to 600
+            response.raise_for_status()
+            # Check if the response status code indicates success
+            if response.status_code == 200:
+                resp = response.json()
+                embeddings = resp.get("data", [])
+                # Sort resulting embeddings by index
+                sorted_embeddings = sorted(embeddings, key=lambda e: e.get("index", 0))
+                # Return just the embeddings
+                embed_results.extend(
+                    [result.get("embedding", []) for result in sorted_embeddings]
+                )
+            else:
+                # Log error or handle unsuccessful response appropriately
+                # Handle 100 <= status_code < 400, not include 200
+                raise RequestException(
+                    f"Error: Received status code {response.status_code} from "
+                    "`BaichuanEmbedding` API"
+                )
+        return embed_results
 
     def embed_documents(self, texts: List[str]) -> Optional[List[List[float]]]:  # type: ignore[override]
         """Public method to get embeddings for a list of documents.

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

@@ -17,3 +17,13 @@ def test_baichuan_embedding_query() -> None:
     embedding = BaichuanTextEmbeddings()  # type: ignore[call-arg]
     output = embedding.embed_query(document)
     assert len(output) == 1024  # type: ignore[arg-type]
+
+
+def test_baichuan_embeddings_multi_documents() -> None:
+    """Test Baichuan Text Embedding for documents with multi texts."""
+    document = "午餐吃了螺蛳粉"
+    doc_amount = 35
+    embeddings = BaichuanTextEmbeddings()  # type: ignore[call-arg]
+    output = embeddings.embed_documents([document] * doc_amount)
+    assert len(output) == doc_amount  # type: ignore[arg-type]
+    assert len(output[0]) == 1024  # type: ignore[index]

libs/community/tests/unit_tests/embeddings/test_baichuan.py

@@ -0,0 +1,18 @@
+from typing import cast
+
+from langchain_core.pydantic_v1 import SecretStr
+
+from langchain_community.embeddings import BaichuanTextEmbeddings
+
+
+def test_sparkllm_initialization_by_alias() -> None:
+    # Effective initialization
+    embeddings = BaichuanTextEmbeddings(  # type: ignore[call-arg]
+        model="embedding_model",  # type: ignore[arg-type]
+        api_key="your-api-key",  # type: ignore[arg-type]
+    )
+    assert embeddings.model_name == "embedding_model"
+    assert (
+        cast(SecretStr, embeddings.baichuan_api_key).get_secret_value()
+        == "your-api-key"
+    )