community[patch]: add detailed paragraph and example for BaichuanTextEmbeddings (#22031)

- **Description:** add detailed paragraph and example for
BaichuanTextEmbeddings
   - **Issue:** the issue #21983

libs/community/langchain_community/embeddings/baichuan.py

@@ -4,6 +4,7 @@ import requests
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+from requests import RequestException
 
 BAICHUAN_API_URL: str = "http://api.baichuan-ai.com/v1/embeddings"
 
@@ -22,11 +23,23 @@ BAICHUAN_API_URL: str = "http://api.baichuan-ai.com/v1/embeddings"
 # NOTE!! BaichuanTextEmbeddings only supports Chinese text embedding.
 # Multi-language support is coming soon.
 class BaichuanTextEmbeddings(BaseModel, Embeddings):
-    """Baichuan Text Embedding models."""
+    """Baichuan Text Embedding models.
+
+    To use, you should set the environment variable ``BAICHUAN_API_KEY`` to
+    your API key or pass it as a named parameter to the constructor.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.embeddings import BaichuanTextEmbeddings
+
+            baichuan = BaichuanTextEmbeddings(baichuan_api_key="my-api-key")
+    """
 
     session: Any  #: :meta private:
     model_name: str = "Baichuan-Text-Embedding"
     baichuan_api_key: Optional[SecretStr] = None
+    """Automatically inferred from env var `BAICHUAN_API_KEY` if not provided."""
 
     @root_validator(allow_reuse=True)
     def validate_environment(cls, values: Dict) -> Dict:
@@ -65,29 +78,26 @@ class BaichuanTextEmbeddings(BaseModel, Embeddings):
             A list of list of floats representing the embeddings, or None if an
             error occurs.
         """
-        try:
-            response = self.session.post(
-                BAICHUAN_API_URL, json={"input": texts, "model": self.model_name}
+        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"
             )
-            # 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
-                print(  # noqa: T201
-                    f"Error: Received status code {response.status_code} from "
-                    "embedding API"
-                )
-                return None
-        except Exception as e:
-            # Log the exception or handle it as needed
-            print(f"Exception occurred while trying to get embeddings: {str(e)}")  # noqa: T201
-            return None
 
     def embed_documents(self, texts: List[str]) -> Optional[List[List[float]]]:  # type: ignore[override]
         """Public method to get embeddings for a list of documents.