community[minor]: Add Baichuan Text Embedding Model and Baichuan Inc introduction (#16568)

- **Description:** Adding Baichuan Text Embedding Model and Baichuan Inc
introduction.

Baichuan Text Embedding ranks #1 in C-MTEB leaderboard:
https://huggingface.co/spaces/mteb/leaderboard

Co-authored-by: BaiChuanHelper <wintergyc@WinterGYCs-MacBook-Pro.local>

libs/community/langchain_community/embeddings/baichuan.py

@@ -0,0 +1,113 @@
+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.utils import convert_to_secret_str, get_from_dict_or_env
+
+BAICHUAN_API_URL: str = "http://api.baichuan-ai.com/v1/embeddings"
+
+# BaichuanTextEmbeddings is an embedding model provided by Baichuan Inc. (https://www.baichuan-ai.com/home).
+# As of today (Jan 25th, 2024) BaichuanTextEmbeddings ranks #1 in C-MTEB
+# (Chinese Multi-Task Embedding Benchmark) leaderboard.
+# Leaderboard (Under Overall -> Chinese section): https://huggingface.co/spaces/mteb/leaderboard
+
+# Official Website: https://platform.baichuan-ai.com/docs/text-Embedding
+# An API-key is required to use this embedding model. You can get one by registering
+# at https://platform.baichuan-ai.com/docs/text-Embedding.
+# BaichuanTextEmbeddings support 512 token window and preduces vectors with
+# 1024 dimensions.
+
+
+# NOTE!! BaichuanTextEmbeddings only supports Chinese text embedding.
+# Multi-language support is coming soon.
+class BaichuanTextEmbeddings(BaseModel, Embeddings):
+    """Baichuan Text Embedding models."""
+
+    session: Any  #: :meta private:
+    model_name: str = "Baichuan-Text-Embedding"
+    baichuan_api_key: Optional[SecretStr] = None
+
+    @root_validator(allow_reuse=True)
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that auth token exists in environment."""
+        try:
+            baichuan_api_key = convert_to_secret_str(
+                get_from_dict_or_env(values, "baichuan_api_key", "BAICHUAN_API_KEY")
+            )
+        except ValueError as original_exc:
+            try:
+                baichuan_api_key = convert_to_secret_str(
+                    get_from_dict_or_env(
+                        values, "baichuan_auth_token", "BAICHUAN_AUTH_TOKEN"
+                    )
+                )
+            except ValueError:
+                raise original_exc
+        session = requests.Session()
+        session.headers.update(
+            {
+                "Authorization": f"Bearer {baichuan_api_key.get_secret_value()}",
+                "Accept-Encoding": "identity",
+                "Content-type": "application/json",
+            }
+        )
+        values["session"] = session
+        return values
+
+    def _embed(self, texts: List[str]) -> Optional[List[List[float]]]:
+        """Internal method to call Baichuan Embedding API and return embeddings.
+
+        Args:
+            texts: A list of texts to embed.
+
+        Returns:
+            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}
+            )
+            # 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(
+                    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)}")
+            return None
+
+    def embed_documents(self, texts: List[str]) -> Optional[List[List[float]]]:
+        """Public method to get embeddings for a list of documents.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            A list of embeddings, one for each text, or None if an error occurs.
+        """
+        return self._embed(texts)
+
+    def embed_query(self, text: str) -> Optional[List[float]]:
+        """Public method to get embedding for a single query text.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text, or None if an error occurs.
+        """
+        result = self._embed([text])
+        return result[0] if result is not None else None