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>

docs/docs/integrations/providers/baichuan.mdx

@@ -0,0 +1,13 @@
+# Baichuan
+
+>[Baichuan Inc.](https://www.baichuan-ai.com/) is a Chinese startup in the era of AGI, dedicated to addressing fundamental human needs: Efficiency, Health, and Happiness.
+
+## Visit Us
+Visit us at https://www.baichuan-ai.com/.
+Register and get an API key if you are trying out our APIs.
+
+## Baichuan Chat Model
+An example is available at [example](/docs/integrations/chat/baichuan).
+
+## Baichuan Text Embedding Model
+An example is available at [example] (/docs/integrations/text_embedding/baichuan)

docs/docs/integrations/text_embedding/baichuan.ipynb

@@ -0,0 +1,75 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Baichuan Text Embeddings\n",
+    "\n",
+    "As of today (Jan 25th, 2024) BaichuanTextEmbeddings ranks #1 in C-MTEB (Chinese Multi-Task Embedding Benchmark) leaderboard.\n",
+    "\n",
+    "Leaderboard (Under Overall -> Chinese section): https://huggingface.co/spaces/mteb/leaderboard\n",
+    "\n",
+    "Official Website: https://platform.baichuan-ai.com/docs/text-Embedding\n",
+    "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.\n",
+    "BaichuanTextEmbeddings support 512 token window and preduces vectors with 1024 dimensions. \n",
+    "\n",
+    "Please NOTE that BaichuanTextEmbeddings only supports Chinese text embedding. Multi-language support is coming soon.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_community.embeddings import BaichuanTextEmbeddings\n",
+    "\n",
+    "# Place your Baichuan API-key here.\n",
+    "embeddings = BaichuanTextEmbeddings(baichuan_api_key=\"sk-*\")\n",
+    "\n",
+    "text_1 = \"今天天气不错\"\n",
+    "text_2 = \"今天阳光很好\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text_1)\n",
+    "query_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "doc_result = embeddings.embed_documents([text_1, text_2])\n",
+    "doc_result"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/vectorstores/kdbai.ipynb

@@ -167,9 +167,9 @@
    ],
    "source": [
     "%%time\n",
-    "URL = 'https://www.conseil-constitutionnel.fr/node/3850/pdf'\n",
-    "PDF = 'Déclaration_des_droits_de_l_homme_et_du_citoyen.pdf'\n",
-    "open(PDF, 'wb').write(requests.get(URL).content)"
+    "URL = \"https://www.conseil-constitutionnel.fr/node/3850/pdf\"\n",
+    "PDF = \"Déclaration_des_droits_de_l_homme_et_du_citoyen.pdf\"\n",
+    "open(PDF, \"wb\").write(requests.get(URL).content)"
    ]
   },
   {
@@ -208,7 +208,7 @@
    ],
    "source": [
     "%%time\n",
-    "print('Read a PDF...')\n",
+    "print(\"Read a PDF...\")\n",
     "loader = PyPDFLoader(PDF)\n",
     "pages = loader.load_and_split()\n",
     "len(pages)"
@@ -252,12 +252,14 @@
    ],
    "source": [
     "%%time\n",
-    "print('Create a Vector Database from PDF text...')\n",
-    "embeddings = OpenAIEmbeddings(model='text-embedding-ada-002')\n",
+    "print(\"Create a Vector Database from PDF text...\")\n",
+    "embeddings = OpenAIEmbeddings(model=\"text-embedding-ada-002\")\n",
     "texts = [p.page_content for p in pages]\n",
     "metadata = pd.DataFrame(index=list(range(len(texts))))\n",
-    "metadata['tag'] = 'law'\n",
-    "metadata['title'] = 'Déclaration des Droits de l\\'Homme et du Citoyen de 1789'.encode('utf-8')\n",
+    "metadata[\"tag\"] = \"law\"\n",
+    "metadata[\"title\"] = \"Déclaration des Droits de l'Homme et du Citoyen de 1789\".encode(\n",
+    "    \"utf-8\"\n",
+    ")\n",
     "vectordb = KDBAI(table, embeddings)\n",
     "vectordb.add_texts(texts=texts, metadatas=metadata)"
    ]
@@ -288,11 +290,13 @@
    ],
    "source": [
     "%%time\n",
-    "print('Create LangChain Pipeline...')\n",
-    "qabot = RetrievalQA.from_chain_type(chain_type='stuff',\n",
-    "                                    llm=ChatOpenAI(model='gpt-3.5-turbo-16k', temperature=TEMP), \n",
-    "                                    retriever=vectordb.as_retriever(search_kwargs=dict(k=K)),\n",
-    "                                    return_source_documents=True)"
+    "print(\"Create LangChain Pipeline...\")\n",
+    "qabot = RetrievalQA.from_chain_type(\n",
+    "    chain_type=\"stuff\",\n",
+    "    llm=ChatOpenAI(model=\"gpt-3.5-turbo-16k\", temperature=TEMP),\n",
+    "    retriever=vectordb.as_retriever(search_kwargs=dict(k=K)),\n",
+    "    return_source_documents=True,\n",
+    ")"
    ]
   },
   {
@@ -325,9 +329,9 @@
    ],
    "source": [
     "%%time\n",
-    "Q = 'Summarize the document in English:'\n",
-    "print(f'\\n\\n{Q}\\n')\n",
-    "print(qabot.invoke(dict(query=Q))['result'])"
+    "Q = \"Summarize the document in English:\"\n",
+    "print(f\"\\n\\n{Q}\\n\")\n",
+    "print(qabot.invoke(dict(query=Q))[\"result\"])"
    ]
   },
   {
@@ -362,9 +366,9 @@
    ],
    "source": [
     "%%time\n",
-    "Q = 'Is it a fair law and why ?'\n",
-    "print(f'\\n\\n{Q}\\n')\n",
-    "print(qabot.invoke(dict(query=Q))['result'])"
+    "Q = \"Is it a fair law and why ?\"\n",
+    "print(f\"\\n\\n{Q}\\n\")\n",
+    "print(qabot.invoke(dict(query=Q))[\"result\"])"
    ]
   },
   {
@@ -414,9 +418,9 @@
    ],
    "source": [
     "%%time\n",
-    "Q = 'What are the rights and duties of the man, the citizen and the society ?'\n",
-    "print(f'\\n\\n{Q}\\n')\n",
-    "print(qabot.invoke(dict(query=Q))['result'])"
+    "Q = \"What are the rights and duties of the man, the citizen and the society ?\"\n",
+    "print(f\"\\n\\n{Q}\\n\")\n",
+    "print(qabot.invoke(dict(query=Q))[\"result\"])"
    ]
   },
   {
@@ -441,9 +445,9 @@
    ],
    "source": [
     "%%time\n",
-    "Q = 'Is this law practical ?'\n",
-    "print(f'\\n\\n{Q}\\n')\n",
-    "print(qabot.invoke(dict(query=Q))['result'])"
+    "Q = \"Is this law practical ?\"\n",
+    "print(f\"\\n\\n{Q}\\n\")\n",
+    "print(qabot.invoke(dict(query=Q))[\"result\"])"
    ]
   },
   {

libs/community/langchain_community/embeddings/__init__.py

@@ -20,6 +20,7 @@ from langchain_community.embeddings.aleph_alpha import (
 )
 from langchain_community.embeddings.awa import AwaEmbeddings
 from langchain_community.embeddings.azure_openai import AzureOpenAIEmbeddings
+from langchain_community.embeddings.baichuan import BaichuanTextEmbeddings
 from langchain_community.embeddings.baidu_qianfan_endpoint import (
     QianfanEmbeddingsEndpoint,
 )
@@ -92,6 +93,7 @@ logger = logging.getLogger(__name__)
 __all__ = [
     "OpenAIEmbeddings",
     "AzureOpenAIEmbeddings",
+    "BaichuanTextEmbeddings",
     "ClarifaiEmbeddings",
     "CohereEmbeddings",
     "DatabricksEmbeddings",

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

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

@@ -0,0 +1,19 @@
+"""Test Baichuan Text Embedding."""
+from langchain_community.embeddings.baichuan import BaichuanTextEmbeddings
+
+
+def test_baichuan_embedding_documents() -> None:
+    """Test Baichuan Text Embedding for documents."""
+    documents = ["今天天气不错", "今天阳光灿烂"]
+    embedding = BaichuanTextEmbeddings()
+    output = embedding.embed_documents(documents)
+    assert len(output) == 2
+    assert len(output[0]) == 1024
+
+
+def test_baichuan_embedding_query() -> None:
+    """Test Baichuan Text Embedding for query."""
+    document = "所有的小学生都会学过只因兔同笼问题。"
+    embedding = BaichuanTextEmbeddings()
+    output = embedding.embed_query(document)
+    assert len(output) == 1024

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

@@ -3,6 +3,7 @@ from langchain_community.embeddings import __all__
 EXPECTED_ALL = [
     "OpenAIEmbeddings",
     "AzureOpenAIEmbeddings",
+    "BaichuanTextEmbeddings",
     "ClarifaiEmbeddings",
     "CohereEmbeddings",
     "DatabricksEmbeddings",