Source code for langchain_community.embeddings.baichuan

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.
[docs]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( # 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
[docs] def embed_documents(self, texts: List[str]) -> Optional[List[List[float]]]: # type: ignore[override] """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)
[docs] def embed_query(self, text: str) -> Optional[List[float]]: # type: ignore[override] """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