mirror of
https://github.com/hwchase17/langchain
synced 2024-11-20 03:25:56 +00:00
114 lines
4.5 KiB
Python
114 lines
4.5 KiB
Python
from typing import Any, Dict, List, Optional
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import requests
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
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BAICHUAN_API_URL: str = "http://api.baichuan-ai.com/v1/embeddings"
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# BaichuanTextEmbeddings is an embedding model provided by Baichuan Inc. (https://www.baichuan-ai.com/home).
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# As of today (Jan 25th, 2024) BaichuanTextEmbeddings ranks #1 in C-MTEB
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# (Chinese Multi-Task Embedding Benchmark) leaderboard.
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# Leaderboard (Under Overall -> Chinese section): https://huggingface.co/spaces/mteb/leaderboard
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# Official Website: https://platform.baichuan-ai.com/docs/text-Embedding
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# An API-key is required to use this embedding model. You can get one by registering
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# at https://platform.baichuan-ai.com/docs/text-Embedding.
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# BaichuanTextEmbeddings support 512 token window and preduces vectors with
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# 1024 dimensions.
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# NOTE!! BaichuanTextEmbeddings only supports Chinese text embedding.
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# Multi-language support is coming soon.
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class BaichuanTextEmbeddings(BaseModel, Embeddings):
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"""Baichuan Text Embedding models."""
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session: Any #: :meta private:
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model_name: str = "Baichuan-Text-Embedding"
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baichuan_api_key: Optional[SecretStr] = None
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@root_validator(allow_reuse=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that auth token exists in environment."""
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try:
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baichuan_api_key = convert_to_secret_str(
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get_from_dict_or_env(values, "baichuan_api_key", "BAICHUAN_API_KEY")
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)
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except ValueError as original_exc:
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try:
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baichuan_api_key = convert_to_secret_str(
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get_from_dict_or_env(
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values, "baichuan_auth_token", "BAICHUAN_AUTH_TOKEN"
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)
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)
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except ValueError:
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raise original_exc
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session = requests.Session()
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session.headers.update(
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{
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"Authorization": f"Bearer {baichuan_api_key.get_secret_value()}",
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"Accept-Encoding": "identity",
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"Content-type": "application/json",
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}
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)
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values["session"] = session
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return values
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def _embed(self, texts: List[str]) -> Optional[List[List[float]]]:
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"""Internal method to call Baichuan Embedding API and return embeddings.
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Args:
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texts: A list of texts to embed.
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Returns:
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A list of list of floats representing the embeddings, or None if an
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error occurs.
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"""
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try:
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response = self.session.post(
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BAICHUAN_API_URL, json={"input": texts, "model": self.model_name}
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)
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# Check if the response status code indicates success
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if response.status_code == 200:
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resp = response.json()
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embeddings = resp.get("data", [])
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# Sort resulting embeddings by index
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sorted_embeddings = sorted(embeddings, key=lambda e: e.get("index", 0))
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# Return just the embeddings
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return [result.get("embedding", []) for result in sorted_embeddings]
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else:
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# Log error or handle unsuccessful response appropriately
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print( # noqa: T201
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f"""Error: Received status code {response.status_code} from
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embedding API"""
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)
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return None
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except Exception as e:
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# Log the exception or handle it as needed
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print(f"Exception occurred while trying to get embeddings: {str(e)}") # noqa: T201
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return None
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def embed_documents(self, texts: List[str]) -> Optional[List[List[float]]]: # type: ignore[override]
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"""Public method to get embeddings for a list of documents.
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Args:
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texts: The list of texts to embed.
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Returns:
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A list of embeddings, one for each text, or None if an error occurs.
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"""
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return self._embed(texts)
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def embed_query(self, text: str) -> Optional[List[float]]: # type: ignore[override]
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"""Public method to get embedding for a single query text.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text, or None if an error occurs.
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"""
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result = self._embed([text])
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return result[0] if result is not None else None
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