mirror of
https://github.com/hwchase17/langchain
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77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
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from typing import Any, Dict, List
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
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from langchain_core.utils import get_from_dict_or_env
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class ZhipuAIEmbeddings(BaseModel, Embeddings):
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"""ZhipuAI embedding models.
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To use, you should have the ``zhipuai`` python package installed, and the
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environment variable ``ZHIPU_API_KEY`` set with your API key or pass it
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as a named parameter to the constructor.
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More instructions about ZhipuAi Embeddings, you can get it
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from https://open.bigmodel.cn/dev/api#vector
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Example:
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.. code-block:: python
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from langchain_community.embeddings import ZhipuAIEmbeddings
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embeddings = ZhipuAIEmbeddings(api_key="your-api-key")
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text = "This is a test query."
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query_result = embeddings.embed_query(text)
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# texts = ["This is a test query1.", "This is a test query2."]
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# query_result = embeddings.embed_query(texts)
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"""
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_client: Any = Field(default=None, exclude=True) #: :meta private:
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model: str = Field(default="embedding-2")
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"""Model name"""
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api_key: str
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"""Automatically inferred from env var `ZHIPU_API_KEY` if not provided."""
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@root_validator(pre=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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values["api_key"] = get_from_dict_or_env(values, "api_key", "ZHIPUAI_API_KEY")
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try:
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from zhipuai import ZhipuAI
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values["_client"] = ZhipuAI(api_key=values["api_key"])
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except ImportError:
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raise ImportError(
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"Could not import zhipuai python package."
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"Please install it with `pip install zhipuai`."
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)
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return values
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def embed_query(self, text: str) -> List[float]:
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"""
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Embeds a text using the AutoVOT algorithm.
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Args:
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text: A text to embed.
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Returns:
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Input document's embedded list.
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"""
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resp = self.embed_documents([text])
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return resp[0]
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""
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Embeds a list of text documents using the AutoVOT algorithm.
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Args:
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texts: A list of text documents to embed.
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Returns:
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A list of embeddings for each document in the input list.
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Each embedding is represented as a list of float values.
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"""
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resp = self._client.embeddings.create(model=self.model, input=texts)
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embeddings = [r.embedding for r in resp.data]
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return embeddings
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