Source code for langchain_community.embeddings.bookend

"""Wrapper around Bookend AI embedding models."""

import json
from typing import Any, List

import requests
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Field

API_URL = ""
DEFAULT_TASK = "embeddings"
PATH = "/models/predict"

[docs]class BookendEmbeddings(BaseModel, Embeddings): """Bookend AI sentence_transformers embedding models. Example: .. code-block:: python from langchain_community.embeddings import BookendEmbeddings bookend = BookendEmbeddings( domain={domain} api_token={api_token} model_id={model_id} ) bookend.embed_documents([ "Please put on these earmuffs because I can't you hear.", "Baby wipes are made of chocolate stardust.", ]) bookend.embed_query( "She only paints with bold colors; she does not like pastels." ) """ domain: str """Request for a domain at to use this embeddings module.""" api_token: str """Request for an API token at to use this embeddings module.""" model_id: str """Embeddings model ID to use.""" auth_header: dict = Field(default_factory=dict) def __init__(self, **kwargs: Any): super().__init__(**kwargs) self.auth_header = {"Authorization": "Basic {}".format(self.api_token)}
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed documents using a Bookend deployed embeddings model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ result = [] headers = self.auth_header headers["Content-Type"] = "application/json; charset=utf-8" params = { "model_id": self.model_id, "task": DEFAULT_TASK, } for text in texts: data = json.dumps( {"text": text, "question": None, "context": None, "instruction": None} ) r = requests.request( "POST", API_URL + self.domain + PATH, headers=headers, params=params, data=data, ) result.append(r.json()[0]["data"]) return result
[docs] def embed_query(self, text: str) -> List[float]: """Embed a query using a Bookend deployed embeddings model. Args: text: The text to embed. Returns: Embeddings for the text. """ return self.embed_documents([text])[0]