Source code for langchain_community.embeddings.sparkllm

import base64
import hashlib
import hmac
import json
import logging
from datetime import datetime
from time import mktime
from typing import Any, Dict, List, Optional
from urllib.parse import urlencode
from wsgiref.handlers import format_date_time

import numpy as np
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
from numpy import ndarray

# Used for document and knowledge embedding
EMBEDDING_P_API_URL: str = "https://cn-huabei-1.xf-yun.com/v1/private/sa8a05c27"
# Used for user questions embedding
EMBEDDING_Q_API_URL: str = "https://cn-huabei-1.xf-yun.com/v1/private/s50d55a16"

# SparkLLMTextEmbeddings is an embedding model provided by iFLYTEK Co., Ltd.. (https://iflytek.com/en/).

# Official Website: https://www.xfyun.cn/doc/spark/Embedding_new_api.html
# Developers need to create an application in the console first, use the appid, APIKey,
# and APISecret provided in the application for authentication,
# and generate an authentication URL for handshake.
# You can get one by registering at https://console.xfyun.cn/services/bm3.
# SparkLLMTextEmbeddings support 2K token window and preduces vectors with
# 2560 dimensions.

logger = logging.getLogger(__name__)


[docs]class Url:
[docs] def __init__(self, host: str, path: str, schema: str) -> None: self.host = host self.path = path self.schema = schema pass
[docs]class SparkLLMTextEmbeddings(BaseModel, Embeddings): """SparkLLM Text Embedding models.""" spark_app_id: SecretStr spark_api_key: SecretStr spark_api_secret: SecretStr @root_validator(allow_reuse=True) def validate_environment(cls, values: Dict) -> Dict: """Validate that auth token exists in environment.""" cls.spark_app_id = convert_to_secret_str( get_from_dict_or_env(values, "spark_app_id", "SPARK_APP_ID") ) cls.spark_api_key = convert_to_secret_str( get_from_dict_or_env(values, "spark_api_key", "SPARK_API_KEY") ) cls.spark_api_secret = convert_to_secret_str( get_from_dict_or_env(values, "spark_api_secret", "SPARK_API_SECRET") ) return values def _embed(self, texts: List[str], host: str) -> Optional[List[List[float]]]: url = self._assemble_ws_auth_url( request_url=host, method="POST", api_key=self.spark_api_key.get_secret_value(), api_secret=self.spark_api_secret.get_secret_value(), ) embed_result: list = [] for text in texts: query_context = {"messages": [{"content": text, "role": "user"}]} content = self._get_body( self.spark_app_id.get_secret_value(), query_context ) response = requests.post( url, json=content, headers={"content-type": "application/json"} ).text res_arr = self._parser_message(response) if res_arr is not None: embed_result.append(res_arr.tolist()) else: embed_result.append(None) return embed_result
[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, EMBEDDING_P_API_URL)
[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], EMBEDDING_Q_API_URL) return result[0] if result is not None else None
@staticmethod def _assemble_ws_auth_url( request_url: str, method: str = "GET", api_key: str = "", api_secret: str = "" ) -> str: u = SparkLLMTextEmbeddings._parse_url(request_url) host = u.host path = u.path now = datetime.now() date = format_date_time(mktime(now.timetuple())) signature_origin = "host: {}\ndate: {}\n{} {} HTTP/1.1".format( host, date, method, path ) signature_sha = hmac.new( api_secret.encode("utf-8"), signature_origin.encode("utf-8"), digestmod=hashlib.sha256, ).digest() signature_sha_str = base64.b64encode(signature_sha).decode(encoding="utf-8") authorization_origin = ( 'api_key="%s", algorithm="%s", headers="%s", signature="%s"' % (api_key, "hmac-sha256", "host date request-line", signature_sha_str) ) authorization = base64.b64encode(authorization_origin.encode("utf-8")).decode( encoding="utf-8" ) values = {"host": host, "date": date, "authorization": authorization} return request_url + "?" + urlencode(values) @staticmethod def _parse_url(request_url: str) -> Url: stidx = request_url.index("://") host = request_url[stidx + 3 :] schema = request_url[: stidx + 3] edidx = host.index("/") if edidx <= 0: raise AssembleHeaderException("invalid request url:" + request_url) path = host[edidx:] host = host[:edidx] u = Url(host, path, schema) return u @staticmethod def _get_body(appid: str, text: dict) -> Dict[str, Any]: body = { "header": {"app_id": appid, "uid": "39769795890", "status": 3}, "parameter": {"emb": {"feature": {"encoding": "utf8"}}}, "payload": { "messages": { "text": base64.b64encode(json.dumps(text).encode("utf-8")).decode() } }, } return body @staticmethod def _parser_message( message: str, ) -> Optional[ndarray]: data = json.loads(message) code = data["header"]["code"] if code != 0: logger.warning(f"Request error: {code}, {data}") return None else: text_base = data["payload"]["feature"]["text"] text_data = base64.b64decode(text_base) dt = np.dtype(np.float32) dt = dt.newbyteorder("<") text = np.frombuffer(text_data, dtype=dt) if len(text) > 2560: array = text[:2560] else: array = text return array
[docs]class AssembleHeaderException(Exception): """Exception raised for errors in the header assembly.""" def __init__(self, msg: str) -> None: self.message = msg