Source code for langchain_community.llms.sparkllm

from __future__ import annotations

import base64
import hashlib
import hmac
import json
import logging
import queue
import threading
from datetime import datetime
from queue import Queue
from time import mktime
from typing import Any, Dict, Generator, Iterator, List, Optional
from urllib.parse import urlencode, urlparse, urlunparse
from wsgiref.handlers import format_date_time

from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.utils import get_from_dict_or_env

logger = logging.getLogger(__name__)

[docs]class SparkLLM(LLM): """iFlyTek Spark large language model. To use, you should pass `app_id`, `api_key`, `api_secret` as a named parameter to the constructor OR set environment variables ``IFLYTEK_SPARK_APP_ID``, ``IFLYTEK_SPARK_API_KEY`` and ``IFLYTEK_SPARK_API_SECRET`` Example: .. code-block:: python client = SparkLLM( spark_app_id="<app_id>", spark_api_key="<api_key>", spark_api_secret="<api_secret>" ) """ client: Any = None #: :meta private: spark_app_id: Optional[str] = None spark_api_key: Optional[str] = None spark_api_secret: Optional[str] = None spark_api_url: Optional[str] = None spark_llm_domain: Optional[str] = None spark_user_id: str = "lc_user" streaming: bool = False request_timeout: int = 30 temperature: float = 0.5 top_k: int = 4 model_kwargs: Dict[str, Any] = Field(default_factory=dict) @root_validator() def validate_environment(cls, values: Dict) -> Dict: values["spark_app_id"] = get_from_dict_or_env( values, "spark_app_id", "IFLYTEK_SPARK_APP_ID", ) values["spark_api_key"] = get_from_dict_or_env( values, "spark_api_key", "IFLYTEK_SPARK_API_KEY", ) values["spark_api_secret"] = get_from_dict_or_env( values, "spark_api_secret", "IFLYTEK_SPARK_API_SECRET", ) values["spark_api_url"] = get_from_dict_or_env( values, "spark_api_url", "IFLYTEK_SPARK_API_URL", "wss://", ) values["spark_llm_domain"] = get_from_dict_or_env( values, "spark_llm_domain", "IFLYTEK_SPARK_LLM_DOMAIN", "generalv3", ) # put extra params into model_kwargs values["model_kwargs"]["temperature"] = values["temperature"] or cls.temperature values["model_kwargs"]["top_k"] = values["top_k"] or cls.top_k values["client"] = _SparkLLMClient( app_id=values["spark_app_id"], api_key=values["spark_api_key"], api_secret=values["spark_api_secret"], api_url=values["spark_api_url"], spark_domain=values["spark_llm_domain"], model_kwargs=values["model_kwargs"], ) return values @property def _llm_type(self) -> str: """Return type of llm.""" return "spark-llm-chat" @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling SparkLLM API.""" normal_params = { "spark_llm_domain": self.spark_llm_domain, "stream": self.streaming, "request_timeout": self.request_timeout, "top_k": self.top_k, "temperature": self.temperature, } return {**normal_params, **self.model_kwargs} def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to an sparkllm for each generation with a prompt. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the llm. Example: .. code-block:: python response = client("Tell me a joke.") """ if self.streaming: completion = "" for chunk in self._stream(prompt, stop, run_manager, **kwargs): completion += chunk.text return completion completion = "" self.client.arun( [{"role": "user", "content": prompt}], self.spark_user_id, self.model_kwargs, self.streaming, ) for content in self.client.subscribe(timeout=self.request_timeout): if "data" not in content: continue completion = content["data"]["content"] return completion def _stream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[GenerationChunk]: [{"role": "user", "content": prompt}], self.spark_user_id, self.model_kwargs, self.streaming, ) for content in self.client.subscribe(timeout=self.request_timeout): if "data" not in content: continue delta = content["data"] if run_manager: run_manager.on_llm_new_token(delta) yield GenerationChunk(text=delta["content"])
class _SparkLLMClient: """ Use websocket-client to call the SparkLLM interface provided by Xfyun, which is the iFlyTek's open platform for AI capabilities """ def __init__( self, app_id: str, api_key: str, api_secret: str, api_url: Optional[str] = None, spark_domain: Optional[str] = None, model_kwargs: Optional[dict] = None, ): try: import websocket self.websocket_client = websocket except ImportError: raise ImportError( "Could not import websocket client python package. " "Please install it with `pip install websocket-client`." ) self.api_url = ( "wss://" if not api_url else api_url ) self.app_id = app_id self.model_kwargs = model_kwargs self.spark_domain = spark_domain or "generalv3" self.queue: Queue[Dict] = Queue() self.blocking_message = {"content": "", "role": "assistant"} self.api_key = api_key self.api_secret = api_secret @staticmethod def _create_url(api_url: str, api_key: str, api_secret: str) -> str: """ Generate a request url with an api key and an api secret. """ # generate timestamp by RFC1123 date = format_date_time(mktime( # urlparse parsed_url = urlparse(api_url) host = parsed_url.netloc path = parsed_url.path signature_origin = f"host: {host}\ndate: {date}\nGET {path} HTTP/1.1" # encrypt using hmac-sha256 signature_sha = api_secret.encode("utf-8"), signature_origin.encode("utf-8"), digestmod=hashlib.sha256, ).digest() signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding="utf-8") authorization_origin = f'api_key="{api_key}", algorithm="hmac-sha256", \ headers="host date request-line", signature="{signature_sha_base64}"' authorization = base64.b64encode(authorization_origin.encode("utf-8")).decode( encoding="utf-8" ) # generate url params_dict = {"authorization": authorization, "date": date, "host": host} encoded_params = urlencode(params_dict) url = urlunparse( ( parsed_url.scheme, parsed_url.netloc, parsed_url.path, parsed_url.params, encoded_params, parsed_url.fragment, ) ) return url def run( self, messages: List[Dict], user_id: str, model_kwargs: Optional[dict] = None, streaming: bool = False, ) -> None: self.websocket_client.enableTrace(False) ws = self.websocket_client.WebSocketApp( _SparkLLMClient._create_url( self.api_url, self.api_key, self.api_secret, ), on_message=self.on_message, on_error=self.on_error, on_close=self.on_close, on_open=self.on_open, ) ws.messages = messages # type: ignore[attr-defined] ws.user_id = user_id # type: ignore[attr-defined] ws.model_kwargs = self.model_kwargs if model_kwargs is None else model_kwargs # type: ignore[attr-defined] ws.streaming = streaming # type: ignore[attr-defined] ws.run_forever() def arun( self, messages: List[Dict], user_id: str, model_kwargs: Optional[dict] = None, streaming: bool = False, ) -> threading.Thread: ws_thread = threading.Thread(, args=( messages, user_id, model_kwargs, streaming, ), ) ws_thread.start() return ws_thread def on_error(self, ws: Any, error: Optional[Any]) -> None: self.queue.put({"error": error}) ws.close() def on_close(self, ws: Any, close_status_code: int, close_reason: str) -> None: logger.debug( { "log": { "close_status_code": close_status_code, "close_reason": close_reason, } } ) self.queue.put({"done": True}) def on_open(self, ws: Any) -> None: self.blocking_message = {"content": "", "role": "assistant"} data = json.dumps( self.gen_params( messages=ws.messages, user_id=ws.user_id, model_kwargs=ws.model_kwargs ) ) ws.send(data) def on_message(self, ws: Any, message: str) -> None: data = json.loads(message) code = data["header"]["code"] if code != 0: self.queue.put( {"error": f"Code: {code}, Error: {data['header']['message']}"} ) ws.close() else: choices = data["payload"]["choices"] status = choices["status"] content = choices["text"][0]["content"] if ws.streaming: self.queue.put({"data": choices["text"][0]}) else: self.blocking_message["content"] += content if status == 2: if not ws.streaming: self.queue.put({"data": self.blocking_message}) usage_data = ( data.get("payload", {}).get("usage", {}).get("text", {}) if data else {} ) self.queue.put({"usage": usage_data}) ws.close() def gen_params( self, messages: list, user_id: str, model_kwargs: Optional[dict] = None ) -> dict: data: Dict = { "header": {"app_id": self.app_id, "uid": user_id}, "parameter": {"chat": {"domain": self.spark_domain}}, "payload": {"message": {"text": messages}}, } if model_kwargs: data["parameter"]["chat"].update(model_kwargs) logger.debug(f"Spark Request Parameters: {data}") return data def subscribe(self, timeout: Optional[int] = 30) -> Generator[Dict, None, None]: while True: try: content = self.queue.get(timeout=timeout) except queue.Empty as _: raise TimeoutError( f"SparkLLMClient wait LLM api response timeout {timeout} seconds" ) if "error" in content: raise ConnectionError(content["error"]) if "usage" in content: yield content continue if "done" in content: break if "data" not in content: break yield content