Source code for langchain_community.chat_models.sparkllm

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, Mapping, Optional, Type
from urllib.parse import urlencode, urlparse, urlunparse
from wsgiref.handlers import format_date_time

from langchain_core.callbacks import (
from langchain_core.language_models.chat_models import (
from langchain_core.messages import (
from langchain_core.outputs import (
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.utils import (

logger = logging.getLogger(__name__)

SPARK_API_URL = "wss://"
SPARK_LLM_DOMAIN = "generalv3.5"

def _convert_message_to_dict(message: BaseMessage) -> dict:
    if isinstance(message, ChatMessage):
        message_dict = {"role": "user", "content": message.content}
    elif isinstance(message, HumanMessage):
        message_dict = {"role": "user", "content": message.content}
    elif isinstance(message, AIMessage):
        message_dict = {"role": "assistant", "content": message.content}
    elif isinstance(message, SystemMessage):
        message_dict = {"role": "system", "content": message.content}
        raise ValueError(f"Got unknown type {message}")

    return message_dict

def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
    msg_role = _dict["role"]
    msg_content = _dict["content"]
    if msg_role == "user":
        return HumanMessage(content=msg_content)
    elif msg_role == "assistant":
        content = msg_content or ""
        return AIMessage(content=content)
    elif msg_role == "system":
        return SystemMessage(content=msg_content)
        return ChatMessage(content=msg_content, role=msg_role)

def _convert_delta_to_message_chunk(
    _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
    msg_role = _dict["role"]
    msg_content = _dict.get("content", "")
    if msg_role == "user" or default_class == HumanMessageChunk:
        return HumanMessageChunk(content=msg_content)
    elif msg_role == "assistant" or default_class == AIMessageChunk:
        return AIMessageChunk(content=msg_content)
    elif msg_role or default_class == ChatMessageChunk:
        return ChatMessageChunk(content=msg_content, role=msg_role)
        return default_class(content=msg_content)  # type: ignore[call-arg]

[docs]class ChatSparkLLM(BaseChatModel): """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 = ChatSparkLLM( spark_app_id="<app_id>", spark_api_key="<api_key>", spark_api_secret="<api_secret>" ) Extra infos: 1. Get app_id, api_key, api_secret from the iFlyTek Open Platform Console: 2. By default, iFlyTek Spark LLM V3.5 is invoked. If you need to invoke other versions, please configure the corresponding parameters(spark_api_url and spark_llm_domain) according to the document: 3. It is necessary to ensure that the app_id used has a license for the corresponding model version. 4. If you encounter problems during use, try getting help at: """
[docs] @classmethod def is_lc_serializable(cls) -> bool: """Return whether this model can be serialized by Langchain.""" return False
@property def lc_secrets(self) -> Dict[str, str]: return { "spark_app_id": "IFLYTEK_SPARK_APP_ID", "spark_api_key": "IFLYTEK_SPARK_API_KEY", "spark_api_secret": "IFLYTEK_SPARK_API_SECRET", "spark_api_url": "IFLYTEK_SPARK_API_URL", "spark_llm_domain": "IFLYTEK_SPARK_LLM_DOMAIN", } client: Any = None #: :meta private: spark_app_id: Optional[str] = Field(default=None, alias="app_id") """Automatically inferred from env var `IFLYTEK_SPARK_APP_ID` if not provided.""" spark_api_key: Optional[str] = Field(default=None, alias="api_key") """Automatically inferred from env var `IFLYTEK_SPARK_API_KEY` if not provided.""" spark_api_secret: Optional[str] = Field(default=None, alias="api_secret") """Automatically inferred from env var `IFLYTEK_SPARK_API_SECRET` if not provided.""" spark_api_url: Optional[str] = Field(default=None, alias="api_url") """Base URL path for API requests, leave blank if not using a proxy or service emulator.""" spark_llm_domain: Optional[str] = Field(default=None, alias="model") """Model name to use.""" spark_user_id: str = "lc_user" streaming: bool = False """Whether to stream the results or not.""" request_timeout: int = Field(30, alias="timeout") """request timeout for chat http requests""" temperature: float = Field(default=0.5) """What sampling temperature to use.""" top_k: int = 4 """What search sampling control to use.""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for API call not explicitly specified.""" class Config: """Configuration for this pydantic object.""" allow_population_by_field_name = True @root_validator(pre=True) def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) for field_name in list(values): if field_name in extra: raise ValueError(f"Found {field_name} supplied twice.") if field_name not in all_required_field_names: logger.warning( f"""WARNING! {field_name} is not default parameter. {field_name} was transferred to model_kwargs. Please confirm that {field_name} is what you intended.""" ) extra[field_name] = values.pop(field_name) invalid_model_kwargs = all_required_field_names.intersection(extra.keys()) if invalid_model_kwargs: raise ValueError( f"Parameters {invalid_model_kwargs} should be specified explicitly. " f"Instead they were passed in as part of `model_kwargs` parameter." ) values["model_kwargs"] = extra return values @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", SPARK_API_URL, ) values["spark_llm_domain"] = get_from_dict_or_env( values, "spark_llm_domain", "IFLYTEK_SPARK_LLM_DOMAIN", SPARK_LLM_DOMAIN, ) # 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 def _stream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: default_chunk_class = AIMessageChunk self.client.arun( [_convert_message_to_dict(m) for m in messages], 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"] chunk = _convert_delta_to_message_chunk(delta, default_chunk_class) cg_chunk = ChatGenerationChunk(message=chunk) if run_manager: run_manager.on_llm_new_token(str(chunk.content), chunk=cg_chunk) yield cg_chunk def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: if self.streaming: stream_iter = self._stream( messages=messages, stop=stop, run_manager=run_manager, **kwargs ) return generate_from_stream(stream_iter) self.client.arun( [_convert_message_to_dict(m) for m in messages], self.spark_user_id, self.model_kwargs, False, ) completion = {} llm_output = {} for content in self.client.subscribe(timeout=self.request_timeout): if "usage" in content: llm_output["token_usage"] = content["usage"] if "data" not in content: continue completion = content["data"] message = _convert_dict_to_message(completion) generations = [ChatGeneration(message=message)] return ChatResult(generations=generations, llm_output=llm_output) @property def _llm_type(self) -> str: return "spark-llm-chat"
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 = SPARK_API_URL if not api_url else api_url self.app_id = app_id self.model_kwargs = model_kwargs self.spark_domain = spark_domain or SPARK_LLM_DOMAIN 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