separate API client and Langchain wrapper.

automation-api/lib/llms/iflytek/__init__.py

@@ -1 +1 @@
-from .spark_api import Spark  # noqa: F401
+from .spark_api import SparkClient  # noqa: F401

automation-api/lib/llms/iflytek/spark_api.py

@@ -8,16 +8,11 @@
 import json
 from datetime import datetime
 from time import mktime
-from typing import Any, Dict, List, Mapping, Optional
+from typing import Any, Dict, Optional
 from urllib.parse import urlencode, urlparse
 from wsgiref.handlers import format_date_time
 
 import websocket
-from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
-from pydantic import root_validator
-
-from lib.config import read_config
 
 
 class Ws_Param(object):
@@ -144,80 +139,3 @@ def generate_text(self, content, **kwargs) -> Dict[str, Any]:
     def chat(self):
         # TODO: add chat function, which accepts some message history and generate new reply.
         raise NotImplementedError()
-
-
-def get_from_dict_or_env(data, key, env_key):
-    if key in data and data[key]:
-        return data[key]
-    else:
-        config = read_config()
-        if env_key in config and config[env_key]:
-            return config[env_key]
-        raise ValueError(
-            f"Did not found {key} in provided dict and {env_key} in environment variables"
-        )
-
-
-class Spark(LLM):
-    # TODO: maybe rewrite based on BaseLLM. Need to implement the more complex _generate method.
-    client: Any
-    iflytek_appid: str
-    iflytek_api_key: str
-    iflytek_api_secret: str
-    temperature: Optional[float] = 0.5
-    max_tokens: Optional[int] = 2048
-    top_k: Optional[int] = 4
-
-    @property
-    def _llm_type(self) -> str:
-        return "iflytek_spark"
-
-    @root_validator()
-    def validate_environment(cls, values: Dict) -> Dict:  # noqa: N805
-        """Validate api key, python package exists."""
-        iflytek_appid = get_from_dict_or_env(values, "iflytek_appid", "IFLYTEK_APPID")
-        iflytek_api_key = get_from_dict_or_env(
-            values, "iflytek_api_key", "IFLYTEK_API_KEY"
-        )
-        iflytek_api_secret = get_from_dict_or_env(
-            values, "iflytek_api_secret", "IFLYTEK_API_SECRET"
-        )
-
-        values["client"] = SparkClient(
-            iflytek_appid, iflytek_api_key, iflytek_api_secret
-        )
-
-        if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
-            raise ValueError("temperature must be in the range [0.0, 1.0]")
-
-        if values["top_k"] is not None and not 1 <= values["top_k"] <= 6:
-            raise ValueError("top_k must be between 1 and 6")
-
-        if values["max_tokens"] is not None and not 1 <= values["max_tokens"] <= 4096:
-            raise ValueError("max_output_tokens must be between 1 and 4096")
-
-        return values
-
-    def _call(
-        self,
-        prompt: str,
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-    ) -> str:
-        if stop is not None:
-            raise ValueError("stop kwargs are not permitted.")
-        return self.client.generate_text(
-            prompt,
-            temperature=self.temperature,
-            max_tokens=self.max_tokens,
-            top_k=self.top_k,
-        )["text"]
-
-    @property
-    def _identifying_params(self) -> Mapping[str, Any]:
-        """Get the identifying parameters."""
-        return {
-            "temperature": self.temperature,
-            "max_tokens": self.max_tokens,
-            "top_k": self.top_k,
-        }

automation-api/lib/llms/utils.py

@@ -12,7 +12,7 @@
 from lib.config import read_config
 
 from .fake import RandomAnswerLLM
-from .iflytek import Spark
+from .spark import Spark
 
 
 def get_openai_model(model_name: str, **kwargs: Any) -> Union[ChatOpenAI, OpenAI]: