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487 lines
16 KiB
Python
487 lines
16 KiB
Python
import base64
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import hashlib
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import hmac
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import json
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import logging
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import queue
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import threading
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from datetime import datetime
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from queue import Queue
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from time import mktime
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from typing import Any, Dict, Generator, Iterator, List, Mapping, Optional, Type
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from urllib.parse import urlencode, urlparse, urlunparse
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from wsgiref.handlers import format_date_time
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from langchain_core.callbacks import (
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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generate_from_stream,
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)
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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)
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from langchain_core.outputs import (
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ChatGeneration,
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ChatGenerationChunk,
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ChatResult,
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)
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from langchain_core.pydantic_v1 import Field, root_validator
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from langchain_core.utils import (
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get_from_dict_or_env,
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get_pydantic_field_names,
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)
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logger = logging.getLogger(__name__)
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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if isinstance(message, ChatMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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else:
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raise ValueError(f"Got unknown type {message}")
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return message_dict
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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msg_role = _dict["role"]
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msg_content = _dict["content"]
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if msg_role == "user":
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return HumanMessage(content=msg_content)
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elif msg_role == "assistant":
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content = msg_content or ""
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return AIMessage(content=content)
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elif msg_role == "system":
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return SystemMessage(content=msg_content)
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else:
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return ChatMessage(content=msg_content, role=msg_role)
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def _convert_delta_to_message_chunk(
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_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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msg_role = _dict["role"]
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msg_content = _dict.get("content", "")
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if msg_role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=msg_content)
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elif msg_role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=msg_content)
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elif msg_role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=msg_content, role=msg_role)
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else:
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return default_class(content=msg_content)
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class ChatSparkLLM(BaseChatModel):
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"""Wrapper around iFlyTek's Spark large language model.
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To use, you should pass `app_id`, `api_key`, `api_secret`
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as a named parameter to the constructor OR set environment
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variables ``IFLYTEK_SPARK_APP_ID``, ``IFLYTEK_SPARK_API_KEY`` and
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``IFLYTEK_SPARK_API_SECRET``
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Example:
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.. code-block:: python
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client = ChatSparkLLM(
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spark_app_id="<app_id>",
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spark_api_key="<api_key>",
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spark_api_secret="<api_secret>"
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)
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Extra infos:
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1. Get app_id, api_key, api_secret from the iFlyTek Open Platform Console:
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https://console.xfyun.cn/services/bm35
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2. By default, iFlyTek Spark LLM V3.0 is invoked.
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If you need to invoke other versions, please configure the corresponding
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parameters(spark_api_url and spark_llm_domain) according to the document:
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https://www.xfyun.cn/doc/spark/Web.html
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3. It is necessary to ensure that the app_id used has a license for
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the corresponding model version.
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4. If you encounter problems during use, try getting help at:
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https://console.xfyun.cn/workorder/commit
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"""
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@classmethod
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def is_lc_serializable(cls) -> bool:
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"""Return whether this model can be serialized by Langchain."""
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return False
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {
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"spark_app_id": "IFLYTEK_SPARK_APP_ID",
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"spark_api_key": "IFLYTEK_SPARK_API_KEY",
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"spark_api_secret": "IFLYTEK_SPARK_API_SECRET",
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"spark_api_url": "IFLYTEK_SPARK_API_URL",
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"spark_llm_domain": "IFLYTEK_SPARK_LLM_DOMAIN",
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}
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client: Any = None #: :meta private:
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spark_app_id: Optional[str] = None
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spark_api_key: Optional[str] = None
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spark_api_secret: Optional[str] = None
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spark_api_url: Optional[str] = None
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spark_llm_domain: Optional[str] = None
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spark_user_id: str = "lc_user"
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streaming: bool = False
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request_timeout: int = 30
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temperature: float = 0.5
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top_k: int = 4
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = get_pydantic_field_names(cls)
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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if field_name not in all_required_field_names:
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logger.warning(
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f"""WARNING! {field_name} is not default parameter.
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{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
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if invalid_model_kwargs:
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raise ValueError(
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f"Parameters {invalid_model_kwargs} should be specified explicitly. "
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f"Instead they were passed in as part of `model_kwargs` parameter."
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)
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values["model_kwargs"] = extra
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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values["spark_app_id"] = get_from_dict_or_env(
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values,
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"spark_app_id",
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"IFLYTEK_SPARK_APP_ID",
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)
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values["spark_api_key"] = get_from_dict_or_env(
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values,
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"spark_api_key",
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"IFLYTEK_SPARK_API_KEY",
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)
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values["spark_api_secret"] = get_from_dict_or_env(
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values,
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"spark_api_secret",
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"IFLYTEK_SPARK_API_SECRET",
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)
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values["spark_app_url"] = get_from_dict_or_env(
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values,
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"spark_app_url",
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"IFLYTEK_SPARK_APP_URL",
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"wss://spark-api.xf-yun.com/v3.1/chat",
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)
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values["spark_llm_domain"] = get_from_dict_or_env(
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values,
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"spark_llm_domain",
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"IFLYTEK_SPARK_LLM_DOMAIN",
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"generalv3",
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)
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# put extra params into model_kwargs
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values["model_kwargs"]["temperature"] = values["temperature"] or cls.temperature
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values["model_kwargs"]["top_k"] = values["top_k"] or cls.top_k
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values["client"] = _SparkLLMClient(
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app_id=values["spark_app_id"],
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api_key=values["spark_api_key"],
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api_secret=values["spark_api_secret"],
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api_url=values["spark_api_url"],
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spark_domain=values["spark_llm_domain"],
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model_kwargs=values["model_kwargs"],
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)
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return values
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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default_chunk_class = AIMessageChunk
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self.client.arun(
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[_convert_message_to_dict(m) for m in messages],
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self.spark_user_id,
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self.model_kwargs,
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self.streaming,
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)
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for content in self.client.subscribe(timeout=self.request_timeout):
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if "data" not in content:
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continue
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delta = content["data"]
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chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
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cg_chunk = ChatGenerationChunk(message=chunk)
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yield cg_chunk
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if run_manager:
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run_manager.on_llm_new_token(str(chunk.content), chunk=cg_chunk)
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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if self.streaming:
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stream_iter = self._stream(
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messages=messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return generate_from_stream(stream_iter)
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self.client.arun(
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[_convert_message_to_dict(m) for m in messages],
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self.spark_user_id,
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self.model_kwargs,
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False,
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)
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completion = {}
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llm_output = {}
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for content in self.client.subscribe(timeout=self.request_timeout):
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if "usage" in content:
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llm_output["token_usage"] = content["usage"]
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if "data" not in content:
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continue
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completion = content["data"]
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message = _convert_dict_to_message(completion)
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generations = [ChatGeneration(message=message)]
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return ChatResult(generations=generations, llm_output=llm_output)
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@property
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def _llm_type(self) -> str:
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return "spark-llm-chat"
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class _SparkLLMClient:
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"""
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Use websocket-client to call the SparkLLM interface provided by Xfyun,
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which is the iFlyTek's open platform for AI capabilities
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"""
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def __init__(
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self,
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app_id: str,
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api_key: str,
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api_secret: str,
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api_url: Optional[str] = None,
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spark_domain: Optional[str] = None,
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model_kwargs: Optional[dict] = None,
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):
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try:
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import websocket
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self.websocket_client = websocket
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except ImportError:
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raise ImportError(
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"Could not import websocket client python package. "
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"Please install it with `pip install websocket-client`."
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)
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self.api_url = (
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"wss://spark-api.xf-yun.com/v3.1/chat" if not api_url else api_url
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)
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self.app_id = app_id
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self.ws_url = _SparkLLMClient._create_url(
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self.api_url,
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api_key,
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api_secret,
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)
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self.model_kwargs = model_kwargs
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self.spark_domain = spark_domain or "generalv3"
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self.queue: Queue[Dict] = Queue()
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self.blocking_message = {"content": "", "role": "assistant"}
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@staticmethod
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def _create_url(api_url: str, api_key: str, api_secret: str) -> str:
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"""
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Generate a request url with an api key and an api secret.
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"""
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# generate timestamp by RFC1123
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date = format_date_time(mktime(datetime.now().timetuple()))
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# urlparse
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parsed_url = urlparse(api_url)
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host = parsed_url.netloc
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path = parsed_url.path
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signature_origin = f"host: {host}\ndate: {date}\nGET {path} HTTP/1.1"
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# encrypt using hmac-sha256
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signature_sha = hmac.new(
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api_secret.encode("utf-8"),
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signature_origin.encode("utf-8"),
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digestmod=hashlib.sha256,
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).digest()
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signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding="utf-8")
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authorization_origin = f'api_key="{api_key}", algorithm="hmac-sha256", \
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headers="host date request-line", signature="{signature_sha_base64}"'
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authorization = base64.b64encode(authorization_origin.encode("utf-8")).decode(
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encoding="utf-8"
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)
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# generate url
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params_dict = {"authorization": authorization, "date": date, "host": host}
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encoded_params = urlencode(params_dict)
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url = urlunparse(
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(
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parsed_url.scheme,
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parsed_url.netloc,
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parsed_url.path,
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parsed_url.params,
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encoded_params,
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parsed_url.fragment,
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)
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)
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return url
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def run(
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self,
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messages: List[Dict],
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user_id: str,
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model_kwargs: Optional[dict] = None,
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streaming: bool = False,
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) -> None:
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self.websocket_client.enableTrace(False)
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ws = self.websocket_client.WebSocketApp(
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self.ws_url,
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on_message=self.on_message,
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on_error=self.on_error,
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on_close=self.on_close,
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on_open=self.on_open,
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)
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ws.messages = messages
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ws.user_id = user_id
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ws.model_kwargs = self.model_kwargs if model_kwargs is None else model_kwargs
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ws.streaming = streaming
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ws.run_forever()
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def arun(
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self,
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messages: List[Dict],
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user_id: str,
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model_kwargs: Optional[dict] = None,
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streaming: bool = False,
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) -> threading.Thread:
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ws_thread = threading.Thread(
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target=self.run,
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args=(
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messages,
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user_id,
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model_kwargs,
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streaming,
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),
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)
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ws_thread.start()
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return ws_thread
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def on_error(self, ws: Any, error: Optional[Any]) -> None:
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self.queue.put({"error": error})
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ws.close()
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def on_close(self, ws: Any, close_status_code: int, close_reason: str) -> None:
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logger.debug(
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{
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"log": {
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"close_status_code": close_status_code,
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"close_reason": close_reason,
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}
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}
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)
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self.queue.put({"done": True})
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def on_open(self, ws: Any) -> None:
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self.blocking_message = {"content": "", "role": "assistant"}
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data = json.dumps(
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self.gen_params(
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messages=ws.messages, user_id=ws.user_id, model_kwargs=ws.model_kwargs
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)
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)
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ws.send(data)
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def on_message(self, ws: Any, message: str) -> None:
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data = json.loads(message)
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code = data["header"]["code"]
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if code != 0:
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self.queue.put(
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{"error": f"Code: {code}, Error: {data['header']['message']}"}
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)
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ws.close()
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else:
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choices = data["payload"]["choices"]
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status = choices["status"]
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content = choices["text"][0]["content"]
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if ws.streaming:
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self.queue.put({"data": choices["text"][0]})
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else:
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self.blocking_message["content"] += content
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if status == 2:
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if not ws.streaming:
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self.queue.put({"data": self.blocking_message})
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usage_data = (
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data.get("payload", {}).get("usage", {}).get("text", {})
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if data
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else {}
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)
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self.queue.put({"usage": usage_data})
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ws.close()
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def gen_params(
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self, messages: list, user_id: str, model_kwargs: Optional[dict] = None
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) -> dict:
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data: Dict = {
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"header": {"app_id": self.app_id, "uid": user_id},
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"parameter": {"chat": {"domain": self.spark_domain}},
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"payload": {"message": {"text": messages}},
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}
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if model_kwargs:
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data["parameter"]["chat"].update(model_kwargs)
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logger.debug(f"Spark Request Parameters: {data}")
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return data
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def subscribe(self, timeout: Optional[int] = 30) -> Generator[Dict, None, None]:
|
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while True:
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try:
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content = self.queue.get(timeout=timeout)
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except queue.Empty as _:
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raise TimeoutError(
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f"SparkLLMClient wait LLM api response timeout {timeout} seconds"
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)
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if "error" in content:
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raise ConnectionError(content["error"])
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if "usage" in content:
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yield content
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continue
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if "done" in content:
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break
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if "data" not in content:
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break
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yield content
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