import json
import logging
from typing import Any, Dict, Iterator, List, Mapping, Optional, Type
import requests
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.chat_models import (
BaseChatModel,
generate_from_stream,
)
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
HumanMessage,
HumanMessageChunk,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
from langchain_core.utils import (
convert_to_secret_str,
get_from_dict_or_env,
get_pydantic_field_names,
)
logger = logging.getLogger(__name__)
DEFAULT_API_BASE = "https://api.baichuan-ai.com/v1/chat/completions"
def _convert_message_to_dict(message: BaseMessage) -> dict:
message_dict: Dict[str, Any]
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "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}
else:
raise TypeError(f"Got unknown type {message}")
return message_dict
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
role = _dict["role"]
if role == "user":
return HumanMessage(content=_dict["content"])
elif role == "assistant":
return AIMessage(content=_dict.get("content", "") or "")
else:
return ChatMessage(content=_dict["content"], role=role)
def _convert_delta_to_message_chunk(
_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
role = _dict.get("role")
content = _dict.get("content") or ""
if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content)
elif role == "assistant" or default_class == AIMessageChunk:
return AIMessageChunk(content=content)
elif role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role)
else:
return default_class(content=content)
[docs]class ChatBaichuan(BaseChatModel):
"""Baichuan chat models API by Baichuan Intelligent Technology.
For more information, see https://platform.baichuan-ai.com/docs/api
"""
@property
def lc_secrets(self) -> Dict[str, str]:
return {
"baichuan_api_key": "BAICHUAN_API_KEY",
}
@property
def lc_serializable(self) -> bool:
return True
baichuan_api_base: str = Field(default=DEFAULT_API_BASE)
"""Baichuan custom endpoints"""
baichuan_api_key: Optional[SecretStr] = None
"""Baichuan API Key"""
baichuan_secret_key: Optional[SecretStr] = None
"""[DEPRECATED, keeping it for for backward compatibility] Baichuan Secret Key"""
streaming: bool = False
"""Whether to stream the results or not."""
request_timeout: int = 60
"""request timeout for chat http requests"""
model = "Baichuan2-Turbo-192K"
"""model name of Baichuan, default is `Baichuan2-Turbo-192K`,
other options include `Baichuan2-Turbo`"""
temperature: float = 0.3
"""What sampling temperature to use."""
top_k: int = 5
"""What search sampling control to use."""
top_p: float = 0.85
"""What probability mass to use."""
with_search_enhance: bool = False
"""Whether to use search enhance, default is False."""
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["baichuan_api_base"] = get_from_dict_or_env(
values,
"baichuan_api_base",
"BAICHUAN_API_BASE",
DEFAULT_API_BASE,
)
values["baichuan_api_key"] = convert_to_secret_str(
get_from_dict_or_env(
values,
"baichuan_api_key",
"BAICHUAN_API_KEY",
)
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Baichuan API."""
normal_params = {
"model": self.model,
"temperature": self.temperature,
"top_p": self.top_p,
"top_k": self.top_k,
"with_search_enhance": self.with_search_enhance,
"stream": self.streaming,
}
return {**normal_params, **self.model_kwargs}
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)
res = self._chat(messages, **kwargs)
if res.status_code != 200:
raise ValueError(f"Error from Baichuan api response: {res}")
response = res.json()
return self._create_chat_result(response)
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
res = self._chat(messages, **kwargs)
if res.status_code != 200:
raise ValueError(f"Error from Baichuan api response: {res}")
default_chunk_class = AIMessageChunk
for chunk in res.iter_lines():
chunk = chunk.decode("utf-8").strip("\r\n")
parts = chunk.split("data: ", 1)
chunk = parts[1] if len(parts) > 1 else None
if chunk is None:
continue
if chunk == "[DONE]":
break
response = json.loads(chunk)
for m in response.get("choices"):
chunk = _convert_delta_to_message_chunk(
m.get("delta"), default_chunk_class
)
default_chunk_class = chunk.__class__
yield ChatGenerationChunk(message=chunk)
if run_manager:
run_manager.on_llm_new_token(chunk.content)
def _chat(self, messages: List[BaseMessage], **kwargs: Any) -> requests.Response:
parameters = {**self._default_params, **kwargs}
model = parameters.pop("model")
headers = parameters.pop("headers", {})
temperature = parameters.pop("temperature", 0.3)
top_k = parameters.pop("top_k", 5)
top_p = parameters.pop("top_p", 0.85)
with_search_enhance = parameters.pop("with_search_enhance", False)
stream = parameters.pop("stream", False)
payload = {
"model": model,
"messages": [_convert_message_to_dict(m) for m in messages],
"top_k": top_k,
"top_p": top_p,
"temperature": temperature,
"with_search_enhance": with_search_enhance,
"stream": stream,
}
url = self.baichuan_api_base
api_key = ""
if self.baichuan_api_key:
api_key = self.baichuan_api_key.get_secret_value()
res = requests.post(
url=url,
timeout=self.request_timeout,
headers={
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}",
**headers,
},
json=payload,
stream=self.streaming,
)
return res
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
generations = []
for c in response["choices"]:
message = _convert_dict_to_message(c["message"])
gen = ChatGeneration(message=message)
generations.append(gen)
token_usage = response["usage"]
llm_output = {"token_usage": token_usage, "model": self.model}
return ChatResult(generations=generations, llm_output=llm_output)
@property
def _llm_type(self) -> str:
return "baichuan-chat"