mirror of https://github.com/hwchase17/langchain
Add Baichuan chat model (#11923)
Description: A large language models developed by Baichuan Intelligent Technology,https://www.baichuan-ai.com/home Issue: None Dependencies: None Tag maintainer: Twitter handle:pull/11938/head
parent
9ecb7240a4
commit
3fb5e4d185
@ -0,0 +1,274 @@
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
import time
|
||||
from typing import Any, Dict, Iterator, List, Mapping, Optional, Type
|
||||
|
||||
import requests
|
||||
|
||||
from langchain.callbacks.manager import CallbackManagerForLLMRun
|
||||
from langchain.chat_models.base import BaseChatModel, _generate_from_stream
|
||||
from langchain.pydantic_v1 import Field, root_validator
|
||||
from langchain.schema import (
|
||||
AIMessage,
|
||||
BaseMessage,
|
||||
ChatGeneration,
|
||||
ChatMessage,
|
||||
ChatResult,
|
||||
HumanMessage,
|
||||
)
|
||||
from langchain.schema.messages import (
|
||||
AIMessageChunk,
|
||||
BaseMessageChunk,
|
||||
ChatMessageChunk,
|
||||
HumanMessageChunk,
|
||||
)
|
||||
from langchain.schema.output import ChatGenerationChunk
|
||||
from langchain.utils import get_from_dict_or_env, get_pydantic_field_names
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
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)
|
||||
|
||||
|
||||
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",
|
||||
"baichuan_secret_key": "BAICHUAN_SECRET_KEY",
|
||||
}
|
||||
|
||||
@property
|
||||
def lc_serializable(self) -> bool:
|
||||
return True
|
||||
|
||||
baichuan_api_base: str = "https://api.baichuan-ai.com"
|
||||
"""Baichuan custom endpoints"""
|
||||
baichuan_api_key: Optional[str] = None
|
||||
"""Baichuan API Key"""
|
||||
baichuan_secret_key: Optional[str] = None
|
||||
"""Baichuan Secret Key"""
|
||||
streaming: Optional[bool] = False
|
||||
"""streaming mode."""
|
||||
request_timeout: Optional[int] = 60
|
||||
"""request timeout for chat http requests"""
|
||||
|
||||
model = "Baichuan2-53B"
|
||||
"""model name of Baichuan, default is `Baichuan2-53B`."""
|
||||
temperature: float = 0.3
|
||||
top_k: int = 5
|
||||
top_p: float = 0.85
|
||||
with_search_enhance: bool = False
|
||||
"""Whether to use search enhance, default is False."""
|
||||
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
||||
|
||||
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",
|
||||
)
|
||||
values["baichuan_api_key"] = get_from_dict_or_env(
|
||||
values,
|
||||
"baichuan_api_key",
|
||||
"BAICHUAN_API_KEY",
|
||||
)
|
||||
values["baichuan_secret_key"] = get_from_dict_or_env(
|
||||
values,
|
||||
"baichuan_secret_key",
|
||||
"BAICHUAN_SECRET_KEY",
|
||||
)
|
||||
|
||||
return values
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the default parameters for calling Baichuan API."""
|
||||
normal_params = {
|
||||
"model": self.model,
|
||||
"top_p": self.top_p,
|
||||
"top_k": self.top_k,
|
||||
"with_search_enhance": self.with_search_enhance,
|
||||
}
|
||||
|
||||
return {**normal_params, **self.model_kwargs}
|
||||
|
||||
def _signature(self, data: Dict[str, Any], timestamp: int) -> str:
|
||||
if self.baichuan_secret_key is None:
|
||||
raise ValueError("Baichuan secret key is not set.")
|
||||
|
||||
input_str = self.baichuan_secret_key + json.dumps(data) + str(timestamp)
|
||||
md5 = hashlib.md5()
|
||||
md5.update(input_str.encode("utf-8"))
|
||||
return md5.hexdigest()
|
||||
|
||||
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)
|
||||
|
||||
response = res.json()
|
||||
|
||||
if response.get("code") != 0:
|
||||
raise ValueError(f"Error from Baichuan api response: {response}")
|
||||
|
||||
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)
|
||||
|
||||
default_chunk_class = AIMessageChunk
|
||||
for chunk in res.iter_lines():
|
||||
response = json.loads(chunk)
|
||||
if response.get("code") != 0:
|
||||
raise ValueError(f"Error from Baichuan api response: {response}")
|
||||
|
||||
data = response.get("data")
|
||||
for m in data.get("messages"):
|
||||
chunk = _convert_delta_to_message_chunk(m, 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", {})
|
||||
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": [convert_message_to_dict(m) for m in messages],
|
||||
"parameters": parameters,
|
||||
}
|
||||
|
||||
timestamp = int(time.time())
|
||||
|
||||
url = f"{self.baichuan_api_base}/v1"
|
||||
if self.streaming:
|
||||
url = f"{url}/stream"
|
||||
url = f"{url}/chat"
|
||||
|
||||
res = requests.post(
|
||||
url=url,
|
||||
timeout=self.request_timeout,
|
||||
headers={
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {self.baichuan_api_key}",
|
||||
"X-BC-Timestamp": str(timestamp),
|
||||
"X-BC-Signature": self._signature(payload, timestamp),
|
||||
"X-BC-Sign-Algo": "MD5",
|
||||
**headers,
|
||||
},
|
||||
json=payload,
|
||||
stream=self.streaming,
|
||||
)
|
||||
return res
|
||||
|
||||
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
|
||||
generations = []
|
||||
for m in response["data"]["messages"]:
|
||||
message = _convert_dict_to_message(m)
|
||||
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"
|
Loading…
Reference in New Issue