import json
import logging
from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
AIMessage,
BaseMessage,
ChatMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import ChatGeneration, ChatResult
from langchain_core.pydantic_v1 import root_validator
from langchain_core.utils import get_pydantic_field_names
logger = logging.getLogger(__name__)
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_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, SystemMessage):
message_dict = {"role": "system", "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
[docs]class WasmChatService(BaseChatModel):
"""Chat with LLMs via `llama-api-server`
For the information about `llama-api-server`, visit https://github.com/second-state/llama-utils
"""
request_timeout: int = 60
"""request timeout for chat http requests"""
service_url: Optional[str] = None
"""URL of WasmChat service"""
model: str = "NA"
"""model name, default is `NA`."""
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
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
res = self._chat(messages, **kwargs)
if res.status_code != 200:
raise ValueError(f"Error code: {res.status_code}, reason: {res.reason}")
response = res.json()
return self._create_chat_result(response)
def _chat(self, messages: List[BaseMessage], **kwargs: Any) -> requests.Response:
if self.service_url is None:
res = requests.models.Response()
res.status_code = 503
res.reason = "The IP address or port of the chat service is incorrect."
return res
service_url = f"{self.service_url}/v1/chat/completions"
payload = {
"model": self.model,
"messages": [_convert_message_to_dict(m) for m in messages],
}
res = requests.post(
url=service_url,
timeout=self.request_timeout,
headers={
"accept": "application/json",
"Content-Type": "application/json",
},
data=json.dumps(payload),
)
return res
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
message = _convert_dict_to_message(response["choices"][0].get("message"))
generations = [ChatGeneration(message=message)]
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 "wasm-chat"