import json
import logging
import re
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,
SystemMessage,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, 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
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) # type: ignore[arg-type]
else:
return default_class(content=content) # type: ignore[call-arg]
[docs]class LlamaEdgeChatService(BaseChatModel):
"""通过`llama-api-server`与LLMs进行聊天
有关`llama-api-server`的信息，请访问https://github.com/second-state/LlamaEdge"""
request_timeout: int = 60
"""聊天http请求的请求超时"""
service_url: Optional[str] = None
"""WasmChat服务的URL"""
model: str = "NA"
"""模型名称，默认为`NA`。"""
streaming: bool = False
"""是否要流式传输结果。"""
class Config:
"""此pydantic对象的配置。"""
allow_population_by_field_name = True
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""从传入的额外参数构建额外的kwargs。"""
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:
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 code: {res.status_code}, reason: {res.reason}")
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)
default_chunk_class = AIMessageChunk
substring = '"object":"chat.completion.chunk"}'
for line in res.iter_lines():
chunks = []
if line:
json_string = line.decode("utf-8")
# Find all positions of the substring
positions = [m.start() for m in re.finditer(substring, json_string)]
positions = [-1 * len(substring)] + positions
for i in range(len(positions) - 1):
chunk = json.loads(
json_string[
positions[i] + len(substring) : positions[i + 1]
+ len(substring)
]
)
chunks.append(chunk)
for chunk in chunks:
if not isinstance(chunk, dict):
chunk = chunk.dict()
if len(chunk["choices"]) == 0:
continue
choice = chunk["choices"][0]
chunk = _convert_delta_to_message_chunk(
choice["delta"], default_chunk_class
)
if (
choice.get("finish_reason") is not None
and choice.get("finish_reason") == "stop"
):
break
finish_reason = choice.get("finish_reason")
generation_info = (
dict(finish_reason=finish_reason)
if finish_reason is not None
else None
)
default_chunk_class = chunk.__class__
cg_chunk = ChatGenerationChunk(
message=chunk, generation_info=generation_info
)
if run_manager:
run_manager.on_llm_new_token(cg_chunk.text, chunk=cg_chunk)
yield cg_chunk
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"
if self.streaming:
payload = {
"model": self.model,
"messages": [_convert_message_to_dict(m) for m in messages],
"stream": self.streaming,
}
else:
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"