import json
import logging
from typing import Any, AsyncIterator, Dict, List, Optional, cast
import requests
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
ChatMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.pydantic_v1 import root_validator
from langchain_core.utils import get_from_dict_or_env
from langchain_community.llms.utils import enforce_stop_tokens
logger = logging.getLogger(__name__)
[docs]class PaiEasChatEndpoint(BaseChatModel):
"""Alibaba Cloud PAI-EAS LLM Service chat model API.
To use, must have a deployed eas chat llm service on AliCloud. One can set the
environment variable ``eas_service_url`` and ``eas_service_token`` set with your eas
service url and service token.
Example:
.. code-block:: python
from langchain_community.chat_models import PaiEasChatEndpoint
eas_chat_endpoint = PaiEasChatEndpoint(
eas_service_url="your_service_url",
eas_service_token="your_service_token"
)
"""
"""PAI-EAS Service URL"""
eas_service_url: str
"""PAI-EAS Service TOKEN"""
eas_service_token: str
"""PAI-EAS Service Infer Params"""
max_new_tokens: Optional[int] = 512
temperature: Optional[float] = 0.8
top_p: Optional[float] = 0.1
top_k: Optional[int] = 10
do_sample: Optional[bool] = False
use_cache: Optional[bool] = True
stop_sequences: Optional[List[str]] = None
"""Enable stream chat mode."""
streaming: bool = False
"""Key/value arguments to pass to the model. Reserved for future use"""
model_kwargs: Optional[dict] = None
version: Optional[str] = "2.0"
timeout: Optional[int] = 5000
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values["eas_service_url"] = get_from_dict_or_env(
values, "eas_service_url", "EAS_SERVICE_URL"
)
values["eas_service_token"] = get_from_dict_or_env(
values, "eas_service_token", "EAS_SERVICE_TOKEN"
)
return values
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
"eas_service_url": self.eas_service_url,
"eas_service_token": self.eas_service_token,
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "pai_eas_chat_endpoint"
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Cohere API."""
return {
"max_new_tokens": self.max_new_tokens,
"temperature": self.temperature,
"top_k": self.top_k,
"top_p": self.top_p,
"stop_sequences": [],
"do_sample": self.do_sample,
"use_cache": self.use_cache,
}
def _invocation_params(
self, stop_sequences: Optional[List[str]], **kwargs: Any
) -> dict:
params = self._default_params
if self.model_kwargs:
params.update(self.model_kwargs)
if self.stop_sequences is not None and stop_sequences is not None:
raise ValueError("`stop` found in both the input and default params.")
elif self.stop_sequences is not None:
params["stop"] = self.stop_sequences
else:
params["stop"] = stop_sequences
return {**params, **kwargs}
def _format_response_payload(
self, output: bytes, stop_sequences: Optional[List[str]]
) -> str:
"""Formats response"""
try:
text = json.loads(output)["response"]
if stop_sequences:
text = enforce_stop_tokens(text, stop_sequences)
return text
except Exception as e:
if isinstance(e, json.decoder.JSONDecodeError):
return output.decode("utf-8")
raise e
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
output_str = self._call(messages, stop=stop, run_manager=run_manager, **kwargs)
message = AIMessage(content=output_str)
generation = ChatGeneration(message=message)
return ChatResult(generations=[generation])
def _call(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
params = self._invocation_params(stop, **kwargs)
request_payload = self.format_request_payload(messages, **params)
response_payload = self._call_eas(request_payload)
generated_text = self._format_response_payload(response_payload, params["stop"])
if run_manager:
run_manager.on_llm_new_token(generated_text)
return generated_text
def _call_eas(self, query_body: dict) -> Any:
"""Generate text from the eas service."""
headers = {
"Content-Type": "application/json",
"Accept": "application/json",
"Authorization": f"{self.eas_service_token}",
}
# make request
response = requests.post(
self.eas_service_url, headers=headers, json=query_body, timeout=self.timeout
)
if response.status_code != 200:
raise Exception(
f"Request failed with status code {response.status_code}"
f" and message {response.text}"
)
return response.text
def _call_eas_stream(self, query_body: dict) -> Any:
"""Generate text from the eas service."""
headers = {
"Content-Type": "application/json",
"Accept": "application/json",
"Authorization": f"{self.eas_service_token}",
}
# make request
response = requests.post(
self.eas_service_url, headers=headers, json=query_body, timeout=self.timeout
)
if response.status_code != 200:
raise Exception(
f"Request failed with status code {response.status_code}"
f" and message {response.text}"
)
return response
def _convert_chunk_to_message_message(
self,
chunk: str,
) -> AIMessageChunk:
data = json.loads(chunk.encode("utf-8"))
return AIMessageChunk(content=data.get("response", ""))
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
params = self._invocation_params(stop, **kwargs)
request_payload = self.format_request_payload(messages, **params)
request_payload["use_stream_chat"] = True
response = self._call_eas_stream(request_payload)
for chunk in response.iter_lines(
chunk_size=8192, decode_unicode=False, delimiter=b"\0"
):
if chunk:
content = self._convert_chunk_to_message_message(chunk)
# identify stop sequence in generated text, if any
stop_seq_found: Optional[str] = None
for stop_seq in params["stop"]:
if stop_seq in content.content:
stop_seq_found = stop_seq
# identify text to yield
text: Optional[str] = None
if stop_seq_found:
content.content = content.content[
: content.content.index(stop_seq_found)
]
# yield text, if any
if text:
cg_chunk = ChatGenerationChunk(message=content)
if run_manager:
await run_manager.on_llm_new_token(
cast(str, content.content), chunk=cg_chunk
)
yield cg_chunk
# break if stop sequence found
if stop_seq_found:
break
