import json
import logging
from contextlib import asynccontextmanager
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Mapping,
Optional,
Sequence,
Type,
Union,
)
import requests
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import (
BaseChatModel,
agenerate_from_stream,
generate_from_stream,
)
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
HumanMessage,
HumanMessageChunk,
SystemMessage,
SystemMessageChunk,
ToolMessage,
)
from langchain_core.output_parsers.openai_tools import (
make_invalid_tool_call,
parse_tool_call,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
from langchain_core.runnables import Runnable
from langchain_core.tools import BaseTool
from langchain_core.utils import (
convert_to_secret_str,
get_from_dict_or_env,
get_pydantic_field_names,
)
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_community.chat_models.llamacpp import (
_lc_invalid_tool_call_to_openai_tool_call,
_lc_tool_call_to_openai_tool_call,
)
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]
content = message.content
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": content}
elif isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": content}
if "tool_calls" in message.additional_kwargs:
message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
elif message.tool_calls or message.invalid_tool_calls:
message_dict["tool_calls"] = [
_lc_tool_call_to_openai_tool_call(tc) for tc in message.tool_calls
] + [
_lc_invalid_tool_call_to_openai_tool_call(tc)
for tc in message.invalid_tool_calls
]
elif isinstance(message, ToolMessage):
message_dict = {
"role": "tool",
"tool_call_id": message.tool_call_id,
"content": content,
"name": message.name or message.additional_kwargs.get("name"),
}
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": 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"]
content = _dict.get("content", "")
if role == "user":
return HumanMessage(content=content)
elif role == "assistant":
tool_calls = []
invalid_tool_calls = []
additional_kwargs = {}
if raw_tool_calls := _dict.get("tool_calls"):
additional_kwargs["tool_calls"] = raw_tool_calls
for raw_tool_call in raw_tool_calls:
try:
tool_calls.append(parse_tool_call(raw_tool_call, return_id=True))
except Exception as e:
invalid_tool_calls.append(
make_invalid_tool_call(raw_tool_call, str(e))
)
return AIMessage(
content=content,
additional_kwargs=additional_kwargs,
tool_calls=tool_calls, # type: ignore[arg-type]
invalid_tool_calls=invalid_tool_calls,
)
elif role == "tool":
additional_kwargs = {}
if "name" in _dict:
additional_kwargs["name"] = _dict["name"]
return ToolMessage(
content=content,
tool_call_id=_dict.get("tool_call_id"), # type: ignore[arg-type]
additional_kwargs=additional_kwargs,
)
elif role == "system":
return SystemMessage(content=content)
else:
return ChatMessage(content=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 == "system" or default_class == SystemMessageChunk:
return SystemMessageChunk(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]@asynccontextmanager
async def aconnect_httpx_sse(
client: Any, method: str, url: str, **kwargs: Any
) -> AsyncIterator:
"""Async context manager for connecting to an SSE stream.
Args:
client: The httpx client.
method: The HTTP method.
url: The URL to connect to.
kwargs: Additional keyword arguments to pass to the client.
Yields:
An EventSource object.
"""
from httpx_sse import EventSource
async with client.stream(method, url, **kwargs) as response:
yield EventSource(response)
[docs]class ChatBaichuan(BaseChatModel):
"""Baichuan chat model integration.
Setup:
To use, you should have the environment variable``BAICHUAN_API_KEY`` set with
your API KEY.
.. code-block:: bash
export BAICHUAN_API_KEY="your-api-key"
Key init args — completion params:
model: Optional[str]
Name of Baichuan model to use.
max_tokens: Optional[int]
Max number of tokens to generate.
streaming: Optional[bool]
Whether to stream the results or not.
temperature: Optional[float]
Sampling temperature.
top_p: Optional[float]
What probability mass to use.
top_k: Optional[int]
What search sampling control to use.
Key init args — client params:
api_key: Optional[str]
Baichuan API key. If not passed in will be read from env var BAICHUAN_API_KEY.
base_url: Optional[str]
Base URL for API requests.
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_community.chat_models import ChatBaichuan
chat = ChatBaichuan(
api_key=api_key,
model='Baichuan4',
# temperature=...,
# other params...
)
Invoke:
.. code-block:: python
messages = [
("system", "你是一名专业的翻译家，可以将用户的中文翻译为英文。"),
("human", "我喜欢编程。"),
]
chat.invoke(messages)
.. code-block:: python
AIMessage(
content='I enjoy programming.',
response_metadata={
'token_usage': {
'prompt_tokens': 93,
'completion_tokens': 5,
'total_tokens': 98
},
'model': 'Baichuan4'
},
id='run-944ff552-6a93-44cf-a861-4e4d849746f9-0'
)
Stream:
.. code-block:: python
for chunk in chat.stream(messages):
print(chunk)
.. code-block:: python
content='I' id='run-f99fcd6f-dd31-46d5-be8f-0b6a22bf77d8'
content=' enjoy programming.' id='run-f99fcd6f-dd31-46d5-be8f-0b6a22bf77d8
.. code-block:: python
stream = chat.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
full
.. code-block:: python
AIMessageChunk(
content='I like programming.',
id='run-74689970-dc31-461d-b729-3b6aa93508d2'
)
Async:
.. code-block:: python
await chat.ainvoke(messages)
# stream
# async for chunk in chat.astream(messages):
# print(chunk)
# batch
# await chat.abatch([messages])
.. code-block:: python
AIMessage(
content='I enjoy programming.',
response_metadata={
'token_usage': {
'prompt_tokens': 93,
'completion_tokens': 5,
'total_tokens': 98
},
'model': 'Baichuan4'
},
id='run-952509ed-9154-4ff9-b187-e616d7ddfbba-0'
)
Tool calling:
.. code-block:: python
class get_current_weather(BaseModel):
'''Get current weather.'''
location: str = Field('City or province, such as Shanghai')
llm_with_tools = ChatBaichuan(model='Baichuan3-Turbo').bind_tools([get_current_weather])
llm_with_tools.invoke('How is the weather today?')
.. code-block:: python
[{'name': 'get_current_weather',
'args': {'location': 'New York'},
'id': '3951017OF8doB0A',
'type': 'tool_call'}]
Response metadata
.. code-block:: python
ai_msg = chat.invoke(messages)
ai_msg.response_metadata
.. code-block:: python
{
'token_usage': {
'prompt_tokens': 93,
'completion_tokens': 5,
'total_tokens': 98
},
'model': 'Baichuan4'
}
""" # noqa: E501
@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, alias="base_url")
"""Baichuan custom endpoints"""
baichuan_api_key: SecretStr = Field(alias="api_key")
"""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."""
max_tokens: Optional[int] = None
"""Maximum number of tokens to generate."""
request_timeout: int = Field(default=60, alias="timeout")
"""request timeout for chat http requests"""
model: str = "Baichuan2-Turbo-192K"
"""model name of Baichuan, default is `Baichuan2-Turbo-192K`,
other options include `Baichuan2-Turbo`"""
temperature: Optional[float] = Field(default=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
"""[DEPRECATED, keeping it for for backward compatibility],
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:
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(pre=True)
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", "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,
"stream": self.streaming,
"max_tokens": self.max_tokens,
}
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, stream=True, **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__
cg_chunk = ChatGenerationChunk(message=chunk)
if run_manager:
run_manager.on_llm_new_token(chunk.content, chunk=cg_chunk)
yield cg_chunk
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
if should_stream:
stream_iter = self._astream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return await agenerate_from_stream(stream_iter)
headers = self._create_headers_parameters(**kwargs)
payload = self._create_payload_parameters(messages, **kwargs)
import httpx
async with httpx.AsyncClient(
headers=headers, timeout=self.request_timeout
) as client:
response = await client.post(self.baichuan_api_base, json=payload)
response.raise_for_status()
return self._create_chat_result(response.json())
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
headers = self._create_headers_parameters(**kwargs)
payload = self._create_payload_parameters(messages, stream=True, **kwargs)
import httpx
async with httpx.AsyncClient(
headers=headers, timeout=self.request_timeout
) as client:
async with aconnect_httpx_sse(
client, "POST", self.baichuan_api_base, json=payload
) as event_source:
async for sse in event_source.aiter_sse():
chunk = json.loads(sse.data)
if len(chunk["choices"]) == 0:
continue
choice = chunk["choices"][0]
chunk = _convert_delta_to_message_chunk(
choice["delta"], AIMessageChunk
)
finish_reason = choice.get("finish_reason", None)
generation_info = (
{"finish_reason": finish_reason}
if finish_reason is not None
else None
)
chunk = ChatGenerationChunk(
message=chunk, generation_info=generation_info
)
if run_manager:
await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
yield chunk
if finish_reason is not None:
break
def _chat(self, messages: List[BaseMessage], **kwargs: Any) -> requests.Response:
payload = self._create_payload_parameters(messages, **kwargs)
url = self.baichuan_api_base
headers = self._create_headers_parameters(**kwargs)
res = requests.post(
url=url,
timeout=self.request_timeout,
headers=headers,
json=payload,
stream=self.streaming,
)
return res
def _create_payload_parameters( # type: ignore[no-untyped-def]
self, messages: List[BaseMessage], **kwargs
) -> Dict[str, Any]:
parameters = {**self._default_params, **kwargs}
temperature = parameters.pop("temperature", 0.3)
top_k = parameters.pop("top_k", 5)
top_p = parameters.pop("top_p", 0.85)
model = parameters.pop("model")
with_search_enhance = parameters.pop("with_search_enhance", False)
stream = parameters.pop("stream", False)
tools = parameters.pop("tools", [])
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,
"tools": tools,
}
return payload
def _create_headers_parameters(self, **kwargs) -> Dict[str, Any]: # type: ignore[no-untyped-def]
parameters = {**self._default_params, **kwargs}
default_headers = parameters.pop("headers", {})
api_key = ""
if self.baichuan_api_key:
api_key = self.baichuan_api_key.get_secret_value()
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}",
**default_headers,
}
return headers
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"
