"""Writer chat wrapper."""
from __future__ import annotations
import logging
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Literal,
Mapping,
Optional,
Sequence,
Tuple,
Type,
Union,
)
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,
ChatMessage,
HumanMessage,
SystemMessage,
ToolMessage,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.runnables import Runnable
from langchain_core.utils.function_calling import convert_to_openai_tool
from pydantic import BaseModel, ConfigDict, Field, SecretStr
logger = logging.getLogger(__name__)
def _convert_message_to_dict(message: BaseMessage) -> dict:
"""Convert a LangChain message to a Writer message dict."""
message_dict = {"role": "", "content": message.content}
if isinstance(message, ChatMessage):
message_dict["role"] = message.role
elif isinstance(message, HumanMessage):
message_dict["role"] = "user"
elif isinstance(message, AIMessage):
message_dict["role"] = "assistant"
if message.tool_calls:
message_dict["tool_calls"] = [
{
"id": tool["id"],
"type": "function",
"function": {"name": tool["name"], "arguments": tool["args"]},
}
for tool in message.tool_calls
]
elif isinstance(message, SystemMessage):
message_dict["role"] = "system"
elif isinstance(message, ToolMessage):
message_dict["role"] = "tool"
message_dict["tool_call_id"] = message.tool_call_id
else:
raise ValueError(f"Got unknown message type: {type(message)}")
if message.name:
message_dict["name"] = message.name
return message_dict
def _convert_dict_to_message(response_dict: Dict[str, Any]) -> BaseMessage:
"""Convert a Writer message dict to a LangChain message."""
role = response_dict["role"]
content = response_dict.get("content", "")
if role == "user":
return HumanMessage(content=content)
elif role == "assistant":
additional_kwargs = {}
if tool_calls := response_dict.get("tool_calls"):
additional_kwargs["tool_calls"] = tool_calls
return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
elif role == "system":
return SystemMessage(content=content)
elif role == "tool":
return ToolMessage(
content=content,
tool_call_id=response_dict["tool_call_id"],
name=response_dict.get("name"),
)
else:
return ChatMessage(content=content, role=role)
[docs]
class ChatWriter(BaseChatModel):
"""Writer chat model.
To use, you should have the ``writer-sdk`` Python package installed, and the
environment variable ``WRITER_API_KEY`` set with your API key.
Example:
.. code-block:: python
from langchain_community.chat_models import ChatWriter
chat = ChatWriter(model="palmyra-x-004")
"""
client: Any = Field(default=None, exclude=True) #: :meta private:
async_client: Any = Field(default=None, exclude=True) #: :meta private:
model_name: str = Field(default="palmyra-x-004", alias="model")
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
writer_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
"""Writer API key."""
writer_api_base: Optional[str] = Field(default=None, alias="base_url")
"""Base URL for API requests."""
streaming: bool = False
"""Whether to stream the results or not."""
n: int = 1
"""Number of chat completions to generate for each prompt."""
max_tokens: Optional[int] = None
"""Maximum number of tokens to generate."""
model_config = ConfigDict(populate_by_name=True)
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "writer-chat"
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {
"model_name": self.model_name,
"temperature": self.temperature,
"streaming": self.streaming,
**self.model_kwargs,
}
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
generations = []
for choice in response["choices"]:
message = _convert_dict_to_message(choice["message"])
gen = ChatGeneration(
message=message,
generation_info=dict(finish_reason=choice.get("finish_reason")),
)
generations.append(gen)
token_usage = response.get("usage", {})
llm_output = {
"token_usage": token_usage,
"model_name": self.model_name,
"system_fingerprint": response.get("system_fingerprint", ""),
}
return ChatResult(generations=generations, llm_output=llm_output)
def _convert_messages_to_dicts(
self, messages: List[BaseMessage], stop: Optional[List[str]] = None
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
params = {
"model": self.model_name,
"temperature": self.temperature,
"n": self.n,
"stream": self.streaming,
**self.model_kwargs,
}
if stop:
params["stop"] = stop
if self.max_tokens is not None:
params["max_tokens"] = self.max_tokens
message_dicts = [_convert_message_to_dict(m) for m in messages]
return message_dicts, params
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
message_dicts, params = self._convert_messages_to_dicts(messages, stop)
params = {**params, **kwargs, "stream": True}
response = self.client.chat.chat(messages=message_dicts, **params)
for chunk in response:
delta = chunk["choices"][0].get("delta")
if not delta or not delta.get("content"):
continue
chunk = _convert_dict_to_message(
{"role": "assistant", "content": delta["content"]}
)
chunk = ChatGenerationChunk(message=chunk)
if run_manager:
run_manager.on_llm_new_token(chunk.text)
yield chunk
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
message_dicts, params = self._convert_messages_to_dicts(messages, stop)
params = {**params, **kwargs, "stream": True}
response = await self.async_client.chat.chat(messages=message_dicts, **params)
async for chunk in response:
delta = chunk["choices"][0].get("delta")
if not delta or not delta.get("content"):
continue
chunk = _convert_dict_to_message(
{"role": "assistant", "content": delta["content"]}
)
chunk = ChatGenerationChunk(message=chunk)
if run_manager:
await run_manager.on_llm_new_token(chunk.text)
yield chunk
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
return generate_from_stream(
self._stream(messages, stop, run_manager, **kwargs)
)
message_dicts, params = self._convert_messages_to_dicts(messages, stop)
params = {**params, **kwargs}
response = self.client.chat.chat(messages=message_dicts, **params)
return self._create_chat_result(response)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
return await agenerate_from_stream(
self._astream(messages, stop, run_manager, **kwargs)
)
message_dicts, params = self._convert_messages_to_dicts(messages, stop)
params = {**params, **kwargs}
response = await self.async_client.chat.chat(messages=message_dicts, **params)
return self._create_chat_result(response)
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Writer API."""
return {
"model": self.model_name,
"temperature": self.temperature,
"stream": self.streaming,
"n": self.n,
"max_tokens": self.max_tokens,
**self.model_kwargs,
}
