from __future__ import annotations
import asyncio
from operator import itemgetter
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Literal,
Optional,
Sequence,
Type,
Union,
)
from google.auth.credentials import Credentials
from langchain_core.callbacks.manager 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.language_models.llms import BaseLLM
from langchain_core.messages import (
AIMessage,
BaseMessage,
)
from langchain_core.outputs import (
ChatGeneration,
ChatGenerationChunk,
ChatResult,
Generation,
LLMResult,
)
from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
from langchain_core.runnables import (
Runnable,
RunnableMap,
RunnablePassthrough,
)
from langchain_core.tools import BaseTool
from langchain_google_vertexai._anthropic_parsers import (
ToolsOutputParser,
_extract_tool_calls,
)
from langchain_google_vertexai._anthropic_utils import (
_format_messages_anthropic,
_make_message_chunk_from_anthropic_event,
_tools_in_params,
convert_to_anthropic_tool,
)
from langchain_google_vertexai._base import _BaseVertexAIModelGarden, _VertexAICommon
[docs]
class VertexAIModelGarden(_BaseVertexAIModelGarden, BaseLLM):
"""Large language models served from Vertex AI Model Garden."""
class Config:
"""Configuration for this pydantic object."""
allow_population_by_field_name = True
# Needed so that mypy doesn't flag missing aliased init args.
def __init__(self, **kwargs: Any) -> None:
super().__init__(**kwargs)
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
instances = self._prepare_request(prompts, **kwargs)
if self.single_example_per_request and len(instances) > 1:
results = []
for instance in instances:
response = self.client.predict(
endpoint=self.endpoint_path, instances=[instance]
)
results.append(self._parse_prediction(response.predictions[0]))
return LLMResult(
generations=[[Generation(text=result)] for result in results]
)
response = self.client.predict(endpoint=self.endpoint_path, instances=instances)
return self._parse_response(response)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
instances = self._prepare_request(prompts, **kwargs)
if self.single_example_per_request and len(instances) > 1:
responses = []
for instance in instances:
responses.append(
self.async_client.predict(
endpoint=self.endpoint_path, instances=[instance]
)
)
responses = await asyncio.gather(*responses)
return LLMResult(
generations=[
[Generation(text=self._parse_prediction(response.predictions[0]))]
for response in responses
]
)
response = await self.async_client.predict(
endpoint=self.endpoint_path, instances=instances
)
return self._parse_response(response)
[docs]
class ChatAnthropicVertex(_VertexAICommon, BaseChatModel):
async_client: Any = None #: :meta private:
model_name: Optional[str] = Field(default=None, alias="model") # type: ignore[assignment]
"Underlying model name."
max_output_tokens: int = Field(default=1024, alias="max_tokens")
access_token: Optional[str] = None
stream_usage: bool = True # Whether to include usage metadata in streaming output
credentials: Optional[Credentials] = None
class Config:
"""Configuration for this pydantic object."""
allow_population_by_field_name = True
# Needed so that mypy doesn't flag missing aliased init args.
def __init__(self, **kwargs: Any) -> None:
super().__init__(**kwargs)
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
from anthropic import ( # type: ignore
AnthropicVertex,
AsyncAnthropicVertex,
)
values["client"] = AnthropicVertex(
project_id=values["project"],
region=values["location"],
max_retries=values["max_retries"],
access_token=values["access_token"],
credentials=values["credentials"],
)
values["async_client"] = AsyncAnthropicVertex(
project_id=values["project"],
region=values["location"],
max_retries=values["max_retries"],
access_token=values["access_token"],
credentials=values["credentials"],
)
return values
@property
def _default_params(self):
return {
"model": self.model_name,
"max_tokens": self.max_output_tokens,
"temperature": self.temperature,
"top_k": self.top_k,
"top_p": self.top_p,
}
def _format_params(
self,
*,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> Dict[str, Any]:
system_message, formatted_messages = _format_messages_anthropic(messages)
params = self._default_params
params.update(kwargs)
if kwargs.get("model_name"):
params["model"] = params["model_name"]
if kwargs.get("model"):
params["model"] = kwargs["model"]
params.pop("model_name", None)
params.update(
{
"system": system_message,
"messages": formatted_messages,
"stop_sequences": stop,
}
)
return {k: v for k, v in params.items() if v is not None}
def _format_output(self, data: Any, **kwargs: Any) -> ChatResult:
data_dict = data.model_dump()
content = [c for c in data_dict["content"] if c["type"] != "tool_use"]
content = content[0]["text"] if len(content) == 1 else content
llm_output = {
k: v for k, v in data_dict.items() if k not in ("content", "role", "type")
}
tool_calls = _extract_tool_calls(data_dict["content"])
if tool_calls:
msg = AIMessage(content=content, tool_calls=tool_calls)
else:
msg = AIMessage(content=content)
# Collect token usage
msg.usage_metadata = {
"input_tokens": data.usage.input_tokens,
"output_tokens": data.usage.output_tokens,
"total_tokens": data.usage.input_tokens + data.usage.output_tokens,
}
return ChatResult(
generations=[ChatGeneration(message=msg)],
llm_output=llm_output,
)
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
params = self._format_params(messages=messages, stop=stop, **kwargs)
if self.streaming:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return generate_from_stream(stream_iter)
data = self.client.messages.create(**params)
return self._format_output(data, **kwargs)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
params = self._format_params(messages=messages, stop=stop, **kwargs)
if self.streaming:
stream_iter = self._astream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return await agenerate_from_stream(stream_iter)
data = await self.async_client.messages.create(**params)
return self._format_output(data, **kwargs)
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "anthropic-chat-vertexai"
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
*,
stream_usage: Optional[bool] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
if stream_usage is None:
stream_usage = self.stream_usage
params = self._format_params(messages=messages, stop=stop, **kwargs)
stream = self.client.messages.create(**params, stream=True)
coerce_content_to_string = not _tools_in_params(params)
for event in stream:
msg = _make_message_chunk_from_anthropic_event(
event,
stream_usage=stream_usage,
coerce_content_to_string=coerce_content_to_string,
)
if msg is not None:
chunk = ChatGenerationChunk(message=msg)
if run_manager and isinstance(msg.content, str):
run_manager.on_llm_new_token(msg.content, chunk=chunk)
yield chunk
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
*,
stream_usage: Optional[bool] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
if stream_usage is None:
stream_usage = self.stream_usage
params = self._format_params(messages=messages, stop=stop, **kwargs)
stream = await self.async_client.messages.create(**params, stream=True)
coerce_content_to_string = not _tools_in_params(params)
async for event in stream:
msg = _make_message_chunk_from_anthropic_event(
event,
stream_usage=stream_usage,
coerce_content_to_string=coerce_content_to_string,
)
if msg is not None:
chunk = ChatGenerationChunk(message=msg)
if run_manager and isinstance(msg.content, str):
await run_manager.on_llm_new_token(msg.content, chunk=chunk)
yield chunk
[docs]
def with_structured_output(
self,
schema: Union[Dict, Type[BaseModel]],
*,
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
"""Model wrapper that returns outputs formatted to match the given schema."""
tool_name = convert_to_anthropic_tool(schema)["name"]
llm = self.bind_tools([schema], tool_choice=tool_name)
if isinstance(schema, type) and issubclass(schema, BaseModel):
output_parser = ToolsOutputParser(
first_tool_only=True, pydantic_schemas=[schema]
)
else:
output_parser = ToolsOutputParser(first_tool_only=True, args_only=True)
if include_raw:
parser_assign = RunnablePassthrough.assign(
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
)
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
parser_with_fallback = parser_assign.with_fallbacks(
[parser_none], exception_key="parsing_error"
)
return RunnableMap(raw=llm) | parser_with_fallback
else:
return llm | output_parser
