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langchain[patch]: support OpenAIAssistantRunnable async (#15302)

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fix https://github.com/langchain-ai/langchain/issues/15299

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
chyroc 2024-01-30 04:19:47 +08:00 committed by GitHub
parent 39eb00d304
commit a08f9a7ff9
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1 changed files with 271 additions and 1 deletions
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272
libs/langchain/langchain/agents/openai_assistant/base.py
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@ -8,7 +8,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple, Un
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import CallbackManager
from langchain_core.load import dumpd
from langchain_core.pydantic_v1 import Field
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.runnables import RunnableConfig, RunnableSerializable, ensure_config
from langchain_core.tools import BaseTool
from langchain_core.utils.function_calling import convert_to_openai_tool
@ -60,6 +60,22 @@ def _get_openai_client() -> openai.OpenAI:
) from e
def _get_openai_async_client() -> openai.AsyncOpenAI:
try:
import openai
return openai.AsyncOpenAI()
except ImportError as e:
raise ImportError(
"Unable to import openai, please install with `pip install openai`."
) from e
except AttributeError as e:
raise AttributeError(
"Please make sure you are using a v1.1-compatible version of openai. You "
'can install with `pip install "openai>=1.1"`.'
) from e
OutputType = Union[
List[OpenAIAssistantAction],
OpenAIAssistantFinish,
@ -148,6 +164,8 @@ class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]):
client: Any = Field(default_factory=_get_openai_client)
"""OpenAI or AzureOpenAI client."""
async_client: Any = None
"""OpenAI or AzureOpenAI async client."""
assistant_id: str
"""OpenAI assistant id."""
check_every_ms: float = 1_000.0
@ -155,6 +173,15 @@ class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]):
as_agent: bool = False
"""Use as a LangChain agent, compatible with the AgentExecutor."""
@root_validator()
def validate_async_client(cls, values: dict) -> dict:
if values["async_client"] is None:
import openai
api_key = values["client"].api_key
values["async_client"] = openai.AsyncOpenAI(api_key=api_key)
return values
@classmethod
def create_assistant(
cls,
@ -273,6 +300,131 @@ class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]):
run_manager.on_chain_end(response)
return response
@classmethod
async def acreate_assistant(
cls,
name: str,
instructions: str,
tools: Sequence[Union[BaseTool, dict]],
model: str,
*,
async_client: Optional[
Union[openai.AsyncOpenAI, openai.AsyncAzureOpenAI]
] = None,
**kwargs: Any,
) -> OpenAIAssistantRunnable:
"""Create an AsyncOpenAI Assistant and instantiate the Runnable.
Args:
name: Assistant name.
instructions: Assistant instructions.
tools: Assistant tools. Can be passed in OpenAI format or as BaseTools.
model: Assistant model to use.
async_client: AsyncOpenAI client.
Will create default async_client if not specified.
Returns:
AsyncOpenAIAssistantRunnable configured to run using the created assistant.
"""
async_client = async_client or _get_openai_async_client()
openai_tools = [convert_to_openai_tool(tool) for tool in tools]
assistant = await async_client.beta.assistants.create(
name=name,
instructions=instructions,
tools=openai_tools,
model=model,
)
return cls(assistant_id=assistant.id, async_client=async_client, **kwargs)
async def ainvoke(
self, input: dict, config: Optional[RunnableConfig] = None, **kwargs: Any
) -> OutputType:
"""Async invoke assistant.
Args:
input: Runnable input dict that can have:
content: User message when starting a new run.
thread_id: Existing thread to use.
run_id: Existing run to use. Should only be supplied when providing
the tool output for a required action after an initial invocation.
file_ids: File ids to include in new run. Used for retrieval.
message_metadata: Metadata to associate with new message.
thread_metadata: Metadata to associate with new thread. Only relevant
when new thread being created.
instructions: Additional run instructions.
model: Override Assistant model for this run.
tools: Override Assistant tools for this run.
run_metadata: Metadata to associate with new run.
config: Runnable config:
Return:
If self.as_agent, will return
Union[List[OpenAIAssistantAction], OpenAIAssistantFinish]. Otherwise,
will return OpenAI types
Union[List[ThreadMessage], List[RequiredActionFunctionToolCall]].
"""
config = config or {}
callback_manager = CallbackManager.configure(
inheritable_callbacks=config.get("callbacks"),
inheritable_tags=config.get("tags"),
inheritable_metadata=config.get("metadata"),
)
run_manager = callback_manager.on_chain_start(
dumpd(self), input, name=config.get("run_name")
)
try:
# Being run within AgentExecutor and there are tool outputs to submit.
if self.as_agent and input.get("intermediate_steps"):
tool_outputs = self._parse_intermediate_steps(
input["intermediate_steps"]
)
run = await self.async_client.beta.threads.runs.submit_tool_outputs(
**tool_outputs
)
# Starting a new thread and a new run.
elif "thread_id" not in input:
thread = {
"messages": [
{
"role": "user",
"content": input["content"],
"file_ids": input.get("file_ids", []),
"metadata": input.get("message_metadata"),
}
],
"metadata": input.get("thread_metadata"),
}
run = await self._create_thread_and_run(input, thread)
# Starting a new run in an existing thread.
elif "run_id" not in input:
_ = await self.async_client.beta.threads.messages.create(
input["thread_id"],
content=input["content"],
role="user",
file_ids=input.get("file_ids", []),
metadata=input.get("message_metadata"),
)
run = await self._create_run(input)
# Submitting tool outputs to an existing run, outside the AgentExecutor
# framework.
else:
run = await self.async_client.beta.threads.runs.submit_tool_outputs(
**input
)
run = await self._wait_for_run(run.id, run.thread_id)
except BaseException as e:
run_manager.on_chain_error(e)
raise e
try:
response = self._get_response(run)
except BaseException as e:
run_manager.on_chain_error(e, metadata=run.dict())
raise e
else:
run_manager.on_chain_end(response)
return response
def _parse_intermediate_steps(
self, intermediate_steps: List[Tuple[OpenAIAssistantAction, str]]
) -> dict:
@ -388,3 +540,121 @@ class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]):
if in_progress:
sleep(self.check_every_ms / 1000)
return run
async def _aparse_intermediate_steps(
self, intermediate_steps: List[Tuple[OpenAIAssistantAction, str]]
) -> dict:
last_action, last_output = intermediate_steps[-1]
run = await self._wait_for_run(last_action.run_id, last_action.thread_id)
required_tool_call_ids = {
tc.id for tc in run.required_action.submit_tool_outputs.tool_calls
}
tool_outputs = [
{"output": str(output), "tool_call_id": action.tool_call_id}
for action, output in intermediate_steps
if action.tool_call_id in required_tool_call_ids
]
submit_tool_outputs = {
"tool_outputs": tool_outputs,
"run_id": last_action.run_id,
"thread_id": last_action.thread_id,
}
return submit_tool_outputs
async def _acreate_run(self, input: dict) -> Any:
params = {
k: v
for k, v in input.items()
if k in ("instructions", "model", "tools", "run_metadata")
}
return await self.async_client.beta.threads.runs.create(
input["thread_id"],
assistant_id=self.assistant_id,
**params,
)
async def _acreate_thread_and_run(self, input: dict, thread: dict) -> Any:
params = {
k: v
for k, v in input.items()
if k in ("instructions", "model", "tools", "run_metadata")
}
run = await self.async_client.beta.threads.create_and_run(
assistant_id=self.assistant_id,
thread=thread,
**params,
)
return run
async def _aget_response(self, run: Any) -> Any:
# TODO: Pagination
if run.status == "completed":
import openai
messages = await self.async_client.beta.threads.messages.list(
run.thread_id, order="asc"
)
new_messages = [msg for msg in messages if msg.run_id == run.id]
if not self.as_agent:
return new_messages
answer: Any = [
msg_content for msg in new_messages for msg_content in msg.content
]
if all(
isinstance(content, openai.types.beta.threads.MessageContentText)
for content in answer
):
answer = "\n".join(content.text.value for content in answer)
return OpenAIAssistantFinish(
return_values={
"output": answer,
"thread_id": run.thread_id,
"run_id": run.id,
},
log="",
run_id=run.id,
thread_id=run.thread_id,
)
elif run.status == "requires_action":
if not self.as_agent:
return run.required_action.submit_tool_outputs.tool_calls
actions = []
for tool_call in run.required_action.submit_tool_outputs.tool_calls:
function = tool_call.function
try:
args = json.loads(function.arguments, strict=False)
except JSONDecodeError as e:
raise ValueError(
f"Received invalid JSON function arguments: "
f"{function.arguments} for function {function.name}"
) from e
if len(args) == 1 and "__arg1" in args:
args = args["__arg1"]
actions.append(
OpenAIAssistantAction(
tool=function.name,
tool_input=args,
tool_call_id=tool_call.id,
log="",
run_id=run.id,
thread_id=run.thread_id,
)
)
return actions
else:
run_info = json.dumps(run.dict(), indent=2)
raise ValueError(
f"Unexpected run status: {run.status}. Full run info:\n\n{run_info})"
)
async def _await_for_run(self, run_id: str, thread_id: str) -> Any:
in_progress = True
while in_progress:
run = await self.async_client.beta.threads.runs.retrieve(
run_id, thread_id=thread_id
)
in_progress = run.status in ("in_progress", "queued")
if in_progress:
sleep(self.check_every_ms / 1000)
return run