OpenAIAssistantRunnable#
- class langchain.agents.openai_assistant.base.OpenAIAssistantRunnable[source]#
Bases:
RunnableSerializable
[Dict
,Union
[List
[OpenAIAssistantAction
],OpenAIAssistantFinish
,List
[ThreadMessage
],List
[RequiredActionFunctionToolCall
]]]Run an OpenAI Assistant.
- Example using OpenAI tools:
from langchain_experimental.openai_assistant import OpenAIAssistantRunnable interpreter_assistant = OpenAIAssistantRunnable.create_assistant( name="langchain assistant", instructions="You are a personal math tutor. Write and run code to answer math questions.", tools=[{"type": "code_interpreter"}], model="gpt-4-1106-preview" ) output = interpreter_assistant.invoke({"content": "What's 10 - 4 raised to the 2.7"})
- Example using custom tools and AgentExecutor:
from langchain_experimental.openai_assistant import OpenAIAssistantRunnable from langchain.agents import AgentExecutor from langchain.tools import E2BDataAnalysisTool tools = [E2BDataAnalysisTool(api_key="...")] agent = OpenAIAssistantRunnable.create_assistant( name="langchain assistant e2b tool", instructions="You are a personal math tutor. Write and run code to answer math questions.", tools=tools, model="gpt-4-1106-preview", as_agent=True ) agent_executor = AgentExecutor(agent=agent, tools=tools) agent_executor.invoke({"content": "What's 10 - 4 raised to the 2.7"})
- Example using custom tools and custom execution:
from langchain_experimental.openai_assistant import OpenAIAssistantRunnable from langchain.agents import AgentExecutor from langchain_core.agents import AgentFinish from langchain.tools import E2BDataAnalysisTool tools = [E2BDataAnalysisTool(api_key="...")] agent = OpenAIAssistantRunnable.create_assistant( name="langchain assistant e2b tool", instructions="You are a personal math tutor. Write and run code to answer math questions.", tools=tools, model="gpt-4-1106-preview", as_agent=True ) def execute_agent(agent, tools, input): tool_map = {tool.name: tool for tool in tools} response = agent.invoke(input) while not isinstance(response, AgentFinish): tool_outputs = [] for action in response: tool_output = tool_map[action.tool].invoke(action.tool_input) tool_outputs.append({"output": tool_output, "tool_call_id": action.tool_call_id}) response = agent.invoke( { "tool_outputs": tool_outputs, "run_id": action.run_id, "thread_id": action.thread_id } ) return response response = execute_agent(agent, tools, {"content": "What's 10 - 4 raised to the 2.7"}) next_response = execute_agent(agent, tools, {"content": "now add 17.241", "thread_id": response.thread_id})
Note
OpenAIAssistantRunnable implements the standard
Runnable Interface
. 🏃The
Runnable Interface
has additional methods that are available on runnables, such aswith_types
,with_retry
,assign
,bind
,get_graph
, and more.- param as_agent: bool = False#
Use as a LangChain agent, compatible with the AgentExecutor.
- param assistant_id: str [Required]#
OpenAI assistant id.
- param async_client: Any = None#
OpenAI or AzureOpenAI async client.
- param check_every_ms: float = 1000.0#
Frequency with which to check run progress in ms.
- param client: Any [Optional]#
OpenAI or AzureOpenAI client.
- async abatch(inputs: List[Input], config: RunnableConfig | List[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) List[Output] #
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.
- Parameters:
inputs (List[Input]) – A list of inputs to the Runnable.
config (RunnableConfig | List[RunnableConfig] | None) – A config to use when invoking the Runnable. The config supports standard keys like ‘tags’, ‘metadata’ for tracing purposes, ‘max_concurrency’ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.
return_exceptions (bool) – Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.
- Returns:
A list of outputs from the Runnable.
- Return type:
List[Output]
- async abatch_as_completed(inputs: Sequence[Input], config: RunnableConfig | Sequence[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) AsyncIterator[Tuple[int, Output | Exception]] #
Run ainvoke in parallel on a list of inputs, yielding results as they complete.
- Parameters:
inputs (Sequence[Input]) – A list of inputs to the Runnable.
config (RunnableConfig | Sequence[RunnableConfig] | None) – A config to use when invoking the Runnable. The config supports standard keys like ‘tags’, ‘metadata’ for tracing purposes, ‘max_concurrency’ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None. Defaults to None.
return_exceptions (bool) – Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.
- Yields:
A tuple of the index of the input and the output from the Runnable.
- Return type:
AsyncIterator[Tuple[int, Output | Exception]]
- async classmethod acreate_assistant(name: str, instructions: str, tools: Sequence[BaseTool | dict], model: str, *, async_client: openai.AsyncOpenAI | openai.AsyncAzureOpenAI | None = None, **kwargs: Any) OpenAIAssistantRunnable [source]#
Async create an AsyncOpenAI Assistant and instantiate the Runnable.
- Parameters:
name (str) – Assistant name.
instructions (str) – Assistant instructions.
tools (Sequence[Union[BaseTool, dict]]) – Assistant tools. Can be passed in OpenAI format or as BaseTools.
model (str) – Assistant model to use.
async_client (Optional[Union[openai.AsyncOpenAI, openai.AsyncAzureOpenAI]]) – AsyncOpenAI client. Will create default async_client if not specified.
kwargs (Any) –
- Returns:
AsyncOpenAIAssistantRunnable configured to run using the created assistant.
- Return type:
- async ainvoke(input: dict, config: RunnableConfig | None = None, **kwargs: Any) OutputType [source]#
Async invoke assistant.
- Parameters:
input (dict) –
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.
message_metadata: Metadata to associate with a new message. thread_metadata: Metadata to associate with new thread. Only relevant
when a new thread is 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 (Optional[RunnableConfig]) – Runnable config. Defaults to None.
kwargs (Any) – Additional arguments.
- Returns:
- If self.as_agent, will return
Union[List[OpenAIAssistantAction], OpenAIAssistantFinish]. Otherwise, will return OpenAI types Union[List[ThreadMessage], List[RequiredActionFunctionToolCall]].
- Return type:
OutputType
- async astream(input: Input, config: RunnableConfig | None = None, **kwargs: Any | None) AsyncIterator[Output] #
Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.
- Parameters:
input (Input) – The input to the Runnable.
config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.
kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.
- Yields:
The output of the Runnable.
- Return type:
AsyncIterator[Output]
- astream_events(input: Any, config: RunnableConfig | None = None, *, version: Literal['v1', 'v2'], include_names: Sequence[str] | None = None, include_types: Sequence[str] | None = None, include_tags: Sequence[str] | None = None, exclude_names: Sequence[str] | None = None, exclude_types: Sequence[str] | None = None, exclude_tags: Sequence[str] | None = None, **kwargs: Any) AsyncIterator[StandardStreamEvent | CustomStreamEvent] #
Beta
This API is in beta and may change in the future.
Generate a stream of events.
Use to create an iterator over StreamEvents that provide real-time information about the progress of the Runnable, including StreamEvents from intermediate results.
A StreamEvent is a dictionary with the following schema:
event
: str - Event names are of theformat: on_[runnable_type]_(start|stream|end).
name
: str - The name of the Runnable that generated the event.run_id
: str - randomly generated ID associated with the given execution ofthe Runnable that emitted the event. A child Runnable that gets invoked as part of the execution of a parent Runnable is assigned its own unique ID.
parent_ids
: List[str] - The IDs of the parent runnables thatgenerated the event. The root Runnable will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list.
tags
: Optional[List[str]] - The tags of the Runnable that generatedthe event.
metadata
: Optional[Dict[str, Any]] - The metadata of the Runnablethat generated the event.
data
: Dict[str, Any]
Below is a table that illustrates some evens that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.
ATTENTION This reference table is for the V2 version of the schema.
event
name
chunk
input
output
on_chat_model_start
[model name]
{“messages”: [[SystemMessage, HumanMessage]]}
on_chat_model_stream
[model name]
AIMessageChunk(content=”hello”)
on_chat_model_end
[model name]
{“messages”: [[SystemMessage, HumanMessage]]}
AIMessageChunk(content=”hello world”)
on_llm_start
[model name]
{‘input’: ‘hello’}
on_llm_stream
[model name]
‘Hello’
on_llm_end
[model name]
‘Hello human!’
on_chain_start
format_docs
on_chain_stream
format_docs
“hello world!, goodbye world!”
on_chain_end
format_docs
[Document(…)]
“hello world!, goodbye world!”
on_tool_start
some_tool
{“x”: 1, “y”: “2”}
on_tool_end
some_tool
{“x”: 1, “y”: “2”}
on_retriever_start
[retriever name]
{“query”: “hello”}
on_retriever_end
[retriever name]
{“query”: “hello”}
[Document(…), ..]
on_prompt_start
[template_name]
{“question”: “hello”}
on_prompt_end
[template_name]
{“question”: “hello”}
ChatPromptValue(messages: [SystemMessage, …])
In addition to the standard events, users can also dispatch custom events (see example below).
Custom events will be only be surfaced with in the v2 version of the API!
A custom event has following format:
Attribute
Type
Description
name
str
A user defined name for the event.
data
Any
The data associated with the event. This can be anything, though we suggest making it JSON serializable.
Here are declarations associated with the standard events shown above:
format_docs:
def format_docs(docs: List[Document]) -> str: '''Format the docs.''' return ", ".join([doc.page_content for doc in docs]) format_docs = RunnableLambda(format_docs)
some_tool:
@tool def some_tool(x: int, y: str) -> dict: '''Some_tool.''' return {"x": x, "y": y}
prompt:
template = ChatPromptTemplate.from_messages( [("system", "You are Cat Agent 007"), ("human", "{question}")] ).with_config({"run_name": "my_template", "tags": ["my_template"]})
Example:
from langchain_core.runnables import RunnableLambda async def reverse(s: str) -> str: return s[::-1] chain = RunnableLambda(func=reverse) events = [ event async for event in chain.astream_events("hello", version="v2") ] # will produce the following events (run_id, and parent_ids # has been omitted for brevity): [ { "data": {"input": "hello"}, "event": "on_chain_start", "metadata": {}, "name": "reverse", "tags": [], }, { "data": {"chunk": "olleh"}, "event": "on_chain_stream", "metadata": {}, "name": "reverse", "tags": [], }, { "data": {"output": "olleh"}, "event": "on_chain_end", "metadata": {}, "name": "reverse", "tags": [], }, ]
Example: Dispatch Custom Event
from langchain_core.callbacks.manager import ( adispatch_custom_event, ) from langchain_core.runnables import RunnableLambda, RunnableConfig import asyncio async def slow_thing(some_input: str, config: RunnableConfig) -> str: """Do something that takes a long time.""" await asyncio.sleep(1) # Placeholder for some slow operation await adispatch_custom_event( "progress_event", {"message": "Finished step 1 of 3"}, config=config # Must be included for python < 3.10 ) await asyncio.sleep(1) # Placeholder for some slow operation await adispatch_custom_event( "progress_event", {"message": "Finished step 2 of 3"}, config=config # Must be included for python < 3.10 ) await asyncio.sleep(1) # Placeholder for some slow operation return "Done" slow_thing = RunnableLambda(slow_thing) async for event in slow_thing.astream_events("some_input", version="v2"): print(event)
- Parameters:
input (Any) – The input to the Runnable.
config (RunnableConfig | None) – The config to use for the Runnable.
version (Literal['v1', 'v2']) – The version of the schema to use either v2 or v1. Users should use v2. v1 is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in v2.
include_names (Sequence[str] | None) – Only include events from runnables with matching names.
include_types (Sequence[str] | None) – Only include events from runnables with matching types.
include_tags (Sequence[str] | None) – Only include events from runnables with matching tags.
exclude_names (Sequence[str] | None) – Exclude events from runnables with matching names.
exclude_types (Sequence[str] | None) – Exclude events from runnables with matching types.
exclude_tags (Sequence[str] | None) – Exclude events from runnables with matching tags.
kwargs (Any) – Additional keyword arguments to pass to the Runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.
- Yields:
An async stream of StreamEvents.
- Raises:
NotImplementedError – If the version is not v1 or v2.
- Return type:
AsyncIterator[StandardStreamEvent | CustomStreamEvent]
- batch(inputs: List[Input], config: RunnableConfig | List[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) List[Output] #
Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.
- Parameters:
inputs (List[Input]) –
config (RunnableConfig | List[RunnableConfig] | None) –
return_exceptions (bool) –
kwargs (Any | None) –
- Return type:
List[Output]
- batch_as_completed(inputs: Sequence[Input], config: RunnableConfig | Sequence[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) Iterator[Tuple[int, Output | Exception]] #
Run invoke in parallel on a list of inputs, yielding results as they complete.
- Parameters:
inputs (Sequence[Input]) –
config (RunnableConfig | Sequence[RunnableConfig] | None) –
return_exceptions (bool) –
kwargs (Any | None) –
- Return type:
Iterator[Tuple[int, Output | Exception]]
- configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) RunnableSerializable[Input, Output] #
Configure alternatives for Runnables that can be set at runtime.
- Parameters:
which (ConfigurableField) – The ConfigurableField instance that will be used to select the alternative.
default_key (str) – The default key to use if no alternative is selected. Defaults to “default”.
prefix_keys (bool) – Whether to prefix the keys with the ConfigurableField id. Defaults to False.
**kwargs (Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) – A dictionary of keys to Runnable instances or callables that return Runnable instances.
- Returns:
A new Runnable with the alternatives configured.
- Return type:
RunnableSerializable[Input, Output]
from langchain_anthropic import ChatAnthropic from langchain_core.runnables.utils import ConfigurableField from langchain_openai import ChatOpenAI model = ChatAnthropic( model_name="claude-3-sonnet-20240229" ).configurable_alternatives( ConfigurableField(id="llm"), default_key="anthropic", openai=ChatOpenAI() ) # uses the default model ChatAnthropic print(model.invoke("which organization created you?").content) # uses ChatOpenAI print( model.with_config( configurable={"llm": "openai"} ).invoke("which organization created you?").content )
- configurable_fields(**kwargs: ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) RunnableSerializable[Input, Output] #
Configure particular Runnable fields at runtime.
- Parameters:
**kwargs (ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) – A dictionary of ConfigurableField instances to configure.
- Returns:
A new Runnable with the fields configured.
- Return type:
RunnableSerializable[Input, Output]
from langchain_core.runnables import ConfigurableField from langchain_openai import ChatOpenAI model = ChatOpenAI(max_tokens=20).configurable_fields( max_tokens=ConfigurableField( id="output_token_number", name="Max tokens in the output", description="The maximum number of tokens in the output", ) ) # max_tokens = 20 print( "max_tokens_20: ", model.invoke("tell me something about chess").content ) # max_tokens = 200 print("max_tokens_200: ", model.with_config( configurable={"output_token_number": 200} ).invoke("tell me something about chess").content )
- classmethod create_assistant(name: str, instructions: str, tools: Sequence[BaseTool | dict], model: str, *, client: openai.OpenAI | openai.AzureOpenAI | None = None, **kwargs: Any) OpenAIAssistantRunnable [source]#
Create an OpenAI Assistant and instantiate the Runnable.
- Parameters:
name (str) – Assistant name.
instructions (str) – Assistant instructions.
tools (Sequence[Union[BaseTool, dict]]) – Assistant tools. Can be passed in OpenAI format or as BaseTools.
model (str) – Assistant model to use.
client (Optional[Union[openai.OpenAI, openai.AzureOpenAI]]) – OpenAI or AzureOpenAI client. Will create a default OpenAI client if not specified.
kwargs (Any) – Additional arguments.
- Returns:
OpenAIAssistantRunnable configured to run using the created assistant.
- Return type:
- invoke(input: dict, config: RunnableConfig | None = None) OutputType [source]#
Invoke assistant.
- Parameters:
input (dict) –
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.
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 (Optional[RunnableConfig]) – Runnable config. Defaults to None.
- Returns:
- If self.as_agent, will return
Union[List[OpenAIAssistantAction], OpenAIAssistantFinish]. Otherwise, will return OpenAI types Union[List[ThreadMessage], List[RequiredActionFunctionToolCall]].
- Return type:
OutputType
- stream(input: Input, config: RunnableConfig | None = None, **kwargs: Any | None) Iterator[Output] #
Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.
- Parameters:
input (Input) – The input to the Runnable.
config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.
kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.
- Yields:
The output of the Runnable.
- Return type:
Iterator[Output]
- to_json() SerializedConstructor | SerializedNotImplemented #
Serialize the Runnable to JSON.
- Returns:
A JSON-serializable representation of the Runnable.
- Return type: