SessionsPythonREPLTool#

class langchain_azure_dynamic_sessions.tools.sessions.SessionsPythonREPLTool[source]#

Bases: BaseTool

Azure Dynamic Sessions tool.

Setup:

Install langchain-azure-dynamic-sessions and create a session pool, which you can do by following the instructions [here](https://learn.microsoft.com/en-us/azure/container-apps/sessions-code-interpreter?tabs=azure-cli#create-a-session-pool-with-azure-cli).

pip install -U langchain-azure-dynamic-sessions

import getpass

POOL_MANAGEMENT_ENDPOINT = getpass.getpass("Enter the management endpoint of the session pool: ")

Instantiation:

from langchain_azure_dynamic_sessions import SessionsPythonREPLTool

tool = SessionsPythonREPLTool(
    pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT
)

Invocation with args:

tool.invoke("6 * 7")

'{\\n  "result": 42,\\n  "stdout": "",\\n  "stderr": ""\\n}'

Invocation with ToolCall:

tool.invoke({"args": {"input":"6 * 7"}, "id": "1", "name": tool.name, "type": "tool_call"})

'{\\n  "result": 42,\\n  "stdout": "",\\n  "stderr": ""\\n}'

Initialize the tool.

Note

SessionsPythonREPLTool implements the standard Runnable Interface. 🏃

The Runnable Interface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more.

param access_token_provider: Callable[[], str | None] = <function _access_token_provider_factory.<locals>.access_token_provider>#: A function that returns the access token to use for the session pool.

param args_schema: TypeBaseModel | None = None#

Pydantic model class to validate and parse the tool’s input arguments.

Args schema should be either:

A subclass of pydantic.BaseModel.

or - A subclass of pydantic.v1.BaseModel if accessing v1 namespace in pydantic 2

param callback_manager: BaseCallbackManager | None = None#: Deprecated since version 0.1.7: Use callbacks instead.

Callback manager to add to the run trace.

param callbacks: Callbacks = None#: Callbacks to be called during tool execution.

param description: str = 'A Python shell. Use this to execute python commands when you need to perform calculations or computations. Input should be a valid python command. Returns a JSON object with the result, stdout, and stderr. '#

Used to tell the model how/when/why to use the tool.

You can provide few-shot examples as a part of the description.

param handle_tool_error: bool | str | Callable[[ToolException], str] | None = False#: Handle the content of the ToolException thrown.

param handle_validation_error: bool | str | Callable[[ValidationError], str] | None = False#: Handle the content of the ValidationError thrown.

param metadata: Dict[str, Any] | None = None#: Optional metadata associated with the tool. Defaults to None. This metadata will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a tool with its use case.

param pool_management_endpoint: str [Required]#: The management endpoint of the session pool. Should end with a ‘/’.

param response_format: Literal['content_and_artifact'] = 'content_and_artifact'#

The tool response format. Defaults to ‘content’.

If “content” then the output of the tool is interpreted as the contents of a ToolMessage. If “content_and_artifact” then the output is expected to be a two-tuple corresponding to the (content, artifact) of a ToolMessage.

param return_direct: bool = False#

Whether to return the tool’s output directly.

Setting this to True means that after the tool is called, the AgentExecutor will stop looping.

param sanitize_input: bool = True#: Whether to sanitize input to the python REPL.

param session_id: str = '2a4e2198-4b49-4cce-859a-56533bcd3c32'#: The session ID to use for the code interpreter. Defaults to a random UUID.

param tags: List[str] | None = None#: Optional list of tags associated with the tool. Defaults to None. These tags will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a tool with its use case.

param verbose: bool = False#: Whether to log the tool’s progress.

__call__(tool_input: str, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None = None) → str#

Deprecated since version langchain-core==0.1.47: Use invoke instead.

Make tool callable.

Parameters:

tool_input (str) –
callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None) –

Return type:

str

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 ainvoke(input: str | Dict | ToolCall, config: RunnableConfig | None = None, **kwargs: Any) → Any#

Default implementation of ainvoke, calls invoke from a thread.

The default implementation allows usage of async code even if the Runnable did not implement a native async version of invoke.

Subclasses should override this method if they can run asynchronously.

Parameters:

input (str | Dict | ToolCall) –
config (RunnableConfig | None) –
kwargs (Any) –

Return type:

Any

Run the tool asynchronously.

Parameters:

tool_input (str | Dict) – The input to the tool.
verbose (bool | None) – Whether to log the tool’s progress. Defaults to None.
start_color (str | None) – The color to use when starting the tool. Defaults to ‘green’.
color (str | None) – The color to use when ending the tool. Defaults to ‘green’.
callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None) – Callbacks to be called during tool execution. Defaults to None.
tags (List[str] | None) – Optional list of tags associated with the tool. Defaults to None.
metadata (Dict[str, Any] | None) – Optional metadata associated with the tool. Defaults to None.
run_name (str | None) – The name of the run. Defaults to None.
run_id (UUID | None) – The id of the run. Defaults to None.
config (RunnableConfig | None) – The configuration for the tool. Defaults to None.
tool_call_id (str | None) – The id of the tool call. Defaults to None.
kwargs (Any) – Additional arguments to pass to the tool

Returns:

The output of the tool.

Raises:

ToolException – If an error occurs during tool execution.

Return type:

Any

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 the
format: 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 of
the 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 that
generated 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 generated
the event.
metadata: Optional[Dict[str, Any]] - The metadata of the Runnable
that 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
)

download_file(*, remote_file_path: str, local_file_path: str | None = None) → BinaryIO[source]#

Download a file from the session.

Parameters:

remote_file_path (str) – The path to download the file from, relative to /mnt/data.
local_file_path (str | None) – The path to save the downloaded file to. If not provided, the file is returned as a BufferedReader.

Returns:

The data of the downloaded file.

Return type:

BinaryIO

execute(python_code: str) → Any[source]#

Execute Python code in the session.

Parameters:: python_code (str) –
Return type:: Any

invoke(input: str | Dict | ToolCall, config: RunnableConfig | None = None, **kwargs: Any) → Any#

Transform a single input into an output. Override to implement.

Parameters:

input (str | Dict | ToolCall) – The input to the Runnable.
config (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.
kwargs (Any) –

Returns:

The output of the Runnable.

Return type:

Any

list_files() → List[RemoteFileMetadata][source]#

List the files in the session.

Returns:: The metadata for the files in the session
Return type:: list[RemoteFileMetadata]

Run the tool.

Parameters:

tool_input (str | Dict[str, Any]) – The input to the tool.
verbose (bool | None) – Whether to log the tool’s progress. Defaults to None.
start_color (str | None) – The color to use when starting the tool. Defaults to ‘green’.
color (str | None) – The color to use when ending the tool. Defaults to ‘green’.
callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None) – Callbacks to be called during tool execution. Defaults to None.
tags (List[str] | None) – Optional list of tags associated with the tool. Defaults to None.
metadata (Dict[str, Any] | None) – Optional metadata associated with the tool. Defaults to None.
run_name (str | None) – The name of the run. Defaults to None.
run_id (UUID | None) – The id of the run. Defaults to None.
config (RunnableConfig | None) – The configuration for the tool. Defaults to None.
tool_call_id (str | None) – The id of the tool call. Defaults to None.
kwargs (Any) – Additional arguments to pass to the tool

Returns:

The output of the tool.

Raises:

ToolException – If an error occurs during tool execution.

Return type:

Any

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:: SerializedConstructor | SerializedNotImplemented

upload_file(*, data: BinaryIO | None = None, remote_file_path: str | None = None, local_file_path: str | None = None) → RemoteFileMetadata[source]#

Upload a file to the session.

Parameters:

data (BinaryIO | None) – The data to upload.
remote_file_path (str | None) – The path to upload the file to, relative to /mnt/data. If local_file_path is provided, this is defaulted to its filename.
local_file_path (str | None) – The path to the local file to upload.

Returns:

The metadata for the uploaded file

Return type:

RemoteFileMetadata

property args: dict#

property is_single_input: bool#: Whether the tool only accepts a single input.

property tool_call_schema: Type[BaseModel]#