From b8df15cd647ca645ef16b2d66be271dc1f5187c1 Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Wed, 9 Aug 2023 12:34:23 +0100 Subject: [PATCH] Adds transform support for runnables (#8762) --------- Co-authored-by: jacoblee93 Co-authored-by: Eugene Yurtsev Co-authored-by: Bagatur --- .../langchain/callbacks/tracers/base.py | 15 +- libs/langchain/langchain/chains/base.py | 20 +- libs/langchain/langchain/chains/transform.py | 16 +- libs/langchain/langchain/chat_models/fake.py | 40 ++- libs/langchain/langchain/llms/fake.py | 30 +- .../langchain/schema/output_parser.py | 59 +++- libs/langchain/langchain/schema/runnable.py | 333 ++++++++++++++++-- libs/langchain/langchain/utils/aiter.py | 191 ++++++++++ .../tests/unit_tests/chains/test_transform.py | 2 +- .../schema/__snapshots__/test_runnable.ambr | 17 +- .../tests/unit_tests/schema/test_runnable.py | 53 ++- 11 files changed, 717 insertions(+), 59 deletions(-) create mode 100644 libs/langchain/langchain/utils/aiter.py diff --git a/libs/langchain/langchain/callbacks/tracers/base.py b/libs/langchain/langchain/callbacks/tracers/base.py index 3a0ee9d2c5..e4f89c85cb 100644 --- a/libs/langchain/langchain/callbacks/tracers/base.py +++ b/libs/langchain/langchain/callbacks/tracers/base.py @@ -47,6 +47,9 @@ class BaseTracer(BaseCallbackHandler, ABC): parent_run = self.run_map[str(run.parent_run_id)] if parent_run: self._add_child_run(parent_run, run) + parent_run.child_execution_order = max( + parent_run.child_execution_order, run.child_execution_order + ) else: logger.debug(f"Parent run with UUID {run.parent_run_id} not found.") self.run_map[str(run.id)] = run @@ -254,7 +257,12 @@ class BaseTracer(BaseCallbackHandler, ABC): self._on_chain_start(chain_run) def on_chain_end( - self, outputs: Dict[str, Any], *, run_id: UUID, **kwargs: Any + self, + outputs: Dict[str, Any], + *, + run_id: UUID, + inputs: Optional[Dict[str, Any]] = None, + **kwargs: Any, ) -> None: """End a trace for a chain run.""" if not run_id: @@ -266,6 +274,8 @@ class BaseTracer(BaseCallbackHandler, ABC): chain_run.outputs = outputs chain_run.end_time = datetime.utcnow() chain_run.events.append({"name": "end", "time": chain_run.end_time}) + if inputs is not None: + chain_run.inputs = inputs self._end_trace(chain_run) self._on_chain_end(chain_run) @@ -273,6 +283,7 @@ class BaseTracer(BaseCallbackHandler, ABC): self, error: Union[Exception, KeyboardInterrupt], *, + inputs: Optional[Dict[str, Any]] = None, run_id: UUID, **kwargs: Any, ) -> None: @@ -286,6 +297,8 @@ class BaseTracer(BaseCallbackHandler, ABC): chain_run.error = repr(error) chain_run.end_time = datetime.utcnow() chain_run.events.append({"name": "error", "time": chain_run.end_time}) + if inputs is not None: + chain_run.inputs = inputs self._end_trace(chain_run) self._on_chain_error(chain_run) diff --git a/libs/langchain/langchain/chains/base.py b/libs/langchain/langchain/chains/base.py index 7f79c4ffd9..301b0143e7 100644 --- a/libs/langchain/langchain/chains/base.py +++ b/libs/langchain/langchain/chains/base.py @@ -1,9 +1,11 @@ """Base interface that all chains should implement.""" +import asyncio import inspect import json import logging import warnings from abc import ABC, abstractmethod +from functools import partial from pathlib import Path from typing import Any, Dict, List, Optional, Union @@ -55,18 +57,26 @@ class Chain(Serializable, Runnable[Dict[str, Any], Dict[str, Any]], ABC): """ def invoke( - self, input: Dict[str, Any], config: Optional[RunnableConfig] = None + self, + input: Dict[str, Any], + config: Optional[RunnableConfig] = None, + **kwargs: Any, ) -> Dict[str, Any]: - return self(input, **(config or {})) + return self(input, **(config or {}), **kwargs) async def ainvoke( - self, input: Dict[str, Any], config: Optional[RunnableConfig] = None + self, + input: Dict[str, Any], + config: Optional[RunnableConfig] = None, + **kwargs: Any, ) -> Dict[str, Any]: if type(self)._acall == Chain._acall: # If the chain does not implement async, fall back to default implementation - return await super().ainvoke(input, config) + return await asyncio.get_running_loop().run_in_executor( + None, partial(self.invoke, input, config, **kwargs) + ) - return await self.acall(input, **(config or {})) + return await self.acall(input, **(config or {}), **kwargs) memory: Optional[BaseMemory] = None """Optional memory object. Defaults to None. diff --git a/libs/langchain/langchain/chains/transform.py b/libs/langchain/langchain/chains/transform.py index 969796ffe7..b03bbe22c3 100644 --- a/libs/langchain/langchain/chains/transform.py +++ b/libs/langchain/langchain/chains/transform.py @@ -3,6 +3,8 @@ import functools import logging from typing import Any, Awaitable, Callable, Dict, List, Optional +from pydantic import Field + from langchain.callbacks.manager import ( AsyncCallbackManagerForChainRun, CallbackManagerForChainRun, @@ -27,9 +29,11 @@ class TransformChain(Chain): """The keys expected by the transform's input dictionary.""" output_variables: List[str] """The keys returned by the transform's output dictionary.""" - transform: Callable[[Dict[str, str]], Dict[str, str]] + transform_cb: Callable[[Dict[str, str]], Dict[str, str]] = Field(alias="transform") """The transform function.""" - atransform: Optional[Callable[[Dict[str, Any]], Awaitable[Dict[str, Any]]]] = None + atransform_cb: Optional[ + Callable[[Dict[str, Any]], Awaitable[Dict[str, Any]]] + ] = Field(None, alias="atransform") """The async coroutine transform function.""" @staticmethod @@ -62,18 +66,18 @@ class TransformChain(Chain): inputs: Dict[str, str], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: - return self.transform(inputs) + return self.transform_cb(inputs) async def _acall( self, inputs: Dict[str, Any], run_manager: Optional[AsyncCallbackManagerForChainRun] = None, ) -> Dict[str, Any]: - if self.atransform is not None: - return await self.atransform(inputs) + if self.atransform_cb is not None: + return await self.atransform_cb(inputs) else: self._log_once( "TransformChain's atransform is not provided, falling" " back to synchronous transform" ) - return self.transform(inputs) + return self.transform_cb(inputs) diff --git a/libs/langchain/langchain/chat_models/fake.py b/libs/langchain/langchain/chat_models/fake.py index 97e69c993f..fe332016e6 100644 --- a/libs/langchain/langchain/chat_models/fake.py +++ b/libs/langchain/langchain/chat_models/fake.py @@ -1,9 +1,13 @@ """Fake ChatModel for testing purposes.""" -from typing import Any, Dict, List, Optional +from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union -from langchain.callbacks.manager import CallbackManagerForLLMRun +from langchain.callbacks.manager import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) from langchain.chat_models.base import SimpleChatModel -from langchain.schema.messages import BaseMessage +from langchain.schema.messages import AIMessageChunk, BaseMessage +from langchain.schema.output import ChatGenerationChunk class FakeListChatModel(SimpleChatModel): @@ -31,6 +35,36 @@ class FakeListChatModel(SimpleChatModel): self.i = 0 return response + def _stream( + self, + messages: List[BaseMessage], + stop: Union[List[str], None] = None, + run_manager: Union[CallbackManagerForLLMRun, None] = None, + **kwargs: Any, + ) -> Iterator[ChatGenerationChunk]: + response = self.responses[self.i] + if self.i < len(self.responses) - 1: + self.i += 1 + else: + self.i = 0 + for c in response: + yield ChatGenerationChunk(message=AIMessageChunk(content=c)) + + async def _astream( + self, + messages: List[BaseMessage], + stop: Union[List[str], None] = None, + run_manager: Union[AsyncCallbackManagerForLLMRun, None] = None, + **kwargs: Any, + ) -> AsyncIterator[ChatGenerationChunk]: + response = self.responses[self.i] + if self.i < len(self.responses) - 1: + self.i += 1 + else: + self.i = 0 + for c in response: + yield ChatGenerationChunk(message=AIMessageChunk(content=c)) + @property def _identifying_params(self) -> Dict[str, Any]: return {"responses": self.responses} diff --git a/libs/langchain/langchain/llms/fake.py b/libs/langchain/langchain/llms/fake.py index c2cf63036d..8aa0fbea75 100644 --- a/libs/langchain/langchain/llms/fake.py +++ b/libs/langchain/langchain/llms/fake.py @@ -1,10 +1,12 @@ -from typing import Any, List, Mapping, Optional +from typing import Any, AsyncIterator, Iterator, List, Mapping, Optional from langchain.callbacks.manager import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) from langchain.llms.base import LLM +from langchain.schema.language_model import LanguageModelInput +from langchain.schema.runnable import RunnableConfig class FakeListLLM(LLM): @@ -51,3 +53,29 @@ class FakeListLLM(LLM): @property def _identifying_params(self) -> Mapping[str, Any]: return {"responses": self.responses} + + +class FakeStreamingListLLM(FakeListLLM): + def stream( + self, + input: LanguageModelInput, + config: Optional[RunnableConfig] = None, + *, + stop: Optional[List[str]] = None, + **kwargs: Any, + ) -> Iterator[str]: + result = self.invoke(input, config) + for c in result: + yield c + + async def astream( + self, + input: LanguageModelInput, + config: Optional[RunnableConfig] = None, + *, + stop: Optional[List[str]] = None, + **kwargs: Any, + ) -> AsyncIterator[str]: + result = await self.ainvoke(input, config) + for c in result: + yield c diff --git a/libs/langchain/langchain/schema/output_parser.py b/libs/langchain/langchain/schema/output_parser.py index cce0f70562..aeeda0880d 100644 --- a/libs/langchain/langchain/schema/output_parser.py +++ b/libs/langchain/langchain/schema/output_parser.py @@ -2,7 +2,17 @@ from __future__ import annotations import asyncio from abc import ABC, abstractmethod -from typing import Any, Dict, Generic, List, Optional, TypeVar, Union +from typing import ( + Any, + AsyncIterator, + Dict, + Generic, + Iterator, + List, + Optional, + TypeVar, + Union, +) from langchain.load.serializable import Serializable from langchain.schema.messages import BaseMessage @@ -47,7 +57,7 @@ class BaseGenerationOutputParser( BaseLLMOutputParser, Runnable[Union[str, BaseMessage], T] ): def invoke( - self, input: str | BaseMessage, config: RunnableConfig | None = None + self, input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None ) -> T: if isinstance(input, BaseMessage): return self._call_with_config( @@ -115,7 +125,7 @@ class BaseOutputParser(BaseLLMOutputParser, Runnable[Union[str, BaseMessage], T] """ # noqa: E501 def invoke( - self, input: str | BaseMessage, config: RunnableConfig | None = None + self, input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None ) -> T: if isinstance(input, BaseMessage): return self._call_with_config( @@ -242,8 +252,47 @@ class BaseOutputParser(BaseLLMOutputParser, Runnable[Union[str, BaseMessage], T] return output_parser_dict -class StrOutputParser(BaseOutputParser[str]): - """OutputParser that parses LLMResult into the top likely string..""" +class BaseTransformOutputParser(BaseOutputParser[T]): + """Base class for an output parser that can handle streaming input.""" + + def _transform(self, input: Iterator[Union[str, BaseMessage]]) -> Iterator[T]: + for chunk in input: + if isinstance(chunk, BaseMessage): + yield self.parse_result([ChatGeneration(message=chunk)]) + else: + yield self.parse_result([Generation(text=chunk)]) + + async def _atransform( + self, input: AsyncIterator[Union[str, BaseMessage]] + ) -> AsyncIterator[T]: + async for chunk in input: + if isinstance(chunk, BaseMessage): + yield self.parse_result([ChatGeneration(message=chunk)]) + else: + yield self.parse_result([Generation(text=chunk)]) + + def transform( + self, + input: Iterator[Union[str, BaseMessage]], + config: Optional[RunnableConfig] = None, + ) -> Iterator[T]: + yield from self._transform_stream_with_config( + input, self._transform, config, run_type="parser" + ) + + async def atransform( + self, + input: AsyncIterator[Union[str, BaseMessage]], + config: Optional[RunnableConfig] = None, + ) -> AsyncIterator[T]: + async for chunk in self._atransform_stream_with_config( + input, self._atransform, config, run_type="parser" + ): + yield chunk + + +class StrOutputParser(BaseTransformOutputParser[str]): + """OutputParser that parses LLMResult into the top likely string.""" @property def lc_serializable(self) -> bool: diff --git a/libs/langchain/langchain/schema/runnable.py b/libs/langchain/langchain/schema/runnable.py index 5ff837ee78..8edafe4599 100644 --- a/libs/langchain/langchain/schema/runnable.py +++ b/libs/langchain/langchain/schema/runnable.py @@ -3,6 +3,7 @@ from __future__ import annotations import asyncio from abc import ABC, abstractmethod from concurrent.futures import ThreadPoolExecutor +from itertools import tee from typing import ( Any, AsyncIterator, @@ -29,6 +30,7 @@ from pydantic import Field from langchain.callbacks.base import BaseCallbackManager, Callbacks from langchain.load.dump import dumpd from langchain.load.serializable import Serializable +from langchain.utils.aiter import atee, py_anext async def _gated_coro(semaphore: asyncio.Semaphore, coro: Coroutine) -> Any: @@ -92,6 +94,8 @@ class Runnable(Generic[Input, Output], ABC): ) -> RunnableSequence[Other, Output]: return RunnableSequence(first=_coerce_to_runnable(other), last=self) + """ --- Public API --- """ + @abstractmethod def invoke(self, input: Input, config: Optional[RunnableConfig] = None) -> Output: ... @@ -99,6 +103,10 @@ class Runnable(Generic[Input, Output], ABC): async def ainvoke( self, input: Input, config: Optional[RunnableConfig] = None ) -> Output: + """ + Default implementation of ainvoke, which calls invoke in a thread pool. + Subclasses should override this method if they can run asynchronously. + """ return await asyncio.get_running_loop().run_in_executor( None, self.invoke, input, config ) @@ -110,6 +118,10 @@ class Runnable(Generic[Input, Output], ABC): *, max_concurrency: Optional[int] = None, ) -> List[Output]: + """ + Default implementation of batch, which calls invoke N times. + Subclasses should override this method if they can batch more efficiently. + """ configs = self._get_config_list(config, len(inputs)) # If there's only one input, don't bother with the executor @@ -126,6 +138,10 @@ class Runnable(Generic[Input, Output], ABC): *, max_concurrency: Optional[int] = None, ) -> List[Output]: + """ + Default implementation of abatch, which calls ainvoke N times. + Subclasses should override this method if they can batch more efficiently. + """ configs = self._get_config_list(config, len(inputs)) coros = map(self.ainvoke, inputs, configs) @@ -134,22 +150,90 @@ class Runnable(Generic[Input, Output], ABC): def stream( self, input: Input, config: Optional[RunnableConfig] = None ) -> Iterator[Output]: + """ + Default implementation of stream, which calls invoke. + Subclasses should override this method if they support streaming output. + """ yield self.invoke(input, config) async def astream( self, input: Input, config: Optional[RunnableConfig] = None ) -> AsyncIterator[Output]: + """ + Default implementation of astream, which calls ainvoke. + Subclasses should override this method if they support streaming output. + """ yield await self.ainvoke(input, config) + def transform( + self, input: Iterator[Input], config: Optional[RunnableConfig] = None + ) -> Iterator[Output]: + """ + Default implementation of transform, which buffers input and then calls stream. + Subclasses should override this method if they can start producing output while + input is still being generated. + """ + final: Union[Input, None] = None + + for chunk in input: + if final is None: + final = chunk + else: + # Make a best effort to gather, for any type that supports `+` + # This method should throw an error if gathering fails. + final += chunk # type: ignore[operator] + if final: + yield from self.stream(final, config) + + async def atransform( + self, input: AsyncIterator[Input], config: Optional[RunnableConfig] = None + ) -> AsyncIterator[Output]: + """ + Default implementation of atransform, which buffers input and calls astream. + Subclasses should override this method if they can start producing output while + input is still being generated. + """ + final: Union[Input, None] = None + + async for chunk in input: + if final is None: + final = chunk + else: + # Make a best effort to gather, for any type that supports `+` + # This method should throw an error if gathering fails. + final += chunk # type: ignore[operator] + + if final: + async for output in self.astream(final, config): + yield output + def bind(self, **kwargs: Any) -> Runnable[Input, Output]: """ Bind arguments to a Runnable, returning a new Runnable. """ return RunnableBinding(bound=self, kwargs=kwargs) + def with_fallbacks( + self, + fallbacks: Sequence[Runnable[Input, Output]], + *, + exceptions_to_handle: Tuple[Type[BaseException]] = (Exception,), + ) -> RunnableWithFallbacks[Input, Output]: + return RunnableWithFallbacks( + runnable=self, + fallbacks=fallbacks, + exceptions_to_handle=exceptions_to_handle, + ) + + """ --- Helper methods for Subclasses --- """ + def _get_config_list( self, config: Optional[Union[RunnableConfig, List[RunnableConfig]]], length: int ) -> List[RunnableConfig]: + """ + Helper method to get a list of configs from a single config or a list of + configs, useful for subclasses overriding batch() or abatch(). + """ if isinstance(config, list) and len(config) != length: raise ValueError( f"config must be a list of the same length as inputs, " @@ -169,6 +253,8 @@ class Runnable(Generic[Input, Output], ABC): config: Optional[RunnableConfig], run_type: Optional[str] = None, ) -> Output: + """Helper method to transform an Input value to an Output value, + with callbacks. Use this method to implement invoke() in subclasses.""" from langchain.callbacks.manager import CallbackManager config = config or {} @@ -200,6 +286,8 @@ class Runnable(Generic[Input, Output], ABC): config: Optional[RunnableConfig], run_type: Optional[str] = None, ) -> Output: + """Helper method to transform an Input value to an Output value, + with callbacks. Use this method to implement ainvoke() in subclasses.""" from langchain.callbacks.manager import AsyncCallbackManager config = config or {} @@ -224,20 +312,154 @@ class Runnable(Generic[Input, Output], ABC): ) return output - def with_fallbacks( + def _transform_stream_with_config( self, - fallbacks: Sequence[Runnable[Input, Output]], - *, - exceptions_to_handle: Tuple[Type[BaseException]] = (Exception,), - ) -> RunnableWithFallbacks[Input, Output]: - return RunnableWithFallbacks( - runnable=self, - fallbacks=fallbacks, - exceptions_to_handle=exceptions_to_handle, + input: Iterator[Input], + transformer: Callable[[Iterator[Input]], Iterator[Output]], + config: Optional[RunnableConfig], + run_type: Optional[str] = None, + ) -> Iterator[Output]: + """Helper method to transform an Iterator of Input values into an Iterator of + Output values, with callbacks. + Use this to implement `stream()` or `transform()` in Runnable subclasses.""" + from langchain.callbacks.manager import CallbackManager + + # tee the input so we can iterate over it twice + input_for_tracing, input_for_transform = tee(input, 2) + # Start the input iterator to ensure the input runnable starts before this one + final_input: Optional[Input] = next(input_for_tracing, None) + final_input_supported = True + final_output: Optional[Output] = None + final_output_supported = True + + 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": ""}, + run_type=run_type, ) + try: + for chunk in transformer(input_for_transform): + yield chunk + if final_output_supported: + if final_output is None: + final_output = chunk + else: + try: + final_output += chunk # type: ignore[operator] + except TypeError: + final_output = None + final_output_supported = False + for ichunk in input_for_tracing: + if final_input_supported: + if final_input is None: + final_input = ichunk + else: + try: + final_input += ichunk # type: ignore[operator] + except TypeError: + final_input = None + final_input_supported = False + except Exception as e: + run_manager.on_chain_error( + e, + inputs=final_input + if isinstance(final_input, dict) + else {"input": final_input}, + ) + raise + else: + run_manager.on_chain_end( + final_output + if isinstance(final_output, dict) + else {"output": final_output}, + inputs=final_input + if isinstance(final_input, dict) + else {"input": final_input}, + ) + + async def _atransform_stream_with_config( + self, + input: AsyncIterator[Input], + transformer: Callable[[AsyncIterator[Input]], AsyncIterator[Output]], + config: Optional[RunnableConfig], + run_type: Optional[str] = None, + ) -> AsyncIterator[Output]: + """Helper method to transform an Async Iterator of Input values into an Async + Iterator of Output values, with callbacks. + Use this to implement `astream()` or `atransform()` in Runnable subclasses.""" + from langchain.callbacks.manager import AsyncCallbackManager + + # tee the input so we can iterate over it twice + input_for_tracing, input_for_transform = atee(input, 2) + # Start the input iterator to ensure the input runnable starts before this one + final_input: Optional[Input] = await py_anext(input_for_tracing, None) + final_input_supported = True + final_output: Optional[Output] = None + final_output_supported = True + + config = config or {} + callback_manager = AsyncCallbackManager.configure( + inheritable_callbacks=config.get("callbacks"), + inheritable_tags=config.get("tags"), + inheritable_metadata=config.get("metadata"), + ) + run_manager = await callback_manager.on_chain_start( + dumpd(self), + {"input": ""}, + run_type=run_type, + ) + try: + async for chunk in transformer(input_for_transform): + yield chunk + if final_output_supported: + if final_output is None: + final_output = chunk + else: + try: + final_output += chunk # type: ignore[operator] + except TypeError: + final_output = None + final_output_supported = False + async for ichunk in input_for_tracing: + if final_input_supported: + if final_input is None: + final_input = ichunk + else: + try: + final_input += ichunk # type: ignore[operator] + except TypeError: + final_input = None + final_input_supported = False + except Exception as e: + await run_manager.on_chain_error( + e, + inputs=final_input + if isinstance(final_input, dict) + else {"input": final_input}, + ) + raise + else: + await run_manager.on_chain_end( + final_output + if isinstance(final_output, dict) + else {"output": final_output}, + inputs=final_input + if isinstance(final_input, dict) + else {"input": final_input}, + ) class RunnableWithFallbacks(Serializable, Runnable[Input, Output]): + """ + A Runnable that can fallback to other Runnables if it fails. + """ + runnable: Runnable[Input, Output] fallbacks: Sequence[Runnable[Input, Output]] exceptions_to_handle: Tuple[Type[BaseException]] = (Exception,) @@ -467,6 +689,10 @@ class RunnableWithFallbacks(Serializable, Runnable[Input, Output]): class RunnableSequence(Serializable, Runnable[Input, Output]): + """ + A sequence of runnables, where the output of each is the input of the next. + """ + first: Runnable[Input, Any] middle: List[Runnable[Any, Any]] = Field(default_factory=list) last: Runnable[Any, Output] @@ -738,9 +964,18 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): dumpd(self), input if isinstance(input, dict) else {"input": input} ) + steps = [self.first] + self.middle + [self.last] + streaming_start_index = 0 + + for i in range(len(steps) - 1, 0, -1): + if type(steps[i]).transform != Runnable.transform: + streaming_start_index = i - 1 + else: + break + # invoke the first steps try: - for step in [self.first] + self.middle: + for step in steps[0:streaming_start_index]: input = step.invoke( input, # mark each step as a child run @@ -750,15 +985,20 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): run_manager.on_chain_error(e) raise - # stream the last step + # stream the last steps final: Union[Output, None] = None final_supported = True try: - for output in self.last.stream( - input, - # mark the last step as a child run - _patch_config(config, run_manager.get_child()), - ): + # stream the first of the last steps with non-streaming input + final_pipeline = steps[streaming_start_index].stream( + input, _patch_config(config, run_manager.get_child()) + ) + # stream the rest of the last steps with streaming input + for step in steps[streaming_start_index + 1 :]: + final_pipeline = step.transform( + final_pipeline, _patch_config(config, run_manager.get_child()) + ) + for output in final_pipeline: yield output # Accumulate output if possible, otherwise disable accumulation if final_supported: @@ -801,9 +1041,18 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): dumpd(self), input if isinstance(input, dict) else {"input": input} ) + steps = [self.first] + self.middle + [self.last] + streaming_start_index = len(steps) - 1 + + for i in range(len(steps) - 1, 0, -1): + if type(steps[i]).transform != Runnable.transform: + streaming_start_index = i - 1 + else: + break + # invoke the first steps try: - for step in [self.first] + self.middle: + for step in steps[0:streaming_start_index]: input = await step.ainvoke( input, # mark each step as a child run @@ -813,15 +1062,20 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): await run_manager.on_chain_error(e) raise - # stream the last step + # stream the last steps final: Union[Output, None] = None final_supported = True try: - async for output in self.last.astream( - input, - # mark the last step as a child run - _patch_config(config, run_manager.get_child()), - ): + # stream the first of the last steps with non-streaming input + final_pipeline = steps[streaming_start_index].astream( + input, _patch_config(config, run_manager.get_child()) + ) + # stream the rest of the last steps with streaming input + for step in steps[streaming_start_index + 1 :]: + final_pipeline = step.atransform( + final_pipeline, _patch_config(config, run_manager.get_child()) + ) + async for output in final_pipeline: yield output # Accumulate output if possible, otherwise disable accumulation if final_supported: @@ -845,6 +1099,11 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): class RunnableMap(Serializable, Runnable[Input, Dict[str, Any]]): + """ + A runnable that runs a mapping of runnables in parallel, + and returns a mapping of their outputs. + """ + steps: Mapping[str, Runnable[Input, Any]] def __init__( @@ -957,6 +1216,10 @@ class RunnableMap(Serializable, Runnable[Input, Dict[str, Any]]): class RunnableLambda(Runnable[Input, Output]): + """ + A runnable that runs a callable. + """ + def __init__(self, func: Callable[[Input], Output]) -> None: if callable(func): self.func = func @@ -977,6 +1240,10 @@ class RunnableLambda(Runnable[Input, Output]): class RunnablePassthrough(Serializable, Runnable[Input, Input]): + """ + A runnable that passes through the input. + """ + @property def lc_serializable(self) -> bool: return True @@ -986,6 +1253,10 @@ class RunnablePassthrough(Serializable, Runnable[Input, Input]): class RunnableBinding(Serializable, Runnable[Input, Output]): + """ + A runnable that binds a runnable to a set of kwargs. + """ + bound: Runnable[Input, Output] kwargs: Mapping[str, Any] @@ -1041,6 +1312,17 @@ class RunnableBinding(Serializable, Runnable[Input, Output]): async for item in self.bound.astream(input, config, **self.kwargs): yield item + def transform( + self, input: Iterator[Input], config: Optional[RunnableConfig] = None + ) -> Iterator[Output]: + yield from self.bound.transform(input, config, **self.kwargs) + + async def atransform( + self, input: AsyncIterator[Input], config: Optional[RunnableConfig] = None + ) -> AsyncIterator[Output]: + async for item in self.bound.atransform(input, config, **self.kwargs): + yield item + class RouterInput(TypedDict): key: str @@ -1050,6 +1332,11 @@ class RouterInput(TypedDict): class RouterRunnable( Serializable, Generic[Input, Output], Runnable[RouterInput, Output] ): + """ + A runnable that routes to a set of runnables based on Input['key']. + Returns the output of the selected runnable. + """ + runnables: Mapping[str, Runnable[Input, Output]] def __init__(self, runnables: Mapping[str, Runnable[Input, Output]]) -> None: diff --git a/libs/langchain/langchain/utils/aiter.py b/libs/langchain/langchain/utils/aiter.py new file mode 100644 index 0000000000..8670c823f9 --- /dev/null +++ b/libs/langchain/langchain/utils/aiter.py @@ -0,0 +1,191 @@ +""" +Adapted from +https://github.com/maxfischer2781/asyncstdlib/blob/master/asyncstdlib/itertools.py +MIT License +""" + +from collections import deque +from typing import ( + Any, + AsyncGenerator, + AsyncIterator, + Awaitable, + Callable, + Deque, + Generic, + Iterator, + List, + Tuple, + TypeVar, + Union, + cast, + overload, +) + +T = TypeVar("T") + +_no_default = object() + + +# https://github.com/python/cpython/blob/main/Lib/test/test_asyncgen.py#L54 +# before 3.10, the builtin anext() was not available +def py_anext( + iterator: AsyncIterator[T], default: Union[T, Any] = _no_default +) -> Awaitable[Union[T, None, Any]]: + """Pure-Python implementation of anext() for testing purposes. + + Closely matches the builtin anext() C implementation. + Can be used to compare the built-in implementation of the inner + coroutines machinery to C-implementation of __anext__() and send() + or throw() on the returned generator. + """ + + try: + __anext__ = cast( + Callable[[AsyncIterator[T]], Awaitable[T]], type(iterator).__anext__ + ) + except AttributeError: + raise TypeError(f"{iterator!r} is not an async iterator") + + if default is _no_default: + return __anext__(iterator) + + async def anext_impl() -> Union[T, Any]: + try: + # The C code is way more low-level than this, as it implements + # all methods of the iterator protocol. In this implementation + # we're relying on higher-level coroutine concepts, but that's + # exactly what we want -- crosstest pure-Python high-level + # implementation and low-level C anext() iterators. + return await __anext__(iterator) + except StopAsyncIteration: + return default + + return anext_impl() + + +async def tee_peer( + iterator: AsyncIterator[T], + # the buffer specific to this peer + buffer: Deque[T], + # the buffers of all peers, including our own + peers: List[Deque[T]], +) -> AsyncGenerator[T, None]: + """An individual iterator of a :py:func:`~.tee`""" + try: + while True: + if not buffer: + # Another peer produced an item while we were waiting for the lock. + # Proceed with the next loop iteration to yield the item. + if buffer: + continue + try: + item = await iterator.__anext__() + except StopAsyncIteration: + break + else: + # Append to all buffers, including our own. We'll fetch our + # item from the buffer again, instead of yielding it directly. + # This ensures the proper item ordering if any of our peers + # are fetching items concurrently. They may have buffered their + # item already. + for peer_buffer in peers: + peer_buffer.append(item) + yield buffer.popleft() + finally: + # this peer is done – remove its buffer + for idx, peer_buffer in enumerate(peers): # pragma: no branch + if peer_buffer is buffer: + peers.pop(idx) + break + # if we are the last peer, try and close the iterator + if not peers and hasattr(iterator, "aclose"): + await iterator.aclose() + + +class Tee(Generic[T]): + """ + Create ``n`` separate asynchronous iterators over ``iterable`` + + This splits a single ``iterable`` into multiple iterators, each providing + the same items in the same order. + All child iterators may advance separately but share the same items + from ``iterable`` -- when the most advanced iterator retrieves an item, + it is buffered until the least advanced iterator has yielded it as well. + A ``tee`` works lazily and can handle an infinite ``iterable``, provided + that all iterators advance. + + .. code-block:: python3 + + async def derivative(sensor_data): + previous, current = a.tee(sensor_data, n=2) + await a.anext(previous) # advance one iterator + return a.map(operator.sub, previous, current) + + Unlike :py:func:`itertools.tee`, :py:func:`~.tee` returns a custom type instead + of a :py:class:`tuple`. Like a tuple, it can be indexed, iterated and unpacked + to get the child iterators. In addition, its :py:meth:`~.tee.aclose` method + immediately closes all children, and it can be used in an ``async with`` context + for the same effect. + + If ``iterable`` is an iterator and read elsewhere, ``tee`` will *not* + provide these items. Also, ``tee`` must internally buffer each item until the + last iterator has yielded it; if the most and least advanced iterator differ + by most data, using a :py:class:`list` is more efficient (but not lazy). + + If the underlying iterable is concurrency safe (``anext`` may be awaited + concurrently) the resulting iterators are concurrency safe as well. Otherwise, + the iterators are safe if there is only ever one single "most advanced" iterator. + To enforce sequential use of ``anext``, provide a ``lock`` + - e.g. an :py:class:`asyncio.Lock` instance in an :py:mod:`asyncio` application - + and access is automatically synchronised. + """ + + def __init__( + self, + iterable: AsyncIterator[T], + n: int = 2, + ): + self._iterator = iterable.__aiter__() # before 3.10 aiter() doesn't exist + self._buffers: List[Deque[T]] = [deque() for _ in range(n)] + self._children = tuple( + tee_peer( + iterator=self._iterator, + buffer=buffer, + peers=self._buffers, + ) + for buffer in self._buffers + ) + + def __len__(self) -> int: + return len(self._children) + + @overload + def __getitem__(self, item: int) -> AsyncIterator[T]: + ... + + @overload + def __getitem__(self, item: slice) -> Tuple[AsyncIterator[T], ...]: + ... + + def __getitem__( + self, item: Union[int, slice] + ) -> Union[AsyncIterator[T], Tuple[AsyncIterator[T], ...]]: + return self._children[item] + + def __iter__(self) -> Iterator[AsyncIterator[T]]: + yield from self._children + + async def __aenter__(self) -> "Tee[T]": + return self + + async def __aexit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> bool: + await self.aclose() + return False + + async def aclose(self) -> None: + for child in self._children: + await child.aclose() + + +atee = Tee diff --git a/libs/langchain/tests/unit_tests/chains/test_transform.py b/libs/langchain/tests/unit_tests/chains/test_transform.py index a4dbca25de..a4a1cd08ff 100644 --- a/libs/langchain/tests/unit_tests/chains/test_transform.py +++ b/libs/langchain/tests/unit_tests/chains/test_transform.py @@ -15,7 +15,7 @@ def dummy_transform(inputs: Dict[str, str]) -> Dict[str, str]: return outputs -def test_tranform_chain() -> None: +def test_transform_chain() -> None: """Test basic transform chain.""" transform_chain = TransformChain( input_variables=["first_name", "last_name"], diff --git a/libs/langchain/tests/unit_tests/schema/__snapshots__/test_runnable.ambr b/libs/langchain/tests/unit_tests/schema/__snapshots__/test_runnable.ambr index c94b4d81a6..fcedca63c3 100644 --- a/libs/langchain/tests/unit_tests/schema/__snapshots__/test_runnable.ambr +++ b/libs/langchain/tests/unit_tests/schema/__snapshots__/test_runnable.ambr @@ -447,7 +447,7 @@ # --- # name: test_combining_sequences.3 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'RunnableLambda']}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=1, child_execution_order=8, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=2, child_execution_order=2, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=3, child_execution_order=3, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, example=False)}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=4, child_execution_order=4, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'RunnableLambda']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=5, child_execution_order=5, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'foobar'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nicer assistant.', additional_kwargs={}), HumanMessage(content='foobar', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=6, child_execution_order=6, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=7, child_execution_order=7, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='baz, qux', additional_kwargs={}, example=False)}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=8, child_execution_order=8, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'RunnableLambda']}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, example=False)}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'RunnableLambda']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'foobar'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nicer assistant.', additional_kwargs={}), HumanMessage(content='foobar', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='baz, qux', additional_kwargs={}, example=False)}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_prompt_with_chat_model @@ -550,7 +550,7 @@ # --- # name: test_prompt_with_chat_model.1 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo', additional_kwargs={}, example=False)}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=1, child_execution_order=3, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=2, child_execution_order=2, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=3, child_execution_order=3, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo', additional_kwargs={}, example=False)}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_prompt_with_chat_model_and_parser @@ -666,7 +666,7 @@ # --- # name: test_prompt_with_chat_model_and_parser.1 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=1, child_execution_order=4, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=2, child_execution_order=2, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=3, child_execution_order=3, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, example=False)}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=4, child_execution_order=4, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, example=False)}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_prompt_with_llm @@ -769,13 +769,13 @@ # --- # name: test_prompt_with_llm.1 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=1, child_execution_order=3, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=2, child_execution_order=2, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=3, child_execution_order=3, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_prompt_with_llm.2 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'bar'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=1, child_execution_order=3, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=2, child_execution_order=2, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bar', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=3, child_execution_order=3, child_runs=[])]), - Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=1, child_execution_order=3, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your favorite color?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=[], execution_order=2, child_execution_order=2, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=[], execution_order=3, child_execution_order=3, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'bar'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bar', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your favorite color?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_router_runnable @@ -994,11 +994,6 @@ } ''' # --- -# name: test_router_runnable.1 - list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableMap'], 'kwargs': {'steps': {'key': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'RunnableLambda']}, 'input': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableMap'], 'kwargs': {'steps': {'question': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'RunnableLambda']}}}}}}}, 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RouterRunnable'], 'kwargs': {'runnables': {'math': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': 'You are a math genius. Answer the question: {question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, 'english': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': 'You are an english major. Answer the question: {question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}}}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'key': 'math', 'question': '2 + 2'}, outputs={'output': '4'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=1, child_execution_order=8, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='RunnableMap', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableMap'], 'kwargs': {'steps': {'key': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'RunnableLambda']}, 'input': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableMap'], 'kwargs': {'steps': {'question': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'RunnableLambda']}}}}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': {'key': 'math', 'question': '2 + 2'}}, outputs={'key': 'math', 'input': {'question': '2 + 2'}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=2, child_execution_order=5, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'RunnableLambda']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'key': 'math', 'question': '2 + 2'}, outputs={'output': 'math'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000001'), tags=[], execution_order=3, child_execution_order=3, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableMap', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableMap'], 'kwargs': {'steps': {'question': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'RunnableLambda']}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': {'key': 'math', 'question': '2 + 2'}}, outputs={'question': '2 + 2'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000001'), tags=[], execution_order=4, child_execution_order=5, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'RunnableLambda']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'key': 'math', 'question': '2 + 2'}, outputs={'output': '2 + 2'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=[], execution_order=5, child_execution_order=5, child_runs=[])])]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': 'You are a math genius. Answer the question: {question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': '2 + 2'}, outputs={'output': '4'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=6, child_execution_order=8, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000006'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': 'You are a math genius. Answer the question: {question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': '2 + 2'}, outputs={'output': ChatPromptValue(messages=[HumanMessage(content='You are a math genius. Answer the question: 2 + 2', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000005'), tags=[], execution_order=7, child_execution_order=7, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000007'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['4'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['Human: You are a math genius. Answer the question: 2 + 2']}, outputs={'generations': [[{'text': '4', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000005'), tags=[], execution_order=8, child_execution_order=8, child_runs=[])])]), - ]) -# --- # name: test_seq_dict_prompt_llm ''' { diff --git a/libs/langchain/tests/unit_tests/schema/test_runnable.py b/libs/langchain/tests/unit_tests/schema/test_runnable.py index 181cf50c3a..4ae6eb1cf2 100644 --- a/libs/langchain/tests/unit_tests/schema/test_runnable.py +++ b/libs/langchain/tests/unit_tests/schema/test_runnable.py @@ -11,7 +11,7 @@ from langchain.callbacks.manager import Callbacks from langchain.callbacks.tracers.base import BaseTracer from langchain.callbacks.tracers.schemas import Run from langchain.chat_models.fake import FakeListChatModel -from langchain.llms.fake import FakeListLLM +from langchain.llms.fake import FakeListLLM, FakeStreamingListLLM from langchain.load.dump import dumpd, dumps from langchain.output_parsers.list import CommaSeparatedListOutputParser from langchain.prompts.chat import ( @@ -22,6 +22,7 @@ from langchain.prompts.chat import ( ) from langchain.schema.document import Document from langchain.schema.messages import AIMessage, HumanMessage, SystemMessage +from langchain.schema.output_parser import StrOutputParser from langchain.schema.retriever import BaseRetriever from langchain.schema.runnable import ( RouterRunnable, @@ -61,6 +62,8 @@ class FakeTracer(BaseTracer): if run.parent_run_id else None, "child_runs": [self._copy_run(child) for child in run.child_runs], + "execution_order": None, + "child_execution_order": None, } ) @@ -302,7 +305,7 @@ async def test_prompt_with_chat_model( tracer = FakeTracer() assert [ *chain.stream({"question": "What is your name?"}, dict(callbacks=[tracer])) - ] == [AIMessage(content="foo")] + ] == [AIMessage(content="f"), AIMessage(content="o"), AIMessage(content="o")] assert prompt_spy.call_args.args[1] == {"question": "What is your name?"} assert chat_spy.call_args.args[1] == ChatPromptValue( messages=[ @@ -678,7 +681,12 @@ async def test_router_runnable( "key": "math", "input": {"question": "2 + 2"}, } - assert tracer.runs == snapshot + assert len([r for r in tracer.runs if r.parent_run_id is None]) == 1 + parent_run = next(r for r in tracer.runs if r.parent_run_id is None) + assert len(parent_run.child_runs) == 2 + router_run = parent_run.child_runs[1] + assert router_run.name == "RunnableSequence" # TODO: should be RunnableRouter + assert len(router_run.child_runs) == 2 @freeze_time("2023-01-01") @@ -758,6 +766,45 @@ def test_bind_bind() -> None: ) == dumpd(llm.bind(stop=["Observation:"], one="two", hello="world")) +def test_deep_stream() -> None: + prompt = ( + SystemMessagePromptTemplate.from_template("You are a nice assistant.") + + "{question}" + ) + llm = FakeStreamingListLLM(responses=["foo-lish"]) + + chain = prompt | llm | StrOutputParser() + + stream = chain.stream({"question": "What up"}) + + chunks = [] + for chunk in stream: + chunks.append(chunk) + + assert len(chunks) == len("foo-lish") + assert "".join(chunks) == "foo-lish" + + +@pytest.mark.asyncio +async def test_deep_astream() -> None: + prompt = ( + SystemMessagePromptTemplate.from_template("You are a nice assistant.") + + "{question}" + ) + llm = FakeStreamingListLLM(responses=["foo-lish"]) + + chain = prompt | llm | StrOutputParser() + + stream = chain.astream({"question": "What up"}) + + chunks = [] + async for chunk in stream: + chunks.append(chunk) + + assert len(chunks) == len("foo-lish") + assert "".join(chunks) == "foo-lish" + + @pytest.fixture() def llm_with_fallbacks() -> RunnableWithFallbacks: error_llm = FakeListLLM(responses=["foo"], i=1)