807 lines
31 KiB
Python
807 lines
31 KiB
Python
"""Base interface that all chains should implement."""
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import builtins
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import contextlib
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import inspect
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import json
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import logging
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import warnings
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from abc import ABC, abstractmethod
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from pathlib import Path
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from typing import Any, cast
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import yaml
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from langchain_core._api import deprecated
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from langchain_core.callbacks import (
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AsyncCallbackManager,
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AsyncCallbackManagerForChainRun,
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BaseCallbackManager,
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CallbackManager,
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CallbackManagerForChainRun,
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Callbacks,
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)
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from langchain_core.outputs import RunInfo
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from langchain_core.runnables import (
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RunnableConfig,
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RunnableSerializable,
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ensure_config,
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run_in_executor,
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)
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from langchain_core.utils.pydantic import create_model
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from pydantic import (
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BaseModel,
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ConfigDict,
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Field,
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field_validator,
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model_validator,
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)
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from typing_extensions import override
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from langchain_classic.base_memory import BaseMemory
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from langchain_classic.schema import RUN_KEY
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logger = logging.getLogger(__name__)
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def _get_verbosity() -> bool:
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from langchain_classic.globals import get_verbose
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return get_verbose()
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class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
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"""Abstract base class for creating structured sequences of calls to components.
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Chains should be used to encode a sequence of calls to components like
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models, document retrievers, other chains, etc., and provide a simple interface
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to this sequence.
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The Chain interface makes it easy to create apps that are:
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- Stateful: add Memory to any Chain to give it state,
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- Observable: pass Callbacks to a Chain to execute additional functionality,
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like logging, outside the main sequence of component calls,
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- Composable: the Chain API is flexible enough that it is easy to combine
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Chains with other components, including other Chains.
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The main methods exposed by chains are:
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- `__call__`: Chains are callable. The `__call__` method is the primary way to
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execute a Chain. This takes inputs as a dictionary and returns a
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dictionary output.
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- `run`: A convenience method that takes inputs as args/kwargs and returns the
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output as a string or object. This method can only be used for a subset of
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chains and cannot return as rich of an output as `__call__`.
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"""
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memory: BaseMemory | None = None
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"""Optional memory object.
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Memory is a class that gets called at the start
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and at the end of every chain. At the start, memory loads variables and passes
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them along in the chain. At the end, it saves any returned variables.
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There are many different types of memory - please see memory docs
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for the full catalog."""
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callbacks: Callbacks = Field(default=None, exclude=True)
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"""Optional list of callback handlers (or callback manager).
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Callback handlers are called throughout the lifecycle of a call to a chain,
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starting with on_chain_start, ending with on_chain_end or on_chain_error.
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Each custom chain can optionally call additional callback methods, see Callback docs
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for full details."""
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verbose: bool = Field(default_factory=_get_verbosity)
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"""Whether or not run in verbose mode. In verbose mode, some intermediate logs
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will be printed to the console. Defaults to the global `verbose` value,
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accessible via `langchain.globals.get_verbose()`."""
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tags: list[str] | None = None
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"""Optional list of tags associated with the chain.
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These tags will be associated with each call to this chain,
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and passed as arguments to the handlers defined in `callbacks`.
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You can use these to eg identify a specific instance of a chain with its use case.
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"""
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metadata: builtins.dict[str, Any] | None = None
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"""Optional metadata associated with the chain.
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This metadata will be associated with each call to this chain,
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and passed as arguments to the handlers defined in `callbacks`.
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You can use these to eg identify a specific instance of a chain with its use case.
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"""
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callback_manager: BaseCallbackManager | None = Field(default=None, exclude=True)
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"""[DEPRECATED] Use `callbacks` instead."""
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model_config = ConfigDict(
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arbitrary_types_allowed=True,
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)
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@override
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def get_input_schema(
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self,
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config: RunnableConfig | None = None,
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) -> type[BaseModel]:
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# This is correct, but pydantic typings/mypy don't think so.
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return create_model("ChainInput", **dict.fromkeys(self.input_keys, (Any, None)))
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@override
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def get_output_schema(
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self,
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config: RunnableConfig | None = None,
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) -> type[BaseModel]:
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# This is correct, but pydantic typings/mypy don't think so.
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return create_model(
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"ChainOutput",
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**dict.fromkeys(self.output_keys, (Any, None)),
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)
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@override
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def invoke(
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self,
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input: dict[str, Any],
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config: RunnableConfig | None = None,
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**kwargs: Any,
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) -> dict[str, Any]:
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config = ensure_config(config)
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callbacks = config.get("callbacks")
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tags = config.get("tags")
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metadata = config.get("metadata")
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run_name = config.get("run_name") or self.get_name()
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run_id = config.get("run_id")
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include_run_info = kwargs.get("include_run_info", False)
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return_only_outputs = kwargs.get("return_only_outputs", False)
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inputs = self.prep_inputs(input)
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callback_manager = CallbackManager.configure(
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callbacks,
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self.callbacks,
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self.verbose,
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tags,
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self.tags,
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metadata,
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self.metadata,
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)
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new_arg_supported = inspect.signature(self._call).parameters.get("run_manager")
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run_manager = callback_manager.on_chain_start(
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None,
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inputs,
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run_id,
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name=run_name,
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)
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try:
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self._validate_inputs(inputs)
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outputs = (
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self._call(inputs, run_manager=run_manager)
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if new_arg_supported
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else self._call(inputs)
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)
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final_outputs: dict[str, Any] = self.prep_outputs(
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inputs,
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outputs,
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return_only_outputs,
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)
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except BaseException as e:
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run_manager.on_chain_error(e)
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raise
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run_manager.on_chain_end(outputs)
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if include_run_info:
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final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id)
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return final_outputs
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@override
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async def ainvoke(
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self,
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input: dict[str, Any],
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config: RunnableConfig | None = None,
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**kwargs: Any,
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) -> dict[str, Any]:
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config = ensure_config(config)
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callbacks = config.get("callbacks")
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tags = config.get("tags")
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metadata = config.get("metadata")
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run_name = config.get("run_name") or self.get_name()
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run_id = config.get("run_id")
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include_run_info = kwargs.get("include_run_info", False)
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return_only_outputs = kwargs.get("return_only_outputs", False)
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inputs = await self.aprep_inputs(input)
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callback_manager = AsyncCallbackManager.configure(
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callbacks,
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self.callbacks,
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self.verbose,
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tags,
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self.tags,
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metadata,
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self.metadata,
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)
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new_arg_supported = inspect.signature(self._acall).parameters.get("run_manager")
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run_manager = await callback_manager.on_chain_start(
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None,
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inputs,
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run_id,
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name=run_name,
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)
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try:
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self._validate_inputs(inputs)
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outputs = (
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await self._acall(inputs, run_manager=run_manager)
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if new_arg_supported
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else await self._acall(inputs)
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)
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final_outputs: dict[str, Any] = await self.aprep_outputs(
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inputs,
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outputs,
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return_only_outputs,
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)
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except BaseException as e:
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await run_manager.on_chain_error(e)
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raise
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await run_manager.on_chain_end(outputs)
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if include_run_info:
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final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id)
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return final_outputs
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@property
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def _chain_type(self) -> str:
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msg = "Saving not supported for this chain type."
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raise NotImplementedError(msg)
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@model_validator(mode="before")
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@classmethod
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def raise_callback_manager_deprecation(cls, values: dict) -> Any:
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"""Raise deprecation warning if callback_manager is used."""
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if values.get("callback_manager") is not None:
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if values.get("callbacks") is not None:
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msg = (
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"Cannot specify both callback_manager and callbacks. "
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"callback_manager is deprecated, callbacks is the preferred "
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"parameter to pass in."
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)
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raise ValueError(msg)
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warnings.warn(
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"callback_manager is deprecated. Please use callbacks instead.",
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DeprecationWarning,
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stacklevel=4,
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)
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values["callbacks"] = values.pop("callback_manager", None)
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return values
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@field_validator("verbose", mode="before")
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@classmethod
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def set_verbose(
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cls,
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verbose: bool | None, # noqa: FBT001
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) -> bool:
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"""Set the chain verbosity.
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Defaults to the global setting if not specified by the user.
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"""
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if verbose is None:
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return _get_verbosity()
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return verbose
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@property
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@abstractmethod
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def input_keys(self) -> list[str]:
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"""Keys expected to be in the chain input."""
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@property
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@abstractmethod
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def output_keys(self) -> list[str]:
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"""Keys expected to be in the chain output."""
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def _validate_inputs(self, inputs: Any) -> None:
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"""Check that all inputs are present."""
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if not isinstance(inputs, dict):
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_input_keys = set(self.input_keys)
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if self.memory is not None:
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# If there are multiple input keys, but some get set by memory so that
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# only one is not set, we can still figure out which key it is.
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_input_keys = _input_keys.difference(self.memory.memory_variables)
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if len(_input_keys) != 1:
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msg = (
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f"A single string input was passed in, but this chain expects "
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f"multiple inputs ({_input_keys}). When a chain expects "
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f"multiple inputs, please call it by passing in a dictionary, "
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"eg `chain({'foo': 1, 'bar': 2})`"
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)
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raise ValueError(msg)
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missing_keys = set(self.input_keys).difference(inputs)
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if missing_keys:
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msg = f"Missing some input keys: {missing_keys}"
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raise ValueError(msg)
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def _validate_outputs(self, outputs: dict[str, Any]) -> None:
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missing_keys = set(self.output_keys).difference(outputs)
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if missing_keys:
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msg = f"Missing some output keys: {missing_keys}"
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raise ValueError(msg)
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@abstractmethod
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def _call(
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self,
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inputs: builtins.dict[str, Any],
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run_manager: CallbackManagerForChainRun | None = None,
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) -> builtins.dict[str, Any]:
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"""Execute the chain.
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This is a private method that is not user-facing. It is only called within
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`Chain.__call__`, which is the user-facing wrapper method that handles
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callbacks configuration and some input/output processing.
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Args:
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inputs: A dict of named inputs to the chain. Assumed to contain all inputs
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specified in `Chain.input_keys`, including any inputs added by memory.
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run_manager: The callbacks manager that contains the callback handlers for
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this run of the chain.
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Returns:
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A dict of named outputs. Should contain all outputs specified in
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`Chain.output_keys`.
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"""
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async def _acall(
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self,
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inputs: builtins.dict[str, Any],
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run_manager: AsyncCallbackManagerForChainRun | None = None,
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) -> builtins.dict[str, Any]:
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"""Asynchronously execute the chain.
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This is a private method that is not user-facing. It is only called within
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`Chain.acall`, which is the user-facing wrapper method that handles
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callbacks configuration and some input/output processing.
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Args:
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inputs: A dict of named inputs to the chain. Assumed to contain all inputs
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specified in `Chain.input_keys`, including any inputs added by memory.
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run_manager: The callbacks manager that contains the callback handlers for
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this run of the chain.
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Returns:
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A dict of named outputs. Should contain all outputs specified in
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`Chain.output_keys`.
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"""
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return await run_in_executor(
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None,
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self._call,
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inputs,
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run_manager.get_sync() if run_manager else None,
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)
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@deprecated("0.1.0", alternative="invoke", removal="1.0")
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def __call__(
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self,
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inputs: dict[str, Any] | Any,
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return_only_outputs: bool = False, # noqa: FBT001,FBT002
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callbacks: Callbacks = None,
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*,
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tags: list[str] | None = None,
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metadata: dict[str, Any] | None = None,
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run_name: str | None = None,
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include_run_info: bool = False,
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) -> dict[str, Any]:
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"""Execute the chain.
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Args:
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inputs: Dictionary of inputs, or single input if chain expects
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only one param. Should contain all inputs specified in
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`Chain.input_keys` except for inputs that will be set by the chain's
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memory.
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return_only_outputs: Whether to return only outputs in the
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response. If `True`, only new keys generated by this chain will be
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returned. If `False`, both input keys and new keys generated by this
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chain will be returned.
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callbacks: Callbacks to use for this chain run. These will be called in
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addition to callbacks passed to the chain during construction, but only
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these runtime callbacks will propagate to calls to other objects.
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tags: List of string tags to pass to all callbacks. These will be passed in
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addition to tags passed to the chain during construction, but only
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these runtime tags will propagate to calls to other objects.
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metadata: Optional metadata associated with the chain.
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run_name: Optional name for this run of the chain.
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include_run_info: Whether to include run info in the response. Defaults
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to False.
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Returns:
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A dict of named outputs. Should contain all outputs specified in
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`Chain.output_keys`.
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"""
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config = {
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"callbacks": callbacks,
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"tags": tags,
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"metadata": metadata,
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"run_name": run_name,
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}
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return self.invoke(
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inputs,
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cast("RunnableConfig", {k: v for k, v in config.items() if v is not None}),
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return_only_outputs=return_only_outputs,
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include_run_info=include_run_info,
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)
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@deprecated("0.1.0", alternative="ainvoke", removal="1.0")
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async def acall(
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self,
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inputs: dict[str, Any] | Any,
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return_only_outputs: bool = False, # noqa: FBT001,FBT002
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callbacks: Callbacks = None,
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*,
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tags: list[str] | None = None,
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metadata: dict[str, Any] | None = None,
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run_name: str | None = None,
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include_run_info: bool = False,
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) -> dict[str, Any]:
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"""Asynchronously execute the chain.
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|
Args:
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inputs: Dictionary of inputs, or single input if chain expects
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only one param. Should contain all inputs specified in
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`Chain.input_keys` except for inputs that will be set by the chain's
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memory.
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return_only_outputs: Whether to return only outputs in the
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response. If `True`, only new keys generated by this chain will be
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returned. If `False`, both input keys and new keys generated by this
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chain will be returned.
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callbacks: Callbacks to use for this chain run. These will be called in
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addition to callbacks passed to the chain during construction, but only
|
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these runtime callbacks will propagate to calls to other objects.
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|
tags: List of string tags to pass to all callbacks. These will be passed in
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addition to tags passed to the chain during construction, but only
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these runtime tags will propagate to calls to other objects.
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metadata: Optional metadata associated with the chain.
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|
run_name: Optional name for this run of the chain.
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include_run_info: Whether to include run info in the response. Defaults
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to False.
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Returns:
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A dict of named outputs. Should contain all outputs specified in
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`Chain.output_keys`.
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"""
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config = {
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"callbacks": callbacks,
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"tags": tags,
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"metadata": metadata,
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"run_name": run_name,
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}
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return await self.ainvoke(
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inputs,
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cast("RunnableConfig", {k: v for k, v in config.items() if k is not None}),
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return_only_outputs=return_only_outputs,
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include_run_info=include_run_info,
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)
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def prep_outputs(
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self,
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inputs: dict[str, str],
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outputs: dict[str, str],
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return_only_outputs: bool = False, # noqa: FBT001,FBT002
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) -> dict[str, str]:
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"""Validate and prepare chain outputs, and save info about this run to memory.
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Args:
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inputs: Dictionary of chain inputs, including any inputs added by chain
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memory.
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outputs: Dictionary of initial chain outputs.
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return_only_outputs: Whether to only return the chain outputs. If `False`,
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inputs are also added to the final outputs.
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Returns:
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A dict of the final chain outputs.
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"""
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self._validate_outputs(outputs)
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if self.memory is not None:
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self.memory.save_context(inputs, outputs)
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if return_only_outputs:
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return outputs
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return {**inputs, **outputs}
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async def aprep_outputs(
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self,
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inputs: dict[str, str],
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outputs: dict[str, str],
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return_only_outputs: bool = False, # noqa: FBT001,FBT002
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) -> dict[str, str]:
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"""Validate and prepare chain outputs, and save info about this run to memory.
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|
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Args:
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inputs: Dictionary of chain inputs, including any inputs added by chain
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memory.
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outputs: Dictionary of initial chain outputs.
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return_only_outputs: Whether to only return the chain outputs. If `False`,
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inputs are also added to the final outputs.
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Returns:
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A dict of the final chain outputs.
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"""
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self._validate_outputs(outputs)
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if self.memory is not None:
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await self.memory.asave_context(inputs, outputs)
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if return_only_outputs:
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return outputs
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return {**inputs, **outputs}
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|
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def prep_inputs(self, inputs: dict[str, Any] | Any) -> dict[str, str]:
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"""Prepare chain inputs, including adding inputs from memory.
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|
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Args:
|
|
inputs: Dictionary of raw inputs, or single input if chain expects
|
|
only one param. Should contain all inputs specified in
|
|
`Chain.input_keys` except for inputs that will be set by the chain's
|
|
memory.
|
|
|
|
Returns:
|
|
A dictionary of all inputs, including those added by the chain's memory.
|
|
"""
|
|
if not isinstance(inputs, dict):
|
|
_input_keys = set(self.input_keys)
|
|
if self.memory is not None:
|
|
# If there are multiple input keys, but some get set by memory so that
|
|
# only one is not set, we can still figure out which key it is.
|
|
_input_keys = _input_keys.difference(self.memory.memory_variables)
|
|
inputs = {next(iter(_input_keys)): inputs}
|
|
if self.memory is not None:
|
|
external_context = self.memory.load_memory_variables(inputs)
|
|
inputs = dict(inputs, **external_context)
|
|
return inputs
|
|
|
|
async def aprep_inputs(self, inputs: dict[str, Any] | Any) -> dict[str, str]:
|
|
"""Prepare chain inputs, including adding inputs from memory.
|
|
|
|
Args:
|
|
inputs: Dictionary of raw inputs, or single input if chain expects
|
|
only one param. Should contain all inputs specified in
|
|
`Chain.input_keys` except for inputs that will be set by the chain's
|
|
memory.
|
|
|
|
Returns:
|
|
A dictionary of all inputs, including those added by the chain's memory.
|
|
"""
|
|
if not isinstance(inputs, dict):
|
|
_input_keys = set(self.input_keys)
|
|
if self.memory is not None:
|
|
# If there are multiple input keys, but some get set by memory so that
|
|
# only one is not set, we can still figure out which key it is.
|
|
_input_keys = _input_keys.difference(self.memory.memory_variables)
|
|
inputs = {next(iter(_input_keys)): inputs}
|
|
if self.memory is not None:
|
|
external_context = await self.memory.aload_memory_variables(inputs)
|
|
inputs = dict(inputs, **external_context)
|
|
return inputs
|
|
|
|
@property
|
|
def _run_output_key(self) -> str:
|
|
if len(self.output_keys) != 1:
|
|
msg = (
|
|
f"`run` not supported when there is not exactly "
|
|
f"one output key. Got {self.output_keys}."
|
|
)
|
|
raise ValueError(msg)
|
|
return self.output_keys[0]
|
|
|
|
@deprecated("0.1.0", alternative="invoke", removal="1.0")
|
|
def run(
|
|
self,
|
|
*args: Any,
|
|
callbacks: Callbacks = None,
|
|
tags: list[str] | None = None,
|
|
metadata: dict[str, Any] | None = None,
|
|
**kwargs: Any,
|
|
) -> Any:
|
|
"""Convenience method for executing chain.
|
|
|
|
The main difference between this method and `Chain.__call__` is that this
|
|
method expects inputs to be passed directly in as positional arguments or
|
|
keyword arguments, whereas `Chain.__call__` expects a single input dictionary
|
|
with all the inputs
|
|
|
|
Args:
|
|
*args: If the chain expects a single input, it can be passed in as the
|
|
sole positional argument.
|
|
callbacks: Callbacks to use for this chain run. These will be called in
|
|
addition to callbacks passed to the chain during construction, but only
|
|
these runtime callbacks will propagate to calls to other objects.
|
|
tags: List of string tags to pass to all callbacks. These will be passed in
|
|
addition to tags passed to the chain during construction, but only
|
|
these runtime tags will propagate to calls to other objects.
|
|
metadata: Optional metadata associated with the chain.
|
|
**kwargs: If the chain expects multiple inputs, they can be passed in
|
|
directly as keyword arguments.
|
|
|
|
Returns:
|
|
The chain output.
|
|
|
|
Example:
|
|
```python
|
|
# Suppose we have a single-input chain that takes a 'question' string:
|
|
chain.run("What's the temperature in Boise, Idaho?")
|
|
# -> "The temperature in Boise is..."
|
|
|
|
# Suppose we have a multi-input chain that takes a 'question' string
|
|
# and 'context' string:
|
|
question = "What's the temperature in Boise, Idaho?"
|
|
context = "Weather report for Boise, Idaho on 07/03/23..."
|
|
chain.run(question=question, context=context)
|
|
# -> "The temperature in Boise is..."
|
|
```
|
|
"""
|
|
# Run at start to make sure this is possible/defined
|
|
_output_key = self._run_output_key
|
|
|
|
if args and not kwargs:
|
|
if len(args) != 1:
|
|
msg = "`run` supports only one positional argument."
|
|
raise ValueError(msg)
|
|
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
|
|
_output_key
|
|
]
|
|
|
|
if kwargs and not args:
|
|
return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
|
|
_output_key
|
|
]
|
|
|
|
if not kwargs and not args:
|
|
msg = (
|
|
"`run` supported with either positional arguments or keyword arguments,"
|
|
" but none were provided."
|
|
)
|
|
raise ValueError(msg)
|
|
msg = (
|
|
f"`run` supported with either positional arguments or keyword arguments"
|
|
f" but not both. Got args: {args} and kwargs: {kwargs}."
|
|
)
|
|
raise ValueError(msg)
|
|
|
|
@deprecated("0.1.0", alternative="ainvoke", removal="1.0")
|
|
async def arun(
|
|
self,
|
|
*args: Any,
|
|
callbacks: Callbacks = None,
|
|
tags: list[str] | None = None,
|
|
metadata: dict[str, Any] | None = None,
|
|
**kwargs: Any,
|
|
) -> Any:
|
|
"""Convenience method for executing chain.
|
|
|
|
The main difference between this method and `Chain.__call__` is that this
|
|
method expects inputs to be passed directly in as positional arguments or
|
|
keyword arguments, whereas `Chain.__call__` expects a single input dictionary
|
|
with all the inputs
|
|
|
|
|
|
Args:
|
|
*args: If the chain expects a single input, it can be passed in as the
|
|
sole positional argument.
|
|
callbacks: Callbacks to use for this chain run. These will be called in
|
|
addition to callbacks passed to the chain during construction, but only
|
|
these runtime callbacks will propagate to calls to other objects.
|
|
tags: List of string tags to pass to all callbacks. These will be passed in
|
|
addition to tags passed to the chain during construction, but only
|
|
these runtime tags will propagate to calls to other objects.
|
|
metadata: Optional metadata associated with the chain.
|
|
**kwargs: If the chain expects multiple inputs, they can be passed in
|
|
directly as keyword arguments.
|
|
|
|
Returns:
|
|
The chain output.
|
|
|
|
Example:
|
|
```python
|
|
# Suppose we have a single-input chain that takes a 'question' string:
|
|
await chain.arun("What's the temperature in Boise, Idaho?")
|
|
# -> "The temperature in Boise is..."
|
|
|
|
# Suppose we have a multi-input chain that takes a 'question' string
|
|
# and 'context' string:
|
|
question = "What's the temperature in Boise, Idaho?"
|
|
context = "Weather report for Boise, Idaho on 07/03/23..."
|
|
await chain.arun(question=question, context=context)
|
|
# -> "The temperature in Boise is..."
|
|
```
|
|
"""
|
|
if len(self.output_keys) != 1:
|
|
msg = (
|
|
f"`run` not supported when there is not exactly "
|
|
f"one output key. Got {self.output_keys}."
|
|
)
|
|
raise ValueError(msg)
|
|
if args and not kwargs:
|
|
if len(args) != 1:
|
|
msg = "`run` supports only one positional argument."
|
|
raise ValueError(msg)
|
|
return (
|
|
await self.acall(
|
|
args[0],
|
|
callbacks=callbacks,
|
|
tags=tags,
|
|
metadata=metadata,
|
|
)
|
|
)[self.output_keys[0]]
|
|
|
|
if kwargs and not args:
|
|
return (
|
|
await self.acall(
|
|
kwargs,
|
|
callbacks=callbacks,
|
|
tags=tags,
|
|
metadata=metadata,
|
|
)
|
|
)[self.output_keys[0]]
|
|
|
|
msg = (
|
|
f"`run` supported with either positional arguments or keyword arguments"
|
|
f" but not both. Got args: {args} and kwargs: {kwargs}."
|
|
)
|
|
raise ValueError(msg)
|
|
|
|
def dict(self, **kwargs: Any) -> dict:
|
|
"""Dictionary representation of chain.
|
|
|
|
Expects `Chain._chain_type` property to be implemented and for memory to be
|
|
null.
|
|
|
|
Args:
|
|
**kwargs: Keyword arguments passed to default `pydantic.BaseModel.dict`
|
|
method.
|
|
|
|
Returns:
|
|
A dictionary representation of the chain.
|
|
|
|
Example:
|
|
```python
|
|
chain.model_dump(exclude_unset=True)
|
|
# -> {"_type": "foo", "verbose": False, ...}
|
|
```
|
|
"""
|
|
_dict = super().model_dump(**kwargs)
|
|
with contextlib.suppress(NotImplementedError):
|
|
_dict["_type"] = self._chain_type
|
|
return _dict
|
|
|
|
def save(self, file_path: Path | str) -> None:
|
|
"""Save the chain.
|
|
|
|
Expects `Chain._chain_type` property to be implemented and for memory to be
|
|
null.
|
|
|
|
Args:
|
|
file_path: Path to file to save the chain to.
|
|
|
|
Example:
|
|
```python
|
|
chain.save(file_path="path/chain.yaml")
|
|
```
|
|
"""
|
|
if self.memory is not None:
|
|
msg = "Saving of memory is not yet supported."
|
|
raise ValueError(msg)
|
|
|
|
# Fetch dictionary to save
|
|
chain_dict = self.model_dump()
|
|
if "_type" not in chain_dict:
|
|
msg = f"Chain {self} does not support saving."
|
|
raise NotImplementedError(msg)
|
|
|
|
# Convert file to Path object.
|
|
save_path = Path(file_path) if isinstance(file_path, str) else file_path
|
|
|
|
directory_path = save_path.parent
|
|
directory_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
if save_path.suffix == ".json":
|
|
with save_path.open("w") as f:
|
|
json.dump(chain_dict, f, indent=4)
|
|
elif save_path.suffix.endswith((".yaml", ".yml")):
|
|
with save_path.open("w") as f:
|
|
yaml.dump(chain_dict, f, default_flow_style=False)
|
|
else:
|
|
msg = f"{save_path} must be json or yaml"
|
|
raise ValueError(msg)
|
|
|
|
@deprecated("0.1.0", alternative="batch", removal="1.0")
|
|
def apply(
|
|
self,
|
|
input_list: list[builtins.dict[str, Any]],
|
|
callbacks: Callbacks = None,
|
|
) -> list[builtins.dict[str, str]]:
|
|
"""Call the chain on all inputs in the list."""
|
|
return [self(inputs, callbacks=callbacks) for inputs in input_list]
|