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709 lines
28 KiB
Python
709 lines
28 KiB
Python
"""Base interface that all chains should implement."""
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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, Dict, List, Optional, Type, Union, 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.load.dump import dumpd
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from langchain_core.memory import BaseMemory
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from langchain_core.outputs import RunInfo
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from langchain_core.pydantic_v1 import BaseModel, Field, root_validator, validator
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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.runnables.utils import create_model
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from langchain.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.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: Optional[BaseMemory] = None
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"""Optional memory object. Defaults to None.
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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). Defaults to None.
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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: Optional[List[str]] = None
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"""Optional list of tags associated with the chain. Defaults to None.
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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: Optional[Dict[str, Any]] = None
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"""Optional metadata associated with the chain. Defaults to None.
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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: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
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"""[DEPRECATED] Use `callbacks` instead."""
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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def get_input_schema(
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self, config: Optional[RunnableConfig] = 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( # type: ignore[call-overload]
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"ChainInput", **{k: (Any, None) for k in self.input_keys}
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)
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def get_output_schema(
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self, config: Optional[RunnableConfig] = 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( # type: ignore[call-overload]
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"ChainOutput", **{k: (Any, None) for k in self.output_keys}
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)
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def invoke(
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self,
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input: Dict[str, Any],
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config: Optional[RunnableConfig] = 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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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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dumpd(self),
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inputs,
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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, outputs, 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 e
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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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async def ainvoke(
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self,
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input: Dict[str, Any],
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config: Optional[RunnableConfig] = 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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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 = 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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dumpd(self),
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inputs,
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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] = self.prep_outputs(
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inputs, outputs, 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 e
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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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raise NotImplementedError("Saving not supported for this chain type.")
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@root_validator()
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def raise_callback_manager_deprecation(cls, values: Dict) -> Dict:
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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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raise ValueError(
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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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warnings.warn(
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"callback_manager is deprecated. Please use callbacks instead.",
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DeprecationWarning,
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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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@validator("verbose", pre=True, always=True)
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def set_verbose(cls, verbose: Optional[bool]) -> 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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else:
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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: Dict[str, 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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raise ValueError(
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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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missing_keys = set(self.input_keys).difference(inputs)
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if missing_keys:
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raise ValueError(f"Missing some input keys: {missing_keys}")
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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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raise ValueError(f"Missing some output keys: {missing_keys}")
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@abstractmethod
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> 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: Dict[str, Any],
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> 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, self._call, inputs, run_manager.get_sync() if run_manager else None
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)
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@deprecated("0.1.0", alternative="invoke", removal="0.2.0")
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def __call__(
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self,
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inputs: Union[Dict[str, Any], Any],
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return_only_outputs: bool = False,
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callbacks: Callbacks = None,
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*,
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tags: Optional[List[str]] = None,
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metadata: Optional[Dict[str, Any]] = None,
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run_name: Optional[str] = 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. Defaults to False.
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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. Defaults to None
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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="0.2.0")
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async def acall(
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self,
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inputs: Union[Dict[str, Any], Any],
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return_only_outputs: bool = False,
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callbacks: Callbacks = None,
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*,
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tags: Optional[List[str]] = None,
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metadata: Optional[Dict[str, Any]] = None,
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run_name: Optional[str] = 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. Defaults to False.
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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.
|
|
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. Defaults to None
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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,
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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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else:
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return {**inputs, **outputs}
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|
|
def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]:
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|
"""Prepare chain inputs, including adding inputs from memory.
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|
|
Args:
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inputs: Dictionary of raw inputs, or single input if chain expects
|
|
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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Returns:
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A dictionary of all inputs, including those added by the chain's memory.
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"""
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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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inputs = {list(_input_keys)[0]: inputs}
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if self.memory is not None:
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external_context = self.memory.load_memory_variables(inputs)
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inputs = dict(inputs, **external_context)
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return inputs
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@property
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def _run_output_key(self) -> str:
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if len(self.output_keys) != 1:
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raise ValueError(
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f"`run` not supported when there is not exactly "
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f"one output key. Got {self.output_keys}."
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)
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return self.output_keys[0]
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|
|
@deprecated("0.1.0", alternative="invoke", removal="0.2.0")
|
|
def run(
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self,
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*args: Any,
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callbacks: Callbacks = None,
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|
tags: Optional[List[str]] = None,
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metadata: Optional[Dict[str, Any]] = None,
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|
**kwargs: Any,
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) -> Any:
|
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"""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.
|
|
**kwargs: If the chain expects multiple inputs, they can be passed in
|
|
directly as keyword arguments.
|
|
|
|
Returns:
|
|
The chain output.
|
|
|
|
Example:
|
|
.. code-block:: 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:
|
|
raise ValueError("`run` supports only one positional argument.")
|
|
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:
|
|
raise ValueError(
|
|
"`run` supported with either positional arguments or keyword arguments,"
|
|
" but none were provided."
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"`run` supported with either positional arguments or keyword arguments"
|
|
f" but not both. Got args: {args} and kwargs: {kwargs}."
|
|
)
|
|
|
|
@deprecated("0.1.0", alternative="ainvoke", removal="0.2.0")
|
|
async def arun(
|
|
self,
|
|
*args: Any,
|
|
callbacks: Callbacks = None,
|
|
tags: Optional[List[str]] = None,
|
|
metadata: Optional[Dict[str, Any]] = 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.
|
|
**kwargs: If the chain expects multiple inputs, they can be passed in
|
|
directly as keyword arguments.
|
|
|
|
Returns:
|
|
The chain output.
|
|
|
|
Example:
|
|
.. code-block:: 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:
|
|
raise ValueError(
|
|
f"`run` not supported when there is not exactly "
|
|
f"one output key. Got {self.output_keys}."
|
|
)
|
|
elif args and not kwargs:
|
|
if len(args) != 1:
|
|
raise ValueError("`run` supports only one positional argument.")
|
|
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]]
|
|
|
|
raise ValueError(
|
|
f"`run` supported with either positional arguments or keyword arguments"
|
|
f" but not both. Got args: {args} and kwargs: {kwargs}."
|
|
)
|
|
|
|
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:
|
|
.. code-block:: python
|
|
|
|
chain.dict(exclude_unset=True)
|
|
# -> {"_type": "foo", "verbose": False, ...}
|
|
"""
|
|
_dict = super().dict(**kwargs)
|
|
try:
|
|
_dict["_type"] = self._chain_type
|
|
except NotImplementedError:
|
|
pass
|
|
return _dict
|
|
|
|
def save(self, file_path: Union[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:
|
|
.. code-block:: python
|
|
|
|
chain.save(file_path="path/chain.yaml")
|
|
"""
|
|
if self.memory is not None:
|
|
raise ValueError("Saving of memory is not yet supported.")
|
|
|
|
# Fetch dictionary to save
|
|
chain_dict = self.dict()
|
|
if "_type" not in chain_dict:
|
|
raise NotImplementedError(f"Chain {self} does not support saving.")
|
|
|
|
# Convert file to Path object.
|
|
if isinstance(file_path, str):
|
|
save_path = Path(file_path)
|
|
else:
|
|
save_path = file_path
|
|
|
|
directory_path = save_path.parent
|
|
directory_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
if save_path.suffix == ".json":
|
|
with open(file_path, "w") as f:
|
|
json.dump(chain_dict, f, indent=4)
|
|
elif save_path.suffix.endswith((".yaml", ".yml")):
|
|
with open(file_path, "w") as f:
|
|
yaml.dump(chain_dict, f, default_flow_style=False)
|
|
else:
|
|
raise ValueError(f"{save_path} must be json or yaml")
|
|
|
|
@deprecated("0.1.0", alternative="batch", removal="0.2.0")
|
|
def apply(
|
|
self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
|
|
) -> List[Dict[str, str]]:
|
|
"""Call the chain on all inputs in the list."""
|
|
return [self(inputs, callbacks=callbacks) for inputs in input_list]
|