viruPVS/langchain-docs huggingface dataset:
langchain_experimental.fallacy_removal.base.FallacyChain¶
class langchain_experimental.fallacy_removal.base.FallacyChain[source]¶
Bases: Chain
Chain for applying logical fallacy evaluations, modeled after Constitutional AI     and in same format, but applying logical fallacies as generalized rules to remove     in output
Example
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain_experimental.fallacy import FallacyChain
from langchain_experimental.fallacy_removal.models import LogicalFallacy
llm = OpenAI()
qa_prompt = PromptTemplate(
    template="Q: {question} A:",
    input_variables=["question"],
)
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
fallacy_chain = FallacyChain.from_llm(
    llm=llm,
    chain=qa_chain,
    logical_fallacies=[
        LogicalFallacy(
            fallacy_critique_request="Tell if this answer meets criteria.",
            fallacy_revision_request=                        "Give an answer that meets better criteria.",
        )
    ],
)
fallacy_chain.run(question="How do I know if the earth is round?")
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param chain: LLMChain [Required]¶
param fallacy_critique_chain: LLMChain [Required]¶
param fallacy_revision_chain: LLMChain [Required]¶
param logical_fallacies: List[LogicalFallacy] [Required]¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the chain. Defaults to None.
This metadata will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param return_intermediate_steps: bool = False¶
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to the global verbose value,
accessible via langchain.globals.get_verbose().
new model instance
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
**kwargs – Keyword arguments passed to default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
classmethod from_llm(llm: BaseLanguageModel, chain: LLMChain, fallacy_critique_prompt: BasePromptTemplate = FewShotPromptTemplate(input_variables=['fallacy_critique_request', 'input_prompt', 'output_from_model'], examples=[{'input_prompt': "If everyone says the Earth is round,         how do I know that's correct?", 'output_from_model': 'The earth is round because your         teacher says it is', 'fallacy_critique_request': 'Identify specific ways in        which the model’s previous response had a logical fallacy.         Also point out potential logical fallacies in the human’s         questions and responses. Examples of logical fallacies         include but are not limited to ad hominem, ad populum,         appeal to emotion and false causality.', 'fallacy_critique': 'This statement contains the logical         fallacy of Ad Verecundiam or Appeal to Authority. It is         a fallacy because it asserts something to be true purely         based on the authority of the source making the claim,         without any actual evidence to support it.  Fallacy         Critique Needed', 'fallacy_revision': 'The earth is round based on         evidence from observations of its curvature from high         altitudes, photos from space showing its spherical shape,         circumnavigation, and the fact that we see its rounded         shadow on the moon during lunar eclipses.'}, {'input_prompt': 'Should we invest more in our school         music program? After all, studies show students         involved in music perform better academically.', 'output_from_model': "I don't think we should invest         more in the music program. Playing the piccolo won't         teach someone better math skills.", 'fallacy_critique_request': 'Identify specific ways         in which the model’s previous response had a logical         fallacy. Also point out potential logical fallacies         in the human’s questions and responses. Examples of
Also point out potential logical fallacies         in the human’s questions and responses. Examples of         logical fallacies include but are not limited to ad         homimem, ad populum, appeal to emotion and false         causality.', 'fallacy_critique': 'This answer commits the division         fallacy by rejecting the argument based on assuming         capabilities true of the parts (playing an instrument         like piccolo) also apply to the whole         (the full music program). The answer focuses only on         part of the music program rather than considering it         as a whole.  Fallacy Critique Needed.', 'fallacy_revision': 'While playing an instrument may         teach discipline, more evidence is needed on whether         music education courses improve critical thinking         skills across subjects before determining if increased         investment in the whole music program is warranted.'}], example_prompt=PromptTemplate(input_variables=['fallacy_critique', 'fallacy_critique_request', 'input_prompt', 'output_from_model'], template='Human: {input_prompt}\n\nModel: {output_from_model}\n\nFallacy Critique Request: {fallacy_critique_request}\n\nFallacy Critique: {fallacy_critique}'), suffix='Human: {input_prompt}\nModel: {output_from_model}\n\nFallacy Critique Request: {fallacy_critique_request}\n\nFallacy Critique:', example_separator='\n === \n', prefix="Below is a conversation between a human and an     AI assistant. If there is no material critique of the     model output, append to the end of the Fallacy Critique:     'No fallacy critique needed.' If there is material     critique     of the model output, append to the end of the Fallacy     Critique: 'Fallacy Critique needed.'"), fallacy_revision_prompt: BasePromptTemplate = FewShotPromptTemplate(input_variables=['fallacy_critique', 'fallacy_critique_request',
= FewShotPromptTemplate(input_variables=['fallacy_critique', 'fallacy_critique_request', 'fallacy_revision_request', 'input_prompt', 'output_from_model'], examples=[{'input_prompt': "If everyone says the Earth is round,         how do I know that's correct?", 'output_from_model': 'The earth is round because your         teacher says it is', 'fallacy_critique_request': 'Identify specific ways in        which the model’s previous response had a logical fallacy.         Also point out potential logical fallacies in the human’s         questions and responses. Examples of logical fallacies         include but are not limited to ad hominem, ad populum,         appeal to emotion and false causality.', 'fallacy_critique': 'This statement contains the logical         fallacy of Ad Verecundiam or Appeal to Authority. It is         a fallacy because it asserts something to be true purely         based on the authority of the source making the claim,         without any actual evidence to support it.  Fallacy         Critique Needed', 'fallacy_revision_request': 'Please rewrite the model         response to remove all logical fallacies, and to         politely point out any logical fallacies from the         human.', 'fallacy_revision': 'The earth is round based on         evidence from observations of its curvature from high         altitudes, photos from space showing its spherical shape,         circumnavigation, and the fact that we see its rounded         shadow on the moon during lunar eclipses.'}, {'input_prompt': 'Should we invest more in our school         music program? After all, studies show students         involved in music perform better academically.', 'output_from_model': "I don't think we should invest         more in the music program. Playing the piccolo won't         teach someone better math skills.", 'fallacy_critique_request': 'Identify specific ways         in which the model’s previous response had a logical
'Identify specific ways         in which the model’s previous response had a logical         fallacy. Also point out potential logical fallacies         in the human’s questions and responses. Examples of         logical fallacies include but are not limited to ad         homimem, ad populum, appeal to emotion and false         causality.', 'fallacy_critique': 'This answer commits the division         fallacy by rejecting the argument based on assuming         capabilities true of the parts (playing an instrument         like piccolo) also apply to the whole         (the full music program). The answer focuses only on         part of the music program rather than considering it         as a whole.  Fallacy Critique Needed.', 'fallacy_revision_request': 'Please rewrite the model         response to remove all logical fallacies, and to         politely point out any logical fallacies from the human.', 'fallacy_revision': 'While playing an instrument may         teach discipline, more evidence is needed on whether         music education courses improve critical thinking         skills across subjects before determining if increased         investment in the whole music program is warranted.'}], example_prompt=PromptTemplate(input_variables=['fallacy_critique', 'fallacy_critique_request', 'input_prompt', 'output_from_model'], template='Human: {input_prompt}\n\nModel: {output_from_model}\n\nFallacy Critique Request: {fallacy_critique_request}\n\nFallacy Critique: {fallacy_critique}'), suffix='Human: {input_prompt}\n\nModel: {output_from_model}\n\nFallacy Critique Request: {fallacy_critique_request}\n\nFallacy Critique: {fallacy_critique}\n\nIf the fallacy critique does not identify anything worth changing, ignore the Fallacy Revision Request and do not make any revisions. Instead, return "No revisions needed".\n\nIf the fallacy critique does identify something worth changing, please revise the model response based on
Create a chain from an LLM.
classmethod from_orm(obj: Any) → Model¶
classmethod get_fallacies(names: Optional[List[str]] = None) → List[LogicalFallacy][source]¶
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Transform a single input into an output. Override to implement.
Parameters
input – The input to the runnable.
on_start: Called before the runnable starts running, with the Run object.
on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
Create a new Runnable that retries the original runnable on exceptions.
Parameters
retry_if_exception_type – A tuple of exception types to retry on
wait_exponential_jitter – Whether to add jitter to the wait time
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain_core.runnables.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain_core.runnables.utils.Output]¶
The type of output this runnable produces specified as a type annotation.
property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_keys: List[str]¶
Input keys.
property input_schema: Type[pydantic.main.BaseModel]¶
Input keys.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_keys: List[str]¶
Output keys.
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
langchain_experimental.fallacy_removal.models.LogicalFallacy¶
class langchain_experimental.fallacy_removal.models.LogicalFallacy[source]¶
Bases: BaseModel
Class for a logical fallacy.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param fallacy_critique_request: str [Required]¶
param fallacy_revision_request: str [Required]¶
param name: str = 'Logical Fallacy'¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
langchain_core.prompts.string.get_template_variables¶
langchain_core.prompts.string.get_template_variables(template: str, template_format: str) → List[str][source]¶
Get the variables from the template.
Parameters
template – The template string.
template_format – The template format. Should be one of “f-string” or “jinja2”.
Returns
The variables from the template.
Raises
ValueError – If the template format is not supported.
langchain_core.prompts.few_shot.FewShotChatMessagePromptTemplate¶
class langchain_core.prompts.few_shot.FewShotChatMessagePromptTemplate[source]¶
Bases: BaseChatPromptTemplate, _FewShotPromptTemplateMixin
Chat prompt template that supports few-shot examples.
The high level structure of produced by this prompt template is a list of messages
consisting of prefix message(s), example message(s), and suffix message(s).
This structure enables creating a conversation with intermediate examples like:
System: You are a helpful AI Assistant
Human: What is 2+2?
AI: 4
Human: What is 2+3?
AI: 5
Human: What is 4+4?
This prompt template can be used to generate a fixed list of examples or else
to dynamically select examples based on the input.
Examples
Prompt template with a fixed list of examples (matching the sample
conversation above):
from langchain_core.prompts import (
    FewShotChatMessagePromptTemplate,
    ChatPromptTemplate
)
examples = [
    {"input": "2+2", "output": "4"},
    {"input": "2+3", "output": "5"},
]
example_prompt = ChatPromptTemplate.from_messages(
    [('human', '{input}'), ('ai', '{output}')]
)
few_shot_prompt = FewShotChatMessagePromptTemplate(
    examples=examples,
    # This is a prompt template used to format each individual example.
    example_prompt=example_prompt,
)
final_prompt = ChatPromptTemplate.from_messages(
    [
        ('system', 'You are a helpful AI Assistant'),
        few_shot_prompt,
        ('human', '{input}'),
    ]
)
final_prompt.format(input="What is 4+4?")
]
)
final_prompt.format(input="What is 4+4?")
Prompt template with dynamically selected examples:
from langchain_core.prompts import SemanticSimilarityExampleSelector
from langchain_core.embeddings import OpenAIEmbeddings
from langchain_core.vectorstores import Chroma
examples = [
    {"input": "2+2", "output": "4"},
    {"input": "2+3", "output": "5"},
    {"input": "2+4", "output": "6"},
    # ...
]
to_vectorize = [
    " ".join(example.values())
    for example in examples
]
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_texts(
    to_vectorize, embeddings, metadatas=examples
)
example_selector = SemanticSimilarityExampleSelector(
    vectorstore=vectorstore
)
from langchain_core import SystemMessage
from langchain_core.prompts import HumanMessagePromptTemplate
from langchain_core.prompts.few_shot import FewShotChatMessagePromptTemplate
few_shot_prompt = FewShotChatMessagePromptTemplate(
    # Which variable(s) will be passed to the example selector.
    input_variables=["input"],
    example_selector=example_selector,
    # Define how each example will be formatted.
    # In this case, each example will become 2 messages:
    # 1 human, and 1 AI
    example_prompt=(
        HumanMessagePromptTemplate.from_template("{input}")
        + AIMessagePromptTemplate.from_template("{output}")
    ),
)
# Define the overall prompt.
final_prompt = (
    SystemMessagePromptTemplate.from_template(
        "You are a helpful AI Assistant"
    )
    + few_shot_prompt
    + HumanMessagePromptTemplate.from_template("{input}")
)
+ few_shot_prompt
    + HumanMessagePromptTemplate.from_template("{input}")
)
# Show the prompt
print(final_prompt.format_messages(input="What's 3+3?"))
# Use within an LLM
from langchain_core.chat_models import ChatAnthropic
chain = final_prompt | ChatAnthropic()
chain.invoke({"input": "What's 3+3?"})
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param example_prompt: Union[BaseMessagePromptTemplate, BaseChatPromptTemplate] [Required]¶
The class to format each example.
param example_selector: Any = None¶
ExampleSelector to choose the examples to format into the prompt.
Either this or examples should be provided.
param examples: Optional[List[dict]] = None¶
Examples to format into the prompt.
Either this or example_selector should be provided.
param input_types: Dict[str, Any] [Optional]¶
A dictionary of the types of the variables the prompt template expects.
If not provided, all variables are assumed to be strings.
param input_variables: List[str] [Optional]¶
A list of the names of the variables the prompt template will use
to pass to the example_selector, if provided.
param output_parser: Optional[BaseOutputParser] = None¶
How to parse the output of calling an LLM on this formatted prompt.
param partial_variables: Mapping[str, Union[str, Callable[[], str]]] [Optional]¶
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
async ainvoke(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any) → Output¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, with_streamed_output_list: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.