Tony20100/langchain_full huggingface dataset:
langchain.chains.combine_documents.reduce.ReduceDocumentsChain¶
class langchain.chains.combine_documents.reduce.ReduceDocumentsChain[source]¶
Bases: BaseCombineDocumentsChain
Combine documents by recursively reducing them.
This involves
combine_documents_chain
collapse_documents_chain
combine_documents_chain is ALWAYS provided. This is final chain that is called.
We pass all previous results to this chain, and the output of this chain is
returned as a final result.
collapse_documents_chain is used if the documents passed in are too many to all
be passed to combine_documents_chain in one go. In this case,
collapse_documents_chain is called recursively on as big of groups of documents
as are allowed.
Example
from langchain.chains import (
    StuffDocumentsChain, LLMChain, ReduceDocumentsChain
)
from langchain_core.prompts import PromptTemplate
from langchain_community.llms import OpenAI
# This controls how each document will be formatted. Specifically,
# it will be passed to `format_document` - see that function for more
# details.
document_prompt = PromptTemplate(
    input_variables=["page_content"],
     template="{page_content}"
)
document_variable_name = "context"
llm = OpenAI()
# The prompt here should take as an input variable the
# `document_variable_name`
prompt = PromptTemplate.from_template(
    "Summarize this content: {context}"
)
llm_chain = LLMChain(llm=llm, prompt=prompt)
combine_documents_chain = StuffDocumentsChain(
    llm_chain=llm_chain,
    document_prompt=document_prompt,
    document_variable_name=document_variable_name
)
chain = ReduceDocumentsChain(
    combine_documents_chain=combine_documents_chain,
)
# If we wanted to, we could also pass in collapse_documents_chain
)
# If we wanted to, we could also pass in collapse_documents_chain
# which is specifically aimed at collapsing documents BEFORE
# the final call.
prompt = PromptTemplate.from_template(
    "Collapse this content: {context}"
)
llm_chain = LLMChain(llm=llm, prompt=prompt)
collapse_documents_chain = StuffDocumentsChain(
    llm_chain=llm_chain,
    document_prompt=document_prompt,
    document_variable_name=document_variable_name
)
chain = ReduceDocumentsChain(
    combine_documents_chain=combine_documents_chain,
    collapse_documents_chain=collapse_documents_chain,
)
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
for full details.
param collapse_documents_chain: Optional[BaseCombineDocumentsChain] = None¶
Chain to use to collapse documents if needed until they can all fit.
If None, will use the combine_documents_chain.
This is typically a StuffDocumentsChain.
param collapse_max_retries: Optional[int] = None¶
The maximum number of retries to collapse documents to fit token_max.
If None, it will keep trying to collapse documents to fit token_max.
Otherwise, after it reaches the max number, it will throw an error
param combine_documents_chain: BaseCombineDocumentsChain [Required]¶
Final chain to call to combine documents.
Final chain to call to combine documents.
This is typically a StuffDocumentsChain.
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 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 token_max: int = 3000¶
The maximum number of tokens to group documents into. For example, if
set to 3000 then documents will be grouped into chunks of no greater than
3000 tokens before trying to combine them into a smaller chunk.
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().
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
Return type
Dict[str, Any]
Notes
Deprecated since version langchain==0.1.0: Use ainvoke instead.
async acombine_docs(docs: List[Document], token_max: Optional[int] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Tuple[str, dict][source]¶
Async combine multiple documents recursively.
Parameters
docs (List[Document]) – List of documents to combine, assumed that each one is less than
token_max.
token_max (Optional[int]) – Recursively creates groups of documents less than this number
of tokens.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to be passed through
**kwargs (Any) – additional parameters to be passed to LLM calls (like other
input variables besides the documents)
Returns
The first element returned is the single string output. The second
element returned is a dictionary of other keys to return.
Return type
Tuple[str, dict]
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
Parameters
input (Dict[str, Any]) – 
config (Optional[RunnableConfig]) – 
kwargs (Any) – 
Return type
Dict[str, Any]
Run invoke in parallel on a list of inputs,
yielding results as they complete.
Parameters
inputs (Sequence[Input]) – 
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
Iterator[Tuple[int, Union[Output, Exception]]]
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
Useful when a runnable in a chain requires an argument that is not
in the output of the previous runnable or included in the user input.
Example:
from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser
llm = ChatOllama(model='llama2')
# Without bind.
chain = (
    llm
    | StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.'
# With bind.
chain = (
    llm.bind(stop=["three"])
    | StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'
Parameters
kwargs (Any) – 
Return type
Runnable[Input, Output]
combine_docs(docs: List[Document], token_max: Optional[int] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Tuple[str, dict][source]¶
Combine multiple documents recursively.
Parameters
docs (List[Document]) – List of documents to combine, assumed that each one is less than
token_max.
token_max (Optional[int]) – Recursively creates groups of documents less than this number
token_max (Optional[int]) – Recursively creates groups of documents less than this number
of tokens.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to be passed through
**kwargs (Any) – additional parameters to be passed to LLM calls (like other
input variables besides the documents)
Returns
The first element returned is the single string output. The second
element returned is a dictionary of other keys to return.
Return type
Tuple[str, dict]
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic model.
To mark a field as configurable, see the configurable_fields
and configurable_alternatives methods.
Parameters
include (Optional[Sequence[str]]) – A list of fields to include in the config schema.
Returns
A pydantic model that can be used to validate config.
Return type
Type[BaseModel]
configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
Configure alternatives for runnables that can be set at runtime.
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatAnthropic(
    model_name="claude-3-sonnet-20240229"
).configurable_alternatives(
    ConfigurableField(id="llm"),
    default_key="anthropic",
    openai=ChatOpenAI()
)
# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
**kwargs (Any) – Keyword arguments passed to default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Return type
Dict
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
# -> [4, 6, 8]
Parameters
others (Union[Runnable[Any, Other], Callable[[Any], Other]]) – 
name (Optional[str]) – 
Return type
RunnableSerializable[Input, Other]
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Prepare chain inputs, including adding inputs from memory.
Parameters
inputs (Union[Dict[str, Any], Any]) – 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.
Return type
Dict[str, str]
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs (Dict[str, str]) – Dictionary of chain inputs, including any inputs added by chain
memory.
outputs (Dict[str, str]) – Dictionary of initial chain outputs.
return_only_outputs (bool) – Whether to only return the chain outputs. If False,
inputs are also added to the final outputs.
Returns
A dict of the final chain outputs.
Return type
Dict[str, str]
prompt_length(docs: List[Document], **kwargs: Any) → Optional[int]¶
Return the prompt length given the documents passed in.
This can be used by a caller to determine whether passing in a list
of documents would exceed a certain prompt length. This useful when
trying to ensure that the size of a prompt remains below a certain
context limit.
Parameters
trying to ensure that the size of a prompt remains below a certain
context limit.
Parameters
docs (List[Document]) – List[Document], a list of documents to use to calculate the
total prompt length.
kwargs (Any) – 
Returns
Returns None if the method does not depend on the prompt length,
otherwise the length of the prompt in tokens.
Return type
Optional[int]
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
[Deprecated] 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
Parameters
*args (Any) – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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 (Optional[List[str]]) – 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 (Any) – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
metadata (Optional[Dict[str, Any]]) – 
**kwargs – 
Returns
The chain output.
Return type
Any
Example
**kwargs – 
Returns
The chain output.
Return type
Any
Example
# 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..."
Notes
Deprecated since version langchain==0.1.0: Use invoke instead.
save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
file_path (Union[Path, str]) – Path to file to save the chain to.
Return type
None
Example
chain.save(file_path="path/chain.yaml")
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
stop_after_attempt (int) – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
Return type
Runnable[Input, Output]
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.
Parameters
input_type (Optional[Type[Input]]) – 
output_type (Optional[Type[Output]]) – 
Return type
Runnable[Input, Output]
property InputType: Type[Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[Output]¶
The type of output this runnable produces specified as a type annotation.
property config_specs: List[ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[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”}
name: Optional[str] = None¶
The name of the runnable. Used for debugging and tracing.
property output_schema: Type[BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using ReduceDocumentsChain¶
Summarize Text
langchain_community.chains.pebblo_retrieval.base.PebbloRetrievalQA¶
class langchain_community.chains.pebblo_retrieval.base.PebbloRetrievalQA[source]¶
Bases: Chain
Retrieval Chain with Identity & Semantic Enforcement for question-answering
against a vector database.
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 api_key: Optional[str] = None¶
Pebblo cloud API key for app.
param app_name: str [Required]¶
App name.
param auth_context_key: str = 'auth_context'¶
Authentication context for identity enforcement.
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
for full details.
param classifier_url: str = 'http://localhost:8000'¶
Classifier endpoint.
param combine_documents_chain: BaseCombineDocumentsChain [Required]¶
Chain to use to combine the documents.
param description: str [Required]¶
Description of app.
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.
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 owner: str [Required]¶
Owner of app.
param retriever: VectorStoreRetriever [Required]¶
VectorStore to use for retrieval.
param return_source_documents: bool = False¶
Return the source documents or not.
param semantic_context_key: str = 'semantic_context'¶
Semantic context for semantic enforcement.
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().
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
[Deprecated] Execute the chain.
Parameters
[Deprecated] Execute the chain.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of 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.
return_only_outputs (bool) – Whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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 (Optional[List[str]]) – 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[Dict[str, Any]]) – Optional metadata associated with the chain. Defaults to None
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
run_name (Optional[str]) – 
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
Return type
Dict[str, Any]
Notes
Deprecated since version langchain==0.1.0: Use invoke instead.
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.
The default implementation of batch works well for IO bound runnables.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
Parameters
inputs (List[Input]) – 
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
List[Output]
async abatch_as_completed(inputs: Sequence[Input], config: Optional[Union[RunnableConfig, Sequence[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → AsyncIterator[Tuple[int, Union[Output, Exception]]]¶
Run ainvoke in parallel on a list of inputs,
yielding results as they complete.
Parameters
inputs (Sequence[Input]) – 
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
AsyncIterator[Tuple[int, Union[Output, Exception]]]
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
[Deprecated] Asynchronously execute the chain.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of 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.