"""Base interface for chains combining documents."""
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Tuple, Type
from langchain_core._api import deprecated
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain_core.documents import Document
from langchain_core.prompts import BasePromptTemplate, PromptTemplate
from langchain_core.runnables.config import RunnableConfig
from langchain_core.runnables.utils import create_model
from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
from pydantic import BaseModel, Field
from langchain.chains.base import Chain
DEFAULT_DOCUMENT_SEPARATOR = "\n\n"
DOCUMENTS_KEY = "context"
DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template("{page_content}")
def _validate_prompt(prompt: BasePromptTemplate, document_variable_name: str) -> None:
if document_variable_name not in prompt.input_variables:
raise ValueError(
f"Prompt must accept {document_variable_name} as an input variable. "
f"Received prompt with input variables: {prompt.input_variables}"
)
[docs]
class BaseCombineDocumentsChain(Chain, ABC):
"""Base interface for chains combining documents.
Subclasses of this chain deal with combining documents in a variety of
ways. This base class exists to add some uniformity in the interface these types
of chains should expose. Namely, they expect an input key related to the documents
to use (default `input_documents`), and then also expose a method to calculate
the length of a prompt from documents (useful for outside callers to use to
determine whether it's safe to pass a list of documents into this chain or whether
that will be longer than the context length).
"""
input_key: str = "input_documents" #: :meta private:
output_key: str = "output_text" #: :meta private:
def get_input_schema(
self, config: Optional[RunnableConfig] = None
) -> Type[BaseModel]:
return create_model(
"CombineDocumentsInput",
**{self.input_key: (List[Document], None)}, # type: ignore[call-overload]
)
def get_output_schema(
self, config: Optional[RunnableConfig] = None
) -> Type[BaseModel]:
return create_model(
"CombineDocumentsOutput",
**{self.output_key: (str, None)}, # type: ignore[call-overload]
)
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return [self.output_key]
[docs]
def prompt_length(self, 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.
Args:
docs: List[Document], a list of documents to use to calculate the
total prompt length.
Returns:
Returns None if the method does not depend on the prompt length,
otherwise the length of the prompt in tokens.
"""
return None
[docs]
@abstractmethod
def combine_docs(self, docs: List[Document], **kwargs: Any) -> Tuple[str, dict]:
"""Combine documents into a single string.
Args:
docs: List[Document], the documents to combine
**kwargs: Other parameters to use in combining documents, often
other inputs to the prompt.
Returns:
The first element returned is the single string output. The second
element returned is a dictionary of other keys to return.
"""
[docs]
@abstractmethod
async def acombine_docs(
self, docs: List[Document], **kwargs: Any
) -> Tuple[str, dict]:
"""Combine documents into a single string.
Args:
docs: List[Document], the documents to combine
**kwargs: Other parameters to use in combining documents, often
other inputs to the prompt.
Returns:
The first element returned is the single string output. The second
element returned is a dictionary of other keys to return.
"""
def _call(
self,
inputs: Dict[str, List[Document]],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""Prepare inputs, call combine docs, prepare outputs."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
docs = inputs[self.input_key]
# Other keys are assumed to be needed for LLM prediction
other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
output, extra_return_dict = self.combine_docs(
docs, callbacks=_run_manager.get_child(), **other_keys
)
extra_return_dict[self.output_key] = output
return extra_return_dict
async def _acall(
self,
inputs: Dict[str, List[Document]],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""Prepare inputs, call combine docs, prepare outputs."""
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
docs = inputs[self.input_key]
# Other keys are assumed to be needed for LLM prediction
other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
output, extra_return_dict = await self.acombine_docs(
docs, callbacks=_run_manager.get_child(), **other_keys
)
extra_return_dict[self.output_key] = output
return extra_return_dict
[docs]
@deprecated(
since="0.2.7",
alternative=(
"example in API reference with more detail: "
"https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.base.AnalyzeDocumentChain.html" # noqa: E501
),
removal="1.0",
)
class AnalyzeDocumentChain(Chain):
"""Chain that splits documents, then analyzes it in pieces.
This chain is parameterized by a TextSplitter and a CombineDocumentsChain.
This chain takes a single document as input, and then splits it up into chunks
and then passes those chucks to the CombineDocumentsChain.
This class is deprecated. See below for alternative implementations which
supports async and streaming modes of operation.
If the underlying combine documents chain takes one ``input_documents`` argument
(e.g., chains generated by ``load_summarize_chain``):
.. code-block:: python
split_text = lambda x: text_splitter.create_documents([x])
summarize_document_chain = split_text | chain
If the underlying chain takes additional arguments (e.g., ``load_qa_chain``, which
takes an additional ``question`` argument), we can use the following:
.. code-block:: python
from operator import itemgetter
from langchain_core.runnables import RunnableLambda, RunnableParallel
split_text = RunnableLambda(
lambda x: text_splitter.create_documents([x])
)
summarize_document_chain = RunnableParallel(
question=itemgetter("question"),
input_documents=itemgetter("input_document") | split_text,
) | chain.pick("output_text")
To additionally return the input parameters, as ``AnalyzeDocumentChain`` does,
we can wrap this construction with ``RunnablePassthrough``:
.. code-block:: python
from operator import itemgetter
from langchain_core.runnables import (
RunnableLambda,
RunnableParallel,
RunnablePassthrough,
)
split_text = RunnableLambda(
lambda x: text_splitter.create_documents([x])
)
summarize_document_chain = RunnablePassthrough.assign(
output_text=RunnableParallel(
question=itemgetter("question"),
input_documents=itemgetter("input_document") | split_text,
) | chain.pick("output_text")
)
"""
input_key: str = "input_document" #: :meta private:
text_splitter: TextSplitter = Field(default_factory=RecursiveCharacterTextSplitter)
combine_docs_chain: BaseCombineDocumentsChain
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return self.combine_docs_chain.output_keys
def get_input_schema(
self, config: Optional[RunnableConfig] = None
) -> Type[BaseModel]:
return create_model(
"AnalyzeDocumentChain",
**{self.input_key: (str, None)}, # type: ignore[call-overload]
)
def get_output_schema(
self, config: Optional[RunnableConfig] = None
) -> Type[BaseModel]:
return self.combine_docs_chain.get_output_schema(config)
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""Split document into chunks and pass to CombineDocumentsChain."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
document = inputs[self.input_key]
docs = self.text_splitter.create_documents([document])
# Other keys are assumed to be needed for LLM prediction
other_keys: Dict = {k: v for k, v in inputs.items() if k != self.input_key}
other_keys[self.combine_docs_chain.input_key] = docs
return self.combine_docs_chain(
other_keys, return_only_outputs=True, callbacks=_run_manager.get_child()
)
