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100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
"""Base interface for chains combining documents."""
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional, Tuple
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from pydantic import Field
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from langchain.chains.base import Chain
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from langchain.docstore.document import Document
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from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
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class BaseCombineDocumentsChain(Chain, ABC):
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"""Base interface for chains combining documents."""
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input_key: str = "input_documents" #: :meta private:
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output_key: str = "output_text" #: :meta private:
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@property
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def input_keys(self) -> List[str]:
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"""Expect input key.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Return output key.
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:meta private:
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"""
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return [self.output_key]
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def prompt_length(self, docs: List[Document], **kwargs: Any) -> Optional[int]:
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"""Return the prompt length given the documents passed in.
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Returns None if the method does not depend on the prompt length.
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"""
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return None
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@abstractmethod
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def combine_docs(self, docs: List[Document], **kwargs: Any) -> Tuple[str, dict]:
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"""Combine documents into a single string."""
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@abstractmethod
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async def acombine_docs(
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self, docs: List[Document], **kwargs: Any
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) -> Tuple[str, dict]:
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"""Combine documents into a single string asynchronously."""
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def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
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docs = inputs[self.input_key]
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# Other keys are assumed to be needed for LLM prediction
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other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
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output, extra_return_dict = self.combine_docs(docs, **other_keys)
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extra_return_dict[self.output_key] = output
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return extra_return_dict
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async def _acall(self, inputs: Dict[str, Any]) -> Dict[str, str]:
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docs = inputs[self.input_key]
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# Other keys are assumed to be needed for LLM prediction
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other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
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output, extra_return_dict = await self.acombine_docs(docs, **other_keys)
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extra_return_dict[self.output_key] = output
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return extra_return_dict
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class AnalyzeDocumentChain(Chain):
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"""Chain that splits documents, then analyzes it in pieces."""
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input_key: str = "input_document" #: :meta private:
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output_key: str = "output_text" #: :meta private:
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text_splitter: TextSplitter = Field(default_factory=RecursiveCharacterTextSplitter)
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combine_docs_chain: BaseCombineDocumentsChain
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@property
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def input_keys(self) -> List[str]:
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"""Expect input key.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Return output key.
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:meta private:
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"""
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return [self.output_key]
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def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
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document = inputs[self.input_key]
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docs = self.text_splitter.create_documents([document])
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# Other keys are assumed to be needed for LLM prediction
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other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
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other_keys[self.combine_docs_chain.input_key] = docs
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return self.combine_docs_chain(other_keys, return_only_outputs=True)
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