mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-21 15:07:35 +00:00
fd69cc7e42
Removed duplicate BaseModel dependencies in class inheritances. Also, sorted imports by `isort`.
102 lines
4.1 KiB
Python
102 lines
4.1 KiB
Python
"""Chain that combines documents by stuffing into context."""
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from typing import Any, Dict, List, Optional, Tuple
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from pydantic import Extra, Field, root_validator
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from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
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from langchain.chains.llm import LLMChain
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from langchain.docstore.document import Document
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from langchain.prompts.base import BasePromptTemplate
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from langchain.prompts.prompt import PromptTemplate
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def _get_default_document_prompt() -> PromptTemplate:
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return PromptTemplate(input_variables=["page_content"], template="{page_content}")
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class StuffDocumentsChain(BaseCombineDocumentsChain):
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"""Chain that combines documents by stuffing into context."""
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llm_chain: LLMChain
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"""LLM wrapper to use after formatting documents."""
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document_prompt: BasePromptTemplate = Field(
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default_factory=_get_default_document_prompt
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)
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"""Prompt to use to format each document."""
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document_variable_name: str
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"""The variable name in the llm_chain to put the documents in.
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If only one variable in the llm_chain, this need not be provided."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@root_validator(pre=True)
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def get_default_document_variable_name(cls, values: Dict) -> Dict:
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"""Get default document variable name, if not provided."""
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if "document_variable_name" not in values:
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llm_chain_variables = values["llm_chain"].prompt.input_variables
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if len(llm_chain_variables) == 1:
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values["document_variable_name"] = llm_chain_variables[0]
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else:
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raise ValueError(
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"document_variable_name must be provided if there are "
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"multiple llm_chain_variables"
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)
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else:
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llm_chain_variables = values["llm_chain"].prompt.input_variables
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if values["document_variable_name"] not in llm_chain_variables:
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raise ValueError(
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f"document_variable_name {values['document_variable_name']} was "
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f"not found in llm_chain input_variables: {llm_chain_variables}"
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)
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return values
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def _get_inputs(self, docs: List[Document], **kwargs: Any) -> dict:
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# Get relevant information from each document.
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doc_dicts = []
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for doc in docs:
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base_info = {"page_content": doc.page_content}
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base_info.update(doc.metadata)
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document_info = {
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k: base_info[k] for k in self.document_prompt.input_variables
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}
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doc_dicts.append(document_info)
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# Format each document according to the prompt
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doc_strings = [self.document_prompt.format(**doc) for doc in doc_dicts]
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# Join the documents together to put them in the prompt.
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inputs = {
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k: v
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for k, v in kwargs.items()
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if k in self.llm_chain.prompt.input_variables
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}
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inputs[self.document_variable_name] = "\n\n".join(doc_strings)
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return inputs
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def prompt_length(self, docs: List[Document], **kwargs: Any) -> Optional[int]:
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"""Get the prompt length by formatting the prompt."""
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inputs = self._get_inputs(docs, **kwargs)
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prompt = self.llm_chain.prompt.format(**inputs)
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return self.llm_chain.llm.get_num_tokens(prompt)
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def combine_docs(self, docs: List[Document], **kwargs: Any) -> Tuple[str, dict]:
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"""Stuff all documents into one prompt and pass to LLM."""
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inputs = self._get_inputs(docs, **kwargs)
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# Call predict on the LLM.
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return self.llm_chain.predict(**inputs), {}
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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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"""Stuff all documents into one prompt and pass to LLM."""
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inputs = self._get_inputs(docs, **kwargs)
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# Call predict on the LLM.
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return await self.llm_chain.apredict(**inputs), {}
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@property
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def _chain_type(self) -> str:
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return "stuff_documents_chain"
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