Source code for langchain.chains.hyde.base
"""假设性文档嵌入。
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
import numpy as np
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.embeddings import Embeddings
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import Extra
from langchain.chains.base import Chain
from langchain.chains.hyde.prompts import PROMPT_MAP
from langchain.chains.llm import LLMChain
[docs]class HypotheticalDocumentEmbedder(Chain, Embeddings):
"""生成用于查询的假设文档,然后嵌入其中。
基于 https://arxiv.org/abs/2212.10496
"""
base_embeddings: Embeddings
llm_chain: LLMChain
class Config:
"""这个pydantic对象的配置。"""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""为Hyde的LLM链输入密钥。"""
return self.llm_chain.input_keys
@property
def output_keys(self) -> List[str]:
"""Hyde的LLM链的输出密钥。"""
return self.llm_chain.output_keys
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""调用基础嵌入。"""
return self.base_embeddings.embed_documents(texts)
[docs] def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]:
"""将嵌入组合成最终嵌入。"""
return list(np.array(embeddings).mean(axis=0))
[docs] def embed_query(self, text: str) -> List[float]:
"""生成一个假设文档并嵌入其中。"""
var_name = self.llm_chain.input_keys[0]
result = self.llm_chain.generate([{var_name: text}])
documents = [generation.text for generation in result.generations[0]]
embeddings = self.embed_documents(documents)
return self.combine_embeddings(embeddings)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""调用内部的llm链。"""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
return self.llm_chain(inputs, callbacks=_run_manager.get_child())
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
base_embeddings: Embeddings,
prompt_key: Optional[str] = None,
custom_prompt: Optional[BasePromptTemplate] = None,
**kwargs: Any,
) -> HypotheticalDocumentEmbedder:
"""使用LLMChain加载并使用特定提示键或自定义提示。"""
if custom_prompt is not None:
prompt = custom_prompt
elif prompt_key is not None and prompt_key in PROMPT_MAP:
prompt = PROMPT_MAP[prompt_key]
else:
raise ValueError(
f"Must specify prompt_key if custom_prompt not provided. Should be one "
f"of {list(PROMPT_MAP.keys())}."
)
llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(base_embeddings=base_embeddings, llm_chain=llm_chain, **kwargs)
@property
def _chain_type(self) -> str:
return "hyde_chain"