| | """Hypothetical Document Embeddings. |
| | |
| | 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.output_parsers import StrOutputParser |
| | from langchain_core.prompts import BasePromptTemplate |
| | from langchain_core.runnables import Runnable |
| | from pydantic import ConfigDict |
| |
|
| | from langchain.chains.base import Chain |
| | from langchain.chains.hyde.prompts import PROMPT_MAP |
| | from langchain.chains.llm import LLMChain |
| |
|
| |
|
| | class HypotheticalDocumentEmbedder(Chain, Embeddings): |
| | """Generate hypothetical document for query, and then embed that. |
| | |
| | Based on https://arxiv.org/abs/2212.10496 |
| | """ |
| |
|
| | base_embeddings: Embeddings |
| | llm_chain: Runnable |
| |
|
| | model_config = ConfigDict( |
| | arbitrary_types_allowed=True, |
| | extra="forbid", |
| | ) |
| |
|
| | @property |
| | def input_keys(self) -> List[str]: |
| | """Input keys for Hyde's LLM chain.""" |
| | return self.llm_chain.input_schema.model_json_schema()["required"] |
| |
|
| | @property |
| | def output_keys(self) -> List[str]: |
| | """Output keys for Hyde's LLM chain.""" |
| | if isinstance(self.llm_chain, LLMChain): |
| | return self.llm_chain.output_keys |
| | else: |
| | return ["text"] |
| |
|
| | def embed_documents(self, texts: List[str]) -> List[List[float]]: |
| | """Call the base embeddings.""" |
| | return self.base_embeddings.embed_documents(texts) |
| |
|
| | def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]: |
| | """Combine embeddings into final embeddings.""" |
| | return list(np.array(embeddings).mean(axis=0)) |
| |
|
| | def embed_query(self, text: str) -> List[float]: |
| | """Generate a hypothetical document and embedded it.""" |
| | var_name = self.input_keys[0] |
| | result = self.llm_chain.invoke({var_name: text}) |
| | if isinstance(self.llm_chain, LLMChain): |
| | documents = [result[self.output_keys[0]]] |
| | else: |
| | documents = [result] |
| | 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]: |
| | """Call the internal llm chain.""" |
| | _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() |
| | return self.llm_chain.invoke( |
| | inputs, config={"callbacks": _run_manager.get_child()} |
| | ) |
| |
|
| | @classmethod |
| | def from_llm( |
| | cls, |
| | llm: BaseLanguageModel, |
| | base_embeddings: Embeddings, |
| | prompt_key: Optional[str] = None, |
| | custom_prompt: Optional[BasePromptTemplate] = None, |
| | **kwargs: Any, |
| | ) -> HypotheticalDocumentEmbedder: |
| | """Load and use LLMChain with either a specific prompt key or custom prompt.""" |
| | 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 = prompt | llm | StrOutputParser() |
| | return cls(base_embeddings=base_embeddings, llm_chain=llm_chain, **kwargs) |
| |
|
| | @property |
| | def _chain_type(self) -> str: |
| | return "hyde_chain" |
| |
|
