| """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" |
|
|
