initial commit

venv/Lib/site-packages/langchain_classic/chains/hyde/__init__.py

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+"""Hypothetical Document Embeddings.
+
+https://arxiv.org/abs/2212.10496
+"""

venv/Lib/site-packages/langchain_classic/chains/hyde/base.py

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+"""Hypothetical Document Embeddings.
+
+https://arxiv.org/abs/2212.10496
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+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_classic.chains.base import Chain
+from langchain_classic.chains.hyde.prompts import PROMPT_MAP
+from langchain_classic.chains.llm import LLMChain
+
+logger = logging.getLogger(__name__)
+
+
+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
+        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."""
+        try:
+            import numpy as np
+
+            return list(np.array(embeddings).mean(axis=0))
+        except ImportError:
+            logger.warning(
+                "NumPy not found in the current Python environment. "
+                "HypotheticalDocumentEmbedder will use a pure Python implementation "
+                "for internal calculations, which may significantly impact "
+                "performance, especially for large datasets. For optimal speed and "
+                "efficiency, consider installing NumPy: pip install numpy",
+            )
+            if not embeddings:
+                return []
+            num_vectors = len(embeddings)
+            return [
+                sum(dim_values) / num_vectors
+                for dim_values in zip(*embeddings, strict=False)
+            ]
+
+    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: CallbackManagerForChainRun | None = 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: str | None = None,
+        custom_prompt: BasePromptTemplate | None = 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:
+            msg = (
+                f"Must specify prompt_key if custom_prompt not provided. Should be one "
+                f"of {list(PROMPT_MAP.keys())}."
+            )
+            raise ValueError(msg)
+
+        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"

venv/Lib/site-packages/langchain_classic/chains/hyde/prompts.py

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+from langchain_core.prompts.prompt import PromptTemplate
+
+web_search_template = """Please write a passage to answer the question
+Question: {QUESTION}
+Passage:"""
+web_search = PromptTemplate(template=web_search_template, input_variables=["QUESTION"])
+sci_fact_template = """Please write a scientific paper passage to support/refute the claim
+Claim: {Claim}
+Passage:"""  # noqa: E501
+sci_fact = PromptTemplate(template=sci_fact_template, input_variables=["Claim"])
+arguana_template = """Please write a counter argument for the passage
+Passage: {PASSAGE}
+Counter Argument:"""
+arguana = PromptTemplate(template=arguana_template, input_variables=["PASSAGE"])
+trec_covid_template = """Please write a scientific paper passage to answer the question
+Question: {QUESTION}
+Passage:"""
+trec_covid = PromptTemplate(template=trec_covid_template, input_variables=["QUESTION"])
+fiqa_template = """Please write a financial article passage to answer the question
+Question: {QUESTION}
+Passage:"""
+fiqa = PromptTemplate(template=fiqa_template, input_variables=["QUESTION"])
+dbpedia_entity_template = """Please write a passage to answer the question.
+Question: {QUESTION}
+Passage:"""
+dbpedia_entity = PromptTemplate(
+    template=dbpedia_entity_template, input_variables=["QUESTION"]
+)
+trec_news_template = """Please write a news passage about the topic.
+Topic: {TOPIC}
+Passage:"""
+trec_news = PromptTemplate(template=trec_news_template, input_variables=["TOPIC"])
+mr_tydi_template = """Please write a passage in Swahili/Korean/Japanese/Bengali to answer the question in detail.
+Question: {QUESTION}
+Passage:"""  # noqa: E501
+mr_tydi = PromptTemplate(template=mr_tydi_template, input_variables=["QUESTION"])
+PROMPT_MAP = {
+    "web_search": web_search,
+    "sci_fact": sci_fact,
+    "arguana": arguana,
+    "trec_covid": trec_covid,
+    "fiqa": fiqa,
+    "dbpedia_entity": dbpedia_entity,
+    "trec_news": trec_news,
+    "mr_tydi": mr_tydi,
+}