Download MemGen-main/data/utils/search_utils.py from Ouzhang/low-high-reference: direct link, hf CLI and curl.
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https://huggingface.co/datasets/Ouzhang/low-high-reference/resolve/main/MemGen-main/data/utils/search_utils.py
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hf download hf://datasets/Ouzhang/low-high-reference/MemGen-main/data/utils/search_utils.py
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curl -L -o search_utils.py https://huggingface.co/datasets/Ouzhang/low-high-reference/resolve/main/MemGen-main/data/utils/search_utils.py
2.68 kB
| from typing import Optional, Union | |
| from langchain.docstore.document import Document | |
| import wikipedia | |
| class LangChainWiki: | |
| def __init__(self) -> None: | |
| self.document: Optional[Document] = None | |
| self.lookup_str = "" | |
| self.lookup_index = 0 | |
| def search(self, search: str) -> Union[str, Document]: | |
| def _try_search(term: str) -> Union[str, Document]: | |
| try: | |
| page_content = wikipedia.page(search).content | |
| url = wikipedia.page(search).url | |
| result: Union[str, Document] = Document( page_content=page_content, metadata={"page": url} ) | |
| return result | |
| except wikipedia.PageError: | |
| return f"Could not find [{term}]. Similar: {wikipedia.search(term)}" | |
| except wikipedia.DisambiguationError: | |
| return f"Could not find [{term}]. Similar: {wikipedia.search(term)}" | |
| except Exception: | |
| return f"Could not find [{term}]. Similar: {wikipedia.search(term)}" | |
| result = _try_search(search) | |
| if isinstance(result, str) and "Similar:" in result: | |
| try: | |
| similar = wikipedia.search(search) | |
| if similar: | |
| fallback = similar[0] | |
| print(f"[INFO] Falling back to similar term: {fallback}") | |
| result = _try_search(fallback) | |
| except Exception as e: | |
| print(f"[ERROR] Could not fetch similar terms: {e}") | |
| if isinstance(result, Document): | |
| self.document = result | |
| return self._sumary | |
| else: | |
| self.document = None | |
| return result | |
| def lookup(self, term: str): | |
| if self.document is None: | |
| raise ValueError("Cannot lookup without a successful search first") | |
| if term.lower() != self.lookup_str: | |
| self.lookup_str = term.lower() | |
| self.lookup_index = 0 | |
| else: | |
| self.lookup_index += 1 | |
| lookups = [p for p in self._paragraphs if self.lookup_str in p.lower()] | |
| if len(lookups) == 0: | |
| return "No Results" | |
| elif self.lookup_index >= len(lookups): | |
| return "No More Results" | |
| else: | |
| result_prefix = f"(Result {self.lookup_index + 1}/{len(lookups)})" | |
| return f"{result_prefix} {lookups[self.lookup_index]}" | |
| def _sumary(self) -> str: | |
| return self._paragraphs[0] | |
| def _paragraphs(self) -> list[str]: | |
| if self.document is None: | |
| raise ValueError("Cannot get paragraphs without a document") | |
| return self.document.page_content.split("\n\n") | |