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Bump versions again to get LangChain extractors
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -2,7 +2,6 @@ | |
| CassandraGraphVectorStore as CassandraGraphStore, | ||
| ) | ||
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|
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| __all__ = [ | ||
| "CassandraGraphStore", | ||
| ] | ||
66 changes: 9 additions & 57 deletions
66
libs/langchain/ragstack_langchain/graph_store/extractors/gliner_link_extractor.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,57 +1,9 @@ | ||
| from typing import Any, Dict, Iterable, List, Optional, Set | ||
|
|
||
| from langchain_core.graph_vectorstores import Link | ||
|
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| from ragstack_langchain.graph_store.extractors.link_extractor import LinkExtractor | ||
|
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| # TypeAlias is not available in Python 2.9, we can't use that or the newer `type`. | ||
| GLiNERInput = str | ||
|
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|
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| class GLiNERLinkExtractor(LinkExtractor[GLiNERInput]): | ||
| def __init__( | ||
| self, | ||
| labels: List[str], | ||
| *, | ||
| kind: str = "entity", | ||
| model: str = "urchade/gliner_mediumv2.1", | ||
| extract_kwargs: Optional[Dict[str, Any]] = None, | ||
| ): | ||
| """Extract keywords using GLiNER. | ||
| Args: | ||
| kind: Kind of links to produce with this extractor. | ||
| labels: List of kinds of entities to extract. | ||
| model: GLiNER model to use. | ||
| extract_kwargs: Keyword arguments to pass to GLiNER. | ||
| """ | ||
| try: | ||
| from gliner import GLiNER | ||
|
|
||
| self._model = GLiNER.from_pretrained(model) | ||
|
|
||
| except ImportError: | ||
| raise ImportError( | ||
| "gliner is required for GLiNERLinkExtractor. " | ||
| "Please install it with `pip install gliner`." | ||
| ) from None | ||
|
|
||
| self._labels = labels | ||
| self._kind = kind | ||
| self._extract_kwargs = extract_kwargs or {} | ||
|
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| def extract_one(self, input: GLiNERInput) -> Set[Link]: # noqa: A002 | ||
| return next(self.extract_many([input])) | ||
|
|
||
| def extract_many( | ||
| self, | ||
| inputs: Iterable[GLiNERInput], | ||
| ) -> Iterable[Set[Link]]: | ||
| strs = [i if isinstance(i, str) else i.page_content for i in inputs] | ||
| for entities in self._model.batch_predict_entities( | ||
| strs, self._labels, **self._extract_kwargs | ||
| ): | ||
| yield { | ||
| Link.bidir(kind=f"{self._kind}:{e['label']}", tag=e["text"]) | ||
| for e in entities | ||
| } | ||
| from langchain_community.graph_vectorstores.extractors import ( | ||
| GLiNERInput, | ||
| GLiNERLinkExtractor, | ||
| ) | ||
|
|
||
| __all__ = [ | ||
| "GLiNERInput", | ||
| "GLiNERLinkExtractor", | ||
| ] |
70 changes: 9 additions & 61 deletions
70
libs/langchain/ragstack_langchain/graph_store/extractors/hierarchy_link_extractor.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,61 +1,9 @@ | ||
| from typing import Callable, List, Set | ||
|
|
||
| from langchain_core.documents import Document | ||
| from langchain_core.graph_vectorstores import Link | ||
|
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| from .link_extractor import LinkExtractor | ||
| from .link_extractor_adapter import LinkExtractorAdapter | ||
|
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| # TypeAlias is not available in Python 2.9, we can't use that or the newer `type`. | ||
| HierarchyInput = List[str] | ||
|
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| class HierarchyLinkExtractor(LinkExtractor[HierarchyInput]): | ||
| def __init__( | ||
| self, | ||
| kind: str = "hierarchy", | ||
| up_links: bool = True, | ||
| down_links: bool = False, | ||
| sibling_links: bool = False, | ||
| ): | ||
| """Extract links from a document hierarchy. | ||
| Args: | ||
| kind: Kind of links to produce with this extractor. | ||
| up_links: Link from a section to it's parent. | ||
| down_links: Link from a section to it's children. | ||
| sibling_links: Link from a section to other sections with the same parent. | ||
| """ | ||
| self._kind = kind | ||
| self._up_links = up_links | ||
| self._down_links = down_links | ||
| self._sibling_links = sibling_links | ||
|
|
||
| def as_document_extractor( | ||
| self, hierarchy: Callable[[Document], HierarchyInput] | ||
| ) -> LinkExtractor[Document]: | ||
| return LinkExtractorAdapter(underlying=self, transform=hierarchy) | ||
|
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||
| def extract_one( | ||
| self, | ||
| input: HierarchyInput, # noqa: A002 | ||
| ) -> Set[Link]: | ||
| this_path = "/".join(input) | ||
| parent_path = None | ||
|
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||
| links = set() | ||
| if self._up_links: | ||
| links.add(Link.incoming(kind=self._kind, tag=f"up:{this_path}")) | ||
| if self._down_links: | ||
| links.add(Link.outgoing(kind=self._kind, tag=f"down:{this_path}")) | ||
|
|
||
| if len(input) >= 1: | ||
| parent_path = "/".join(input[0:-1]) | ||
| if self._up_links and len(input) > 1: | ||
| links.add(Link.outgoing(kind=self._kind, tag=f"up:{parent_path}")) | ||
| if self._down_links and len(input) > 1: | ||
| links.add(Link.incoming(kind=self._kind, tag=f"down:{parent_path}")) | ||
| if self._sibling_links: | ||
| links.add(Link.bidir(kind=self._kind, tag=f"sib:{parent_path}")) | ||
|
|
||
| return links | ||
| from langchain_community.graph_vectorstores.extractors import ( | ||
| HierarchyInput, | ||
| HierarchyLinkExtractor, | ||
| ) | ||
|
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||
| __all__ = [ | ||
| "HierarchyInput", | ||
| "HierarchyLinkExtractor", | ||
| ] |
128 changes: 9 additions & 119 deletions
128
libs/langchain/ragstack_langchain/graph_store/extractors/html_link_extractor.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,119 +1,9 @@ | ||
| from dataclasses import dataclass | ||
| from typing import TYPE_CHECKING, Set, Union | ||
| from urllib.parse import urldefrag, urljoin, urlparse | ||
|
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| from langchain_core.documents import Document | ||
| from langchain_core.graph_vectorstores import Link | ||
|
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||
| from .link_extractor import LinkExtractor | ||
| from .link_extractor_adapter import LinkExtractorAdapter | ||
|
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||
| if TYPE_CHECKING: | ||
| from bs4 import BeautifulSoup | ||
|
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| def _parse_url(link, page_url, drop_fragments: bool = True): | ||
| href = link.get("href") | ||
| if href is None: | ||
| return None | ||
| url = urlparse(href) | ||
| if url.scheme not in ["http", "https", ""]: | ||
| return None | ||
|
|
||
| # Join the HREF with the page_url to convert relative paths to absolute. | ||
| url = urljoin(page_url, href) | ||
|
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||
| # Fragments would be useful if we chunked a page based on section. | ||
| # Then, each chunk would have a different URL based on the fragment. | ||
| # Since we aren't doing that yet, they just "break" links. So, drop | ||
| # the fragment. | ||
| if drop_fragments: | ||
| return urldefrag(url).url | ||
| return url | ||
|
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||
|
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||
| def _parse_hrefs( | ||
| soup: "BeautifulSoup", url: str, drop_fragments: bool = True | ||
| ) -> Set[str]: | ||
| links = soup.find_all("a") | ||
| links = { | ||
| _parse_url(link, page_url=url, drop_fragments=drop_fragments) for link in links | ||
| } | ||
|
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| # Remove entries for any 'a' tag that failed to parse (didn't have href, | ||
| # or invalid domain, etc.) | ||
| links.discard(None) | ||
|
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||
| # Remove self links. | ||
| links.discard(url) | ||
|
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| return links | ||
|
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| @dataclass | ||
| class HtmlInput: | ||
| content: Union[str, "BeautifulSoup"] | ||
| base_url: str | ||
|
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|
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| class HtmlLinkExtractor(LinkExtractor[HtmlInput]): | ||
| def __init__(self, *, kind: str = "hyperlink", drop_fragments: bool = True): | ||
| """Extract hyperlinks from HTML content. | ||
| Expects the input to be an HTML string or a `BeautifulSoup` object. | ||
| Args: | ||
| kind: The kind of edge to extract. Defaults to "hyperlink". | ||
| drop_fragments: Whether fragments in URLs and links shoud be | ||
| dropped. Defaults to `True`. | ||
| """ | ||
| try: | ||
| import bs4 # noqa:F401 | ||
| except ImportError as e: | ||
| raise ImportError( | ||
| "BeautifulSoup4 is required for HtmlLinkExtractor. " | ||
| "Please install it with `pip install beautifulsoup4`." | ||
| ) from e | ||
|
|
||
| self._kind = kind | ||
| self.drop_fragments = drop_fragments | ||
|
|
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| def as_document_extractor( | ||
| self, url_metadata_key: str = "source" | ||
| ) -> LinkExtractor[Document]: | ||
| """Return a LinkExtractor that applies to documents. | ||
| NOTE: Since the HtmlLinkExtractor parses HTML, if you use with other similar | ||
| link extractors it may be more efficient to call the link extractors directly | ||
| on the parsed BeautifulSoup object. | ||
| Args: | ||
| url_metadata_key: The name of the filed in document metadata with the URL of | ||
| the document. | ||
| """ | ||
| return LinkExtractorAdapter( | ||
| underlying=self, | ||
| transform=lambda doc: HtmlInput( | ||
| doc.page_content, doc.metadata[url_metadata_key] | ||
| ), | ||
| ) | ||
|
|
||
| def extract_one( | ||
| self, | ||
| input: HtmlInput, # noqa: A002 | ||
| ) -> Set[Link]: | ||
| content = input.content | ||
| if isinstance(content, str): | ||
| from bs4 import BeautifulSoup | ||
|
|
||
| content = BeautifulSoup(content, "html.parser") | ||
|
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| base_url = input.base_url | ||
| if self.drop_fragments: | ||
| base_url = urldefrag(base_url).url | ||
|
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| hrefs = _parse_hrefs(content, base_url, self.drop_fragments) | ||
|
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| links = {Link.outgoing(kind=self._kind, tag=url) for url in hrefs} | ||
| links.add(Link.incoming(kind=self._kind, tag=base_url)) | ||
| return links | ||
| from langchain_community.graph_vectorstores.extractors import ( | ||
| HtmlInput, | ||
| HtmlLinkExtractor, | ||
| ) | ||
|
|
||
| __all__ = [ | ||
| "HtmlInput", | ||
| "HtmlLinkExtractor", | ||
| ] |
72 changes: 9 additions & 63 deletions
72
libs/langchain/ragstack_langchain/graph_store/extractors/keybert_link_extractor.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,63 +1,9 @@ | ||
| from typing import Any, Dict, Iterable, Optional, Set, Union | ||
|
|
||
| from langchain_core.documents import Document | ||
| from langchain_core.graph_vectorstores import Link | ||
|
|
||
| from ragstack_langchain.graph_store.extractors.link_extractor import LinkExtractor | ||
|
|
||
| # TypeAlias is not available in Python 2.9, we can't use that or the newer `type`. | ||
| KeybertInput = Union[str, Document] | ||
|
|
||
|
|
||
| class KeybertLinkExtractor(LinkExtractor[KeybertInput]): | ||
| def __init__( | ||
| self, | ||
| *, | ||
| kind: str = "kw", | ||
| embedding_model: str = "all-MiniLM-L6-v2", | ||
| extract_keywords_kwargs: Optional[Dict[str, Any]] = None, | ||
| ): | ||
| """Extract keywords using Keybert. | ||
| Args: | ||
| kind: Kind of links to produce with this extractor. | ||
| embedding_model: Name of the embedding model to use with Keybert. | ||
| extract_keywords_kwargs: Keyword arguments to pass to Keybert's | ||
| `extract_keywords` method. | ||
| """ | ||
| try: | ||
| import keybert | ||
|
|
||
| self._kw_model = keybert.KeyBERT(model=embedding_model) | ||
| except ImportError: | ||
| raise ImportError( | ||
| "keybert is required for KeybertLinkExtractor. " | ||
| "Please install it with `pip install keybert`." | ||
| ) from None | ||
|
|
||
| self._kind = kind | ||
| self._extract_keywords_kwargs = extract_keywords_kwargs or {} | ||
|
|
||
| def extract_one(self, input: KeybertInput) -> Set[Link]: # noqa: A002 | ||
| keywords = self._kw_model.extract_keywords( | ||
| input if isinstance(input, str) else input.page_content, | ||
| **self._extract_keywords_kwargs, | ||
| ) | ||
| return {Link.bidir(kind=self._kind, tag=kw[0]) for kw in keywords} | ||
|
|
||
| def extract_many( | ||
| self, | ||
| inputs: Iterable[KeybertInput], | ||
| ) -> Iterable[Set[Link]]: | ||
| if len(inputs) == 1: | ||
| # Even though we pass a list, if it contains one item, keybert will | ||
| # flatten it. This means it's easier to just call the special case | ||
| # for one item. | ||
| yield self.extract_one(inputs[0]) | ||
| elif len(inputs) > 1: | ||
| strs = [i if isinstance(i, str) else i.page_content for i in inputs] | ||
| extracted = self._kw_model.extract_keywords( | ||
| strs, **self._extract_keywords_kwargs | ||
| ) | ||
| for keywords in extracted: | ||
| yield {Link.bidir(kind=self._kind, tag=kw[0]) for kw in keywords} | ||
| from langchain_community.graph_vectorstores.extractors import ( | ||
| KeybertInput, | ||
| KeybertLinkExtractor, | ||
| ) | ||
|
|
||
| __all__ = [ | ||
| "KeybertInput", | ||
| "KeybertLinkExtractor", | ||
| ] |
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