mirror of
https://github.com/hwchase17/langchain.git
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8516a03a02
This PR upgrades langchain-community to pydantic 2.
* Most of this PR was auto-generated using code mods with gritql
(https://github.com/eyurtsev/migrate-pydantic/tree/main)
* Subsequently, some code was fixed manually due to accommodate
differences between pydantic 1 and 2
Breaking Changes:
- Use TEXTEMBED_API_KEY and TEXTEMBEB_API_URL for env variables for text
embed integrations:
cbea780492
Other changes:
- Added pydantic_settings as a required dependency for community. This
may be removed if we have enough time to convert the dependency into an
optional one.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
91 lines
2.8 KiB
Python
91 lines
2.8 KiB
Python
"""Utils for interacting with the Semantic Scholar API."""
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import logging
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel, model_validator
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logger = logging.getLogger(__name__)
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class SemanticScholarAPIWrapper(BaseModel):
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"""Wrapper around semanticscholar.org API.
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https://github.com/danielnsilva/semanticscholar
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You should have this library installed.
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`pip install semanticscholar`
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Semantic Scholar API can conduct searches and fetch document metadata
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like title, abstract, authors, etc.
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Attributes:
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top_k_results: number of the top-scored document used for the Semantic Scholar tool
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load_max_docs: a limit to the number of loaded documents
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Example:
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.. code-block:: python
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from langchain_community.utilities.semanticscholar import SemanticScholarAPIWrapper
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ss = SemanticScholarAPIWrapper(
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top_k_results = 3,
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load_max_docs = 3
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)
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ss.run("biases in large language models")
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"""
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semanticscholar_search: Any #: :meta private:
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top_k_results: int = 5
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S2_MAX_QUERY_LENGTH: int = 300
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load_max_docs: int = 100
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doc_content_chars_max: Optional[int] = 4000
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returned_fields: List[str] = [
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"title",
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"abstract",
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"venue",
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"year",
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"paperId",
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"citationCount",
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"openAccessPdf",
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"authors",
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"externalIds",
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]
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@model_validator(mode="before")
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@classmethod
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def validate_environment(cls, values: Dict) -> Any:
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"""Validate that the python package exists in environment."""
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try:
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from semanticscholar import SemanticScholar
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sch = SemanticScholar()
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values["semanticscholar_search"] = sch.search_paper
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except ImportError:
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raise ImportError(
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"Could not import Semanticscholar python package. "
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"Please install it with `pip install semanticscholar`."
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)
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return values
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def run(self, query: str) -> str:
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"""Run the Semantic Scholar API."""
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results = self.semanticscholar_search(
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query, limit=self.load_max_docs, fields=self.returned_fields
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)
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documents = []
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for item in results[: self.top_k_results]:
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authors = ", ".join(
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author["name"] for author in getattr(item, "authors", [])
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)
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documents.append(
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f"Published year: {getattr(item, 'year', None)}\n"
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f"Title: {getattr(item, 'title', None)}\n"
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f"Authors: {authors}\n"
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f"Abstract: {getattr(item, 'abstract', None)}\n"
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)
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if documents:
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return "\n\n".join(documents)[: self.doc_content_chars_max]
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else:
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return "No results found."
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