Source code for langchain_community.utilities.stackexchange
import html
from typing import Any, Dict, Literal
from pydantic import BaseModel, Field, model_validator
[docs]
class StackExchangeAPIWrapper(BaseModel):
"""Wrapper for Stack Exchange API."""
client: Any = None #: :meta private:
max_results: int = 3
"""Max number of results to include in output."""
query_type: Literal["all", "title", "body"] = "all"
"""Which part of StackOverflows items to match against. One of 'all', 'title',
'body'. Defaults to 'all'.
"""
fetch_params: Dict[str, Any] = Field(default_factory=dict)
"""Additional params to pass to StackApi.fetch."""
result_separator: str = "\n\n"
"""Separator between question,answer pairs."""
@model_validator(mode="before")
@classmethod
def validate_environment(cls, values: Dict) -> Any:
"""Validate that the required Python package exists."""
try:
from stackapi import StackAPI
values["client"] = StackAPI("stackoverflow")
except ImportError:
raise ImportError(
"The 'stackapi' Python package is not installed. "
"Please install it with `pip install stackapi`."
)
return values
[docs]
def run(self, query: str) -> str:
"""Run query through StackExchange API and parse results."""
query_key = "q" if self.query_type == "all" else self.query_type
output = self.client.fetch(
"search/excerpts", **{query_key: query}, **self.fetch_params
)
if len(output["items"]) < 1:
return f"No relevant results found for '{query}' on Stack Overflow."
questions = [
item for item in output["items"] if item["item_type"] == "question"
][: self.max_results]
answers = [item for item in output["items"] if item["item_type"] == "answer"]
results = []
for question in questions:
res_text = f"Question: {question['title']}\n{question['excerpt']}"
relevant_answers = [
answer
for answer in answers
if answer["question_id"] == question["question_id"]
]
accepted_answers = [
answer for answer in relevant_answers if answer["is_accepted"]
]
if relevant_answers:
top_answer = (
accepted_answers[0] if accepted_answers else relevant_answers[0]
)
excerpt = html.unescape(top_answer["excerpt"])
res_text += f"\nAnswer: {excerpt}"
results.append(res_text)
return self.result_separator.join(results)