_get_relevant_documents() — langchain Function Reference

Architecture documentation for the _get_relevant_documents() function in retrievers.py from the langchain codebase.

Function python LangChainCore Runnables

Entity Profile

LangChainCore→ Runnables→ _get_relevant_documents() — langchain Function Reference

Dependency Diagram

graph TD
  43f1b02b_af65_58a4_a314_2cbec97ed62d["_get_relevant_documents()"]
  9fed5a40_61d5_cd0e_7e35_8a2bf939a75e["PerplexitySearchRetriever"]
  43f1b02b_af65_58a4_a314_2cbec97ed62d -->|defined in| 9fed5a40_61d5_cd0e_7e35_8a2bf939a75e
  style 43f1b02b_af65_58a4_a314_2cbec97ed62d fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/perplexity/langchain_perplexity/retrievers.py lines 40–68

    def _get_relevant_documents(
        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
    ) -> list[Document]:
        params = {
            "query": query,
            "max_results": self.k,
            "max_tokens": self.max_tokens,
            "max_tokens_per_page": self.max_tokens_per_page,
            "country": self.country,
            "search_domain_filter": self.search_domain_filter,
            "search_recency_filter": self.search_recency_filter,
            "search_after_date": self.search_after_date,
            "search_before_date": self.search_before_date,
        }
        params = {k: v for k, v in params.items() if v is not None}
        response = self.client.search.create(**params)

        return [
            Document(
                page_content=result.snippet,
                metadata={
                    "title": result.title,
                    "url": result.url,
                    "date": result.date,
                    "last_updated": result.last_updated,
                },
            )
            for result in response.results
        ]