LangChain and LlamaIndex

Use SciSpace as a retriever so an existing RAG application gains scholarly grounding.

Community adapters, not first-party packages

There is no official SciSpace package for either framework. The recipes below use the Python SDK directly, which is stable and versioned. Roughly 30 lines gets you a working retriever, and you keep control of how citations flow through your chain.

LangChain retriever

Python

from typing import List
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from scispace import Scispace

class SciSpaceRetriever(BaseRetriever):
    client: Scispace = Scispace()
    depth: str = "standard"
    max_papers: int = 20

    def _get_relevant_documents(self, query: str) -> List[Document]:
        search = self.client.searches.create(
            query=query, depth=self.depth, max_papers=self.max_papers
        ).wait()
        return [
            Document(
                page_content=c.quote or "",
                metadata={
                    "source_id": c.source.id,
                    "title": c.source.title,
                    "source_type": c.source.type,   # paper | document | model
                    "page": c.locations[0].page if c.locations else None,
                    "url": c.source.url,
                },
            )
            for c in search.answer.citations
        ]

LlamaIndex

Wrap the same call in a BaseRetriever returning NodeWithScore, carrying the identical metadata keys. The mapping is what matters, not the framework.

Preserving citations through the chain

This is the whole point of the integration, and the easiest thing to lose.

Keep source_id and page on every node. A chain that concatenates page_content into a prompt and discards metadata produces an ungrounded answer with extra steps.
Drop or label source_type == "model" nodes. They carry no quote and no locator.
Render citations from metadata, not from the LLM's output. Never let a downstream model invent citation numbers — map them from source_id.

Cost

Each retriever call is one search: 5 credits at standard. A chain that retrieves on every turn of a conversation multiplies that — cache by normalized query. See cost-control.

searches · grounded-answers · tool-use · python

Last updated 2026-08-10

LangChain retriever

LlamaIndex

Preserving citations through the chain

Cost

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