REPOGEO REPORT · LITE

Future-House/paper-qa

Default branch main · commit d7675d7b · scanned 6/20/2026, 5:21:51 AM

GitHub: 8,727 stars · 883 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Future-House/paper-qa, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README's opening statement

Why:

CURRENT

PaperQA2 is a package for doing high-accuracy retrieval augmented generation (RAG) on PDFs, text files, Microsoft Office documents, and source code files, with a focus on the scientific literature.

COPY-PASTE FIX

PaperQA2 is a specialized, high-accuracy Retrieval Augmented Generation (RAG) solution for scientific documents, research papers, and academic PDFs. It provides verifiable, cited answers with superhuman performance in scientific tasks like question answering, summarization, and contradiction detection.

mediumtopics#2

Add more specific topics

Why:

CURRENT

ai, rag, science, search

COPY-PASTE FIX

ai, rag, science, search, scientific-rag, academic-papers, citation-generation

mediumfaq#3

Add a FAQ entry comparing PaperQA2 to general RAG frameworks

Why:

COPY-PASTE FIX

Add a new FAQ entry to the README:
**Q: How does PaperQA2 differ from general RAG frameworks like LlamaIndex or LangChain?**
A: PaperQA2 is purpose-built for scientific and academic documents, focusing on high-accuracy, verifiable answers with direct citations. While general frameworks offer broad RAG capabilities, PaperQA2's specialized algorithms and architecture deliver superhuman performance for scientific tasks like question answering, summarization, and contradiction detection in research literature.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface Future-House/paper-qa

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LlamaIndex

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LlamaIndex · recommended 2×
LangChain · recommended 2×
Haystack · recommended 2×
OpenAI API · recommended 1×
Cohere · recommended 1×

CATEGORY QUERY
Looking for a RAG solution to accurately answer questions from scientific research papers.
you: not recommended
AI recommended (in order):
1. LlamaIndex
2. LangChain
3. Haystack
4. OpenAI API
5. Cohere
6. Weaviate
7. Chroma
AI recommended 7 alternatives but never named Future-House/paper-qa. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Which RAG framework performs best for extracting information from academic PDFs and documents?
you: not recommended
AI recommended (in order):
1. LlamaIndex
2. LangChain
3. pypdf
4. unstructured
5. Haystack
6. RAGatouille
7. DSPy
8. GPT-4V
9. Gemini
AI recommended 9 alternatives but never named Future-House/paper-qa. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of Future-House/paper-qa?
pass
AI named Future-House/paper-qa explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Future-House/paper-qa in production, what risks or prerequisites should they evaluate first?
pass
AI named Future-House/paper-qa explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo Future-House/paper-qa solve, and who is the primary audience?
pass
AI named Future-House/paper-qa explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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Future-House/paper-qa — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite