RRepoGEO

REPOGEO REPORT · LITE

IntelLabs/fastRAG

Default branch main · commit ab3d19d5 · scanned 6/25/2026, 2:36:54 PM

GitHub: 1,782 stars · 168 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)

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

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 IntelLabs/fastRAG, 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
  • highabout#1
    Update the 'about' description to clarify archived status and unique focus

    Why:

    CURRENT
    Efficient Retrieval Augmentation and Generation Framework
    COPY-PASTE FIX
    ARCHIVED: IntelLabs' research framework for efficient, optimized Retrieval Augmented Generation (RAG) pipelines, with a focus on Intel hardware. Provides tools for advancing RAG research and benchmarking.
  • mediumreadme#2
    Add a 'Key Differentiators' section to the README

    Why:

    COPY-PASTE FIX
    ## Key Differentiators
- **Intel Hardware Optimization:** Engineered for high-performance, low-latency, and high-throughput RAG on Intel CPUs and GPUs.
- **Research Framework:** Provides a comprehensive toolkit for advancing retrieval augmented generation research.
  • lowreadme#3
    Add a 'Historical Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Historical Comparison
While fastRAG is archived, it offered unique optimizations for Intel hardware compared to general-purpose frameworks like LlamaIndex and LangChain, and focused on research-grade benchmarking capabilities similar to Ragas.

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 IntelLabs/fastRAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Haystack · recommended 2×
  4. Weaviate · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How can I build efficient and optimized retrieval-augmented generation pipelines for large language models?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Weaviate
    5. Pinecone
    6. FAISS
    7. Sentence-Transformers

    AI recommended 7 alternatives but never named IntelLabs/fastRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a research framework to benchmark retrieval-augmented generative AI models efficiently.
    you: not recommended
    AI recommended (in order):
    1. Ragas
    2. LlamaIndex
    3. LangChain
    4. DeepEval
    5. Phoenix
    6. Haystack

    AI recommended 6 alternatives but never named IntelLabs/fastRAG. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 IntelLabs/fastRAG?
    pass
    AI did not name IntelLabs/fastRAG — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts IntelLabs/fastRAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named IntelLabs/fastRAG 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 IntelLabs/fastRAG solve, and who is the primary audience?
    pass
    AI named IntelLabs/fastRAG explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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IntelLabs/fastRAG — 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