RRepoGEO

REPOGEO REPORT · LITE

Dicklesworthstone/swiss_army_llama

Default branch main · commit 7bd15541 · scanned 5/10/2026, 1:13:17 AM

GitHub: 1,052 stars · 66 forks

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 Dicklesworthstone/swiss_army_llama, 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 README opening to clarify core identity as a FastAPI service

    Why:

    CURRENT
    The Swiss Army Llama is designed to facilitate and optimize the process of working with local LLMs by using FastAPI to expose convenient REST endpoints for various tasks, including obtaining text embeddings and completions using different LLMs via llama_cpp, as well as automating the process of obtaining all the embeddings for most common document types, including PDFs (even ones that require OCR), Word files, etc; it even allows you to submit an audio file and automatically transcribes it with the Whisper model, cleans up the resulting text, and then computes the embeddings for it.
    COPY-PASTE FIX
    The Swiss Army Llama is a FastAPI service that provides a comprehensive suite of tools for semantic text search, precomputed embeddings, and advanced similarity measures, with built-in support for processing various document and audio file types.
  • highlicense#2
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Create a file named LICENSE in the repository root and add the text of your chosen open-source license (e.g., MIT, Apache-2.0).
  • mediumtopics#3
    Add more specific topics to highlight FastAPI service and document processing

    Why:

    CURRENT
    embedding-similarity, embedding-vectors, embeddings, llama2, llamacpp, semantic-search
    COPY-PASTE FIX
    embedding-similarity, embedding-vectors, embeddings, llama2, llamacpp, semantic-search, fastapi, rest-api, document-processing, audio-transcription, ocr

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 Dicklesworthstone/swiss_army_llama
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. Faiss · recommended 2×
  5. Weaviate · recommended 2×
  • CATEGORY QUERY
    How to build a semantic search API with local LLMs and document processing?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Faiss
    5. Sentence Transformers
    6. FastAPI
    7. Weaviate
    8. Qdrant

    AI recommended 8 alternatives but never named Dicklesworthstone/swiss_army_llama. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for precomputing and caching text embeddings from various document and audio types?
    you: not recommended
    AI recommended (in order):
    1. Haystack
    2. LlamaIndex
    3. LangChain
    4. Faiss
    5. Weaviate
    6. Milvus
    7. Zilliz Cloud

    AI recommended 7 alternatives but never named Dicklesworthstone/swiss_army_llama. 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 Dicklesworthstone/swiss_army_llama?
    pass
    AI did not name Dicklesworthstone/swiss_army_llama — 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 Dicklesworthstone/swiss_army_llama in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Dicklesworthstone/swiss_army_llama 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 Dicklesworthstone/swiss_army_llama solve, and who is the primary audience?
    pass
    AI named Dicklesworthstone/swiss_army_llama explicitly

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

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Dicklesworthstone/swiss_army_llama — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
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