REPOGEO REPORT · LITE

Dicklesworthstone/swiss_army_llama

Default branch main · commit 7bd15541 · scanned 6/19/2026, 10:32:56 PM

GitHub: 1,052 stars · 66 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

35 /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

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 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 the README H1 to specify category

Why:

CURRENT

# 🇨🇭🎖️🦙 Swiss Army Llama

COPY-PASTE FIX

# 🇨🇭🎖️🦙 Swiss Army Llama: A FastAPI Service for Local LLM Tooling and Document Processing

highlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a `LICENSE` file in the repository root containing the text of a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that you wish to apply to the project.

mediumtopics#3

Update repository topics for better categorization

Why:

CURRENT

embedding-similarity, embedding-vectors, embeddings, llama2, llamacpp, semantic-search

COPY-PASTE FIX

embedding-similarity, embedding-vectors, embeddings, llama2, llamacpp, semantic-search, fastapi, llm-service, document-processing, 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

Of all named tools, what % are you?

Top rival

facebookresearch/faiss

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

facebookresearch/faiss · recommended 1×
Pinecone · recommended 1×
weaviate/weaviate · recommended 1×
elastic/elasticsearch · recommended 1×
nmslib/hnswlib · recommended 1×

CATEGORY QUERY
How to build a semantic search API for various document types using precomputed embeddings?
you: not recommended
AI recommended (in order):
1. Faiss (facebookresearch/faiss)
2. Pinecone
3. Weaviate (weaviate/weaviate)
4. Elasticsearch (elastic/elasticsearch)
5. Hnswlib (nmslib/hnswlib)
6. Qdrant (qdrant/qdrant)
AI recommended 6 alternatives but never named Dicklesworthstone/swiss_army_llama. This is the gap to close.
Show full AI answer
CATEGORY QUERY
FastAPI service for local LLM text embeddings and document processing with OCR capabilities?
you: not recommended
AI recommended (in order):
1. FastAPI (tiangolo/fastapi)
2. Sentence-Transformers (UKPLab/sentence-transformers)
3. Tesseract OCR (tesseract-ocr/tesseract)
4. unstructured.io (Unstructured-IO/unstructured)
5. Hugging Face Transformers (huggingface/transformers)
6. PaddleOCR (PaddlePaddle/PaddleOCR)
7. InstructorEmbeddings (HKUNLP/instructor-embedding)
8. LlamaParse (run-llama/llama_index)
9. ONNX Runtime (microsoft/onnxruntime)
10. spaCy (explosion/spaCy)
11. pdfminer.six (pdfminer/pdfminer.six)
AI recommended 11 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 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?
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?

Embed your GEO score

Drop this badge into the README of Dicklesworthstone/swiss_army_llama. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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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
Prioritized action items8 vs 3 in Lite