RRepoGEO

REPOGEO REPORT · LITE

D-Star-AI/dsRAG

Default branch main · commit 5215e979 · scanned 5/22/2026, 4:24:32 PM

GitHub: 1,583 stars · 129 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 D-Star-AI/dsRAG, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    rag, retrieval-augmented-generation, llm, nlp, information-retrieval, unstructured-data, document-qa, question-answering, ai, machine-learning, financebench, legal-tech, academic-research
  • highreadme#2
    Reposition the README's opening to emphasize core value

    Why:

    CURRENT
    The two creators of dsRAG, Zach and Nick McCormick, run a small applied AI consulting firm. We specialize in building high-performance RAG-based applications (naturally). As former startup founders and YC alums, we bring a business and product-centric perspective to the projects we work on. We do a mix of advisory and implementation work. If you'd like to hire us, fill out this form and we'll be in touch. ## What is dsRAG? dsRAG is a retrieval engine for unstructured data. It is especially good at handling challenging queries over dense text, like financial reports, legal documents, and academic papers. dsRAG achieves substantially higher accuracy than vanilla RAG baselines on complex open-book question answering tasks.
    COPY-PASTE FIX
    dsRAG is a high-performance retrieval engine for unstructured data, specifically engineered to achieve substantially higher accuracy than vanilla RAG baselines on complex open-book question answering tasks. It excels at handling challenging queries over dense text, such as financial reports, legal documents, and academic papers, by employing innovative methods like Semantic Sectioning, AutoContext, and Relevant Segment Extraction (RSE). The two creators of dsRAG, Zach and Nick McCormick, run a small applied AI consulting firm specializing in high-performance RAG-based applications. If you'd like to hire us, fill out this form and we'll be in touch.
  • mediumhomepage#3
    Add the official project homepage URL

    Why:

    COPY-PASTE FIX
    https://d-star-ai.github.io/dsRAG/

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 D-Star-AI/dsRAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. weaviate/weaviate · recommended 2×
  3. elastic/elasticsearch · recommended 2×
  4. huggingface/transformers · recommended 2×
  5. OpenAI GPT-4 · recommended 1×
  • CATEGORY QUERY
    Seeking a high-accuracy RAG solution for complex queries on dense legal documents.
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. Azure AI Search
    3. Azure OpenAI Service
    4. Anthropic Claude 3 Opus
    5. Pinecone
    6. Weaviate (weaviate/weaviate)
    7. Cohere Command R+
    8. ElasticSearch (elastic/elasticsearch)
    9. Google Gemini 1.5 Pro
    10. Google Cloud Vertex AI Search
    11. Hugging Face Transformers (huggingface/transformers)
    12. Llama 3
    13. Mistral
    14. FAISS (facebookresearch/faiss)
    15. Annoy (spotify/annoy)

    AI recommended 15 alternatives but never named D-Star-AI/dsRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods to enhance retrieval performance for unstructured data in RAG systems?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. BioBERT
    4. SciBERT
    5. OpenAI's `text-embedding-3-large`
    6. Cohere's Embed v3
    7. Google's `text-embedding-004`
    8. CLIP (openai/CLIP)
    9. Google's Gemini
    10. BM25
    11. TF-IDF
    12. Pinecone
    13. Weaviate (weaviate/weaviate)
    14. Elasticsearch (elastic/elasticsearch)
    15. Qdrant (qdrant/qdrant)
    16. Cohere Rerank
    17. bge-reranker-large
    18. BERT
    19. OpenAI GPT models
    20. Milvus (milvus-io/milvus)
    21. DPR - Dense Passage Retriever
    22. ColBERT (stanford-futuredata/ColBERT)
    23. Hugging Face Transformers (huggingface/transformers)
    24. Pytorch-Lightning (Lightning-AI/lightning)

    AI recommended 24 alternatives but never named D-Star-AI/dsRAG. 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 D-Star-AI/dsRAG?
    pass
    AI named D-Star-AI/dsRAG explicitly

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

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

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

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D-Star-AI/dsRAG — 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