RRepoGEO

REPOGEO REPORT · LITE

parthsarthi03/raptor

Default branch master · commit 7da1d48a · scanned 5/20/2026, 7:53:09 PM

GitHub: 1,669 stars · 220 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 parthsarthi03/raptor, 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 to clearly state the project type and primary use case

    Why:

    CURRENT
    RAPTOR introduces a novel approach to retrieval-augmented language models by constructing a recursive tree structure from documents.
    COPY-PASTE FIX
    RAPTOR is a Python library that introduces a novel approach to retrieval-augmented language models by constructing a recursive tree structure from documents. It enables more efficient and context-aware information retrieval across large texts, specifically designed for RAG applications.
  • hightopics#2
    Add more specific topics to improve category visibility for RAG and hierarchical retrieval

    Why:

    CURRENT
    agents, clustering, framework, language-model, llm, machine-learning, rag, retrieval, retrieval-augmented-generation, vector-database
    COPY-PASTE FIX
    agents, clustering, framework, language-model, llm, machine-learning, rag, retrieval, retrieval-augmented-generation, vector-database, hierarchical-retrieval, tree-structure, long-context-window, document-summarization
  • mediumreadme#3
    Add a 'Why RAPTOR?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why RAPTOR?

RAPTOR differentiates itself from traditional RAG frameworks and document chunking strategies by focusing on a recursive, tree-organized approach to document processing. While tools like LlamaIndex and LangChain provide comprehensive RAG ecosystems, RAPTOR offers a specialized method for abstractive summarization and hierarchical retrieval, particularly effective for very long and complex documents where standard chunking falls short. This allows for more nuanced context understanding and improved retrieval accuracy compared to flat document indexing.

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 parthsarthi03/raptor
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
run-llama/llama_index
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. run-llama/llama_index · recommended 3×
  2. LlamaIndex · recommended 1×
  3. LangChain · recommended 1×
  4. Unstructured.io · recommended 1×
  5. OpenAI's `text-embedding-3-large` · recommended 1×
  • CATEGORY QUERY
    How to improve RAG performance for very long documents and complex information retrieval?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Unstructured.io
    4. OpenAI's `text-embedding-3-large`
    5. Cohere Embed v3
    6. E5-large-v2
    7. Voyage AI Embeddings
    8. Cohere Rerank
    9. OpenAI Function Calling / Tool Use
    10. bge-reranker-large
    11. Sentence-Transformers library
    12. Neo4j
    13. GraphRAG
    14. GPT-4 / Claude 3 Opus
    15. OpenAI Fine-tuning API
    16. Hugging Face Transformers
    17. Elasticsearch
    18. Pinecone
    19. Weaviate

    AI recommended 19 alternatives but never named parthsarthi03/raptor. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for organizing document chunks into hierarchical structures for LLM retrieval?
    you: not recommended
    AI recommended (in order):
    1. RecursiveCharacterTextSplitter (langchain-ai/langchain)
    2. SentenceSplitter (run-llama/llama_index)
    3. NLTK (nltk/nltk)
    4. Pandoc (jgm/pandoc)
    5. Beautiful Soup (crummy/bs4)
    6. PyPDF2 (py-pdf/pypdf)
    7. pdfminer.six (pdfminer/pdfminer.six)
    8. OpenAI API
    9. Hugging Face Transformers (huggingface/transformers)
    10. Summary Index (run-llama/llama_index)
    11. Neo4j (neo4j/neo4j)
    12. NetworkX (networkx/networkx)
    13. Knowledge Graph Index (run-llama/llama_index)
    14. OpenAI Embeddings
    15. Sentence-Transformers (UKPLab/sentence-transformers)
    16. scikit-learn (scikit-learn/scikit-learn)
    17. UMAP (lmcinnes/umap)
    18. t-SNE

    AI recommended 18 alternatives but never named parthsarthi03/raptor. 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 parthsarthi03/raptor?
    pass
    AI named parthsarthi03/raptor explicitly

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

  • If a team adopts parthsarthi03/raptor in production, what risks or prerequisites should they evaluate first?
    pass
    AI named parthsarthi03/raptor 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 parthsarthi03/raptor solve, and who is the primary audience?
    pass
    AI named parthsarthi03/raptor explicitly

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

Embed your GEO score

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parthsarthi03/raptor — 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