RRepoGEO

REPOGEO REPORT · LITE

tonykipkemboi/ollama_pdf_rag

Default branch main · commit f17f9c98 · scanned 6/13/2026, 3:02:05 PM

GitHub: 524 stars · 190 forks

AI VISIBILITY SCORE
27 /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
1 / 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 tonykipkemboi/ollama_pdf_rag, 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 sentence to emphasize 'full-stack demo application'

    Why:

    CURRENT
    A powerful local RAG (Retrieval Augmented Generation) application that lets you chat with your PDF documents using Ollama and LangChain. This project includes multiple interfaces: a modern Next.js web app, a Streamlit interface, and Jupyter notebooks for experimentation.
    COPY-PASTE FIX
    This is a **ready-to-run, full-stack demo application** for local RAG (Retrieval Augmented Generation) that lets you chat with your PDF documents using Ollama and LangChain. This project includes multiple interfaces: a modern Next.js web app, a Streamlit interface, and Jupyter notebooks for experimentation.
  • mediumtopics#2
    Add more application-oriented and privacy-focused topics

    Why:

    CURRENT
    langchain, nextjs, ollama, pdf, rag, vercel-ai-sdk
    COPY-PASTE FIX
    langchain, nextjs, ollama, pdf, rag, vercel-ai-sdk, full-stack-demo, local-rag-app, private-ai, llm-application
  • lowreadme#3
    Add a 'Why this project?' section to clarify its role as an integrated solution

    Why:

    COPY-PASTE FIX
    ## 🤔 Why `ollama_pdf_rag`? (vs. just using LangChain/LlamaIndex)

While `ollama_pdf_rag` leverages powerful libraries like LangChain and LlamaIndex, it is designed as a complete, ready-to-deploy full-stack application. It provides a pre-built web UI (Next.js/Streamlit), a FastAPI backend, and a fully configured RAG pipeline, allowing you to get a local chat-with-PDF system running quickly without assembling individual components.

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 tonykipkemboi/ollama_pdf_rag
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. FastAPI · recommended 2×
  4. Flask · recommended 2×
  5. LM Studio · recommended 1×
  • CATEGORY QUERY
    How can I set up a local system to chat with my PDF documents?
    you: not recommended
    AI recommended (in order):
    1. LM Studio
    2. PrivateGPT (imartinez/privateGPT)
    3. LocalGPT (PromtEngineer/localGPT)
    4. Ollama (ollama/ollama)
    5. LangChain (langchain-ai/langchain)
    6. LlamaIndex (run-llama/llama_index)
    7. all-MiniLM-L6-v2
    8. Chroma (chroma-core/chroma)
    9. FAISS (facebookresearch/faiss)
    10. AnythingLLM (Mintplex-Labs/anything-llm)

    AI recommended 10 alternatives but never named tonykipkemboi/ollama_pdf_rag. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a full-stack solution for local document interaction and Q&A with a web interface.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. Streamlit
    3. ChromaDB
    4. FAISS
    5. LangChain
    6. FastAPI
    7. PostgreSQL
    8. pgvector
    9. Haystack
    10. Flask
    11. Elasticsearch
    12. Gradio
    13. LlamaIndex
    14. LangChain
    15. Next.js
    16. FastAPI
    17. Flask
    18. Django
    19. Weaviate
    20. Qdrant

    AI recommended 20 alternatives but never named tonykipkemboi/ollama_pdf_rag. 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 tonykipkemboi/ollama_pdf_rag?
    pass
    AI did not name tonykipkemboi/ollama_pdf_rag — 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 tonykipkemboi/ollama_pdf_rag in production, what risks or prerequisites should they evaluate first?
    pass
    AI named tonykipkemboi/ollama_pdf_rag 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 tonykipkemboi/ollama_pdf_rag solve, and who is the primary audience?
    pass
    AI did not name tonykipkemboi/ollama_pdf_rag — 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?

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tonykipkemboi/ollama_pdf_rag — 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