RRepoGEO

REPOGEO REPORT · LITE

jacoblee93/fully-local-pdf-chatbot

Default branch main · commit 74145548 · scanned 5/20/2026, 10:43:44 PM

GitHub: 1,816 stars · 329 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
22 /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
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 jacoblee93/fully-local-pdf-chatbot, 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, local-llm, offline-chatbot, pdf-chatbot, webllm, ollama, gemini-nano, nextjs, client-side, privacy
  • highreadme#2
    Strengthen README opening to emphasize "fully local application" and unique tech stack

    Why:

    CURRENT
    Yes, it's another chat over documents implementation... but this one is entirely local! You can run it in three different ways: - 🦙 Exposing a port to a local LLM running on your desktop via Ollama. - 🌐 Downloading weights into your browser and running via WebLLM. - ♊ Joining the early preview program for Chrome's experimental built-in Gemini Nano model and using it directly! It's a Next.js app that read the content of an uploaded PDF, chunks it, adds it to a vector store, and performs RAG, all client side. You can even turn off your WiFi after the site loads.
    COPY-PASTE FIX
    This is a complete, fully local RAG (Retrieval Augmented Generation) application for chatting over PDF documents, designed for ultimate privacy and offline capability. Unlike cloud-dependent solutions, it performs all LLM inference, embedding, and vector store operations entirely client-side. You can run it in three distinct ways: 🦙 via a local LLM like Ollama, 🌐 directly in your browser with WebLLM, or ♊ leveraging Chrome's experimental built-in Gemini Nano model. Built with Next.js, it allows you to upload a PDF, chunk its content, add it to a vector store, and perform RAG, even with your WiFi turned off after the site loads.
  • mediumabout#3
    Refine the repository description for clarity and keyword density

    Why:

    CURRENT
    Yes, it's another chat over documents implementation... but this one is entirely local!
    COPY-PASTE FIX
    A complete, fully local RAG application for chatting over PDF documents, running entirely client-side with Ollama, WebLLM, or Gemini Nano for ultimate privacy.

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 jacoblee93/fully-local-pdf-chatbot
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 1×
  2. PyMuPDFReader · recommended 1×
  3. HuggingFaceEmbedding · recommended 1×
  4. ChromaDB · recommended 1×
  5. FAISS · recommended 1×
  • CATEGORY QUERY
    How to build a completely offline chatbot for querying local PDF documents?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. PyMuPDFReader
    3. HuggingFaceEmbedding
    4. ChromaDB
    5. FAISS
    6. Ollama
    7. LM Studio
    8. CTransformers
    9. llama-cpp-python
    10. LangChain
    11. PyPDFLoader
    12. UnstructuredFileLoader
    13. unstructured
    14. HuggingFaceEmbeddings
    15. Chroma
    16. ChatOllama
    17. LlamaCpp
    18. PyMuPDF
    19. PyPDF2
    20. sentence-transformers
    21. Haystack
    22. PyPDFToTextConverter
    23. UnstructuredFileConverter
    24. SentenceTransformersDocumentEmbedder
    25. SentenceTransformersTextEmbedder
    26. InMemoryDocumentStore
    27. ChromaDocumentStore
    28. OllamaGenerator
    29. LocalGPTQGenerator

    AI recommended 29 alternatives but never named jacoblee93/fully-local-pdf-chatbot. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are options for running RAG with large language models directly in the browser?
    you: not recommended
    AI recommended (in order):
    1. LangChain.js
    2. Transformers.js
    3. HNSWLib.js
    4. LanceDB.js
    5. OpenAI GPT-4
    6. Anthropic Claude
    7. Google Gemini
    8. LlamaIndex.TS
    9. ONNX Runtime Web
    10. Web LLM
    11. DuckDB-Wasm

    AI recommended 11 alternatives but never named jacoblee93/fully-local-pdf-chatbot. 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 jacoblee93/fully-local-pdf-chatbot?
    pass
    AI did not name jacoblee93/fully-local-pdf-chatbot — 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 jacoblee93/fully-local-pdf-chatbot in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jacoblee93/fully-local-pdf-chatbot 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 jacoblee93/fully-local-pdf-chatbot solve, and who is the primary audience?
    pass
    AI did not name jacoblee93/fully-local-pdf-chatbot — 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?

Embed your GEO score

Drop this badge into the README of jacoblee93/fully-local-pdf-chatbot. 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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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite