RRepoGEO

REPOGEO REPORT · LITE

pixegami/langchain-rag-tutorial

Default branch main · commit c6e04543 · scanned 6/14/2026, 1:22:34 AM

GitHub: 961 stars · 516 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 pixegami/langchain-rag-tutorial, 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
    Clarify the README's opening statement to position the repo as a tutorial/example

    Why:

    CURRENT
    The current README starts directly with installation instructions after the H1.
    COPY-PASTE FIX
    Add this sentence immediately after the `# Langchain RAG Tutorial` heading: "This repository provides a beginner-friendly, step-by-step tutorial and a complete example application for building a Retrieval-Augmented Generation (RAG) system using LangChain and ChromaDB."
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    langchain, rag, tutorial, python, chromadb, llm, generative-ai, example-application
  • mediumlicense#3
    Add a standard open-source license file

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root of the repository containing the text of the MIT License.

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 pixegami/langchain-rag-tutorial
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pandas
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Pandas · recommended 1×
  2. BeautifulSoup4 · recommended 1×
  3. LangChain Document Loaders · recommended 1×
  4. PyPDFLoader · recommended 1×
  5. UnstructuredHTMLLoader · recommended 1×
  • CATEGORY QUERY
    What are the steps to build a simple retrieval augmented generation application in Python?
    you: not recommended
    AI recommended (in order):
    1. Pandas
    2. BeautifulSoup4
    3. LangChain Document Loaders
    4. PyPDFLoader
    5. UnstructuredHTMLLoader
    6. CSVLoader
    7. LangChain Text Splitters
    8. RecursiveCharacterTextSplitter
    9. CharacterTextSplitter
    10. Hugging Face Transformers
    11. sentence-transformers/all-MiniLM-L6-v2
    12. BAAI/bge-small-en-v1.5
    13. OpenAI Embeddings
    14. text-embedding-ada-002
    15. Cohere Embeddings
    16. FAISS
    17. Pinecone
    18. Weaviate
    19. Chroma
    20. LangChain Retrievers
    21. OpenAI GPT-4
    22. GPT-3.5 Turbo
    23. Anthropic Claude
    24. Claude 3 Opus
    25. Sonnet
    26. Haiku
    27. Llama 3
    28. Mistral 7B
    29. Mixtral 8x7B
    30. Hugging Face Inference Endpoints
    31. Replicate
    32. LangChain
    33. LlamaIndex
    34. HuggingFaceEmbeddings
    35. OpenAIEmbeddings
    36. FAISS.from_documents
    37. Chroma.from_documents
    38. ChatOpenAI
    39. HuggingFacePipeline
    40. RetrievalQA.from_chain_type
    41. create_retrieval_chain

    AI recommended 41 alternatives but never named pixegami/langchain-rag-tutorial. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a beginner-friendly guide to implement a RAG pipeline with a vector database.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex (llamaindex/llamaindex)
    2. LangChain (langchain-ai/langchain)
    3. Haystack (deepset-ai/haystack)
    4. Hugging Face Transformers (huggingface/transformers)
    5. FAISS (facebookresearch/faiss)
    6. ChromaDB (chroma-core/chroma)
    7. Weaviate (weaviate/weaviate)

    AI recommended 7 alternatives but never named pixegami/langchain-rag-tutorial. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 pixegami/langchain-rag-tutorial?
    pass
    AI did not name pixegami/langchain-rag-tutorial — 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 pixegami/langchain-rag-tutorial in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pixegami/langchain-rag-tutorial 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 pixegami/langchain-rag-tutorial solve, and who is the primary audience?
    pass
    AI named pixegami/langchain-rag-tutorial explicitly

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

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pixegami/langchain-rag-tutorial — 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