RRepoGEO

REPOGEO REPORT · LITE

langchain-ai/rag-from-scratch

Default branch main · commit 1fdb7d0e · scanned 5/23/2026, 1:58:36 AM

GitHub: 8,315 stars · 2,007 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
30 /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
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 langchain-ai/rag-from-scratch, 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.

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 langchain-ai/rag-from-scratch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 3×
  2. run-llama/llama_index · recommended 3×
  3. Pinecone · recommended 2×
  4. chroma-core/chroma · recommended 2×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    How can I make an LLM use my own documents for generating more accurate responses?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Pinecone
    4. Weaviate
    5. Chroma
    6. OpenAI API
    7. Hugging Face Transformers
    8. Ludwig
    9. OpenAI Playground
    10. Anthropic Console

    AI recommended 10 alternatives but never named langchain-ai/rag-from-scratch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the fundamental steps to implement retrieval augmented generation with custom data?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Unstructured (Unstructured-IO/unstructured)
    4. Pandas (pandas-dev/pandas)
    5. OpenAI Embeddings
    6. Hugging Face Transformers (Sentence Transformers) (UKPLab/sentence-transformers)
    7. Cohere Embeddings
    8. Chroma (chroma-core/chroma)
    9. Pinecone
    10. Weaviate (weaviate/weaviate)
    11. Qdrant (qdrant/qdrant)
    12. FAISS (Facebook AI Similarity Search) (facebookresearch/faiss)
    13. LangChain (langchain-ai/langchain)
    14. LlamaIndex (run-llama/llama_index)
    15. Pinecone Python Client (pinecone-io/pinecone-python-client)
    16. ChromaDB Python Client (chroma-core/chroma)
    17. OpenAI API (GPT-3.5, GPT-4)
    18. Anthropic API (Claude)
    19. Hugging Face Transformers (e.g., Llama 2, Mistral, Falcon) (huggingface/transformers)
    20. Google Cloud Vertex AI (PaLM 2, Gemini)
    21. Ragas (explodinggradients/ragas)
    22. LangChain Evaluation (langchain-ai/langchain)
    23. LlamaIndex Evaluation (run-llama/llama_index)

    AI recommended 23 alternatives but never named langchain-ai/rag-from-scratch. 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 langchain-ai/rag-from-scratch?
    pass
    AI named langchain-ai/rag-from-scratch explicitly

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

  • If a team adopts langchain-ai/rag-from-scratch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named langchain-ai/rag-from-scratch 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 langchain-ai/rag-from-scratch solve, and who is the primary audience?
    pass
    AI named langchain-ai/rag-from-scratch 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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langchain-ai/rag-from-scratch — 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