RRepoGEO

REPOGEO REPORT · LITE

deepset-ai/haystack-cookbook

Default branch main · commit 924d9bca · scanned 6/5/2026, 4:28:17 AM

GitHub: 541 stars · 123 forks

AI VISIBILITY SCORE
28 /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
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 deepset-ai/haystack-cookbook, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Emphasize official status in README opening

    Why:

    CURRENT
    A collection of example notebooks using Haystack 💚
    COPY-PASTE FIX
    The official, community-driven collection of practical example notebooks for building with Haystack 💚
  • mediumlicense#2
    Add a LICENSE file

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root, specifying the intended open-source license (e.g., Apache-2.0, MIT).

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 deepset-ai/haystack-cookbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Haystack · recommended 2×
  4. Microsoft Semantic Kernel · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to build a robust RAG system with different model providers and vector databases?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Microsoft Semantic Kernel
    5. Pinecone
    6. Weaviate
    7. Qdrant
    8. Chroma
    9. PostgreSQL with pgvector
    10. OpenAI Embeddings
    11. Hugging Face Transformers
    12. Cohere Embeddings
    13. OpenAI GPT Models
    14. Anthropic Claude Models
    15. Google Gemini Models
    16. Mistral AI Models
    17. Llama 3
    18. Cohere Rerank
    19. LangChain's Tracing
    20. Arize AI

    AI recommended 20 alternatives but never named deepset-ai/haystack-cookbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical Python examples for developing agentic AI applications and generative AI use cases.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. AutoGen
    5. Transformers
    6. OpenAI Python Library
    7. Instructor

    AI recommended 7 alternatives but never named deepset-ai/haystack-cookbook. 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 deepset-ai/haystack-cookbook?
    pass
    AI did not name deepset-ai/haystack-cookbook — 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 deepset-ai/haystack-cookbook in production, what risks or prerequisites should they evaluate first?
    pass
    AI named deepset-ai/haystack-cookbook 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 deepset-ai/haystack-cookbook solve, and who is the primary audience?
    pass
    AI named deepset-ai/haystack-cookbook explicitly

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite