RRepoGEO

REPOGEO REPORT · LITE

pig-mesh/deepseek4j

Default branch main · commit b0c38715 · scanned 6/6/2026, 2:06:56 PM

GitHub: 752 stars · 144 forks

AI VISIBILITY SCORE
33 /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
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 pig-mesh/deepseek4j, 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
    Add a concise English value proposition to the README's opening

    Why:

    COPY-PASTE FIX
    Add the following English paragraph immediately after the main title `# deepseek4j （DeepSeek Java SDK）`:

"deepseek4j is the definitive Java SDK for DeepSeek AI models, offering comprehensive support for DeepSeek R1 and V3, including conversation, function calling, JSON output, and OpenAI-compatible embeddings. It provides seamless integration for Spring Boot 2.x/3.x and Solon applications with out-of-the-box configuration and robust reactive streaming support."
  • mediumtopics#2
    Expand repository topics with relevant LLM and reactive keywords

    Why:

    CURRENT
    deepseek, deepseek-r1, java, openai, spring-boot, spring-boot-3, spring-boot-4
    COPY-PASTE FIX
    deepseek, deepseek-r1, java, openai, spring-boot, spring-boot-3, llm, generative-ai, ai-sdk, reactive, streaming
  • mediumreadme#3
    Add a 'Why deepseek4j?' section to highlight differentiators

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why deepseek4j?' to the README, detailing its specific advantages for DeepSeek users, such as full DeepSeek API support, enhanced Reactor reactive capabilities, and seamless Spring Boot integration, especially compared to generic LLM SDKs or unmaintained projects like OpenAI4J.

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 pig-mesh/deepseek4j
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
spring-projects/spring-ai
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. spring-projects/spring-ai · recommended 1×
  2. openai/openai-java · recommended 1×
  3. googleapis/google-cloud-java · recommended 1×
  4. Hugging Face Inference API · recommended 1×
  5. huggingface/huggingface-java · recommended 1×
  • CATEGORY QUERY
    How to integrate AI model capabilities into a Spring Boot application using Java?
    you: not recommended
    AI recommended (in order):
    1. Spring AI (spring-projects/spring-ai)
    2. OpenAI Java Library (openai/openai-java)
    3. Google Cloud Client Libraries for Java (googleapis/google-cloud-java)
    4. Hugging Face Inference API
    5. huggingface-java (huggingface/huggingface-java)
    6. Deeplearning4j (deeplearning4j/deeplearning4j)
    7. ONNX Runtime (microsoft/onnxruntime)
    8. TensorFlow Lite

    AI recommended 8 alternatives but never named pig-mesh/deepseek4j. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are Java SDKs for large language models supporting reactive streaming responses?
    you: not recommended
    AI recommended (in order):
    1. Spring AI
    2. OpenAI Java Client
    3. Google Cloud Client Libraries for Java
    4. LangChain4j
    5. Hugging Face Inference API Java Client (huggingface-java/huggingface-java)

    AI recommended 5 alternatives but never named pig-mesh/deepseek4j. 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 pig-mesh/deepseek4j?
    pass
    AI did not name pig-mesh/deepseek4j — 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 pig-mesh/deepseek4j in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pig-mesh/deepseek4j 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 pig-mesh/deepseek4j solve, and who is the primary audience?
    pass
    AI named pig-mesh/deepseek4j 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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pig-mesh/deepseek4j — 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