EduPLEx_API
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Recommendation, reporting & analytics
Recommendation, reporting & analytics
  • Experiments report
    • Key concepts
    • Data sources
    • First demonstrator: ESCO ontologies and semantic matching
    • Software design
      • Endpoints Sbert_eduplex
      • Setup Sbert_eduplex
    • AI Applications
    • Conclusions
    • Recommendation
    • Bibliography
  • Recommendation Engine
  • Reporting and predictive analytics
  • LRS User Journey Visualizer
  • AI Tutor - RAG system
    • LLM-augmented Retrieval and Ranking for Course Recommendations
    • Retrieval of course candidates when searching via title.
    • Answer Generation Evaluation
    • Chunk Size and Retrieval Evaluation
    • Chunking Techniques – Splitters
    • Golden Case CLAPNQ
    • Comparative Retrieval Performance: Modules vs Golden Case
    • LLM-based Evaluator for Context Relevance
    • Retrieval Performance Indexing pdf vs xapi, and Keywords vs Questions
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On this page
  • Endpoints usage
  • Find ESCO skill best match based on different text (label and description) and languages (English and Deutsch)
  • Vectorise a text string based on the default LLM
  • Compute skills similariry of existing skills in english
  • Vectorise a text string based on the default LLM
  • Vectorise a text string based on the default LLM
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  1. Experiments report
  2. Software design

Endpoints Sbert_eduplex

PreviousSoftware designNextSetup Sbert_eduplex

Last updated 1 year ago

Endpoints usage

Find ESCO skill best match based on different text (label and description) and languages (English and Deutsch)

GET (deprecated)

GET (deprecated)

GET (deprecated)

Vectorise a text string based on the default LLM

GET (not publicly available)

POST (publicly available, Authorization header is needed)

Request example:

{
    "vectorise": [
        "Text A to vectorise",
        "Text B to vectorise",
        "Text C to vectorise"
    ]
}

@return: a JSON file with dimension, llm model, text to vectorise, and the numerical vector for eact text within the vectorise array

Compute skills similariry of existing skills in english

Takes a JSON file with a pre-defined language and computes similarity scores for skills based on ESCO descriptions Similarity score is computed for each pair of skills. Currently only english is supported

Request example:

{
    "language": "en",
    "skill": "Manage musical staff",
    "skills_eval": [
        "Manage musical staff",
        "supervise correctional procedures",
        "apply anti-oppressive practices"
    ]
}

Response example:

JSON file with cosine similarity scores

{
    "0": {
        "base_skill": "Manage musical staff",
        "base_skill_id": 1,
        "eval_skill": "Manage musical staff",
        "eval_skill_id": -1,
        "score": -1
    },
    "1": {
        "base_skill": "Manage musical staff",
        "base_skill_id": 1,
        "eval_skill": "supervise correctional procedures",
        "eval_skill_id": 2,
        "score": 0.840195
    },
    "2": {
        "base_skill": "Manage musical staff",
        "base_skill_id": 1,
        "eval_skill": "apply anti-oppressive practices",
        "eval_skill_id": 3,
        "score": 0.788353
    }
}

Vectorise a text string based on the default LLM

Takes a JSON file with a pre-defined serialised LLM and retrieves similarity scores for skills based on ESCO descriptions. Scores are retrieved from a previously vectorised model containing all vectors for all ESCO's skills trained with ESCO descriptions. Thus vector embeddings are not computed but retrieved, but semantic similarity is computed.

  {
    "language": "en",
    "skill": "Manage musical staff",
    "skills_eval": [
      "manage musical staff",
      "supervise correctional procedures",
      "apply anti-oppressive practices"
    ]
  }

@return: JSON file with cosine similarity scores

Vectorise a text string based on the default LLM

Takes a JSON.

  {
    "language": "en",
    "title": "Manage musical staff",
    "description": "Manage musical staff",
    "learning_goals": "Manage musical staff"
  }

@return: JSON file with top k matches for title, description, and learning goals

GET

POST (Authorization header is needed)

GET

POST (Authorization header is needed)

GET (not publicly available)

POST (Authorization header is needed, also including boolean param sanitize_all)

http://localhost:5000/match_desc_en/{string}
http://localhost:5000/match_desc_de/{string}
http://localhost:5000/match_label_en/{string}
http://localhost:5000/vectorise
https://proto.eduplex.eu/edu/api/v1/ai/sbert/vectorise
http://localhost:5000/compute_compare_skills/
https://proto.eduplex.eu/edu/api/v1/ai/sbert/computeCompareSkills
http://localhost:5000/precomputed_compare_skills/
https://proto.eduplex.eu/edu/api/v1/ai/sbert/preComputedCompareSkills
http://localhost:5000/match_course_skills/
https://proto.eduplex.eu/edu/api/v1/ai/sbert/matchCourseSkills