# Endpoints Sbert\_eduplex

## Endpoints usage

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

GET <http://localhost:5000/match_desc_en/{string}> (deprecated)

GET <http://localhost:5000/match_desc_de/{string}> (deprecated)

GET <http://localhost:5000/match_label_en/{string}> (deprecated)

### Vectorise a text string based on the default LLM

GET <http://localhost:5000/vectorise> (not publicly available)

POST <https://proto.eduplex.eu/edu/api/v1/ai/sbert/vectorise> (publicly available, Authorization header is needed)

Request example:

```json
{
    "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

GET <http://localhost:5000/compute_compare_skills/>

POST <https://proto.eduplex.eu/edu/api/v1/ai/sbert/computeCompareSkills> (Authorization header is needed)

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:

```json
{
    "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

```json
{
    "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

GET <http://localhost:5000/precomputed_compare_skills/>

POST <https://proto.eduplex.eu/edu/api/v1/ai/sbert/preComputedCompareSkills> (Authorization header is needed)

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.

```json
  {
    "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

GET <http://localhost:5000/match_course_skills/> (not publicly available)

POST <https://proto.eduplex.eu/edu/api/v1/ai/sbert/matchCourseSkills> (Authorization header is needed, also including boolean param `sanitize_all`)

Takes a JSON.

```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


---

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