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c8ab7aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | # Python AI service contract
## Endpoint
```http
POST /ai/analyze-post
```
## Request
```json
{
"postId": 101,
"content": "Hom nay hoc Spring Boot Kafka kha hay"
}
```
## Response
```json
{
"postId": 101,
"modelName": "sentence-transformers/all-MiniLM-L6-v2",
"dimension": 384,
"embedding": [0.012, -0.034, 0.221],
"moderation": {
"status": "SAFE",
"spamScore": 0.05,
"toxicityScore": 0.01,
"scamScore": 0.02
}
}
```
## Responsibilities
Python should:
1. Receive `postId` and `content`.
2. Clean text lightly.
3. Generate embedding with `sentence-transformers/all-MiniLM-L6-v2`.
4. Calculate spam, toxicity, and scam scores.
5. Return the response above.
Python should not return or calculate:
- topic
- hashtag
- keyword
- summary
## Moderation Status
Allowed values:
- `SAFE`
- `LIMITED`
- `BLOCKED`
Scores should be normalized from `0.0` to `1.0`.
Spring Boot derives `qualityScore` from moderation scores:
```text
qualityScore = 1 - (spamScore * 0.4 + toxicityScore * 0.4 + scamScore * 0.2)
```
The value is clamped to `0.0..1.0`.
## Minimal FastAPI Shape
```python
from fastapi import FastAPI
from pydantic import BaseModel
from sentence_transformers import SentenceTransformer
app = FastAPI()
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
class AnalyzePostRequest(BaseModel):
postId: int
content: str
@app.post("/ai/analyze-post")
def analyze_post(request: AnalyzePostRequest):
content = " ".join((request.content or "").split())
embedding = model.encode(content).tolist()
return {
"postId": request.postId,
"modelName": "sentence-transformers/all-MiniLM-L6-v2",
"dimension": len(embedding),
"embedding": embedding,
"moderation": {
"status": "SAFE",
"spamScore": 0.0,
"toxicityScore": 0.0,
"scamScore": 0.0,
},
}
```
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