first-step

.gitignore

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+.env
+.venv/
+__pycache__/

app.py

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+import os
+from FlagEmbedding import BGEM3FlagModel
+from dotenv import load_dotenv
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import List
+
+
+
+# --- ساخت اپلیکیشن ---
+def create_app() -> FastAPI:
+    app = FastAPI(
+        title="embeding-services Backend",
+        version="0.1.0",
+    )
+
+    origins = ["*"]
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=origins,
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    @app.get("/")
+    async def simple():
+        return "embeding-services OK"
+
+    @app.get("/ping")
+    async def ping():
+        return "embeding-services OK"
+
+    return app
+
+
+# ---------- Model ----------
+load_dotenv()
+bge_m3_path = os.getenv("EMBED_BGE")
+
+bgem3_model = BGEM3FlagModel(
+    model_name_or_path=bge_m3_path,
+    use_fp16=True,
+    devices="cpu",
+    cache_dir="./cache",
+    normalize_embeddings=True,
+    trust_remote_code=False,
+)
+
+# ---------- Schema ----------
+class InputForm(BaseModel):
+    id: str
+    sentence: str
+
+class EmbedInput(BaseModel):
+    max_length: int = 1024
+    batch_size: int = 64
+    sentences: List[InputForm]
+
+class OutputForm(BaseModel):
+    id: str
+    sentence: str
+    vec: List[float]
+
+class EmbedOut(BaseModel):
+    result: List[OutputForm]
+
+
+def chunks(lst, n):
+    """Split list to batches"""
+    for i in range(0, len(lst), n):
+        yield lst[i:i+n]
+
+
+# ---------- Endpoint ----------
+# ✅ نمونه‌سازی نهایی
+app = create_app()
+
+@app.post("/embedding/bge-m3", response_model=EmbedOut)
+async def embed_bge(payload: EmbedInput):
+
+    sentences_text = [s.sentence for s in payload.sentences]
+    ids = [s.id for s in payload.sentences]
+
+    results = []
+
+    for batch_texts, batch_ids in zip(
+            chunks(sentences_text, payload.batch_size),
+            chunks(ids, payload.batch_size)):
+
+        emb = bgem3_model.encode(
+            sentences=batch_texts,
+            max_length=payload.max_length
+        )["dense_vecs"]
+
+        for i in range(len(batch_texts)):
+            results.append(
+                OutputForm(
+                    id=batch_ids[i],
+                    sentence=batch_texts[i],
+                    vec=emb[i].tolist() if hasattr(emb[i], "tolist") else emb[i]
+                )
+            )
+
+    return EmbedOut(result=results)

requierments.txt

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+FlagEmbedding
+fastapi
+uvicorn

run_bash.bash

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+# python3 -m venv .venv
+source /home2/.venv/bin/activate
+pip install -r requierments.txt
+uvicorn app:app --host=0.0.0.0 --port=8002 --workers 4 --reload 
+