Initial commit: DeepRes Go backend — bot + worker + embed service

embed-service/Dockerfile

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+FROM python:3.12-slim
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY main.py .
+
+EXPOSE 8081
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8081"]

embed-service/main.py

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+"""
+E5-small-v2 embedding server — OpenAI-compatible /v1/embeddings endpoint.
+Minimal, CPU-only, ~100MB RAM.
+"""
+from fastapi import FastAPI
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+import numpy as np
+
+app = FastAPI()
+model = SentenceTransformer("intfloat/e5-small-v2", device="cpu")
+
+
+class EmbedRequest(BaseModel):
+    model: str = "intfloat/e5-small-v2"
+    input: list[str]
+
+
+class EmbedData(BaseModel):
+    object: str = "embedding"
+    index: int
+    embedding: list[float]
+
+
+class EmbedResponse(BaseModel):
+    object: str = "list"
+    data: list[EmbedData]
+    model: str
+
+
+@app.post("/v1/embeddings")
+def embed(req: EmbedRequest):
+    # E5 requires "query: " or "passage: " prefix
+    prefixed = [f"passage: {t}" if "\n" not in t else t for t in req.input]
+    embs = model.encode(prefixed, normalize_embeddings=True, show_progress_bar=False)
+    data = [
+        EmbedData(index=i, embedding=emb.tolist())
+        for i, emb in enumerate(embs)
+    ]
+    return EmbedResponse(data=data, model=req.model)
+
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8081)

embed-service/requirements.txt

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+sentence-transformers>=3.0
+fastapi>=0.115
+uvicorn>=0.30