graph-rag-local-ui-scl / embedding_proxy.py
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import json
from fastapi import FastAPI, HTTPException
import uvicorn
import httpx
from pydantic import BaseModel
from typing import List, Union
app = FastAPI()
OLLAMA_URL = "http://localhost:11434" # Default Ollama URL
class EmbeddingRequest(BaseModel):
input: Union[str, List[str]]
model: str
class EmbeddingResponse(BaseModel):
object: str
data: List[dict]
model: str
usage: dict
@app.post("/v1/embeddings")
async def create_embedding(request: EmbeddingRequest):
async with httpx.AsyncClient() as client:
if isinstance(request.input, str):
request.input = [request.input]
ollama_requests = [{"model": request.model, "prompt": text} for text in request.input]
embeddings = []
for i, ollama_request in enumerate(ollama_requests):
response = await client.post(f"{OLLAMA_URL}/api/embeddings", json=ollama_request)
if response.status_code != 200:
raise HTTPException(status_code=response.status_code, detail="Ollama API error")
result = response.json()
embeddings.append({
"object": "embedding",
"embedding": result["embedding"],
"index": i
})
return EmbeddingResponse(
object="list",
data=embeddings,
model=request.model,
)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Run the embedding proxy server")
parser.add_argument("--port", type=int, default=11435, help="Port to run the server on")
parser.add_argument("--host", type=str, default="http://localhost:11434", help="URL of the Ollama server")
parser.add_argument("--reload", action="store_true", help="Enable auto-reload for development")
args = parser.parse_args()
OLLAMA_URL = args.host
uvicorn.run("embedding_proxy:app", host="0.0.0.0", port=args.port, reload=args.reload)