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Merge pull request #6 from nomyo-ai/dev-v0.3.x

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This is adding quite a few improvements, fixes and already preparations for v0.4
increasing compatibility and stability and even performance.
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Alpha Nerd 2025-09-21 16:23:18 +02:00 • committed by GitHub
commit ffee2baab8
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2 changed files with 76 additions and 52 deletions
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126
router.py
View file

@ -100,6 +100,11 @@ app.add_middleware(
allow_methods=["GET", "POST", "DELETE"],
allow_headers=["Authorization", "Content-Type"],
)
default_headers={
"HTTP-Referer": "https://nomyo.ai",
"X-Title": "NOMYO Router",
}
# -------------------------------------------------------------
# 3. Global state: per‑endpoint per‑model active connection counters
# -------------------------------------------------------------
@ -271,68 +276,83 @@ def iso8601_ns():
class rechunk:
def openai_chat_completion2ollama(chunk: dict, stream: bool, start_ts: float):
rechunk = { "model": chunk.model,
"created_at": iso8601_ns() ,
"done_reason": chunk.choices[0].finish_reason,
"load_duration": None,
"prompt_eval_count": None,
"prompt_eval_duration": None,
"eval_count": None,
"eval_duration": None,
"eval_count": (chunk.usage.completion_tokens if chunk.usage is not None else None),
"prompt_eval_count": (chunk.usage.prompt_tokens if chunk.usage is not None else None),
"eval_duration": (int((time.perf_counter() - start_ts) * 1000) if chunk.usage is not None else None),
"response_token/s": (round(chunk.usage.total_tokens / (time.perf_counter() - start_ts), 2) if chunk.usage is not None else None)
}
if stream == True:
rechunk["message"] = {"role": chunk.choices[0].delta.role or "assistant", "content": chunk.choices[0].delta.content, "thinking": None, "images": None, "tool_name": None, "tool_calls": None}
assistant_msg = ollama.Message(
role=chunk.choices[0].delta.role or "assistant",
content=chunk.choices[0].delta.content,
thinking=None,
images=None,
tool_name=None,
tool_calls=None
)
else:
rechunk["message"] = {"role": chunk.choices[0].message.role or "assistant", "content": chunk.choices[0].message.content, "thinking": None, "images": None, "tool_name": None, "tool_calls": None}
assistant_msg = ollama.Message(
role=chunk.choices[0].message.role or "assistant",
content=chunk.choices[0].message.content,
thinking=None,
images=None,
tool_name=None,
tool_calls=None
)
rechunk = ollama.ChatResponse(model=chunk.model,
created_at=iso8601_ns(),
done_reason=chunk.choices[0].finish_reason,
load_duration=100000,
prompt_eval_duration=(int((time.perf_counter() - start_ts) * 1_000_000_000 * (chunk.usage.prompt_tokens / chunk.usage.completion_tokens / 100)) if chunk.usage is not None else None),
eval_count= (chunk.usage.completion_tokens if chunk.usage is not None else None),
prompt_eval_count=(chunk.usage.prompt_tokens if chunk.usage is not None else None),
eval_duration=(int((time.perf_counter() - start_ts) * 1_000_000_000) if chunk.usage is not None else None),
total_duration=(int((time.perf_counter() - start_ts) * 1_000_000_000) if chunk.usage is not None else None),
message=assistant_msg)
return rechunk
def openai_completion2ollama(chunk: dict, stream: bool, start_ts: float):
with_thinking = chunk.choices[0] if chunk.choices[0] else None
thinking = getattr(with_thinking, "reasoning", None) if with_thinking else None
rechunk = { "model": chunk.model,
"created_at": iso8601_ns(),
"load_duration": None,
"done_reason": chunk.choices[0].finish_reason,
"total_duration": None,
"eval_duration": (int((time.perf_counter() - start_ts) * 1000) if chunk.usage is not None else None),
"thinking": thinking,
"context": None,
"response": chunk.choices[0].text
}
rechunk = ollama.GenerateResponse(model=chunk.model,
created_at=iso8601_ns(),
load_duration=10000,
done_reason=chunk.choices[0].finish_reason,
done=None, #True if chunk.choices[0].finish_reason is not None else False,
total_duration=(int((time.perf_counter() - start_ts) * 1000) if chunk.usage is not None else None),
eval_duration=(int((time.perf_counter() - start_ts) * 1000) if chunk.usage is not None else None),
thinking=thinking,
response=chunk.choices[0].text
)
return rechunk
def openai_embeddings2ollama(chunk: dict):
rechunk = {"embedding": chunk.data[0].embedding}
rechunk = ollama.EmbeddingsResponse(embedding=chunk.data[0].embedding)
return rechunk
def openai_embed2ollama(chunk: dict, model: str):
rechunk = { "model": model,
"created_at": iso8601_ns(),
"done": None,
"done_reason": None,
"total_duration": None,
"load_duration": None,
"prompt_eval_count": None,
"prompt_eval_duration": None,
"eval_count": None,
"eval_duration": None,
"embeddings": [chunk.data[0].embedding]
}
rechunk = ollama.EmbedResponse(model=model,
created_at=iso8601_ns(),
done=None,
done_reason=None,
total_duration=None,
load_duration=None,
prompt_eval_count=None,
prompt_eval_duration=None,
eval_count=None,
eval_duration=None,
embeddings=[chunk.data[0].embedding]
)
return rechunk
# ------------------------------------------------------------------
# SSE Helpser
# ------------------------------------------------------------------
async def publish_snapshot():
snapshot = json.dumps({"usage_counts": usage_counts})
async with usage_lock:
snapshot = json.dumps({"usage_counts": usage_counts}, sort_keys=True)
async with _subscribers_lock:
for q in _subscribers:
# If the queue is full, drop the message to avoid back‑pressure.
if q.full():
continue
try:
await q.get()
except asyncio.QueueEmpty:
pass
await q.put(snapshot)
async def close_all_sse_queues():
@ -497,7 +517,7 @@ async def proxy(request: Request):
}
params.update({k: v for k, v in optional_params.items() if v is not None})
oclient = openai.AsyncOpenAI(base_url=endpoint, api_key=config.api_keys[endpoint])
oclient = openai.AsyncOpenAI(base_url=endpoint, default_headers=default_headers, api_key=config.api_keys[endpoint])
else:
client = ollama.AsyncClient(host=endpoint)
await increment_usage(endpoint, model)
@ -560,9 +580,10 @@ async def chat_proxy(request: Request):
tools = payload.get("tools")
stream = payload.get("stream")
think = payload.get("think")
format = payload.get("format")
_format = payload.get("format")
options = payload.get("options")
keep_alive = payload.get("keep_alive")
options = payload.get("options")
if not model:
raise HTTPException(
@ -572,6 +593,10 @@ async def chat_proxy(request: Request):
raise HTTPException(
status_code=400, detail="Missing or invalid 'messages' field (must be a list)"
)
if options is not None and not isinstance(options, dict):
raise HTTPException(
status_code=400, detail="`options` must be a JSON object"
)
except json.JSONDecodeError as e:
raise HTTPException(status_code=400, detail=f"Invalid JSON: {e}") from e
@ -591,9 +616,8 @@ async def chat_proxy(request: Request):
"tools": tools,
"stream": stream,
}
params.update({k: v for k, v in optional_params.items() if v is not None})
oclient = openai.AsyncOpenAI(base_url=endpoint, api_key=config.api_keys[endpoint])
oclient = openai.AsyncOpenAI(base_url=endpoint, default_headers=default_headers, api_key=config.api_keys[endpoint])
else:
client = ollama.AsyncClient(host=endpoint)
await increment_usage(endpoint, model)
@ -605,7 +629,7 @@ async def chat_proxy(request: Request):
start_ts = time.perf_counter()
async_gen = await oclient.chat.completions.create(**params)
else:
async_gen = await client.chat(model=model, messages=messages, tools=tools, stream=stream, think=think, format=format, options=options, keep_alive=keep_alive)
async_gen = await client.chat(model=model, messages=messages, tools=tools, stream=stream, think=think, format=_format, options=options, keep_alive=keep_alive)
if stream == True:
async for chunk in async_gen:
if is_openai_endpoint:
@ -685,7 +709,7 @@ async def embedding_proxy(request: Request):
try:
# The chat method returns a generator of dicts (or GenerateResponse)
if is_openai_endpoint:
async_gen = await client.embeddings.create(input=[prompt], model=model)
async_gen = await client.embeddings.create(input=prompt, model=model)
async_gen = rechunk.openai_embeddings2ollama(async_gen)
else:
async_gen = await client.embeddings(model=model, prompt=prompt, options=options, keep_alive=keep_alive)
@ -751,7 +775,7 @@ async def embed_proxy(request: Request):
try:
# The chat method returns a generator of dicts (or GenerateResponse)
if is_openai_endpoint:
async_gen = await client.embeddings.create(input=[_input], model=model)
async_gen = await client.embeddings.create(input=_input, model=model)
async_gen = rechunk.openai_embed2ollama(async_gen, model)
else:
async_gen = await client.embed(model=model, input=_input, truncate=truncate, options=options, keep_alive=keep_alive)
@ -1176,10 +1200,10 @@ async def openai_embedding_proxy(request: Request):
else:
api_key = "ollama"
base_url = ep2base(endpoint)
oclient = openai.AsyncOpenAI(base_url=base_url, api_key=api_key)
oclient = openai.AsyncOpenAI(base_url=base_url, default_headers=default_headers, api_key=api_key)
# 3. Async generator that streams embedding data and decrements the counter
async_gen = await oclient.embeddings.create(input=[doc], model=model)
async_gen = await oclient.embeddings.create(input=doc, model=model)
await decrement_usage(endpoint, model)
@ -1256,7 +1280,7 @@ async def openai_chat_completions_proxy(request: Request):
endpoint = await choose_endpoint(model)
await increment_usage(endpoint, model)
base_url = ep2base(endpoint)
oclient = openai.AsyncOpenAI(base_url=base_url, api_key=config.api_keys[endpoint])
oclient = openai.AsyncOpenAI(base_url=base_url, default_headers=default_headers, api_key=config.api_keys[endpoint])
# 3. Async generator that streams completions data and decrements the counter
async def stream_ochat_response():
@ -1359,7 +1383,7 @@ async def openai_completions_proxy(request: Request):
endpoint = await choose_endpoint(model)
await increment_usage(endpoint, model)
base_url = ep2base(endpoint)
oclient = openai.AsyncOpenAI(base_url=base_url, api_key=config.api_keys[endpoint])
oclient = openai.AsyncOpenAI(base_url=base_url, default_headers=default_headers, api_key=config.api_keys[endpoint])
# 3. Async generator that streams completions data and decrements the counter
async def stream_ocompletions_response():

2
static/index.html
View file

@ -346,7 +346,7 @@
const body = document.getElementById("tags-body");
body.innerHTML = data.models
.map((m) => {
let modelCell = `${m.id || m.name}`;
let modelCell = `${m.model}`;
if (m.digest) {
modelCell += `<a href="#" class="delete-link" data-model="${m.name}">delete</a>`;
modelCell += `<a href="#" class="copy-link" data-source="${m.name}">copy</a>`;