132 lines
4.1 KiB
Python
132 lines
4.1 KiB
Python
"""Endpoint URL, model-name, and endpoint-classification helpers.
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The endpoint classifiers read live config via ``get_config()`` so that the
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startup-time rebind of ``config`` in router.py is picked up at call time.
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"""
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from config import get_config
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def _normalize_llama_model_name(name: str) -> str:
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"""Extract the model name from a huggingface-style identifier.
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e.g. 'unsloth/gpt-oss-20b-GGUF:F16' -> 'gpt-oss-20b-GGUF'
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"""
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if "/" in name:
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name = name.rsplit("/", 1)[1]
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if ":" in name:
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name = name.split(":")[0]
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return name
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def _extract_llama_quant(name: str) -> str:
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"""Extract the quantization level from a huggingface-style identifier.
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e.g. 'unsloth/gpt-oss-20b-GGUF:Q8_0' -> 'Q8_0'
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Returns empty string if no quant suffix is present.
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"""
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if ":" in name:
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return name.rsplit(":", 1)[1]
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return ""
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def ep2base(ep):
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if "/v1" in ep:
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base_url = ep
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else:
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base_url = ep + "/v1"
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return base_url
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def dedupe_on_keys(dicts, key_fields):
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"""
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Helper function to deduplicate endpoint details based on given dict keys.
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"""
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seen = set()
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out = []
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for d in dicts:
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# Build a tuple of the values for the chosen keys
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key = tuple(d.get(k) for k in key_fields)
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if key not in seen:
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seen.add(key)
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out.append(d)
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return out
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def is_llama_swap(endpoint: str) -> bool:
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"""True if the endpoint is a configured llama-swap front."""
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return endpoint in get_config().llama_swap_endpoints
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def is_llama_server(endpoint: str) -> bool:
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"""True for a llama.cpp llama-server OR a llama-swap front.
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Both speak the same OpenAI-compatible surface, so the router treats them
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identically everywhere except loaded-model detection and model unload.
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"""
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cfg = get_config()
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return endpoint in cfg.llama_server_endpoints or endpoint in cfg.llama_swap_endpoints
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def llama_endpoints(cfg) -> list:
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"""Combined, de-duplicated llama-server + llama-swap endpoints (order preserved)."""
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return list(dict.fromkeys([*cfg.llama_server_endpoints, *cfg.llama_swap_endpoints]))
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def is_ext_openai_endpoint(endpoint: str) -> bool:
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"""
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Determine if an endpoint is an external OpenAI-compatible endpoint (not Ollama, llama-server or llama-swap).
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Returns True for:
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- External services like OpenAI.com, Groq, etc.
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Returns False for:
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- Ollama endpoints (without /v1, or with /v1 but default port 11434)
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- llama-server / llama-swap endpoints (explicitly configured)
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"""
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# Check if it's a llama-server / llama-swap endpoint (has /v1 and is in a configured list)
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if is_llama_server(endpoint):
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return False
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if "/v1" not in endpoint:
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return False
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base_endpoint = endpoint.replace('/v1', '')
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if base_endpoint in get_config().endpoints:
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return False # It's Ollama's /v1
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# Check for default Ollama port
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if ':11434' in endpoint:
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return False # It's Ollama
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return True # It's an external OpenAI endpoint
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def is_openai_compatible(endpoint: str) -> bool:
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"""
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Return True if the endpoint speaks the OpenAI API (not native Ollama).
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This includes external OpenAI endpoints AND llama-server / llama-swap endpoints.
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"""
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return "/v1" in endpoint or is_llama_server(endpoint)
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def get_tracking_model(endpoint: str, model: str) -> str:
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"""
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Normalize model name for tracking purposes so it matches the PS table key.
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- For llama-server endpoints: strips HF prefix and quantization suffix
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- For Ollama endpoints: appends ":latest" if no version suffix is present
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- For external OpenAI endpoints: returns as-is (not shown in PS)
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This ensures consistent model naming across all routes for usage tracking.
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"""
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# External OpenAI endpoints are not shown in PS, keep as-is
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if is_ext_openai_endpoint(endpoint):
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return model
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# llama-server / llama-swap endpoints use normalized names in PS
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if is_llama_server(endpoint):
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return _normalize_llama_model_name(model)
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# Ollama endpoints: append ":latest" if no version suffix
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if ":" not in model:
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return model + ":latest"
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return model
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