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6b3874e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 | """Pluggable inference client: vllm | ollama | mock backends.
All backends implement the same contract:
health() -> BackendStatus (never raises)
generate(prompt, system, max_tokens) -> InferenceResult with token counts
and wall-clock latency
validate() -> deterministic test-prompt round trip
The mock backend is the default for tests: fully deterministic, no network.
The ollama backend follows the platform's existing local pattern
(sensorflow/evaluation/copilot.py). The vllm backend targets any
OpenAI-compatible endpoint and is only exercisable on CUDA/ROCm hosts —
see sensorflow/retro/inference/vllm_server/.
"""
from __future__ import annotations
import hashlib
import os
import time
from abc import ABC, abstractmethod
from typing import List, Optional
import httpx
from pydantic import BaseModel, Field
VALIDATION_PROMPT = "Respond with exactly the token RETRO-OK and nothing else."
VALIDATION_EXPECT = "RETRO-OK"
DEFAULT_OLLAMA_URL = os.environ.get("RETRO_OLLAMA_URL", "http://localhost:11434")
DEFAULT_VLLM_URL = os.environ.get("RETRO_VLLM_URL", "http://localhost:8001/v1")
class InferenceResult(BaseModel):
backend: str
model: str
text: str
prompt_tokens: int
completion_tokens: int
latency_ms: float
notes: str = ""
class BackendStatus(BaseModel):
backend: str
available: bool
endpoint: Optional[str] = None
model: Optional[str] = None
detail: str = ""
health_latency_ms: Optional[float] = None
class ValidationResult(BaseModel):
backend: str
passed: bool
expected: str
got: str
latency_ms: float
prompt_tokens: int
completion_tokens: int
detail: str = ""
class InferenceBackend(ABC):
name: str = "abstract"
@abstractmethod
def health(self) -> BackendStatus: ...
@abstractmethod
def generate(self, prompt: str, system: Optional[str] = None,
max_tokens: int = 1024) -> InferenceResult: ...
def validate(self) -> ValidationResult:
"""Deterministic test-prompt inference validation with latency."""
t0 = time.perf_counter()
try:
res = self.generate(VALIDATION_PROMPT, max_tokens=16)
except Exception as exc:
return ValidationResult(backend=self.name, passed=False,
expected=VALIDATION_EXPECT, got="",
latency_ms=(time.perf_counter() - t0) * 1000,
prompt_tokens=0, completion_tokens=0,
detail=f"generate failed: {exc}")
passed = VALIDATION_EXPECT in res.text
return ValidationResult(backend=self.name, passed=passed,
expected=VALIDATION_EXPECT, got=res.text.strip()[:200],
latency_ms=res.latency_ms,
prompt_tokens=res.prompt_tokens,
completion_tokens=res.completion_tokens,
detail="" if passed else
"model reply did not contain the expected token")
def _approx_tokens(text: str) -> int:
"""Deterministic whitespace-ish token approximation (mock backend only)."""
return max(1, len(text.split())) if text else 0
class MockBackend(InferenceBackend):
"""Deterministic, offline. Replies are pure functions of the prompt.
The retrospective agent does NOT parse mock replies for analysis content —
in mock mode the analysis itself is produced by the deterministic scripted
path (sensorflow/retro/agent). The mock backend exists to satisfy the
client contract (health/generate/validate) without any model.
"""
name = "mock"
def health(self) -> BackendStatus:
return BackendStatus(backend="mock", available=True, endpoint=None,
model="deterministic-mock",
detail="always available; replies are deterministic "
"functions of the prompt", health_latency_ms=0.0)
def generate(self, prompt: str, system: Optional[str] = None,
max_tokens: int = 1024) -> InferenceResult:
t0 = time.perf_counter()
if VALIDATION_EXPECT in prompt or "RETRO-OK" in prompt:
text = VALIDATION_EXPECT
else:
digest = hashlib.sha256(((system or "") + "\n" + prompt).encode()).hexdigest()[:12]
text = f"[mock-response {digest}] Deterministic placeholder reply."
return InferenceResult(
backend="mock", model="deterministic-mock", text=text,
prompt_tokens=_approx_tokens((system or "") + " " + prompt),
completion_tokens=_approx_tokens(text),
latency_ms=(time.perf_counter() - t0) * 1000,
notes="deterministic mock; token counts are whitespace approximations")
class OllamaBackend(InferenceBackend):
"""Local Ollama server (Metal/CPU on this machine — NOT vLLM numbers)."""
name = "ollama"
def __init__(self, base_url: str = DEFAULT_OLLAMA_URL,
model: Optional[str] = None, timeout: float = 120.0):
self.base_url = base_url.rstrip("/")
self.model = model or os.environ.get("RETRO_OLLAMA_MODEL", "")
self.timeout = timeout
def _pick_model(self) -> Optional[str]:
if self.model:
return self.model
try:
res = httpx.get(f"{self.base_url}/api/tags", timeout=3.0)
models = [m.get("name") for m in res.json().get("models", [])]
self.model = models[0] if models else None
except Exception:
self.model = None
return self.model
def health(self) -> BackendStatus:
t0 = time.perf_counter()
try:
res = httpx.get(f"{self.base_url}/api/tags", timeout=3.0)
ms = (time.perf_counter() - t0) * 1000
if res.status_code == 200:
models = [m.get("name") for m in res.json().get("models", [])]
return BackendStatus(backend="ollama", available=True,
endpoint=self.base_url,
model=self.model or (models[0] if models else None),
detail=f"{len(models)} model(s) installed",
health_latency_ms=round(ms, 1))
return BackendStatus(backend="ollama", available=False,
endpoint=self.base_url,
detail=f"HTTP {res.status_code} from /api/tags")
except Exception as exc:
return BackendStatus(backend="ollama", available=False,
endpoint=self.base_url,
detail=f"unreachable: {exc.__class__.__name__}: {exc}")
def generate(self, prompt: str, system: Optional[str] = None,
max_tokens: int = 1024) -> InferenceResult:
model = self._pick_model()
if not model:
raise RuntimeError(f"no Ollama model available at {self.base_url}")
messages = ([{"role": "system", "content": system}] if system else []) + [
{"role": "user", "content": prompt}]
t0 = time.perf_counter()
res = httpx.post(f"{self.base_url}/api/chat",
json={"model": model, "messages": messages, "stream": False,
"options": {"num_predict": max_tokens}},
timeout=self.timeout)
latency_ms = (time.perf_counter() - t0) * 1000
res.raise_for_status()
body = res.json()
return InferenceResult(
backend="ollama", model=model,
text=body.get("message", {}).get("content", ""),
prompt_tokens=int(body.get("prompt_eval_count", 0)),
completion_tokens=int(body.get("eval_count", 0)),
latency_ms=round(latency_ms, 1),
notes="Ollama on local CPU/Metal — these are NOT vLLM/GPU numbers")
class VLLMBackend(InferenceBackend):
"""OpenAI-compatible vLLM endpoint. Only runnable against a CUDA/ROCm host
(see vllm_server/); on this macOS machine health() honestly reports it
unavailable unless a remote endpoint is configured."""
name = "vllm"
def __init__(self, base_url: str = DEFAULT_VLLM_URL,
model: Optional[str] = None, timeout: float = 120.0):
self.base_url = base_url.rstrip("/")
self.model = model or os.environ.get("RETRO_VLLM_MODEL", "")
self.timeout = timeout
def health(self) -> BackendStatus:
t0 = time.perf_counter()
try:
res = httpx.get(f"{self.base_url}/models", timeout=3.0)
ms = (time.perf_counter() - t0) * 1000
if res.status_code == 200:
models = [m.get("id") for m in res.json().get("data", [])]
return BackendStatus(backend="vllm", available=True,
endpoint=self.base_url,
model=self.model or (models[0] if models else None),
detail=f"OpenAI-compatible server, models: {models}",
health_latency_ms=round(ms, 1))
return BackendStatus(backend="vllm", available=False,
endpoint=self.base_url,
detail=f"HTTP {res.status_code} from /models")
except Exception as exc:
return BackendStatus(backend="vllm", available=False,
endpoint=self.base_url,
detail=f"unreachable ({exc.__class__.__name__}); vLLM "
"cannot run on this macOS machine — point "
"RETRO_VLLM_URL at a CUDA/ROCm host")
def generate(self, prompt: str, system: Optional[str] = None,
max_tokens: int = 1024) -> InferenceResult:
model = self.model
if not model:
res = httpx.get(f"{self.base_url}/models", timeout=5.0)
data = res.json().get("data", [])
if not data:
raise RuntimeError(f"no models served at {self.base_url}")
model = data[0]["id"]
messages = ([{"role": "system", "content": system}] if system else []) + [
{"role": "user", "content": prompt}]
t0 = time.perf_counter()
res = httpx.post(f"{self.base_url}/chat/completions",
json={"model": model, "messages": messages,
"max_tokens": max_tokens, "temperature": 0.0},
timeout=self.timeout)
latency_ms = (time.perf_counter() - t0) * 1000
res.raise_for_status()
body = res.json()
usage = body.get("usage", {})
return InferenceResult(
backend="vllm", model=model,
text=body["choices"][0]["message"]["content"],
prompt_tokens=int(usage.get("prompt_tokens", 0)),
completion_tokens=int(usage.get("completion_tokens", 0)),
latency_ms=round(latency_ms, 1))
BACKENDS = {"mock": MockBackend, "ollama": OllamaBackend, "vllm": VLLMBackend}
def get_backend(name: str = "mock", **kwargs) -> InferenceBackend:
if name not in BACKENDS:
raise ValueError(f"unknown backend {name!r}; expected one of {sorted(BACKENDS)}")
return BACKENDS[name](**kwargs)
def all_backend_statuses() -> List[BackendStatus]:
return [get_backend(n).health() for n in ("mock", "ollama", "vllm")]
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