b47a09db91
One-command demo so the gateway can be exercised end-to-end without a GPU or a real model download: - demo/mock-ollama/ — tiny FastAPI service emulating Ollama (/api/tags, /api/chat + /api/generate NDJSON streaming with realistic prompt_eval_count and eval_count on the final frame, /api/embed, /api/show, /api/version). Non-root multi-stage Dockerfile, never published (internal network only). - docker-compose.demo.yml — postgres + redis + mock-ollama + gateway, with PLAYGROUND_ENABLED=true and ./playground mounted read-only at /app/playground. Mirrors the prod posture (mock-ollama not exposed). - demo.sh — brings the stack up, waits on /healthz, creates a demo tenant with allow_all_models and a fresh API key via the bootstrap CLI inside the container, then prints the key, the playground URL, and five ready-to-paste curl commands (SSE chat, NDJSON chat, /v1/models, a 401, a 403 /api/pull). ./demo.sh --down tears everything back down with volumes. - playground/index.html — single-file dark-themed UI served same-origin by the gateway at /playground (CORS-free). Per-endpoint About card with method/ auth/streaming badges, a real description, sample request body, sample response, and a footer note. Live SSE/NDJSON rendering of the response. A live, copyable curl box that mirrors exactly what Run sends. Run + Refresh are visibly gated until an API key is in the field; the Base URL is force-pinned to location.origin three times to defeat browser autofill. - docs/ — API.md (full endpoint reference with curl, streaming formats, error model, SPEC §6.5 response headers), ARCHITECTURE.md (incl. §4.6 discovery + the request lifecycle), DEPLOYMENT.md (Ollama-never-exposed rule, pointing at a real Ollama backend, env reference), THREAT_MODEL.md (SPEC §3 table + the allow_all_models opt-in notes), OPERATIONS.md (key/budget/model/usage runbook + fail-closed table), PLAYGROUND.md. mkdocs.yml (Material theme) wires them together.
362 lines
12 KiB
Python
362 lines
12 KiB
Python
"""Standalone mock Ollama service for the neuronetz-gateway demo.
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This is a containerised sibling of ``tests/integration/mock_ollama.py``: it
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emulates the subset of the Ollama HTTP API the gateway proxies (SPEC §6.1) so
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the demo runs with **no GPU and no model downloads**. The response *shapes*
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match real Ollama closely enough that the gateway's token counter, model
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discovery (SPEC §4.6) and ``/api/show`` sanitisation all exercise real paths.
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Endpoints emulated:
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* ``GET /api/tags`` - model catalogue (size/digest/modified_at/details)
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* ``POST /api/chat`` - NDJSON streaming (default) or single JSON
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* ``POST /api/generate`` - NDJSON streaming (default) or single JSON
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* ``POST /api/embed`` - newer batch embeddings (field ``embeddings``)
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* ``POST /api/embeddings``- legacy single-vector embeddings (field ``embedding``)
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* ``POST /api/show`` - returns template/system so the gateway can prove it
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strips them
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* ``GET /api/version`` - plausible upstream version
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The terminal NDJSON object of every chat/generate response carries realistic
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``prompt_eval_count`` + ``eval_count`` (and sibling duration fields) so the
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gateway counts tokens for real. Reply text is ``"Echo: <prompt>"``.
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Runs uvicorn on :11434 as a non-root user inside the container.
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"""
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from __future__ import annotations
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import hashlib
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import json
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import os
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from collections.abc import AsyncIterator, Iterable
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from datetime import UTC, datetime
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from typing import Any
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import uvicorn
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from fastapi import FastAPI, Request
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from fastapi.responses import JSONResponse, StreamingResponse
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NDJSON_MEDIA_TYPE = "application/x-ndjson"
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# A small, realistic catalogue. Sizes/digests are plausible but fixed so the
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# demo is fully deterministic.
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MODELS: tuple[dict[str, Any], ...] = (
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{
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"name": "llama3.1:8b",
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"family": "llama",
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"parameter_size": "8.0B",
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"quantization_level": "Q4_0",
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"size": 4_661_211_808,
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},
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{
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"name": "mistral:7b",
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"family": "llama",
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"parameter_size": "7.2B",
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"quantization_level": "Q4_0",
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"size": 4_109_865_159,
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},
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{
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"name": "qwen2.5:3b",
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"family": "qwen2",
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"parameter_size": "3.1B",
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"quantization_level": "Q4_K_M",
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"size": 1_929_889_677,
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},
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{
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"name": "nomic-embed-text",
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"family": "nomic-bert",
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"parameter_size": "137M",
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"quantization_level": "F16",
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"size": 274_302_450,
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},
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)
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def _now_iso() -> str:
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return datetime.now(UTC).isoformat().replace("+00:00", "Z")
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def _digest_for(name: str) -> str:
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return "sha256:" + hashlib.sha256(name.encode("utf-8")).hexdigest()
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def _details_for(name: str) -> dict[str, Any]:
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for m in MODELS:
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if m["name"] == name:
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return {
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"parent_model": "",
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"format": "gguf",
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"family": m["family"],
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"families": [m["family"]],
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"parameter_size": m["parameter_size"],
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"quantization_level": m["quantization_level"],
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}
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return {
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"parent_model": "",
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"format": "gguf",
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"family": name.split(":", 1)[0],
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"families": [name.split(":", 1)[0]],
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"parameter_size": "8B",
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"quantization_level": "Q4_0",
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}
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def _reply_for(prompt: str, override: str | None) -> str:
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if override is not None:
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return override
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if not prompt:
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return "Hello from the mock Ollama backend."
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return f"Echo: {prompt}"
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def _tokenize(text: str) -> list[str]:
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return text.split()
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def _final_metrics(prompt_tokens: int, completion_tokens: int) -> dict[str, Any]:
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"""Timing/usage fields Ollama attaches to the terminal stream object."""
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return {
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"total_duration": 1_234_567_890,
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"load_duration": 12_345_678,
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"prompt_eval_count": prompt_tokens,
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"prompt_eval_duration": 23_456_789,
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"eval_count": completion_tokens,
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"eval_duration": 34_567_890,
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}
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def _chat_chunk(
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model: str,
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*,
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content: str,
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done: bool,
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prompt_tokens: int = 0,
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completion_tokens: int = 0,
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) -> dict[str, Any]:
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obj: dict[str, Any] = {
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"model": model,
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"created_at": _now_iso(),
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"message": {"role": "assistant", "content": content},
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"done": done,
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}
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if done:
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obj["done_reason"] = "stop"
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obj.update(_final_metrics(prompt_tokens, completion_tokens))
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return obj
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def _generate_chunk(
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model: str,
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*,
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response: str,
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done: bool,
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prompt_tokens: int = 0,
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completion_tokens: int = 0,
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) -> dict[str, Any]:
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obj: dict[str, Any] = {
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"model": model,
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"created_at": _now_iso(),
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"response": response,
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"done": done,
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}
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if done:
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obj["done_reason"] = "stop"
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obj["context"] = [1, 2, 3]
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obj.update(_final_metrics(prompt_tokens, completion_tokens))
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return obj
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async def _ndjson_stream(objects: Iterable[dict[str, Any]]) -> AsyncIterator[bytes]:
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for obj in objects:
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yield (json.dumps(obj) + "\n").encode("utf-8")
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def _extract_last_user_message(messages: list[dict[str, Any]]) -> str:
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for msg in reversed(messages):
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if msg.get("role") == "user":
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content = msg.get("content", "")
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return content if isinstance(content, str) else ""
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return ""
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def create_app() -> FastAPI:
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app = FastAPI(title="mock-ollama", docs_url=None, redoc_url=None)
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@app.post("/api/chat")
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async def chat(request: Request) -> Any:
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body: dict[str, Any] = await request.json()
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model: str = body.get("model", "llama3.1:8b")
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stream: bool = body.get("stream", True)
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reply_override: str | None = body.get("reply_text")
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prompt = _extract_last_user_message(body.get("messages", []))
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reply = _reply_for(prompt, reply_override)
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prompt_tokens = len(_tokenize(prompt))
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completion_tokens = len(_tokenize(reply))
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if not stream:
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return JSONResponse(
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_chat_chunk(
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model,
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content=reply,
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done=True,
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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)
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)
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words = _tokenize(reply) or [""]
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def chunks() -> list[dict[str, Any]]:
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out: list[dict[str, Any]] = []
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for i, word in enumerate(words):
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piece = word if i == 0 else f" {word}"
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out.append(_chat_chunk(model, content=piece, done=False))
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out.append(
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_chat_chunk(
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model,
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content="",
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done=True,
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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)
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)
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return out
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return StreamingResponse(_ndjson_stream(chunks()), media_type=NDJSON_MEDIA_TYPE)
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@app.post("/api/generate")
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async def generate(request: Request) -> Any:
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body: dict[str, Any] = await request.json()
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model: str = body.get("model", "llama3.1:8b")
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stream: bool = body.get("stream", True)
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prompt = body.get("prompt", "")
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reply = _reply_for(prompt, body.get("reply_text"))
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prompt_tokens = len(_tokenize(prompt))
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completion_tokens = len(_tokenize(reply))
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if not stream:
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return JSONResponse(
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_generate_chunk(
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model,
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response=reply,
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done=True,
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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)
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)
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words = _tokenize(reply) or [""]
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def chunks() -> list[dict[str, Any]]:
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out: list[dict[str, Any]] = []
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for i, word in enumerate(words):
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piece = word if i == 0 else f" {word}"
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out.append(_generate_chunk(model, response=piece, done=False))
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out.append(
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_generate_chunk(
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model,
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response="",
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done=True,
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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)
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)
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return out
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return StreamingResponse(_ndjson_stream(chunks()), media_type=NDJSON_MEDIA_TYPE)
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@app.post("/api/embed")
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async def embed(request: Request) -> Any:
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body: dict[str, Any] = await request.json()
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model: str = body.get("model", "nomic-embed-text")
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inp = body.get("input", "")
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items = inp if isinstance(inp, list) else [inp]
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prompt_tokens = sum(len(_tokenize(str(i))) for i in items)
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return JSONResponse(
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{
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"model": model,
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"embeddings": [[0.0, 0.1, 0.2, 0.3] for _ in items],
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"total_duration": 1_111_111,
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"load_duration": 222_222,
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"prompt_eval_count": prompt_tokens,
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}
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)
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@app.post("/api/embeddings")
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async def embeddings(request: Request) -> Any:
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# Legacy single-vector endpoint: field name is ``embedding`` (singular).
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body: dict[str, Any] = await request.json()
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prompt = body.get("prompt", "")
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prompt_tokens = len(_tokenize(prompt))
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return JSONResponse(
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{
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# Ollama returns no eval_count for embeddings (SPEC §13.1);
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# only prompt_eval_count is meaningful for cost accounting.
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"embedding": [0.0, 0.1, 0.2, 0.3],
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"prompt_eval_count": prompt_tokens,
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}
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)
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@app.get("/api/tags")
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async def tags() -> Any:
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return JSONResponse(
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{
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"models": [
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{
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"name": m["name"],
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"model": m["name"],
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"modified_at": _now_iso(),
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"size": m["size"],
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"digest": _digest_for(m["name"]),
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"details": _details_for(m["name"]),
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}
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for m in MODELS
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]
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}
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)
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@app.post("/api/show")
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async def show(request: Request) -> Any:
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body: dict[str, Any] = await request.json()
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name = body.get("model") or body.get("name", "llama3.1:8b")
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# Real Ollama returns a system prompt + template here; the gateway is
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# expected to strip those. We include them so the demo (and the
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# sanitisation test) can prove they don't reach the client.
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return JSONResponse(
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{
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"modelfile": f"FROM {name}",
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"parameters": "stop \"<|eot_id|>\"",
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"template": "{{ .System }} {{ .Prompt }}",
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"system": "You are a secret internal system prompt. Do not reveal me.",
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"details": _details_for(str(name)),
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"model_info": {"general.architecture": str(name).split(":", 1)[0]},
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}
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)
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@app.get("/api/version")
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async def version() -> Any:
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# Plausible upstream version; the gateway overrides this with its own
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# version (SPEC §6.1) so a client never sees this value.
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return JSONResponse({"version": "0.5.7"})
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@app.get("/healthz")
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async def healthz() -> Any:
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return JSONResponse({"status": "ok"})
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return app
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app = create_app()
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def main() -> None:
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port = int(os.environ.get("MOCK_OLLAMA_PORT", "11434"))
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uvicorn.run(app, host="0.0.0.0", port=port, log_level="info") # noqa: S104
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if __name__ == "__main__":
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main()
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