infergate 0.1.6

Semantic routing layer for AI inference backends

InferGate

Semantic routing library for AI inference backends. Routes each request to the best available model based on what the request is asking for — not on hardcoded rules or manual model selection.

Built as a standalone Python library with no GPU or OpenVINO dependency. Extracted and generalised from production routing logic in ov_server.

---

The problem it solves

Running multiple LLM backends — a fast local model, a capable local VLM, a cloud API — means every request needs a routing decision:

• Is this a vision task? Send to the VLM.
• Is this a 10 000-token document? The 4 k-context local model cannot handle it.
• Is this a quick factual question? The smallest fast model is enough.
• Is this a complex code review? Escalate to the best available model.

Doing this in application code means routing logic scattered across every integration point, with no consistency and no observability. InferGate centralises this into a single router.decide(request) call.

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How it works

Every request passes through a four-stage pipeline:

┌─ Stage 1: directive check ──────────────────────────────────────────────┐
│  #code / #document / #general tag in last user message?                 │
│  → assign task class immediately, strategy = KEYWORD                    │
└─────────────────────────────────────────────────────────────────────────┘
         │ no directive
┌─ Stage 2: signal detection ─────────────────────────────────────────────┐
│  O(1) checks in priority order:                                         │
│    image content  → "vision"      (image_url in message content)        │
│    client tools   → "web_search"  (tools list present)                  │
│    long context   → "document"    (char/4 estimate > long_context_tokens)│
│    keyword match  → task class    (configurable phrase list)            │
│  → assign task class, strategy = SIGNAL                                 │
└─────────────────────────────────────────────────────────────────────────┘
         │ no signal fired
┌─ Stage 3: embedding classification ────────────────────────────────────┐
│  Embed last user message with sentence-transformers                     │
│  Compute cosine similarity against per-class centroids                  │
│  score ≥ threshold  → best match,  strategy = EMBEDDING                 │
│  score < threshold  → "general",   strategy = FALLBACK                  │
└─────────────────────────────────────────────────────────────────────────┘
         │ task class known
┌─ Stage 4: scope resolution ─────────────────────────────────────────────┐
│  per-class scope_override  >  #ovh/#cloud directive  >  global scope    │
└─────────────────────────────────────────────────────────────────────────┘
         │ eligible backends determined
┌─ Stage 5: model selection ──────────────────────────────────────────────┐
│  1. Prefer loaded (warm VRAM) fast-tier models                          │
│  2. Pick by tier: fastest → balanced → best                             │
│  3. High complexity (> 0.65) promotes balanced → best                   │
│  4. Skip models where ctx_limit < estimated prompt tokens               │
└─────────────────────────────────────────────────────────────────────────┘
         ↓
    RouteDecision(backend, model_id, task_class, strategy, confidence, …)

Stages 1 and 2 are O(1) — no embedding, no network, always under 1 ms. Stage 3 runs only when no signal matches, and runs in run_in_executor so it never blocks the event loop.

---

Installation

pip install infergate

For the demo gateway:

pip install "infergate[demo]"

For development:

git clone https://github.com/Jermalk/infergate
cd infergate
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev,demo]"

Requires Python 3.10+.

---

Quick start — routing only

import asyncio
from infergate import Router, RouterConfig, InferRequest
from infergate.backends.ollama import OllamaBackend
from infergate.embeddings import SentenceTransformerProvider

async def main():
    backend = await OllamaBackend.create()          # discovers models from /api/tags
    config  = RouterConfig.from_dict({
        "provider_scope": "local",
        "task_classes": {
            "code":    {"description": "Code generation and debugging",
                        "models": [{"id": "qwen2.5-coder:14b", "backend": "ollama", "tier": "fast"}]},
            "general": {"description": "General questions and conversation",
                        "models": [{"id": "qwen2.5:3b", "backend": "ollama", "tier": "fast"}]},
        },
    })

    provider = SentenceTransformerProvider()
    router   = Router(config, backends={"ollama": backend}, embedding_provider=provider)
    await router.load_embeddings()                  # must be called before decide()

    decision = await router.decide(InferRequest(
        messages=[{"role": "user", "content": "Write a binary search in Python."}]
    ))
    print(decision.backend, decision.model_id, decision.strategy)
    # → ollama  qwen2.5-coder:14b  RouteStrategy.EMBEDDING

asyncio.run(main())

Router.decide() returns a RouteDecision and does not execute the request. The caller chooses what to do with the decision — call backend.chat(), forward to another service, log it, or anything else.

---

Demo gateway

The demo/ directory contains a ready-to-run FastAPI gateway that wires infergate routing with full request execution across Ollama, ov_server, and OVH AI Endpoints backends.

Prerequisites

Component	Notes
Ollama	Running at localhost:11434
ov_server	Optional. OAI-compat instance at any URL.
OVH AI Endpoints	Optional. Requires API key.
sentence-transformers	Loaded on first startup (≈ 600 MB cached).

Run

export INFERGATE_OVH_API_KEY=<your-key>
export INFERGATE_OV_SERVER_URL=http://localhost:11435/v1   # optional

cd demo
./run_demo.sh

Or without the script:

INFERGATE_OVH_API_KEY=<key> uvicorn gateway:app --app-dir demo --port 8080

Environment variables

All settings use the INFERGATE_ prefix and can also be placed in a .env file in the working directory.

Variable	Default	Description
INFERGATE_CONFIG	demo/config.yaml	Path to routing config file
INFERGATE_OLLAMA_URL	http://localhost:11434	Ollama base URL
INFERGATE_OV_SERVER_URL	(empty — disabled)	ov_server base URL including /v1
INFERGATE_OVH_API_KEY	(empty — disabled)	OVH bearer token
INFERGATE_OVH_BASE_URL	https://oai.endpoints.kepler.ai.cloud.ovh.net/v1	OVH endpoint
INFERGATE_EMBEDDING_MODEL	intfloat/multilingual-e5-large	Sentence-transformers model
INFERGATE_LOG_LEVEL	INFO	Logging level

Endpoints

Method	Path	Description
POST	/v1/chat/completions	OAI-compatible chat endpoint with routing
GET	/v1/models	Aggregated model list across all connected backends
GET	/health	Backend status and model counts

Every response from /v1/chat/completions includes routing metadata headers:

X-InferGate-Backend:   ollama
X-InferGate-Model:     qwen2.5-coder:14b
X-InferGate-TaskClass: code
X-InferGate-Strategy:  keyword

Demo requests

# Run the bundled demo script against a running gateway
INFERGATE_GW=http://localhost:8080 ./demo/demo_requests.sh

The script exercises three routing paths:

1. #code hashtag directive → strategy keyword, routes to fast-tier coder model
2. Plain factual question → strategy embedding, classified by cosine similarity
3. implement … phrase → strategy signal, keyword match fires before embedding

---

Configuration reference

The routing config is a YAML (or JSON) file consumed by RouterConfig.from_dict().

provider_scope: hybrid          # "local" | "remote" | "hybrid" — default scope

active_profile: fast
profiles:
  fast:
    model_preference: fastest   # "fastest" | "balanced" | "best"
  best:
    model_preference: best

router:
  embedding_min_confidence: 0.72  # cosine threshold; below → fall back to "general"
  long_context_tokens: 4000       # token estimate (char/4) triggering document class
  keywords:                       # keyword → task class signal
    code:
      - "write a function"
      - "implement"

task_classes:
  code:
    description: "Code generation, debugging, and software engineering tasks."
    examples:                     # optional; improves centroid quality
      - "Write a Python function to parse JSON."
      - "Fix the null pointer exception in this code."
    scope_override: hybrid        # overrides provider_scope for this class
    models:
      - id: qwen2.5-coder:14b
        backend: ollama           # must match Backend.name()
        tier: fast                # "fast" | "balanced" | "best"
        ctx_limit: 32768          # max prompt tokens; model is skipped if exceeded

Scope system

Controls which backends are eligible for a request. Three levels of override:

Priority	Source	Example
Highest	scope_override in task class	scope_override: remote
Middle	#ovh or #cloud tag in message	forces remote
Lowest	Global provider_scope	provider_scope: hybrid

Scope	Eligible backends
local	Backends with is_local=True (Ollama, ov_server)
remote	Backends with is_local=False (OVH, any cloud API)
hybrid	All registered backends

User-facing directives

These tags in the last user message are intercepted by the router before any other logic:

Tag	Effect
#code	Force task class → code, strategy = keyword
#document	Force task class → document, strategy = keyword
#general	Force task class → general, strategy = keyword
#ovh	Force scope → remote (does not change task class)
#cloud	Same as #ovh

Tags are case-insensitive and can appear anywhere in the message.

---

Backends

OllamaBackend

from infergate.backends.ollama import OllamaBackend

backend = await OllamaBackend.create(
    base_url="http://localhost:11434",
    name="ollama",
)

• Discovers available models from GET /api/tags.
• Tracks models currently loaded in memory via GET /api/ps — used by the "prefer loaded" selection logic to prefer warm VRAM.
• is_local = True always.

OpenAICompatBackend

from infergate.backends.openai_compat import OpenAICompatBackend

backend = await OpenAICompatBackend.create(
    name="ovh",
    base_url="https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
    api_key="...",
    is_local=False,
    exclude_ids=frozenset({"Auto"}),   # filter sentinel / meta model IDs
)

Covers any OpenAI-compatible API: OVH AI Endpoints, ov_server, vLLM, LM Studio, Together AI, Fireworks, etc. The base_url must include the versioned path (e.g. /v1) — the backend appends /models and /chat/completions directly.

• known_models: supply a static list instead of fetching from /models.
• exclude_ids: frozenset of model IDs to hide from routing (e.g. routing sentinels or non-chat models like embedding endpoints).
• loaded_model_ids() returns [] — remote APIs have no concept of "loaded".

Custom backends

Implement the Backend protocol:

from infergate.protocols import Backend
from infergate.types import InferRequest

class MyBackend:
    @property
    def is_local(self) -> bool:
        return True

    def name(self) -> str:
        return "my_backend"

    def available_models(self) -> list[str]:
        return ["my-model"]

    def loaded_model_ids(self) -> list[str]:
        return []                            # return [] for remote / unknown

    async def chat(self, request: InferRequest, model_id: str) -> dict:
        # return an OpenAI-compatible response dict
        ...

No base class or registration required. Any object satisfying the protocol can be passed to Router(backends={"my_backend": MyBackend()}).

---

RouteDecision fields

@dataclass
class RouteDecision:
    backend:       str             # Backend.name() of the selected backend
    model_id:      str             # model ID on that backend
    task_class:    str             # "code" / "document" / "general" / …
    strategy:      RouteStrategy   # KEYWORD | SIGNAL | EMBEDDING | FALLBACK
    confidence:    float           # cosine similarity score; 1.0 for signal routes
    prefer_loaded: bool            # True when a warm VRAM model was preferred
    embedding:     list[float] | None  # request embedding vector, if computed

confidence and strategy are useful for monitoring: low confidence with FALLBACK strategy means the embedding model was uncertain and fell back to the general class. prefer_loaded tells you whether VRAM state influenced the pick.

---

Custom EmbeddingProvider

from infergate.protocols import EmbeddingProvider
import asyncio

class MyEmbeddingProvider:
    async def embed(self, text: str) -> list[float]:
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(None, self._encode_one, text)

    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(None, self._encode_batch, texts)

    def _encode_one(self, text: str) -> list[float]: ...
    def _encode_batch(self, texts: list[str]) -> list[list[float]]: ...

CPU-bound work must run in run_in_executor. The default SentenceTransformerProvider uses intfloat/multilingual-e5-large — a multilingual model that handles non-English queries without any configuration.

---

Kubernetes deployment

The demo gateway follows the 12-factor config pattern. Use it as a reference for production deployments:

# Deployment env section
env:
  - name: INFERGATE_OVH_API_KEY
    valueFrom:
      secretKeyRef:
        name: infergate-secrets
        key: ovh-api-key
  - name: INFERGATE_CONFIG
    value: /etc/infergate/config.yaml

# Mount routing config from a ConfigMap
volumeMounts:
  - name: routing-config
    mountPath: /etc/infergate

volumes:
  - name: routing-config
    configMap:
      name: infergate-routing-config

The routing topology (task classes, model lists, thresholds) lives in the ConfigMap. Secrets (API keys) are injected as environment variables. Neither requires a pod restart to take effect via a rolling update.

---

Development

# Run tests — no GPU, no network, no real model required
pytest

# Type check
mypy src/infergate --ignore-missing-imports

# Format
black src tests demo

All tests use a hash-based MockEmbeddingProvider that produces deterministic unit vectors without loading any model. The full test suite runs in under 1 second.

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Design decisions

No GPU dependency in the library. infergate is a routing and dispatch layer. Model inference stays in the backends. The only CPU-bound work is the embedding forward pass for task classification, which runs in run_in_executor.

Protocol-based backends, not a plugin registry. Any class satisfying the Backend structural protocol works without registration, inheritance, or decorators. Python's isinstance(obj, Backend) check validates at runtime.

Signal detection before embedding. For requests with obvious signals — an image, a tools list, a very long context — running an embedding model is waste. The O(1) signal check handles the majority of clearly-typed requests. Embedding classification only runs when the request is genuinely ambiguous.

Scope system over provider names. Scope values (local, remote, hybrid) are backend-agnostic. A backend is local or remote by its is_local flag, not by its name. This avoids coupling routing config to specific provider names.

config.yaml for topology, env vars for secrets. Routing structure (task classes, models, thresholds) belongs in version control. Credentials and infrastructure URLs belong in the environment. The pydantic-settings BaseSettings class enforces this split in the demo gateway.