proviz-elekto 0.6.0

Smart LLM model router - auto-starts proviz-server binary, no Docker required

ProvizElekto

Smart LLM model router. Picks the best model for each call based on context size, rate limits, and capabilities — and retries automatically on failure.

Your app → pz.call(step, fn)               → CallResult
           pz.call_litellm(step, messages) → CallResult
                    ↕  (automatic)
           select → LLM call → report → retry on failure
                    ↕
              proviz-server (Rust)
          rate-limit state · catalog

Key difference from LiteLLM fallback: LiteLLM retries after failure. ProvizElekto picks the right model before the call — skipping models that are rate-limited or near their quota, can't fit the context, or lack required capabilities — then retries with the next eligible model automatically.

Features

• Context-aware selection - don't waste a 128k model on a 1k prompt
• Proactive quota tracking - sliding-window counters (RPM/TPM/RPD/TPD) plus atomic in-flight reservations; avoids over-booking before any 429 fires
• Provider-anchored windows - every successful call forwards x-ratelimit-remaining-* headers back to the server; the window floor is clamped to provider reality so internal estimates can't drift below what the provider actually sees
• Scored selection - multi-component scoring: fast headroom (RPS/RPM/TPM, 25%), daily budget (RPD/TPD, 20%), quality (20%), cost (15%), latency (10%), traffic balance (10%). Over-quota models stay eligible with lower scores — AllModelsExhausted only fires when every model is in reactive 429 cooldown.
• Traffic shaping - per-brand traffic_weight steers load proportionally across providers in a 5-minute rolling window; under-served brands get a higher score on the traffic component
• Capability filtering - hard requirements for function calling, JSON mode
• Quality floor - reject models below a quality threshold per step
• Model groups - define named pools of models (e.g. "fast-chat", "coding-tier1") and restrict selection to that pool
• Your keys, your models - curated catalog, no vendor proxy
• Zero-infra - pip install proviz-elekto auto-starts the Rust server as a subprocess
• Any language - HTTP API, not a library binding
• Pluggable storage - SQLite (default) or PostgreSQL

Installation

ProvizElekto consists of a Rust server and various clients.

pip install proviz-elekto          # core only
pip install proviz-elekto[litellm] # + built-in LiteLLM integration

The proviz-server binary is bundled in the wheel.

CLI tool (proviz) is also included:

proviz --help

Documentation

• Catalog Setup — Seeding brands/models, adding rules, model groups
• Selection Algorithm — Scoring, headroom, priority, quality scores, retry hints
• HTTP API Reference — /select, /report, /health, /catalog/reload
• Deployment & Docker — Running the server, env vars, Docker, building from source
• Data Model — Table schemas for all pz_* tables

Quickstart

With LiteLLM (recommended)

from proviz_elekto import ProvizElekto

pz = ProvizElekto(db_path="./proviz.db")
# or PostgreSQL: pz = ProvizElekto(database_url=os.environ["DATABASE_URL"])

result = pz.call_litellm(
    step="verdict",
    messages=[{"role": "user", "content": "Summarize this document..."}],
    estimated_tokens=2500,
    requires_json_mode=True,
)
print(result.provider, result.candidate.model_slug, result.total_tokens)
# → mistral mistral-small-latest 312

call_litellm() selects the best available model, calls it, reports the outcome, and retries with the next eligible model on any failure — automatically.

With a custom LLM caller

import anthropic

client = anthropic.Anthropic()

def my_llm(candidate):
    return client.messages.create(
        model=candidate.model_slug,
        max_tokens=1024,
        messages=[{"role": "user", "content": "Hello"}],
    )

result = pz.call("verdict", my_llm, estimated_tokens=100)
print(result.candidate.brand_slug, result.prompt_tokens)

Pass any callable that accepts a ModelCandidate and returns a response. ProvizElekto wraps it with the same select → report → retry loop.

Low-level API

If you need direct control over selection and reporting:

candidate = pz.select(step="verdict", estimated_tokens=2500)
try:
    response = my_llm_call(candidate)

    # Read provider rate-limit headers (Mistral/OpenAI style; Anthropic style also supported)
    hdrs = getattr(response, "_hidden_params", {}).get("additional_headers") or {}
    rem_req = hdrs.get("x-ratelimit-remaining-requests")
    rem_tok = hdrs.get("x-ratelimit-remaining-tokens")

    pz.report_success(
        candidate.model_id,
        estimated_tokens=candidate.estimated_tokens,  # releases in-flight reservation
        actual_tokens=response.usage.total_tokens,    # improves TPM window accuracy
        remaining_requests=int(rem_req) if rem_req is not None else None,
        remaining_tokens=int(rem_tok)   if rem_tok is not None else None,
    )
    # report_success is fire-and-forget — returns immediately, HTTP call runs in background
except RateLimitError as exc:
    msg = str(exc).lower()
    if "day" in msg or "daily" in msg:
        error_type = "tpd"
    elif "token" in msg:
        error_type = "tpm"
    else:
        error_type = "rpm"
    pz.report_rate_limit(candidate.model_id, error_type)  # synchronous — must complete before retry
except Exception:
    pz.report_error(candidate.model_id, "other")

estimated_tokens in each report call releases the in-flight reservation made at selection time. Omitting it is safe (legacy clients work unchanged) but leaves the in-flight counter inflated until the next selection clears it.

report_success is non-blocking: the HTTP call to proviz runs in a background daemon thread so the caller receives the LLM result without waiting for the round-trip. report_rate_limit and report_error remain synchronous because the model must be blocked in proviz before the retry select() call.

License

Apache-2.0