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remove model_metrics_sources and selection_policy from demo and docs
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Adil Hafeez
parent
ba701264be
commit
0ff166e0f6
3 changed files with 7 additions and 168 deletions
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@ -13,7 +13,6 @@ Plano is an AI-native proxy and data plane for agentic apps — with built-in or
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- **One endpoint, many models** — apps call Plano using standard OpenAI/Anthropic APIs; Plano handles provider selection, keys, and failover
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- **Intelligent routing** — a lightweight 1.5B router model classifies user intent and picks the best model per request
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- **Cost & latency ranking** — models are ranked by live cost (DigitalOcean pricing API) or latency (Prometheus) before returning the fallback list
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- **Platform governance** — centralize API keys, rate limits, guardrails, and observability without touching app code
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- **Runs anywhere** — single binary; self-host the router for full data privacy
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@ -30,38 +29,24 @@ routing_preferences:
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models:
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- openai/gpt-4o
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- openai/gpt-4o-mini
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selection_policy:
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prefer: cheapest # rank by live cost data
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- name: code_generation
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description: generating new code, writing functions, or creating boilerplate
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models:
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- anthropic/claude-sonnet-4-20250514
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- openai/gpt-4o
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selection_policy:
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prefer: fastest # rank by Prometheus p95 latency
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```
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### `selection_policy.prefer` values
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| Value | Behavior |
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|---|---|
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| `cheapest` | Sort models by ascending cost. Requires a `type: cost` source in `model_metrics_sources`. |
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| `fastest` | Sort models by ascending P95 latency. Requires a `type: latency` source in `model_metrics_sources`. |
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| `random` | Shuffle the model list on each request. |
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| `none` | Return models in definition order — no reordering. |
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When a request arrives, Plano:
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1. Sends the conversation + route descriptions to Arch-Router for intent classification
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2. Looks up the matched route and ranks its candidate models by cost or latency
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2. Looks up the matched route and returns its candidate models
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3. Returns an ordered list — client uses `models[0]`, falls back to `models[1]` on 429/5xx
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```
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1. Request arrives → "Write binary search in Python"
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2. Arch-Router classifies → route: "code_generation"
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3. Rank by latency → claude-sonnet (0.85s) < gpt-4o (1.2s)
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4. Response → models: ["anthropic/claude-sonnet-4-20250514", "openai/gpt-4o"]
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3. Response → models: ["anthropic/claude-sonnet-4-20250514", "openai/gpt-4o"]
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```
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No match? Arch-Router returns `null` route → client falls back to the model in the original request.
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@ -77,28 +62,12 @@ export OPENAI_API_KEY=<your-key>
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export ANTHROPIC_API_KEY=<your-key>
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```
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Start Prometheus and the mock latency metrics server:
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Start Plano:
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```bash
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cd demos/llm_routing/model_routing_service
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docker compose up -d
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planoai up demos/llm_routing/model_routing_service/config.yaml
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```
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Then start Plano:
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```bash
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planoai up config.yaml
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```
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On startup you should see logs like:
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```
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fetched digitalocean pricing: N models
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fetched prometheus latency metrics: 3 models
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```
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If a model in `routing_preferences` has no matching pricing or latency data, Plano logs a warning at startup — the model is still included but ranked last.
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## Run the demo
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```bash
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@ -135,35 +104,7 @@ Response:
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}
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```
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The response contains the ranked model list — your client should try `models[0]` first and fall back to `models[1]` on 429 or 5xx errors.
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## Metrics Sources
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### Cost Metrics (provider: digitalocean)
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Fetches public model pricing from the DigitalOcean Gen-AI catalog (no auth required). Model IDs are normalized as `lowercase(creator)/model_id`. Cost scalar = `input_price_per_million + output_price_per_million`.
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```yaml
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model_metrics_sources:
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- type: cost
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provider: digitalocean
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refresh_interval: 3600 # re-fetch every hour
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```
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### Latency Metrics (provider: prometheus)
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Queries a Prometheus instance for P95 latency. The PromQL expression must return an instant vector with a `model_name` label matching the model names in `routing_preferences`.
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```yaml
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model_metrics_sources:
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- type: latency
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provider: prometheus
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url: http://localhost:9090
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query: model_latency_p95_seconds
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refresh_interval: 60
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```
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The demo's `metrics_server.py` exposes mock latency data; `docker compose up -d` starts it alongside Prometheus.
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The response contains the model list — your client should try `models[0]` first and fall back to `models[1]` on 429 or 5xx errors.
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## Kubernetes Deployment (Self-hosted Arch-Router on GPU)
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@ -22,28 +22,9 @@ routing_preferences:
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models:
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- openai/gpt-4o
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- openai/gpt-4o-mini
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selection_policy:
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prefer: cheapest
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- name: code_generation
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description: generating new code, writing functions, or creating boilerplate
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models:
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- anthropic/claude-sonnet-4-20250514
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- openai/gpt-4o
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selection_policy:
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prefer: fastest
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model_metrics_sources:
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- type: cost
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provider: digitalocean
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refresh_interval: 3600
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model_aliases:
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openai-gpt-4o: openai/gpt-4o
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openai-gpt-4o-mini: openai/gpt-4o-mini
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anthropic-claude-sonnet-4: anthropic/claude-sonnet-4-20250514
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- type: latency
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provider: prometheus
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url: http://localhost:9090
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query: model_latency_p95_seconds
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refresh_interval: 60
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@ -21,14 +21,12 @@ POST /v1/chat/completions
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{
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"name": "code generation",
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"description": "generating new code snippets",
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"models": ["anthropic/claude-sonnet-4-20250514", "openai/gpt-4o", "openai/gpt-4o-mini"],
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"selection_policy": {"prefer": "fastest"}
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"models": ["anthropic/claude-sonnet-4-20250514", "openai/gpt-4o", "openai/gpt-4o-mini"]
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},
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{
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"name": "general questions",
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"description": "casual conversation and simple queries",
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"models": ["openai/gpt-4o-mini"],
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"selection_policy": {"prefer": "cheapest"}
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"models": ["openai/gpt-4o-mini"]
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}
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]
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}
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@ -41,15 +39,6 @@ POST /v1/chat/completions
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| `name` | string | yes | Route identifier. Must match the LLM router's route classification. |
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| `description` | string | yes | Natural language description used by the router to match user intent. |
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| `models` | string[] | yes | Ordered candidate pool. At least one entry required. Must be declared in `model_providers`. |
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| `selection_policy.prefer` | enum | yes | How to rank models: `cheapest`, `fastest`, or `none`. |
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### `selection_policy.prefer` values
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| Value | Behavior |
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| `cheapest` | Sort by ascending cost from the metrics endpoint. Models with no data appended last. |
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| `fastest` | Sort by ascending latency from the metrics endpoint. Models with no data appended last. |
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| `none` | Return models in the order they were defined — no reordering. |
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### Notes
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@ -121,86 +110,14 @@ routing_preferences:
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models:
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- anthropic/claude-sonnet-4-20250514
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- openai/gpt-4o
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selection_policy:
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prefer: fastest
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- name: general questions
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description: casual conversation and simple queries
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models:
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- openai/gpt-4o-mini
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- openai/gpt-4o
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selection_policy:
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prefer: cheapest
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# Optional: live cost and latency data sources (max one per type)
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model_metrics_sources:
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- type: cost
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provider: digitalocean
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refresh_interval: 3600
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- type: latency
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provider: prometheus
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url: https://internal-prometheus/
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query: histogram_quantile(0.95, sum by (model_name, le) (rate(model_latency_seconds_bucket[5m])))
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refresh_interval: 60
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```
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### Startup validation
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Plano validates metric source configuration at startup and exits with a clear error if:
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| Condition | Error |
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| `prefer: cheapest` with no cost source | `prefer: cheapest requires a cost metrics source` |
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| `prefer: fastest` with no latency source | `prefer: fastest requires a latency metrics source` |
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| Two `type: cost` entries | `only one cost metrics source is allowed` |
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| Two `type: latency` entries | `only one latency metrics source is allowed` |
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If a model listed in `routing_preferences` has no matching entry in the fetched pricing or latency data, Plano logs a `WARN` at startup — the model is still included but ranked last. The same warning is also emitted per routing request when a model has no data in cache at decision time (relevant for inline `routing_preferences` overrides that reference models not covered by the configured metrics sources).
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### Cost metrics (provider: digitalocean)
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Fetches public model pricing from the DigitalOcean Gen-AI catalog. No authentication required.
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```yaml
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model_metrics_sources:
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- type: cost
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provider: digitalocean
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refresh_interval: 3600 # re-fetch every hour; omit to fetch once on startup
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model_aliases:
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openai-gpt-4o: openai/gpt-4o
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openai-gpt-4o-mini: openai/gpt-4o-mini
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anthropic-claude-sonnet-4: anthropic/claude-sonnet-4-20250514
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```
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DO catalog entries are stored by their `model_id` field (e.g. `openai-gpt-4o`). The cost scalar is `input_price_per_million + output_price_per_million`.
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**`model_aliases`** — optional. Maps DO `model_id` values to the model names used in `routing_preferences`. Without aliases, cost data is stored under the DO model_id (e.g. `openai-gpt-4o`), which won't match models configured as `openai/gpt-4o`. Aliases let you bridge the naming gap without changing your routing config.
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**Constraints:**
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- Only one `type: cost` entry is allowed.
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### Latency metrics (provider: prometheus)
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Plano queries `{url}/api/v1/query?query={query}` on startup and each `refresh_interval`. The PromQL expression must return an instant vector with a `model_name` label:
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```json
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{
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"status": "success",
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"data": {
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"resultType": "vector",
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"result": [
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{"metric": {"model_name": "anthropic/claude-sonnet-4-20250514"}, "value": [1234567890, "120.5"]},
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{"metric": {"model_name": "openai/gpt-4o"}, "value": [1234567890, "200.3"]}
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]
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}
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}
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```
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- The PromQL query is responsible for computing the percentile (e.g. `histogram_quantile(0.95, ...)`)
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- Latency units are arbitrary — only relative order matters
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- Models missing from the result are appended at the end of the ranked list
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---
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## Version Requirements
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