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+# Endpoint Picker Protocol
+
+The Endpoint Picker, or EPP, is a core component of the inference extension. Ultimately it's
+responsible for picking an endpoint from the `InferencePool`. A reference implementation can be
+found [here](../../../pkg/ext-proc/).
+
+## Proxy Protocol
+
+This is the protocol between the EPP and the proxy (e.g, Envoy).
+
+The EPP MUST implement the Envoy
+[external processing service](https://www.envoyproxy.io/docs/envoy/latest/api-v3/service/ext_proc/v3/external_processor)protocol.
+
+For each HTTP request, the EPP MUST communicate to the proxy the picked model server endpoint, via
+adding the `target-pod` HTTP header in the request, or otherwise return an error.
+
+## Model Server Protocol
+
+This is the protocol between the EPP and the model servers.
+
+### Inference API Protocol
+
+The model server MUST implement OpenAI’s [Completions](https://platform.openai.com/docs/api-reference/completions)
+and [Chat](https://platform.openai.com/docs/api-reference/chat) APIs.
+
+### Metrics Reporting
+
+The inference extension scrapes metrics from the model servers to make optimal request scheduling
+decisions. The model servers MUST provide the following metrics via a Prometheus endpoint. The exact
+metric names don't necessarily need to be the same as the recommended names here, however the
+metric types and semantics MUST follow this doc.
+
+Note the requirements here are aligned with the
+[model server metrics standardization](https://docs.google.com/document/d/1SpSp1E6moa4HSrJnS4x3NpLuj88sMXr2tbofKlzTZpk)
+effort.
+
+The corresponding metrics in vLLM are also shown in the table below, as vLLM is already integrated
+into the reference endpoint picker implementation.
+
+| Metric | Type | Description | vLLM metric |
+| ----- | ---- | ---- | ---- |
+| TotalQueuedRequests         | Gauge     | The current total number of requests in the queue.| `vllm:num_requests_waiting`|
+| KVCacheUtilization| Gauge     | The current KV cache utilization in percentage.| `vllm:gpu_cache_usage_perc`|
+
+
+### LoRA Adapter Serving
+
+Model servers that support dynamic LoRA serving can benefit from the LoRA affinity algorithm. Note
+the current algorithm in the reference EPP is highly biased towards vLLM's current dynamic LoRA 
+implementation.
+
+The model servers MUST support serving a LoRA adapter specified in the `model` argument of the
+request, provided the requested adapter is valid.
+
+The model server MUST expose the following LoRA adapter metrics via the same Prometheus endpoint:
+
+* Metric name implemented in vLLM: `vllm:lora_requests_info` 
+* Metric type: Gauge
+* Metric value: The last updated timestamp (so the EPP can find the latest).
+* Metric labels: 
+  * `max_lora`: The maximum number of adapters that can be loaded to GPU memory to serve a batch.
+  Requests will be queued if the model server has reached MaxActiveAdapter and canno load the
+  requested adapter. Example: `"max_lora": "8"`.
+  * `running_lora_adapters`: A comma separated list of adapters that are currently loaded in GPU
+    memory and ready to serve requests. Example: `"running_lora_adapters": "adapter1, adapter2"`
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