Token Rate Limit Header Keys

Extends token-rate-limit.yaml with M5 header dimensions (ai#123 / ai#129): each distinct x-tenant-id value gets its own token budget under the same catch-all rule

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Category: Setup-dependent integration
Task: Extends token-rate-limit.yaml with M5 header dimensions (ai#123 / ai#129): each distinct x-tenant-id value gets its own token budget under the same catch-all rule

Prerequisites: The external service, credentials, or certificates referenced by this configuration.

This configuration comes from the selected release. The example has not been run here; external services are not bundled.

Download the source file.

Companion resources from the same snapshot:

# Token Rate Limiting -- Per-Header Bucket Keys
#
# Extends token-rate-limit.yaml with M5 header dimensions (ai#123 /
# ai#129): each distinct `x-tenant-id` value gets its own token budget
# under the same catch-all rule. Missing `x-tenant-id` is rejected (400)
# unless you set `missing: fallback`.
#
# Usage:
#   cargo run -p praxis-ai-proxy -- -c examples/configs/token-rate-limit-header-keys.yaml
#   curl -i http://localhost:8080/v1/chat/completions -H 'x-tenant-id: alpha' \
#     -d '{"model":"gpt-4","messages":[{"role":"user","content":"hi"}]}'
#
# See token-rate-limit.yaml for reservation-based admission (M2),
# reconciliation, M4 token-type weights, and the `rules:` schema.
# CEL-expression matchers/keys remain deferred.
#
# Supported token_count provider values:
#   openai | anthropic | google | bedrock | azure

listeners:
  - name: default
    address: "127.0.0.1:8080"
    filter_chains:
      - main

filter_chains:
  - name: main
    filters:
      - filter: router
        routes:
          - path: "/v1/chat/completions"
            cluster: backend

      - filter: token_rate_limit
        key:
          - header: x-tenant-id
        default_weights:
          input: 1.0
          output: 1.0
          cached_input: 0.1
          cache_write: 1.25
          reasoning: 0.9
        rules:
          - name: default
            algorithm: sliding_window
            window: 1h
            capacity: 100000
            reserved_tokens: 500

      - filter: token_count
        provider: openai

      - filter: access_log

      - filter: load_balancer
        clusters:
          - name: backend
            endpoints:
              - "127.0.0.1:3000"

insecure_options:
  allow_private_endpoints: true