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OpenAI-compatible compute-backend preset (config only, reuses LLM_BACKEND=openai) for pointing a supplier node at the HuggingFace Inference Providers router. Adds a boot guard that rejects the OpenRouter-only reasoning param against the HF router, unit tests, and an operator runbook. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
115 lines
5.5 KiB
Plaintext
115 lines
5.5 KiB
Plaintext
# supplier/.env.huggingface-chat.example
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#
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# Preset for a chat supplier that uses the HuggingFace Inference Providers router
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# (https://router.huggingface.co) as its COMPUTE BACKEND to fulfil bonded
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# inference work commissioned on Vector. It is the OpenAI-compatible on-ramp:
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# the router is OpenAI-compatible, so this reuses the existing LLM_BACKEND=openai
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# path with NO code changes — it is config only.
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#
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# Copy to supplier/.env on the host (chmod 600) and fill in real values.
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# .env is gitignored — NEVER commit a populated copy.
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#
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# Runbook: docs/HUGGINGFACE_ROUTER_SETUP.md
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#
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# To generate a fresh Ed25519 wallet seed: openssl rand -hex 32
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# Lines marked SECRET must never be shared, logged, or committed.
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# ─── Required ─────────────────────────────────────────────────────────────
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# 64-char hex Ed25519 private key seed for this supplier's wallet. SECRET.
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SUPPLIER_PRIV_KEY_HEX=PASTE_64_HEX_CHARS_HERE
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# Ogmios WebSocket URL. Testnet shown; use the mainnet URL for NETWORK_ID=1:
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# testnet: wss://ogmios.vector.testnet.apexfusion.org
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# mainnet: wss://ogmios.vector.mainnet.apexfusion.org
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OGMIOS_URL=wss://ogmios.vector.testnet.apexfusion.org
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# OutputReference of this supplier's on-chain advertisement UTxO.
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# Format "<64-hex-tx-hash>#<int>". Post it first (see tx:post-advert below),
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# then paste the printed "<txHash>#0" here.
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ADVERT_REF=PASTE_TXHASH_HERE#0
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# Cardano network ID. "0" = testnet, "1" = mainnet. Keep "0" until you have
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# validated the setup end-to-end on testnet.
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NETWORK_ID=0
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# ─── Backend: HuggingFace router (OpenAI-compatible) ──────────────────────
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# Route the chat job runner at the HF Inference Providers router instead of a
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# local Ollama. The supplier POSTs to ${OPENAI_BASE_URL}/v1/chat/completions.
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LLM_BACKEND=openai
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# HF router base URL. The supplier appends /v1/chat/completions itself, so do
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# NOT include the /v1 suffix here.
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OPENAI_BASE_URL=https://router.huggingface.co
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# HuggingFace access token (starts with "hf_"). Create one at
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# https://huggingface.co/settings/tokens with "Make calls to Inference
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# Providers" permission. Forwarded as `Authorization: Bearer <token>`. SECRET.
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OPENAI_API_KEY=PASTE_HF_TOKEN_HERE
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# First-token latency across hosted providers can exceed local Ollama — keep a
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# generous request timeout.
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OPENAI_TIMEOUT_MS=180000
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# ─── IMPORTANT: do NOT set OPENAI_REASONING here ──────────────────────────
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# `reasoning:{enabled:false}` is an OpenRouter-only param. The HF router
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# rejects it with HTTP 400 on every request. The supplier refuses to boot if
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# OPENAI_REASONING is disabled while OPENAI_BASE_URL points at the HF router.
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# (This is the #1 footgun when copying an OpenRouter env — leave it unset.)
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# OPENAI_REASONING=
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# Optional hard ceiling forwarded as `max_tokens`. Unset = provider grants the
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# max available (context − prompt). Keep below the model's context window.
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# OPENAI_MAX_TOKENS=
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# ─── Enable real chain submissions ────────────────────────────────────────
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# LIVE_CHAIN: only the literal "1" opts in to real Claim/Submit txs. Leave
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# unset for a dry run (read-only, no chain writes) while testing.
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LIVE_CHAIN=
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# HTTP listen port inside the container. Compose maps this to the host.
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PORT=8080
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# ─── tx:post-advert — run once before first deploy ────────────────────────
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# Execute (from repo root):
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# pnpm --filter @marketplace/supplier tx:post-advert \
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# --capability-id llm.text.generate.v1 \
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# --model deepseek-ai/DeepSeek-V3:fastest \
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# --max-output-tokens 512 \
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# --max-processing-ms 60000 \
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# --price-lovelace 2000000 \
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# --endpoint-url https://mp-supplier.vector.testnet.apexfusion.org
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# Copy the printed "<txHash>#0" into ADVERT_REF above.
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#
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# NOTE on --model: HF uses PROVIDER-QUALIFIED model names. The bare id picks a
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# default provider; a ":<hint>" suffix pins routing:
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# deepseek-ai/DeepSeek-V3:fastest # cheapest/fastest provider for the model
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# deepseek-ai/DeepSeek-V3:together # pin a specific provider
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# openai/gpt-oss-120b # open-weight, HF-hosted
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# Browse models + providers at https://huggingface.co/models?inference_provider=all
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# The value lives on-chain in AdvertDatum.model — it is data, not code, so you
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# can advertise any router-supported model without touching the supplier.
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ADVERT_CAPABILITY_ID=llm.text.generate.v1
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ADVERT_MODEL=deepseek-ai/DeepSeek-V3:fastest
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ADVERT_MAX_OUTPUT_TOKENS=512
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ADVERT_MAX_PROCESSING_MS=60000
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# Flat AP3X amount committed in escrow per job (1 AP3X = 1_000_000 lovelace).
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# The HF backend meters PER TOKEN while this committed amount is fixed per job,
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# so size it for your worst-case prompt length and cap output tightly via
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# ADVERT_MAX_OUTPUT_TOKENS. See docs/HUGGINGFACE_ROUTER_SETUP.md §4.
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ADVERT_PRICE_LOVELACE=2000000
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ADVERT_SUPPLIER_BOND_LOVELACE=1000000
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ADVERT_BUYER_BOND_LOVELACE=1000000
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# HTTPS endpoint buyers will call (must be publicly reachable).
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ADVERT_ENDPOINT_URL=https://mp-supplier.vector.testnet.apexfusion.org
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# Optional off-chain detail pointer + its sha256 (leave empty for v1).
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ADVERT_DETAIL_URI=
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ADVERT_DETAIL_HASH=
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# ─── Optional: CIP-33 reference scripts (recommended on mainnet) ──────────
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# ESCROW_REF_UTXO=<txhash>#0
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# ADVERT_REF_UTXO=<txhash>#1
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