openpay/ (+3 crates, ~8400 LOC, 162 tests)
op-iso8583/ ISO 8583 messages for direct acquirer / card-network
connectivity. MTIs, primary+secondary bitmaps, full
DE catalogue, BCD/ASCII/EBCDIC/LLVAR/LLLVAR codecs,
five dialects (Visa Base I, Mastercard MDS, Amex GNS,
Discover, JCB), network-management messages
(sign-on/echo/cutover), DE 55 EMV TLV.
op-mobile-wallets/ Apple Pay (ECDH-P256 + AES-256-GCM), Google Pay ECv2
(ECDH + HKDF + AES-256-CTR + HMAC), Samsung Pay. PAN
never leaves the decryption boundary as a String;
lands in VaultRef. No-PAN-leakage assertions per
wallet.
op-routing/ Least-cost routing + MCC-aware routing + intelligent
retry / soft-decline recovery. 231-MCC catalogue.
Real ISO 8583 DE-39 soft/hard decline taxonomies.
smart-byte/smart-byte-rs/ (+3 crates, ~7100 LOC, 96 tests)
smart-byte-vc/ W3C Verifiable Credentials 2.0: VCDM, presentations,
Data Integrity proofs (eddsa-jcs-2022), VC-JWT,
SD-JWT (selective disclosure), Bitstring Status
List 2021. Cargo-bridge maps VCs to Smart Byte
envelopes with SAID stability.
smart-byte-did/ W3C DID resolution: did:key (Ed25519/P-256/secp256k1),
did:web (HTTPS), did:peer (numalgo 2+4), did:jwk,
did:ion (feature-gated stub). DID URL dereferencer.
smart-byte-pq/ Post-quantum signatures: ML-DSA-44/65/87 (FIPS 204),
SLH-DSA across 12 parameter sets (FIPS 205), FN-DSA
(draft FIPS 206, feature-gated). Hybrid Ed25519 +
ML-DSA pattern for the transition. Secret-key
zeroize-on-drop.
eoc/eoc-rs/ (+3 crates, ~6450 LOC, 76 tests)
eoc-vendor-api/ NeuralBackend implementations: Anthropic, OpenAI,
Google, Mistral, Cohere, Groq, Together, Fireworks.
SSE streaming. Prompt-caching where supported.
Joule estimates per published energy coefficient
(12-model curated table).
eoc-embeddings/ 21 embedding models behind one Embedder trait:
OpenAI v3 (3), Cohere v3 (3), Voyage (5), Mistral,
Jina, plus local ONNX (BGE x3, nomic, mxbai, GTE,
E5) behind 'local' feature. Matryoshka-style
dimension projection. Per-content-hash cache.
eoc-route-learned/ RouteLLM-style matrix-factorisation router +
multinomial-logreg classifier + LinUCB / Thompson
bandits for online learning. LearnedCascade wraps
the rule-based cascade with a confidence-threshold
skip policy. Synthetic-eval accuracy: MF 0.935,
LogReg 0.980.
Quality:
- cargo check across all three workspaces: clean
- cargo clippy --all-targets -- -D warnings on every new crate: clean
- 334 new tests total; all passing
- #![forbid(unsafe_code)] across every new crate
- No unwrap/expect outside #[cfg(test)]
Rust 1.95.0 edition 2024 (inherited via root rust-toolchain.toml).
89 lines
2.3 KiB
Rust
89 lines
2.3 KiB
Rust
//! Mistral chat-completions backend (OpenAI-compatible schema).
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use async_trait::async_trait;
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use eoc_core::{JouleCost, JouleSource, Query, Response, Stage};
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use eoc_neural::NeuralBackend;
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use tracing::field;
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use crate::auth::Auth;
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use crate::config::VendorConfig;
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use crate::error::VendorResult;
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use crate::openai_compat;
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/// Default Mistral endpoint.
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pub const DEFAULT_ENDPOINT: &str = "https://api.mistral.ai/v1/chat/completions";
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/// Mistral chat backend.
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pub struct MistralBackend {
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client: reqwest::Client,
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auth: Auth,
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model: String,
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stream: bool,
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config: VendorConfig,
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}
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impl MistralBackend {
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/// Construct with an API key and target model.
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pub fn new(api_key: impl Into<String>, model: impl Into<String>) -> Self {
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Self {
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client: reqwest::Client::new(),
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auth: Auth::Bearer(api_key.into()),
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model: model.into(),
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stream: true,
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config: VendorConfig::new(),
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}
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}
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/// Override the [`VendorConfig`].
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pub fn with_config(mut self, config: VendorConfig) -> Self {
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self.config = config;
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self
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}
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/// Disable SSE streaming.
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pub fn without_stream(mut self) -> Self {
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self.stream = false;
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self
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}
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fn endpoint(&self) -> &str {
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self.config
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.endpoint
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.as_deref()
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.unwrap_or(DEFAULT_ENDPOINT)
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}
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async fn try_infer(&self, q: &Query) -> VendorResult<Response> {
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tracing::debug!(
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target: "mistral.infer",
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model = %self.model,
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api_key = field::Empty,
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"dispatching mistral inference"
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);
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openai_compat::execute(
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&self.client,
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self.endpoint(),
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&self.auth,
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&self.model,
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q,
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self.stream,
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&self.config,
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)
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.await
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}
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}
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#[async_trait]
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impl NeuralBackend for MistralBackend {
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async fn infer(&self, q: &Query) -> Response {
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match self.try_infer(q).await {
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Ok(r) => r,
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Err(e) => Response::new(
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q.id,
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format!("[mistral-error: {e}]"),
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Stage::Neural,
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JouleCost { microjoules: 0, source: JouleSource::Estimated },
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),
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}
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}
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}
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