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feat: more models, make decisions a wrapper for system one

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This commit is contained in:
Jacob
2026-10-10 19:06:02 -04:00
parent 9a848e2c78
commit e0e1f0781e
7 changed files with 248 additions and 167 deletions
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269
src/server/decisions.rs
View File

@@ -1,25 +1,16 @@
use crate::CloudflareCredentials;
use crate::openai;
use crate::server::AppState;
use crate::server::Provider;
use crate::server::Upstream;
use crate::server::take_random_by_swapping;
use axum::Json;
use axum::extract::State;
use axum::http::StatusCode;
use axum::response::IntoResponse;
use axum::response::Response;
use axum_extra::routing::TypedPath;
use ecow::EcoString;
use headers::Header;
use headers::authorization::Bearer;
use mitsein::vec1::Vec1;
use reqwest::Certificate;
use secrecy::ExposeSecret;
use serde::Deserialize;
use serde_with::DeserializeFromStr;
use serde_with::SerializeDisplay;
use std::collections::VecDeque;
use std::collections::HashMap;
use std::hash::RandomState;
use strum::EnumString;
use strum::VariantArray;
@@ -31,20 +22,12 @@ pub struct V1DecisionsPath;
Clone, Debug, EnumString, DeserializeFromStr, strum::Display, SerializeDisplay, VariantArray,
)]
#[strum(serialize_all = "lowercase")]
pub enum Shortcut {}
impl Shortcut {
fn suggestions(&self) -> Vec<(Provider, EcoString)> {
use Provider as P;
use Shortcut as S;
match self {
_ => unreachable!()
}
}
pub enum Shortcut {
S,
M,
}
pub struct DecisionsResponse(openai::embeddings::CreateEmbeddingResponse<String>);
pub struct DecisionsResponse(openai::decisions::DecisionResponse<String>);
impl IntoResponse for DecisionsResponse {
fn into_response(self) -> Response {
@@ -66,95 +49,175 @@ impl IntoResponse for DecisionsError {
}
}
#[tracing::instrument(skip(state, request))]
#[tracing::instrument(skip(app_state, request))]
pub async fn create_decision(
_: V1DecisionsPath,
State(state): State<AppState>,
State(app_state): State<AppState>,
Json(request): Json<openai::decisions::DecisionRequest<Shortcut>>,
) -> Result<DecisionsResponse, DecisionsError> {
let shortcut = request.model.clone();
let mut model_upstreams = VecDeque::new();
use super::system_one;
for (provider, model) in shortcut.suggestions() {
let upstreams: Vec<Upstream> = match provider {
Provider::CloudflareWorkersAi => unreachable!(),
Provider::GoogleAiStudio => unreachable!(),
Provider::Groq => unreachable!(),
Provider::HuggingFace => unreachable!(),
Provider::LiquidAi => unreachable!(),
Provider::Mistral => unreachable!(),
Provider::OpenRouter => unreachable!(),
Provider::ZAi => unreachable!(),
};
let openai::decisions::DecisionRequest {
model,
input,
questions,
} = request;
if let Ok(upstreams) = Vec1::try_from(upstreams) {
model_upstreams.push_back((model, upstreams));
}
}
let model = match model {
Shortcut::S => system_one::Shortcut::S,
Shortcut::M => system_one::Shortcut::M,
};
while let Some((model, upstreams)) = model_upstreams.pop_front() {
let (upstream, upstreams) = take_random_by_swapping(upstreams, &mut rand::rng());
if let Some(upstreams) = upstreams {
model_upstreams.push_back((model.clone(), upstreams));
}
let (state, images, files) = match input {
openai::decisions::DecisionInput::Messages(messages) => {
let mut images = vec![];
let mut texts = vec![];
tracing::info!(?model, ?upstream);
let openai::decisions::DecisionRequest { model: _shortcut, input, questions } = request.clone();
let request = openai::decisions::DecisionRequest { model, input, questions };
let client = reqwest::Client::builder()
.tls_certs_only(
webpki_root_certs::TLS_SERVER_ROOT_CERTS
.iter()
.filter_map(|cert| Certificate::from_der(cert).ok()),
)
.build()
.expect("TODO");
let mut upstream_request = client.post(upstream.url).json(&request);
if let Some(api_key) = upstream.api_key {
let authorization =
headers::Authorization::bearer(api_key.expose_secret()).expect("TODO");
upstream_request = upstream_request.header(headers::Authorization::<Bearer>::name(), {
let mut vec = vec![];
authorization.encode(&mut vec);
let [one] = vec.try_into().expect("TODO");
one
});
}
let upstream_response = match upstream_request.send().await {
Ok(upstream_response) => upstream_response,
Err(upstream_error) => {
tracing::error!(?upstream_error);
continue;
}
};
if let Err(upstream_error) = upstream_response.error_for_status_ref() {
tracing::error!(?upstream_error);
match upstream_response.text().await {
Ok(body) => tracing::error!(?body),
Err(body_error) => tracing::error!(?body_error),
for openai::decisions::DecisionInputMessage { role, content } in messages {
match content {
openai::decisions::DecisionInputContent::Text(text) => texts.push(text),
openai::decisions::DecisionInputContent::Parts(decision_input_parts) => {
for decision_input_part in decision_input_parts {
match decision_input_part {
openai::decisions::DecisionInputPart::Image { image_url, detail } => images.push(image_url),
openai::decisions::DecisionInputPart::Text { text } => texts.push(text),
}
}
},
}
};
continue;
};
let response = match upstream_response.json().await {
Ok(response) => response,
Err(json_error) => {
tracing::error!(?json_error);
continue;
}
};
let text = texts.join("\n\n");
return Ok(DecisionsResponse(response));
}
Err(DecisionsError::NoneSucceeded)
let files = images.clone();
(openai::system_one::State::Text(text), Some(images), Some(files))
}
openai::decisions::DecisionInput::Text(text) => (openai::system_one::State::Text(text), None, None),
};
let questions = questions.into_iter().map(|question| match question {
openai::decisions::DecisionQuestion::Predicate(predicate_question) => todo!(),
openai::decisions::DecisionQuestion::Choice(choice_question) => todo!(),
openai::decisions::DecisionQuestion::Score(score_question) => todo!(),
} ).collect();
let request = openai::system_one::SystemOneRequest {
model,
state,
files,
images,
questions,
};
system_one::create_system_one(system_one::V1SystemOnePath, State(app_state), Json(request))
.await
.map(
|system_one::SystemOneResponse(openai::system_one::SystemOneResponse {
model,
answers,
usage,
})| {
let answers = answers
.into_iter()
.map(|(id, answer)| match answer {
openai::system_one::Answer::Noul(openai::system_one::NoulAnswer {
noul,
}) => openai::decisions::DecisionAnswer::Predicate(
openai::decisions::PredicateAnswer {
name: Some(id),
probability: noul,
},
),
openai::system_one::Answer::Choice(openai::system_one::ChoiceAnswer {
choice,
probabilities,
confidence,
}) => openai::decisions::DecisionAnswer::Choice(
openai::decisions::ChoiceAnswer {
name: Some(id),
choice: openai::decisions::Choice::String(choice),
confidence,
probabilities: probabilities
.into_iter()
.map(|(choice_id, probability)| {
openai::decisions::ChoiceProbability {
value: openai::decisions::Choice::String(choice_id),
probability,
}
})
.collect(),
},
),
openai::system_one::Answer::Score(openai::system_one::ScoreAnswer {
score,
legend,
probabilities,
confidence,
}) => openai::decisions::DecisionAnswer::Score(
openai::decisions::ScoreAnswer {
name: Some(id),
score,
confidence,
probabilities: {
let reverse_legend = HashMap::<_, _, RandomState>::from_iter(
legend.into_iter().map(|(index, value)| {
(
value,
index
.parse::<i32>()
.expect("this should be an integer, I believe"),
)
}),
);
probabilities
.into_iter()
.map(|(choice_id, probability)| {
openai::decisions::ScoreProbability {
value: reverse_legend
.get(&choice_id)
.expect("it should exist, I think")
.clone(),
label: choice_id,
probability,
}
})
.collect()
},
},
),
})
.collect();
let openai::system_one::Usage {
input_tokens,
output_tokens,
} = usage;
let input_tokens = input_tokens as i32;
let output_tokens = output_tokens as i32;
let total_tokens = input_tokens + output_tokens;
let input_tokens_details = openai::decisions::TokenUsageDetails {
cache_write_tokens: 0,
cached_tokens: 0,
};
let output_tokens_details = openai::decisions::OutputTokenDetails {
reasoning_tokens: 0,
};
let usage = openai::decisions::TokenUsage {
input_tokens,
input_tokens_details,
output_tokens,
output_tokens_details,
total_tokens,
};
DecisionsResponse(openai::decisions::DecisionResponse {
model,
answers,
usage,
})
},
)
.map_err(|_| DecisionsError::NoneSucceeded)
}

24
src/server/embeddings.rs
View File

@@ -32,8 +32,17 @@ pub struct V1EmbeddingsPath;
)]
#[strum(serialize_all = "lowercase")]
pub enum Shortcut {
#[strum(serialize = "lfm-2.5-embedding-350m")]
Lfm2_5Embedding350m,
#[strum(serialize = "codestral-embed-2505")]
CodestralEmbed2505,
#[strum(serialize = "lfm2.5-embedding-350m")]
Lfm2_5Embedding350M,
#[strum(serialize = "mistral-embed-2312")]
MistralEmbed2312,
#[strum(serialize = "nemotron-3-embed-1b")]
Nemotron3Embed1B,
}
impl Shortcut {
@@ -42,8 +51,17 @@ impl Shortcut {
use Shortcut as S;
match self {
S::Lfm2_5Embedding350m => {
S::CodestralEmbed2505 => vec![
(P::Mistral, "codestral-embed-2505".into())
],
S::Lfm2_5Embedding350M => {
vec![(P::OpenRouter, "liquid/lfm-2.5-embedding-350m:free".into())]
},
S::MistralEmbed2312 => vec![
(P::Mistral, "mistral-embed-2312".into())
],
S::Nemotron3Embed1B => {
vec![(P::OpenRouter, "nvidia/nemotron-3-embed-1b:free".into())]
}
}
}

51
src/server/system_one.rs
View File

@@ -32,6 +32,7 @@ pub struct V1SystemOnePath;
)]
#[strum(serialize_all = "lowercase")]
pub enum Shortcut {
S,
M,
}
@@ -41,14 +42,26 @@ impl Shortcut {
use Shortcut as S;
match self {
S::S => {
vec![
(P::LiquidAi, "d1:free".into()),
(P::CloudflareWorkersAi, "clef-omni".into()),
(P::CloudflareWorkersAi, "clef-flash".into()),
(P::OpenRouter, "respan/span-01-lite:free".into()),
(P::OpenRouter, "respan/span-01-lite".into()),
]
}
S::M => {
vec![(P::LiquidAi, "d1:free".into())]
vec![
(P::CloudflareWorkersAi, "clef".into()),
(P::OpenRouter, "inception/mercury-decide:free".into()),
]
}
}
}
}
pub struct SystemOneResponse(openai::system_one::SystemOneResponse<String>);
pub struct SystemOneResponse(pub openai::system_one::SystemOneResponse<String>);
impl IntoResponse for SystemOneResponse {
fn into_response(self) -> Response {
@@ -94,7 +107,7 @@ pub async fn create_system_one(
}| {
Upstream::builder()
.url(
format!("https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/v1/systemone")
format!("https://api.cloudflare.com/client/v4/accounts/{account_id}/run/@cf/cloudflare/{model}")
.parse()
.expect("TODO"),
)
@@ -147,15 +160,22 @@ pub async fn create_system_one(
tracing::info!(?model, ?upstream);
let openai::system_one::SystemOneRequest { model: _shortcut, state, questions } = request.clone();
let openai::system_one::SystemOneRequest {
model: _shortcut,
state,
files,
images,
questions,
} = request.clone();
let request = openai::system_one::SystemOneRequest {
model,
state,
files,
images,
questions,
};
let client = reqwest::Client::builder()
.tls_certs_only(
webpki_root_certs::TLS_SERVER_ROOT_CERTS
@@ -165,9 +185,7 @@ pub async fn create_system_one(
.build()
.expect("TODO");
let mut upstream_request = client
.post(upstream.url)
.json(&request);
let mut upstream_request = client.post(upstream.url).json(&request);
if let Some(api_key) = upstream.api_key {
let authorization =
@@ -200,16 +218,15 @@ pub async fn create_system_one(
continue;
};
let response = match upstream_response.json().await {
Ok(response) => response,
Err(json_error) => {
tracing::error!(?json_error);
continue;
}
};
let response = match upstream_response.json().await {
Ok(response) => response,
Err(json_error) => {
tracing::error!(?json_error);
continue;
}
};
return Ok(SystemOneResponse(response));
return Ok(SystemOneResponse(response));
}
Err(SystemOneError::NoneSucceeded)
}