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hf_public_repos/text-generation-inference/router | hf_public_repos/text-generation-inference/router/src/health.rs | use std::sync::atomic::{AtomicBool, Ordering};
use std::sync::Arc;
use text_generation_client::{
Batch, NextTokenChooserParameters, Request, ShardedClient, StoppingCriteriaParameters,
};
// Note: Request ids and batch ids cannot collide.
const LIVENESS_ID: u64 = u64::MAX;
const BATCH_ID: u64 = u64::MAX;
#[derive(Clone, Debug)]
pub(crate) struct Health {
client: ShardedClient,
generation_health: Arc<AtomicBool>,
}
impl Health {
pub(crate) fn new(client: ShardedClient, generation_health: Arc<AtomicBool>) -> Self {
Self {
client,
generation_health,
}
}
pub(crate) async fn check(&mut self) -> bool {
if self.generation_health.load(Ordering::SeqCst) {
// Generation is healthy, we only check that the shards are answering gRPC calls
self.client.health().await.is_ok()
} else {
// Generation is unhealthy or have not sent any generation request yet
// Dummy batch of 1 token and 1 generated token
let liveness_request = Request {
id: LIVENESS_ID,
inputs: "liveness".to_string(),
truncate: 10,
prefill_logprobs: false,
parameters: Some(NextTokenChooserParameters {
temperature: 1.0,
top_k: 0,
top_p: 1.0,
typical_p: 1.0,
do_sample: false,
seed: 0,
repetition_penalty: 1.0,
watermark: false,
}),
stopping_parameters: Some(StoppingCriteriaParameters {
max_new_tokens: 1,
stop_sequences: vec![],
ignore_eos_token: false,
}),
};
let batch = Batch {
id: BATCH_ID,
requests: vec![liveness_request],
size: 1,
max_tokens: 2,
};
// Skips the queue
let value = self.client.prefill(batch).await.is_ok();
// Update generation health
self.generation_health.store(value, Ordering::SeqCst);
value
}
}
}
| 0 |
hf_public_repos/text-generation-inference/router | hf_public_repos/text-generation-inference/router/src/server.rs | /// HTTP Server logic
use crate::health::Health;
use crate::infer::{InferError, InferResponse, InferStreamResponse};
use crate::validation::ValidationError;
use crate::{
BestOfSequence, CompatGenerateRequest, Details, ErrorResponse, FinishReason,
GenerateParameters, GenerateRequest, GenerateResponse, HubModelInfo, Infer, Info, PrefillToken,
StreamDetails, StreamResponse, Token, Validation,
};
use axum::extract::Extension;
use axum::http::{HeaderMap, Method, StatusCode};
use axum::response::sse::{Event, KeepAlive, Sse};
use axum::response::{IntoResponse, Response};
use axum::routing::{get, post};
use axum::{http, Json, Router};
use axum_tracing_opentelemetry::opentelemetry_tracing_layer;
use futures::stream::StreamExt;
use futures::Stream;
use metrics_exporter_prometheus::{Matcher, PrometheusBuilder, PrometheusHandle};
use std::convert::Infallible;
use std::net::SocketAddr;
use std::sync::atomic::AtomicBool;
use std::sync::Arc;
use text_generation_client::{ShardInfo, ShardedClient};
use tokenizers::Tokenizer;
use tokio::signal;
use tokio::time::Instant;
use tower_http::cors::{AllowOrigin, CorsLayer};
use tracing::{info_span, instrument, Instrument};
use utoipa::OpenApi;
use utoipa_swagger_ui::SwaggerUi;
/// Generate tokens if `stream == false` or a stream of token if `stream == true`
#[utoipa::path(
post,
tag = "Text Generation Inference",
path = "/",
request_body = CompatGenerateRequest,
responses(
(status = 200, description = "Generated Text",
content(
("application/json" = GenerateResponse),
("text/event-stream" = StreamResponse),
)),
(status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"})),
(status = 429, description = "Model is overloaded", body = ErrorResponse,
example = json ! ({"error": "Model is overloaded"})),
(status = 422, description = "Input validation error", body = ErrorResponse,
example = json ! ({"error": "Input validation error"})),
(status = 500, description = "Incomplete generation", body = ErrorResponse,
example = json ! ({"error": "Incomplete generation"})),
)
)]
#[instrument(skip(infer, req))]
async fn compat_generate(
default_return_full_text: Extension<bool>,
infer: Extension<Infer>,
req: Json<CompatGenerateRequest>,
) -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
let mut req = req.0;
// default return_full_text given the pipeline_tag
if req.parameters.return_full_text.is_none() {
req.parameters.return_full_text = Some(default_return_full_text.0)
}
// switch on stream
if req.stream {
Ok(generate_stream(infer, Json(req.into()))
.await
.into_response())
} else {
let (headers, generation) = generate(infer, Json(req.into())).await?;
// wrap generation inside a Vec to match api-inference
Ok((headers, Json(vec![generation.0])).into_response())
}
}
/// Text Generation Inference endpoint info
#[utoipa::path(
get,
tag = "Text Generation Inference",
path = "/info",
responses((status = 200, description = "Served model info", body = Info))
)]
#[instrument]
async fn get_model_info(info: Extension<Info>) -> Json<Info> {
Json(info.0)
}
#[utoipa::path(
get,
tag = "Text Generation Inference",
path = "/health",
responses(
(status = 200, description = "Everything is working fine"),
(status = 503, description = "Text generation inference is down", body = ErrorResponse,
example = json ! ({"error": "unhealthy", "error_type": "healthcheck"})),
)
)]
#[instrument(skip(health))]
/// Health check method
async fn health(mut health: Extension<Health>) -> Result<(), (StatusCode, Json<ErrorResponse>)> {
match health.check().await {
true => Ok(()),
false => Err((
StatusCode::SERVICE_UNAVAILABLE,
Json(ErrorResponse {
error: "unhealthy".to_string(),
error_type: "healthcheck".to_string(),
}),
)),
}
}
/// Generate tokens
#[utoipa::path(
post,
tag = "Text Generation Inference",
path = "/generate",
request_body = GenerateRequest,
responses(
(status = 200, description = "Generated Text", body = GenerateResponse),
(status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"})),
(status = 429, description = "Model is overloaded", body = ErrorResponse,
example = json ! ({"error": "Model is overloaded"})),
(status = 422, description = "Input validation error", body = ErrorResponse,
example = json ! ({"error": "Input validation error"})),
(status = 500, description = "Incomplete generation", body = ErrorResponse,
example = json ! ({"error": "Incomplete generation"})),
)
)]
#[instrument(
skip_all,
fields(
parameters = ? req.0.parameters,
total_time,
validation_time,
queue_time,
inference_time,
time_per_token,
seed,
)
)]
async fn generate(
infer: Extension<Infer>,
req: Json<GenerateRequest>,
) -> Result<(HeaderMap, Json<GenerateResponse>), (StatusCode, Json<ErrorResponse>)> {
let span = tracing::Span::current();
let start_time = Instant::now();
metrics::increment_counter!("tgi_request_count");
tracing::debug!("Input: {}", req.0.inputs);
let compute_characters = req.0.inputs.chars().count();
let mut add_prompt = None;
if req.0.parameters.return_full_text.unwrap_or(false) {
add_prompt = Some(req.0.inputs.clone());
}
let details = req.0.parameters.details || req.0.parameters.decoder_input_details;
// Inference
let (response, best_of_responses) = match req.0.parameters.best_of {
Some(best_of) if best_of > 1 => {
let (response, best_of_responses) = infer.generate_best_of(req.0, best_of).await?;
(response, Some(best_of_responses))
}
_ => (infer.generate(req.0).await?, None),
};
// Token details
let details = match details {
true => {
// convert best_of_responses
let best_of_sequences = best_of_responses.map(|responses: Vec<InferResponse>| {
responses
.into_iter()
.map(|response: InferResponse| {
// Add prompt if return_full_text
let mut output_text = response.generated_text.text;
if let Some(prompt) = &add_prompt {
output_text = prompt.clone() + &output_text;
}
BestOfSequence {
generated_text: output_text,
finish_reason: FinishReason::from(
response.generated_text.finish_reason,
),
generated_tokens: response.generated_text.generated_tokens,
prefill: response.prefill,
tokens: response.tokens,
seed: response.generated_text.seed,
}
})
.collect()
});
Some(Details {
finish_reason: FinishReason::from(response.generated_text.finish_reason),
generated_tokens: response.generated_text.generated_tokens,
prefill: response.prefill,
tokens: response.tokens,
seed: response.generated_text.seed,
best_of_sequences,
})
}
false => None,
};
// Timings
let total_time = start_time.elapsed();
let validation_time = response.queued - start_time;
let queue_time = response.start - response.queued;
let inference_time = Instant::now() - response.start;
let time_per_token = inference_time / response.generated_text.generated_tokens;
// Tracing metadata
span.record("total_time", format!("{total_time:?}"));
span.record("validation_time", format!("{validation_time:?}"));
span.record("queue_time", format!("{queue_time:?}"));
span.record("inference_time", format!("{inference_time:?}"));
span.record("time_per_token", format!("{time_per_token:?}"));
span.record("seed", format!("{:?}", response.generated_text.seed));
// Headers
let mut headers = HeaderMap::new();
headers.insert("x-compute-type", "gpu+optimized".parse().unwrap());
headers.insert(
"x-compute-time",
total_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-compute-characters",
compute_characters.to_string().parse().unwrap(),
);
headers.insert(
"x-total-time",
total_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-validation-time",
validation_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-queue-time",
queue_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-inference-time",
inference_time.as_millis().to_string().parse().unwrap(),
);
headers.insert(
"x-time-per-token",
time_per_token.as_millis().to_string().parse().unwrap(),
);
// Metrics
metrics::increment_counter!("tgi_request_success");
metrics::histogram!("tgi_request_duration", total_time.as_secs_f64());
metrics::histogram!(
"tgi_request_validation_duration",
validation_time.as_secs_f64()
);
metrics::histogram!("tgi_request_queue_duration", queue_time.as_secs_f64());
metrics::histogram!(
"tgi_request_inference_duration",
inference_time.as_secs_f64()
);
metrics::histogram!(
"tgi_request_mean_time_per_token_duration",
time_per_token.as_secs_f64()
);
metrics::histogram!(
"tgi_request_generated_tokens",
response.generated_text.generated_tokens as f64
);
// Send response
let mut output_text = response.generated_text.text;
if let Some(prompt) = add_prompt {
output_text = prompt + &output_text;
}
tracing::debug!("Output: {}", output_text);
tracing::info!("Success");
let response = GenerateResponse {
generated_text: output_text,
details,
};
Ok((headers, Json(response)))
}
/// Generate a stream of token using Server-Sent Events
#[utoipa::path(
post,
tag = "Text Generation Inference",
path = "/generate_stream",
request_body = GenerateRequest,
responses(
(status = 200, description = "Generated Text", body = StreamResponse,
content_type = "text/event-stream"),
(status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"}),
content_type = "text/event-stream"),
(status = 429, description = "Model is overloaded", body = ErrorResponse,
example = json ! ({"error": "Model is overloaded"}),
content_type = "text/event-stream"),
(status = 422, description = "Input validation error", body = ErrorResponse,
example = json ! ({"error": "Input validation error"}),
content_type = "text/event-stream"),
(status = 500, description = "Incomplete generation", body = ErrorResponse,
example = json ! ({"error": "Incomplete generation"}),
content_type = "text/event-stream"),
)
)]
#[instrument(
skip_all,
fields(
parameters = ? req.0.parameters,
total_time,
validation_time,
queue_time,
inference_time,
time_per_token,
seed,
)
)]
async fn generate_stream(
infer: Extension<Infer>,
req: Json<GenerateRequest>,
) -> (
HeaderMap,
Sse<impl Stream<Item = Result<Event, Infallible>>>,
) {
let span = tracing::Span::current();
let start_time = Instant::now();
metrics::increment_counter!("tgi_request_count");
tracing::debug!("Input: {}", req.0.inputs);
let compute_characters = req.0.inputs.chars().count();
let mut headers = HeaderMap::new();
headers.insert("x-compute-type", "gpu+optimized".parse().unwrap());
headers.insert(
"x-compute-characters",
compute_characters.to_string().parse().unwrap(),
);
headers.insert("X-Accel-Buffering", "no".parse().unwrap());
let stream = async_stream::stream! {
// Inference
let mut end_reached = false;
let mut error = false;
let mut add_prompt = None;
if req.0.parameters.return_full_text.unwrap_or(false) {
add_prompt = Some(req.0.inputs.clone());
}
let details = req.0.parameters.details;
let best_of = req.0.parameters.best_of.unwrap_or(1);
if best_of != 1 {
let err = InferError::from(ValidationError::BestOfStream);
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
tracing::error!("{err}");
yield Ok(Event::from(err));
} else if req.0.parameters.decoder_input_details {
let err = InferError::from(ValidationError::PrefillDetailsStream);
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
tracing::error!("{err}");
yield Ok(Event::from(err));
} else {
match infer.generate_stream(req.0).instrument(info_span!(parent: &span, "async_stream")).await {
// Keep permit as long as generate_stream lives
Ok((_permit, mut response_stream)) => {
// Server-Sent Event stream
while let Some(response) = response_stream.next().await {
match response {
Ok(response) => {
match response {
// Prefill is ignored
InferStreamResponse::Prefill(_) => {}
// Yield event for every new token
InferStreamResponse::Token(token) => {
tracing::debug!(parent: &span, "Token: {:?}", token);
// StreamResponse
let stream_token = StreamResponse {
token,
generated_text: None,
details: None,
};
yield Ok(Event::default().json_data(stream_token).unwrap())
}
// Yield event for last token and compute timings
InferStreamResponse::End {
token,
generated_text,
start,
queued,
} => {
// Token details
let details = match details {
true => Some(StreamDetails {
finish_reason: FinishReason::from(generated_text.finish_reason),
generated_tokens: generated_text.generated_tokens,
seed: generated_text.seed,
}),
false => None,
};
// Timings
let total_time = start_time.elapsed();
let validation_time = queued - start_time;
let queue_time = start - queued;
let inference_time = Instant::now() - start;
let time_per_token = inference_time / generated_text.generated_tokens;
// Tracing metadata
span.record("total_time", format!("{total_time:?}"));
span.record("validation_time", format!("{validation_time:?}"));
span.record("queue_time", format!("{queue_time:?}"));
span.record("inference_time", format!("{inference_time:?}"));
span.record("time_per_token", format!("{time_per_token:?}"));
span.record("seed", format!("{:?}", generated_text.seed));
// Metrics
metrics::increment_counter!("tgi_request_success");
metrics::histogram!("tgi_request_duration", total_time.as_secs_f64());
metrics::histogram!("tgi_request_validation_duration", validation_time.as_secs_f64());
metrics::histogram!("tgi_request_queue_duration", queue_time.as_secs_f64());
metrics::histogram!("tgi_request_inference_duration", inference_time.as_secs_f64());
metrics::histogram!("tgi_request_mean_time_per_token_duration", time_per_token.as_secs_f64());
metrics::histogram!("tgi_request_generated_tokens", generated_text.generated_tokens as f64);
// StreamResponse
end_reached = true;
let mut output_text = generated_text.text;
if let Some(prompt) = add_prompt {
output_text = prompt + &output_text;
}
tracing::debug!(parent: &span, "Output: {}", output_text);
tracing::info!(parent: &span, "Success");
let stream_token = StreamResponse {
token,
generated_text: Some(output_text),
details
};
yield Ok(Event::default().json_data(stream_token).unwrap());
break;
}
}
}
// yield error
Err(err) => {
error = true;
yield Ok(Event::from(err));
break;
}
}
}
},
// yield error
Err(err) => {
error = true;
yield Ok(Event::from(err));
}
}
// Check if generation reached the end
// Skip if we already sent an error
if !end_reached && !error {
let err = InferError::IncompleteGeneration;
metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
tracing::error!("{err}");
yield Ok(Event::from(err));
}
}
};
(headers, Sse::new(stream).keep_alive(KeepAlive::default()))
}
/// Prometheus metrics scrape endpoint
#[utoipa::path(
get,
tag = "Text Generation Inference",
path = "/metrics",
responses((status = 200, description = "Prometheus Metrics", body = String))
)]
async fn metrics(prom_handle: Extension<PrometheusHandle>) -> String {
prom_handle.render()
}
/// Serving method
#[allow(clippy::too_many_arguments)]
pub async fn run(
model_info: HubModelInfo,
shard_info: ShardInfo,
compat_return_full_text: bool,
max_concurrent_requests: usize,
max_best_of: usize,
max_stop_sequences: usize,
max_input_length: usize,
max_total_tokens: usize,
waiting_served_ratio: f32,
max_batch_prefill_tokens: u32,
max_batch_total_tokens: u32,
max_waiting_tokens: usize,
client: ShardedClient,
tokenizer: Option<Tokenizer>,
validation_workers: usize,
addr: SocketAddr,
allow_origin: Option<AllowOrigin>,
ngrok: bool,
ngrok_authtoken: Option<String>,
ngrok_edge: Option<String>,
) -> Result<(), axum::BoxError> {
// OpenAPI documentation
#[derive(OpenApi)]
#[openapi(
paths(
health,
get_model_info,
compat_generate,
generate,
generate_stream,
metrics,
),
components(
schemas(
Info,
CompatGenerateRequest,
GenerateRequest,
GenerateParameters,
PrefillToken,
Token,
GenerateResponse,
BestOfSequence,
Details,
FinishReason,
StreamResponse,
StreamDetails,
ErrorResponse,
)
),
tags(
(name = "Text Generation Inference", description = "Hugging Face Text Generation Inference API")
),
info(
title = "Text Generation Inference",
license(
name = "Apache 2.0",
url = "https://www.apache.org/licenses/LICENSE-2.0"
)
)
)]
struct ApiDoc;
// Create state
let validation = Validation::new(
validation_workers,
tokenizer,
max_best_of,
max_stop_sequences,
max_input_length,
max_total_tokens,
);
let generation_health = Arc::new(AtomicBool::new(false));
let health_ext = Health::new(client.clone(), generation_health.clone());
let infer = Infer::new(
client,
validation,
waiting_served_ratio,
max_batch_prefill_tokens,
max_batch_total_tokens,
max_waiting_tokens,
max_concurrent_requests,
shard_info.requires_padding,
generation_health,
);
// Duration buckets
let duration_matcher = Matcher::Suffix(String::from("duration"));
let n_duration_buckets = 35;
let mut duration_buckets = Vec::with_capacity(n_duration_buckets);
// Minimum duration in seconds
let mut value = 0.0001;
for _ in 0..n_duration_buckets {
// geometric sequence
value *= 1.5;
duration_buckets.push(value);
}
// Input Length buckets
let input_length_matcher = Matcher::Full(String::from("tgi_request_input_length"));
let input_length_buckets: Vec<f64> = (0..100)
.map(|x| (max_input_length as f64 / 100.0) * (x + 1) as f64)
.collect();
// Generated tokens buckets
let generated_tokens_matcher = Matcher::Full(String::from("tgi_request_generated_tokens"));
let generated_tokens_buckets: Vec<f64> = (0..100)
.map(|x| (max_total_tokens as f64 / 100.0) * (x + 1) as f64)
.collect();
// Input Length buckets
let max_new_tokens_matcher = Matcher::Full(String::from("tgi_request_max_new_tokens"));
let max_new_tokens_buckets: Vec<f64> = (0..100)
.map(|x| (max_total_tokens as f64 / 100.0) * (x + 1) as f64)
.collect();
// Batch size buckets
let batch_size_matcher = Matcher::Full(String::from("tgi_batch_next_size"));
let batch_size_buckets: Vec<f64> = (0..1024).map(|x| (x + 1) as f64).collect();
// Prometheus handler
let builder = PrometheusBuilder::new()
.set_buckets_for_metric(duration_matcher, &duration_buckets)
.unwrap()
.set_buckets_for_metric(input_length_matcher, &input_length_buckets)
.unwrap()
.set_buckets_for_metric(generated_tokens_matcher, &generated_tokens_buckets)
.unwrap()
.set_buckets_for_metric(max_new_tokens_matcher, &max_new_tokens_buckets)
.unwrap()
.set_buckets_for_metric(batch_size_matcher, &batch_size_buckets)
.unwrap();
let prom_handle = builder
.install_recorder()
.expect("failed to install metrics recorder");
// CORS layer
let allow_origin = allow_origin.unwrap_or(AllowOrigin::any());
let cors_layer = CorsLayer::new()
.allow_methods([Method::GET, Method::POST])
.allow_headers([http::header::CONTENT_TYPE])
.allow_origin(allow_origin);
// Endpoint info
let info = Info {
model_id: model_info.model_id,
model_sha: model_info.sha,
model_dtype: shard_info.dtype,
model_device_type: shard_info.device_type,
model_pipeline_tag: model_info.pipeline_tag,
max_concurrent_requests,
max_best_of,
max_stop_sequences,
max_input_length,
max_total_tokens,
waiting_served_ratio,
max_batch_total_tokens,
max_waiting_tokens,
validation_workers,
version: env!("CARGO_PKG_VERSION"),
sha: option_env!("VERGEN_GIT_SHA"),
docker_label: option_env!("DOCKER_LABEL"),
};
// Create router
let app = Router::new()
.merge(SwaggerUi::new("/docs").url("/api-doc/openapi.json", ApiDoc::openapi()))
// Base routes
.route("/", post(compat_generate))
.route("/info", get(get_model_info))
.route("/generate", post(generate))
.route("/generate_stream", post(generate_stream))
// AWS Sagemaker route
.route("/invocations", post(compat_generate))
// Base Health route
.route("/health", get(health))
// Inference API health route
.route("/", get(health))
// AWS Sagemaker health route
.route("/ping", get(health))
// Prometheus metrics route
.route("/metrics", get(metrics))
.layer(Extension(info))
.layer(Extension(health_ext.clone()))
.layer(Extension(compat_return_full_text))
.layer(Extension(infer))
.layer(Extension(prom_handle.clone()))
.layer(opentelemetry_tracing_layer())
.layer(cors_layer);
if ngrok {
#[cfg(feature = "ngrok")]
{
use ngrok::config::TunnelBuilder;
let _ = addr;
let authtoken =
ngrok_authtoken.expect("`ngrok-authtoken` must be set when using ngrok tunneling");
let edge = ngrok_edge.expect("`ngrok-edge` must be set when using ngrok tunneling");
let tunnel = ngrok::Session::builder()
.authtoken(authtoken)
.connect()
.await
.unwrap()
.labeled_tunnel()
.label("edge", edge);
let listener = tunnel.listen().await.unwrap();
// Run prom metrics and health locally too
tokio::spawn(
axum::Server::bind(&addr)
.serve(
Router::new()
.route("/health", get(health))
.route("/metrics", get(metrics))
.layer(Extension(health_ext))
.layer(Extension(prom_handle))
.into_make_service(),
)
//Wait until all requests are finished to shut down
.with_graceful_shutdown(shutdown_signal()),
);
// Run server
axum::Server::builder(listener)
.serve(app.into_make_service())
//Wait until all requests are finished to shut down
.with_graceful_shutdown(shutdown_signal())
.await?;
}
#[cfg(not(feature = "ngrok"))]
{
let _ngrok_authtoken = ngrok_authtoken;
let _ngrok_domain = ngrok_domain;
let _ngrok_username = ngrok_username;
let _ngrok_password = ngrok_password;
panic!("`text-generation-router` was compiled without the `ngrok` feature");
}
} else {
// Run server
axum::Server::bind(&addr)
.serve(app.into_make_service())
// Wait until all requests are finished to shut down
.with_graceful_shutdown(shutdown_signal())
.await?;
}
Ok(())
}
/// Shutdown signal handler
async fn shutdown_signal() {
let ctrl_c = async {
signal::ctrl_c()
.await
.expect("failed to install Ctrl+C handler");
};
#[cfg(unix)]
let terminate = async {
signal::unix::signal(signal::unix::SignalKind::terminate())
.expect("failed to install signal handler")
.recv()
.await;
};
#[cfg(not(unix))]
let terminate = std::future::pending::<()>();
tokio::select! {
_ = ctrl_c => {},
_ = terminate => {},
}
tracing::info!("signal received, starting graceful shutdown");
opentelemetry::global::shutdown_tracer_provider();
}
impl From<i32> for FinishReason {
fn from(finish_reason: i32) -> Self {
let finish_reason = text_generation_client::FinishReason::from_i32(finish_reason).unwrap();
match finish_reason {
text_generation_client::FinishReason::Length => FinishReason::Length,
text_generation_client::FinishReason::EosToken => FinishReason::EndOfSequenceToken,
text_generation_client::FinishReason::StopSequence => FinishReason::StopSequence,
}
}
}
/// Convert to Axum supported formats
impl From<InferError> for (StatusCode, Json<ErrorResponse>) {
fn from(err: InferError) -> Self {
let status_code = match err {
InferError::GenerationError(_) => StatusCode::FAILED_DEPENDENCY,
InferError::Overloaded(_) => StatusCode::TOO_MANY_REQUESTS,
InferError::ValidationError(_) => StatusCode::UNPROCESSABLE_ENTITY,
InferError::IncompleteGeneration => StatusCode::INTERNAL_SERVER_ERROR,
};
(
status_code,
Json(ErrorResponse {
error: err.to_string(),
error_type: err.error_type().to_string(),
}),
)
}
}
impl From<InferError> for Event {
fn from(err: InferError) -> Self {
Event::default()
.json_data(ErrorResponse {
error: err.to_string(),
error_type: err.error_type().to_string(),
})
.unwrap()
}
}
| 0 |
hf_public_repos/text-generation-inference/router | hf_public_repos/text-generation-inference/router/src/infer.rs | /// Batching and inference logic
use crate::validation::{Validation, ValidationError};
use crate::{Entry, Queue, Token};
use crate::{GenerateRequest, PrefillToken};
use flume::r#async::RecvStream;
use flume::SendTimeoutError;
use futures::future::try_join_all;
use futures::stream::StreamExt;
use nohash_hasher::IntMap;
use std::sync::{
atomic::{AtomicBool, Ordering},
Arc,
};
use std::time::Duration;
use text_generation_client::{
Batch, CachedBatch, ClientError, GeneratedText, Generation, PrefillTokens, ShardedClient,
};
use thiserror::Error;
use tokio::sync::{Notify, OwnedSemaphorePermit, Semaphore, TryAcquireError};
use tokio::time::Instant;
use tracing::{info_span, instrument, Instrument, Span};
/// Inference struct
#[derive(Clone)]
pub struct Infer {
/// Validation
validation: Validation,
/// Request queue
queue: Queue,
/// Shared state
shared: Arc<Shared>,
/// Inference limit
limit_concurrent_requests: Arc<Semaphore>,
}
/// Infer shared state
struct Shared {
/// Batching background Tokio task notifier
batching_task: Notify,
}
impl Infer {
#[allow(clippy::too_many_arguments)]
pub(crate) fn new(
client: ShardedClient,
validation: Validation,
waiting_served_ratio: f32,
max_batch_prefill_tokens: u32,
max_batch_total_tokens: u32,
max_waiting_tokens: usize,
max_concurrent_requests: usize,
requires_padding: bool,
generation_health: Arc<AtomicBool>,
) -> Self {
// Infer shared state
let queue = Queue::new(requires_padding, 16);
let shared = Arc::new(Shared {
batching_task: Notify::new(),
});
// Spawn batching background task that contains all the inference logic
tokio::spawn(batching_task(
client,
waiting_served_ratio,
max_batch_prefill_tokens,
max_batch_total_tokens,
max_waiting_tokens,
queue.clone(),
shared.clone(),
generation_health,
));
// Inference limit with a semaphore
let semaphore = Arc::new(Semaphore::new(max_concurrent_requests));
Self {
validation,
queue,
shared,
limit_concurrent_requests: semaphore,
}
}
/// Add a new request to the queue and return a stream of InferStreamResponse
#[instrument(skip(self))]
pub(crate) async fn generate_stream(
&self,
request: GenerateRequest,
) -> Result<
(
OwnedSemaphorePermit,
RecvStream<Result<InferStreamResponse, InferError>>,
),
InferError,
> {
// Limit concurrent requests by acquiring a permit from the semaphore
let permit = self
.clone()
.limit_concurrent_requests
.try_acquire_owned()
.map_err(|err| {
metrics::increment_counter!("tgi_request_failure", "err" => "overloaded");
tracing::error!("{err}");
err
})?;
// Validate request
let valid_request = self.validation.validate(request).await.map_err(|err| {
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
tracing::error!("{err}");
err
})?;
// MPSC channel to communicate with the background batching task
let (response_tx, response_rx) = flume::unbounded();
// Append the request to the queue
self.queue.append(Entry {
request: valid_request,
response_tx,
span: Span::current(),
temp_span: None,
queue_time: Instant::now(),
batch_time: None,
});
// Notify the background task that we have a new entry in the queue that needs
// to be batched
self.shared.batching_task.notify_one();
// Return stream
Ok((permit, response_rx.into_stream()))
}
/// Add a new request to the queue and return a InferResponse
#[instrument(skip(self))]
pub(crate) async fn generate(
&self,
request: GenerateRequest,
) -> Result<InferResponse, InferError> {
// Create stream and keep semaphore permit as long as generate lives
let (_permit, mut stream) = self.generate_stream(request).await?;
// Return values
let mut result_prefill = Vec::new();
let mut result_tokens = Vec::new();
let mut result_generated_text = None;
let mut result_start = None;
let mut result_queued = None;
// Iterate on stream
while let Some(response) = stream.next().await {
match response? {
// Add prefill tokens
InferStreamResponse::Prefill(tokens) => {
// Create Token objects
// We do that here instead of in the Python code as Rust for loops are faster
result_prefill = tokens
.ids
.into_iter()
.zip(tokens.logprobs.into_iter())
.zip(tokens.texts.into_iter())
.map(|((id, logprob), text)| PrefillToken { id, text, logprob })
.collect();
}
// Push last token
InferStreamResponse::Token(token) => result_tokens.push(token),
// Final message
// Set return values
InferStreamResponse::End {
token,
generated_text,
start,
queued,
} => {
result_tokens.push(token);
result_generated_text = Some(generated_text);
result_start = Some(start);
result_queued = Some(queued)
}
}
}
// Check that we received a `InferStreamResponse::End` message
if let (Some(generated_text), Some(queued), Some(start)) =
(result_generated_text, result_queued, result_start)
{
Ok(InferResponse {
prefill: result_prefill,
tokens: result_tokens,
generated_text,
queued,
start,
})
} else {
let err = InferError::IncompleteGeneration;
metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
tracing::error!("{err}");
Err(err)
}
}
/// Add best_of new requests to the queue and return a InferResponse of the sequence with
/// the highest log probability per token
#[instrument(skip(self))]
pub(crate) async fn generate_best_of(
&self,
request: GenerateRequest,
best_of: usize,
) -> Result<(InferResponse, Vec<InferResponse>), InferError> {
// validate best_of parameter separately
let best_of = self.validation.validate_best_of(best_of)?;
// create multiple generate requests
let mut infer_responses: Vec<InferResponse> =
try_join_all((0..best_of).map(|_| self.generate(request.clone()))).await?;
// get the sequence with the highest log probability per token
let mut max_index = 0;
let mut max_logprob: f32 = f32::MIN;
for (i, response) in infer_responses.iter().enumerate() {
// mean logprobs of the generated tokens
let sequence_logprob = response
.tokens
.iter()
.map(|token| token.logprob)
.sum::<f32>()
/ response.tokens.len() as f32;
// set best sequence
if sequence_logprob > max_logprob {
max_index = i;
max_logprob = sequence_logprob;
}
}
let best_response = infer_responses.remove(max_index);
Ok((best_response, infer_responses))
}
}
/// Batching logic
/// Will be launched in a background Tokio task
///
/// Batches requests and sends them to the inference server
#[allow(clippy::too_many_arguments)]
async fn batching_task(
mut client: ShardedClient,
waiting_served_ratio: f32,
max_batch_prefill_tokens: u32,
max_batch_total_tokens: u32,
max_waiting_tokens: usize,
queue: Queue,
shared: Arc<Shared>,
generation_health: Arc<AtomicBool>,
) {
// Infinite loop
loop {
// Wait for a notification from the Infer struct
shared.batching_task.notified().await;
// Get the next batch from the queue
// This batch might be smaller than the maximum batch size if there are not enough requests
// waiting in the queue
while let Some((mut entries, batch, span)) = queue
.next_batch(None, max_batch_prefill_tokens, max_batch_total_tokens)
.await
{
let mut cached_batch = prefill(&mut client, batch, &mut entries, &generation_health)
.instrument(span)
.await;
let mut waiting_tokens = 1;
// We loop until we do not receive any cached batch from the inference server (== until
// all requests have met their stopping criteria)
while let Some(batch) = cached_batch {
// Get current batch info
let batch_size = batch.size;
let batch_max_tokens = batch.max_tokens;
let mut batches = vec![batch];
metrics::gauge!("tgi_batch_current_size", batch_size as f64);
metrics::gauge!("tgi_batch_current_max_tokens", batch_max_tokens as f64);
let min_size = if waiting_tokens >= max_waiting_tokens {
// If we didn't onboard any new requests since >= max_waiting_tokens, we try
// to add a new batch even though its size might be small
None
} else {
// Minimum batch size
Some((batch_size as f32 * waiting_served_ratio).floor() as usize)
};
let token_budget = max_batch_total_tokens.saturating_sub(batch_max_tokens);
// Try to get a new batch
if let Some((mut new_entries, new_batch, span)) = queue
.next_batch(min_size, max_batch_prefill_tokens, token_budget)
.await
{
// Tracking metrics
if min_size.is_some() {
metrics::increment_counter!("tgi_batch_concat", "reason" => "backpressure");
} else {
metrics::increment_counter!("tgi_batch_concat", "reason" => "wait_exceeded");
}
entries.iter_mut().for_each(|(_, entry)| {
// Create a new span to add the info that this entry is waiting
// because a new batch is being computed
let entry_waiting_span = info_span!(parent: &entry.span, "waiting");
// Add relationships
span.follows_from(&entry_waiting_span);
entry_waiting_span.follows_from(&span);
// Update entry
entry.temp_span = Some(entry_waiting_span);
});
// Generate one token for this new batch to have the attention past in cache
let new_cached_batch =
prefill(&mut client, new_batch, &mut new_entries, &generation_health)
.instrument(span)
.await;
// Reset waiting counter
waiting_tokens = 1;
// Extend current batch with the new batch
if let Some(new_cached_batch) = new_cached_batch {
entries.extend(new_entries);
batches.push(new_cached_batch);
}
}
// Create span for this batch to add context to inference calls
let next_batch_size = entries.len();
let next_batch_span =
info_span!(parent: None, "batch", batch_size = next_batch_size);
entries.iter_mut().for_each(|(_, entry)| {
// Create a new span to link the batch back to this entry
let entry_batch_span = info_span!(parent: &entry.span, "infer");
// Add relationships
next_batch_span.follows_from(&entry_batch_span);
entry_batch_span.follows_from(&next_batch_span);
// Update entry
entry.temp_span = Some(entry_batch_span);
});
cached_batch = decode(&mut client, batches, &mut entries, &generation_health)
.instrument(next_batch_span)
.await;
waiting_tokens += 1;
}
metrics::gauge!("tgi_batch_current_size", 0.0);
metrics::gauge!("tgi_batch_current_max_tokens", 0.0);
}
}
}
#[instrument(skip_all)]
async fn prefill(
client: &mut ShardedClient,
batch: Batch,
entries: &mut IntMap<u64, Entry>,
generation_health: &Arc<AtomicBool>,
) -> Option<CachedBatch> {
let start_time = Instant::now();
let batch_id = batch.id;
metrics::increment_counter!("tgi_batch_inference_count", "method" => "prefill");
match client.prefill(batch).await {
Ok((generations, next_batch)) => {
// Update health
generation_health.store(true, Ordering::SeqCst);
// Send generated tokens and filter stopped entries
filter_send_generations(generations, entries);
// Filter next batch and remove requests that were stopped
let next_batch = filter_batch(client, next_batch, entries).await;
metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "prefill");
metrics::increment_counter!("tgi_batch_inference_success", "method" => "prefill");
next_batch
}
// If we have an error, we discard the whole batch
Err(err) => {
// Update health
generation_health.store(false, Ordering::SeqCst);
let _ = client.clear_cache(Some(batch_id)).await;
send_errors(err, entries);
metrics::increment_counter!("tgi_batch_inference_failure", "method" => "prefill");
None
}
}
}
#[instrument(skip_all)]
async fn decode(
client: &mut ShardedClient,
batches: Vec<CachedBatch>,
entries: &mut IntMap<u64, Entry>,
generation_health: &Arc<AtomicBool>,
) -> Option<CachedBatch> {
let start_time = Instant::now();
let batch_ids: Vec<u64> = batches.iter().map(|b| b.id).collect();
metrics::increment_counter!("tgi_batch_inference_count", "method" => "decode");
match client.decode(batches).await {
Ok((generations, next_batch)) => {
// Update health
generation_health.store(true, Ordering::SeqCst);
// Send generated tokens and filter stopped entries
filter_send_generations(generations, entries);
// Filter next batch and remove requests that were stopped
let next_batch = filter_batch(client, next_batch, entries).await;
metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "decode");
metrics::increment_counter!("tgi_batch_inference_success", "method" => "decode");
next_batch
}
// If we have an error, we discard the whole batch
Err(err) => {
generation_health.store(false, Ordering::SeqCst);
for id in batch_ids {
let _ = client.clear_cache(Some(id)).await;
}
send_errors(err, entries);
metrics::increment_counter!("tgi_batch_inference_failure", "method" => "decode");
None
}
}
}
/// Filter a `batch` and remove all requests not present in `entries`
#[instrument(skip_all)]
async fn filter_batch(
client: &mut ShardedClient,
next_batch: Option<CachedBatch>,
entries: &IntMap<u64, Entry>,
) -> Option<CachedBatch> {
let mut batch = next_batch?;
// No need to filter
if batch.size as usize == entries.len() {
return Some(batch);
}
let id = batch.id;
// Retain only requests that are still in entries
batch.request_ids.retain(|id| entries.contains_key(id));
if batch.request_ids.is_empty() {
// All requests have been filtered out
// Next batch is now empty
// Clear it from the Python shards cache
// We unwrap here as we need to panic since we cannot recover if this method fails
client.clear_cache(Some(id)).await.unwrap();
None
} else {
// Filter Python shard cache
// We unwrap here as we need to panic since we cannot recover if this method fails
client.filter_batch(id, batch.request_ids).await.unwrap()
}
}
/// Send one or multiple `InferStreamResponse` to Infer for all `entries`
/// and filter entries
#[instrument(skip_all)]
fn filter_send_generations(generations: Vec<Generation>, entries: &mut IntMap<u64, Entry>) {
generations.into_iter().for_each(|generation| {
let id = generation.request_id;
// Get entry
// We can `expect` here as the request id should always be in the entries
let entry = entries
.get(&id)
.expect("ID not found in entries. This is a bug.");
// Create and enter a span to link this function back to the entry
let _span = info_span!(parent: entry.temp_span.as_ref().expect("batch_span is None. This is a bug."), "send_generation", generation = ?generation).entered();
// Send generation responses back to the infer task
// If the receive an error from the Flume channel, it means that the client dropped the
// request and we need to stop generating hence why we unwrap_or(true)
let stopped = send_responses(generation, entry).map_err(|err| {
if let SendTimeoutError::Timeout(_) = *err {
tracing::error!("Entry response channel timed out.")
}
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
err
}).unwrap_or(true);
if stopped {
entries.remove(&id).expect("ID not found in entries. This is a bug.");
}
});
}
/// Send responses through the `entry` response channel
fn send_responses(
generation: Generation,
entry: &Entry,
) -> Result<bool, Box<SendTimeoutError<Result<InferStreamResponse, InferError>>>> {
// Return directly if the channel is disconnected
if entry.response_tx.is_disconnected() {
return Ok(true);
}
let mut stopped = false;
if let Some(prefill_tokens) = generation.prefill_tokens {
// Send message
entry.response_tx.send_timeout(
Ok(InferStreamResponse::Prefill(prefill_tokens)),
Duration::from_millis(10),
)?;
}
// Create last Token
let token = Token {
id: generation.token_id,
text: generation.token_text,
logprob: generation.token_logprob,
special: generation.token_is_special,
};
if let Some(generated_text) = generation.generated_text {
// Generation has ended
stopped = true;
// Send message
entry.response_tx.send_timeout(
Ok(InferStreamResponse::End {
token,
generated_text,
queued: entry.queue_time,
start: entry.batch_time.unwrap(),
}),
Duration::from_millis(10),
)?;
} else {
// Send message
entry.response_tx.send_timeout(
Ok(InferStreamResponse::Token(token)),
Duration::from_millis(10),
)?;
}
Ok(stopped)
}
/// Send errors to Infer for all `entries`
#[instrument(skip_all)]
fn send_errors(error: ClientError, entries: &mut IntMap<u64, Entry>) {
entries.drain().for_each(|(_, entry)| {
// Create and enter a span to link this function back to the entry
let _send_error_span = info_span!(parent: entry.temp_span.as_ref().expect("batch_span is None. This is a bug."), "send_error").entered();
let err = InferError::GenerationError(error.to_string());
metrics::increment_counter!("tgi_request_failure", "err" => "generation");
tracing::error!("{err}");
// unwrap_or is valid here as we don't care if the receiver is gone.
entry
.response_tx
.send_timeout(Err(err), Duration::from_millis(10))
.unwrap_or(());
});
}
#[derive(Debug)]
pub(crate) enum InferStreamResponse {
// Optional first message
Prefill(PrefillTokens),
// Intermediate messages
Token(Token),
// Last message
End {
token: Token,
generated_text: GeneratedText,
start: Instant,
queued: Instant,
},
}
#[derive(Debug)]
pub(crate) struct InferResponse {
pub(crate) prefill: Vec<PrefillToken>,
pub(crate) tokens: Vec<Token>,
pub(crate) generated_text: GeneratedText,
pub(crate) queued: Instant,
pub(crate) start: Instant,
}
#[derive(Debug, Error)]
pub enum InferError {
#[error("Request failed during generation: {0}")]
GenerationError(String),
#[error("Model is overloaded")]
Overloaded(#[from] TryAcquireError),
#[error("Input validation error: {0}")]
ValidationError(#[from] ValidationError),
#[error("Incomplete generation")]
IncompleteGeneration,
}
impl InferError {
pub(crate) fn error_type(&self) -> &str {
match self {
InferError::GenerationError(_) => "generation",
InferError::Overloaded(_) => "overloaded",
InferError::ValidationError(_) => "validation",
InferError::IncompleteGeneration => "incomplete_generation",
}
}
}
| 0 |
hf_public_repos/text-generation-inference/router | hf_public_repos/text-generation-inference/router/src/lib.rs | mod health;
/// Text Generation Inference Webserver
mod infer;
mod queue;
pub mod server;
mod validation;
use infer::Infer;
use queue::{Entry, Queue};
use serde::{Deserialize, Serialize};
use utoipa::ToSchema;
use validation::Validation;
/// Hub type
#[derive(Clone, Debug, Deserialize)]
pub struct HubModelInfo {
#[serde(rename(deserialize = "id"))]
pub model_id: String,
pub sha: Option<String>,
pub pipeline_tag: Option<String>,
}
#[derive(Clone, Debug, Serialize, ToSchema)]
pub struct Info {
/// Model info
#[schema(example = "bigscience/blomm-560m")]
pub model_id: String,
#[schema(nullable = true, example = "e985a63cdc139290c5f700ff1929f0b5942cced2")]
pub model_sha: Option<String>,
#[schema(example = "torch.float16")]
pub model_dtype: String,
#[schema(example = "cuda")]
pub model_device_type: String,
#[schema(nullable = true, example = "text-generation")]
pub model_pipeline_tag: Option<String>,
/// Router Parameters
#[schema(example = "128")]
pub max_concurrent_requests: usize,
#[schema(example = "2")]
pub max_best_of: usize,
#[schema(example = "4")]
pub max_stop_sequences: usize,
#[schema(example = "1024")]
pub max_input_length: usize,
#[schema(example = "2048")]
pub max_total_tokens: usize,
#[schema(example = "1.2")]
pub waiting_served_ratio: f32,
#[schema(example = "32000")]
pub max_batch_total_tokens: u32,
#[schema(example = "20")]
pub max_waiting_tokens: usize,
#[schema(example = "2")]
pub validation_workers: usize,
/// Router Info
#[schema(example = "0.5.0")]
pub version: &'static str,
#[schema(nullable = true, example = "null")]
pub sha: Option<&'static str>,
#[schema(nullable = true, example = "null")]
pub docker_label: Option<&'static str>,
}
#[derive(Clone, Debug, Deserialize, ToSchema)]
pub(crate) struct GenerateParameters {
#[serde(default)]
#[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 1)]
pub best_of: Option<usize>,
#[serde(default)]
#[schema(
exclusive_minimum = 0.0,
nullable = true,
default = "null",
example = 0.5
)]
pub temperature: Option<f32>,
#[serde(default)]
#[schema(
exclusive_minimum = 0.0,
nullable = true,
default = "null",
example = 1.03
)]
pub repetition_penalty: Option<f32>,
#[serde(default)]
#[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 10)]
pub top_k: Option<i32>,
#[serde(default)]
#[schema(
exclusive_minimum = 0.0,
maximum = 1.0,
nullable = true,
default = "null",
example = 0.95
)]
pub top_p: Option<f32>,
#[serde(default)]
#[schema(
exclusive_minimum = 0.0,
maximum = 1.0,
nullable = true,
default = "null",
example = 0.95
)]
pub typical_p: Option<f32>,
#[serde(default)]
#[schema(default = "false", example = true)]
pub do_sample: bool,
#[serde(default = "default_max_new_tokens")]
#[schema(exclusive_minimum = 0, exclusive_maximum = 512, default = "20")]
pub max_new_tokens: u32,
#[serde(default)]
#[schema(nullable = true, default = "null", example = false)]
pub return_full_text: Option<bool>,
#[serde(default)]
#[schema(inline, max_items = 4, example = json ! (["photographer"]))]
pub stop: Vec<String>,
#[serde(default)]
#[schema(nullable = true, default = "null", example = "null")]
pub truncate: Option<usize>,
#[serde(default)]
#[schema(default = "false", example = true)]
pub watermark: bool,
#[serde(default)]
#[schema(default = "true")]
pub details: bool,
#[serde(default)]
#[schema(default = "true")]
pub decoder_input_details: bool,
#[serde(default)]
#[schema(
exclusive_minimum = 0,
nullable = true,
default = "null",
example = "null"
)]
pub seed: Option<u64>,
}
fn default_max_new_tokens() -> u32 {
20
}
fn default_parameters() -> GenerateParameters {
GenerateParameters {
best_of: None,
temperature: None,
repetition_penalty: None,
top_k: None,
top_p: None,
typical_p: None,
do_sample: false,
max_new_tokens: default_max_new_tokens(),
return_full_text: None,
stop: Vec::new(),
truncate: None,
watermark: false,
details: false,
decoder_input_details: false,
seed: None,
}
}
#[derive(Clone, Debug, Deserialize, ToSchema)]
pub(crate) struct GenerateRequest {
#[schema(example = "My name is Olivier and I")]
pub inputs: String,
#[serde(default = "default_parameters")]
pub parameters: GenerateParameters,
}
#[derive(Clone, Debug, Deserialize, ToSchema)]
pub(crate) struct CompatGenerateRequest {
#[schema(example = "My name is Olivier and I")]
pub inputs: String,
#[serde(default = "default_parameters")]
pub parameters: GenerateParameters,
#[serde(default)]
#[schema(default = "false")]
pub stream: bool,
}
impl From<CompatGenerateRequest> for GenerateRequest {
fn from(req: CompatGenerateRequest) -> Self {
Self {
inputs: req.inputs,
parameters: req.parameters,
}
}
}
#[derive(Debug, Serialize, ToSchema)]
pub struct PrefillToken {
#[schema(example = 0)]
id: u32,
#[schema(example = "test")]
text: String,
#[schema(nullable = true, example = - 0.34)]
logprob: f32,
}
#[derive(Debug, Serialize, ToSchema)]
pub struct Token {
#[schema(example = 0)]
id: u32,
#[schema(example = "test")]
text: String,
#[schema(nullable = true, example = - 0.34)]
logprob: f32,
#[schema(example = "false")]
special: bool,
}
#[derive(Serialize, ToSchema)]
#[serde(rename_all(serialize = "snake_case"))]
pub(crate) enum FinishReason {
#[schema(rename = "length")]
Length,
#[serde(rename = "eos_token")]
#[schema(rename = "eos_token")]
EndOfSequenceToken,
#[schema(rename = "stop_sequence")]
StopSequence,
}
#[derive(Serialize, ToSchema)]
pub(crate) struct BestOfSequence {
#[schema(example = "test")]
pub generated_text: String,
#[schema(example = "length")]
pub finish_reason: FinishReason,
#[schema(example = 1)]
pub generated_tokens: u32,
#[schema(nullable = true, example = 42)]
pub seed: Option<u64>,
pub prefill: Vec<PrefillToken>,
pub tokens: Vec<Token>,
}
#[derive(Serialize, ToSchema)]
pub(crate) struct Details {
#[schema(example = "length")]
pub finish_reason: FinishReason,
#[schema(example = 1)]
pub generated_tokens: u32,
#[schema(nullable = true, example = 42)]
pub seed: Option<u64>,
pub prefill: Vec<PrefillToken>,
pub tokens: Vec<Token>,
#[serde(skip_serializing_if = "Option::is_none")]
pub best_of_sequences: Option<Vec<BestOfSequence>>,
}
#[derive(Serialize, ToSchema)]
pub(crate) struct GenerateResponse {
#[schema(example = "test")]
pub generated_text: String,
#[serde(skip_serializing_if = "Option::is_none")]
pub details: Option<Details>,
}
#[derive(Serialize, ToSchema)]
pub(crate) struct StreamDetails {
#[schema(example = "length")]
pub finish_reason: FinishReason,
#[schema(example = 1)]
pub generated_tokens: u32,
#[schema(nullable = true, example = 42)]
pub seed: Option<u64>,
}
#[derive(Serialize, ToSchema)]
pub(crate) struct StreamResponse {
pub token: Token,
#[schema(nullable = true, default = "null", example = "test")]
pub generated_text: Option<String>,
#[schema(nullable = true, default = "null")]
pub details: Option<StreamDetails>,
}
#[derive(Serialize, ToSchema)]
pub(crate) struct ErrorResponse {
pub error: String,
pub error_type: String,
}
#[cfg(test)]
mod tests {
use std::io::Write;
use tokenizers::Tokenizer;
pub(crate) async fn get_tokenizer() -> Tokenizer {
if !std::path::Path::new("tokenizer.json").exists() {
let content = reqwest::get("https://huggingface.co/gpt2/raw/main/tokenizer.json")
.await
.unwrap()
.bytes()
.await
.unwrap();
let mut file = std::fs::File::create("tokenizer.json").unwrap();
file.write_all(&content).unwrap();
}
Tokenizer::from_file("tokenizer.json").unwrap()
}
}
| 0 |
hf_public_repos/text-generation-inference/clients | hf_public_repos/text-generation-inference/clients/python/README.md | # Text Generation
The Hugging Face Text Generation Python library provides a convenient way of interfacing with a
`text-generation-inference` instance running on
[Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints) or on the Hugging Face Hub.
## Get Started
### Install
```shell
pip install text-generation
```
### Inference API Usage
```python
from text_generation import InferenceAPIClient
client = InferenceAPIClient("bigscience/bloomz")
text = client.generate("Why is the sky blue?").generated_text
print(text)
# ' Rayleigh scattering'
# Token Streaming
text = ""
for response in client.generate_stream("Why is the sky blue?"):
if not response.token.special:
text += response.token.text
print(text)
# ' Rayleigh scattering'
```
or with the asynchronous client:
```python
from text_generation import InferenceAPIAsyncClient
client = InferenceAPIAsyncClient("bigscience/bloomz")
response = await client.generate("Why is the sky blue?")
print(response.generated_text)
# ' Rayleigh scattering'
# Token Streaming
text = ""
async for response in client.generate_stream("Why is the sky blue?"):
if not response.token.special:
text += response.token.text
print(text)
# ' Rayleigh scattering'
```
Check all currently deployed models on the Huggingface Inference API with `Text Generation` support:
```python
from text_generation.inference_api import deployed_models
print(deployed_models())
```
### Hugging Face Inference Endpoint usage
```python
from text_generation import Client
endpoint_url = "https://YOUR_ENDPOINT.endpoints.huggingface.cloud"
client = Client(endpoint_url)
text = client.generate("Why is the sky blue?").generated_text
print(text)
# ' Rayleigh scattering'
# Token Streaming
text = ""
for response in client.generate_stream("Why is the sky blue?"):
if not response.token.special:
text += response.token.text
print(text)
# ' Rayleigh scattering'
```
or with the asynchronous client:
```python
from text_generation import AsyncClient
endpoint_url = "https://YOUR_ENDPOINT.endpoints.huggingface.cloud"
client = AsyncClient(endpoint_url)
response = await client.generate("Why is the sky blue?")
print(response.generated_text)
# ' Rayleigh scattering'
# Token Streaming
text = ""
async for response in client.generate_stream("Why is the sky blue?"):
if not response.token.special:
text += response.token.text
print(text)
# ' Rayleigh scattering'
```
### Types
```python
# Request Parameters
class Parameters:
# Activate logits sampling
do_sample: bool
# Maximum number of generated tokens
max_new_tokens: int
# The parameter for repetition penalty. 1.0 means no penalty.
# See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
repetition_penalty: Optional[float]
# Whether to prepend the prompt to the generated text
return_full_text: bool
# Stop generating tokens if a member of `stop_sequences` is generated
stop: List[str]
# Random sampling seed
seed: Optional[int]
# The value used to module the logits distribution.
temperature: Optional[float]
# The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_k: Optional[int]
# If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
# higher are kept for generation.
top_p: Optional[float]
# truncate inputs tokens to the given size
truncate: Optional[int]
# Typical Decoding mass
# See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
typical_p: Optional[float]
# Generate best_of sequences and return the one if the highest token logprobs
best_of: Optional[int]
# Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
watermark: bool
# Get decoder input token logprobs and ids
decoder_input_details: bool
# Decoder input tokens
class InputToken:
# Token ID from the model tokenizer
id: int
# Token text
text: str
# Logprob
# Optional since the logprob of the first token cannot be computed
logprob: Optional[float]
# Generated tokens
class Token:
# Token ID from the model tokenizer
id: int
# Token text
text: str
# Logprob
logprob: float
# Is the token a special token
# Can be used to ignore tokens when concatenating
special: bool
# Generation finish reason
class FinishReason(Enum):
# number of generated tokens == `max_new_tokens`
Length = "length"
# the model generated its end of sequence token
EndOfSequenceToken = "eos_token"
# the model generated a text included in `stop_sequences`
StopSequence = "stop_sequence"
# Additional sequences when using the `best_of` parameter
class BestOfSequence:
# Generated text
generated_text: str
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int]
# Decoder input tokens, empty if decoder_input_details is False
prefill: List[InputToken]
# Generated tokens
tokens: List[Token]
# `generate` details
class Details:
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int]
# Decoder input tokens, empty if decoder_input_details is False
prefill: List[InputToken]
# Generated tokens
tokens: List[Token]
# Additional sequences when using the `best_of` parameter
best_of_sequences: Optional[List[BestOfSequence]]
# `generate` return value
class Response:
# Generated text
generated_text: str
# Generation details
details: Details
# `generate_stream` details
class StreamDetails:
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int]
# `generate_stream` return value
class StreamResponse:
# Generated token
token: Token
# Complete generated text
# Only available when the generation is finished
generated_text: Optional[str]
# Generation details
# Only available when the generation is finished
details: Optional[StreamDetails]
# Inference API currently deployed model
class DeployedModel:
model_id: str
sha: str
``` | 0 |
hf_public_repos/text-generation-inference/clients | hf_public_repos/text-generation-inference/clients/python/pyproject.toml | [tool.poetry]
name = "text-generation"
version = "0.6.0"
description = "Hugging Face Text Generation Python Client"
license = "Apache-2.0"
authors = ["Olivier Dehaene <[email protected]>"]
maintainers = ["Olivier Dehaene <[email protected]>"]
readme = "README.md"
homepage = "https://github.com/huggingface/text-generation-inference"
repository = "https://github.com/huggingface/text-generation-inference"
[tool.poetry.dependencies]
python = "^3.7"
pydantic = "^1.10"
aiohttp = "^3.8"
huggingface-hub = ">= 0.12, < 1.0"
[tool.poetry.dev-dependencies]
pytest = "^6.2.5"
pytest-asyncio = "^0.17.2"
pytest-cov = "^3.0.0"
[tool.pytest.ini_options]
asyncio_mode = "auto"
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
| 0 |
hf_public_repos/text-generation-inference/clients | hf_public_repos/text-generation-inference/clients/python/poetry.lock | [[package]]
name = "aiohttp"
version = "3.8.4"
description = "Async http client/server framework (asyncio)"
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
aiosignal = ">=1.1.2"
async-timeout = ">=4.0.0a3,<5.0"
asynctest = {version = "0.13.0", markers = "python_version < \"3.8\""}
attrs = ">=17.3.0"
charset-normalizer = ">=2.0,<4.0"
frozenlist = ">=1.1.1"
multidict = ">=4.5,<7.0"
typing-extensions = {version = ">=3.7.4", markers = "python_version < \"3.8\""}
yarl = ">=1.0,<2.0"
[package.extras]
speedups = ["Brotli", "aiodns", "cchardet"]
[[package]]
name = "aiosignal"
version = "1.3.1"
description = "aiosignal: a list of registered asynchronous callbacks"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
frozenlist = ">=1.1.0"
[[package]]
name = "async-timeout"
version = "4.0.2"
description = "Timeout context manager for asyncio programs"
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
typing-extensions = {version = ">=3.6.5", markers = "python_version < \"3.8\""}
[[package]]
name = "asynctest"
version = "0.13.0"
description = "Enhance the standard unittest package with features for testing asyncio libraries"
category = "main"
optional = false
python-versions = ">=3.5"
[[package]]
name = "atomicwrites"
version = "1.4.1"
description = "Atomic file writes."
category = "dev"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
[[package]]
name = "attrs"
version = "22.2.0"
description = "Classes Without Boilerplate"
category = "main"
optional = false
python-versions = ">=3.6"
[package.extras]
cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"]
dev = ["attrs[docs,tests]"]
docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"]
tests = ["attrs[tests-no-zope]", "zope.interface"]
tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=0.971,<0.990)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
tests_no_zope = ["cloudpickle", "hypothesis", "mypy (>=0.971,<0.990)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
[[package]]
name = "certifi"
version = "2022.12.7"
description = "Python package for providing Mozilla's CA Bundle."
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "charset-normalizer"
version = "3.1.0"
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
category = "main"
optional = false
python-versions = ">=3.7.0"
[[package]]
name = "colorama"
version = "0.4.6"
description = "Cross-platform colored terminal text."
category = "main"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
[[package]]
name = "coverage"
version = "7.2.1"
description = "Code coverage measurement for Python"
category = "dev"
optional = false
python-versions = ">=3.7"
[package.dependencies]
tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""}
[package.extras]
toml = ["tomli"]
[[package]]
name = "filelock"
version = "3.10.0"
description = "A platform independent file lock."
category = "main"
optional = false
python-versions = ">=3.7"
[package.extras]
docs = ["furo (>=2022.12.7)", "sphinx (>=6.1.3)", "sphinx-autodoc-typehints (>=1.22,!=1.23.4)"]
testing = ["covdefaults (>=2.3)", "coverage (>=7.2.1)", "pytest (>=7.2.2)", "pytest-cov (>=4)", "pytest-timeout (>=2.1)"]
[[package]]
name = "frozenlist"
version = "1.3.3"
description = "A list-like structure which implements collections.abc.MutableSequence"
category = "main"
optional = false
python-versions = ">=3.7"
[[package]]
name = "huggingface-hub"
version = "0.13.2"
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
category = "main"
optional = false
python-versions = ">=3.7.0"
[package.dependencies]
filelock = "*"
importlib-metadata = {version = "*", markers = "python_version < \"3.8\""}
packaging = ">=20.9"
pyyaml = ">=5.1"
requests = "*"
tqdm = ">=4.42.1"
typing-extensions = ">=3.7.4.3"
[package.extras]
all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "black (>=23.1,<24.0)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
cli = ["InquirerPy (==0.3.4)"]
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "black (>=23.1,<24.0)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
quality = ["black (>=23.1,<24.0)", "mypy (==0.982)", "ruff (>=0.0.241)"]
tensorflow = ["graphviz", "pydot", "tensorflow"]
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "jedi", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "soundfile"]
torch = ["torch"]
typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
[[package]]
name = "idna"
version = "3.4"
description = "Internationalized Domain Names in Applications (IDNA)"
category = "main"
optional = false
python-versions = ">=3.5"
[[package]]
name = "importlib-metadata"
version = "6.0.0"
description = "Read metadata from Python packages"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
typing-extensions = {version = ">=3.6.4", markers = "python_version < \"3.8\""}
zipp = ">=0.5"
[package.extras]
docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
perf = ["ipython"]
testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"]
[[package]]
name = "iniconfig"
version = "2.0.0"
description = "brain-dead simple config-ini parsing"
category = "dev"
optional = false
python-versions = ">=3.7"
[[package]]
name = "multidict"
version = "6.0.4"
description = "multidict implementation"
category = "main"
optional = false
python-versions = ">=3.7"
[[package]]
name = "packaging"
version = "23.0"
description = "Core utilities for Python packages"
category = "main"
optional = false
python-versions = ">=3.7"
[[package]]
name = "pluggy"
version = "1.0.0"
description = "plugin and hook calling mechanisms for python"
category = "dev"
optional = false
python-versions = ">=3.6"
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importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""}
[package.extras]
dev = ["pre-commit", "tox"]
testing = ["pytest", "pytest-benchmark"]
[[package]]
name = "py"
version = "1.11.0"
description = "library with cross-python path, ini-parsing, io, code, log facilities"
category = "dev"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
[[package]]
name = "pydantic"
version = "1.10.6"
description = "Data validation and settings management using python type hints"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
typing-extensions = ">=4.2.0"
[package.extras]
dotenv = ["python-dotenv (>=0.10.4)"]
email = ["email-validator (>=1.0.3)"]
[[package]]
name = "pytest"
version = "6.2.5"
description = "pytest: simple powerful testing with Python"
category = "dev"
optional = false
python-versions = ">=3.6"
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atomicwrites = {version = ">=1.0", markers = "sys_platform == \"win32\""}
attrs = ">=19.2.0"
colorama = {version = "*", markers = "sys_platform == \"win32\""}
importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""}
iniconfig = "*"
packaging = "*"
pluggy = ">=0.12,<2.0"
py = ">=1.8.2"
toml = "*"
[package.extras]
testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"]
[[package]]
name = "pytest-asyncio"
version = "0.17.2"
description = "Pytest support for asyncio"
category = "dev"
optional = false
python-versions = ">=3.7"
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pytest = ">=6.1.0"
typing-extensions = {version = ">=4.0", markers = "python_version < \"3.8\""}
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testing = ["coverage (==6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (==0.931)"]
[[package]]
name = "pytest-cov"
version = "3.0.0"
description = "Pytest plugin for measuring coverage."
category = "dev"
optional = false
python-versions = ">=3.6"
[package.dependencies]
coverage = {version = ">=5.2.1", extras = ["toml"]}
pytest = ">=4.6"
[package.extras]
testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"]
[[package]]
name = "PyYAML"
version = "6.0"
description = "YAML parser and emitter for Python"
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "requests"
version = "2.28.2"
description = "Python HTTP for Humans."
category = "main"
optional = false
python-versions = ">=3.7, <4"
[package.dependencies]
certifi = ">=2017.4.17"
charset-normalizer = ">=2,<4"
idna = ">=2.5,<4"
urllib3 = ">=1.21.1,<1.27"
[package.extras]
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"]
[[package]]
name = "toml"
version = "0.10.2"
description = "Python Library for Tom's Obvious, Minimal Language"
category = "dev"
optional = false
python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
[[package]]
name = "tomli"
version = "2.0.1"
description = "A lil' TOML parser"
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{file = "yarl-1.8.2-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:48dd18adcf98ea9cd721a25313aef49d70d413a999d7d89df44f469edfb38a06"},
{file = "yarl-1.8.2-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:e59399dda559688461762800d7fb34d9e8a6a7444fd76ec33220a926c8be1516"},
{file = "yarl-1.8.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:d617c241c8c3ad5c4e78a08429fa49e4b04bedfc507b34b4d8dceb83b4af3588"},
{file = "yarl-1.8.2-cp39-cp39-win32.whl", hash = "sha256:cb6d48d80a41f68de41212f3dfd1a9d9898d7841c8f7ce6696cf2fd9cb57ef83"},
{file = "yarl-1.8.2-cp39-cp39-win_amd64.whl", hash = "sha256:6604711362f2dbf7160df21c416f81fac0de6dbcf0b5445a2ef25478ecc4c778"},
{file = "yarl-1.8.2.tar.gz", hash = "sha256:49d43402c6e3013ad0978602bf6bf5328535c48d192304b91b97a3c6790b1562"},
]
zipp = [
{file = "zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"},
{file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"},
]
| 0 |
hf_public_repos/text-generation-inference/clients | hf_public_repos/text-generation-inference/clients/python/Makefile | unit-tests:
python -m pytest --cov=text_generation tests
install:
pip install pip --upgrade
pip install -e . | 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/tests/test_inference_api.py | import pytest
from text_generation import (
InferenceAPIClient,
InferenceAPIAsyncClient,
Client,
AsyncClient,
)
from text_generation.errors import NotSupportedError, NotFoundError
from text_generation.inference_api import check_model_support, deployed_models
def test_check_model_support(flan_t5_xxl, unsupported_model, fake_model):
assert check_model_support(flan_t5_xxl)
assert not check_model_support(unsupported_model)
with pytest.raises(NotFoundError):
check_model_support(fake_model)
def test_deployed_models():
deployed_models()
def test_client(flan_t5_xxl):
client = InferenceAPIClient(flan_t5_xxl)
assert isinstance(client, Client)
def test_client_unsupported_model(unsupported_model):
with pytest.raises(NotSupportedError):
InferenceAPIClient(unsupported_model)
def test_async_client(flan_t5_xxl):
client = InferenceAPIAsyncClient(flan_t5_xxl)
assert isinstance(client, AsyncClient)
def test_async_client_unsupported_model(unsupported_model):
with pytest.raises(NotSupportedError):
InferenceAPIAsyncClient(unsupported_model)
| 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/tests/conftest.py | import pytest
from text_generation import __version__
from huggingface_hub.utils import build_hf_headers
@pytest.fixture
def flan_t5_xxl():
return "google/flan-t5-xxl"
@pytest.fixture
def fake_model():
return "fake/model"
@pytest.fixture
def unsupported_model():
return "gpt2"
@pytest.fixture
def base_url():
return "https://api-inference.huggingface.co/models"
@pytest.fixture
def bloom_url(base_url, bloom_model):
return f"{base_url}/{bloom_model}"
@pytest.fixture
def flan_t5_xxl_url(base_url, flan_t5_xxl):
return f"{base_url}/{flan_t5_xxl}"
@pytest.fixture
def fake_url(base_url, fake_model):
return f"{base_url}/{fake_model}"
@pytest.fixture
def unsupported_url(base_url, unsupported_model):
return f"{base_url}/{unsupported_model}"
@pytest.fixture(scope="session")
def hf_headers():
return build_hf_headers(
library_name="text-generation-tests", library_version=__version__
)
| 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/tests/test_types.py | import pytest
from text_generation.types import Parameters, Request
from text_generation.errors import ValidationError
def test_parameters_validation():
# Test best_of
Parameters(best_of=1)
with pytest.raises(ValidationError):
Parameters(best_of=0)
with pytest.raises(ValidationError):
Parameters(best_of=-1)
Parameters(best_of=2, do_sample=True)
with pytest.raises(ValidationError):
Parameters(best_of=2)
with pytest.raises(ValidationError):
Parameters(best_of=2, seed=1)
# Test repetition_penalty
Parameters(repetition_penalty=1)
with pytest.raises(ValidationError):
Parameters(repetition_penalty=0)
with pytest.raises(ValidationError):
Parameters(repetition_penalty=-1)
# Test seed
Parameters(seed=1)
with pytest.raises(ValidationError):
Parameters(seed=-1)
# Test temperature
Parameters(temperature=1)
with pytest.raises(ValidationError):
Parameters(temperature=0)
with pytest.raises(ValidationError):
Parameters(temperature=-1)
# Test top_k
Parameters(top_k=1)
with pytest.raises(ValidationError):
Parameters(top_k=0)
with pytest.raises(ValidationError):
Parameters(top_k=-1)
# Test top_p
Parameters(top_p=0.5)
with pytest.raises(ValidationError):
Parameters(top_p=0)
with pytest.raises(ValidationError):
Parameters(top_p=-1)
with pytest.raises(ValidationError):
Parameters(top_p=1)
# Test truncate
Parameters(truncate=1)
with pytest.raises(ValidationError):
Parameters(truncate=0)
with pytest.raises(ValidationError):
Parameters(truncate=-1)
# Test typical_p
Parameters(typical_p=0.5)
with pytest.raises(ValidationError):
Parameters(typical_p=0)
with pytest.raises(ValidationError):
Parameters(typical_p=-1)
with pytest.raises(ValidationError):
Parameters(typical_p=1)
def test_request_validation():
Request(inputs="test")
with pytest.raises(ValidationError):
Request(inputs="")
Request(inputs="test", stream=True)
Request(inputs="test", parameters=Parameters(best_of=2, do_sample=True))
with pytest.raises(ValidationError):
Request(
inputs="test", parameters=Parameters(best_of=2, do_sample=True), stream=True
)
| 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/tests/test_client.py | import pytest
from text_generation import Client, AsyncClient
from text_generation.errors import NotFoundError, ValidationError
from text_generation.types import FinishReason, InputToken
def test_generate(flan_t5_xxl_url, hf_headers):
client = Client(flan_t5_xxl_url, hf_headers)
response = client.generate("test", max_new_tokens=1, decoder_input_details=True)
assert response.generated_text == ""
assert response.details.finish_reason == FinishReason.Length
assert response.details.generated_tokens == 1
assert response.details.seed is None
assert len(response.details.prefill) == 1
assert response.details.prefill[0] == InputToken(id=0, text="<pad>", logprob=None)
assert len(response.details.tokens) == 1
assert response.details.tokens[0].id == 3
assert response.details.tokens[0].text == " "
assert not response.details.tokens[0].special
def test_generate_best_of(flan_t5_xxl_url, hf_headers):
client = Client(flan_t5_xxl_url, hf_headers)
response = client.generate(
"test", max_new_tokens=1, best_of=2, do_sample=True, decoder_input_details=True
)
assert response.details.seed is not None
assert response.details.best_of_sequences is not None
assert len(response.details.best_of_sequences) == 1
assert response.details.best_of_sequences[0].seed is not None
def test_generate_not_found(fake_url, hf_headers):
client = Client(fake_url, hf_headers)
with pytest.raises(NotFoundError):
client.generate("test")
def test_generate_validation_error(flan_t5_xxl_url, hf_headers):
client = Client(flan_t5_xxl_url, hf_headers)
with pytest.raises(ValidationError):
client.generate("test", max_new_tokens=10_000)
def test_generate_stream(flan_t5_xxl_url, hf_headers):
client = Client(flan_t5_xxl_url, hf_headers)
responses = [
response for response in client.generate_stream("test", max_new_tokens=1)
]
assert len(responses) == 1
response = responses[0]
assert response.generated_text == ""
assert response.details.finish_reason == FinishReason.Length
assert response.details.generated_tokens == 1
assert response.details.seed is None
def test_generate_stream_not_found(fake_url, hf_headers):
client = Client(fake_url, hf_headers)
with pytest.raises(NotFoundError):
list(client.generate_stream("test"))
def test_generate_stream_validation_error(flan_t5_xxl_url, hf_headers):
client = Client(flan_t5_xxl_url, hf_headers)
with pytest.raises(ValidationError):
list(client.generate_stream("test", max_new_tokens=10_000))
@pytest.mark.asyncio
async def test_generate_async(flan_t5_xxl_url, hf_headers):
client = AsyncClient(flan_t5_xxl_url, hf_headers)
response = await client.generate(
"test", max_new_tokens=1, decoder_input_details=True
)
assert response.generated_text == ""
assert response.details.finish_reason == FinishReason.Length
assert response.details.generated_tokens == 1
assert response.details.seed is None
assert len(response.details.prefill) == 1
assert response.details.prefill[0] == InputToken(id=0, text="<pad>", logprob=None)
assert len(response.details.tokens) == 1
assert response.details.tokens[0].id == 3
assert response.details.tokens[0].text == " "
assert not response.details.tokens[0].special
@pytest.mark.asyncio
async def test_generate_async_best_of(flan_t5_xxl_url, hf_headers):
client = AsyncClient(flan_t5_xxl_url, hf_headers)
response = await client.generate(
"test", max_new_tokens=1, best_of=2, do_sample=True, decoder_input_details=True
)
assert response.details.seed is not None
assert response.details.best_of_sequences is not None
assert len(response.details.best_of_sequences) == 1
assert response.details.best_of_sequences[0].seed is not None
@pytest.mark.asyncio
async def test_generate_async_not_found(fake_url, hf_headers):
client = AsyncClient(fake_url, hf_headers)
with pytest.raises(NotFoundError):
await client.generate("test")
@pytest.mark.asyncio
async def test_generate_async_validation_error(flan_t5_xxl_url, hf_headers):
client = AsyncClient(flan_t5_xxl_url, hf_headers)
with pytest.raises(ValidationError):
await client.generate("test", max_new_tokens=10_000)
@pytest.mark.asyncio
async def test_generate_stream_async(flan_t5_xxl_url, hf_headers):
client = AsyncClient(flan_t5_xxl_url, hf_headers)
responses = [
response async for response in client.generate_stream("test", max_new_tokens=1)
]
assert len(responses) == 1
response = responses[0]
assert response.generated_text == ""
assert response.details.finish_reason == FinishReason.Length
assert response.details.generated_tokens == 1
assert response.details.seed is None
@pytest.mark.asyncio
async def test_generate_stream_async_not_found(fake_url, hf_headers):
client = AsyncClient(fake_url, hf_headers)
with pytest.raises(NotFoundError):
async for _ in client.generate_stream("test"):
pass
@pytest.mark.asyncio
async def test_generate_stream_async_validation_error(flan_t5_xxl_url, hf_headers):
client = AsyncClient(flan_t5_xxl_url, hf_headers)
with pytest.raises(ValidationError):
async for _ in client.generate_stream("test", max_new_tokens=10_000):
pass
| 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/tests/test_errors.py | from text_generation.errors import (
parse_error,
GenerationError,
IncompleteGenerationError,
OverloadedError,
ValidationError,
BadRequestError,
ShardNotReadyError,
ShardTimeoutError,
NotFoundError,
RateLimitExceededError,
UnknownError,
)
def test_generation_error():
payload = {"error_type": "generation", "error": "test"}
assert isinstance(parse_error(400, payload), GenerationError)
def test_incomplete_generation_error():
payload = {"error_type": "incomplete_generation", "error": "test"}
assert isinstance(parse_error(400, payload), IncompleteGenerationError)
def test_overloaded_error():
payload = {"error_type": "overloaded", "error": "test"}
assert isinstance(parse_error(400, payload), OverloadedError)
def test_validation_error():
payload = {"error_type": "validation", "error": "test"}
assert isinstance(parse_error(400, payload), ValidationError)
def test_bad_request_error():
payload = {"error": "test"}
assert isinstance(parse_error(400, payload), BadRequestError)
def test_shard_not_ready_error():
payload = {"error": "test"}
assert isinstance(parse_error(403, payload), ShardNotReadyError)
assert isinstance(parse_error(424, payload), ShardNotReadyError)
def test_shard_timeout_error():
payload = {"error": "test"}
assert isinstance(parse_error(504, payload), ShardTimeoutError)
def test_not_found_error():
payload = {"error": "test"}
assert isinstance(parse_error(404, payload), NotFoundError)
def test_rate_limit_exceeded_error():
payload = {"error": "test"}
assert isinstance(parse_error(429, payload), RateLimitExceededError)
def test_unknown_error():
payload = {"error": "test"}
assert isinstance(parse_error(500, payload), UnknownError)
| 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/text_generation/__init__.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__version__ = "0.3.0"
from text_generation.client import Client, AsyncClient
from text_generation.inference_api import InferenceAPIClient, InferenceAPIAsyncClient
| 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/text_generation/client.py | import json
import requests
from aiohttp import ClientSession, ClientTimeout
from pydantic import ValidationError
from typing import Dict, Optional, List, AsyncIterator, Iterator
from text_generation.types import (
StreamResponse,
Response,
Request,
Parameters,
)
from text_generation.errors import parse_error
class Client:
"""Client to make calls to a text-generation-inference instance
Example:
```python
>>> from text_generation import Client
>>> client = Client("https://api-inference.huggingface.co/models/bigscience/bloomz")
>>> client.generate("Why is the sky blue?").generated_text
' Rayleigh scattering'
>>> result = ""
>>> for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
"""
def __init__(
self,
base_url: str,
headers: Optional[Dict[str, str]] = None,
cookies: Optional[Dict[str, str]] = None,
timeout: int = 10,
):
"""
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
"""
self.base_url = base_url
self.headers = headers
self.cookies = cookies
self.timeout = timeout
def generate(
self,
prompt: str,
do_sample: bool = False,
max_new_tokens: int = 20,
best_of: Optional[int] = None,
repetition_penalty: Optional[float] = None,
return_full_text: bool = False,
seed: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
temperature: Optional[float] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
truncate: Optional[int] = None,
typical_p: Optional[float] = None,
watermark: bool = False,
decoder_input_details: bool = False,
) -> Response:
"""
Given a prompt, generate the following text
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
best_of (`int`):
Generate best_of sequences and return the one if the highest token logprobs
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
decoder_input_details (`bool`):
Return the decoder input token logprobs and ids
Returns:
Response: generated response
"""
# Validate parameters
parameters = Parameters(
best_of=best_of,
details=True,
do_sample=do_sample,
max_new_tokens=max_new_tokens,
repetition_penalty=repetition_penalty,
return_full_text=return_full_text,
seed=seed,
stop=stop_sequences if stop_sequences is not None else [],
temperature=temperature,
top_k=top_k,
top_p=top_p,
truncate=truncate,
typical_p=typical_p,
watermark=watermark,
decoder_input_details=decoder_input_details,
)
request = Request(inputs=prompt, stream=False, parameters=parameters)
resp = requests.post(
self.base_url,
json=request.dict(),
headers=self.headers,
cookies=self.cookies,
timeout=self.timeout,
)
payload = resp.json()
if resp.status_code != 200:
raise parse_error(resp.status_code, payload)
return Response(**payload[0])
def generate_stream(
self,
prompt: str,
do_sample: bool = False,
max_new_tokens: int = 20,
repetition_penalty: Optional[float] = None,
return_full_text: bool = False,
seed: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
temperature: Optional[float] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
truncate: Optional[int] = None,
typical_p: Optional[float] = None,
watermark: bool = False,
) -> Iterator[StreamResponse]:
"""
Given a prompt, generate the following stream of tokens
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
Returns:
Iterator[StreamResponse]: stream of generated tokens
"""
# Validate parameters
parameters = Parameters(
best_of=None,
details=True,
decoder_input_details=False,
do_sample=do_sample,
max_new_tokens=max_new_tokens,
repetition_penalty=repetition_penalty,
return_full_text=return_full_text,
seed=seed,
stop=stop_sequences if stop_sequences is not None else [],
temperature=temperature,
top_k=top_k,
top_p=top_p,
truncate=truncate,
typical_p=typical_p,
watermark=watermark,
)
request = Request(inputs=prompt, stream=True, parameters=parameters)
resp = requests.post(
self.base_url,
json=request.dict(),
headers=self.headers,
cookies=self.cookies,
timeout=self.timeout,
stream=True,
)
if resp.status_code != 200:
raise parse_error(resp.status_code, resp.json())
# Parse ServerSentEvents
for byte_payload in resp.iter_lines():
# Skip line
if byte_payload == b"\n":
continue
payload = byte_payload.decode("utf-8")
# Event data
if payload.startswith("data:"):
# Decode payload
json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
# Parse payload
try:
response = StreamResponse(**json_payload)
except ValidationError:
# If we failed to parse the payload, then it is an error payload
raise parse_error(resp.status_code, json_payload)
yield response
class AsyncClient:
"""Asynchronous Client to make calls to a text-generation-inference instance
Example:
```python
>>> from text_generation import AsyncClient
>>> client = AsyncClient("https://api-inference.huggingface.co/models/bigscience/bloomz")
>>> response = await client.generate("Why is the sky blue?")
>>> response.generated_text
' Rayleigh scattering'
>>> result = ""
>>> async for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
"""
def __init__(
self,
base_url: str,
headers: Optional[Dict[str, str]] = None,
cookies: Optional[Dict[str, str]] = None,
timeout: int = 10,
):
"""
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
"""
self.base_url = base_url
self.headers = headers
self.cookies = cookies
self.timeout = ClientTimeout(timeout * 60)
async def generate(
self,
prompt: str,
do_sample: bool = False,
max_new_tokens: int = 20,
best_of: Optional[int] = None,
repetition_penalty: Optional[float] = None,
return_full_text: bool = False,
seed: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
temperature: Optional[float] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
truncate: Optional[int] = None,
typical_p: Optional[float] = None,
watermark: bool = False,
decoder_input_details: bool = False,
) -> Response:
"""
Given a prompt, generate the following text asynchronously
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
best_of (`int`):
Generate best_of sequences and return the one if the highest token logprobs
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
decoder_input_details (`bool`):
Return the decoder input token logprobs and ids
Returns:
Response: generated response
"""
# Validate parameters
parameters = Parameters(
best_of=best_of,
details=True,
decoder_input_details=decoder_input_details,
do_sample=do_sample,
max_new_tokens=max_new_tokens,
repetition_penalty=repetition_penalty,
return_full_text=return_full_text,
seed=seed,
stop=stop_sequences if stop_sequences is not None else [],
temperature=temperature,
top_k=top_k,
top_p=top_p,
truncate=truncate,
typical_p=typical_p,
watermark=watermark,
)
request = Request(inputs=prompt, stream=False, parameters=parameters)
async with ClientSession(
headers=self.headers, cookies=self.cookies, timeout=self.timeout
) as session:
async with session.post(self.base_url, json=request.dict()) as resp:
payload = await resp.json()
if resp.status != 200:
raise parse_error(resp.status, payload)
return Response(**payload[0])
async def generate_stream(
self,
prompt: str,
do_sample: bool = False,
max_new_tokens: int = 20,
repetition_penalty: Optional[float] = None,
return_full_text: bool = False,
seed: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
temperature: Optional[float] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
truncate: Optional[int] = None,
typical_p: Optional[float] = None,
watermark: bool = False,
) -> AsyncIterator[StreamResponse]:
"""
Given a prompt, generate the following stream of tokens asynchronously
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
Returns:
AsyncIterator[StreamResponse]: stream of generated tokens
"""
# Validate parameters
parameters = Parameters(
best_of=None,
details=True,
decoder_input_details=False,
do_sample=do_sample,
max_new_tokens=max_new_tokens,
repetition_penalty=repetition_penalty,
return_full_text=return_full_text,
seed=seed,
stop=stop_sequences if stop_sequences is not None else [],
temperature=temperature,
top_k=top_k,
top_p=top_p,
truncate=truncate,
typical_p=typical_p,
watermark=watermark,
)
request = Request(inputs=prompt, stream=True, parameters=parameters)
async with ClientSession(
headers=self.headers, cookies=self.cookies, timeout=self.timeout
) as session:
async with session.post(self.base_url, json=request.dict()) as resp:
if resp.status != 200:
raise parse_error(resp.status, await resp.json())
# Parse ServerSentEvents
async for byte_payload in resp.content:
# Skip line
if byte_payload == b"\n":
continue
payload = byte_payload.decode("utf-8")
# Event data
if payload.startswith("data:"):
# Decode payload
json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
# Parse payload
try:
response = StreamResponse(**json_payload)
except ValidationError:
# If we failed to parse the payload, then it is an error payload
raise parse_error(resp.status, json_payload)
yield response
| 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/text_generation/inference_api.py | import os
import requests
from typing import Dict, Optional, List
from huggingface_hub.utils import build_hf_headers
from text_generation import Client, AsyncClient, __version__
from text_generation.types import DeployedModel
from text_generation.errors import NotSupportedError, parse_error
INFERENCE_ENDPOINT = os.environ.get(
"HF_INFERENCE_ENDPOINT", "https://api-inference.huggingface.co"
)
def deployed_models(headers: Optional[Dict] = None) -> List[DeployedModel]:
"""
Get all currently deployed models with text-generation-inference-support
Returns:
List[DeployedModel]: list of all currently deployed models
"""
resp = requests.get(
f"https://api-inference.huggingface.co/framework/text-generation-inference",
headers=headers,
timeout=5,
)
payload = resp.json()
if resp.status_code != 200:
raise parse_error(resp.status_code, payload)
models = [DeployedModel(**raw_deployed_model) for raw_deployed_model in payload]
return models
def check_model_support(repo_id: str, headers: Optional[Dict] = None) -> bool:
"""
Check if a given model is supported by text-generation-inference
Returns:
bool: whether the model is supported by this client
"""
resp = requests.get(
f"https://api-inference.huggingface.co/status/{repo_id}",
headers=headers,
timeout=5,
)
payload = resp.json()
if resp.status_code != 200:
raise parse_error(resp.status_code, payload)
framework = payload["framework"]
supported = framework == "text-generation-inference"
return supported
class InferenceAPIClient(Client):
"""Client to make calls to the HuggingFace Inference API.
Only supports a subset of the available text-generation or text2text-generation models that are served using
text-generation-inference
Example:
```python
>>> from text_generation import InferenceAPIClient
>>> client = InferenceAPIClient("bigscience/bloomz")
>>> client.generate("Why is the sky blue?").generated_text
' Rayleigh scattering'
>>> result = ""
>>> for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
"""
def __init__(self, repo_id: str, token: Optional[str] = None, timeout: int = 10):
"""
Init headers and API information
Args:
repo_id (`str`):
Id of repository (e.g. `bigscience/bloom`).
token (`str`, `optional`):
The API token to use as HTTP bearer authorization. This is not
the authentication token. You can find the token in
https://huggingface.co/settings/token. Alternatively, you can
find both your organizations and personal API tokens using
`HfApi().whoami(token)`.
timeout (`int`):
Timeout in seconds
"""
headers = build_hf_headers(
token=token, library_name="text-generation", library_version=__version__
)
# Text Generation Inference client only supports a subset of the available hub models
if not check_model_support(repo_id, headers):
raise NotSupportedError(repo_id)
base_url = f"{INFERENCE_ENDPOINT}/models/{repo_id}"
super(InferenceAPIClient, self).__init__(
base_url, headers=headers, timeout=timeout
)
class InferenceAPIAsyncClient(AsyncClient):
"""Aynschronous Client to make calls to the HuggingFace Inference API.
Only supports a subset of the available text-generation or text2text-generation models that are served using
text-generation-inference
Example:
```python
>>> from text_generation import InferenceAPIAsyncClient
>>> client = InferenceAPIAsyncClient("bigscience/bloomz")
>>> response = await client.generate("Why is the sky blue?")
>>> response.generated_text
' Rayleigh scattering'
>>> result = ""
>>> async for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
"""
def __init__(self, repo_id: str, token: Optional[str] = None, timeout: int = 10):
"""
Init headers and API information
Args:
repo_id (`str`):
Id of repository (e.g. `bigscience/bloom`).
token (`str`, `optional`):
The API token to use as HTTP bearer authorization. This is not
the authentication token. You can find the token in
https://huggingface.co/settings/token. Alternatively, you can
find both your organizations and personal API tokens using
`HfApi().whoami(token)`.
timeout (`int`):
Timeout in seconds
"""
headers = build_hf_headers(
token=token, library_name="text-generation", library_version=__version__
)
# Text Generation Inference client only supports a subset of the available hub models
if not check_model_support(repo_id, headers):
raise NotSupportedError(repo_id)
base_url = f"{INFERENCE_ENDPOINT}/models/{repo_id}"
super(InferenceAPIAsyncClient, self).__init__(
base_url, headers=headers, timeout=timeout
)
| 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/text_generation/types.py | from enum import Enum
from pydantic import BaseModel, validator
from typing import Optional, List
from text_generation.errors import ValidationError
class Parameters(BaseModel):
# Activate logits sampling
do_sample: bool = False
# Maximum number of generated tokens
max_new_tokens: int = 20
# The parameter for repetition penalty. 1.0 means no penalty.
# See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
repetition_penalty: Optional[float] = None
# Whether to prepend the prompt to the generated text
return_full_text: bool = False
# Stop generating tokens if a member of `stop_sequences` is generated
stop: List[str] = []
# Random sampling seed
seed: Optional[int]
# The value used to module the logits distribution.
temperature: Optional[float]
# The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_k: Optional[int]
# If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
# higher are kept for generation.
top_p: Optional[float]
# truncate inputs tokens to the given size
truncate: Optional[int]
# Typical Decoding mass
# See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
typical_p: Optional[float]
# Generate best_of sequences and return the one if the highest token logprobs
best_of: Optional[int]
# Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
watermark: bool = False
# Get generation details
details: bool = False
# Get decoder input token logprobs and ids
decoder_input_details: bool = False
@validator("best_of")
def valid_best_of(cls, field_value, values):
if field_value is not None:
if field_value <= 0:
raise ValidationError("`best_of` must be strictly positive")
if field_value > 1 and values["seed"] is not None:
raise ValidationError("`seed` must not be set when `best_of` is > 1")
sampling = (
values["do_sample"]
| (values["temperature"] is not None)
| (values["top_k"] is not None)
| (values["top_p"] is not None)
| (values["typical_p"] is not None)
)
if field_value > 1 and not sampling:
raise ValidationError("you must use sampling when `best_of` is > 1")
return field_value
@validator("repetition_penalty")
def valid_repetition_penalty(cls, v):
if v is not None and v <= 0:
raise ValidationError("`repetition_penalty` must be strictly positive")
return v
@validator("seed")
def valid_seed(cls, v):
if v is not None and v < 0:
raise ValidationError("`seed` must be positive")
return v
@validator("temperature")
def valid_temp(cls, v):
if v is not None and v <= 0:
raise ValidationError("`temperature` must be strictly positive")
return v
@validator("top_k")
def valid_top_k(cls, v):
if v is not None and v <= 0:
raise ValidationError("`top_k` must be strictly positive")
return v
@validator("top_p")
def valid_top_p(cls, v):
if v is not None and (v <= 0 or v >= 1.0):
raise ValidationError("`top_p` must be > 0.0 and < 1.0")
return v
@validator("truncate")
def valid_truncate(cls, v):
if v is not None and v <= 0:
raise ValidationError("`truncate` must be strictly positive")
return v
@validator("typical_p")
def valid_typical_p(cls, v):
if v is not None and (v <= 0 or v >= 1.0):
raise ValidationError("`typical_p` must be > 0.0 and < 1.0")
return v
class Request(BaseModel):
# Prompt
inputs: str
# Generation parameters
parameters: Optional[Parameters]
# Whether to stream output tokens
stream: bool = False
@validator("inputs")
def valid_input(cls, v):
if not v:
raise ValidationError("`inputs` cannot be empty")
return v
@validator("stream")
def valid_best_of_stream(cls, field_value, values):
parameters = values["parameters"]
if (
parameters is not None
and parameters.best_of is not None
and parameters.best_of > 1
and field_value
):
raise ValidationError(
"`best_of` != 1 is not supported when `stream` == True"
)
return field_value
# Decoder input tokens
class InputToken(BaseModel):
# Token ID from the model tokenizer
id: int
# Token text
text: str
# Logprob
# Optional since the logprob of the first token cannot be computed
logprob: Optional[float]
# Generated tokens
class Token(BaseModel):
# Token ID from the model tokenizer
id: int
# Token text
text: str
# Logprob
logprob: float
# Is the token a special token
# Can be used to ignore tokens when concatenating
special: bool
# Generation finish reason
class FinishReason(str, Enum):
# number of generated tokens == `max_new_tokens`
Length = "length"
# the model generated its end of sequence token
EndOfSequenceToken = "eos_token"
# the model generated a text included in `stop_sequences`
StopSequence = "stop_sequence"
# Additional sequences when using the `best_of` parameter
class BestOfSequence(BaseModel):
# Generated text
generated_text: str
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int]
# Decoder input tokens, empty if decoder_input_details is False
prefill: List[InputToken]
# Generated tokens
tokens: List[Token]
# `generate` details
class Details(BaseModel):
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int]
# Decoder input tokens, empty if decoder_input_details is False
prefill: List[InputToken]
# Generated tokens
tokens: List[Token]
# Additional sequences when using the `best_of` parameter
best_of_sequences: Optional[List[BestOfSequence]]
# `generate` return value
class Response(BaseModel):
# Generated text
generated_text: str
# Generation details
details: Details
# `generate_stream` details
class StreamDetails(BaseModel):
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int]
# `generate_stream` return value
class StreamResponse(BaseModel):
# Generated token
token: Token
# Complete generated text
# Only available when the generation is finished
generated_text: Optional[str]
# Generation details
# Only available when the generation is finished
details: Optional[StreamDetails]
# Inference API currently deployed model
class DeployedModel(BaseModel):
model_id: str
sha: str
| 0 |
hf_public_repos/text-generation-inference/clients/python | hf_public_repos/text-generation-inference/clients/python/text_generation/errors.py | from typing import Dict
# Text Generation Inference Errors
class ValidationError(Exception):
def __init__(self, message: str):
super().__init__(message)
class GenerationError(Exception):
def __init__(self, message: str):
super().__init__(message)
class OverloadedError(Exception):
def __init__(self, message: str):
super().__init__(message)
class IncompleteGenerationError(Exception):
def __init__(self, message: str):
super().__init__(message)
# API Inference Errors
class BadRequestError(Exception):
def __init__(self, message: str):
super().__init__(message)
class ShardNotReadyError(Exception):
def __init__(self, message: str):
super().__init__(message)
class ShardTimeoutError(Exception):
def __init__(self, message: str):
super().__init__(message)
class NotFoundError(Exception):
def __init__(self, message: str):
super().__init__(message)
class RateLimitExceededError(Exception):
def __init__(self, message: str):
super().__init__(message)
class NotSupportedError(Exception):
def __init__(self, model_id: str):
message = (
f"Model `{model_id}` is not available for inference with this client. \n"
"Use `huggingface_hub.inference_api.InferenceApi` instead."
)
super(NotSupportedError, self).__init__(message)
# Unknown error
class UnknownError(Exception):
def __init__(self, message: str):
super().__init__(message)
def parse_error(status_code: int, payload: Dict[str, str]) -> Exception:
"""
Parse error given an HTTP status code and a json payload
Args:
status_code (`int`):
HTTP status code
payload (`Dict[str, str]`):
Json payload
Returns:
Exception: parsed exception
"""
# Try to parse a Text Generation Inference error
message = payload["error"]
if "error_type" in payload:
error_type = payload["error_type"]
if error_type == "generation":
return GenerationError(message)
if error_type == "incomplete_generation":
return IncompleteGenerationError(message)
if error_type == "overloaded":
return OverloadedError(message)
if error_type == "validation":
return ValidationError(message)
# Try to parse a APIInference error
if status_code == 400:
return BadRequestError(message)
if status_code == 403 or status_code == 424:
return ShardNotReadyError(message)
if status_code == 504:
return ShardTimeoutError(message)
if status_code == 404:
return NotFoundError(message)
if status_code == 429:
return RateLimitExceededError(message)
# Fallback to an unknown error
return UnknownError(message)
| 0 |
hf_public_repos | hf_public_repos/chat-ui/package.json | {
"name": "chat-ui",
"version": "0.4.0",
"private": true,
"packageManager": "[email protected]",
"scripts": {
"dev": "vite dev",
"build": "vite build",
"preview": "vite preview",
"check": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json",
"check:watch": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json --watch",
"lint": "prettier --plugin-search-dir . --check . && eslint .",
"format": "prettier --plugin-search-dir . --write .",
"test": "MONGODB_URL=mongodb://127.0.0.1:27017/ vitest"
},
"devDependencies": {
"@iconify-json/carbon": "^1.1.16",
"@iconify-json/eos-icons": "^1.1.6",
"@sveltejs/adapter-node": "^1.2.4",
"@sveltejs/kit": "^1.15.10",
"@tailwindcss/typography": "^0.5.9",
"@types/jsdom": "^21.1.1",
"@types/marked": "^4.0.8",
"@types/parquetjs": "^0.10.3",
"@typescript-eslint/eslint-plugin": "^5.45.0",
"@typescript-eslint/parser": "^5.45.0",
"eslint": "^8.28.0",
"eslint-config-prettier": "^8.5.0",
"eslint-plugin-svelte": "^2.27.3",
"prettier": "^2.8.0",
"prettier-plugin-svelte": "^2.8.1",
"prettier-plugin-tailwindcss": "^0.2.7",
"svelte": "^3.58.0",
"svelte-check": "^3.2.0",
"tslib": "^2.4.1",
"typescript": "^4.9.3",
"unplugin-icons": "^0.16.1",
"vite": "^4.0.0",
"vitest": "^0.31.0"
},
"type": "module",
"dependencies": {
"@huggingface/hub": "^0.5.1",
"@huggingface/inference": "^2.2.0",
"autoprefixer": "^10.4.14",
"date-fns": "^2.29.3",
"dotenv": "^16.0.3",
"highlight.js": "^11.7.0",
"jsdom": "^22.0.0",
"marked": "^4.3.0",
"mongodb": "^5.3.0",
"nanoid": "^4.0.2",
"openid-client": "^5.4.2",
"parquetjs": "^0.11.2",
"postcss": "^8.4.21",
"serpapi": "^1.1.1",
"tailwind-scrollbar": "^3.0.0",
"tailwindcss": "^3.3.1",
"zod": "^3.21.4"
}
}
| 0 |
hf_public_repos | hf_public_repos/chat-ui/README.md | ---
title: chat-ui
emoji: 🔥
colorFrom: purple
colorTo: purple
sdk: docker
pinned: false
license: apache-2.0
base_path: /chat
app_port: 3000
---
# Chat UI
A chat interface using open source models, eg OpenAssistant. It is a SvelteKit app and it powers the [HuggingChat app on hf.co/chat](https://huggingface.co/chat).
0. [No Setup Deploy](#no-setup-deploy)
1. [Setup](#setup)
2. [Launch](#launch)
3. [Extra parameters](#extra-parameters)
4. [Deploying to a HF Space](#deploying-to-a-hf-space)
5. [Building](#building)
## No Setup Deploy
If you don't want to configure, setup, and launch your own Chat UI yourself, you can use this option as a fast deploy alternative.
You can deploy your own customized Chat UI instance with any supported LLM of your choice with only a few clicks to Hugging Face Spaces thanks to the Chat UI Spaces Docker template. Get started [here](https://huggingface.co/new-space?template=huggingchat/chat-ui-template).
Read the full tutorial [here](https://huggingface.co/docs/hub/spaces-sdks-docker-chatui#chatui-on-spaces).
## Setup
The default config for Chat UI is stored in the `.env` file. You will need to override some values to get Chat UI to run locally. This is done in `.env.local`.
Start by creating a `.env.local` file in the root of the repository. The bare minimum config you need to get Chat UI to run locally is the following:
```bash
MONGODB_URL=<the URL to your mongoDB instance>
HF_ACCESS_TOKEN=<your access token>
```
### Database
The chat history is stored in a MongoDB instance, and having a DB instance available is needed for Chat UI to work.
You can use a local MongoDB instance. The easiest way is to spin one up using docker:
```bash
docker run -d -p 27017:27017 --name mongo-chatui mongo:latest
```
In which case the url of your DB will be `MONGODB_URL=mongodb://localhost:27017`.
Alternatively, you can use a [free MongoDB Atlas](https://www.mongodb.com/pricing) instance for this, Chat UI should fit comfortably within the free tier. After which you can set the `MONGODB_URL` variable in `.env.local` to match your instance.
### Hugging Face Access Token
You will need a Hugging Face access token to run Chat UI locally, using the remote inference endpoints. You can get one from [your Hugging Face profile](https://huggingface.co/settings/tokens).
## Launch
After you're done with the `.env.local` file you can run Chat UI locally with:
```bash
npm install
npm run dev
```
## Extra parameters
### OpenID connect
The login feature is disabled by default and users are attributed a unique ID based on their browser. But if you want to use OpenID to authenticate your users, you can add the following to your `.env.local` file:
```bash
OPENID_PROVIDER_URL=<your OIDC issuer>
OPENID_CLIENT_ID=<your OIDC client ID>
OPENID_CLIENT_SECRET=<your OIDC client secret>
```
These variables will enable the openID sign-in modal for users.
### Theming
You can use a few environment variables to customize the look and feel of chat-ui. These are by default:
```
PUBLIC_APP_NAME=ChatUI
PUBLIC_APP_ASSETS=chatui
PUBLIC_APP_COLOR=blue
PUBLIC_APP_DATA_SHARING=
PUBLIC_APP_DISCLAIMER=
```
- `PUBLIC_APP_NAME` The name used as a title throughout the app.
- `PUBLIC_APP_ASSETS` Is used to find logos & favicons in `static/$PUBLIC_APP_ASSETS`, current options are `chatui` and `huggingchat`.
- `PUBLIC_APP_COLOR` Can be any of the [tailwind colors](https://tailwindcss.com/docs/customizing-colors#default-color-palette).
- `PUBLIC_APP_DATA_SHARING` Can be set to 1 to add a toggle in the user settings that lets your users opt-in to data sharing with models creator.
- `PUBLIC_APP_DISCLAIMER` If set to 1, we show a disclaimer about generated outputs on login.
### Web Search
You can enable the web search by adding either `SERPER_API_KEY` ([serper.dev](https://serper.dev/)) or `SERPAPI_KEY` ([serpapi.com](https://serpapi.com/)) to your `.env.local`.
### Custom models
You can customize the parameters passed to the model or even use a new model by updating the `MODELS` variable in your `.env.local`. The default one can be found in `.env` and looks like this :
```
MODELS=`[
{
"name": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
"datasetName": "OpenAssistant/oasst1",
"description": "A good alternative to ChatGPT",
"websiteUrl": "https://open-assistant.io",
"userMessageToken": "<|prompter|>",
"assistantMessageToken": "<|assistant|>",
"messageEndToken": "</s>",
"preprompt": "Below are a series of dialogues between various people and an AI assistant. The AI tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed. It also tries to avoid giving false or misleading information, and it caveats when it isn't entirely sure about the right answer. That said, the assistant is practical and really does its best, and doesn't let caution get too much in the way of being useful.\n-----\n",
"promptExamples": [
{
"title": "Write an email from bullet list",
"prompt": "As a restaurant owner, write a professional email to the supplier to get these products every week: \n\n- Wine (x10)\n- Eggs (x24)\n- Bread (x12)"
}, {
"title": "Code a snake game",
"prompt": "Code a basic snake game in python, give explanations for each step."
}, {
"title": "Assist in a task",
"prompt": "How do I make a delicious lemon cheesecake?"
}
],
"parameters": {
"temperature": 0.9,
"top_p": 0.95,
"repetition_penalty": 1.2,
"top_k": 50,
"truncate": 1000,
"max_new_tokens": 1024
}
}
]`
```
You can change things like the parameters, or customize the preprompt to better suit your needs. You can also add more models by adding more objects to the array, with different preprompts for example.
#### Running your own models using a custom endpoint
If you want to, you can even run your own models locally, by having a look at our endpoint project, [text-generation-inference](https://github.com/huggingface/text-generation-inference). You can then add your own endpoints to the `MODELS` variable in `.env.local`, by adding an `"endpoints"` key for each model in `MODELS`.
```
{
// rest of the model config here
"endpoints": [{"url": "https://HOST:PORT/generate_stream"}]
}
```
If `endpoints` is left unspecified, ChatUI will look for the model on the hosted Hugging Face inference API using the model name.
#### Custom endpoint authorization
Custom endpoints may require authorization, depending on how you configure them. Authentication will usually be set either with `Basic` or `Bearer`.
For `Basic` we will need to generate a base64 encoding of the username and password.
`echo -n "USER:PASS" | base64`
> VVNFUjpQQVNT
For `Bearer` you can use a token, which can be grabbed from [here](https://huggingface.co/settings/tokens).
You can then add the generated information and the `authorization` parameter to your `.env.local`.
```
"endpoints": [
{
"url": "https://HOST:PORT/generate_stream",
"authorization": "Basic VVNFUjpQQVNT",
}
]
```
#### Models hosted on multiple custom endpoints
If the model being hosted will be available on multiple servers/instances add the `weight` parameter to your `.env.local`. The `weight` will be used to determine the probability of requesting a particular endpoint.
```
"endpoints": [
{
"url": "https://HOST:PORT/generate_stream",
"weight": 1
}
{
"url": "https://HOST:PORT/generate_stream",
"weight": 2
}
...
]
```
## Deploying to a HF Space
Create a `DOTENV_LOCAL` secret to your HF space with the content of your .env.local, and they will be picked up automatically when you run.
## Building
To create a production version of your app:
```bash
npm run build
```
You can preview the production build with `npm run preview`.
> To deploy your app, you may need to install an [adapter](https://kit.svelte.dev/docs/adapters) for your target environment.
| 0 |
hf_public_repos | hf_public_repos/chat-ui/.prettierignore | .DS_Store
node_modules
/build
/.svelte-kit
/package
.env
.env.*
!.env.example
# Ignore files for PNPM, NPM and YARN
pnpm-lock.yaml
package-lock.json
yarn.lock
| 0 |
hf_public_repos | hf_public_repos/chat-ui/.prettierrc | {
"useTabs": true,
"trailingComma": "es5",
"printWidth": 100,
"plugins": ["prettier-plugin-svelte", "prettier-plugin-tailwindcss"],
"pluginSearchDirs": ["."],
"overrides": [{ "files": "*.svelte", "options": { "parser": "svelte" } }]
}
| 0 |
hf_public_repos | hf_public_repos/chat-ui/.eslintrc.cjs | module.exports = {
root: true,
parser: "@typescript-eslint/parser",
extends: [
"eslint:recommended",
"plugin:@typescript-eslint/recommended",
"plugin:svelte/recommended",
"prettier",
],
plugins: ["@typescript-eslint"],
ignorePatterns: ["*.cjs"],
overrides: [
{
files: ["*.svelte"],
parser: "svelte-eslint-parser",
parserOptions: {
parser: "@typescript-eslint/parser",
},
},
],
parserOptions: {
sourceType: "module",
ecmaVersion: 2020,
extraFileExtensions: [".svelte"],
},
rules: {
"no-shadow": ["error"],
"@typescript-eslint/no-explicit-any": "error",
"@typescript-eslint/no-non-null-assertion": "error",
"@typescript-eslint/no-unused-vars": [
// prevent variables with a _ prefix from being marked as unused
"error",
{
argsIgnorePattern: "^_",
},
],
},
env: {
browser: true,
es2017: true,
node: true,
},
};
| 0 |
hf_public_repos | hf_public_repos/chat-ui/Dockerfile | # read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
# you will also find guides on how best to write your Dockerfile
FROM node:19 as builder-production
WORKDIR /app
COPY --link --chown=1000 package-lock.json package.json ./
RUN --mount=type=cache,target=/app/.npm \
npm set cache /app/.npm && \
npm ci --omit=dev
FROM builder-production as builder
RUN --mount=type=cache,target=/app/.npm \
npm set cache /app/.npm && \
npm ci
COPY --link --chown=1000 . .
RUN --mount=type=secret,id=DOTENV_LOCAL,dst=.env.local \
npm run build
FROM node:19-slim
RUN npm install -g pm2
COPY --from=builder-production /app/node_modules /app/node_modules
COPY --link --chown=1000 package.json /app/package.json
COPY --from=builder /app/build /app/build
CMD pm2 start /app/build/index.js -i $CPU_CORES --no-daemon
| 0 |
hf_public_repos | hf_public_repos/chat-ui/.eslintignore | .DS_Store
node_modules
/build
/.svelte-kit
/package
.env
.env.*
!.env.example
# Ignore files for PNPM, NPM and YARN
pnpm-lock.yaml
package-lock.json
yarn.lock
| 0 |
hf_public_repos | hf_public_repos/chat-ui/PRIVACY.md | ## Privacy
> Last updated: May 15, 2023
Starting with `v0.2` of HuggingChat, users are authenticated through their HF user account.
By default, your conversations are shared with the model's authors (for the `v0.2` model, to <a target="_blank" href="https://open-assistant.io/dashboard">Open Assistant</a>) to improve their training data and model over time. Model authors are the custodians of the data collected by their model, even if it's hosted on our platform.
If you disable data sharing in your settings, your conversations will not be used for any downstream usage (including for research or model training purposes), and they will only be stored to let you access past conversations. You can click on the Delete icon to delete any past conversation at any moment.
🗓 Please also consult huggingface.co's main privacy policy at https://huggingface.co/privacy. To exercise any of your legal privacy rights, please send an email to [email protected].
## About available LLMs
The goal of this app is to showcase that it is now (May 2023) possible to build an open source alternative to ChatGPT. 💪
For now, it's running OpenAssistant's [latest LLaMA based model](https://huggingface.co/OpenAssistant/oasst-sft-6-llama-30b-xor) (which is one of the current best open source chat models), but the plan in the longer-term is to expose all good-quality chat models from the Hub.
We are not affiliated with Open Assistant, but if you want to contribute to the training data for the next generation of open models, please consider contributing to https://open-assistant.io/ ❤️
## Technical details
This app is running in a [Space](https://huggingface.co/docs/hub/spaces-overview), which entails that the code for this UI is publicly visible [inside the Space repo](https://huggingface.co/spaces/huggingchat/chat-ui/tree/main).
**Further development takes place on the [huggingface/chat-ui GitHub repo](https://github.com/huggingface/chat-ui).**
The inference backend is running the optimized [text-generation-inference](https://github.com/huggingface/text-generation-inference) on HuggingFace's Inference API infrastructure.
It is therefore possible to deploy a copy of this app to a Space and customize it (swap model, add some UI elements, or store user messages according to your own Terms and conditions)
We welcome any feedback on this app: please participate to the public discussion at https://huggingface.co/spaces/huggingchat/chat-ui/discussions
<a target="_blank" href="https://huggingface.co/spaces/huggingchat/chat-ui/discussions"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-a-discussion-xl.svg" title="open a discussion"></a>
## Coming soon
- User setting to share conversations with model authors (done ✅)
- LLM watermarking
| 0 |
hf_public_repos | hf_public_repos/chat-ui/LICENSE | Copyright 2018- The Hugging Face team. All rights reserved.
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limitations under the License. | 0 |
hf_public_repos | hf_public_repos/chat-ui/.dockerignore | Dockerfile
.vscode/
.idea
.gitignore
LICENSE
README.md
node_modules/
.svelte-kit/
| 0 |
hf_public_repos | hf_public_repos/chat-ui/vite.config.ts | import { sveltekit } from "@sveltejs/kit/vite";
import { defineConfig } from "vite";
import Icons from "unplugin-icons/vite";
export default defineConfig({
plugins: [
sveltekit(),
Icons({
compiler: "svelte",
}),
],
});
| 0 |
hf_public_repos | hf_public_repos/chat-ui/tailwind.config.cjs | const defaultTheme = require("tailwindcss/defaultTheme");
const colors = require("tailwindcss/colors");
import dotenv from "dotenv";
dotenv.config({ path: "./.env" });
/** @type {import('tailwindcss').Config} */
export default {
darkMode: "class",
content: ["./src/**/*.{html,js,svelte,ts}"],
theme: {
extend: {
colors: {
primary: colors[process.env.PUBLIC_APP_COLOR],
},
// fontFamily: {
// sans: ['"Inter"', ...defaultTheme.fontFamily.sans]
// },
fontSize: {
xxs: "0.625rem",
smd: "0.94rem",
},
},
},
plugins: [
require("tailwind-scrollbar")({ nocompatible: true }),
require("@tailwindcss/typography"),
],
};
| 0 |
hf_public_repos | hf_public_repos/chat-ui/tsconfig.json | {
"extends": "./.svelte-kit/tsconfig.json",
"compilerOptions": {
"allowJs": true,
"checkJs": true,
"esModuleInterop": true,
"forceConsistentCasingInFileNames": true,
"resolveJsonModule": true,
"skipLibCheck": true,
"sourceMap": true,
"strict": true,
"target": "ES2018"
}
// Path aliases are handled by https://kit.svelte.dev/docs/configuration#alias
//
// If you want to overwrite includes/excludes, make sure to copy over the relevant includes/excludes
// from the referenced tsconfig.json - TypeScript does not merge them in
}
| 0 |
hf_public_repos | hf_public_repos/chat-ui/postcss.config.js | export default {
plugins: {
tailwindcss: {},
autoprefixer: {},
},
};
| 0 |
hf_public_repos | hf_public_repos/chat-ui/package-lock.json | {
"name": "chat-ui",
"version": "0.4.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "chat-ui",
"version": "0.4.0",
"dependencies": {
"@huggingface/hub": "^0.5.1",
"@huggingface/inference": "^2.2.0",
"autoprefixer": "^10.4.14",
"date-fns": "^2.29.3",
"dotenv": "^16.0.3",
"highlight.js": "^11.7.0",
"jsdom": "^22.0.0",
"marked": "^4.3.0",
"mongodb": "^5.3.0",
"nanoid": "^4.0.2",
"openid-client": "^5.4.2",
"parquetjs": "^0.11.2",
"postcss": "^8.4.21",
"serpapi": "^1.1.1",
"tailwind-scrollbar": "^3.0.0",
"tailwindcss": "^3.3.1",
"zod": "^3.21.4"
},
"devDependencies": {
"@iconify-json/carbon": "^1.1.16",
"@iconify-json/eos-icons": "^1.1.6",
"@sveltejs/adapter-node": "^1.2.4",
"@sveltejs/kit": "^1.15.10",
"@tailwindcss/typography": "^0.5.9",
"@types/jsdom": "^21.1.1",
"@types/marked": "^4.0.8",
"@types/parquetjs": "^0.10.3",
"@typescript-eslint/eslint-plugin": "^5.45.0",
"@typescript-eslint/parser": "^5.45.0",
"eslint": "^8.28.0",
"eslint-config-prettier": "^8.5.0",
"eslint-plugin-svelte": "^2.27.3",
"prettier": "^2.8.0",
"prettier-plugin-svelte": "^2.8.1",
"prettier-plugin-tailwindcss": "^0.2.7",
"svelte": "^3.58.0",
"svelte-check": "^3.2.0",
"tslib": "^2.4.1",
"typescript": "^4.9.3",
"unplugin-icons": "^0.16.1",
"vite": "^4.0.0",
"vitest": "^0.31.0"
}
},
"node_modules/@antfu/install-pkg": {
"version": "0.1.1",
"resolved": "https://registry.npmjs.org/@antfu/install-pkg/-/install-pkg-0.1.1.tgz",
"integrity": "sha512-LyB/8+bSfa0DFGC06zpCEfs89/XoWZwws5ygEa5D+Xsm3OfI+aXQ86VgVG7Acyef+rSZ5HE7J8rrxzrQeM3PjQ==",
"dev": true,
"dependencies": {
"execa": "^5.1.1",
"find-up": "^5.0.0"
},
"funding": {
"url": "https://github.com/sponsors/antfu"
}
},
"node_modules/@antfu/utils": {
"version": "0.7.2",
"resolved": "https://registry.npmjs.org/@antfu/utils/-/utils-0.7.2.tgz",
"integrity": "sha512-vy9fM3pIxZmX07dL+VX1aZe7ynZ+YyB0jY+jE6r3hOK6GNY2t6W8rzpFC4tgpbXUYABkFQwgJq2XYXlxbXAI0g==",
"dev": true,
"funding": {
"url": "https://github.com/sponsors/antfu"
}
},
"node_modules/@esbuild/android-arm": {
"version": "0.17.16",
"resolved": "https://registry.npmjs.org/@esbuild/android-arm/-/android-arm-0.17.16.tgz",
"integrity": "sha512-baLqRpLe4JnKrUXLJChoTN0iXZH7El/mu58GE3WIA6/H834k0XWvLRmGLG8y8arTRS9hJJibPnF0tiGhmWeZgw==",
"cpu": [
"arm"
],
"dev": true,
"optional": true,
"os": [
"android"
],
"engines": {
"node": ">=12"
}
},
"node_modules/@esbuild/android-arm64": {
"version": "0.17.16",
"resolved": "https://registry.npmjs.org/@esbuild/android-arm64/-/android-arm64-0.17.16.tgz",
"integrity": "sha512-QX48qmsEZW+gcHgTmAj+x21mwTz8MlYQBnzF6861cNdQGvj2jzzFjqH0EBabrIa/WVZ2CHolwMoqxVryqKt8+Q==",
"cpu": [
"arm64"
],
"dev": true,
"optional": true,
"os": [
"android"
],
"engines": {
"node": ">=12"
}
},
"node_modules/@esbuild/android-x64": {
"version": "0.17.16",
"resolved": "https://registry.npmjs.org/@esbuild/android-x64/-/android-x64-0.17.16.tgz",
"integrity": "sha512-G4wfHhrrz99XJgHnzFvB4UwwPxAWZaZBOFXh+JH1Duf1I4vIVfuYY9uVLpx4eiV2D/Jix8LJY+TAdZ3i40tDow==",
"cpu": [
"x64"
],
"dev": true,
"optional": true,
"os": [
"android"
],
"engines": {
"node": ">=12"
}
},
"node_modules/@esbuild/darwin-arm64": {
"version": "0.17.16",
"resolved": "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.17.16.tgz",
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| 0 |
hf_public_repos | hf_public_repos/chat-ui/.env | # Use .env.local to change these variables
# DO NOT EDIT THIS FILE WITH SENSITIVE DATA
MONGODB_URL=#your mongodb URL here
MONGODB_DB_NAME=chat-ui
MONGODB_DIRECT_CONNECTION=false
COOKIE_NAME=hf-chat
HF_ACCESS_TOKEN=#hf_<token> from from https://huggingface.co/settings/token
HF_API_ROOT=https://api-inference.huggingface.co/models
# used to activate search with web functionality. disabled if none are defined. choose one of the following:
SERPER_API_KEY=#your serper.dev api key here
SERPAPI_KEY=#your serpapi key here
# Parameters to enable "Sign in with HF"
OPENID_CLIENT_ID=
OPENID_CLIENT_SECRET=
OPENID_SCOPES="openid profile" # Add "email" for some providers like Google that do not provide preferred_username
OPENID_PROVIDER_URL=https://huggingface.co # for Google, use https://accounts.google.com
# 'name', 'userMessageToken', 'assistantMessageToken' are required
MODELS=`[
{
"name": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
"datasetName": "OpenAssistant/oasst1",
"description": "A good alternative to ChatGPT",
"websiteUrl": "https://open-assistant.io",
"userMessageToken": "<|prompter|>",
"assistantMessageToken": "<|assistant|>",
"messageEndToken": "</s>",
"preprompt": "Below are a series of dialogues between various people and an AI assistant. The AI tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed. It also tries to avoid giving false or misleading information, and it caveats when it isn't entirely sure about the right answer. That said, the assistant is practical and really does its best, and doesn't let caution get too much in the way of being useful.\n-----\n",
"promptExamples": [
{
"title": "Write an email from bullet list",
"prompt": "As a restaurant owner, write a professional email to the supplier to get these products every week: \n\n- Wine (x10)\n- Eggs (x24)\n- Bread (x12)"
}, {
"title": "Code a snake game",
"prompt": "Code a basic snake game in python, give explanations for each step."
}, {
"title": "Assist in a task",
"prompt": "How do I make a delicious lemon cheesecake?"
}
],
"parameters": {
"temperature": 0.9,
"top_p": 0.95,
"repetition_penalty": 1.2,
"top_k": 50,
"truncate": 1000,
"max_new_tokens": 1024
}
}
]`
OLD_MODELS=`[]`# any removed models, `{ name: string, displayName?: string, id?: string }`
PUBLIC_ORIGIN=#https://huggingface.co
PUBLIC_SHARE_PREFIX=#https://hf.co/chat
PUBLIC_GOOGLE_ANALYTICS_ID=#G-XXXXXXXX / Leave empty to disable
PUBLIC_DEPRECATED_GOOGLE_ANALYTICS_ID=#UA-XXXXXXXX-X / Leave empty to disable
PUBLIC_ANNOUNCEMENT_BANNERS=`[
{
"title": "Llama v2 is live on HuggingChat! 🦙",
"linkTitle": "Announcement",
"linkHref": "https://huggingface.co/blog/llama2"
}
]`
PARQUET_EXPORT_DATASET=
PARQUET_EXPORT_HF_TOKEN=
PARQUET_EXPORT_SECRET=
RATE_LIMIT= # requests per minute
MESSAGES_BEFORE_LOGIN=# how many messages a user can send in a conversation before having to login. set to 0 to force login right away
PUBLIC_APP_NAME=ChatUI # name used as title throughout the app
PUBLIC_APP_ASSETS=chatui # used to find logos & favicons in static/$PUBLIC_APP_ASSETS
PUBLIC_APP_COLOR=blue # can be any of tailwind colors: https://tailwindcss.com/docs/customizing-colors#default-color-palette
PUBLIC_APP_DATA_SHARING=#set to 1 to enable options & text regarding data sharing
PUBLIC_APP_DISCLAIMER=#set to 1 to show a disclaimer on login page
# PUBLIC_APP_NAME=HuggingChat
# PUBLIC_APP_ASSETS=huggingchat
# PUBLIC_APP_COLOR=yellow
# PUBLIC_APP_DATA_SHARING=1
# PUBLIC_APP_DISCLAIMER=1 | 0 |
hf_public_repos | hf_public_repos/chat-ui/svelte.config.js | import adapter from "@sveltejs/adapter-node";
import { vitePreprocess } from "@sveltejs/kit/vite";
import dotenv from "dotenv";
dotenv.config({ path: "./.env.local" });
dotenv.config({ path: "./.env" });
process.env.PUBLIC_VERSION = process.env.npm_package_version;
/** @type {import('@sveltejs/kit').Config} */
const config = {
// Consult https://kit.svelte.dev/docs/integrations#preprocessors
// for more information about preprocessors
preprocess: vitePreprocess(),
kit: {
adapter: adapter(),
paths: {
base: process.env.APP_BASE || "",
},
csrf: {
// handled in hooks.server.ts, because we can have multiple valid origins
checkOrigin: false,
},
},
};
export default config;
| 0 |
hf_public_repos | hf_public_repos/chat-ui/.npmrc | engine-strict=true
| 0 |
hf_public_repos/chat-ui/static | hf_public_repos/chat-ui/static/huggingchat/favicon.svg | <svg width="30" height="30" viewBox="0 0 13 12" fill="none" xmlns="http://www.w3.org/2000/svg">
<path
fill="#FFD21E"
d="M1.76 5.63a3.7 3.7 0 0 1 3.7-3.7h1.7a3.43 3.43 0 0 1 0 6.87H3.07L2.01 9.83a.14.14 0 0 1-.25-.1v-4.1Z"
/>
<path
fill="#32343D"
d="M7.37 4.8c.13.05.19.33.33.25a.54.54 0 0 0 .22-.73.54.54 0 0 0-.73-.22.54.54 0 0 0-.22.73c.06.13.27-.08.4-.03ZM4.83 4.8c-.14.05-.2.33-.33.25a.54.54 0 0 1-.23-.73A.54.54 0 0 1 5 4.1c.26.14.36.47.22.73-.06.13-.27-.08-.4-.03ZM6.12 7.4c1.06 0 1.4-.96 1.4-1.44 0-.49-.62.26-1.4.26-.77 0-1.4-.75-1.4-.26 0 .48.34 1.43 1.4 1.43Z"
/>
<path
fill="#FF323D"
d="M6.97 7.12c-.2.16-.49.27-.85.27-.34 0-.6-.1-.81-.24a.94.94 0 0 1 .57-.49c.04-.01.09.06.13.14.05.07.1.15.14.15.05 0 .1-.08.14-.15.05-.08.1-.15.14-.13a.93.93 0 0 1 .54.45Z"
/>
</svg> | 0 |
hf_public_repos/chat-ui/static | hf_public_repos/chat-ui/static/chatui/favicon.svg | <svg width="30" height="30" viewBox="0 0 30 30"
xmlns="http://www.w3.org/2000/svg">
<path
d="M4.06151 14.1464C4.06151 11.8818 4.9611 9.71004 6.56237 8.10877C8.16364 6.5075 10.3354 5.60791 12.6 5.60791H16.5231C18.6254 5.60791 20.6416 6.44307 22.1282 7.92965C23.6148 9.41624 24.45 11.4325 24.45 13.5348C24.45 15.6372 23.6148 17.6534 22.1282 19.14C20.6416 20.6266 18.6254 21.4618 16.5231 21.4618H7.08459L4.63844 23.8387C4.59547 23.8942 4.53557 23.9343 4.4678 23.9527C4.40004 23.9712 4.32811 23.9671 4.2629 23.941C4.1977 23.9149 4.14276 23.8683 4.10643 23.8082C4.07009 23.7481 4.05432 23.6778 4.06151 23.6079V14.1464Z"
style="fill:rgb(107, 114, 128)"></path>
</svg> | 0 |
hf_public_repos/chat-ui | hf_public_repos/chat-ui/.vscode/settings.json | {
"editor.formatOnSave": true,
"editor.defaultFormatter": "esbenp.prettier-vscode",
"editor.codeActionsOnSave": {
"source.fixAll": true
},
"eslint.validate": ["javascript", "svelte"]
}
| 0 |
hf_public_repos/chat-ui | hf_public_repos/chat-ui/src/app.html | <!DOCTYPE html>
<html lang="en" class="h-full">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no" />
<script>
if (
localStorage.theme === "dark" ||
(!("theme" in localStorage) && window.matchMedia("(prefers-color-scheme: dark)").matches)
) {
document.documentElement.classList.add("dark");
}
// For some reason, Sveltekit doesn't let us load env variables from .env here, so we load it from hooks.server.ts
window.gaId = "%gaId%";
window.gaIdDeprecated = "%gaIdDeprecated%";
</script>
%sveltekit.head%
</head>
<body data-sveltekit-preload-data="hover" class="h-full dark:bg-gray-900">
<div id="app" class="contents h-full">%sveltekit.body%</div>
<!-- Google Tag Manager -->
<script>
if (window.gaId) {
const script = document.createElement("script");
script.src = "https://www.googletagmanager.com/gtag/js?id=" + window.gaId;
script.async = true;
document.head.appendChild(script);
window.dataLayer = window.dataLayer || [];
function gtag() {
dataLayer.push(arguments);
}
gtag("js", new Date());
/// ^ See https://developers.google.com/tag-platform/gtagjs/install
gtag("config", window.gaId);
gtag("consent", "default", { ad_storage: "denied", analytics_storage: "denied" });
/// ^ See https://developers.google.com/tag-platform/gtagjs/reference#consent
/// TODO: ask the user for their consent and update this with gtag('consent', 'update')
}
</script>
<!-- Google Analytics v3 (deprecated on 1 July 2023) -->
<script>
if (window.gaIdDeprecated) {
(function (i, s, o, g, r, a, m) {
i["GoogleAnalyticsObject"] = r;
(i[r] =
i[r] ||
function () {
(i[r].q = i[r].q || []).push(arguments);
}),
(i[r].l = 1 * new Date());
(a = s.createElement(o)), (m = s.getElementsByTagName(o)[0]);
a.async = 1;
a.src = g;
m.parentNode.insertBefore(a, m);
})(
window,
document,
"script",
"https://www.google-analytics.com/analytics.js",
"ganalytics"
);
ganalytics("create", window.gaIdDeprecated, "auto");
ganalytics("send", "pageview");
}
</script>
</body>
</html>
| 0 |
hf_public_repos/chat-ui | hf_public_repos/chat-ui/src/hooks.server.ts | import { COOKIE_NAME, MESSAGES_BEFORE_LOGIN } from "$env/static/private";
import type { Handle } from "@sveltejs/kit";
import {
PUBLIC_GOOGLE_ANALYTICS_ID,
PUBLIC_DEPRECATED_GOOGLE_ANALYTICS_ID,
PUBLIC_ORIGIN,
PUBLIC_APP_DISCLAIMER,
} from "$env/static/public";
import { collections } from "$lib/server/database";
import { base } from "$app/paths";
import { refreshSessionCookie, requiresUser } from "$lib/server/auth";
import { ERROR_MESSAGES } from "$lib/stores/errors";
export const handle: Handle = async ({ event, resolve }) => {
const token = event.cookies.get(COOKIE_NAME);
event.locals.sessionId = token || crypto.randomUUID();
const user = await collections.users.findOne({ sessionId: event.locals.sessionId });
if (user) {
event.locals.user = user;
}
function errorResponse(status: number, message: string) {
const sendJson =
event.request.headers.get("accept")?.includes("application/json") ||
event.request.headers.get("content-type")?.includes("application/json");
return new Response(sendJson ? JSON.stringify({ error: message }) : message, {
status,
headers: {
"content-type": sendJson ? "application/json" : "text/plain",
},
});
}
// CSRF protection
const requestContentType = event.request.headers.get("content-type")?.split(";")[0] ?? "";
/** https://developer.mozilla.org/en-US/docs/Web/HTML/Element/form#attr-enctype */
const nativeFormContentTypes = [
"multipart/form-data",
"application/x-www-form-urlencoded",
"text/plain",
];
if (event.request.method === "POST" && nativeFormContentTypes.includes(requestContentType)) {
const referer = event.request.headers.get("referer");
if (!referer) {
return errorResponse(403, "Non-JSON form requests need to have a referer");
}
const validOrigins = [
new URL(event.request.url).origin,
...(PUBLIC_ORIGIN ? [new URL(PUBLIC_ORIGIN).origin] : []),
];
if (!validOrigins.includes(new URL(referer).origin)) {
return errorResponse(403, "Invalid referer for POST request");
}
}
if (
!event.url.pathname.startsWith(`${base}/login`) &&
!event.url.pathname.startsWith(`${base}/admin`) &&
!["GET", "OPTIONS", "HEAD"].includes(event.request.method)
) {
if (
!user &&
requiresUser &&
!((MESSAGES_BEFORE_LOGIN ? parseInt(MESSAGES_BEFORE_LOGIN) : 0) > 0)
) {
return errorResponse(401, ERROR_MESSAGES.authOnly);
}
// if login is not required and the call is not from /settings and we display the ethics modal with PUBLIC_APP_DISCLAIMER
// we check if the user has accepted the ethics modal first.
// If login is required, `ethicsModalAcceptedAt` is already true at this point, so do not pass this condition. This saves a DB call.
if (
!requiresUser &&
!event.url.pathname.startsWith(`${base}/settings`) &&
!!PUBLIC_APP_DISCLAIMER
) {
const hasAcceptedEthicsModal = await collections.settings.countDocuments({
sessionId: event.locals.sessionId,
ethicsModalAcceptedAt: { $exists: true },
});
if (!hasAcceptedEthicsModal) {
return errorResponse(405, "You need to accept the welcome modal first");
}
}
}
refreshSessionCookie(event.cookies, event.locals.sessionId);
let replaced = false;
const response = await resolve(event, {
transformPageChunk: (chunk) => {
// For some reason, Sveltekit doesn't let us load env variables from .env in the app.html template
if (replaced || !chunk.html.includes("%gaId%") || !chunk.html.includes("%gaIdDeprecated%")) {
return chunk.html;
}
replaced = true;
return chunk.html
.replace("%gaId%", PUBLIC_GOOGLE_ANALYTICS_ID)
.replace("%gaIdDeprecated%", PUBLIC_DEPRECATED_GOOGLE_ANALYTICS_ID);
},
});
return response;
};
| 0 |
hf_public_repos/chat-ui | hf_public_repos/chat-ui/src/app.d.ts | /// <reference types="@sveltejs/kit" />
/// <reference types="unplugin-icons/types/svelte" />
import type { User } from "$lib/types/User";
// See https://kit.svelte.dev/docs/types#app
// for information about these interfaces
declare global {
namespace App {
// interface Error {}
interface Locals {
sessionId: string;
user?: User;
}
// interface PageData {}
// interface Platform {}
}
}
export {};
| 0 |
hf_public_repos/chat-ui/src | hf_public_repos/chat-ui/src/styles/highlight-js.css | @import "highlight.js/styles/atom-one-dark";
| 0 |
hf_public_repos/chat-ui/src | hf_public_repos/chat-ui/src/styles/main.css | @import "./highlight-js.css";
@tailwind base;
@tailwind components;
@tailwind utilities;
@layer components {
.btn {
@apply inline-flex flex-shrink-0 cursor-pointer select-none items-center justify-center whitespace-nowrap outline-none transition-all focus:ring disabled:cursor-default;
}
}
@layer utilities {
.scrollbar-custom {
@apply scrollbar-thin scrollbar-track-transparent scrollbar-thumb-black/10 scrollbar-thumb-rounded-full scrollbar-w-1 hover:scrollbar-thumb-black/20 dark:scrollbar-thumb-white/10 dark:hover:scrollbar-thumb-white/20;
}
}
| 0 |
hf_public_repos/chat-ui/src | hf_public_repos/chat-ui/src/routes/+page.svelte | <script lang="ts">
import { goto } from "$app/navigation";
import { base } from "$app/paths";
import ChatWindow from "$lib/components/chat/ChatWindow.svelte";
import { ERROR_MESSAGES, error } from "$lib/stores/errors";
import { pendingMessage } from "$lib/stores/pendingMessage";
import { findCurrentModel } from "$lib/utils/models";
export let data;
let loading = false;
async function createConversation(message: string) {
try {
loading = true;
const res = await fetch(`${base}/conversation`, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({ model: data.settings.activeModel }),
});
if (!res.ok) {
error.set("Error while creating conversation, try again.");
console.error("Error while creating conversation: " + (await res.text()));
return;
}
const { conversationId } = await res.json();
// Ugly hack to use a store as temp storage, feel free to improve ^^
pendingMessage.set(message);
// invalidateAll to update list of conversations
await goto(`${base}/conversation/${conversationId}`, { invalidateAll: true });
} catch (err) {
error.set(ERROR_MESSAGES.default);
console.error(err);
} finally {
loading = false;
}
}
</script>
<ChatWindow
on:message={(ev) => createConversation(ev.detail)}
{loading}
currentModel={findCurrentModel([...data.models, ...data.oldModels], data.settings.activeModel)}
models={data.models}
settings={data.settings}
/>
| 0 |
hf_public_repos/chat-ui/src | hf_public_repos/chat-ui/src/routes/+layout.svelte | <script lang="ts">
import { onDestroy } from "svelte";
import { goto, invalidate } from "$app/navigation";
import { page } from "$app/stores";
import "../styles/main.css";
import { base } from "$app/paths";
import { PUBLIC_ORIGIN, PUBLIC_APP_DISCLAIMER } from "$env/static/public";
import { shareConversation } from "$lib/shareConversation";
import { UrlDependency } from "$lib/types/UrlDependency";
import { error } from "$lib/stores/errors";
import MobileNav from "$lib/components/MobileNav.svelte";
import NavMenu from "$lib/components/NavMenu.svelte";
import Toast from "$lib/components/Toast.svelte";
import SettingsModal from "$lib/components/SettingsModal.svelte";
import LoginModal from "$lib/components/LoginModal.svelte";
import { PUBLIC_APP_ASSETS, PUBLIC_APP_NAME } from "$env/static/public";
export let data;
let isNavOpen = false;
let isSettingsOpen = false;
let errorToastTimeout: ReturnType<typeof setTimeout>;
let currentError: string | null;
async function onError() {
// If a new different error comes, wait for the current error to hide first
if ($error && currentError && $error !== currentError) {
clearTimeout(errorToastTimeout);
currentError = null;
await new Promise((resolve) => setTimeout(resolve, 300));
}
currentError = $error;
errorToastTimeout = setTimeout(() => {
$error = null;
currentError = null;
}, 3000);
}
async function deleteConversation(id: string) {
try {
const res = await fetch(`${base}/conversation/${id}`, {
method: "DELETE",
headers: {
"Content-Type": "application/json",
},
});
if (!res.ok) {
$error = "Error while deleting conversation, try again.";
return;
}
if ($page.params.id !== id) {
await invalidate(UrlDependency.ConversationList);
} else {
await goto(`${base}/`, { invalidateAll: true });
}
} catch (err) {
console.error(err);
$error = String(err);
}
}
async function editConversationTitle(id: string, title: string) {
try {
const res = await fetch(`${base}/conversation/${id}`, {
method: "PATCH",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({ title }),
});
if (!res.ok) {
$error = "Error while editing title, try again.";
return;
}
await invalidate(UrlDependency.ConversationList);
} catch (err) {
console.error(err);
$error = String(err);
}
}
onDestroy(() => {
clearTimeout(errorToastTimeout);
});
$: if ($error) onError();
const requiresLogin =
!$page.error &&
!$page.route.id?.startsWith("/r/") &&
(data.requiresLogin
? !data.user
: !data.settings.ethicsModalAcceptedAt && !!PUBLIC_APP_DISCLAIMER);
</script>
<svelte:head>
<title>{PUBLIC_APP_NAME}</title>
<meta name="description" content="The first open source alternative to ChatGPT. 💪" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta property="og:title" content={PUBLIC_APP_NAME} />
<meta property="og:type" content="website" />
<meta property="og:url" content="{PUBLIC_ORIGIN || $page.url.origin}{base}" />
<meta
property="og:image"
content="{PUBLIC_ORIGIN || $page.url.origin}{base}/{PUBLIC_APP_ASSETS}/thumbnail.png"
/>
<link
rel="icon"
href="{PUBLIC_ORIGIN || $page.url.origin}{base}/{PUBLIC_APP_ASSETS}/favicon.svg"
type="image/svg+xml"
/>
<link
rel="icon"
href="{PUBLIC_ORIGIN || $page.url.origin}{base}/{PUBLIC_APP_ASSETS}/favicon.png"
type="image/png"
/>
<!-- Icon Support for iOS Bookmark Home Screen -->
<link
rel="apple-touch-icon"
href="{PUBLIC_ORIGIN || $page.url.origin}{base}/{PUBLIC_APP_ASSETS}/touch-icon-ipad-retina.png"
sizes="167x167"
type="image/png"
/>
<link
rel="apple-touch-icon"
href="{PUBLIC_ORIGIN || $page.url.origin}{base}/{PUBLIC_APP_ASSETS}/touch-icon-ipad.png"
sizes="152x152"
type="image/png"
/>
<link
rel="apple-touch-icon"
href="{PUBLIC_ORIGIN ||
$page.url.origin}{base}/{PUBLIC_APP_ASSETS}/touch-icon-iphone-retina.png"
sizes="180x180"
type="image/png"
/>
</svelte:head>
<div
class="grid h-full w-screen grid-cols-1 grid-rows-[auto,1fr] overflow-hidden text-smd dark:text-gray-300 md:grid-cols-[280px,1fr] md:grid-rows-[1fr]"
>
<MobileNav
isOpen={isNavOpen}
on:toggle={(ev) => (isNavOpen = ev.detail)}
title={data.conversations.find((conv) => conv.id === $page.params.id)?.title}
>
<NavMenu
conversations={data.conversations}
user={data.user}
on:shareConversation={(ev) => shareConversation(ev.detail.id, ev.detail.title)}
on:deleteConversation={(ev) => deleteConversation(ev.detail)}
on:clickSettings={() => (isSettingsOpen = true)}
on:editConversationTitle={(ev) => editConversationTitle(ev.detail.id, ev.detail.title)}
/>
</MobileNav>
<nav class="grid max-h-screen grid-cols-1 grid-rows-[auto,1fr,auto] max-md:hidden">
<NavMenu
conversations={data.conversations}
user={data.user}
on:shareConversation={(ev) => shareConversation(ev.detail.id, ev.detail.title)}
on:deleteConversation={(ev) => deleteConversation(ev.detail)}
on:clickSettings={() => (isSettingsOpen = true)}
on:editConversationTitle={(ev) => editConversationTitle(ev.detail.id, ev.detail.title)}
/>
</nav>
{#if currentError}
<Toast message={currentError} />
{/if}
{#if isSettingsOpen}
<SettingsModal on:close={() => (isSettingsOpen = false)} settings={data.settings} />
{/if}
{#if requiresLogin && data.messagesBeforeLogin === 0}
<LoginModal settings={data.settings} />
{/if}
<slot />
</div>
| 0 |
hf_public_repos/chat-ui/src | hf_public_repos/chat-ui/src/routes/+error.svelte | <script lang="ts">
import { page } from "$app/stores";
</script>
<div
class="flex items-center justify-center bg-gradient-to-t from-gray-200 text-gray-800 dark:from-gray-700 dark:text-gray-300"
>
<div
class="align-center -mt-24 flex flex-col justify-center rounded-xl border bg-white px-8 pb-2 pt-4 text-center dark:border-gray-700 dark:bg-gray-800"
>
<h1 class="mb-2 text-5xl font-semibold">{$page.status}</h1>
<div class="-mx-8 my-2 h-px bg-gray-200 dark:bg-gray-700" />
<h2 class="max-w-sm text-lg">{$page.error?.message}</h2>
</div>
</div>
| 0 |
hf_public_repos/chat-ui/src | hf_public_repos/chat-ui/src/routes/+layout.server.ts | import { redirect } from "@sveltejs/kit";
import type { LayoutServerLoad } from "./$types";
import { collections } from "$lib/server/database";
import type { Conversation } from "$lib/types/Conversation";
import { UrlDependency } from "$lib/types/UrlDependency";
import { defaultModel, models, oldModels, validateModel } from "$lib/server/models";
import { authCondition, requiresUser } from "$lib/server/auth";
import { DEFAULT_SETTINGS } from "$lib/types/Settings";
import { SERPAPI_KEY, SERPER_API_KEY, MESSAGES_BEFORE_LOGIN } from "$env/static/private";
export const load: LayoutServerLoad = async ({ locals, depends, url }) => {
const { conversations } = collections;
const urlModel = url.searchParams.get("model");
depends(UrlDependency.ConversationList);
if (urlModel) {
const isValidModel = validateModel(models).safeParse(urlModel).success;
if (isValidModel) {
await collections.settings.updateOne(
authCondition(locals),
{ $set: { activeModel: urlModel } },
{ upsert: true }
);
}
throw redirect(302, url.pathname);
}
const settings = await collections.settings.findOne(authCondition(locals));
// If the active model in settings is not valid, set it to the default model. This can happen if model was disabled.
if (settings && !validateModel(models).safeParse(settings?.activeModel).success) {
settings.activeModel = defaultModel.id;
await collections.settings.updateOne(authCondition(locals), {
$set: { activeModel: defaultModel.id },
});
}
return {
conversations: await conversations
.find(authCondition(locals))
.sort({ updatedAt: -1 })
.project<Pick<Conversation, "title" | "model" | "_id" | "updatedAt" | "createdAt">>({
title: 1,
model: 1,
_id: 1,
updatedAt: 1,
createdAt: 1,
})
.map((conv) => ({
id: conv._id.toString(),
title: conv.title,
model: conv.model ?? defaultModel,
}))
.toArray(),
settings: {
shareConversationsWithModelAuthors:
settings?.shareConversationsWithModelAuthors ??
DEFAULT_SETTINGS.shareConversationsWithModelAuthors,
ethicsModalAcceptedAt: settings?.ethicsModalAcceptedAt ?? null,
activeModel: settings?.activeModel ?? DEFAULT_SETTINGS.activeModel,
searchEnabled: !!(SERPAPI_KEY || SERPER_API_KEY),
},
models: models.map((model) => ({
id: model.id,
name: model.name,
websiteUrl: model.websiteUrl,
modelUrl: model.modelUrl,
datasetName: model.datasetName,
datasetUrl: model.datasetUrl,
displayName: model.displayName,
description: model.description,
promptExamples: model.promptExamples,
parameters: model.parameters,
})),
oldModels,
user: locals.user && {
username: locals.user.username,
avatarUrl: locals.user.avatarUrl,
email: locals.user.email,
},
requiresLogin: requiresUser,
messagesBeforeLogin: MESSAGES_BEFORE_LOGIN ? parseInt(MESSAGES_BEFORE_LOGIN) : 0,
};
};
| 0 |
hf_public_repos/chat-ui/src/routes | hf_public_repos/chat-ui/src/routes/logout/+page.server.ts | import { dev } from "$app/environment";
import { base } from "$app/paths";
import { COOKIE_NAME } from "$env/static/private";
import { redirect } from "@sveltejs/kit";
export const actions = {
default: async function ({ cookies }) {
cookies.delete(COOKIE_NAME, {
path: "/",
// So that it works inside the space's iframe
sameSite: dev ? "lax" : "none",
secure: !dev,
httpOnly: true,
});
throw redirect(303, `${base}/`);
},
};
| 0 |
hf_public_repos/chat-ui/src/routes | hf_public_repos/chat-ui/src/routes/settings/+page.server.ts | import { base } from "$app/paths";
import { collections } from "$lib/server/database";
import { redirect } from "@sveltejs/kit";
import { z } from "zod";
import { models, validateModel } from "$lib/server/models";
import { authCondition } from "$lib/server/auth";
import { DEFAULT_SETTINGS } from "$lib/types/Settings";
export const actions = {
default: async function ({ request, locals }) {
const formData = await request.formData();
const { ethicsModalAccepted, ...settings } = z
.object({
shareConversationsWithModelAuthors: z
.boolean({ coerce: true })
.default(DEFAULT_SETTINGS.shareConversationsWithModelAuthors),
ethicsModalAccepted: z.boolean({ coerce: true }).optional(),
activeModel: validateModel(models),
})
.parse({
shareConversationsWithModelAuthors: formData.get("shareConversationsWithModelAuthors"),
ethicsModalAccepted: formData.get("ethicsModalAccepted"),
activeModel: formData.get("activeModel") ?? DEFAULT_SETTINGS.activeModel,
});
await collections.settings.updateOne(
authCondition(locals),
{
$set: {
...settings,
...(ethicsModalAccepted && { ethicsModalAcceptedAt: new Date() }),
updatedAt: new Date(),
},
$setOnInsert: {
createdAt: new Date(),
},
},
{
upsert: true,
}
);
throw redirect(303, request.headers.get("referer") || `${base}/`);
},
};
| 0 |
hf_public_repos/chat-ui/src/routes | hf_public_repos/chat-ui/src/routes/conversation/+server.ts | import type { RequestHandler } from "./$types";
import { collections } from "$lib/server/database";
import { ObjectId } from "mongodb";
import { error, redirect } from "@sveltejs/kit";
import { base } from "$app/paths";
import { z } from "zod";
import type { Message } from "$lib/types/Message";
import { models, validateModel } from "$lib/server/models";
import { authCondition } from "$lib/server/auth";
export const POST: RequestHandler = async ({ locals, request }) => {
const body = await request.text();
let title = "";
let messages: Message[] = [];
const values = z
.object({
fromShare: z.string().optional(),
model: validateModel(models),
})
.parse(JSON.parse(body));
if (values.fromShare) {
const conversation = await collections.sharedConversations.findOne({
_id: values.fromShare,
});
if (!conversation) {
throw error(404, "Conversation not found");
}
title = conversation.title;
messages = conversation.messages;
values.model = conversation.model;
}
const res = await collections.conversations.insertOne({
_id: new ObjectId(),
title:
title ||
"Untitled " + ((await collections.conversations.countDocuments(authCondition(locals))) + 1),
messages,
model: values.model,
createdAt: new Date(),
updatedAt: new Date(),
...(locals.user ? { userId: locals.user._id } : { sessionId: locals.sessionId }),
...(values.fromShare ? { meta: { fromShareId: values.fromShare } } : {}),
});
return new Response(
JSON.stringify({
conversationId: res.insertedId.toString(),
}),
{ headers: { "Content-Type": "application/json" } }
);
};
export const GET: RequestHandler = async () => {
throw redirect(302, `${base}/`);
};
| 0 |
hf_public_repos/chat-ui/src/routes/conversation | hf_public_repos/chat-ui/src/routes/conversation/[id]/+page.server.ts | import { collections } from "$lib/server/database";
import { ObjectId } from "mongodb";
import { error } from "@sveltejs/kit";
import { authCondition } from "$lib/server/auth";
import type { WebSearchMessageResult } from "$lib/types/WebSearch";
import { UrlDependency } from "$lib/types/UrlDependency";
export const load = async ({ params, depends, locals }) => {
// todo: add validation on params.id
const conversation = await collections.conversations.findOne({
_id: new ObjectId(params.id),
...authCondition(locals),
});
depends(UrlDependency.Conversation);
if (!conversation) {
const conversationExists =
(await collections.conversations.countDocuments({
_id: new ObjectId(params.id),
})) !== 0;
if (conversationExists) {
throw error(
403,
"You don't have access to this conversation. If someone gave you this link, ask them to use the 'share' feature instead."
);
}
throw error(404, "Conversation not found.");
}
const webSearchesId = conversation.messages
.filter((message) => message.webSearchId)
.map((message) => new ObjectId(message.webSearchId));
const results = await collections.webSearches.find({ _id: { $in: webSearchesId } }).toArray();
const searches = Object.fromEntries(
results.map((x) => [
x._id.toString(),
[...x.messages, { type: "result", id: x._id.toString() } satisfies WebSearchMessageResult],
])
);
return {
messages: conversation.messages,
title: conversation.title,
model: conversation.model,
searches,
};
};
| 0 |
hf_public_repos/chat-ui/src/routes/conversation | hf_public_repos/chat-ui/src/routes/conversation/[id]/+page.svelte | <script lang="ts">
import ChatWindow from "$lib/components/chat/ChatWindow.svelte";
import { pendingMessage } from "$lib/stores/pendingMessage";
import { pendingMessageIdToRetry } from "$lib/stores/pendingMessageIdToRetry";
import { onMount } from "svelte";
import { page } from "$app/stores";
import { textGenerationStream, type Options } from "@huggingface/inference";
import { invalidate } from "$app/navigation";
import { base } from "$app/paths";
import { shareConversation } from "$lib/shareConversation";
import { UrlDependency } from "$lib/types/UrlDependency";
import { ERROR_MESSAGES, error } from "$lib/stores/errors";
import { randomUUID } from "$lib/utils/randomUuid";
import { findCurrentModel } from "$lib/utils/models";
import { webSearchParameters } from "$lib/stores/webSearchParameters";
import type { WebSearchMessage } from "$lib/types/WebSearch";
import type { Message } from "$lib/types/Message";
import { PUBLIC_APP_DISCLAIMER } from "$env/static/public";
export let data;
let messages = data.messages;
let lastLoadedMessages = data.messages;
let isAborted = false;
let webSearchMessages: WebSearchMessage[] = [];
// Since we modify the messages array locally, we don't want to reset it if an old version is passed
$: if (data.messages !== lastLoadedMessages) {
messages = data.messages;
lastLoadedMessages = data.messages;
}
let loading = false;
let pending = false;
let loginRequired = false;
async function getTextGenerationStream(
inputs: string,
messageId: string,
isRetry = false,
webSearchId?: string
) {
let conversationId = $page.params.id;
const responseId = randomUUID();
const response = textGenerationStream(
{
model: $page.url.href,
inputs,
parameters: {
...data.models.find((m) => m.id === data.model)?.parameters,
return_full_text: false,
},
},
{
id: messageId,
response_id: responseId,
is_retry: isRetry,
use_cache: false,
web_search_id: webSearchId,
} as Options
);
for await (const output of response) {
pending = false;
if (!output) {
break;
}
if (conversationId !== $page.params.id) {
fetch(`${base}/conversation/${conversationId}/stop-generating`, {
method: "POST",
}).catch(console.error);
break;
}
if (isAborted) {
isAborted = false;
fetch(`${base}/conversation/${conversationId}/stop-generating`, {
method: "POST",
}).catch(console.error);
break;
}
// final message
if (output.generated_text) {
const lastMessage = messages[messages.length - 1];
if (lastMessage) {
lastMessage.content = output.generated_text;
lastMessage.webSearchId = webSearchId;
messages = [...messages];
}
break;
}
if (!output.token.special) {
const lastMessage = messages[messages.length - 1];
if (lastMessage?.from !== "assistant") {
// First token has a space at the beginning, trim it
messages = [
...messages,
// id doesn't match the backend id but it's not important for assistant messages
{ from: "assistant", content: output.token.text.trimStart(), id: responseId },
];
} else {
lastMessage.content += output.token.text;
messages = [...messages];
}
}
}
}
async function summarizeTitle(id: string) {
await fetch(`${base}/conversation/${id}/summarize`, {
method: "POST",
});
}
async function writeMessage(message: string, messageId = randomUUID()) {
if (!message.trim()) return;
try {
isAborted = false;
loading = true;
pending = true;
let retryMessageIndex = messages.findIndex((msg) => msg.id === messageId);
const isRetry = retryMessageIndex !== -1;
if (!isRetry) {
retryMessageIndex = messages.length;
}
messages = [
...messages.slice(0, retryMessageIndex),
{ from: "user", content: message, id: messageId },
];
let searchResponseId: string | null = "";
if ($webSearchParameters.useSearch) {
webSearchMessages = [];
const res = await fetch(
`${base}/conversation/${$page.params.id}/web-search?` +
new URLSearchParams({ prompt: message }),
{
method: "GET",
}
);
// required bc linting doesn't see TextDecoderStream for some reason?
// eslint-disable-next-line no-undef
const encoder = new TextDecoderStream();
const reader = res?.body?.pipeThrough(encoder).getReader();
while (searchResponseId === "") {
await new Promise((r) => setTimeout(r, 25));
if (isAborted) {
reader?.cancel();
return;
}
reader
?.read()
.then(async ({ done, value }) => {
if (done) {
reader.cancel();
return;
}
try {
webSearchMessages = (JSON.parse(value) as { messages: WebSearchMessage[] })
.messages;
} catch (parseError) {
// in case of parsing error we wait for the next message
return;
}
const lastSearchMessage = webSearchMessages[webSearchMessages.length - 1];
if (lastSearchMessage.type === "result") {
searchResponseId = lastSearchMessage.id;
reader.cancel();
return;
}
})
.catch(() => {
searchResponseId = null;
});
}
}
await getTextGenerationStream(message, messageId, isRetry, searchResponseId ?? undefined);
webSearchMessages = [];
if (messages.filter((m) => m.from === "user").length === 1) {
summarizeTitle($page.params.id)
.then(() => invalidate(UrlDependency.ConversationList))
.catch(console.error);
} else {
await invalidate(UrlDependency.ConversationList);
}
} catch (err) {
if (err instanceof Error && err.message.includes("overloaded")) {
$error = "Too much traffic, please try again.";
} else if (err instanceof Error && err.message.includes("429")) {
$error = ERROR_MESSAGES.rateLimited;
} else if (err instanceof Error) {
$error = err.message;
} else {
$error = ERROR_MESSAGES.default;
}
console.error(err);
} finally {
loading = false;
pending = false;
}
}
async function voteMessage(score: Message["score"], messageId: string) {
let conversationId = $page.params.id;
let oldScore: Message["score"] | undefined;
// optimistic update to avoid waiting for the server
messages = messages.map((message) => {
if (message.id === messageId) {
oldScore = message.score;
return { ...message, score: score };
}
return message;
});
try {
await fetch(`${base}/conversation/${conversationId}/message/${messageId}/vote`, {
method: "POST",
body: JSON.stringify({ score }),
});
} catch {
// revert score on any error
messages = messages.map((message) => {
return message.id !== messageId ? message : { ...message, score: oldScore };
});
}
}
onMount(async () => {
if ($pendingMessage) {
const val = $pendingMessage;
const messageId = $pendingMessageIdToRetry || undefined;
$pendingMessage = "";
$pendingMessageIdToRetry = null;
writeMessage(val, messageId);
}
});
$: $page.params.id, (isAborted = true);
$: title = data.conversations.find((conv) => conv.id === $page.params.id)?.title ?? data.title;
$: loginRequired =
(data.requiresLogin
? !data.user
: !data.settings.ethicsModalAcceptedAt && !!PUBLIC_APP_DISCLAIMER) &&
messages.length >= data.messagesBeforeLogin;
</script>
<svelte:head>
<title>{title}</title>
</svelte:head>
<ChatWindow
{loading}
{pending}
{messages}
bind:webSearchMessages
searches={{ ...data.searches }}
on:message={(event) => writeMessage(event.detail)}
on:retry={(event) => writeMessage(event.detail.content, event.detail.id)}
on:vote={(event) => voteMessage(event.detail.score, event.detail.id)}
on:share={() => shareConversation($page.params.id, data.title)}
on:stop={() => (isAborted = true)}
models={data.models}
currentModel={findCurrentModel([...data.models, ...data.oldModels], data.model)}
settings={data.settings}
{loginRequired}
/>
| 0 |
hf_public_repos/chat-ui/src/routes/conversation | hf_public_repos/chat-ui/src/routes/conversation/[id]/+server.ts | import { MESSAGES_BEFORE_LOGIN, RATE_LIMIT } from "$env/static/private";
import { buildPrompt } from "$lib/buildPrompt";
import { PUBLIC_SEP_TOKEN } from "$lib/constants/publicSepToken";
import { abortedGenerations } from "$lib/server/abortedGenerations";
import { authCondition, requiresUser } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { modelEndpoint } from "$lib/server/modelEndpoint";
import { models } from "$lib/server/models";
import { ERROR_MESSAGES } from "$lib/stores/errors.js";
import type { Message } from "$lib/types/Message";
import { concatUint8Arrays } from "$lib/utils/concatUint8Arrays";
import { streamToAsyncIterable } from "$lib/utils/streamToAsyncIterable";
import { trimPrefix } from "$lib/utils/trimPrefix";
import { trimSuffix } from "$lib/utils/trimSuffix";
import type { TextGenerationStreamOutput } from "@huggingface/inference";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
import { z } from "zod";
export async function POST({ request, fetch, locals, params }) {
const id = z.string().parse(params.id);
const convId = new ObjectId(id);
const date = new Date();
const userId = locals.user?._id ?? locals.sessionId;
if (!userId) {
throw error(401, "Unauthorized");
}
const conv = await collections.conversations.findOne({
_id: convId,
...authCondition(locals),
});
if (!conv) {
throw error(404, "Conversation not found");
}
if (
!locals.user?._id &&
requiresUser &&
conv.messages.length > (MESSAGES_BEFORE_LOGIN ? parseInt(MESSAGES_BEFORE_LOGIN) : 0)
) {
throw error(429, "Exceeded number of messages before login");
}
const nEvents = await collections.messageEvents.countDocuments({ userId });
if (RATE_LIMIT != "" && nEvents > parseInt(RATE_LIMIT)) {
throw error(429, ERROR_MESSAGES.rateLimited);
}
const model = models.find((m) => m.id === conv.model);
if (!model) {
throw error(410, "Model not available anymore");
}
const json = await request.json();
const {
inputs: newPrompt,
options: { id: messageId, is_retry, web_search_id, response_id: responseId },
} = z
.object({
inputs: z.string().trim().min(1),
options: z.object({
id: z.optional(z.string().uuid()),
response_id: z.optional(z.string().uuid()),
is_retry: z.optional(z.boolean()),
web_search_id: z.ostring(),
}),
})
.parse(json);
const messages = (() => {
if (is_retry && messageId) {
let retryMessageIdx = conv.messages.findIndex((message) => message.id === messageId);
if (retryMessageIdx === -1) {
retryMessageIdx = conv.messages.length;
}
return [
...conv.messages.slice(0, retryMessageIdx),
{ content: newPrompt, from: "user", id: messageId as Message["id"], updatedAt: new Date() },
];
}
return [
...conv.messages,
{
content: newPrompt,
from: "user",
id: (messageId as Message["id"]) || crypto.randomUUID(),
createdAt: new Date(),
updatedAt: new Date(),
},
];
})() satisfies Message[];
const prompt = await buildPrompt(messages, model, web_search_id);
const randomEndpoint = modelEndpoint(model);
const abortController = new AbortController();
const resp = await fetch(randomEndpoint.url, {
headers: {
"Content-Type": request.headers.get("Content-Type") ?? "application/json",
Authorization: randomEndpoint.authorization,
},
method: "POST",
body: JSON.stringify({
...json,
inputs: prompt,
}),
signal: abortController.signal,
});
if (!resp.body) {
throw new Error("Response body is empty");
}
const [stream1, stream2] = resp.body.tee();
async function saveMessage() {
let generated_text = await parseGeneratedText(stream2, convId, date, abortController);
// We could also check if PUBLIC_ASSISTANT_MESSAGE_TOKEN is present and use it to slice the text
if (generated_text.startsWith(prompt)) {
generated_text = generated_text.slice(prompt.length);
}
generated_text = trimSuffix(
trimPrefix(generated_text, "<|startoftext|>"),
PUBLIC_SEP_TOKEN
).trimEnd();
for (const stop of [...(model?.parameters?.stop ?? []), "<|endoftext|>"]) {
if (generated_text.endsWith(stop)) {
generated_text = generated_text.slice(0, -stop.length).trimEnd();
}
}
messages.push({
from: "assistant",
content: generated_text,
webSearchId: web_search_id,
id: (responseId as Message["id"]) || crypto.randomUUID(),
createdAt: new Date(),
updatedAt: new Date(),
});
await collections.messageEvents.insertOne({
userId: userId,
createdAt: new Date(),
});
await collections.conversations.updateOne(
{
_id: convId,
},
{
$set: {
messages,
updatedAt: new Date(),
},
}
);
}
saveMessage().catch(console.error);
// Todo: maybe we should wait for the message to be saved before ending the response - in case of errors
return new Response(stream1, {
headers: Object.fromEntries(resp.headers.entries()),
status: resp.status,
statusText: resp.statusText,
});
}
export async function DELETE({ locals, params }) {
const convId = new ObjectId(params.id);
const conv = await collections.conversations.findOne({
_id: convId,
...authCondition(locals),
});
if (!conv) {
throw error(404, "Conversation not found");
}
await collections.conversations.deleteOne({ _id: conv._id });
return new Response();
}
async function parseGeneratedText(
stream: ReadableStream,
conversationId: ObjectId,
promptedAt: Date,
abortController: AbortController
): Promise<string> {
const inputs: Uint8Array[] = [];
for await (const input of streamToAsyncIterable(stream)) {
inputs.push(input);
const date = abortedGenerations.get(conversationId.toString());
if (date && date > promptedAt) {
abortController.abort("Cancelled by user");
const completeInput = concatUint8Arrays(inputs);
const lines = new TextDecoder()
.decode(completeInput)
.split("\n")
.filter((line) => line.startsWith("data:"));
const tokens = lines.map((line) => {
try {
const json: TextGenerationStreamOutput = JSON.parse(line.slice("data:".length));
return json.token.text;
} catch {
return "";
}
});
return tokens.join("");
}
}
// Merge inputs into a single Uint8Array
const completeInput = concatUint8Arrays(inputs);
// Get last line starting with "data:" and parse it as JSON to get the generated text
const message = new TextDecoder().decode(completeInput);
let lastIndex = message.lastIndexOf("\ndata:");
if (lastIndex === -1) {
lastIndex = message.indexOf("data");
}
if (lastIndex === -1) {
console.error("Could not parse last message", message);
}
let lastMessage = message.slice(lastIndex).trim().slice("data:".length);
if (lastMessage.includes("\n")) {
lastMessage = lastMessage.slice(0, lastMessage.indexOf("\n"));
}
const lastMessageJSON = JSON.parse(lastMessage);
if (lastMessageJSON.error) {
throw new Error(lastMessageJSON.error);
}
const res = lastMessageJSON.generated_text;
if (typeof res !== "string") {
throw new Error("Could not parse generated text");
}
return res;
}
export async function PATCH({ request, locals, params }) {
const { title } = z
.object({ title: z.string().trim().min(1).max(100) })
.parse(await request.json());
const convId = new ObjectId(params.id);
const conv = await collections.conversations.findOne({
_id: convId,
...authCondition(locals),
});
if (!conv) {
throw error(404, "Conversation not found");
}
await collections.conversations.updateOne(
{
_id: convId,
},
{
$set: {
title,
},
}
);
return new Response();
}
| 0 |
hf_public_repos/chat-ui/src/routes/conversation/[id] | hf_public_repos/chat-ui/src/routes/conversation/[id]/stop-generating/+server.ts | import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
/**
* Ideally, we'd be able to detect the client-side abort, see https://github.com/huggingface/chat-ui/pull/88#issuecomment-1523173850
*/
export async function POST({ params, locals }) {
const conversationId = new ObjectId(params.id);
const conversation = await collections.conversations.findOne({
_id: conversationId,
...authCondition(locals),
});
if (!conversation) {
throw error(404, "Conversation not found");
}
await collections.abortedGenerations.updateOne(
{ conversationId },
{ $set: { updatedAt: new Date() }, $setOnInsert: { createdAt: new Date() } },
{ upsert: true }
);
return new Response();
}
| 0 |
hf_public_repos/chat-ui/src/routes/conversation/[id] | hf_public_repos/chat-ui/src/routes/conversation/[id]/web-search/+server.ts | import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { defaultModel } from "$lib/server/models";
import { searchWeb } from "$lib/server/websearch/searchWeb";
import type { Message } from "$lib/types/Message";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
import { z } from "zod";
import type { WebSearch } from "$lib/types/WebSearch";
import { generateQuery } from "$lib/server/websearch/generateQuery";
import { parseWeb } from "$lib/server/websearch/parseWeb";
import { summarizeWeb } from "$lib/server/websearch/summarizeWeb";
interface GenericObject {
[key: string]: GenericObject | unknown;
}
function removeLinks(obj: GenericObject) {
for (const prop in obj) {
if (prop.endsWith("link")) delete obj[prop];
else if (typeof obj[prop] === "object") removeLinks(obj[prop] as GenericObject);
}
return obj;
}
export async function GET({ params, locals, url }) {
const model = defaultModel;
const convId = new ObjectId(params.id);
const searchId = new ObjectId();
const conv = await collections.conversations.findOne({
_id: convId,
...authCondition(locals),
});
if (!conv) {
throw error(404, "Conversation not found");
}
const prompt = z.string().trim().min(1).parse(url.searchParams.get("prompt"));
const messages = (() => {
return [...conv.messages, { content: prompt, from: "user", id: crypto.randomUUID() }];
})() satisfies Message[];
const stream = new ReadableStream({
async start(controller) {
const webSearch: WebSearch = {
_id: searchId,
convId: convId,
prompt: prompt,
searchQuery: "",
knowledgeGraph: "",
answerBox: "",
results: [],
summary: "",
messages: [],
createdAt: new Date(),
updatedAt: new Date(),
};
function appendUpdate(message: string, args?: string[], type?: "error" | "update") {
webSearch.messages.push({
type: type ?? "update",
message,
args,
});
controller.enqueue(JSON.stringify({ messages: webSearch.messages }));
}
try {
appendUpdate("Generating search query");
webSearch.searchQuery = await generateQuery(messages, model);
appendUpdate("Searching Google", [webSearch.searchQuery]);
const results = await searchWeb(webSearch.searchQuery);
let text = "";
webSearch.results =
(results.organic_results &&
results.organic_results.map((el: { link: string }) => el.link)) ??
[];
if (results.answer_box) {
// if google returns an answer box, we use it
webSearch.answerBox = JSON.stringify(removeLinks(results.answer_box));
text = webSearch.answerBox;
appendUpdate("Found a Google answer box");
} else if (results.knowledge_graph) {
// if google returns a knowledge graph, we use it
webSearch.knowledgeGraph = JSON.stringify(removeLinks(results.knowledge_graph));
text = webSearch.knowledgeGraph;
appendUpdate("Found a Google knowledge page");
} else if (webSearch.results.length > 0) {
let tries = 0;
while (!text && tries < 3) {
const searchUrl = webSearch.results[tries];
appendUpdate("Browsing result", [JSON.stringify(searchUrl)]);
try {
text = await parseWeb(searchUrl);
if (!text) throw new Error("text of the webpage is null");
} catch (e) {
appendUpdate("Error parsing webpage", [], "error");
tries++;
}
}
if (!text) throw new Error("No text found on the first 3 results");
} else {
throw new Error("No results found for this search query");
}
appendUpdate("Creating summary");
webSearch.summary = await summarizeWeb(text, webSearch.searchQuery, model);
appendUpdate("Injecting summary", [JSON.stringify(webSearch.summary)]);
} catch (searchError) {
if (searchError instanceof Error) {
webSearch.messages.push({
type: "error",
message: "An error occurred with the web search",
args: [JSON.stringify(searchError.message)],
});
}
}
const res = await collections.webSearches.insertOne(webSearch);
webSearch.messages.push({
type: "result",
id: res.insertedId.toString(),
});
controller.enqueue(JSON.stringify({ messages: webSearch.messages }));
},
});
return new Response(stream, { headers: { "Content-Type": "application/json" } });
}
| 0 |
hf_public_repos/chat-ui/src/routes/conversation/[id] | hf_public_repos/chat-ui/src/routes/conversation/[id]/share/+server.ts | import { base } from "$app/paths";
import { PUBLIC_ORIGIN, PUBLIC_SHARE_PREFIX } from "$env/static/public";
import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import type { SharedConversation } from "$lib/types/SharedConversation";
import { hashConv } from "$lib/utils/hashConv.js";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
import { nanoid } from "nanoid";
export async function POST({ params, url, locals }) {
const conversation = await collections.conversations.findOne({
_id: new ObjectId(params.id),
...authCondition(locals),
});
if (!conversation) {
throw error(404, "Conversation not found");
}
const hash = await hashConv(conversation);
const existingShare = await collections.sharedConversations.findOne({ hash });
if (existingShare) {
return new Response(
JSON.stringify({
url: getShareUrl(url, existingShare._id),
}),
{ headers: { "Content-Type": "application/json" } }
);
}
const shared: SharedConversation = {
_id: nanoid(7),
createdAt: new Date(),
messages: conversation.messages,
hash,
updatedAt: new Date(),
title: conversation.title,
model: conversation.model,
};
await collections.sharedConversations.insertOne(shared);
return new Response(
JSON.stringify({
url: getShareUrl(url, shared._id),
}),
{ headers: { "Content-Type": "application/json" } }
);
}
function getShareUrl(url: URL, shareId: string): string {
return `${PUBLIC_SHARE_PREFIX || `${PUBLIC_ORIGIN || url.origin}${base}`}/r/${shareId}`;
}
| 0 |
hf_public_repos/chat-ui/src/routes/conversation/[id]/message/[messageId] | hf_public_repos/chat-ui/src/routes/conversation/[id]/message/[messageId]/vote/+server.ts | import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
import { z } from "zod";
export async function POST({ params, request, locals }) {
const { score } = z
.object({
score: z.number().int().min(-1).max(1),
})
.parse(await request.json());
const conversationId = new ObjectId(params.id);
const messageId = params.messageId;
const document = await collections.conversations.updateOne(
{
_id: conversationId,
...authCondition(locals),
"messages.id": messageId,
},
{
...(score !== 0
? {
$set: {
"messages.$.score": score,
},
}
: { $unset: { "messages.$.score": "" } }),
}
);
if (!document.matchedCount) {
throw error(404, "Message not found");
}
return new Response();
}
| 0 |
hf_public_repos/chat-ui/src/routes/conversation/[id]/message/[messageId] | hf_public_repos/chat-ui/src/routes/conversation/[id]/message/[messageId]/prompt/+server.ts | import { buildPrompt } from "$lib/buildPrompt";
import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { models } from "$lib/server/models";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
export async function GET({ params, locals }) {
const convId = new ObjectId(params.id);
const conv = await collections.conversations.findOne({
_id: convId,
...authCondition(locals),
});
if (!conv) {
throw error(404, "Conversation not found");
}
const messageId = params.messageId;
const messageIndex = conv.messages.findIndex((msg) => msg.id === messageId);
if (messageIndex === -1) {
throw error(404, "Message not found");
}
const model = models.find((m) => m.id === conv.model);
if (!model) {
throw error(404, "Conversation model not found");
}
const prompt = await buildPrompt(conv.messages.slice(0, messageIndex + 1), model);
return new Response(
JSON.stringify(
{
note: "This is a preview of the prompt that will be sent to the model when retrying the message. It may differ from what was sent in the past if the parameters have been updated since",
prompt,
model: model.name,
parameters: {
...model.parameters,
return_full_text: false,
},
},
null,
2
),
{ headers: { "Content-Type": "application/json" } }
);
}
| 0 |
hf_public_repos/chat-ui/src/routes/conversation/[id] | hf_public_repos/chat-ui/src/routes/conversation/[id]/summarize/+server.ts | import { buildPrompt } from "$lib/buildPrompt";
import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { generateFromDefaultEndpoint } from "$lib/server/generateFromDefaultEndpoint";
import { defaultModel } from "$lib/server/models";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
export async function POST({ params, locals }) {
const convId = new ObjectId(params.id);
const conversation = await collections.conversations.findOne({
_id: convId,
...authCondition(locals),
});
if (!conversation) {
throw error(404, "Conversation not found");
}
const firstMessage = conversation.messages.find((m) => m.from === "user");
const userPrompt =
`Please summarize the following message as a single sentence of less than 5 words:\n` +
firstMessage?.content;
const prompt = await buildPrompt([{ from: "user", content: userPrompt }], defaultModel);
const generated_text = await generateFromDefaultEndpoint(prompt);
if (generated_text) {
await collections.conversations.updateOne(
{
_id: convId,
...authCondition(locals),
},
{
$set: { title: generated_text },
}
);
}
return new Response(
JSON.stringify(
generated_text
? {
title: generated_text,
}
: {}
),
{ headers: { "Content-Type": "application/json" } }
);
}
| 0 |
hf_public_repos/chat-ui/src/routes | hf_public_repos/chat-ui/src/routes/conversations/+page.server.ts | import { base } from "$app/paths";
import { authCondition } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { redirect } from "@sveltejs/kit";
export const actions = {
delete: async function ({ locals }) {
// double check we have a user to delete conversations for
if (locals.user?._id || locals.sessionId) {
await collections.conversations.deleteMany({
...authCondition(locals),
});
}
throw redirect(303, `${base}/`);
},
};
| 0 |
hf_public_repos/chat-ui/src/routes | hf_public_repos/chat-ui/src/routes/privacy/+page.svelte | <script lang="ts">
import { marked } from "marked";
import privacy from "../../../PRIVACY.md?raw";
</script>
<div class="overflow-auto p-6">
<div class="prose mx-auto px-4 pb-24 pt-6 dark:prose-invert md:pt-12">
<!-- eslint-disable-next-line svelte/no-at-html-tags -->
{@html marked(privacy, { gfm: true })}
</div>
</div>
| 0 |
hf_public_repos/chat-ui/src/routes/admin | hf_public_repos/chat-ui/src/routes/admin/export/+server.ts | import {
PARQUET_EXPORT_DATASET,
PARQUET_EXPORT_HF_TOKEN,
PARQUET_EXPORT_SECRET,
} from "$env/static/private";
import { collections } from "$lib/server/database";
import type { Message } from "$lib/types/Message";
import { error } from "@sveltejs/kit";
import { pathToFileURL } from "node:url";
import { unlink } from "node:fs/promises";
import { uploadFile } from "@huggingface/hub";
import parquet from "parquetjs";
import { z } from "zod";
// Triger like this:
// curl -X POST "http://localhost:5173/chat/admin/export" -H "Authorization: Bearer <PARQUET_EXPORT_SECRET>" -H "Content-Type: application/json" -d '{"model": "OpenAssistant/oasst-sft-6-llama-30b-xor"}'
export async function POST({ request }) {
if (!PARQUET_EXPORT_SECRET || !PARQUET_EXPORT_DATASET || !PARQUET_EXPORT_HF_TOKEN) {
throw error(500, "Parquet export is not configured.");
}
if (request.headers.get("Authorization") !== `Bearer ${PARQUET_EXPORT_SECRET}`) {
throw error(403);
}
const { model } = z
.object({
model: z.string(),
})
.parse(await request.json());
const schema = new parquet.ParquetSchema({
title: { type: "UTF8" },
created_at: { type: "TIMESTAMP_MILLIS" },
updated_at: { type: "TIMESTAMP_MILLIS" },
messages: {
repeated: true,
fields: {
from: { type: "UTF8" },
content: { type: "UTF8" },
score: { type: "INT_8", optional: true },
},
},
});
const fileName = `/tmp/conversations-${new Date().toJSON().slice(0, 10)}-${Date.now()}.parquet`;
const writer = await parquet.ParquetWriter.openFile(schema, fileName);
let count = 0;
console.log("Exporting conversations for model", model);
for await (const conversation of collections.settings.aggregate<{
title: string;
created_at: Date;
updated_at: Date;
messages: Message[];
}>([
{
$match: {
shareConversationsWithModelAuthors: true,
sessionId: { $exists: true },
userId: { $exists: false },
},
},
{
$lookup: {
from: "conversations",
localField: "sessionId",
foreignField: "sessionId",
as: "conversations",
pipeline: [{ $match: { model, userId: { $exists: false } } }],
},
},
{ $unwind: "$conversations" },
{
$project: {
title: "$conversations.title",
created_at: "$conversations.createdAt",
updated_at: "$conversations.updatedAt",
messages: "$conversations.messages",
},
},
])) {
await writer.appendRow({
title: conversation.title,
created_at: conversation.created_at,
updated_at: conversation.updated_at,
messages: conversation.messages.map((message: Message) => ({
from: message.from,
content: message.content,
...(message.score ? { score: message.score } : undefined),
})),
});
++count;
if (count % 1_000 === 0) {
console.log("Exported", count, "conversations");
}
}
console.log("exporting convos with userId");
for await (const conversation of collections.settings.aggregate<{
title: string;
created_at: Date;
updated_at: Date;
messages: Message[];
}>([
{ $match: { shareConversationsWithModelAuthors: true, userId: { $exists: true } } },
{
$lookup: {
from: "conversations",
localField: "userId",
foreignField: "userId",
as: "conversations",
pipeline: [{ $match: { model } }],
},
},
{ $unwind: "$conversations" },
{
$project: {
title: "$conversations.title",
created_at: "$conversations.createdAt",
updated_at: "$conversations.updatedAt",
messages: "$conversations.messages",
},
},
])) {
await writer.appendRow({
title: conversation.title,
created_at: conversation.created_at,
updated_at: conversation.updated_at,
messages: conversation.messages.map((message: Message) => ({
from: message.from,
content: message.content,
...(message.score ? { score: message.score } : undefined),
})),
});
++count;
if (count % 1_000 === 0) {
console.log("Exported", count, "conversations");
}
}
await writer.close();
console.log("Uploading", fileName, "to Hugging Face Hub");
await uploadFile({
file: pathToFileURL(fileName),
credentials: { accessToken: PARQUET_EXPORT_HF_TOKEN },
repo: {
type: "dataset",
name: PARQUET_EXPORT_DATASET,
},
});
console.log("Upload done");
await unlink(fileName);
return new Response();
}
| 0 |
hf_public_repos/chat-ui/src/routes/search | hf_public_repos/chat-ui/src/routes/search/[id]/+server.ts | import { collections } from "$lib/server/database";
import { hashConv } from "$lib/utils/hashConv.js";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
export async function GET({ params, locals }) {
const searchId = new ObjectId(params.id);
const search = await collections.webSearches.findOne({
_id: searchId,
});
if (!search) {
throw error(404, "Search query not found");
}
const conv = await collections.conversations.findOne({
_id: search.convId,
});
if (!conv) {
throw error(404, "Conversation not found");
}
// there's no better way to see if a conversation has been shared, so we hash the messages and see if there's a shared conversation with the same hash
const hash = await hashConv(conv);
const sharedConv = await collections.sharedConversations.findOne({
hash: hash,
});
const userShouldSeeConv =
(conv.userId && locals.user?._id.toString() === conv.userId.toString()) || sharedConv !== null;
if (!userShouldSeeConv) {
throw error(403, "You don't have access to the conversation here.");
}
return new Response(JSON.stringify(search), { headers: { "Content-Type": "application/json" } });
}
| 0 |
hf_public_repos/chat-ui/src/routes | hf_public_repos/chat-ui/src/routes/login/+page.server.ts | import { redirect } from "@sveltejs/kit";
import { getOIDCAuthorizationUrl } from "$lib/server/auth";
import { base } from "$app/paths";
export const actions = {
default: async function ({ url, locals, request }) {
// TODO: Handle errors if provider is not responding
const referer = request.headers.get("referer");
const authorizationUrl = await getOIDCAuthorizationUrl(
{ redirectURI: `${(referer ? new URL(referer) : url).origin}${base}/login/callback` },
{ sessionId: locals.sessionId }
);
throw redirect(303, authorizationUrl);
},
};
| 0 |
hf_public_repos/chat-ui/src/routes/login | hf_public_repos/chat-ui/src/routes/login/callback/+page.server.ts | import { redirect, error } from "@sveltejs/kit";
import { getOIDCUserData, validateAndParseCsrfToken } from "$lib/server/auth";
import { z } from "zod";
import { base } from "$app/paths";
import { updateUser } from "./updateUser";
export async function load({ url, locals, cookies }) {
const { error: errorName, error_description: errorDescription } = z
.object({
error: z.string().optional(),
error_description: z.string().optional(),
})
.parse(Object.fromEntries(url.searchParams.entries()));
if (errorName) {
throw error(400, errorName + (errorDescription ? ": " + errorDescription : ""));
}
const { code, state } = z
.object({
code: z.string(),
state: z.string(),
})
.parse(Object.fromEntries(url.searchParams.entries()));
const csrfToken = Buffer.from(state, "base64").toString("utf-8");
const validatedToken = await validateAndParseCsrfToken(csrfToken, locals.sessionId);
if (!validatedToken) {
throw error(403, "Invalid or expired CSRF token");
}
const { userData } = await getOIDCUserData({ redirectURI: validatedToken.redirectUrl }, code);
await updateUser({ userData, locals, cookies });
throw redirect(302, `${base}/`);
}
| 0 |
hf_public_repos/chat-ui/src/routes/login | hf_public_repos/chat-ui/src/routes/login/callback/updateUser.ts | import { authCondition, refreshSessionCookie } from "$lib/server/auth";
import { collections } from "$lib/server/database";
import { ObjectId } from "mongodb";
import { DEFAULT_SETTINGS } from "$lib/types/Settings";
import { z } from "zod";
import type { UserinfoResponse } from "openid-client";
import type { Cookies } from "@sveltejs/kit";
export async function updateUser(params: {
userData: UserinfoResponse;
locals: App.Locals;
cookies: Cookies;
}) {
const { userData, locals, cookies } = params;
const {
preferred_username: username,
name,
email,
picture: avatarUrl,
sub: hfUserId,
} = z
.object({
preferred_username: z.string().optional(),
name: z.string(),
picture: z.string(),
sub: z.string(),
email: z.string().email().optional(),
})
.refine((data) => data.preferred_username || data.email, {
message: "Either preferred_username or email must be provided by the provider.",
})
.parse(userData);
const existingUser = await collections.users.findOne({ hfUserId });
let userId = existingUser?._id;
if (existingUser) {
// update existing user if any
await collections.users.updateOne(
{ _id: existingUser._id },
{ $set: { username, name, avatarUrl } }
);
// refresh session cookie
refreshSessionCookie(cookies, existingUser.sessionId);
} else {
// user doesn't exist yet, create a new one
const { insertedId } = await collections.users.insertOne({
_id: new ObjectId(),
createdAt: new Date(),
updatedAt: new Date(),
username,
name,
email,
avatarUrl,
hfUserId,
sessionId: locals.sessionId,
});
userId = insertedId;
// update pre-existing settings
const { matchedCount } = await collections.settings.updateOne(authCondition(locals), {
$set: { userId, updatedAt: new Date() },
$unset: { sessionId: "" },
});
if (!matchedCount) {
// create new default settings
await collections.settings.insertOne({
userId,
ethicsModalAcceptedAt: new Date(),
updatedAt: new Date(),
createdAt: new Date(),
...DEFAULT_SETTINGS,
});
}
}
// migrate pre-existing conversations
await collections.conversations.updateMany(authCondition(locals), {
$set: { userId },
$unset: { sessionId: "" },
});
}
| 0 |
hf_public_repos/chat-ui/src/routes/login | hf_public_repos/chat-ui/src/routes/login/callback/updateUser.spec.ts | import { assert, it, describe, afterEach, vi, expect } from "vitest";
import type { Cookies } from "@sveltejs/kit";
import { collections } from "$lib/server/database";
import { updateUser } from "./updateUser";
import { ObjectId } from "mongodb";
import { DEFAULT_SETTINGS } from "$lib/types/Settings";
import { defaultModel } from "$lib/server/models";
const userData = {
preferred_username: "new-username",
name: "name",
picture: "https://example.com/avatar.png",
sub: "1234567890",
};
const locals = {
userId: "1234567890",
sessionId: "1234567890",
};
// @ts-expect-error SvelteKit cookies dumb mock
const cookiesMock: Cookies = {
set: vi.fn(),
};
const insertRandomUser = async () => {
const res = await collections.users.insertOne({
_id: new ObjectId(),
createdAt: new Date(),
updatedAt: new Date(),
username: "base-username",
name: userData.name,
avatarUrl: userData.picture,
hfUserId: userData.sub,
sessionId: locals.sessionId,
});
return res.insertedId;
};
const insertRandomConversations = async (count: number) => {
const res = await collections.conversations.insertMany(
new Array(count).fill(0).map(() => ({
_id: new ObjectId(),
title: "random title",
messages: [],
model: defaultModel.id,
createdAt: new Date(),
updatedAt: new Date(),
sessionId: locals.sessionId,
}))
);
return res.insertedIds;
};
describe("login", () => {
it("should update user if existing", async () => {
await insertRandomUser();
await updateUser({ userData, locals, cookies: cookiesMock });
const existingUser = await collections.users.findOne({ hfUserId: userData.sub });
assert.equal(existingUser?.name, userData.name);
expect(cookiesMock.set).toBeCalledTimes(1);
});
it("should migrate pre-existing conversations for new user", async () => {
const insertedId = await insertRandomUser();
await insertRandomConversations(2);
await updateUser({ userData, locals, cookies: cookiesMock });
const conversationCount = await collections.conversations.countDocuments({
userId: insertedId,
sessionId: { $exists: false },
});
assert.equal(conversationCount, 2);
await collections.conversations.deleteMany({ userId: insertedId });
});
it("should create default settings for new user", async () => {
await updateUser({ userData, locals, cookies: cookiesMock });
const user = await collections.users.findOne({ sessionId: locals.sessionId });
assert.exists(user);
const settings = await collections.settings.findOne({ userId: user?._id });
expect(settings).toMatchObject({
userId: user?._id,
updatedAt: expect.any(Date),
createdAt: expect.any(Date),
ethicsModalAcceptedAt: expect.any(Date),
...DEFAULT_SETTINGS,
});
await collections.settings.deleteOne({ userId: user?._id });
});
it("should migrate pre-existing settings for pre-existing user", async () => {
const { insertedId } = await collections.settings.insertOne({
sessionId: locals.sessionId,
ethicsModalAcceptedAt: new Date(),
updatedAt: new Date(),
createdAt: new Date(),
...DEFAULT_SETTINGS,
shareConversationsWithModelAuthors: false,
});
await updateUser({ userData, locals, cookies: cookiesMock });
const settings = await collections.settings.findOne({
_id: insertedId,
sessionId: { $exists: false },
});
assert.exists(settings);
const user = await collections.users.findOne({ hfUserId: userData.sub });
expect(settings).toMatchObject({
userId: user?._id,
updatedAt: expect.any(Date),
createdAt: expect.any(Date),
ethicsModalAcceptedAt: expect.any(Date),
...DEFAULT_SETTINGS,
shareConversationsWithModelAuthors: false,
});
await collections.settings.deleteOne({ userId: user?._id });
});
});
afterEach(async () => {
await collections.users.deleteMany({ hfUserId: userData.sub });
vi.clearAllMocks();
});
| 0 |
hf_public_repos/chat-ui/src/routes/r | hf_public_repos/chat-ui/src/routes/r/[id]/+page.server.ts | import type { PageServerLoad } from "./$types";
import { collections } from "$lib/server/database";
import { error } from "@sveltejs/kit";
import { ObjectId } from "mongodb";
import type { WebSearchMessageResult } from "$lib/types/WebSearch";
export const load: PageServerLoad = async ({ params }) => {
const conversation = await collections.sharedConversations.findOne({
_id: params.id,
});
if (!conversation) {
throw error(404, "Conversation not found");
}
const webSearchesId = conversation.messages
.filter((message) => message.webSearchId)
.map((message) => new ObjectId(message.webSearchId));
const results = await collections.webSearches.find({ _id: { $in: webSearchesId } }).toArray();
const searches = Object.fromEntries(
results.map((x) => [
x._id.toString(),
[...x.messages, { type: "result", id: x._id.toString() } satisfies WebSearchMessageResult],
])
);
return {
messages: conversation.messages,
title: conversation.title,
model: conversation.model,
searches,
};
};
| 0 |
hf_public_repos/chat-ui/src/routes/r | hf_public_repos/chat-ui/src/routes/r/[id]/+page.svelte | <script lang="ts">
import { goto } from "$app/navigation";
import { base } from "$app/paths";
import { page } from "$app/stores";
import { PUBLIC_APP_DISCLAIMER } from "$env/static/public";
import ChatWindow from "$lib/components/chat/ChatWindow.svelte";
import { ERROR_MESSAGES, error } from "$lib/stores/errors";
import { pendingMessage } from "$lib/stores/pendingMessage";
import { pendingMessageIdToRetry } from "$lib/stores/pendingMessageIdToRetry";
import { findCurrentModel } from "$lib/utils/models";
import { share } from "$lib/utils/share";
import type { PageData } from "./$types";
export let data: PageData;
let loading = false;
async function createConversation() {
try {
loading = true;
const res = await fetch(`${base}/conversation`, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({
fromShare: $page.params.id,
model: data.model,
}),
});
if (!res.ok) {
error.set("Error while creating conversation, try again.");
console.error("Error while creating conversation: " + (await res.text()));
return;
}
const { conversationId } = await res.json();
return conversationId;
} catch (err) {
error.set(ERROR_MESSAGES.default);
console.error(String(err));
throw err;
}
}
async function shareConversation() {
const url = `${window.location.origin}${window.location.pathname}`;
share(url, data.title);
}
</script>
<svelte:head>
<title>{data.title}</title>
</svelte:head>
<ChatWindow
{loading}
shared={true}
messages={data.messages}
searches={data.searches}
on:message={(ev) =>
createConversation()
.then((convId) => {
$pendingMessage = ev.detail;
return goto(`${base}/conversation/${convId}`, { invalidateAll: true });
})
.finally(() => (loading = false))}
on:share={shareConversation}
on:retry={(ev) =>
createConversation()
.then((convId) => {
$pendingMessageIdToRetry = ev.detail.id;
$pendingMessage = ev.detail.content;
return goto(`${base}/conversation/${convId}`, { invalidateAll: true });
})
.finally(() => (loading = false))}
models={data.models}
currentModel={findCurrentModel(data.models, data.model)}
settings={data.settings}
loginRequired={!$page.error &&
(data.requiresLogin
? !data.user
: !data.settings.ethicsModalAcceptedAt && !!PUBLIC_APP_DISCLAIMER)}
/>
| 0 |
hf_public_repos/chat-ui/src/routes/r/[id]/message/[messageId] | hf_public_repos/chat-ui/src/routes/r/[id]/message/[messageId]/prompt/+server.ts | import { buildPrompt } from "$lib/buildPrompt";
import { collections } from "$lib/server/database";
import { models } from "$lib/server/models";
import { error } from "@sveltejs/kit";
export async function GET({ params }) {
const conv = await collections.sharedConversations.findOne({
_id: params.id,
});
if (!conv) {
throw error(404, "Conversation not found");
}
const messageId = params.messageId;
const messageIndex = conv.messages.findIndex((msg) => msg.id === messageId);
if (messageIndex === -1) {
throw error(404, "Message not found");
}
const model = models.find((m) => m.id === conv.model);
if (!model) {
throw error(404, "Conversation model not found");
}
const prompt = await buildPrompt(conv.messages.slice(0, messageIndex + 1), model);
return new Response(
JSON.stringify(
{
note: "This is a preview of the prompt that will be sent to the model when retrying the message. It may differ from what was sent in the past if the parameters have been updated since",
prompt,
model: model.name,
parameters: {
...model.parameters,
return_full_text: false,
},
},
null,
2
),
{ headers: { "Content-Type": "application/json" } }
);
}
| 0 |
hf_public_repos/chat-ui/src | hf_public_repos/chat-ui/src/lib/switchTheme.ts | export function switchTheme() {
const { classList } = document.querySelector("html") as HTMLElement;
if (classList.contains("dark")) {
classList.remove("dark");
localStorage.theme = "light";
} else {
classList.add("dark");
localStorage.theme = "dark";
}
}
| 0 |
hf_public_repos/chat-ui/src | hf_public_repos/chat-ui/src/lib/shareConversation.ts | import { base } from "$app/paths";
import { ERROR_MESSAGES, error } from "$lib/stores/errors";
import { share } from "./utils/share";
export async function shareConversation(id: string, title: string) {
try {
const res = await fetch(`${base}/conversation/${id}/share`, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
});
if (!res.ok) {
error.set("Error while sharing conversation, try again.");
console.error("Error while sharing conversation: " + (await res.text()));
return;
}
const { url } = await res.json();
share(url, title);
} catch (err) {
error.set(ERROR_MESSAGES.default);
console.error(err);
}
}
| 0 |
hf_public_repos/chat-ui/src | hf_public_repos/chat-ui/src/lib/buildPrompt.ts | import type { BackendModel } from "./server/models";
import type { Message } from "./types/Message";
import { collections } from "$lib/server/database";
import { ObjectId } from "mongodb";
/**
* Convert [{user: "assistant", content: "hi"}, {user: "user", content: "hello"}] to:
*
* <|assistant|>hi<|endoftext|><|prompter|>hello<|endoftext|><|assistant|>
*/
export async function buildPrompt(
messages: Pick<Message, "from" | "content">[],
model: BackendModel,
webSearchId?: string
): Promise<string> {
const prompt =
messages
.map(
(m) =>
(m.from === "user"
? model.userMessageToken + m.content
: model.assistantMessageToken + m.content) +
(model.messageEndToken
? m.content.endsWith(model.messageEndToken)
? ""
: model.messageEndToken
: "")
)
.join("") + model.assistantMessageToken;
let webPrompt = "";
if (webSearchId) {
const webSearch = await collections.webSearches.findOne({
_id: new ObjectId(webSearchId),
});
if (!webSearch) throw new Error("Web search not found");
if (webSearch.summary) {
webPrompt =
model.assistantMessageToken +
`The following context was found while searching the internet: ${webSearch.summary}` +
model.messageEndToken;
}
}
const finalPrompt =
model.preprompt +
webPrompt +
prompt
.split(" ")
.slice(-(model.parameters?.truncate ?? 0))
.join(" ");
// Not super precise, but it's truncated in the model's backend anyway
return finalPrompt;
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/actions/snapScrollToBottom.ts | import { navigating } from "$app/stores";
import { tick } from "svelte";
import { get } from "svelte/store";
const detachedOffset = 10;
/**
* @param node element to snap scroll to bottom
* @param dependency pass in a dependency to update scroll on changes.
*/
export const snapScrollToBottom = (node: HTMLElement, dependency: unknown) => {
let prevScrollValue = node.scrollTop;
let isDetached = false;
const handleScroll = () => {
// if user scrolled up, we detach
if (node.scrollTop < prevScrollValue) {
isDetached = true;
}
// if user scrolled back to within 10px of bottom, we reattach
if (node.scrollTop - (node.scrollHeight - node.clientHeight) >= -detachedOffset) {
isDetached = false;
}
prevScrollValue = node.scrollTop;
};
const updateScroll = async (_options: { force?: boolean } = {}) => {
const defaultOptions = { force: false };
const options = { ...defaultOptions, ..._options };
const { force } = options;
if (!force && isDetached && !get(navigating)) return;
// wait for next tick to ensure that the DOM is updated
await tick();
node.scrollTo({ top: node.scrollHeight });
};
node.addEventListener("scroll", handleScroll);
if (dependency) {
updateScroll({ force: true });
}
return {
update: updateScroll,
destroy: () => {
node.removeEventListener("scroll", handleScroll);
},
};
};
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/types/MessageEvent.ts | import type { Timestamps } from "./Timestamps";
import type { User } from "./User";
export interface MessageEvent extends Pick<Timestamps, "createdAt"> {
userId: User["_id"] | User["sessionId"];
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/types/User.ts | import type { ObjectId } from "mongodb";
import type { Timestamps } from "./Timestamps";
export interface User extends Timestamps {
_id: ObjectId;
username?: string;
name: string;
email?: string;
avatarUrl: string;
hfUserId: string;
// Session identifier, stored in the cookie
sessionId: string;
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/types/Timestamps.ts | export interface Timestamps {
createdAt: Date;
updatedAt: Date;
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/types/UrlDependency.ts | /* eslint-disable no-shadow */
export enum UrlDependency {
ConversationList = "conversation:list",
Conversation = "conversation",
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/types/SharedConversation.ts | import type { Message } from "./Message";
import type { Timestamps } from "./Timestamps";
export interface SharedConversation extends Timestamps {
_id: string;
hash: string;
model: string;
title: string;
messages: Message[];
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/types/Model.ts | import type { BackendModel } from "$lib/server/models";
export type Model = Pick<
BackendModel,
| "id"
| "name"
| "displayName"
| "websiteUrl"
| "datasetName"
| "promptExamples"
| "parameters"
| "description"
| "modelUrl"
| "datasetUrl"
>;
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/types/AbortedGeneration.ts | // Ideally shouldn't be needed, see https://github.com/huggingface/chat-ui/pull/88#issuecomment-1523173850
import type { Conversation } from "./Conversation";
import type { Timestamps } from "./Timestamps";
export interface AbortedGeneration extends Timestamps {
conversationId: Conversation["_id"];
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/types/Settings.ts | import { defaultModel } from "$lib/server/models";
import type { Timestamps } from "./Timestamps";
import type { User } from "./User";
export interface Settings extends Timestamps {
userId?: User["_id"];
sessionId?: string;
/**
* Note: Only conversations with this settings explicitly set to true should be shared.
*
* This setting is explicitly set to true when users accept the ethics modal.
* */
shareConversationsWithModelAuthors: boolean;
ethicsModalAcceptedAt: Date | null;
activeModel: string;
}
// TODO: move this to a constant file along with other constants
export const DEFAULT_SETTINGS = {
shareConversationsWithModelAuthors: true,
activeModel: defaultModel.id,
};
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/types/Message.ts | import type { Timestamps } from "./Timestamps";
export type Message = Partial<Timestamps> & {
from: "user" | "assistant";
id: ReturnType<typeof crypto.randomUUID>;
content: string;
webSearchId?: string;
score?: -1 | 0 | 1;
};
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/types/WebSearch.ts | import type { ObjectId } from "mongodb";
import type { Conversation } from "./Conversation";
import type { Timestamps } from "./Timestamps";
export interface WebSearch extends Timestamps {
_id: ObjectId;
convId: Conversation["_id"];
prompt: string;
searchQuery: string;
results: string[];
knowledgeGraph: string;
answerBox: string;
summary: string;
messages: WebSearchMessage[];
}
export type WebSearchMessageUpdate = {
type: "update";
message: string;
args?: string[];
};
export type WebSearchMessageError = {
type: "error";
message: string;
args?: string[];
};
export type WebSearchMessageResult = {
type: "result";
id: string;
};
export type WebSearchMessage =
| WebSearchMessageUpdate
| WebSearchMessageResult
| WebSearchMessageError;
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/types/Conversation.ts | import type { ObjectId } from "mongodb";
import type { Message } from "./Message";
import type { Timestamps } from "./Timestamps";
import type { User } from "./User";
export interface Conversation extends Timestamps {
_id: ObjectId;
sessionId?: string;
userId?: User["_id"];
model: string;
title: string;
messages: Message[];
meta?: {
fromShareId?: string;
};
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/utils/trimSuffix.ts | export function trimSuffix(input: string, end: string): string {
if (input.endsWith(end)) {
return input.slice(0, input.length - end.length);
}
return input;
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/utils/deepestChild.ts | export function deepestChild(el: HTMLElement): HTMLElement {
if (el.lastElementChild && el.lastElementChild.nodeType !== Node.TEXT_NODE) {
return deepestChild(el.lastElementChild as HTMLElement);
}
return el;
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/utils/sum.ts | export function sum(nums: number[]): number {
return nums.reduce((a, b) => a + b, 0);
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/utils/concatUint8Arrays.ts | import { sum } from "./sum";
export function concatUint8Arrays(arrays: Uint8Array[]): Uint8Array {
const totalLength = sum(arrays.map((a) => a.length));
const result = new Uint8Array(totalLength);
let offset = 0;
for (const array of arrays) {
result.set(array, offset);
offset += array.length;
}
return result;
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/utils/analytics.ts | export interface GAEvent {
hitType: "event";
eventCategory: string;
eventAction: string;
eventLabel?: string;
eventValue?: number;
}
// Send a Google Analytics event
export function sendAnalyticsEvent({
eventCategory,
eventAction,
eventLabel,
eventValue,
}: Omit<GAEvent, "hitType">): void {
// Mandatory fields
const event: GAEvent = {
hitType: "event",
eventCategory,
eventAction,
};
// Optional fields
if (eventLabel) {
event.eventLabel = eventLabel;
}
if (eventValue) {
event.eventValue = eventValue;
}
// @ts-expect-error typescript doesn't know gtag is on the window object
if (!!window?.gtag && typeof window?.gtag === "function") {
// @ts-expect-error typescript doesn't know gtag is on the window object
window?.gtag("event", eventAction, {
event_category: event.eventCategory,
event_label: event.eventLabel,
value: event.eventValue,
});
}
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/utils/sha256.ts | export async function sha256(input: string): Promise<string> {
const utf8 = new TextEncoder().encode(input);
const hashBuffer = await crypto.subtle.digest("SHA-256", utf8);
const hashArray = Array.from(new Uint8Array(hashBuffer));
const hashHex = hashArray.map((bytes) => bytes.toString(16).padStart(2, "0")).join("");
return hashHex;
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/utils/trimPrefix.ts | export function trimPrefix(input: string, prefix: string) {
if (input.startsWith(prefix)) {
return input.slice(prefix.length);
}
return input;
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/utils/models.ts | import type { Model } from "$lib/types/Model";
export const findCurrentModel = (models: Model[], id?: string) =>
models.find((m) => m.id === id) ?? models[0];
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/utils/share.ts | export function share(url: string, title: string) {
if (navigator.share) {
navigator.share({ url, title });
} else {
prompt("Copy this public url to share:", url);
}
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/utils/streamToAsyncIterable.ts | // https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Statements/for-await...of#iterating_over_async_generators
export async function* streamToAsyncIterable(
stream: ReadableStream<Uint8Array>
): AsyncIterableIterator<Uint8Array> {
const reader = stream.getReader();
try {
while (true) {
const { done, value } = await reader.read();
if (done) return;
yield value;
}
} finally {
reader.releaseLock();
}
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/utils/randomUuid.ts | type UUID = ReturnType<typeof crypto.randomUUID>;
export function randomUUID(): UUID {
// Only on old safari / ios
if (!("randomUUID" in crypto)) {
return "10000000-1000-4000-8000-100000000000".replace(/[018]/g, (c) =>
(
Number(c) ^
(crypto.getRandomValues(new Uint8Array(1))[0] & (15 >> (Number(c) / 4)))
).toString(16)
) as UUID;
}
return crypto.randomUUID();
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/utils/hashConv.ts | import type { Conversation } from "$lib/types/Conversation";
import { sha256 } from "./sha256";
export async function hashConv(conv: Conversation) {
// messages contains the conversation message but only the immutable part
const messages = conv.messages.map((message) => {
return (({ from, id, content, webSearchId }) => ({ from, id, content, webSearchId }))(message);
});
const hash = await sha256(JSON.stringify(messages));
return hash;
}
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/stores/webSearchParameters.ts | import { writable } from "svelte/store";
export interface WebSearchParameters {
useSearch: boolean;
nItems: number;
}
export const webSearchParameters = writable<WebSearchParameters>({
useSearch: false,
nItems: 5,
});
| 0 |
hf_public_repos/chat-ui/src/lib | hf_public_repos/chat-ui/src/lib/stores/pendingMessageIdToRetry.ts | import type { Message } from "$lib/types/Message";
import { writable } from "svelte/store";
export const pendingMessageIdToRetry = writable<Message["id"] | null>(null);
| 0 |
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