Update the docs to include newer models. (#1492)

router/src/lib.rs

@@ -188,18 +188,20 @@ fn default_parameters() -> GenerateParameters {
     }
 }
 
-#[derive(Clone, Deserialize, Serialize)]
+#[derive(Clone, Deserialize, Serialize, ToSchema)]
 pub(crate) struct ChatCompletion {
     pub id: String,
     pub object: String,
+    #[schema(example = "1706270835")]
     pub created: u64,
+    #[schema(example = "mistralai/Mistral-7B-Instruct-v0.2")]
     pub model: String,
     pub system_fingerprint: String,
     pub choices: Vec<ChatCompletionComplete>,
     pub usage: Usage,
 }
 
-#[derive(Clone, Deserialize, Serialize)]
+#[derive(Clone, Deserialize, Serialize, ToSchema)]
 pub(crate) struct ChatCompletionComplete {
     pub index: u32,
     pub message: Message,
@@ -248,17 +250,19 @@ impl ChatCompletion {
     }
 }
 
-#[derive(Clone, Deserialize, Serialize)]
+#[derive(Clone, Deserialize, Serialize, ToSchema)]
 pub(crate) struct ChatCompletionChunk {
     pub id: String,
     pub object: String,
+    #[schema(example = "1706270978")]
     pub created: u64,
+    #[schema(example = "mistralai/Mistral-7B-Instruct-v0.2")]
     pub model: String,
     pub system_fingerprint: String,
     pub choices: Vec<ChatCompletionChoice>,
 }
 
-#[derive(Clone, Deserialize, Serialize)]
+#[derive(Clone, Deserialize, Serialize, ToSchema)]
 pub(crate) struct ChatCompletionChoice {
     pub index: u32,
     pub delta: ChatCompletionDelta,
@@ -266,9 +270,11 @@ pub(crate) struct ChatCompletionChoice {
     pub finish_reason: Option<String>,
 }
 
-#[derive(Clone, Debug, Deserialize, Serialize)]
+#[derive(Clone, Debug, Deserialize, Serialize, ToSchema)]
 pub(crate) struct ChatCompletionDelta {
+    #[schema(example = "user")]
     pub role: String,
+    #[schema(example = "What is Deep Learning?")]
     pub content: String,
 }
 
@@ -311,7 +317,7 @@ fn default_request_messages() -> Vec<Message> {
 #[derive(Clone, Deserialize, ToSchema, Serialize)]
 pub(crate) struct ChatRequest {
     /// UNUSED
-    #[schema(example = "bigscience/blomm-560m")]
+    #[schema(example = "mistralai/Mistral-7B-Instruct-v0.2")]
     /// ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.
     pub model: String, /* NOTE: UNUSED */
 
@@ -322,6 +328,7 @@ pub(crate) struct ChatRequest {
     /// Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far,
     /// decreasing the model's likelihood to repeat the same line verbatim.
     #[serde(default)]
+    #[schema(example = "1.0")]
     pub frequency_penalty: Option<f32>,
 
     /// UNUSED
@@ -336,28 +343,33 @@ pub(crate) struct ChatRequest {
     /// Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each
     /// output token returned in the content of message.
     #[serde(default)]
+    #[schema(example = "false")]
     pub logprobs: Option<bool>,
 
     /// UNUSED
     /// An integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with
     /// an associated log probability. logprobs must be set to true if this parameter is used.
     #[serde(default)]
+    #[schema(example = "5")]
     pub top_logprobs: Option<u32>,
 
     /// The maximum number of tokens that can be generated in the chat completion.
     #[serde(default)]
+    #[schema(example = "32")]
     pub max_tokens: Option<u32>,
 
     /// UNUSED
     /// How many chat completion choices to generate for each input message. Note that you will be charged based on the
     /// number of generated tokens across all of the choices. Keep n as 1 to minimize costs.
     #[serde(default)]
+    #[schema(nullable = true, example = "2")]
     pub n: Option<u32>,
 
     /// UNUSED
     /// Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far,
     /// increasing the model's likelihood to talk about new topics
     #[serde(default)]
+    #[schema(nullable = true, example = 0.1)]
     pub presence_penalty: Option<f32>,
 
     #[serde(default = "bool::default")]
@@ -371,11 +383,13 @@ pub(crate) struct ChatRequest {
     ///
     /// We generally recommend altering this or `top_p` but not both.
     #[serde(default)]
+    #[schema(nullable = true, example = 1.0)]
     pub temperature: Option<f32>,
 
     /// An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the
     /// tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.
     #[serde(default)]
+    #[schema(nullable = true, example = 0.95)]
     pub top_p: Option<f32>,
 }
 
@@ -458,6 +472,7 @@ pub struct SimpleToken {
 
 #[derive(Serialize, ToSchema)]
 #[serde(rename_all(serialize = "snake_case"))]
+#[schema(example = "Length")]
 pub(crate) enum FinishReason {
     #[schema(rename = "length")]
     Length,
@@ -518,6 +533,10 @@ pub(crate) struct GenerateResponse {
     pub details: Option<Details>,
 }
 
+#[derive(Serialize, ToSchema)]
+#[serde(transparent)]
+pub(crate) struct TokenizeResponse(Vec<SimpleToken>);
+
 #[derive(Serialize, ToSchema)]
 pub(crate) struct StreamDetails {
     #[schema(example = "length")]

router/src/server.rs

@@ -3,10 +3,10 @@ use crate::health::Health;
 use crate::infer::{InferError, InferResponse, InferStreamResponse};
 use crate::validation::ValidationError;
 use crate::{
-    BestOfSequence, ChatCompletion, ChatCompletionChunk, ChatRequest, CompatGenerateRequest,
-    Details, ErrorResponse, FinishReason, GenerateParameters, GenerateRequest, GenerateResponse,
-    HubModelInfo, HubTokenizerConfig, Infer, Info, PrefillToken, SimpleToken, StreamDetails,
-    StreamResponse, Token, Validation,
+    BestOfSequence, ChatCompletion, ChatCompletionChoice, ChatCompletionChunk, ChatCompletionDelta,
+    ChatRequest, CompatGenerateRequest, Details, ErrorResponse, FinishReason, GenerateParameters,
+    GenerateRequest, GenerateResponse, HubModelInfo, HubTokenizerConfig, Infer, Info, Message,
+    PrefillToken, SimpleToken, StreamDetails, StreamResponse, Token, TokenizeResponse, Validation,
 };
 use axum::extract::Extension;
 use axum::http::{HeaderMap, Method, StatusCode};
@@ -677,7 +677,7 @@ async fn chat_completions(
     post,
     tag = "Text Generation Inference",
     path = "/tokenize",
-    request_body = TokenizeRequest,
+    request_body = GenerateRequest,
     responses(
     (status = 200, description = "Tokenized ids", body = TokenizeResponse),
     (status = 404, description = "No tokenizer found", body = ErrorResponse,
@@ -688,7 +688,7 @@ async fn chat_completions(
 async fn tokenize(
     Extension(infer): Extension<Infer>,
     Json(req): Json<GenerateRequest>,
-) -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
+) -> Result<Json<TokenizeResponse>, (StatusCode, Json<ErrorResponse>)> {
     let input = req.inputs.clone();
     let encoding = infer.tokenize(req).await?;
     if let Some(encoding) = encoding {
@@ -706,7 +706,7 @@ async fn tokenize(
                 }
             })
             .collect();
-        Ok(Json(tokens).into_response())
+        Ok(Json(TokenizeResponse(tokens)))
     } else {
         Err((
             StatusCode::NOT_FOUND,
@@ -774,10 +774,18 @@ pub async fn run(
     Info,
     CompatGenerateRequest,
     GenerateRequest,
+    ChatRequest,
+    Message,
+    ChatCompletionChoice,
+    ChatCompletionDelta,
+    ChatCompletionChunk,
+    ChatCompletion,
     GenerateParameters,
     PrefillToken,
     Token,
     GenerateResponse,
+    TokenizeResponse,
+    SimpleToken,
     BestOfSequence,
     Details,
     FinishReason,