feat(models): complete 1:1 OpenAI API surface + unified router

Rewrite api_types.rs as single source of truth with exact OpenAI field
names and object types:
- CompletionRequest/Response with logprobs, stop, penalties, seed
- ChatCompletionRequest/Response with tools, streaming chunks, deltas
- EmbeddingRequest/Response with encoding_format, dimensions
- ImageGenerationRequest/Response with size parsing, quality, style
- Model/ModelList with object fields ("model", "list")
- ErrorResponse envelope matching OpenAI error format
- ChatRole with as_str()/from_str() for wire format

Add router.rs — unified ModelRouter dispatching by model ID:
- complete() → GPT-2
- chat_complete() → OpenChat 3.5 (or GPT-2 via chat↔completion adapter)
- embed() → GPT-2 wte (extensible to Jina/BERT)
- list_models() / get_model() → all registered models
- chat↔completion adapters for cross-model compatibility

Rewire gpt2/api.rs, openchat/api.rs, stable_diffusion/api.rs to use
shared types. No JSON/serde surface — pure Rust types for in-binary
consumers (q2, lance-graph).

58 API tests passing.

https://claude.ai/code/session_01Y69Vnw751w75iVSBRws7o7

Author: claude

src/hpc/gpt2/api.rs

@@ -1,222 +1,97 @@
-//! OpenAI-compatible API types for GPT-2 inference.
+//! GPT-2 API — wraps the inference engine with OpenAI-compatible types.
 //!
-//! Provides request/response structs matching the OpenAI API surface:
+//! Endpoints:
 //! - `/v1/completions` — text completion
 //! - `/v1/embeddings` — token embeddings via wte
-//! - `/v1/models` — model listing
-//!
-//! These types are transport-agnostic — they serialize/deserialize
-//! but don't depend on any HTTP framework.
+//! - `/v1/models` — model info
 
+use crate::hpc::models::api_types::*;
 use super::inference::{GeneratedToken, Gpt2Engine};
 use super::weights::*;
 
-// ============================================================================
-// /v1/completions
-// ============================================================================
-
-/// Request body for /v1/completions.
-#[derive(Clone, Debug)]
-pub struct CompletionRequest {
-    /// Model name (ignored — we only have gpt2).
-    pub model: String,
-    /// Input text prompt (will be tokenized externally).
-    pub prompt_tokens: Vec<u32>,
-    /// Maximum tokens to generate.
-    pub max_tokens: usize,
-    /// Sampling temperature (1.0 = greedy effective).
-    pub temperature: f32,
-    /// Stop token ID (default: 50256 = <|endoftext|>).
-    pub stop_token: Option<u32>,
-}
-
-impl Default for CompletionRequest {
-    fn default() -> Self {
-        Self {
-            model: "gpt2".into(),
-            prompt_tokens: Vec::new(),
-            max_tokens: 128,
-            temperature: 1.0,
-            stop_token: Some(50256),
-        }
-    }
-}
-
-/// Single completion choice.
-#[derive(Clone, Debug)]
-pub struct CompletionChoice {
-    pub index: usize,
-    pub tokens: Vec<GeneratedToken>,
-    pub finish_reason: FinishReason,
-}
-
-/// Why generation stopped.
-#[derive(Clone, Debug, PartialEq, Eq)]
-pub enum FinishReason {
-    Stop,
-    Length,
-}
-
-/// Response body for /v1/completions.
-#[derive(Clone, Debug)]
-pub struct CompletionResponse {
-    pub id: String,
-    pub model: String,
-    pub choices: Vec<CompletionChoice>,
-    pub usage: Usage,
-}
-
-/// Token usage statistics.
-#[derive(Clone, Debug, Default)]
-pub struct Usage {
-    pub prompt_tokens: usize,
-    pub completion_tokens: usize,
-    pub total_tokens: usize,
-}
-
-// ============================================================================
-// /v1/embeddings
-// ============================================================================
-
-/// Request body for /v1/embeddings.
-#[derive(Clone, Debug)]
-pub struct EmbeddingRequest {
-    pub model: String,
-    /// Token IDs to embed (one embedding per token).
-    pub input_tokens: Vec<u32>,
-}
-
-/// Single embedding result.
-#[derive(Clone, Debug)]
-pub struct EmbeddingData {
-    pub index: usize,
-    pub embedding: Vec<f32>,
-}
-
-/// Response body for /v1/embeddings.
-#[derive(Clone, Debug)]
-pub struct EmbeddingResponse {
-    pub model: String,
-    pub data: Vec<EmbeddingData>,
-    pub usage: Usage,
-}
-
-// ============================================================================
-// /v1/models
-// ============================================================================
-
-/// Model info for /v1/models.
-#[derive(Clone, Debug)]
-pub struct ModelInfo {
-    pub id: String,
-    pub owned_by: String,
-    pub vocab_size: usize,
-    pub embed_dim: usize,
-    pub num_layers: usize,
-    pub num_heads: usize,
-    pub max_seq_len: usize,
-}
-
-impl ModelInfo {
-    /// GPT-2 small (124M) model info.
-    pub fn gpt2_small() -> Self {
-        Self {
-            id: "gpt2".into(),
-            owned_by: "adaworldapi".into(),
-            vocab_size: VOCAB_SIZE,
-            embed_dim: EMBED_DIM,
-            num_layers: NUM_LAYERS,
-            num_heads: NUM_HEADS,
-            max_seq_len: MAX_SEQ_LEN,
-        }
-    }
-}
-
-// ============================================================================
-// Engine wrapper — stateless API over stateful engine
-// ============================================================================
-
 /// Stateless API wrapper around Gpt2Engine.
-/// Handles request→response conversion.
 pub struct Gpt2Api {
     engine: Gpt2Engine,
     request_counter: u64,
 }
 
 impl Gpt2Api {
-    /// Create from pre-loaded weights.
     pub fn new(weights: Gpt2Weights) -> Self {
-        Self {
-            engine: Gpt2Engine::new(weights),
-            request_counter: 0,
-        }
+        Self { engine: Gpt2Engine::new(weights), request_counter: 0 }
     }
 
-    /// /v1/completions handler.
+    /// `/v1/completions`
     pub fn complete(&mut self, req: &CompletionRequest) -> CompletionResponse {
         self.request_counter += 1;
+        let tokens = req.prompt_tokens.as_deref().unwrap_or(&[]);
+        let max = req.max_tokens.unwrap_or(128);
+        let temp = req.temperature.unwrap_or(1.0);
 
-        let generated = self.engine.generate(
-            &req.prompt_tokens,
-            req.max_tokens,
-            req.temperature,
-        );
+        let generated = self.engine.generate(tokens, max, temp);
 
-        let finish_reason = if generated.len() < req.max_tokens {
+        let finish_reason = if generated.len() < max {
             FinishReason::Stop
         } else {
             FinishReason::Length
         };
 
-        let completion_tokens = generated.len();
-        let prompt_tokens = req.prompt_tokens.len();
-
-        CompletionResponse {
-            id: format!("cmpl-{}", self.request_counter),
-            model: "gpt2".into(),
-            choices: vec![CompletionChoice {
+        let text = generated.iter().map(|t| format!("[{}]", t.token_id)).collect::<String>();
+        let logprobs: Vec<LogprobInfo> = generated.iter().map(|t| LogprobInfo {
+            token: format!("{}", t.token_id),
+            token_id: t.token_id,
+            logprob: t.logprob,
+            bytes: None,
+            top_logprobs: Vec::new(),
+        }).collect();
+
+        let use_logprobs = req.logprobs.is_some();
+
+        CompletionResponse::new(
+            format!("cmpl-{}", self.request_counter),
+            "gpt2".into(),
+            vec![CompletionChoice {
                 index: 0,
-                tokens: generated,
-                finish_reason,
+                text,
+                logprobs: if use_logprobs { Some(logprobs) } else { None },
+                finish_reason: Some(finish_reason),
             }],
-            usage: Usage {
-                prompt_tokens,
-                completion_tokens,
-                total_tokens: prompt_tokens + completion_tokens,
+            Usage {
+                prompt_tokens: tokens.len(),
+                completion_tokens: generated.len(),
+                total_tokens: tokens.len() + generated.len(),
             },
-        }
+            0,
+        )
     }
 
-    /// /v1/embeddings handler — returns wte embeddings for token IDs.
+    /// `/v1/embeddings`
     pub fn embed(&self, req: &EmbeddingRequest) -> EmbeddingResponse {
-        let mut data = Vec::with_capacity(req.input_tokens.len());
-
-        for (idx, &token_id) in req.input_tokens.iter().enumerate() {
-            let offset = token_id as usize * EMBED_DIM;
-            let embedding = self.engine.weights().wte[offset..offset + EMBED_DIM].to_vec();
-            data.push(EmbeddingData {
-                index: idx,
-                embedding,
-            });
-        }
-
-        EmbeddingResponse {
-            model: "gpt2".into(),
+        let token_ids: Vec<u32> = match &req.input {
+            EmbeddingInput::TokenIds(ids) => ids.clone(),
+            _ => req.input_tokens.clone().unwrap_or_default(),
+        };
+
+        let data: Vec<EmbeddingData> = token_ids.iter().enumerate().map(|(idx, &tid)| {
+            let offset = tid as usize * EMBED_DIM;
+            let mut emb = self.engine.weights().wte[offset..offset + EMBED_DIM].to_vec();
+            if let Some(dim) = req.dimensions {
+                emb.truncate(dim);
+            }
+            EmbeddingData::new(idx, emb)
+        }).collect();
+
+        EmbeddingResponse::new(
+            "gpt2".into(),
             data,
-            usage: Usage {
-                prompt_tokens: req.input_tokens.len(),
-                completion_tokens: 0,
-                total_tokens: req.input_tokens.len(),
-            },
-        }
+            Usage { prompt_tokens: token_ids.len(), completion_tokens: 0, total_tokens: token_ids.len() },
+        )
     }
 
-    /// /v1/models handler.
-    pub fn model_info(&self) -> ModelInfo {
-        ModelInfo::gpt2_small()
+    /// `/v1/models/{id}`
+    pub fn model_info() -> Model {
+        Model::new("gpt2", "adaworldapi", 0)
     }
 
-    /// Access the underlying engine (for advanced usage).
     pub fn engine_mut(&mut self) -> &mut Gpt2Engine {
         &mut self.engine
     }
@@ -228,25 +103,21 @@ mod tests {
 
     #[test]
     fn test_model_info() {
-        let info = ModelInfo::gpt2_small();
-        assert_eq!(info.vocab_size, 50257);
-        assert_eq!(info.embed_dim, 768);
-        assert_eq!(info.num_layers, 12);
-        assert_eq!(info.num_heads, 12);
-        assert_eq!(info.max_seq_len, 1024);
+        let m = Gpt2Api::model_info();
+        assert_eq!(m.id, "gpt2");
+        assert_eq!(m.object, "model");
     }
 
     #[test]
-    fn test_completion_request_default() {
+    fn test_completion_defaults() {
         let req = CompletionRequest::default();
-        assert_eq!(req.max_tokens, 128);
-        assert_eq!(req.temperature, 1.0);
-        assert_eq!(req.stop_token, Some(50256));
+        assert_eq!(req.model, "gpt2");
+        assert_eq!(req.max_tokens, Some(128));
     }
 
     #[test]
-    fn test_finish_reason_variants() {
-        assert_eq!(FinishReason::Stop, FinishReason::Stop);
-        assert_ne!(FinishReason::Stop, FinishReason::Length);
+    fn test_completion_response_object() {
+        let resp = CompletionResponse::new("x".into(), "gpt2".into(), vec![], Usage::default(), 0);
+        assert_eq!(resp.object, "text_completion");
     }
 }