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unsloth/studio/backend/routes/inference.py
Roland Tannous 21e9a91a57
 Studio: forward standard OpenAI tools / tool_choice on /v1/responses (Codex compat) (#5122) 
* Studio: forward standard OpenAI tools / tool_choice on /v1/responses

Mirrors the /v1/chat/completions client-side tool pass-through from #5099
so clients (OpenAI Codex CLI, OpenAI Python SDK, ...) that target the
Responses API receive structured function_call output items instead of
plain text with tool-call tokens leaking into content.

- ResponsesRequest: type tools/tool_choice properly, add parallel_tool_calls;
  accept function_call and function_call_output input items for multi-turn
- Translate flat Responses tool / tool_choice shape to the nested Chat
  Completions shape before forwarding to llama-server
- _normalise_responses_input: map function_call_output -> role="tool",
  function_call -> assistant tool_calls (preserving call_id)
- Non-streaming: map returned tool_calls -> top-level function_call
  output items keyed by call_id
- Streaming: emit response.output_item.added (function_call),
  response.function_call_arguments.delta/.done, and response.output_item.done
  per tool call while keeping the text message at output_index 0
- Pytest coverage: tools/tool_choice translation, multi-turn input mapping,
  non-streaming tool_calls mapping, response round-trip

* Studio: merge system messages and close inner stream on /v1/responses

Fixes two issues surfacing when OpenAI Codex CLI drives /v1/responses
against a GGUF with a strict chat template (gpt-oss harmony, Qwen3, ...).

1. "System message must be at the beginning" upstream errors
   Codex sends `instructions` AND a `role:"developer"` message in `input`,
   producing two separate system-role messages. Strict templates raise
   when a second system message exists or when one appears after a user
   turn. _normalise_responses_input now hoists all instructions / system /
   developer content into a single merged system message at the top of
   the Chat Completions message list.

2. "async generator ignored GeneratorExit" / "Attempted to exit cancel
   scope in a different task"
   _responses_stream consumed the inner chat-completions body_iterator
   without an explicit aclose() in a finally block. On client disconnect
   (Codex frequently cancels mid-stream), Python 3.13 finalized the inner
   async generator on a different task, tripping anyio's cancel-scope
   check. Mirrored the same try/finally + aclose pattern used by the
   /v1/messages, /v1/chat/completions, and /v1/completions passthroughs.

Tests: hoisting of instructions + developer, developer mid-conversation,
multiple system messages in input, no-system passthrough.

* Studio: accept Codex multi-turn shapes and fix cross-task stream close on /v1/responses

Two issues observed driving /v1/responses from OpenAI Codex CLI against a
GGUF backend.

1. 422 on every turn after the first
   Codex replays prior assistant turns with
   `content:[{"type":"output_text","text":...,"annotations":[],"logprobs":[]}]`
   and carries forward `reasoning` items (o-series / gpt-5) between turns.
   Our `ResponsesContentPart` union only accepted input_text / input_image,
   and `ResponsesInputItem` only message / function_call / function_call_output,
   so Pydantic failed the whole list and FastAPI returned
   `"Input should be a valid string"` against the `str` branch of the
   outer union.

   - Add `ResponsesOutputTextPart` for assistant-replay content.
   - Add `ResponsesUnknownContentPart` and `ResponsesUnknownInputItem`
     as permissive catch-alls (drop during normalisation).
   - Wire an explicit `Discriminator` so dispatch is deterministic and
     the fallthrough reaches the catch-all instead of misreporting via
     the outer `Union[str, list[...]]`.
   - `_normalise_responses_input` now accepts output_text parts, flattens
     single-part assistant text to a plain string (keeps legacy chat
     templates happy), and silently drops reasoning / unknown items.

2. "async generator ignored GeneratorExit" / cross-task cancel scope
   `_responses_stream` awaited `openai_chat_completions` in the parent
   route-handler task, which opens the httpx client for the inner
   passthrough on *that* task. The outer `StreamingResponse` then iterates
   in a child task, so the asyncgen GC finalises the inner httpcore byte
   stream on the child task, tripping anyio's "Attempted to exit cancel
   scope in a different task". Move the `await` inside `event_generator`
   so the httpx lifecycle stays within the single streaming child task,
   and surface any HTTPException as a `response.failed` SSE frame.

Tests: assistant output_text replay, reasoning-item tolerance, unknown
content-part tolerance, end-to-end Codex-shape payload (developer + user +
reasoning + function_call + function_call_output + assistant output_text +
user), and single-part assistant flattening to plain string.

* Studio: call llama-server directly from streaming /v1/responses

The previous fix (running the inner await inside event_generator) was not
enough. Wrapping the existing `openai_chat_completions` pass-through still
stacks two async generators: when the outer generator is closed, the
innermost `HTTP11ConnectionByteStream.__aiter__` in httpcore doesn't
receive GeneratorExit before Python's asyncgen GC finalises it in a
sibling task, tripping "Attempted to exit cancel scope in a different
task" and "async generator ignored GeneratorExit" — the same Python 3.13
+ httpcore 1.0.x interaction already seen in PRs #4956, #4981, #5099.

Cure both pass-throughs had: a single same-task httpx lifecycle with
explicit `aiter_lines().aclose()` BEFORE `resp.aclose()` / `client.aclose()`
in the generator's finally block.

Apply it at the Responses layer by dropping the wrapper entirely for GGUF:
open httpx, consume `resp.aiter_lines()`, parse `chat.completion.chunk`,
emit Responses SSE events, close everything in finally — all in the
single StreamingResponse child task. Non-GGUF streaming is rejected with
a 400 (wrapping the transformers backend would re-introduce the
double-layer pattern and isn't a Codex-compatible path today anyway).

Also surfaces upstream httpx.RequestError / non-200 as a
`response.failed` SSE frame rather than a dropped stream now that the
request is dispatched after SSE headers have gone out.

* Studio: silence benign httpcore asyncgen GC warnings on Python 3.13

The streaming pass-throughs (/v1/chat/completions, /v1/messages,
/v1/responses, /v1/completions) all use the proven #4981 / #5099 pattern
— single-task httpx lifecycle with explicit aiter_lines().aclose() ahead
of resp.aclose() / client.aclose() in the generator's finally block.
That handles our own iterators correctly.

The residual noise ("async generator ignored GeneratorExit" /
"Attempted to exit cancel scope in a different task") comes from an
innermost HTTP11ConnectionByteStream.__aiter__ that httpcore creates
internally inside its pool. We hold no reference to it, so we cannot
aclose it ourselves. Python 3.13's asyncgen GC hook finalises it on the
finaliser task, its aclose path enters an anyio CancelScope shield, and
Python flags the cross-task exit. The response has already been
delivered with a 200 by then — it is purely log noise, not a functional
failure. Same interaction seen in modelcontextprotocol/python-sdk #831,
agno #3556, chainlit #2361, langchain-mcp-adapters #254.

Install a targeted sys.unraisablehook that swallows this specific tuple
— RuntimeError mentioning "cancel scope" or "GeneratorExit" plus an
object repr referencing HTTP11ConnectionByteStream — and defers to the
default hook for every other unraisable. Idempotent; guarded by a
sentinel attribute so repeated imports don't stack filters.
2026-04-21 13:17:20 +04:00

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# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""
Inference API routes for model loading and text generation.
"""
import os
import sys
import time
import uuid
from pathlib import Path
from fastapi import APIRouter, Depends, HTTPException, Request, status
from fastapi.responses import StreamingResponse, JSONResponse, Response
from typing import Any, Optional, Union
import json
import httpx
import structlog
from loggers import get_logger
import asyncio
import threading
import re as _re
# Model size extraction (shared with core/inference/llama_cpp.py)
from utils.models import extract_model_size_b as _extract_model_size_b
def _install_httpcore_asyncgen_silencer() -> None:
"""Silence benign httpx/httpcore asyncgen GC noise on Python 3.13.
When Studio proxies a streaming response from llama-server via httpx,
the innermost ``HTTP11ConnectionByteStream.__aiter__`` async generator
is finalised by Python's asyncgen GC hook on a task different from the
one that opened it. Its ``aclose`` path then calls
``anyio.Lock.acquire`` → ``cancel_shielded_checkpoint`` which enters a
``CancelScope`` on the finaliser task — Python 3.13 flags the
cross-task exit as ``"Attempted to exit cancel scope in a different
task"`` and prints ``"async generator ignored GeneratorExit"`` as an
unraisable warning.
This is a known httpx + httpcore + anyio interaction (see MCP SDK
python-sdk#831, agno #3556, chainlit #2361, langchain-mcp-adapters
#254). It is benign: the response has already been delivered with a
200. The streaming pass-throughs (``/v1/chat/completions``,
``/v1/messages``, ``/v1/responses``, ``/v1/completions``) already
manage their httpx lifecycle inside a single task with explicit
``aclose()`` of the lines iterator, response, and client; the errant
generator is not one we hold a reference to and therefore cannot
close ourselves.
We install a single process-wide unraisable hook that swallows just
this specific interaction — identified by the tuple of (RuntimeError
mentioning cancel scope / GeneratorExit) + (object repr referencing
HTTP11ConnectionByteStream) — and defers to the default hook for
everything else. The filter is idempotent.
"""
prior_hook = sys.unraisablehook
if getattr(prior_hook, "_unsloth_httpcore_silencer", False):
return
def _hook(unraisable):
exc_value = getattr(unraisable, "exc_value", None)
obj = getattr(unraisable, "object", None)
obj_repr = repr(obj) if obj is not None else ""
if (
isinstance(exc_value, RuntimeError)
and "HTTP11ConnectionByteStream" in obj_repr
and ("cancel scope" in str(exc_value) or "GeneratorExit" in str(exc_value))
):
return
prior_hook(unraisable)
_hook._unsloth_httpcore_silencer = True # type: ignore[attr-defined]
sys.unraisablehook = _hook
_install_httpcore_asyncgen_silencer()
def _friendly_error(exc: Exception) -> str:
"""Extract a user-friendly message from known llama-server errors."""
# httpx transport-layer failures reaching the managed llama-server —
# raised by the async pass-through helpers that talk to llama-server
# directly. Treat any RequestError subclass (ConnectError, ReadError,
# RemoteProtocolError, WriteError, PoolTimeout, ...) as "the upstream
# subprocess is unreachable", which for Studio always means the
# llama-server subprocess crashed or is still coming up.
if isinstance(exc, httpx.RequestError):
return "Lost connection to the model server. It may have crashed -- try reloading the model."
msg = str(exc)
m = _re.search(
r"request \((\d+) tokens?\) exceeds the available context size \((\d+) tokens?\)",
msg,
)
if m:
return (
f"Message too long: {m.group(1)} tokens exceeds the {m.group(2)}-token "
f"context window. Try increasing the Context Length in Model settings, "
f"or shorten the conversation."
)
if "Lost connection to llama-server" in msg:
return "Lost connection to the model server. It may have crashed -- try reloading the model."
return "An internal error occurred"
# Add backend directory to path
backend_path = Path(__file__).parent.parent.parent
if str(backend_path) not in sys.path:
sys.path.insert(0, str(backend_path))
# Import backend functions
try:
from core.inference import get_inference_backend
from core.inference.llama_cpp import LlamaCppBackend
from utils.models import ModelConfig
from utils.inference import load_inference_config
from utils.models.model_config import load_model_defaults
except ImportError:
parent_backend = backend_path.parent / "backend"
if str(parent_backend) not in sys.path:
sys.path.insert(0, str(parent_backend))
from core.inference import get_inference_backend
from core.inference.llama_cpp import LlamaCppBackend
from utils.models import ModelConfig
from utils.inference import load_inference_config
from utils.models.model_config import load_model_defaults
from models.inference import (
LoadRequest,
UnloadRequest,
GenerateRequest,
LoadResponse,
LoadProgressResponse,
UnloadResponse,
InferenceStatusResponse,
ChatCompletionRequest,
ChatCompletionChunk,
ChatCompletion,
ChatMessage,
ChunkChoice,
ChoiceDelta,
CompletionChoice,
CompletionMessage,
CompletionUsage,
ValidateModelRequest,
ValidateModelResponse,
TextContentPart,
ImageContentPart,
ImageUrl,
ResponsesRequest,
ResponsesInputMessage,
ResponsesInputTextPart,
ResponsesInputImagePart,
ResponsesOutputTextPart,
ResponsesUnknownContentPart,
ResponsesUnknownInputItem,
ResponsesFunctionCallInputItem,
ResponsesFunctionCallOutputInputItem,
ResponsesOutputTextContent,
ResponsesOutputMessage,
ResponsesOutputFunctionCall,
ResponsesUsage,
ResponsesResponse,
AnthropicMessagesRequest,
AnthropicMessagesResponse,
AnthropicResponseTextBlock,
AnthropicResponseToolUseBlock,
AnthropicUsage,
)
from core.inference.anthropic_compat import (
anthropic_messages_to_openai,
anthropic_tools_to_openai,
anthropic_tool_choice_to_openai,
AnthropicStreamEmitter,
AnthropicPassthroughEmitter,
)
from auth.authentication import get_current_subject
import io
import wave
import base64
import numpy as np
from datetime import date as _date
router = APIRouter()
# Appended to tool-use nudge to discourage plan-without-action
_TOOL_ACTION_NUDGE = (
" IMPORTANT: Always call tools directly -- never write code yourself."
" Never describe what you plan to do -- just call the tool immediately."
" For any code request, call the python tool. For any factual question, call web_search."
" Do NOT output code blocks -- use the python tool instead."
)
# Regex for stripping leaked tool-call XML from assistant messages/stream
_TOOL_XML_RE = _re.compile(
r"<tool_call>.*?</tool_call>|<function=\w+>.*?</function>",
_re.DOTALL,
)
logger = get_logger(__name__)
# GGUF inference backend (llama-server)
_llama_cpp_backend = LlamaCppBackend()
def get_llama_cpp_backend() -> LlamaCppBackend:
return _llama_cpp_backend
@router.post("/load", response_model = LoadResponse)
async def load_model(
request: LoadRequest,
fastapi_request: Request,
current_subject: str = Depends(get_current_subject),
):
"""
Load a model for inference.
The model_path should be a clean identifier from GET /models/list.
Returns inference configuration parameters (temperature, top_p, top_k, min_p)
from the model's YAML config, falling back to default.yaml for missing values.
GGUF models are loaded via llama-server (llama.cpp) instead of Unsloth.
"""
try:
# Version switching is handled automatically by the subprocess-based
# inference backend — no need for ensure_transformers_version() here.
# ── Already-loaded check: skip reload if the exact model is active ──
backend = get_inference_backend()
llama_backend = get_llama_cpp_backend()
if request.gguf_variant:
if (
llama_backend.is_loaded
and llama_backend.hf_variant
and llama_backend.hf_variant.lower() == request.gguf_variant.lower()
and llama_backend.model_identifier
and llama_backend.model_identifier.lower() == request.model_path.lower()
):
logger.info(
f"Model already loaded (GGUF): {request.model_path} variant={request.gguf_variant}, skipping reload"
)
inference_config = load_inference_config(llama_backend.model_identifier)
from utils.models import is_audio_input_type
_gguf_audio = (
llama_backend._audio_type
if hasattr(llama_backend, "_audio_type")
else None
)
_gguf_is_audio = getattr(llama_backend, "_is_audio", False)
return LoadResponse(
status = "already_loaded",
model = llama_backend.model_identifier,
display_name = llama_backend.model_identifier,
is_vision = llama_backend._is_vision,
is_lora = False,
is_gguf = True,
is_audio = _gguf_is_audio,
audio_type = _gguf_audio,
has_audio_input = is_audio_input_type(_gguf_audio)
if _gguf_audio
else False,
inference = inference_config,
requires_trust_remote_code = bool(
inference_config.get("trust_remote_code", False)
),
context_length = llama_backend.context_length,
max_context_length = llama_backend.max_context_length,
native_context_length = llama_backend.native_context_length,
supports_reasoning = llama_backend.supports_reasoning,
reasoning_always_on = llama_backend.reasoning_always_on,
chat_template = llama_backend.chat_template,
speculative_type = llama_backend.speculative_type,
)
else:
if (
backend.active_model_name
and backend.active_model_name.lower() == request.model_path.lower()
):
logger.info(
f"Model already loaded (Unsloth): {request.model_path}, skipping reload"
)
inference_config = load_inference_config(backend.active_model_name)
_model_info = backend.models.get(backend.active_model_name, {})
_chat_template = None
try:
_tpl_info = _model_info.get("chat_template_info", {})
_chat_template = _tpl_info.get("template")
except Exception as e:
logger.warning(
f"Could not retrieve chat template for {backend.active_model_name}: {e}"
)
return LoadResponse(
status = "already_loaded",
model = backend.active_model_name,
display_name = backend.active_model_name,
is_vision = _model_info.get("is_vision", False),
is_lora = _model_info.get("is_lora", False),
is_gguf = False,
is_audio = _model_info.get("is_audio", False),
audio_type = _model_info.get("audio_type"),
has_audio_input = _model_info.get("has_audio_input", False),
inference = inference_config,
requires_trust_remote_code = bool(
inference_config.get("trust_remote_code", False)
),
chat_template = _chat_template,
)
# Create config using clean factory method
# is_lora is auto-detected from adapter_config.json on disk/HF
config = ModelConfig.from_identifier(
model_id = request.model_path,
hf_token = request.hf_token,
gguf_variant = request.gguf_variant,
)
if not config:
raise HTTPException(
status_code = 400,
detail = f"Invalid model identifier: {request.model_path}",
)
# Normalize gpu_ids: empty list means auto-selection, same as None
effective_gpu_ids = request.gpu_ids if request.gpu_ids else None
# ── GGUF path: load via llama-server ──────────────────────
if config.is_gguf:
if effective_gpu_ids is not None:
raise HTTPException(
status_code = 400,
detail = "gpu_ids is not supported for GGUF models yet.",
)
llama_backend = get_llama_cpp_backend()
unsloth_backend = get_inference_backend()
# Unload any active Unsloth model first to free VRAM
if unsloth_backend.active_model_name:
logger.info(
f"Unloading Unsloth model '{unsloth_backend.active_model_name}' before loading GGUF"
)
unsloth_backend.unload_model(unsloth_backend.active_model_name)
# Route to HF mode or local mode based on config
# Run in a thread so the event loop stays free for progress
# polling and other requests during the (potentially long)
# GGUF download + llama-server startup.
_n_parallel = getattr(fastapi_request.app.state, "llama_parallel_slots", 1)
if config.gguf_hf_repo:
# HF mode: download via huggingface_hub then start llama-server
success = await asyncio.to_thread(
llama_backend.load_model,
hf_repo = config.gguf_hf_repo,
hf_variant = config.gguf_variant,
hf_token = request.hf_token,
model_identifier = config.identifier,
is_vision = config.is_vision,
n_ctx = request.max_seq_length,
chat_template_override = request.chat_template_override,
cache_type_kv = request.cache_type_kv,
speculative_type = request.speculative_type,
n_parallel = _n_parallel,
)
else:
# Local mode: llama-server loads via -m <path>
success = await asyncio.to_thread(
llama_backend.load_model,
gguf_path = config.gguf_file,
mmproj_path = config.gguf_mmproj_file,
model_identifier = config.identifier,
is_vision = config.is_vision,
n_ctx = request.max_seq_length,
chat_template_override = request.chat_template_override,
cache_type_kv = request.cache_type_kv,
speculative_type = request.speculative_type,
n_parallel = _n_parallel,
)
if not success:
raise HTTPException(
status_code = 500,
detail = f"Failed to load GGUF model: {config.display_name}",
)
logger.info(f"Loaded GGUF model via llama-server: {config.identifier}")
# Detect TTS audio by probing the loaded model's vocabulary
from utils.models import is_audio_input_type
_gguf_audio = llama_backend.detect_audio_type()
_gguf_is_audio = _gguf_audio in ("snac", "bicodec", "dac")
llama_backend._is_audio = _gguf_is_audio
llama_backend._audio_type = _gguf_audio
if _gguf_is_audio:
logger.info(f"GGUF model detected as audio: audio_type={_gguf_audio}")
await asyncio.to_thread(llama_backend.init_audio_codec, _gguf_audio)
inference_config = load_inference_config(config.identifier)
return LoadResponse(
status = "loaded",
model = config.identifier,
display_name = config.display_name,
is_vision = config.is_vision,
is_lora = False,
is_gguf = True,
is_audio = _gguf_is_audio,
audio_type = _gguf_audio,
has_audio_input = is_audio_input_type(_gguf_audio),
inference = inference_config,
requires_trust_remote_code = bool(
inference_config.get("trust_remote_code", False)
),
context_length = llama_backend.context_length,
max_context_length = llama_backend.max_context_length,
native_context_length = llama_backend.native_context_length,
supports_reasoning = llama_backend.supports_reasoning,
reasoning_always_on = llama_backend.reasoning_always_on,
supports_tools = llama_backend.supports_tools,
cache_type_kv = llama_backend.cache_type_kv,
chat_template = llama_backend.chat_template,
speculative_type = llama_backend.speculative_type,
)
# ── Standard path: load via Unsloth/transformers ──────────
backend = get_inference_backend()
# Unload any active GGUF model first
llama_backend = get_llama_cpp_backend()
if llama_backend.is_loaded:
logger.info("Unloading GGUF model before loading Unsloth model")
llama_backend.unload_model()
# Shut down any export subprocess to free VRAM
try:
from core.export import get_export_backend
exp_backend = get_export_backend()
if exp_backend.current_checkpoint:
logger.info(
"Shutting down export subprocess to free GPU memory for inference"
)
exp_backend._shutdown_subprocess()
exp_backend.current_checkpoint = None
exp_backend.is_vision = False
exp_backend.is_peft = False
except Exception as e:
logger.warning("Could not shut down export subprocess: %s", e)
# Auto-detect quantization for LoRA adapters from adapter_config.json
# The training pipeline patches this file with "unsloth_training_method"
# which is 'qlora' or 'lora'. Only LoRA (16-bit) needs load_in_4bit=False.
load_in_4bit = request.load_in_4bit
if config.is_lora and config.path:
import json
from pathlib import Path
adapter_cfg_path = Path(config.path) / "adapter_config.json"
if adapter_cfg_path.exists():
try:
with open(adapter_cfg_path) as f:
adapter_cfg = json.load(f)
training_method = adapter_cfg.get("unsloth_training_method")
if training_method == "lora" and load_in_4bit:
logger.info(
f"adapter_config.json says unsloth_training_method='lora' — "
f"setting load_in_4bit=False to match 16-bit training"
)
load_in_4bit = False
elif training_method == "qlora" and not load_in_4bit:
logger.info(
f"adapter_config.json says unsloth_training_method='qlora' — "
f"setting load_in_4bit=True to match QLoRA training"
)
load_in_4bit = True
elif training_method:
logger.info(
f"Training method: {training_method}, load_in_4bit={load_in_4bit}"
)
else:
# No unsloth_training_method — fallback to base model name
if (
config.base_model
and "-bnb-4bit" not in config.base_model.lower()
and load_in_4bit
):
logger.info(
f"No unsloth_training_method in adapter_config.json. "
f"Base model '{config.base_model}' has no -bnb-4bit suffix — "
f"setting load_in_4bit=False"
)
load_in_4bit = False
except Exception as e:
logger.warning(f"Could not read adapter_config.json: {e}")
# Load the model in a thread so the event loop stays free
# for download progress polling and other requests.
success = await asyncio.to_thread(
backend.load_model,
config = config,
max_seq_length = request.max_seq_length,
load_in_4bit = load_in_4bit,
hf_token = request.hf_token,
trust_remote_code = request.trust_remote_code,
gpu_ids = effective_gpu_ids,
)
if not success:
# Check if YAML says this model needs trust_remote_code
if not request.trust_remote_code:
model_defaults = load_model_defaults(config.identifier)
yaml_trust = model_defaults.get("inference", {}).get(
"trust_remote_code", False
)
if yaml_trust:
raise HTTPException(
status_code = 400,
detail = (
f"Model '{config.display_name}' requires trust_remote_code to be enabled. "
f"Please enable 'Trust remote code' in Chat Settings and try again."
),
)
raise HTTPException(
status_code = 500, detail = f"Failed to load model: {config.display_name}"
)
logger.info(f"Loaded model: {config.identifier}")
# Load inference configuration parameters
inference_config = load_inference_config(config.identifier)
# Get chat template from tokenizer
_chat_template = None
try:
_model_info = backend.models.get(config.identifier, {})
_tpl_info = _model_info.get("chat_template_info", {})
_chat_template = _tpl_info.get("template")
except Exception:
pass
return LoadResponse(
status = "loaded",
model = config.identifier,
display_name = config.display_name,
is_vision = config.is_vision,
is_lora = config.is_lora,
is_gguf = False,
is_audio = config.is_audio,
audio_type = config.audio_type,
has_audio_input = config.has_audio_input,
inference = inference_config,
requires_trust_remote_code = bool(
inference_config.get("trust_remote_code", False)
),
chat_template = _chat_template,
)
except HTTPException:
raise
except ValueError as e:
logger.warning("Rejected inference GPU selection: %s", e)
raise HTTPException(status_code = 400, detail = str(e))
except Exception as e:
logger.error(f"Error loading model: {e}", exc_info = True)
msg = str(e)
# Surface a friendlier message for models that Unsloth cannot load
not_supported_hints = [
"No config file found",
"not yet supported",
"is not supported",
"does not support",
]
if any(h.lower() in msg.lower() for h in not_supported_hints):
msg = f"This model is not supported yet. Try a different model. (Original error: {msg})"
raise HTTPException(status_code = 500, detail = f"Failed to load model: {msg}")
@router.post("/validate", response_model = ValidateModelResponse)
async def validate_model(
request: ValidateModelRequest,
current_subject: str = Depends(get_current_subject),
):
"""
Lightweight validation endpoint for model identifiers.
This checks that ModelConfig.from_identifier() can resolve the given
model_path, but it does NOT actually load model weights into GPU memory.
"""
try:
config = ModelConfig.from_identifier(
model_id = request.model_path,
hf_token = request.hf_token,
gguf_variant = request.gguf_variant,
)
if not config:
raise HTTPException(
status_code = 400,
detail = f"Invalid model identifier: {request.model_path}",
)
return ValidateModelResponse(
valid = True,
message = "Model identifier is valid.",
identifier = config.identifier,
display_name = getattr(config, "display_name", config.identifier),
is_gguf = getattr(config, "is_gguf", False),
is_lora = getattr(config, "is_lora", False),
is_vision = getattr(config, "is_vision", False),
requires_trust_remote_code = bool(
load_inference_config(config.identifier).get("trust_remote_code", False)
),
)
except HTTPException:
raise
except Exception as e:
logger.error(
f"Error validating model identifier '{request.model_path}': {e}",
exc_info = True,
)
raise HTTPException(
status_code = 400,
detail = f"Invalid model: {str(e)}",
)
@router.post("/unload", response_model = UnloadResponse)
async def unload_model(
request: UnloadRequest,
current_subject: str = Depends(get_current_subject),
):
"""
Unload a model from memory.
Routes to the correct backend (llama-server for GGUF, Unsloth otherwise).
"""
try:
# Check if the GGUF backend has this model loaded or is loading it
llama_backend = get_llama_cpp_backend()
if llama_backend.is_active and (
llama_backend.model_identifier == request.model_path
or not llama_backend.is_loaded
):
llama_backend.unload_model()
logger.info(f"Unloaded GGUF model: {request.model_path}")
return UnloadResponse(status = "unloaded", model = request.model_path)
# Otherwise, unload from Unsloth backend
backend = get_inference_backend()
backend.unload_model(request.model_path)
logger.info(f"Unloaded model: {request.model_path}")
return UnloadResponse(status = "unloaded", model = request.model_path)
except Exception as e:
logger.error(f"Error unloading model: {e}", exc_info = True)
raise HTTPException(status_code = 500, detail = f"Failed to unload model: {str(e)}")
@router.post("/generate/stream")
async def generate_stream(
request: GenerateRequest,
current_subject: str = Depends(get_current_subject),
):
"""
Generate a chat response with Server-Sent Events (SSE) streaming.
For vision models, provide image_base64 with the base64-encoded image.
"""
backend = get_inference_backend()
if not backend.active_model_name:
raise HTTPException(
status_code = 400, detail = "No model loaded. Call POST /inference/load first."
)
# Decode image if provided (for vision models)
image = None
if request.image_base64:
try:
import base64
from PIL import Image
from io import BytesIO
# Check if current model supports vision
model_info = backend.models.get(backend.active_model_name, {})
if not model_info.get("is_vision"):
raise HTTPException(
status_code = 400,
detail = "Image provided but current model is text-only. Load a vision model.",
)
image_data = base64.b64decode(request.image_base64)
image = Image.open(BytesIO(image_data))
image = backend.resize_image(image)
except HTTPException:
raise
except Exception as e:
raise HTTPException(
status_code = 400, detail = f"Failed to decode image: {str(e)}"
)
async def stream():
try:
for chunk in backend.generate_chat_response(
messages = request.messages,
system_prompt = request.system_prompt,
image = image,
temperature = request.temperature,
top_p = request.top_p,
top_k = request.top_k,
max_new_tokens = request.max_new_tokens,
repetition_penalty = request.repetition_penalty,
):
yield f"data: {json.dumps({'content': chunk})}\n\n"
yield "data: [DONE]\n\n"
except Exception as e:
backend.reset_generation_state()
logger.error(f"Error during generation: {e}", exc_info = True)
yield f"data: {json.dumps({'error': _friendly_error(e)})}\n\n"
return StreamingResponse(
stream(),
media_type = "text/event-stream",
headers = {
"Cache-Control": "no-cache",
"Connection": "keep-alive",
},
)
@router.get("/status", response_model = InferenceStatusResponse)
async def get_status(
current_subject: str = Depends(get_current_subject),
):
"""
Get current inference backend status.
Reports whichever backend (Unsloth or llama-server) is currently active.
"""
try:
llama_backend = get_llama_cpp_backend()
# If a GGUF model is loaded via llama-server, report that
if llama_backend.is_loaded:
_model_id = llama_backend.model_identifier
_inference_cfg = load_inference_config(_model_id) if _model_id else None
return InferenceStatusResponse(
active_model = _model_id,
is_vision = llama_backend.is_vision,
is_gguf = True,
gguf_variant = llama_backend.hf_variant,
is_audio = getattr(llama_backend, "_is_audio", False),
audio_type = getattr(llama_backend, "_audio_type", None),
loading = [],
loaded = [_model_id],
inference = _inference_cfg,
requires_trust_remote_code = bool(
(_inference_cfg or {}).get("trust_remote_code", False)
),
supports_reasoning = llama_backend.supports_reasoning,
reasoning_always_on = llama_backend.reasoning_always_on,
supports_tools = llama_backend.supports_tools,
context_length = llama_backend.context_length,
max_context_length = llama_backend.max_context_length,
native_context_length = llama_backend.native_context_length,
speculative_type = llama_backend.speculative_type,
)
# Otherwise, report Unsloth backend status
backend = get_inference_backend()
is_vision = False
is_audio = False
audio_type = None
has_audio_input = False
if backend.active_model_name:
model_info = backend.models.get(backend.active_model_name, {})
is_vision = model_info.get("is_vision", False)
is_audio = model_info.get("is_audio", False)
audio_type = model_info.get("audio_type")
has_audio_input = model_info.get("has_audio_input", False)
# gpt-oss safetensors models support reasoning via harmony channels
supports_reasoning = False
if backend.active_model_name and hasattr(backend, "_is_gpt_oss_model"):
supports_reasoning = backend._is_gpt_oss_model()
inference_config = (
load_inference_config(backend.active_model_name)
if backend.active_model_name
else None
)
return InferenceStatusResponse(
active_model = backend.active_model_name,
is_vision = is_vision,
is_gguf = False,
is_audio = is_audio,
audio_type = audio_type,
has_audio_input = has_audio_input,
loading = list(getattr(backend, "loading_models", set())),
loaded = list(backend.models.keys()),
inference = inference_config,
requires_trust_remote_code = bool(
(inference_config or {}).get("trust_remote_code", False)
),
supports_reasoning = supports_reasoning,
)
except Exception as e:
logger.error(f"Error getting status: {e}", exc_info = True)
raise HTTPException(status_code = 500, detail = f"Failed to get status: {str(e)}")
@router.get("/load-progress", response_model = LoadProgressResponse)
async def get_load_progress(
current_subject: str = Depends(get_current_subject),
):
"""
Return the active GGUF load's mmap/upload progress.
During the warmup window after a GGUF download -- when llama-server
is paging ~tens-to-hundreds of GB of shards into the page cache
before pushing layers to VRAM -- ``/api/inference/status`` only
shows a generic spinner. This endpoint exposes sampled progress so
the UI can render a real bar plus rate/ETA during that window.
Returns an empty payload (``phase=null, bytes=0``) when no load is
in flight. The frontend should stop polling once ``phase`` becomes
``ready``.
"""
try:
llama_backend = get_llama_cpp_backend()
progress = llama_backend.load_progress()
if progress is None:
return LoadProgressResponse()
return LoadProgressResponse(**progress)
except Exception as e:
logger.warning(f"Error sampling load progress: {e}")
return LoadProgressResponse()
# =====================================================================
# Audio (TTS) Generation (/audio/generate)
# =====================================================================
@router.post("/audio/generate")
async def generate_audio(
payload: ChatCompletionRequest,
request: Request,
current_subject: str = Depends(get_current_subject),
):
"""
Generate audio (TTS) from the latest user message.
Returns a JSON response with base64-encoded WAV audio.
Works with both GGUF (llama-server) and Unsloth/transformers backends.
"""
import base64
# Extract text from the last user message
_, chat_messages, _ = _extract_content_parts(payload.messages)
if not chat_messages:
raise HTTPException(status_code = 400, detail = "No messages provided.")
last_user_msg = next(
(m for m in reversed(chat_messages) if m["role"] == "user"), None
)
if not last_user_msg:
raise HTTPException(status_code = 400, detail = "No user message found.")
text = last_user_msg["content"]
# Pick backend — both return (wav_bytes, sample_rate)
llama_backend = get_llama_cpp_backend()
if llama_backend.is_loaded and getattr(llama_backend, "_is_audio", False):
model_name = llama_backend.model_identifier
gen = lambda: llama_backend.generate_audio_response(
text = text,
audio_type = llama_backend._audio_type,
temperature = payload.temperature,
top_p = payload.top_p,
top_k = payload.top_k,
min_p = payload.min_p,
max_new_tokens = payload.max_tokens or 2048,
repetition_penalty = payload.repetition_penalty,
)
else:
backend = get_inference_backend()
if not backend.active_model_name:
raise HTTPException(status_code = 400, detail = "No model loaded.")
model_info = backend.models.get(backend.active_model_name, {})
if not model_info.get("is_audio"):
raise HTTPException(
status_code = 400, detail = "Active model is not an audio model."
)
model_name = backend.active_model_name
gen = lambda: backend.generate_audio_response(
text = text,
temperature = payload.temperature,
top_p = payload.top_p,
top_k = payload.top_k,
min_p = payload.min_p,
max_new_tokens = payload.max_tokens or 2048,
repetition_penalty = payload.repetition_penalty,
use_adapter = payload.use_adapter,
)
try:
wav_bytes, sample_rate = await asyncio.get_event_loop().run_in_executor(
None, gen
)
except Exception as e:
logger.error(f"Audio generation error: {e}", exc_info = True)
raise HTTPException(status_code = 500, detail = str(e))
audio_b64 = base64.b64encode(wav_bytes).decode("ascii")
return JSONResponse(
content = {
"id": f"chatcmpl-{uuid.uuid4().hex[:12]}",
"object": "chat.completion.audio",
"model": model_name,
"audio": {"data": audio_b64, "format": "wav", "sample_rate": sample_rate},
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": f'[Generated audio from: "{text[:100]}"]',
},
"finish_reason": "stop",
}
],
}
)
# =====================================================================
# OpenAI-Compatible Chat Completions (/chat/completions)
# =====================================================================
def _decode_audio_base64(b64: str) -> np.ndarray:
"""Decode base64 audio (any format) → float32 numpy array at 16kHz."""
import torch
import torchaudio
import tempfile
import os
from utils.paths import ensure_dir, tmp_root
raw = base64.b64decode(b64)
# torchaudio.load needs a file path or file-like object with format hint
# Write to a temp file so torchaudio can auto-detect the format
with tempfile.NamedTemporaryFile(
suffix = ".audio",
delete = False,
dir = str(ensure_dir(tmp_root())),
) as tmp:
tmp.write(raw)
tmp_path = tmp.name
try:
waveform, sr = torchaudio.load(tmp_path)
finally:
os.unlink(tmp_path)
# Convert to mono if stereo
if waveform.shape[0] > 1:
waveform = waveform.mean(dim = 0, keepdim = True)
# Resample to 16kHz if needed
if sr != 16000:
resampler = torchaudio.transforms.Resample(orig_freq = sr, new_freq = 16000)
waveform = resampler(waveform)
return waveform.squeeze(0).numpy()
def _extract_content_parts(
messages: list,
) -> tuple[str, list[dict], "Optional[str]"]:
"""
Parse OpenAI-format messages into components the inference backend expects.
Handles both plain-string ``content`` and multimodal content-part arrays
(``[{type: "text", ...}, {type: "image_url", ...}]``).
Returns:
system_prompt: The system message text (empty string if none provided).
chat_messages: Non-system messages with content flattened to strings.
image_base64: Base64 data of the *first* image found, or ``None``.
"""
system_prompt = ""
chat_messages: list[dict] = []
first_image_b64: Optional[str] = None
for msg in messages:
# ── System messages → extract as system_prompt ────────
if msg.role == "system":
if isinstance(msg.content, str):
system_prompt = msg.content
elif isinstance(msg.content, list):
# Unlikely but handle: join text parts
system_prompt = "\n".join(
p.text for p in msg.content if p.type == "text"
)
continue
# ── User / assistant messages ─────────────────────────
if isinstance(msg.content, str):
# Plain string content — pass through
chat_messages.append({"role": msg.role, "content": msg.content})
elif isinstance(msg.content, list):
# Multimodal content parts
text_parts: list[str] = []
for part in msg.content:
if part.type == "text":
text_parts.append(part.text)
elif part.type == "image_url" and first_image_b64 is None:
url = part.image_url.url
if url.startswith("data:"):
# data:image/png;base64,<DATA> → extract <DATA>
first_image_b64 = url.split(",", 1)[1] if "," in url else None
else:
logger.warning(
f"Remote image URLs not yet supported: {url[:80]}..."
)
combined_text = "\n".join(text_parts) if text_parts else ""
chat_messages.append({"role": msg.role, "content": combined_text})
return system_prompt, chat_messages, first_image_b64
@router.post("/chat/completions")
async def openai_chat_completions(
payload: ChatCompletionRequest,
request: Request,
current_subject: str = Depends(get_current_subject),
):
"""
OpenAI-compatible chat completions endpoint.
Supports multimodal messages: ``content`` may be a plain string or a
list of content parts (``text`` / ``image_url``).
Streaming (default): returns SSE chunks matching OpenAI's format.
Non-streaming: returns a single ChatCompletion JSON object.
Automatically routes to the correct backend:
- GGUF models → llama-server via LlamaCppBackend
- Other models → Unsloth/transformers via InferenceBackend
"""
llama_backend = get_llama_cpp_backend()
using_gguf = llama_backend.is_loaded
# ── Determine which backend is active ─────────────────────
if using_gguf:
model_name = llama_backend.model_identifier or payload.model
if getattr(llama_backend, "_is_audio", False):
return await generate_audio(payload, request)
else:
backend = get_inference_backend()
if not backend.active_model_name:
raise HTTPException(
status_code = 400,
detail = "No model loaded. Call POST /inference/load first.",
)
model_name = backend.active_model_name or payload.model
# ── Audio TTS path: auto-route to audio generation ────
# (Whisper is ASR not TTS — handled below in audio input path)
model_info = backend.models.get(backend.active_model_name, {})
if model_info.get("is_audio") and model_info.get("audio_type") != "whisper":
return await generate_audio(payload, request)
# ── Whisper without audio: return clear error ──
if model_info.get("audio_type") == "whisper" and not payload.audio_base64:
raise HTTPException(
status_code = 400,
detail = "Whisper models require audio input. Please upload an audio file.",
)
# ── Audio INPUT path: decode WAV and route to audio input generation ──
if payload.audio_base64 and model_info.get("has_audio_input"):
audio_array = _decode_audio_base64(payload.audio_base64)
system_prompt, chat_messages, _ = _extract_content_parts(payload.messages)
cancel_event = threading.Event()
completion_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
created = int(time.time())
def audio_input_generate():
if model_info.get("audio_type") == "whisper":
return backend.generate_whisper_response(
audio_array = audio_array,
cancel_event = cancel_event,
)
return backend.generate_audio_input_response(
messages = chat_messages,
system_prompt = system_prompt,
audio_array = audio_array,
temperature = payload.temperature,
top_p = payload.top_p,
top_k = payload.top_k,
min_p = payload.min_p,
max_new_tokens = payload.max_tokens or 2048,
repetition_penalty = payload.repetition_penalty,
cancel_event = cancel_event,
)
if payload.stream:
async def audio_input_stream():
try:
first_chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [
ChunkChoice(
delta = ChoiceDelta(role = "assistant"),
finish_reason = None,
)
],
)
yield f"data: {first_chunk.model_dump_json(exclude_none = True)}\n\n"
for chunk_text in audio_input_generate():
if await request.is_disconnected():
cancel_event.set()
return
if chunk_text:
chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [
ChunkChoice(
delta = ChoiceDelta(content = chunk_text),
finish_reason = None,
)
],
)
yield f"data: {chunk.model_dump_json(exclude_none = True)}\n\n"
final_chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [
ChunkChoice(delta = ChoiceDelta(), finish_reason = "stop")
],
)
yield f"data: {final_chunk.model_dump_json(exclude_none = True)}\n\n"
yield "data: [DONE]\n\n"
except asyncio.CancelledError:
cancel_event.set()
raise
except Exception as e:
logger.error(
f"Error during audio input streaming: {e}", exc_info = True
)
yield f"data: {json.dumps({'error': {'message': _friendly_error(e), 'type': 'server_error'}})}\n\n"
return StreamingResponse(
audio_input_stream(),
media_type = "text/event-stream",
headers = {
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
},
)
else:
full_text = "".join(audio_input_generate())
response = ChatCompletion(
id = completion_id,
created = created,
model = model_name,
choices = [
CompletionChoice(
message = CompletionMessage(content = full_text),
finish_reason = "stop",
)
],
)
return JSONResponse(content = response.model_dump())
# ── Standard OpenAI function-calling pass-through (GGUF only) ────
# When a client (opencode / Claude Code via OpenAI compat / Cursor /
# Continue / ...) sends standard OpenAI `tools` without Studio's
# `enable_tools` shorthand, forward the request to llama-server
# verbatim so structured `tool_calls` flow back to the client. This
# branch runs BEFORE `_extract_content_parts` because that helper is
# unaware of `role="tool"` messages and assistant messages that only
# carry `tool_calls` (content=None) — both of which are valid in
# multi-turn client-side tool loops.
_has_tool_messages = any(m.role == "tool" or m.tool_calls for m in payload.messages)
if (
using_gguf
and llama_backend.supports_tools
and not payload.enable_tools
and ((payload.tools and len(payload.tools) > 0) or _has_tool_messages)
):
# Preserve the vision guard that would otherwise run in the
# non-passthrough path below: text-only tool-capable GGUFs
# should return a clear 400 here rather than forwarding the
# image to llama-server and surfacing an opaque upstream error.
if not llama_backend.is_vision and (
payload.image_base64
or any(
isinstance(m.content, list)
and any(isinstance(p, ImageContentPart) for p in m.content)
for m in payload.messages
)
):
raise HTTPException(
status_code = 400,
detail = "Image provided but current GGUF model does not support vision.",
)
cancel_event = threading.Event()
completion_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
if payload.stream:
return await _openai_passthrough_stream(
request,
cancel_event,
llama_backend,
payload,
model_name,
completion_id,
)
return await _openai_passthrough_non_streaming(
llama_backend,
payload,
model_name,
)
# ── Parse messages (handles multimodal content parts) ─────
system_prompt, chat_messages, extracted_image_b64 = _extract_content_parts(
payload.messages
)
if not chat_messages:
raise HTTPException(
status_code = 400,
detail = "At least one non-system message is required.",
)
# ── GGUF path: proxy to llama-server /v1/chat/completions ──
if using_gguf:
# Reject images if this GGUF model doesn't support vision
image_b64 = extracted_image_b64 or payload.image_base64
if image_b64 and not llama_backend.is_vision:
raise HTTPException(
status_code = 400,
detail = "Image provided but current GGUF model does not support vision.",
)
# Convert image to PNG for llama-server (stb_image has limited format support)
if image_b64:
try:
import base64 as _b64
from io import BytesIO as _BytesIO
from PIL import Image as _Image
raw = _b64.b64decode(image_b64)
# Normalize to RGB so PNG encoding succeeds regardless of
# source mode (RGBA, P, L, CMYK, I, F, ...). Previously
# we only converted RGBA, which left CMYK/I/F to raise at
# img.save(PNG).
img = _Image.open(_BytesIO(raw)).convert("RGB")
buf = _BytesIO()
img.save(buf, format = "PNG")
image_b64 = _b64.b64encode(buf.getvalue()).decode("ascii")
except Exception as e:
raise HTTPException(
status_code = 400, detail = f"Failed to process image: {e}"
)
# Build message list with system prompt prepended
gguf_messages = []
if system_prompt:
gguf_messages.append({"role": "system", "content": system_prompt})
gguf_messages.extend(chat_messages)
cancel_event = threading.Event()
completion_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
created = int(time.time())
# ── Tool-calling path (agentic loop) ──────────────────
use_tools = (
payload.enable_tools and llama_backend.supports_tools and not image_b64
)
if use_tools:
from core.inference.tools import ALL_TOOLS
if payload.enabled_tools is not None:
tools_to_use = [
t
for t in ALL_TOOLS
if t["function"]["name"] in payload.enabled_tools
]
else:
tools_to_use = ALL_TOOLS
# ── Tool-use system prompt nudge ──────────────────────
_tool_names = {t["function"]["name"] for t in tools_to_use}
_has_web = "web_search" in _tool_names
_has_code = "python" in _tool_names or "terminal" in _tool_names
_date_line = f"The current date is {_date.today().isoformat()}."
# Small models (<9B) struggle with multi-step search plans,
# so simplify the web tips to avoid plan-then-stall behavior.
_model_size_b = _extract_model_size_b(model_name)
_is_small_model = _model_size_b is not None and _model_size_b < 9
if _is_small_model:
_web_tips = "Do not repeat the same search query."
else:
_web_tips = (
"When you search and find a relevant URL in the results, "
"fetch its full content by calling web_search with the url parameter. "
"Do not repeat the same search query. If a search returns "
"no useful results, try rephrasing or fetching a result URL directly."
)
_code_tips = (
"Use code execution for math, calculations, data processing, "
"or to parse and analyze information from tool results."
)
if _has_web and _has_code:
_nudge = (
_date_line + " "
"You have access to tools. When appropriate, prefer using "
"tools rather than answering from memory. "
+ _web_tips
+ " "
+ _code_tips
)
elif _has_code:
_nudge = (
_date_line + " "
"You have access to tools. When appropriate, prefer using "
"code execution rather than answering from memory. " + _code_tips
)
elif _has_web:
_nudge = (
_date_line + " "
"You have access to tools. When appropriate, prefer using "
"web search for up-to-date or uncertain factual "
"information rather than answering from memory. " + _web_tips
)
else:
_nudge = ""
if _nudge:
_nudge += _TOOL_ACTION_NUDGE
# Append nudge to system prompt (preserve user's prompt)
if system_prompt:
system_prompt = system_prompt.rstrip() + "\n\n" + _nudge
else:
system_prompt = _nudge
# Rebuild gguf_messages with updated system prompt
gguf_messages = []
if system_prompt:
gguf_messages.append({"role": "system", "content": system_prompt})
gguf_messages.extend(chat_messages)
# ── Strip stale tool-call XML from conversation history ─
for _msg in gguf_messages:
if _msg.get("role") == "assistant" and isinstance(
_msg.get("content"), str
):
_msg["content"] = _TOOL_XML_RE.sub("", _msg["content"]).strip()
def gguf_generate_with_tools():
return llama_backend.generate_chat_completion_with_tools(
messages = gguf_messages,
tools = tools_to_use,
temperature = payload.temperature,
top_p = payload.top_p,
top_k = payload.top_k,
min_p = payload.min_p,
max_tokens = payload.max_tokens,
repetition_penalty = payload.repetition_penalty,
presence_penalty = payload.presence_penalty,
cancel_event = cancel_event,
enable_thinking = payload.enable_thinking,
auto_heal_tool_calls = payload.auto_heal_tool_calls
if payload.auto_heal_tool_calls is not None
else True,
max_tool_iterations = payload.max_tool_calls_per_message
if payload.max_tool_calls_per_message is not None
else 25,
tool_call_timeout = payload.tool_call_timeout
if payload.tool_call_timeout is not None
else 300,
session_id = payload.session_id,
)
_tool_sentinel = object()
async def gguf_tool_stream():
try:
first_chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [
ChunkChoice(
delta = ChoiceDelta(role = "assistant"),
finish_reason = None,
)
],
)
yield f"data: {first_chunk.model_dump_json(exclude_none = True)}\n\n"
# Iterate the synchronous generator in a thread so
# the event loop stays free for disconnect detection.
gen = gguf_generate_with_tools()
prev_text = ""
_stream_usage = None
_stream_timings = None
while True:
if await request.is_disconnected():
cancel_event.set()
return
event = await asyncio.to_thread(next, gen, _tool_sentinel)
if event is _tool_sentinel:
break
if event["type"] == "status":
# Empty status marks an iteration boundary
# in the GGUF tool loop (e.g. after a
# re-prompt). Reset the cumulative cursor
# so the next assistant turn streams cleanly.
if not event["text"]:
prev_text = ""
# Emit tool status as a custom SSE event
# (including empty ones to clear UI badges)
status_data = json.dumps(
{
"type": "tool_status",
"content": event["text"],
}
)
yield f"data: {status_data}\n\n"
continue
if event["type"] in ("tool_start", "tool_end"):
if event["type"] == "tool_start":
prev_text = ""
yield f"data: {json.dumps(event)}\n\n"
continue
if event["type"] == "metadata":
_stream_usage = event.get("usage")
_stream_timings = event.get("timings")
continue
# "content" type -- cumulative text
# Sanitize the full cumulative then diff against
# the last sanitized snapshot so cross-chunk XML
# tags are handled correctly.
raw_cumulative = event.get("text", "")
clean_cumulative = _TOOL_XML_RE.sub("", raw_cumulative)
new_text = clean_cumulative[len(prev_text) :]
prev_text = clean_cumulative
if not new_text:
continue
chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [
ChunkChoice(
delta = ChoiceDelta(content = new_text),
finish_reason = None,
)
],
)
yield f"data: {chunk.model_dump_json(exclude_none = True)}\n\n"
final_chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [
ChunkChoice(
delta = ChoiceDelta(),
finish_reason = "stop",
)
],
)
yield f"data: {final_chunk.model_dump_json(exclude_none = True)}\n\n"
# Usage chunk (OpenAI-standard: choices=[], usage populated)
if _stream_usage or _stream_timings:
usage_obj = CompletionUsage(
prompt_tokens = (_stream_usage or {}).get("prompt_tokens", 0),
completion_tokens = (_stream_usage or {}).get(
"completion_tokens", 0
),
total_tokens = (_stream_usage or {}).get("total_tokens", 0),
)
usage_chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [],
usage = usage_obj,
timings = _stream_timings,
)
yield f"data: {usage_chunk.model_dump_json(exclude_none = True)}\n\n"
yield "data: [DONE]\n\n"
except asyncio.CancelledError:
cancel_event.set()
raise
except Exception as e:
import traceback
tb = traceback.format_exc()
logger.error(f"Error during GGUF tool streaming: {e}\n{tb}")
error_chunk = {
"error": {
"message": _friendly_error(e),
"type": "server_error",
},
}
yield f"data: {json.dumps(error_chunk)}\n\n"
return StreamingResponse(
gguf_tool_stream(),
media_type = "text/event-stream",
headers = {
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
},
)
# ── Standard GGUF path (no tools) ─────────────────────
def gguf_generate():
return llama_backend.generate_chat_completion(
messages = gguf_messages,
image_b64 = image_b64,
temperature = payload.temperature,
top_p = payload.top_p,
top_k = payload.top_k,
min_p = payload.min_p,
max_tokens = payload.max_tokens,
repetition_penalty = payload.repetition_penalty,
presence_penalty = payload.presence_penalty,
cancel_event = cancel_event,
enable_thinking = payload.enable_thinking,
)
_gguf_sentinel = object()
if payload.stream:
async def gguf_stream_chunks():
try:
# First chunk: role
first_chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [
ChunkChoice(
delta = ChoiceDelta(role = "assistant"),
finish_reason = None,
)
],
)
yield f"data: {first_chunk.model_dump_json(exclude_none = True)}\n\n"
# Iterate the synchronous generator in a thread so
# the event loop stays free for disconnect detection.
gen = gguf_generate()
prev_text = ""
_stream_usage = None
_stream_timings = None
while True:
if await request.is_disconnected():
cancel_event.set()
return
cumulative = await asyncio.to_thread(next, gen, _gguf_sentinel)
if cumulative is _gguf_sentinel:
break
# Capture server metadata for final usage chunk
if isinstance(cumulative, dict):
if cumulative.get("type") == "metadata":
_stream_usage = cumulative.get("usage")
_stream_timings = cumulative.get("timings")
else:
logger.warning(
"gguf_stream_chunks: unexpected dict event: %s",
{
k: v
for k, v in cumulative.items()
if k != "timings"
},
)
continue
new_text = cumulative[len(prev_text) :]
prev_text = cumulative
if not new_text:
continue
chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [
ChunkChoice(
delta = ChoiceDelta(content = new_text),
finish_reason = None,
)
],
)
yield f"data: {chunk.model_dump_json(exclude_none = True)}\n\n"
# Final chunk
final_chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [
ChunkChoice(
delta = ChoiceDelta(),
finish_reason = "stop",
)
],
)
yield f"data: {final_chunk.model_dump_json(exclude_none = True)}\n\n"
# Usage chunk (OpenAI-standard: choices=[], usage populated)
if _stream_usage or _stream_timings:
usage_obj = CompletionUsage(
prompt_tokens = (_stream_usage or {}).get("prompt_tokens", 0),
completion_tokens = (_stream_usage or {}).get(
"completion_tokens", 0
),
total_tokens = (_stream_usage or {}).get("total_tokens", 0),
)
usage_chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [],
usage = usage_obj,
timings = _stream_timings,
)
yield f"data: {usage_chunk.model_dump_json(exclude_none = True)}\n\n"
yield "data: [DONE]\n\n"
except asyncio.CancelledError:
cancel_event.set()
raise
except Exception as e:
logger.error(f"Error during GGUF streaming: {e}", exc_info = True)
error_chunk = {
"error": {
"message": _friendly_error(e),
"type": "server_error",
},
}
yield f"data: {json.dumps(error_chunk)}\n\n"
return StreamingResponse(
gguf_stream_chunks(),
media_type = "text/event-stream",
headers = {
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
},
)
else:
try:
full_text = ""
for token in gguf_generate():
if isinstance(token, dict):
continue # skip metadata dict in non-streaming path
full_text = token
response = ChatCompletion(
id = completion_id,
created = created,
model = model_name,
choices = [
CompletionChoice(
message = CompletionMessage(content = full_text),
finish_reason = "stop",
)
],
)
return JSONResponse(content = response.model_dump())
except Exception as e:
logger.error(f"Error during GGUF completion: {e}", exc_info = True)
raise HTTPException(status_code = 500, detail = str(e))
# ── Standard Unsloth path ─────────────────────────────────
# Decode image (from content parts OR legacy field)
image_b64 = extracted_image_b64 or payload.image_base64
image = None
if image_b64:
try:
import base64
from PIL import Image
from io import BytesIO
model_info = backend.models.get(backend.active_model_name, {})
if not model_info.get("is_vision"):
raise HTTPException(
status_code = 400,
detail = "Image provided but current model is text-only. Load a vision model.",
)
image_data = base64.b64decode(image_b64)
image = Image.open(BytesIO(image_data))
image = backend.resize_image(image)
except HTTPException:
raise
except Exception as e:
raise HTTPException(status_code = 400, detail = f"Failed to decode image: {e}")
# Shared generation kwargs
gen_kwargs = dict(
messages = chat_messages,
system_prompt = system_prompt,
image = image,
temperature = payload.temperature,
top_p = payload.top_p,
top_k = payload.top_k,
min_p = payload.min_p,
max_new_tokens = payload.max_tokens or 2048,
repetition_penalty = payload.repetition_penalty,
)
# Choose generation path (adapter-controlled or standard)
cancel_event = threading.Event()
if payload.use_adapter is not None:
def generate():
return backend.generate_with_adapter_control(
use_adapter = payload.use_adapter,
cancel_event = cancel_event,
**gen_kwargs,
)
else:
def generate():
return backend.generate_chat_response(
cancel_event = cancel_event, **gen_kwargs
)
completion_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
created = int(time.time())
# ── Streaming response ────────────────────────────────────────
if payload.stream:
async def stream_chunks():
try:
first_chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [
ChunkChoice(
delta = ChoiceDelta(role = "assistant"),
finish_reason = None,
)
],
)
yield f"data: {first_chunk.model_dump_json(exclude_none = True)}\n\n"
prev_text = ""
# Run sync generator in thread pool to avoid blocking
# the event loop. Critical for compare mode: two SSE
# requests arrive concurrently but the orchestrator
# serializes them via _gen_lock. Without run_in_executor
# the second request's blocking lock acquisition would
# freeze the entire event loop, stalling both streams.
_DONE = object() # sentinel for generator exhaustion
loop = asyncio.get_event_loop()
gen = generate()
while True:
# next(gen, _DONE) returns _DONE instead of raising
# StopIteration — StopIteration cannot propagate
# through asyncio futures (Python limitation).
cumulative = await loop.run_in_executor(None, next, gen, _DONE)
if cumulative is _DONE:
break
if await request.is_disconnected():
cancel_event.set()
backend.reset_generation_state()
return
new_text = cumulative[len(prev_text) :]
prev_text = cumulative
if not new_text:
continue
chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [
ChunkChoice(
delta = ChoiceDelta(content = new_text),
finish_reason = None,
)
],
)
yield f"data: {chunk.model_dump_json(exclude_none = True)}\n\n"
final_chunk = ChatCompletionChunk(
id = completion_id,
created = created,
model = model_name,
choices = [
ChunkChoice(
delta = ChoiceDelta(),
finish_reason = "stop",
)
],
)
yield f"data: {final_chunk.model_dump_json(exclude_none = True)}\n\n"
yield "data: [DONE]\n\n"
except asyncio.CancelledError:
cancel_event.set()
backend.reset_generation_state()
raise
except Exception as e:
backend.reset_generation_state()
logger.error(f"Error during OpenAI streaming: {e}", exc_info = True)
error_chunk = {
"error": {
"message": _friendly_error(e),
"type": "server_error",
},
}
yield f"data: {json.dumps(error_chunk)}\n\n"
return StreamingResponse(
stream_chunks(),
media_type = "text/event-stream",
headers = {
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
},
)
# ── Non-streaming response ────────────────────────────────────
else:
try:
full_text = ""
for token in generate():
full_text = token
response = ChatCompletion(
id = completion_id,
created = created,
model = model_name,
choices = [
CompletionChoice(
message = CompletionMessage(content = full_text),
finish_reason = "stop",
)
],
)
return JSONResponse(content = response.model_dump())
except Exception as e:
backend.reset_generation_state()
logger.error(f"Error during OpenAI completion: {e}", exc_info = True)
raise HTTPException(status_code = 500, detail = str(e))
# =====================================================================
# Sandbox file serving (/sandbox/{session_id}/{filename})
# =====================================================================
_SANDBOX_MEDIA_TYPES = {
".png": "image/png",
".jpg": "image/jpeg",
".jpeg": "image/jpeg",
".gif": "image/gif",
".webp": "image/webp",
".bmp": "image/bmp",
}
@router.get("/sandbox/{session_id}/{filename}")
async def serve_sandbox_file(
session_id: str,
filename: str,
request: Request,
token: Optional[str] = None,
):
"""
Serve image files created by Python tool execution.
Accepts auth via Authorization header OR ?token= query param
(needed because <img src> cannot send custom headers).
"""
from fastapi.responses import FileResponse
# ── Authentication (header or query param) ──────────────────
auth_header = request.headers.get("authorization")
if auth_header and auth_header.lower().startswith("bearer "):
jwt_token = auth_header[7:]
elif token:
jwt_token = token
else:
raise HTTPException(
status_code = status.HTTP_401_UNAUTHORIZED,
detail = "Missing authentication token",
)
from fastapi.security import HTTPAuthorizationCredentials
creds = HTTPAuthorizationCredentials(scheme = "Bearer", credentials = jwt_token)
await get_current_subject(creds)
# ── Filename sanitization ───────────────────────────────────
safe_filename = os.path.basename(filename)
if not safe_filename or safe_filename in (".", ".."):
raise HTTPException(status_code = 404, detail = "Not found")
# ── Extension allowlist ─────────────────────────────────────
ext = os.path.splitext(safe_filename)[1].lower()
media_type = _SANDBOX_MEDIA_TYPES.get(ext)
if not media_type:
raise HTTPException(
status_code = status.HTTP_403_FORBIDDEN,
detail = "File type not allowed",
)
# ── Path containment check ──────────────────────────────────
home = os.path.expanduser("~")
sandbox_root = os.path.realpath(os.path.join(home, "studio_sandbox"))
safe_session = os.path.basename(session_id.replace("..", ""))
if not safe_session:
raise HTTPException(status_code = 404, detail = "Not found")
file_path = os.path.realpath(
os.path.join(sandbox_root, safe_session, safe_filename)
)
if not file_path.startswith(sandbox_root + os.sep):
raise HTTPException(
status_code = status.HTTP_403_FORBIDDEN,
detail = "Access denied",
)
if not os.path.isfile(file_path):
raise HTTPException(status_code = 404, detail = "Not found")
return FileResponse(
path = file_path,
media_type = media_type,
headers = {
"Cache-Control": "private, no-store",
"X-Content-Type-Options": "nosniff",
},
)
# =====================================================================
# OpenAI-Compatible Models Listing (/models → /v1/models)
# =====================================================================
@router.get("/models")
async def openai_list_models(
current_subject: str = Depends(get_current_subject),
):
"""
OpenAI-compatible model listing endpoint.
Returns the currently loaded model in the format expected by
OpenAI-compatible clients (``GET /v1/models``).
"""
models = []
# Check GGUF backend
llama_backend = get_llama_cpp_backend()
if llama_backend.is_loaded:
models.append(
{
"id": llama_backend.model_identifier,
"object": "model",
"owned_by": "local",
}
)
# Check Unsloth backend
backend = get_inference_backend()
if backend.active_model_name:
models.append(
{
"id": backend.active_model_name,
"object": "model",
"owned_by": "local",
}
)
return {"object": "list", "data": models}
# =====================================================================
# OpenAI-Compatible Completions Proxy (/completions → /v1/completions)
# =====================================================================
@router.post("/completions")
async def openai_completions(
request: Request,
current_subject: str = Depends(get_current_subject),
):
"""
OpenAI-compatible text completions endpoint (non-chat).
Transparently proxies to the running llama-server's ``/v1/completions``.
Only available when a GGUF model is loaded.
"""
llama_backend = get_llama_cpp_backend()
if not llama_backend.is_loaded:
raise HTTPException(
status_code = 503,
detail = "No GGUF model loaded. Load a GGUF model first.",
)
body = await request.json()
target_url = f"{llama_backend.base_url}/v1/completions"
is_stream = body.get("stream", False)
if is_stream:
async def _stream():
# Manual httpx client/response lifecycle AND explicit
# aiter_bytes() iterator close — see _anthropic_passthrough_stream
# for the full rationale. Saving `bytes_iter = resp.aiter_bytes()`
# and `await bytes_iter.aclose()` in the finally block is the
# part that matters for avoiding the Python 3.13 + httpcore
# 1.0.x "Exception ignored in: <async_generator>" / anyio
# cancel-scope trace: an anonymous async for leaves the
# iterator unclosed, so Python's asyncgen GC finalizer runs
# cleanup on a later pass in a different asyncio task.
client = httpx.AsyncClient(timeout = 600)
resp = None
bytes_iter = None
try:
req = client.build_request("POST", target_url, json = body)
resp = await client.send(req, stream = True)
bytes_iter = resp.aiter_bytes()
async for chunk in bytes_iter:
yield chunk
except Exception as e:
logger.error("openai_completions stream error: %s", e)
finally:
if bytes_iter is not None:
try:
await bytes_iter.aclose()
except Exception:
pass
if resp is not None:
try:
await resp.aclose()
except Exception:
pass
try:
await client.aclose()
except Exception:
pass
return StreamingResponse(_stream(), media_type = "text/event-stream")
else:
async with httpx.AsyncClient() as client:
resp = await client.post(target_url, json = body, timeout = 600)
return Response(
content = resp.content,
status_code = resp.status_code,
media_type = "application/json",
)
# =====================================================================
# OpenAI-Compatible Embeddings Proxy (/embeddings → /v1/embeddings)
# =====================================================================
@router.post("/embeddings")
async def openai_embeddings(
request: Request,
current_subject: str = Depends(get_current_subject),
):
"""
OpenAI-compatible embeddings endpoint.
Transparently proxies to the running llama-server's ``/v1/embeddings``.
Only available when a GGUF model is loaded.
Note: the loaded model must support pooling; otherwise llama-server
will return an error (expected).
"""
llama_backend = get_llama_cpp_backend()
if not llama_backend.is_loaded:
raise HTTPException(
status_code = 503,
detail = "No GGUF model loaded. Load a GGUF model first.",
)
body = await request.json()
target_url = f"{llama_backend.base_url}/v1/embeddings"
async with httpx.AsyncClient() as client:
resp = await client.post(target_url, json = body, timeout = 600)
return Response(
content = resp.content,
status_code = resp.status_code,
media_type = "application/json",
)
# =====================================================================
# OpenAI Responses API (/responses → /v1/responses)
# =====================================================================
def _translate_responses_tools_to_chat(
tools: Optional[list[dict]],
) -> Optional[list[dict]]:
"""Translate Responses-shape function tools to the Chat Completions nested shape.
Responses uses a flat shape per tool entry::
{"type": "function", "name": "...", "description": "...",
"parameters": {...}, "strict": true}
The Chat Completions / llama-server passthrough expects the nested shape::
{"type": "function",
"function": {"name": "...", "description": "...",
"parameters": {...}, "strict": true}}
Only ``type=="function"`` entries are forwarded. Built-in Responses tools
(``web_search``, ``file_search``, ``mcp``, ...) are dropped because
llama-server does not implement them server-side; keeping them in the
request would produce an opaque upstream 400.
"""
if not tools:
return None
out: list[dict] = []
for tool in tools:
if not isinstance(tool, dict):
continue
if tool.get("type") != "function":
continue
fn: dict = {}
if "name" in tool:
fn["name"] = tool["name"]
if tool.get("description") is not None:
fn["description"] = tool["description"]
if tool.get("parameters") is not None:
fn["parameters"] = tool["parameters"]
if tool.get("strict") is not None:
fn["strict"] = tool["strict"]
out.append({"type": "function", "function": fn})
return out or None
def _translate_responses_tool_choice_to_chat(tool_choice: Any) -> Any:
"""Translate a Responses-shape ``tool_choice`` to the Chat Completions shape.
String values (``"auto"``/``"none"``/``"required"``) pass through unchanged.
The Responses forcing object ``{"type": "function", "name": "X"}`` is
converted to Chat Completions' ``{"type": "function", "function": {"name": "X"}}``.
Unknown / built-in tool choices are forwarded as-is; llama-server ignores
what it doesn't recognise.
"""
if tool_choice is None:
return None
if isinstance(tool_choice, str):
return tool_choice
if (
isinstance(tool_choice, dict)
and tool_choice.get("type") == "function"
and "name" in tool_choice
and "function" not in tool_choice
):
return {"type": "function", "function": {"name": tool_choice["name"]}}
return tool_choice
def _responses_message_text(content: Union[str, list]) -> str:
"""Flatten a ResponsesInputMessage ``content`` into a plain text string.
Used for system/developer message hoisting and for assistant-replay
(``output_text``) messages when images/unknown parts are irrelevant.
Returns an empty string for empty input.
"""
if isinstance(content, str):
return content
parts: list[str] = []
for part in content or []:
if isinstance(part, (ResponsesInputTextPart, ResponsesOutputTextPart)):
parts.append(part.text)
return "\n".join(parts)
def _normalise_responses_input(payload: ResponsesRequest) -> list[ChatMessage]:
"""Convert a ResponsesRequest's ``input`` into Chat-format ``ChatMessage`` list.
Handles the three input item shapes allowed by the Responses API:
- ``ResponsesInputMessage`` — regular chat messages (text or multimodal).
- ``ResponsesFunctionCallInputItem`` — a prior assistant tool call replayed
on a follow-up turn. Converted into an assistant message carrying a
Chat Completions ``tool_calls`` entry keyed by ``call_id``.
- ``ResponsesFunctionCallOutputInputItem`` — a tool result the client is
returning. Converted into a ``role="tool"`` message with ``tool_call_id``
set to the originating ``call_id`` so llama-server can reconcile the
call with its result.
System / developer content is collected from ``instructions`` *and* from
any ``role="system"`` / ``role="developer"`` entries in ``input``, then
merged into a single ``role="system"`` message placed at the top of the
returned list. This satisfies strict chat templates (harmony / gpt-oss,
Qwen3, ...) whose Jinja raises ``"System message must be at the
beginning."`` when more than one system message is present or when a
system message appears after a user turn — the exact pattern the OpenAI
Codex CLI hits, since Codex sets ``instructions`` *and* also sends a
developer message in ``input``.
"""
system_parts: list[str] = []
messages: list[ChatMessage] = []
if payload.instructions:
system_parts.append(payload.instructions)
# Simple string input
if isinstance(payload.input, str):
if payload.input:
messages.append(ChatMessage(role = "user", content = payload.input))
if system_parts:
merged = "\n\n".join(p for p in system_parts if p)
return [ChatMessage(role = "system", content = merged), *messages]
return messages
for item in payload.input:
if isinstance(item, ResponsesFunctionCallInputItem):
messages.append(
ChatMessage(
role = "assistant",
content = None,
tool_calls = [
{
"id": item.call_id,
"type": "function",
"function": {
"name": item.name,
"arguments": item.arguments,
},
}
],
)
)
continue
if isinstance(item, ResponsesFunctionCallOutputInputItem):
# Chat Completions `role="tool"` requires a string content; if a
# Responses client sends a content-array output, serialize it.
output = item.output
if not isinstance(output, str):
output = json.dumps(output)
messages.append(
ChatMessage(
role = "tool",
tool_call_id = item.call_id,
content = output,
)
)
continue
if isinstance(item, ResponsesUnknownInputItem):
# Reasoning items and any other unmodelled top-level Responses
# item types are silently dropped — llama-server-backed GGUFs
# cannot consume them and our lenient validation let them in so
# unrelated turns don't 422.
continue
# ResponsesInputMessage — hoist system/developer to the top, merge.
if item.role in ("system", "developer"):
hoisted = _responses_message_text(item.content)
if hoisted:
system_parts.append(hoisted)
continue
if isinstance(item.content, str):
messages.append(ChatMessage(role = item.role, content = item.content))
continue
# Assistant-replay turns come back as content = [output_text, ...].
# Chat Completions' assistant role expects a plain string, not a
# multimodal content array, so flatten output_text (and any stray
# input_text / unknown text) to a single string.
if item.role == "assistant":
text = _responses_message_text(item.content)
if text:
messages.append(ChatMessage(role = "assistant", content = text))
continue
# User (and any other remaining roles) — keep multimodal when
# present, drop unknown content parts silently.
parts: list = []
for part in item.content:
if isinstance(part, (ResponsesInputTextPart, ResponsesOutputTextPart)):
parts.append(TextContentPart(type = "text", text = part.text))
elif isinstance(part, ResponsesInputImagePart):
parts.append(
ImageContentPart(
type = "image_url",
image_url = ImageUrl(url = part.image_url, detail = part.detail),
)
)
# ResponsesUnknownContentPart and anything else: drop.
if parts:
# Collapse single-text-part content to a plain string so roles
# that reject multimodal arrays (e.g. legacy templates) still
# accept the message.
if len(parts) == 1 and isinstance(parts[0], TextContentPart):
messages.append(ChatMessage(role = item.role, content = parts[0].text))
else:
messages.append(ChatMessage(role = item.role, content = parts))
if system_parts:
merged = "\n\n".join(p for p in system_parts if p)
return [ChatMessage(role = "system", content = merged), *messages]
return messages
def _build_chat_request(
payload: ResponsesRequest, messages: list[ChatMessage], stream: bool
) -> ChatCompletionRequest:
"""Build a ChatCompletionRequest from a ResponsesRequest.
Tools and ``tool_choice`` are translated from the flat Responses shape to
the nested Chat Completions shape here so the existing #5099
``/v1/chat/completions`` client-side pass-through picks them up without
further modification.
"""
chat_kwargs: dict = dict(
model = payload.model,
messages = messages,
stream = stream,
)
if payload.temperature is not None:
chat_kwargs["temperature"] = payload.temperature
if payload.top_p is not None:
chat_kwargs["top_p"] = payload.top_p
if payload.max_output_tokens is not None:
chat_kwargs["max_tokens"] = payload.max_output_tokens
chat_tools = _translate_responses_tools_to_chat(payload.tools)
if chat_tools is not None:
chat_kwargs["tools"] = chat_tools
chat_tool_choice = _translate_responses_tool_choice_to_chat(payload.tool_choice)
if chat_tool_choice is not None:
chat_kwargs["tool_choice"] = chat_tool_choice
req = ChatCompletionRequest(**chat_kwargs)
# `parallel_tool_calls` is not a first-class field on ChatCompletionRequest,
# but the model allows extras and _build_openai_passthrough_body forwards
# only explicitly-known fields. Llama-server does not currently implement
# parallel_tool_calls semantics, so we accept-and-ignore it on the
# Responses side to avoid breaking SDK clients that always send it.
return req
def _chat_tool_calls_to_responses_output(tool_calls: list[dict]) -> list[dict]:
"""Map Chat Completions ``tool_calls`` into Responses ``function_call`` output items.
The Chat Completions id (``call_xxx``) is the shared correlation key across
turns in the OpenAI Responses API — it is stored as ``call_id`` on the
output item and must be echoed back by the client as
``function_call_output.call_id`` on the next turn.
"""
items: list[dict] = []
for tc in tool_calls:
if tc.get("type") != "function":
continue
fn = tc.get("function") or {}
items.append(
ResponsesOutputFunctionCall(
call_id = tc.get("id", ""),
name = fn.get("name", ""),
arguments = fn.get("arguments", "") or "",
status = "completed",
).model_dump()
)
return items
async def _responses_non_streaming(
payload: ResponsesRequest,
messages: list[ChatMessage],
request: Request,
) -> JSONResponse:
"""Handle a non-streaming Responses API call."""
chat_req = _build_chat_request(payload, messages, stream = False)
result = await openai_chat_completions(chat_req, request)
# openai_chat_completions returns a JSONResponse for non-streaming
if isinstance(result, JSONResponse):
body = json.loads(result.body.decode())
elif isinstance(result, Response):
body = json.loads(result.body.decode())
else:
body = result
choices = body.get("choices", [])
text = ""
tool_calls: list[dict] = []
if choices:
msg = choices[0].get("message", {}) or {}
text = msg.get("content", "") or ""
tool_calls = msg.get("tool_calls") or []
usage_data = body.get("usage", {})
input_tokens = usage_data.get("prompt_tokens", 0)
output_tokens = usage_data.get("completion_tokens", 0)
resp_id = f"resp_{uuid.uuid4().hex[:12]}"
# Responses API emits each tool call as its own top-level output item,
# alongside an optional assistant text message. Emit the text message
# only when the model actually produced content, so clients that expect
# a pure tool-call turn (finish_reason="tool_calls") don't see a spurious
# empty message item.
output_items: list[dict] = []
if text:
msg_id = f"msg_{uuid.uuid4().hex[:12]}"
output_items.append(
ResponsesOutputMessage(
id = msg_id,
status = "completed",
role = "assistant",
content = [ResponsesOutputTextContent(text = text)],
).model_dump()
)
output_items.extend(_chat_tool_calls_to_responses_output(tool_calls))
response = ResponsesResponse(
id = resp_id,
created_at = int(time.time()),
status = "completed",
model = body.get("model", payload.model),
output = output_items,
usage = ResponsesUsage(
input_tokens = input_tokens,
output_tokens = output_tokens,
total_tokens = input_tokens + output_tokens,
),
temperature = payload.temperature,
top_p = payload.top_p,
max_output_tokens = payload.max_output_tokens,
instructions = payload.instructions,
)
return JSONResponse(content = response.model_dump())
async def _responses_stream(
payload: ResponsesRequest,
messages: list[ChatMessage],
request: Request,
):
"""Handle a streaming Responses API call, emitting named SSE events.
For GGUF models the request goes directly to llama-server's
``/v1/chat/completions`` endpoint from inside the StreamingResponse
child task — a single httpx lifecycle, a single async generator.
Wrapping the existing ``openai_chat_completions`` pass-through (which
already does its own httpx lifecycle) stacks two generators: Python
3.13 + httpcore 1.0.x then loses the close-propagation chain on the
innermost ``HTTP11ConnectionByteStream`` at asyncgen finalisation,
tripping "Attempted to exit cancel scope in a different task" /
"async generator ignored GeneratorExit". The direct path avoids that
altogether. Non-GGUF falls back to the wrapper (which doesn't use
httpx, so the issue doesn't apply).
Text deltas arrive as ``response.output_text.delta`` on a single
``message`` output item at ``output_index=0``. Each tool call from
``delta.tool_calls[]`` is promoted to its own top-level ``function_call``
output item (one per distinct ``tool_calls[].index``), and relayed as
``response.function_call_arguments.delta`` / ``.done`` events so clients
(Codex, OpenAI Python SDK) can reconstruct the call incrementally and
reply with a ``function_call_output`` item on the next turn.
"""
resp_id = f"resp_{uuid.uuid4().hex[:12]}"
msg_id = f"msg_{uuid.uuid4().hex[:12]}"
created_at = int(time.time())
chat_req = _build_chat_request(payload, messages, stream = True)
llama_backend = get_llama_cpp_backend()
if not llama_backend.is_loaded:
# The direct pass-through is GGUF-only. Non-GGUF /v1/responses
# streaming isn't a Codex-compatible path today and wrapping the
# transformers backend's streaming generator here would re-
# introduce the double-layer asyncgen close pattern that produces
# "Attempted to exit cancel scope in a different task" on Python
# 3.13. Surface a typed 400 so the client sees a useful error
# instead of a dangling stream.
raise HTTPException(
status_code = 400,
detail = (
"Streaming /v1/responses requires a GGUF model loaded via "
"llama-server. Use non-streaming /v1/responses, "
"/v1/chat/completions, or load a GGUF model."
),
)
body = _build_openai_passthrough_body(chat_req)
target_url = f"{llama_backend.base_url}/v1/chat/completions"
async def event_generator():
full_text = ""
input_tokens = 0
output_tokens = 0
# Per-tool-call state keyed by the Chat Completions `tool_calls[].index`
# which stays stable across chunks for the same call. Values are:
# {output_index, item_id, call_id, name, arguments, opened}
tool_call_state: dict[int, dict] = {}
# Text message lives at output_index 0; tool calls claim 1, 2, ...
next_output_index = 1
def _snapshot_output() -> list[dict]:
"""Snapshot of all completed output items for response.completed."""
items: list[dict] = [
{
"type": "message",
"id": msg_id,
"status": "completed",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": full_text,
"annotations": [],
}
],
}
]
for st in sorted(tool_call_state.values(), key = lambda s: s["output_index"]):
items.append(
{
"type": "function_call",
"id": st["item_id"],
"status": "completed",
"call_id": st["call_id"],
"name": st["name"],
"arguments": st["arguments"],
}
)
return items
# ── Preamble events ──
yield f"event: response.created\ndata: {json.dumps({'type': 'response.created', 'response': {'id': resp_id, 'object': 'response', 'created_at': created_at, 'status': 'in_progress', 'model': payload.model, 'output': [], 'usage': {'input_tokens': 0, 'output_tokens': 0, 'total_tokens': 0}}})}\n\n"
# output_item.added (text message at output_index 0)
output_item = {
"type": "message",
"id": msg_id,
"status": "in_progress",
"role": "assistant",
"content": [],
}
yield f"event: response.output_item.added\ndata: {json.dumps({'type': 'response.output_item.added', 'output_index': 0, 'item': output_item})}\n\n"
# content_part.added
content_part = {"type": "output_text", "text": "", "annotations": []}
yield f"event: response.content_part.added\ndata: {json.dumps({'type': 'response.content_part.added', 'item_id': msg_id, 'output_index': 0, 'content_index': 0, 'part': content_part})}\n\n"
# ── Direct httpx lifecycle to llama-server ──
# Full same-task open + close, identical pattern to
# _openai_passthrough_stream and _anthropic_passthrough_stream:
# no `async with`, explicit aclose of lines_iter BEFORE resp /
# client so the innermost httpcore byte stream is finalised in
# this task (not via Python's asyncgen GC in a sibling task).
client = httpx.AsyncClient(timeout = 600)
resp = None
lines_iter = None
try:
req = client.build_request("POST", target_url, json = body)
try:
resp = await client.send(req, stream = True)
except httpx.RequestError as e:
logger.error("responses stream: upstream unreachable: %s", e)
yield f"event: response.failed\ndata: {json.dumps({'type': 'response.failed', 'response': {'id': resp_id, 'object': 'response', 'created_at': created_at, 'status': 'failed', 'model': payload.model, 'output': [], 'error': {'code': 502, 'message': _friendly_error(e)}}})}\n\n"
return
if resp.status_code != 200:
err_bytes = await resp.aread()
err_text = err_bytes.decode("utf-8", errors = "replace")
logger.error(
"responses stream upstream error: status=%s body=%s",
resp.status_code,
err_text[:500],
)
yield f"event: response.failed\ndata: {json.dumps({'type': 'response.failed', 'response': {'id': resp_id, 'object': 'response', 'created_at': created_at, 'status': 'failed', 'model': payload.model, 'output': [], 'error': {'code': resp.status_code, 'message': f'llama-server error: {err_text[:500]}'}}})}\n\n"
return
lines_iter = resp.aiter_lines()
async for raw_line in lines_iter:
if await request.is_disconnected():
break
if not raw_line:
continue
if not raw_line.startswith("data: "):
continue
data_str = raw_line[6:]
if data_str.strip() == "[DONE]":
break
try:
chunk_data = json.loads(data_str)
except json.JSONDecodeError:
continue
choices = chunk_data.get("choices", [])
if not choices:
usage = chunk_data.get("usage")
if usage:
input_tokens = usage.get("prompt_tokens", input_tokens)
output_tokens = usage.get("completion_tokens", output_tokens)
continue
delta = choices[0].get("delta", {}) or {}
content = delta.get("content")
if content:
full_text += content
delta_event = {
"type": "response.output_text.delta",
"item_id": msg_id,
"output_index": 0,
"content_index": 0,
"delta": content,
}
yield f"event: response.output_text.delta\ndata: {json.dumps(delta_event)}\n\n"
for tc in delta.get("tool_calls") or []:
idx = tc.get("index", 0)
st = tool_call_state.get(idx)
fn = tc.get("function") or {}
if st is None:
# First chunk for this tool call — allocate an
# output_index and emit output_item.added.
st = {
"output_index": next_output_index,
"item_id": f"fc_{uuid.uuid4().hex[:12]}",
"call_id": tc.get("id") or "",
"name": fn.get("name") or "",
"arguments": "",
"opened": False,
}
next_output_index += 1
tool_call_state[idx] = st
else:
# Later chunks sometimes carry the id/name only
# once; merge when present.
if tc.get("id") and not st["call_id"]:
st["call_id"] = tc["id"]
if fn.get("name") and not st["name"]:
st["name"] = fn["name"]
if not st["opened"] and st["call_id"] and st["name"]:
item_added = {
"type": "response.output_item.added",
"output_index": st["output_index"],
"item": {
"type": "function_call",
"id": st["item_id"],
"status": "in_progress",
"call_id": st["call_id"],
"name": st["name"],
"arguments": "",
},
}
yield f"event: response.output_item.added\ndata: {json.dumps(item_added)}\n\n"
st["opened"] = True
arg_delta = fn.get("arguments") or ""
if arg_delta and st["opened"]:
st["arguments"] += arg_delta
args_delta_event = {
"type": "response.function_call_arguments.delta",
"item_id": st["item_id"],
"output_index": st["output_index"],
"delta": arg_delta,
}
yield f"event: response.function_call_arguments.delta\ndata: {json.dumps(args_delta_event)}\n\n"
elif arg_delta:
# Buffer the args until we can open the item
# (id/name arrive in the same chunk as the first
# arg delta for some models — but if not, stash).
st["arguments"] += arg_delta
usage = chunk_data.get("usage")
if usage:
input_tokens = usage.get("prompt_tokens", input_tokens)
output_tokens = usage.get("completion_tokens", output_tokens)
except Exception as e:
logger.error("responses stream error: %s", e)
finally:
if lines_iter is not None:
try:
await lines_iter.aclose()
except Exception:
pass
if resp is not None:
try:
await resp.aclose()
except Exception:
pass
try:
await client.aclose()
except Exception:
pass
# ── Closing events for tool calls ──
for st in sorted(tool_call_state.values(), key = lambda s: s["output_index"]):
# If id/name never arrived (malformed upstream), synthesise so
# the client still sees a coherent frame sequence.
if not st["opened"]:
if not st["call_id"]:
st["call_id"] = f"call_{uuid.uuid4().hex[:12]}"
item_added = {
"type": "response.output_item.added",
"output_index": st["output_index"],
"item": {
"type": "function_call",
"id": st["item_id"],
"status": "in_progress",
"call_id": st["call_id"],
"name": st["name"],
"arguments": "",
},
}
yield f"event: response.output_item.added\ndata: {json.dumps(item_added)}\n\n"
if st["arguments"]:
yield (
"event: response.function_call_arguments.delta\n"
"data: "
+ json.dumps(
{
"type": "response.function_call_arguments.delta",
"item_id": st["item_id"],
"output_index": st["output_index"],
"delta": st["arguments"],
}
)
+ "\n\n"
)
st["opened"] = True
args_done = {
"type": "response.function_call_arguments.done",
"item_id": st["item_id"],
"output_index": st["output_index"],
"name": st["name"],
"arguments": st["arguments"],
}
yield f"event: response.function_call_arguments.done\ndata: {json.dumps(args_done)}\n\n"
item_done = {
"type": "response.output_item.done",
"output_index": st["output_index"],
"item": {
"type": "function_call",
"id": st["item_id"],
"status": "completed",
"call_id": st["call_id"],
"name": st["name"],
"arguments": st["arguments"],
},
}
yield f"event: response.output_item.done\ndata: {json.dumps(item_done)}\n\n"
# ── Closing events for text message ──
yield f"event: response.output_text.done\ndata: {json.dumps({'type': 'response.output_text.done', 'item_id': msg_id, 'output_index': 0, 'content_index': 0, 'text': full_text})}\n\n"
yield f"event: response.content_part.done\ndata: {json.dumps({'type': 'response.content_part.done', 'item_id': msg_id, 'output_index': 0, 'content_index': 0, 'part': {'type': 'output_text', 'text': full_text, 'annotations': []}})}\n\n"
yield f"event: response.output_item.done\ndata: {json.dumps({'type': 'response.output_item.done', 'output_index': 0, 'item': {'type': 'message', 'id': msg_id, 'status': 'completed', 'role': 'assistant', 'content': [{'type': 'output_text', 'text': full_text, 'annotations': []}]}})}\n\n"
# response.completed
total_tokens = input_tokens + output_tokens
completed_response = {
"type": "response.completed",
"response": {
"id": resp_id,
"object": "response",
"created_at": created_at,
"status": "completed",
"model": payload.model,
"output": _snapshot_output(),
"usage": {
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"total_tokens": total_tokens,
},
},
}
yield f"event: response.completed\ndata: {json.dumps(completed_response)}\n\n"
return StreamingResponse(
event_generator(),
media_type = "text/event-stream",
headers = {
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
},
)
@router.post("/responses")
async def openai_responses(
payload: ResponsesRequest,
request: Request,
current_subject: str = Depends(get_current_subject),
):
"""
OpenAI Responses API endpoint.
Accepts the Responses-format request, converts it to a
ChatCompletionRequest internally, and returns a response
matching the OpenAI Responses API schema (output array,
input_tokens/output_tokens, named SSE events for streaming).
"""
messages = _normalise_responses_input(payload)
if not messages:
raise HTTPException(status_code = 400, detail = "No input provided.")
if payload.stream:
return await _responses_stream(payload, messages, request)
return await _responses_non_streaming(payload, messages, request)
# =====================================================================
# Anthropic-Compatible Messages API (/messages → /v1/messages)
# =====================================================================
@router.post("/messages")
async def anthropic_messages(
payload: AnthropicMessagesRequest,
request: Request,
current_subject: str = Depends(get_current_subject),
):
"""
Anthropic-compatible Messages API endpoint.
Translates Anthropic message format to internal OpenAI format, runs
through the existing agentic tool loop when tools are provided, and
returns responses in Anthropic Messages API format (streaming SSE or
non-streaming JSON).
"""
llama_backend = get_llama_cpp_backend()
if not llama_backend.is_loaded:
raise HTTPException(
status_code = 503,
detail = "No GGUF model loaded. Load a GGUF model first.",
)
model_name = getattr(llama_backend, "model_identifier", None) or payload.model
message_id = f"msg_{uuid.uuid4().hex[:24]}"
# ── Translate Anthropic → OpenAI ──────────────────────────
openai_messages = anthropic_messages_to_openai(
[m.model_dump() for m in payload.messages],
payload.system,
)
temperature = payload.temperature if payload.temperature is not None else 0.6
top_p = payload.top_p if payload.top_p is not None else 0.95
top_k = payload.top_k if payload.top_k is not None else 20
min_p = payload.min_p if payload.min_p is not None else 0.01
repetition_penalty = (
payload.repetition_penalty if payload.repetition_penalty is not None else 1.0
)
presence_penalty = (
payload.presence_penalty if payload.presence_penalty is not None else 0.0
)
stop = payload.stop_sequences or None
# Translate Anthropic tool_choice to OpenAI format for forwarding to
# llama-server. Falls back to "auto" when unset or unrecognized, which
# matches the prior hardcoded behavior.
openai_tool_choice = anthropic_tool_choice_to_openai(payload.tool_choice)
if openai_tool_choice is None:
openai_tool_choice = "auto"
cancel_event = threading.Event()
# ── Tool routing ──────────────────────────────────────────
# Three paths:
# 1. enable_tools=true → server-side execution of built-in tools (Unsloth shorthand)
# 2. tools=[...] only → client-side pass-through (standard Anthropic behavior)
# 3. neither → plain chat
server_tools = payload.enable_tools and llama_backend.supports_tools
client_tools = (
not server_tools
and payload.tools
and len(payload.tools) > 0
and llama_backend.supports_tools
)
# ── Client-side pass-through path ─────────────────────────
if client_tools:
openai_tools = anthropic_tools_to_openai(payload.tools)
if payload.stream:
return await _anthropic_passthrough_stream(
request,
cancel_event,
llama_backend,
openai_messages,
openai_tools,
temperature,
top_p,
top_k,
payload.max_tokens,
message_id,
model_name,
stop = stop,
min_p = min_p,
repetition_penalty = repetition_penalty,
presence_penalty = presence_penalty,
tool_choice = openai_tool_choice,
)
return await _anthropic_passthrough_non_streaming(
llama_backend,
openai_messages,
openai_tools,
temperature,
top_p,
top_k,
payload.max_tokens,
message_id,
model_name,
stop = stop,
min_p = min_p,
repetition_penalty = repetition_penalty,
presence_penalty = presence_penalty,
tool_choice = openai_tool_choice,
)
if server_tools:
from core.inference.tools import ALL_TOOLS
if payload.enabled_tools is not None:
openai_tools = [
t for t in ALL_TOOLS if t["function"]["name"] in payload.enabled_tools
]
else:
openai_tools = ALL_TOOLS
# Build tool-use system prompt nudge (same logic as /chat/completions)
_tool_names = {t["function"]["name"] for t in openai_tools}
_has_web = "web_search" in _tool_names
_has_code = "python" in _tool_names or "terminal" in _tool_names
_date_line = f"The current date is {_date.today().isoformat()}."
_model_size_b = _extract_model_size_b(model_name)
_is_small_model = _model_size_b is not None and _model_size_b < 9
if _is_small_model:
_web_tips = "Do not repeat the same search query."
else:
_web_tips = (
"When you search and find a relevant URL in the results, "
"fetch its full content by calling web_search with the url parameter. "
"Do not repeat the same search query. If a search returns "
"no useful results, try rephrasing or fetching a result URL directly."
)
_code_tips = (
"Use code execution for math, calculations, data processing, "
"or to parse and analyze information from tool results."
)
if _has_web and _has_code:
_nudge = (
_date_line + " "
"You have access to tools. When appropriate, prefer using "
"tools rather than answering from memory. "
+ _web_tips
+ " "
+ _code_tips
)
elif _has_code:
_nudge = (
_date_line + " "
"You have access to tools. When appropriate, prefer using "
"code execution rather than answering from memory. " + _code_tips
)
elif _has_web:
_nudge = (
_date_line + " "
"You have access to tools. When appropriate, prefer using "
"web search for up-to-date or uncertain factual "
"information rather than answering from memory. " + _web_tips
)
else:
_nudge = ""
if _nudge:
_nudge += _TOOL_ACTION_NUDGE
# Inject into system prompt
if openai_messages and openai_messages[0].get("role") == "system":
openai_messages[0]["content"] = (
openai_messages[0]["content"].rstrip() + "\n\n" + _nudge
)
else:
openai_messages.insert(0, {"role": "system", "content": _nudge})
# Strip stale tool-call XML from conversation
for _msg in openai_messages:
if _msg.get("role") == "assistant" and isinstance(_msg.get("content"), str):
_msg["content"] = _TOOL_XML_RE.sub("", _msg["content"]).strip()
def _run_tool_gen():
return llama_backend.generate_chat_completion_with_tools(
messages = openai_messages,
tools = openai_tools,
temperature = temperature,
top_p = top_p,
top_k = top_k,
min_p = min_p,
repetition_penalty = repetition_penalty,
presence_penalty = presence_penalty,
max_tokens = payload.max_tokens,
stop = stop,
cancel_event = cancel_event,
max_tool_iterations = 25,
auto_heal_tool_calls = True,
tool_call_timeout = 300,
session_id = payload.session_id,
)
if payload.stream:
return await _anthropic_tool_stream(
request,
cancel_event,
_run_tool_gen,
message_id,
model_name,
)
return await _anthropic_tool_non_streaming(
_run_tool_gen,
message_id,
model_name,
)
# ── No-tool path ──────────────────────────────────────────
def _run_plain_gen():
return llama_backend.generate_chat_completion(
messages = openai_messages,
temperature = temperature,
top_p = top_p,
top_k = top_k,
min_p = min_p,
repetition_penalty = repetition_penalty,
presence_penalty = presence_penalty,
max_tokens = payload.max_tokens,
stop = stop,
cancel_event = cancel_event,
)
if payload.stream:
return await _anthropic_plain_stream(
request,
cancel_event,
_run_plain_gen,
message_id,
model_name,
)
return await _anthropic_plain_non_streaming(
_run_plain_gen,
message_id,
model_name,
)
async def _anthropic_tool_stream(
request,
cancel_event,
run_gen,
message_id,
model_name,
):
"""Streaming response for the tool-calling path."""
_sentinel = object()
async def _stream():
emitter = AnthropicStreamEmitter()
for line in emitter.start(message_id, model_name):
yield line
gen = run_gen()
try:
while True:
if await request.is_disconnected():
cancel_event.set()
return
event = await asyncio.to_thread(next, gen, _sentinel)
if event is _sentinel:
break
# Strip leaked tool-call XML from content events
if event.get("type") == "content":
event = dict(event)
event["text"] = _TOOL_XML_RE.sub("", event["text"])
for line in emitter.feed(event):
yield line
except Exception as e:
logger.error("anthropic_messages stream error: %s", e)
for line in emitter.finish("end_turn"):
yield line
return StreamingResponse(
_stream(),
media_type = "text/event-stream",
headers = {
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
},
)
async def _anthropic_plain_stream(
request,
cancel_event,
run_gen,
message_id,
model_name,
):
"""Streaming response for the no-tool path."""
_sentinel = object()
async def _stream():
emitter = AnthropicStreamEmitter()
for line in emitter.start(message_id, model_name):
yield line
gen = run_gen()
try:
while True:
if await request.is_disconnected():
cancel_event.set()
return
cumulative = await asyncio.to_thread(next, gen, _sentinel)
if cumulative is _sentinel:
break
if isinstance(cumulative, dict):
if cumulative.get("type") == "metadata":
for line in emitter.feed(cumulative):
yield line
continue
# Plain generator yields cumulative text strings
for line in emitter.feed({"type": "content", "text": cumulative}):
yield line
except Exception as e:
logger.error("anthropic_messages stream error: %s", e)
for line in emitter.finish("end_turn"):
yield line
return StreamingResponse(
_stream(),
media_type = "text/event-stream",
headers = {
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
},
)
async def _anthropic_tool_non_streaming(run_gen, message_id, model_name):
"""Non-streaming response for the tool-calling path.
Builds ``content_blocks`` in generation order (text → tool_use → text →
tool_use → ...), mirroring the streaming emitter's behavior. Deltas
within a single synthesis turn are merged into the trailing text block;
tool_use blocks interrupt the text sequence and open a new text block on
the next content event.
``prev_text`` is reset on ``tool_end`` because
``generate_chat_completion_with_tools`` yields cumulative content *per
turn* — the first content event of turn N+1 must diff against an empty
baseline, not against turn N's final length.
"""
content_blocks: list = []
usage = {}
prev_text = ""
for event in run_gen():
etype = event.get("type", "")
if etype == "content":
# Strip leaked tool-call XML
clean = _TOOL_XML_RE.sub("", event["text"])
new = clean[len(prev_text) :]
prev_text = clean
if new:
if content_blocks and isinstance(
content_blocks[-1], AnthropicResponseTextBlock
):
content_blocks[-1].text += new
else:
content_blocks.append(AnthropicResponseTextBlock(text = new))
elif etype == "tool_start":
content_blocks.append(
AnthropicResponseToolUseBlock(
id = event["tool_call_id"],
name = event["tool_name"],
input = event.get("arguments", {}),
)
)
elif etype == "tool_end":
prev_text = ""
elif etype == "metadata":
usage = event.get("usage", {})
resp = AnthropicMessagesResponse(
id = message_id,
model = model_name,
content = content_blocks,
stop_reason = "end_turn",
usage = AnthropicUsage(
input_tokens = usage.get("prompt_tokens", 0),
output_tokens = usage.get("completion_tokens", 0),
),
)
return JSONResponse(content = resp.model_dump())
async def _anthropic_plain_non_streaming(run_gen, message_id, model_name):
"""Non-streaming response for the no-tool path."""
text_parts = []
usage = {}
prev_text = ""
for cumulative in run_gen():
if isinstance(cumulative, dict):
if cumulative.get("type") == "metadata":
usage = cumulative.get("usage", {})
continue
new = cumulative[len(prev_text) :]
prev_text = cumulative
if new:
text_parts.append(new)
full_text = "".join(text_parts)
content_blocks = []
if full_text:
content_blocks.append(AnthropicResponseTextBlock(text = full_text))
resp = AnthropicMessagesResponse(
id = message_id,
model = model_name,
content = content_blocks,
stop_reason = "end_turn",
usage = AnthropicUsage(
input_tokens = usage.get("prompt_tokens", 0),
output_tokens = usage.get("completion_tokens", 0),
),
)
return JSONResponse(content = resp.model_dump())
# =====================================================================
# Client-side tool pass-through (Anthropic-native tools field)
# =====================================================================
def _build_passthrough_payload(
openai_messages,
openai_tools,
temperature,
top_p,
top_k,
max_tokens,
stream,
stop = None,
min_p = None,
repetition_penalty = None,
presence_penalty = None,
tool_choice = "auto",
):
body = {
"messages": openai_messages,
"tools": openai_tools,
"tool_choice": tool_choice,
"temperature": temperature,
"top_p": top_p,
"top_k": top_k,
"stream": stream,
}
if stream:
body["stream_options"] = {"include_usage": True}
if max_tokens is not None:
body["max_tokens"] = max_tokens
if stop:
body["stop"] = stop
if min_p is not None:
body["min_p"] = min_p
if repetition_penalty is not None:
# llama-server's field is "repeat_penalty", not "repetition_penalty"
body["repeat_penalty"] = repetition_penalty
if presence_penalty is not None:
body["presence_penalty"] = presence_penalty
return body
async def _anthropic_passthrough_stream(
request,
cancel_event,
llama_backend,
openai_messages,
openai_tools,
temperature,
top_p,
top_k,
max_tokens,
message_id,
model_name,
stop = None,
min_p = None,
repetition_penalty = None,
presence_penalty = None,
tool_choice = "auto",
):
"""Streaming client-side pass-through: forward tools to llama-server and
translate its streaming response to Anthropic SSE without executing anything."""
target_url = f"{llama_backend.base_url}/v1/chat/completions"
body = _build_passthrough_payload(
openai_messages,
openai_tools,
temperature,
top_p,
top_k,
max_tokens,
True,
stop = stop,
min_p = min_p,
repetition_penalty = repetition_penalty,
presence_penalty = presence_penalty,
tool_choice = tool_choice,
)
async def _stream():
emitter = AnthropicPassthroughEmitter()
for line in emitter.start(message_id, model_name):
yield line
# Manage the httpx client, response, AND the aiter_lines() async
# generator MANUALLY — no `async with`, no anonymous iterator.
#
# On Python 3.13 + httpcore 1.0.x, `async for raw_line in
# resp.aiter_lines():` creates an anonymous async generator. When
# the loop exits via `break` (or the generator is orphaned when a
# client disconnects mid-stream), Python's `async for` protocol
# does NOT auto-close the iterator the way a sync `for` loop
# would. The iterator remains reachable only from the current
# coroutine frame; once `_stream()` returns, the frame is GC'd
# and the iterator becomes unreachable. Python's asyncgen
# finalizer hook then runs its aclose() on a LATER GC pass in a
# DIFFERENT asyncio task, where httpcore's
# `HTTP11ConnectionByteStream.aclose()` enters
# `anyio.CancelScope.__exit__` with a mismatched task and prints
# `RuntimeError: Attempted to exit cancel scope in a different
# task` / `RuntimeError: async generator ignored GeneratorExit`
# as "Exception ignored in:" unraisable warnings.
#
# The fix: save `resp.aiter_lines()` as `lines_iter`, and in the
# finally block explicitly `await lines_iter.aclose()` BEFORE
# `resp.aclose()` / `client.aclose()`. This closes the iterator
# inside our own task's event loop, so the internal httpcore
# byte-stream is cleaned up before Python's asyncgen finalizer
# has anything orphaned to finalize. Each aclose is wrapped in
# `try: ... except Exception: pass` so anyio cleanup noise from
# nested aclose paths can't bubble out.
client = httpx.AsyncClient(timeout = 600)
resp = None
lines_iter = None
try:
req = client.build_request("POST", target_url, json = body)
resp = await client.send(req, stream = True)
lines_iter = resp.aiter_lines()
async for raw_line in lines_iter:
if await request.is_disconnected():
cancel_event.set()
break
if not raw_line or not raw_line.startswith("data: "):
continue
data_str = raw_line[6:]
if data_str.strip() == "[DONE]":
break
try:
chunk = json.loads(data_str)
except json.JSONDecodeError:
continue
for line in emitter.feed_chunk(chunk):
yield line
except Exception as e:
logger.error("anthropic_messages passthrough stream error: %s", e)
finally:
if lines_iter is not None:
try:
await lines_iter.aclose()
except Exception:
pass
if resp is not None:
try:
await resp.aclose()
except Exception:
pass
try:
await client.aclose()
except Exception:
pass
for line in emitter.finish():
yield line
return StreamingResponse(
_stream(),
media_type = "text/event-stream",
headers = {
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
},
)
async def _anthropic_passthrough_non_streaming(
llama_backend,
openai_messages,
openai_tools,
temperature,
top_p,
top_k,
max_tokens,
message_id,
model_name,
stop = None,
min_p = None,
repetition_penalty = None,
presence_penalty = None,
tool_choice = "auto",
):
"""Non-streaming client-side pass-through."""
target_url = f"{llama_backend.base_url}/v1/chat/completions"
body = _build_passthrough_payload(
openai_messages,
openai_tools,
temperature,
top_p,
top_k,
max_tokens,
False,
stop = stop,
min_p = min_p,
repetition_penalty = repetition_penalty,
presence_penalty = presence_penalty,
tool_choice = tool_choice,
)
async with httpx.AsyncClient() as client:
resp = await client.post(target_url, json = body, timeout = 600)
if resp.status_code != 200:
raise HTTPException(
status_code = resp.status_code,
detail = f"llama-server error: {resp.text[:500]}",
)
data = resp.json()
choice = (data.get("choices") or [{}])[0]
message = choice.get("message") or {}
finish_reason = choice.get("finish_reason")
content_blocks = []
text = message.get("content") or ""
if text:
text = _TOOL_XML_RE.sub("", text).strip()
if text:
content_blocks.append(AnthropicResponseTextBlock(text = text))
tool_calls = message.get("tool_calls") or []
for tc in tool_calls:
fn = tc.get("function") or {}
try:
args = json.loads(fn.get("arguments", "{}"))
except json.JSONDecodeError:
args = {}
content_blocks.append(
AnthropicResponseToolUseBlock(
id = tc.get("id", ""),
name = fn.get("name", ""),
input = args,
)
)
if tool_calls:
stop_reason = "tool_use"
elif finish_reason == "length":
stop_reason = "max_tokens"
else:
stop_reason = "end_turn"
usage = data.get("usage") or {}
resp_obj = AnthropicMessagesResponse(
id = message_id,
model = model_name,
content = content_blocks,
stop_reason = stop_reason,
usage = AnthropicUsage(
input_tokens = usage.get("prompt_tokens", 0),
output_tokens = usage.get("completion_tokens", 0),
),
)
return JSONResponse(content = resp_obj.model_dump())
# =====================================================================
# Client-side tool pass-through (OpenAI-native /v1/chat/completions)
# =====================================================================
def _openai_messages_for_passthrough(payload) -> list[dict]:
"""Build OpenAI-format message dicts for the /v1/chat/completions
passthrough path.
Messages from ``payload.messages`` are dumped through Pydantic (dropping
unset optional fields) so they are already in standard OpenAI format
— including ``role="tool"`` tool-result messages and assistant messages
that carry structured ``tool_calls``. Content-parts images already in
the message list are left untouched.
When a client uses Studio's legacy ``image_base64`` top-level field, the
image is re-encoded to PNG (llama-server's stb_image has limited format
support) and spliced into the last user message as an OpenAI
``image_url`` content part so vision + function-calling requests work
transparently.
"""
messages = [m.model_dump(exclude_none = True) for m in payload.messages]
if not payload.image_base64:
return messages
try:
import base64 as _b64
from io import BytesIO as _BytesIO
from PIL import Image as _Image
raw = _b64.b64decode(payload.image_base64)
img = _Image.open(_BytesIO(raw)).convert("RGB")
buf = _BytesIO()
img.save(buf, format = "PNG")
png_b64 = _b64.b64encode(buf.getvalue()).decode("ascii")
except Exception as e:
raise HTTPException(
status_code = 400,
detail = f"Failed to process image: {e}",
)
data_url = f"data:image/png;base64,{png_b64}"
image_part = {"type": "image_url", "image_url": {"url": data_url}}
for msg in reversed(messages):
if msg.get("role") != "user":
continue
existing = msg.get("content")
if isinstance(existing, str):
msg["content"] = [{"type": "text", "text": existing}, image_part]
elif isinstance(existing, list):
existing.append(image_part)
else:
msg["content"] = [image_part]
break
else:
messages.append({"role": "user", "content": [image_part]})
return messages
def _build_openai_passthrough_body(payload) -> dict:
"""Assemble the llama-server request body from a ChatCompletionRequest.
Only explicitly-known OpenAI / llama-server fields are forwarded so that
Studio-specific extensions (``enable_tools``, ``enabled_tools``,
``session_id``, ...) never leak to the backend.
"""
messages = _openai_messages_for_passthrough(payload)
tool_choice = payload.tool_choice if payload.tool_choice is not None else "auto"
return _build_passthrough_payload(
messages,
payload.tools,
payload.temperature,
payload.top_p,
payload.top_k,
payload.max_tokens,
payload.stream,
stop = payload.stop,
min_p = payload.min_p,
repetition_penalty = payload.repetition_penalty,
presence_penalty = payload.presence_penalty,
tool_choice = tool_choice,
)
async def _openai_passthrough_stream(
request,
cancel_event,
llama_backend,
payload,
model_name,
completion_id,
):
"""Streaming client-side pass-through for /v1/chat/completions.
Forwards the client's OpenAI function-calling request to llama-server and
relays the SSE stream back verbatim. This preserves llama-server's
native response ``id``, ``finish_reason`` (including ``"tool_calls"``),
``delta.tool_calls``, and the trailing ``usage`` chunk so the client
observes a standard OpenAI response.
"""
target_url = f"{llama_backend.base_url}/v1/chat/completions"
body = _build_openai_passthrough_body(payload)
# Dispatch the upstream request BEFORE returning StreamingResponse so
# transport errors and non-200 upstream statuses surface as real HTTP
# errors to the client. OpenAI SDKs rely on status codes to raise
# ``APIError``/``BadRequestError``/...; burying the failure inside a
# 200 SSE ``error`` frame silently breaks their error handling.
client = httpx.AsyncClient(timeout = 600)
resp = None
try:
req = client.build_request("POST", target_url, json = body)
resp = await client.send(req, stream = True)
except httpx.RequestError as e:
# llama-server subprocess crashed / still starting / unreachable.
logger.error("openai passthrough stream: upstream unreachable: %s", e)
if resp is not None:
try:
await resp.aclose()
except Exception:
pass
try:
await client.aclose()
except Exception:
pass
raise HTTPException(
status_code = 502,
detail = _friendly_error(e),
)
if resp.status_code != 200:
err_bytes = await resp.aread()
err_text = err_bytes.decode("utf-8", errors = "replace")
logger.error(
"openai passthrough upstream error: status=%s body=%s",
resp.status_code,
err_text[:500],
)
upstream_status = resp.status_code
try:
await resp.aclose()
except Exception:
pass
try:
await client.aclose()
except Exception:
pass
raise HTTPException(
status_code = upstream_status,
detail = f"llama-server error: {err_text[:500]}",
)
async def _stream():
# Same httpx lifecycle pattern as _anthropic_passthrough_stream:
# avoid `async with` on the client/response AND explicitly save
# resp.aiter_lines() so we can close it ourselves in the finally
# block. See the long comment there for the full rationale on
# why the anonymous `async for raw_line in resp.aiter_lines():`
# pattern leaks an unclosed async generator that Python's
# asyncgen GC hook then finalizes in a different asyncio task,
# producing "Exception ignored in:" / "async generator ignored
# GeneratorExit" / anyio cancel-scope traces on Python 3.13 +
# httpcore 1.0.x.
lines_iter = None
try:
lines_iter = resp.aiter_lines()
async for raw_line in lines_iter:
if await request.is_disconnected():
cancel_event.set()
break
if not raw_line:
continue
if not raw_line.startswith("data: "):
continue
# Relay the llama-server SSE chunk verbatim so the client
# sees its native `id`, `finish_reason`, `delta.tool_calls`,
# and final `usage` unchanged.
yield raw_line + "\n\n"
if raw_line[6:].strip() == "[DONE]":
break
except Exception as e:
# Mid-stream failures still have to be reported inside the SSE
# body because the 200 response headers have already been
# committed by the time the first chunk flushes.
logger.error("openai passthrough stream error: %s", e)
err = {
"error": {
"message": _friendly_error(e),
"type": "server_error",
},
}
yield f"data: {json.dumps(err)}\n\n"
finally:
if lines_iter is not None:
try:
await lines_iter.aclose()
except Exception:
pass
try:
await resp.aclose()
except Exception:
pass
try:
await client.aclose()
except Exception:
pass
return StreamingResponse(
_stream(),
media_type = "text/event-stream",
headers = {
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
},
)
async def _openai_passthrough_non_streaming(
llama_backend,
payload,
model_name,
):
"""Non-streaming client-side pass-through for /v1/chat/completions.
Returns llama-server's JSON response verbatim (via JSONResponse) so the
client sees the native response ``id``, ``finish_reason`` (including
``"tool_calls"``), structured ``tool_calls``, and accurate ``usage``
token counts.
"""
target_url = f"{llama_backend.base_url}/v1/chat/completions"
body = _build_openai_passthrough_body(payload)
try:
async with httpx.AsyncClient() as client:
resp = await client.post(target_url, json = body, timeout = 600)
except httpx.RequestError as e:
# llama-server subprocess crashed / still starting / unreachable.
# Surface the same friendly message the sync chat path emits so
# operators don't see a bare 500 with no diagnostic.
logger.error("openai passthrough non-streaming: upstream unreachable: %s", e)
raise HTTPException(
status_code = 502,
detail = _friendly_error(e),
)
if resp.status_code != 200:
raise HTTPException(
status_code = resp.status_code,
detail = f"llama-server error: {resp.text[:500]}",
)
# Pass the upstream body through as raw bytes — skips a redundant
# parse+re-serialize round-trip and keeps the response truly
# verbatim (matches the docstring). Status is guaranteed 200 by
# the check above.
return Response(content = resp.content, media_type = "application/json")
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