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feat: Add WebSocket endpoint support for OpenAI Responses API
- Add WebSocket route handling for /v1/responses and /responses endpoints - Add WebSocket message types to schema (ORWebSocketClientMessage, ORWebSocketServerEvent, etc.) - Add connection-local cache types for response storage - Implement initial WebSocket infrastructure (handler to be added in next commit) Signed-off-by: team-coding-agent-1 <team-coding-agent-1@localai.dev>
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‎IMPLEMENTATION_PLAN.md‎

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# WebSocket Mode Implementation Plan for OpenAI Responses API
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## Overview
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Implement WebSocket support for LocalAI's OpenAI API-compatible Responses endpoint, enabling persistent WebSocket connections for long-running, tool-call-heavy agentic workflows.
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## Technical Requirements
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### 1. WebSocket Endpoint
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- **Endpoint**: `ws://<host>:<port>/v1/responses`
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- **Upgrade**: HTTP upgrade from POST /v1/responses when `Upgrade: websocket` header is present
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### 2. Message Types (Client → Server)
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#### response.create (Initial Turn)
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```json
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{
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"type": "response.create",
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"model": "gpt-4o",
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"store": false,
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"input": [...],
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"tools": []
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}
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```
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#### response.create with Continuation (Subsequent Turns)
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```json
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{
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"type": "response.create",
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"model": "gpt-4o",
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"store": false,
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"previous_response_id": "resp_123",
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"input": [...],
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"tools": []
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}
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```
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### 3. Response Events (Server → Client)
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1. **response.created** - Response object created
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2. **response.progress** - Incremental output
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3. **response.function_call_arguments.delta** - Streaming function arguments
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4. **response.function_call_arguments.done** - Function call complete
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5. **response.done** - Final response
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### 4. Connection Management
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- Track active connections with 60-minute timeout
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- Connection-local cache for responses (when store=false)
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- One in-flight response at a time per connection
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### 5. Error Handling
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- `previous_response_not_found` (400)
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- `websocket_connection_limit_reached` (400)
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## Implementation Steps
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### Step 1: Add WebSocket Schema Types
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- Add WebSocket message types to `core/schema/openresponses.go`
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- Add connection-related types
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### Step 2: Add WebSocket Route
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- Modify `core/http/routes/openresponses.go` to handle WebSocket upgrade
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- Add GET /v1/responses WebSocket endpoint
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### Step 3: Create WebSocket Handler
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- Create `core/http/endpoints/openresponses/websocket.go`
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- Implement connection handling
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- Implement message parsing
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- Implement event streaming
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### Step 4: Add Connection Store
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- Implement connection management in store
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- Add 60-minute timeout
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- Add connection-local cache
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## Files to Modify/Create
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1. `core/schema/openresponses.go` - Add WebSocket types
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2. `core/http/routes/openresponses.go` - Add WebSocket route
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3. `core/http/endpoints/openresponses/websocket.go` - New WebSocket handler (create)
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4. `core/http/endpoints/openresponses/store.go` - Add connection management

‎backend/python/nemo/backend.py‎

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#!/usr/bin/env python3
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"""
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gRPC server of LocalAI for NVIDIA NEMO Toolkit ASR.
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GRPC server of LocalAI for NVIDIA NEMO Toolkit ASR.
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"""
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from concurrent import futures
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import time
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import backend_pb2_grpc
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import torch
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import nemo.collections.asr as nemo_asr
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import numpy as np
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try:
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import torchaudio
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TORCHAUDIO_AVAILABLE = True
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except ImportError:
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TORCHAUDIO_AVAILABLE = False
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print("[WARNING] torchaudio not available, will use fallback audio loading", file=sys.stderr)
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import grpc
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MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
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def load_audio_np(audio_path, target_sample_rate=16000):
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"""Load audio file as numpy array using available methods."""
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if TORCHAUDIO_AVAILABLE:
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try:
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waveform, sample_rate = torchaudio.load(audio_path)
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# Convert to mono if stereo
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if waveform.shape[0] > 1:
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waveform = waveform.mean(dim=0, keepdim=True)
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# Resample if needed
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if sample_rate != target_sample_rate:
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resampler = torchaudio.transforms.Resample(sample_rate, target_sample_rate)
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waveform = resampler(waveform)
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# Convert to numpy
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audio_np = waveform.squeeze().numpy()
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return audio_np, target_sample_rate
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except Exception as e:
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print(f"[WARNING] torchaudio loading failed: {e}, trying fallback", file=sys.stderr)
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# Fallback: try using scipy or soundfile
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try:
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import soundfile as sf
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audio_np, sample_rate = sf.read(audio_path)
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if audio_np.ndim > 1:
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audio_np = audio_np.mean(axis=1)
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if sample_rate != target_sample_rate:
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from scipy.signal import resample
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num_samples = int(len(audio_np) * target_sample_rate / sample_rate)
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audio_np = resample(audio_np, num_samples)
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return audio_np, target_sample_rate
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except ImportError:
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pass
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try:
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from scipy.io import wavfile
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sample_rate, audio_np = wavfile.read(audio_path)
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if audio_np.ndim > 1:
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audio_np = audio_np.mean(axis=1)
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return audio_np, sample_rate
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except ImportError:
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pass
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raise RuntimeError("No audio loading library available (torchaudio, soundfile, scipy)")
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class BackendServicer(backend_pb2_grpc.BackendServicer):
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def Health(self, request, context):
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return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
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print(f"Error: Audio file not found: {audio_path}", file=sys.stderr)
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return backend_pb2.TranscriptResult(segments=[], text="")
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# NEMO's transcribe method accepts a list of audio paths and returns a list of transcripts
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results = self.model.transcribe([audio_path])
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# Load audio as numpy array to avoid lhotse dataloader issues
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audio_np, sample_rate = load_audio_np(audio_path, target_sample_rate=16000)
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# Convert to torch tensor
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audio_tensor = torch.from_numpy(audio_np).float()
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audio_tensor = audio_tensor.unsqueeze(0) # Add batch dimension
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# Use the model's transcribe method with the tensor directly
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# Some NEMO models accept audio tensors directly
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try:
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# Try passing the waveform tensor directly
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results = self.model.transcribe(audio_tensor, return_char_alignments=False)
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except TypeError:
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# Fallback: try with dict format
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results = self.model.transcribe(
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[{"audio_file": audio_path}],
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return_char_alignments=False
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)
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if not results or len(results) == 0:
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print("[WARNING] No transcription results returned", file=sys.stderr)
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return backend_pb2.TranscriptResult(segments=[], text="")
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# Get the transcript text from the first result
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text = results[0]
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if isinstance(results, list) and len(results) > 0:
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text = results[0]
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elif isinstance(results, dict) and "text" in results:
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text = results["text"]
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else:
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text = str(results) if results else ""
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if text:
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# Create a single segment with the full transcription
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result_segments.append(backend_pb2.TranscriptSegment(

‎backend/python/nemo/requirements.txt‎

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packaging==24.1
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setuptools
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pyarrow==20.0.0
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torchaudio
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soundfile
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scipy
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numpy

‎core/http/routes/openresponses.go‎

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cancelResponseHandler := openresponses.CancelResponseEndpoint()
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app.POST("/v1/responses/:id/cancel", cancelResponseHandler, middleware.TraceMiddleware(application))
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app.POST("/responses/:id/cancel", cancelResponseHandler, middleware.TraceMiddleware(application))
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// WebSocket endpoint for OpenAI Responses API WebSocket Mode
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websocketHandler := openresponses.WebSocketEndpoint(
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application.ModelConfigLoader(),
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application.ModelLoader(),
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application.TemplatesEvaluator(),
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application.ApplicationConfig(),
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)
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// WebSocket at /v1/responses (GET method for upgrade)
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app.GET("/v1/responses", websocketHandler, middleware.TraceMiddleware(application))
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app.GET("/responses", websocketHandler, middleware.TraceMiddleware(application))
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}
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// setOpenResponsesRequestContext sets up the context and cancel function for Open Responses requests

‎core/schema/openresponses.go‎

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package schema
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import (
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"time"
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"context"
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)
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Logprobs: orLogprobs, // REQUIRED - must always be present as array (empty if none)
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}
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}
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// WebSocket message types for Open Responses API WebSocket Mode
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// https://developers.openai.com/api/docs/guides/websocket-mode
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// ORWebSocketMessage represents a WebSocket message (client -> server or server -> client)
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type ORWebSocketMessage struct {
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Type string `json:"type"` // response.create, response.created, response.progress, etc.
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}
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// ORWebSocketClientMessage represents a client message to the WebSocket endpoint
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type ORWebSocketClientMessage struct {
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Type string `json:"type"` // "response.create"
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Model string `json:"model,omitempty"`
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Input interface{} `json:"input,omitempty"`
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Tools []ORFunctionTool `json:"tools,omitempty"`
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ToolChoice interface{} `json:"tool_choice,omitempty"`
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MaxOutputTokens *int `json:"max_output_tokens,omitempty"`
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Temperature *float64 `json:"temperature,omitempty"`
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TopP *float64 `json:"top_p,omitempty"`
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Truncation string `json:"truncation,omitempty"`
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Instructions string `json:"instructions,omitempty"`
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Reasoning *ORReasoningParam `json:"reasoning,omitempty"`
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Metadata map[string]string `json:"metadata,omitempty"`
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PreviousResponseID string `json:"previous_response_id,omitempty"`
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Store *bool `json:"store,omitempty"`
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TextFormat interface{} `json:"text_format,omitempty"`
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ServiceTier string `json:"service_tier,omitempty"`
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AllowedTools []string `json:"allowed_tools,omitempty"`
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ParallelToolCalls *bool `json:"parallel_tool_calls,omitempty"`
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PresencePenalty *float64 `json:"presence_penalty,omitempty"`
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FrequencyPenalty *float64 `json:"frequency_penalty,omitempty"`
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TopLogprobs *int `json:"top_logprobs,omitempty"`
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MaxToolCalls *int `json:"max_tool_calls,omitempty"`
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Generate *bool `json:"generate,omitempty"` // If false, just warm up and return response_id
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}
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// ORWebSocketServerEvent represents a server event to the WebSocket
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type ORWebSocketServerEvent struct {
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Type string `json:"type"` // response.created, response.progress, etc.
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ResponseID string `json:"response_id,omitempty"`
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Response *ORResponseResource `json:"response,omitempty"`
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OutputIndex *int `json:"output_index,omitempty"`
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Output []ORItemField `json:"output,omitempty"`
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ItemID string `json:"item_id,omitempty"`
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Item *ORItemField `json:"item,omitempty"`
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ContentIndex *int `json:"content_index,omitempty"`
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Delta *string `json:"delta,omitempty"`
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Text *string `json:"text,omitempty"`
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CallID string `json:"call_id,omitempty"`
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Arguments *string `json:"arguments,omitempty"`
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Error *ORError `json:"error,omitempty"`
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}
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// ORWebSocketError represents a WebSocket error event
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type ORWebSocketError struct {
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Type string `json:"type"` // error
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Code string `json:"code,omitempty"` // previous_response_not_found, websocket_connection_limit_reached, etc.
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Message string `json:"message"`
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Param string `json:"param,omitempty"`
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}
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// ConnectionLocalCacheEntry represents a cached response in connection-local storage
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type ConnectionLocalCacheEntry struct {
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ResponseID string
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Response *ORResponseResource
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Input *ORWebSocketClientMessage
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CachedAt time.Time
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ExpiresAt *time.Time
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}

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