Spaces:
Running
Running
File size: 8,354 Bytes
1e08a19 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 | """Convert AI SDK v6 Data Stream SSE to OpenAI chat-completions SSE format."""
from __future__ import annotations
import json
import time
from collections.abc import AsyncIterator
def _make_chunk(
request_id: str,
model: str,
*,
delta: dict,
finish_reason: str | None = None,
usage: dict | None = None,
) -> str:
"""Format a single OpenAI SSE chunk."""
chunk: dict = {
"id": request_id,
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": model,
"choices": [
{
"index": 0,
"delta": delta,
"finish_reason": finish_reason,
}
],
}
if usage is not None:
chunk["usage"] = usage
return f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
def _extract_usage(event: dict) -> dict | None:
"""Extract usage from finish event (handles both /api/chat and /api/doc/chat)."""
meta = event.get("messageMetadata", {})
# /api/doc/chat nests under custom.usage
raw = meta.get("custom", {}).get("usage")
# /api/chat puts usage directly in messageMetadata
if not raw:
raw = meta.get("usage")
if not raw:
return None
return {
"prompt_tokens": raw.get("inputTokens", 0),
"completion_tokens": raw.get("outputTokens", 0),
"total_tokens": raw.get("totalTokens", 0),
}
# ---------------------------------------------------------------------------
# Streaming conversion
# ---------------------------------------------------------------------------
async def convert_stream(
lines: AsyncIterator[str],
model: str,
request_id: str,
) -> AsyncIterator[str]:
"""Yield OpenAI-compatible SSE strings from an AI SDK data-stream."""
role_sent = False
# Accumulate tool call argument deltas per toolCallId
tool_calls_index: dict[str, int] = {} # toolCallId → index
next_tool_index = 0
async for raw_line in lines:
line = raw_line.rstrip("\r\n")
if not line:
continue
if line == "data: [DONE]":
yield "data: [DONE]\n\n"
return
if not line.startswith("data: "):
continue
try:
event = json.loads(line[6:])
except json.JSONDecodeError:
continue
event_type = event.get("type")
# --- Text events ---
if event_type == "text-start":
if not role_sent:
yield _make_chunk(
request_id, model, delta={"role": "assistant", "content": ""}
)
role_sent = True
elif event_type == "text-delta":
if not role_sent:
yield _make_chunk(
request_id, model, delta={"role": "assistant", "content": ""}
)
role_sent = True
yield _make_chunk(
request_id, model, delta={"content": event.get("delta", "")}
)
# --- Tool call events ---
elif event_type == "tool-input-start":
tc_id = event.get("toolCallId", "")
tool_name = event.get("toolName", "")
idx = next_tool_index
tool_calls_index[tc_id] = idx
next_tool_index += 1
delta: dict = {"tool_calls": [{
"index": idx,
"id": tc_id,
"type": "function",
"function": {"name": tool_name, "arguments": ""},
}]}
if not role_sent:
delta["role"] = "assistant"
role_sent = True
yield _make_chunk(request_id, model, delta=delta)
elif event_type == "tool-input-delta":
tc_id = event.get("toolCallId", "")
idx = tool_calls_index.get(tc_id, 0)
yield _make_chunk(
request_id, model,
delta={"tool_calls": [{
"index": idx,
"function": {"arguments": event.get("inputTextDelta", "")},
}]},
)
# tool-input-available — full args ready; nothing extra needed for
# streaming (client already accumulated deltas), but we can skip it.
# --- Finish events ---
elif event_type == "finish":
finish_reason = event.get("finishReason", "stop")
if finish_reason == "tool-calls":
finish_reason = "tool_calls"
usage = _extract_usage(event)
yield _make_chunk(
request_id, model,
delta={},
finish_reason=finish_reason,
usage=usage,
)
yield "data: [DONE]\n\n"
# ---------------------------------------------------------------------------
# Non-streaming helpers
# ---------------------------------------------------------------------------
def parse_full_response(lines: list[str]) -> tuple[str, list[dict], str, dict | None]:
"""Parse all SSE lines into (content, tool_calls, finish_reason, usage)."""
content_parts: list[str] = []
tool_calls: list[dict] = []
# Accumulate args per toolCallId
tool_args: dict[str, list[str]] = {}
tool_meta: dict[str, dict] = {} # toolCallId → {name, id}
finish_reason = "stop"
usage = None
for raw_line in lines:
line = raw_line.rstrip("\r\n")
if not line or not line.startswith("data: ") or line == "data: [DONE]":
continue
try:
event = json.loads(line[6:])
except json.JSONDecodeError:
continue
etype = event.get("type")
if etype == "text-delta":
content_parts.append(event.get("delta", ""))
elif etype == "tool-input-start":
tc_id = event.get("toolCallId", "")
tool_args[tc_id] = []
tool_meta[tc_id] = {
"name": event.get("toolName", ""),
"id": tc_id,
}
elif etype == "tool-input-delta":
tc_id = event.get("toolCallId", "")
tool_args.setdefault(tc_id, []).append(event.get("inputTextDelta", ""))
elif etype == "tool-input-available":
tc_id = event.get("toolCallId", "")
meta = tool_meta.get(tc_id, {"name": event.get("toolName", ""), "id": tc_id})
tool_calls.append({
"id": meta["id"],
"type": "function",
"function": {
"name": meta["name"],
"arguments": json.dumps(event.get("input", {}), ensure_ascii=False),
},
})
elif etype == "finish":
finish_reason = event.get("finishReason", "stop")
if finish_reason == "tool-calls":
finish_reason = "tool_calls"
usage = _extract_usage(event)
# If we got tool-input-start/delta but no tool-input-available, build from deltas
for tc_id, meta in tool_meta.items():
if not any(tc.get("id") == tc_id for tc in tool_calls):
tool_calls.append({
"id": meta["id"],
"type": "function",
"function": {
"name": meta["name"],
"arguments": "".join(tool_args.get(tc_id, [])),
},
})
return "".join(content_parts), tool_calls, finish_reason, usage
def build_non_stream_response(
request_id: str,
model: str,
content: str,
finish_reason: str = "stop",
usage: dict | None = None,
tool_calls: list[dict] | None = None,
) -> dict:
"""Build a non-streaming chat.completions response object."""
message: dict = {"role": "assistant", "content": content or None}
if tool_calls:
message["tool_calls"] = tool_calls
if not content:
message["content"] = None
resp: dict = {
"id": request_id,
"object": "chat.completion",
"created": int(time.time()),
"model": model,
"choices": [
{
"index": 0,
"message": message,
"finish_reason": finish_reason,
}
],
"usage": usage or {
"prompt_tokens": 0,
"completion_tokens": 0,
"total_tokens": 0,
},
}
return resp
|