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Dockerfile

@@ -0,0 +1,18 @@
+FROM python:3.13-slim
+
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
+
+WORKDIR /app
+
+COPY pyproject.toml uv.lock ./
+RUN uv sync --frozen --no-dev --no-install-project
+
+COPY config.py main.py ./
+COPY api/ api/
+
+ENV API_HOST=0.0.0.0
+ENV API_PORT=8080
+
+EXPOSE 8080
+
+CMD ["uv", "run", "uvicorn", "api.server:app", "--host", "0.0.0.0", "--port", "8080"]

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 XXXxx7258
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

api/converter/messages.py

@@ -0,0 +1,192 @@
+"""Convert between OpenAI chat format and AI SDK v6 format."""
+
+from __future__ import annotations
+
+import json
+
+from nanoid import generate as nanoid
+
+from config import MODEL_MAP
+
+_ALPHABET = "abcdefghijklmnopqrstuvwxyz0123456789"
+
+
+def _gen_id(prefix: str = "msg", size: int = 12) -> str:
+    return f"{prefix}_{nanoid(_ALPHABET, size)}"
+
+
+def _resolve_model(model: str) -> str:
+    """Map short model name to assistant-ui API identifier."""
+    if model in MODEL_MAP:
+        return MODEL_MAP[model]
+    if "/" in model:
+        return model
+    return f"openai/{model}"
+
+
+def _guess_media_type(url: str) -> str:
+    """Infer media type from a data-URI or file extension."""
+    if url.startswith("data:"):
+        # data:image/png;base64,...
+        header = url.split(",", 1)[0]
+        if ";" in header:
+            return header[5:].split(";")[0]  # strip "data:" prefix
+        return header[5:]
+    lower = url.lower()
+    for ext, mt in (
+        (".png", "image/png"), (".jpg", "image/jpeg"), (".jpeg", "image/jpeg"),
+        (".gif", "image/gif"), (".webp", "image/webp"), (".svg", "image/svg+xml"),
+    ):
+        if ext in lower:
+            return mt
+    return "image/png"
+
+
+def _convert_tools(tools: list[dict] | None) -> dict:
+    """Convert OpenAI tools list to AI SDK frontend tools format.
+
+    OpenAI format:
+        [{"type": "function", "function": {"name": "...", "description": "...",
+          "parameters": {...}}}]
+
+    AI SDK format:
+        {"tool_name": {"description": "...", "parameters": {...}}}
+    """
+    if not tools:
+        return {}
+    result = {}
+    for tool in tools:
+        if tool.get("type") != "function":
+            continue
+        func = tool.get("function", {})
+        name = func.get("name", "")
+        if not name:
+            continue
+        entry: dict = {"parameters": func.get("parameters", {"type": "object"})}
+        if func.get("description"):
+            entry["description"] = func["description"]
+        result[name] = entry
+    return result
+
+
+def openai_to_ai_sdk(
+    messages: list[dict],
+    model: str,
+    tools: list[dict] | None = None,
+) -> dict:
+    """Convert an OpenAI chat-completions request to AI SDK v6 payload."""
+    sdk_messages: list[dict] = []
+
+    for msg in messages:
+        role = msg.get("role", "")
+        content = msg.get("content", "")
+
+        if role == "system":
+            # Inject as AI SDK system message in the messages array.
+            # convertToModelMessages() handles role:"system" and forwards
+            # it as a model-level system message — bypasses the server's
+            # missing top-level "system" param in streamText().
+            text = content if isinstance(content, str) else ""
+            if text:
+                sdk_messages.append({
+                    "role": "system",
+                    "parts": [{"type": "text", "text": text}],
+                    "metadata": {"custom": {}},
+                    "id": _gen_id("sys"),
+                })
+            continue
+
+        if role == "user":
+            if isinstance(content, list):
+                parts = []
+                for part in content:
+                    if isinstance(part, str):
+                        parts.append({"type": "text", "text": part})
+                    elif isinstance(part, dict):
+                        ptype = part.get("type", "")
+                        if ptype == "text":
+                            parts.append({"type": "text", "text": part["text"]})
+                        elif ptype == "image_url":
+                            # OpenAI vision format → AI SDK file part
+                            img = part.get("image_url", {})
+                            url = img.get("url", "") if isinstance(img, dict) else str(img)
+                            media_type = _guess_media_type(url)
+                            parts.append({
+                                "type": "file",
+                                "mediaType": media_type,
+                                "url": url,
+                            })
+            else:
+                parts = [{"type": "text", "text": str(content)}]
+            sdk_messages.append({
+                "role": "user",
+                "parts": parts,
+                "metadata": {"custom": {}},
+                "id": _gen_id("msg"),
+            })
+
+        elif role == "assistant":
+            parts = []
+            # Text content
+            if isinstance(content, str) and content:
+                parts.append({"type": "text", "text": content})
+            elif isinstance(content, list):
+                for part in content:
+                    if isinstance(part, dict) and part.get("type") == "text":
+                        parts.append({"type": "text", "text": part["text"]})
+            # Tool calls → tool-invocation parts
+            # Initially set state to "input-available" (args ready, no result yet)
+            for tc in msg.get("tool_calls") or []:
+                func = tc.get("function", {})
+                try:
+                    args = json.loads(func.get("arguments", "{}"))
+                except (json.JSONDecodeError, TypeError):
+                    args = {}
+                parts.append({
+                    "type": "tool-invocation",
+                    "toolCallId": tc.get("id", _gen_id("call")),
+                    "toolName": func.get("name", ""),
+                    "input": args,
+                    "state": "input-available",
+                })
+            sdk_messages.append({
+                "role": "assistant",
+                "parts": parts,
+                "metadata": {"custom": {}},
+                "id": _gen_id("msg"),
+            })
+
+        elif role == "tool":
+            # Tool result — attach output to the matching tool-invocation
+            # in the preceding assistant message, using AI SDK v6 field names.
+            tool_call_id = msg.get("tool_call_id", "")
+            # Parse result: try JSON object first, fall back to string
+            if isinstance(content, str):
+                try:
+                    result_obj = json.loads(content)
+                except (json.JSONDecodeError, TypeError):
+                    result_obj = content
+            else:
+                result_obj = content
+            for prev in reversed(sdk_messages):
+                if prev["role"] != "assistant":
+                    continue
+                for part in prev["parts"]:
+                    if (
+                        part.get("type") == "tool-invocation"
+                        and part.get("toolCallId") == tool_call_id
+                    ):
+                        part["state"] = "output-available"
+                        part["output"] = result_obj
+                        break
+                break
+
+    return {
+        "system": "",
+        "config": {"modelName": _resolve_model(model)},
+        "tools": _convert_tools(tools),
+        "id": _gen_id("thread"),
+        "messages": sdk_messages,
+        "trigger": "submit-message",
+        "metadata": {},
+    }

api/converter/stream.py

@@ -0,0 +1,260 @@
+"""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

api/provider.py

@@ -0,0 +1,41 @@
+"""Call the assistant-ui upstream LLM endpoint."""
+
+from __future__ import annotations
+
+from collections.abc import AsyncIterator
+
+import httpx
+
+from config import UPSTREAM_HEADERS, UPSTREAM_URL
+
+
+class UpstreamError(Exception):
+    def __init__(self, status_code: int, message: str):
+        self.status_code = status_code
+        super().__init__(message)
+
+
+async def call_upstream(payload: dict) -> AsyncIterator[str]:
+    """POST to assistant-ui and yield raw SSE lines as they arrive."""
+    async with httpx.AsyncClient(timeout=httpx.Timeout(120, connect=10)) as client:
+        async with client.stream(
+            "POST",
+            UPSTREAM_URL,
+            json=payload,
+            headers=UPSTREAM_HEADERS,
+        ) as resp:
+            if resp.status_code == 429:
+                raise UpstreamError(429, "Rate limit exceeded (upstream: 5 req / 30s per IP)")
+            if resp.status_code >= 400:
+                body = await resp.aread()
+                raise UpstreamError(resp.status_code, body.decode(errors="replace")[:200])
+            async for line in resp.aiter_lines():
+                yield line
+
+
+async def call_upstream_full(payload: dict) -> list[str]:
+    """POST to assistant-ui and collect all SSE lines (for non-stream mode)."""
+    lines: list[str] = []
+    async for line in call_upstream(payload):
+        lines.append(line)
+    return lines

api/routes/chat.py

@@ -0,0 +1,110 @@
+"""POST /v1/chat/completions — OpenAI-compatible chat endpoint."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from fastapi import APIRouter, Request
+from fastapi.responses import JSONResponse, StreamingResponse
+from pydantic import BaseModel
+
+from api.converter.messages import openai_to_ai_sdk, _gen_id
+from api.converter.stream import build_non_stream_response, convert_stream, parse_full_response
+from api.provider import call_upstream, call_upstream_full
+from config import DEFAULT_MODEL
+
+router = APIRouter()
+
+
+class FunctionDef(BaseModel):
+    name: str
+    description: str = ""
+    parameters: dict = {}
+
+
+class ToolDef(BaseModel):
+    type: str = "function"
+    function: FunctionDef
+
+
+class ChatMessage(BaseModel):
+    role: str
+    content: Any = ""
+    tool_calls: list[dict] | None = None
+    tool_call_id: str | None = None
+
+
+class ChatRequest(BaseModel):
+    model: str = DEFAULT_MODEL
+    messages: list[ChatMessage]
+    stream: bool = False
+    tools: list[ToolDef] | None = None
+    tool_choice: Any = None
+    temperature: float | None = None
+    max_tokens: int | None = None
+
+
+@router.post("/v1/chat/completions")
+async def chat_completions(body: ChatRequest, request: Request):
+    request_id = f"chatcmpl-{_gen_id('', 24)}"
+    model = body.model
+
+    tools_raw = [t.model_dump() for t in body.tools] if body.tools else None
+
+    try:
+        payload = openai_to_ai_sdk(
+            [m.model_dump() for m in body.messages],
+            model,
+            tools=tools_raw,
+        )
+    except Exception as e:
+        return JSONResponse(
+            status_code=400,
+            content=_error_body(f"Invalid request: {e}", "invalid_request_error"),
+        )
+
+    if body.stream:
+        return StreamingResponse(
+            _stream_generator(payload, model, request_id),
+            media_type="text/event-stream",
+            headers={
+                "Cache-Control": "no-cache",
+                "X-Accel-Buffering": "no",
+            },
+        )
+
+    # Non-streaming
+    try:
+        lines = await call_upstream_full(payload)
+    except Exception as e:
+        return JSONResponse(
+            status_code=502,
+            content=_error_body(f"Upstream error: {e}", "upstream_error"),
+        )
+
+    content, tool_calls, finish_reason, usage = parse_full_response(lines)
+    return build_non_stream_response(
+        request_id, model, content, finish_reason, usage,
+        tool_calls=tool_calls or None,
+    )
+
+
+async def _stream_generator(payload: dict, model: str, request_id: str):
+    try:
+        upstream = call_upstream(payload)
+        async for chunk in convert_stream(upstream, model, request_id):
+            yield chunk
+    except Exception as e:
+        error_chunk = {
+            "id": request_id,
+            "object": "chat.completion.chunk",
+            "choices": [{"index": 0, "delta": {}, "finish_reason": "error"}],
+            "error": {"message": str(e), "type": "upstream_error"},
+        }
+        import json
+        yield f"data: {json.dumps(error_chunk)}\n\n"
+        yield "data: [DONE]\n\n"
+
+
+def _error_body(message: str, error_type: str) -> dict:
+    return {"error": {"message": message, "type": error_type}}

api/routes/models.py

@@ -0,0 +1,26 @@
+"""GET /v1/models — list available models."""
+
+from __future__ import annotations
+
+import time
+
+from fastapi import APIRouter
+
+from config import MODEL_MAP
+
+router = APIRouter()
+
+
+@router.get("/v1/models")
+async def list_models() -> dict:
+    now = int(time.time())
+    data = [
+        {
+            "id": short_name,
+            "object": "model",
+            "created": now,
+            "owned_by": full_name.split("/")[0],
+        }
+        for short_name, full_name in MODEL_MAP.items()
+    ]
+    return {"object": "list", "data": data}

api/server.py

@@ -0,0 +1,46 @@
+"""FastAPI application — assistant-ui 2API service."""
+
+from __future__ import annotations
+
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
+
+from api.routes.chat import router as chat_router
+from api.routes.models import router as models_router
+from config import API_KEY
+
+app = FastAPI(title="assistant-ui 2API", version="1.0.0")
+
+
+# ---------- Auth middleware ----------
+@app.middleware("http")
+async def auth_middleware(request: Request, call_next):
+    # Skip auth for health / docs
+    if request.url.path in ("/", "/health", "/docs", "/openapi.json"):
+        return await call_next(request)
+
+    if API_KEY:
+        auth = request.headers.get("authorization", "")
+        token = auth.removeprefix("Bearer ").strip()
+        if token != API_KEY:
+            return JSONResponse(
+                status_code=401,
+                content={"error": {"message": "Invalid API key", "type": "auth_error"}},
+            )
+
+    return await call_next(request)
+
+
+# ---------- Routes ----------
+app.include_router(chat_router)
+app.include_router(models_router)
+
+
+@app.get("/")
+async def root():
+    return {"status": "ok", "service": "assistant-ui 2API"}
+
+
+@app.get("/health")
+async def health():
+    return {"status": "ok"}

config.py

@@ -0,0 +1,33 @@
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+API_HOST = os.getenv("API_HOST", "0.0.0.0")
+API_PORT = int(os.getenv("API_PORT", "8080"))
+API_KEY = os.getenv("API_KEY", "sk-assistant-2api-free")
+
+# /api/chat — clean endpoint, no hardcoded system prompt
+# /api/doc/chat — docs assistant with hardcoded persona + tools (avoid)
+UPSTREAM_URL = os.getenv(
+    "UPSTREAM_URL", "https://www.assistant-ui.com/api/chat"
+)
+
+# OpenAI-compatible name → assistant-ui API identifier
+# Note: claude-sonnet-4.6 is disabled server-side (falls back to gpt-5.4)
+MODEL_MAP: dict[str, str] = {
+    "gpt-5.4": "openai/gpt-5.4",
+    "gpt-5-nano": "openai/gpt-5-nano",
+    "gemini-3-flash": "google/gemini-3-flash",
+    "kimi-k2.5": "moonshotai/kimi-k2.5",
+    "deepseek-v3.2": "deepseek/deepseek-v3.2",
+}
+
+DEFAULT_MODEL = "gpt-5.4"
+
+UPSTREAM_HEADERS: dict[str, str] = {
+    "content-type": "application/json",
+    "user-agent": "ai-sdk/6.0.116 runtime/browser",
+    "origin": "https://www.assistant-ui.com",
+    "referer": "https://www.assistant-ui.com/docs",
+}

docker-compose.yml

@@ -0,0 +1,8 @@
+services:
+  assistant-2api:
+    image: zhigengniao/assistant-2api:latest
+    ports:
+      - "${API_PORT:-8080}:8080"
+    env_file:
+      - .env
+    restart: unless-stopped

main.py

@@ -0,0 +1,6 @@
+import uvicorn
+
+from config import API_HOST, API_PORT
+
+if __name__ == "__main__":
+    uvicorn.run("api.server:app", host=API_HOST, port=API_PORT)

pyproject.toml

@@ -0,0 +1,14 @@
+[project]
+name = "assistant"
+version = "0.1.0"
+description = "Add your description here"
+requires-python = ">=3.13"
+dependencies = [
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+    "httpx>=0.28.1",
+    "nanoid>=2.0.0",
+    "openai>=2.26.0",
+    "pydantic>=2.12.5",
+    "python-dotenv>=1.2.2",
+    "uvicorn>=0.41.0",
+]

uv.lock

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