two version of R2R are here HEAD master

2 files changed, 163 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/realtime_api/README.md b/.venv/lib/python3.12/site-packages/litellm/realtime_api/README.md
new file mode 100644
index 00000000..6b467c05
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/realtime_api/README.md
@@ -0,0 +1 @@
+Abstraction / Routing logic for OpenAI's `/v1/realtime` endpoint.
\ No newline at end of file
diff --git a/.venv/lib/python3.12/site-packages/litellm/realtime_api/main.py b/.venv/lib/python3.12/site-packages/litellm/realtime_api/main.py
new file mode 100644
index 00000000..8330de0f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/realtime_api/main.py
@@ -0,0 +1,162 @@
+"""Abstraction function for OpenAI's realtime API"""
+
+from typing import Any, Optional
+
+import litellm
+from litellm import get_llm_provider
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.router import GenericLiteLLMParams
+
+from ..litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
+from ..llms.azure.realtime.handler import AzureOpenAIRealtime
+from ..llms.openai.realtime.handler import OpenAIRealtime
+from ..utils import client as wrapper_client
+
+azure_realtime = AzureOpenAIRealtime()
+openai_realtime = OpenAIRealtime()
+
+
+@wrapper_client
+async def _arealtime(
+    model: str,
+    websocket: Any,  # fastapi websocket
+    api_base: Optional[str] = None,
+    api_key: Optional[str] = None,
+    api_version: Optional[str] = None,
+    azure_ad_token: Optional[str] = None,
+    client: Optional[Any] = None,
+    timeout: Optional[float] = None,
+    **kwargs,
+):
+    """
+    Private function to handle the realtime API call.
+
+    For PROXY use only.
+    """
+    litellm_logging_obj: LiteLLMLogging = kwargs.get("litellm_logging_obj")  # type: ignore
+    litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
+    proxy_server_request = kwargs.get("proxy_server_request", None)
+    model_info = kwargs.get("model_info", None)
+    metadata = kwargs.get("metadata", {})
+    user = kwargs.get("user", None)
+    litellm_params = GenericLiteLLMParams(**kwargs)
+
+    model, _custom_llm_provider, dynamic_api_key, dynamic_api_base = get_llm_provider(
+        model=model,
+        api_base=api_base,
+        api_key=api_key,
+    )
+
+    litellm_logging_obj.update_environment_variables(
+        model=model,
+        user=user,
+        optional_params={},
+        litellm_params={
+            "litellm_call_id": litellm_call_id,
+            "proxy_server_request": proxy_server_request,
+            "model_info": model_info,
+            "metadata": metadata,
+            "preset_cache_key": None,
+            "stream_response": {},
+        },
+        custom_llm_provider=_custom_llm_provider,
+    )
+
+    if _custom_llm_provider == "azure":
+        api_base = (
+            dynamic_api_base
+            or litellm_params.api_base
+            or litellm.api_base
+            or get_secret_str("AZURE_API_BASE")
+        )
+        # set API KEY
+        api_key = (
+            dynamic_api_key
+            or litellm.api_key
+            or litellm.openai_key
+            or get_secret_str("AZURE_API_KEY")
+        )
+
+        await azure_realtime.async_realtime(
+            model=model,
+            websocket=websocket,
+            api_base=api_base,
+            api_key=api_key,
+            api_version="2024-10-01-preview",
+            azure_ad_token=None,
+            client=None,
+            timeout=timeout,
+            logging_obj=litellm_logging_obj,
+        )
+    elif _custom_llm_provider == "openai":
+        api_base = (
+            dynamic_api_base
+            or litellm_params.api_base
+            or litellm.api_base
+            or "https://api.openai.com/"
+        )
+        # set API KEY
+        api_key = (
+            dynamic_api_key
+            or litellm.api_key
+            or litellm.openai_key
+            or get_secret_str("OPENAI_API_KEY")
+        )
+
+        await openai_realtime.async_realtime(
+            model=model,
+            websocket=websocket,
+            logging_obj=litellm_logging_obj,
+            api_base=api_base,
+            api_key=api_key,
+            client=None,
+            timeout=timeout,
+        )
+    else:
+        raise ValueError(f"Unsupported model: {model}")
+
+
+async def _realtime_health_check(
+    model: str,
+    custom_llm_provider: str,
+    api_key: Optional[str],
+    api_base: Optional[str] = None,
+    api_version: Optional[str] = None,
+):
+    """
+    Health check for realtime API - tries connection to the realtime API websocket
+
+    Args:
+        model: str - model name
+        api_base: str - api base
+        api_version: Optional[str] - api version
+        api_key: str - api key
+        custom_llm_provider: str - custom llm provider
+
+    Returns:
+        bool - True if connection is successful, False otherwise
+    Raises:
+        Exception - if the connection is not successful
+    """
+    import websockets
+
+    url: Optional[str] = None
+    if custom_llm_provider == "azure":
+        url = azure_realtime._construct_url(
+            api_base=api_base or "",
+            model=model,
+            api_version=api_version or "2024-10-01-preview",
+        )
+    elif custom_llm_provider == "openai":
+        url = openai_realtime._construct_url(
+            api_base=api_base or "https://api.openai.com/", model=model
+        )
+    else:
+        raise ValueError(f"Unsupported model: {model}")
+    async with websockets.connect(  # type: ignore
+        url,
+        extra_headers={
+            "api-key": api_key,  # type: ignore
+        },
+    ):
+        return True