two version of R2R are here HEAD master

1 files changed, 218 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/batches/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/batches/handler.py
new file mode 100644
index 00000000..b82268be
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/batches/handler.py
@@ -0,0 +1,218 @@
+import json
+from typing import Any, Coroutine, Dict, Optional, Union
+
+import httpx
+
+import litellm
+from litellm.llms.custom_httpx.http_handler import (
+    _get_httpx_client,
+    get_async_httpx_client,
+)
+from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
+from litellm.types.llms.openai import CreateBatchRequest
+from litellm.types.llms.vertex_ai import (
+    VERTEX_CREDENTIALS_TYPES,
+    VertexAIBatchPredictionJob,
+)
+from litellm.types.utils import LiteLLMBatch
+
+from .transformation import VertexAIBatchTransformation
+
+
+class VertexAIBatchPrediction(VertexLLM):
+    def __init__(self, gcs_bucket_name: str, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.gcs_bucket_name = gcs_bucket_name
+
+    def create_batch(
+        self,
+        _is_async: bool,
+        create_batch_data: CreateBatchRequest,
+        api_base: Optional[str],
+        vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
+        vertex_project: Optional[str],
+        vertex_location: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+    ) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
+
+        sync_handler = _get_httpx_client()
+
+        access_token, project_id = self._ensure_access_token(
+            credentials=vertex_credentials,
+            project_id=vertex_project,
+            custom_llm_provider="vertex_ai",
+        )
+
+        default_api_base = self.create_vertex_url(
+            vertex_location=vertex_location or "us-central1",
+            vertex_project=vertex_project or project_id,
+        )
+
+        if len(default_api_base.split(":")) > 1:
+            endpoint = default_api_base.split(":")[-1]
+        else:
+            endpoint = ""
+
+        _, api_base = self._check_custom_proxy(
+            api_base=api_base,
+            custom_llm_provider="vertex_ai",
+            gemini_api_key=None,
+            endpoint=endpoint,
+            stream=None,
+            auth_header=None,
+            url=default_api_base,
+        )
+
+        headers = {
+            "Content-Type": "application/json; charset=utf-8",
+            "Authorization": f"Bearer {access_token}",
+        }
+
+        vertex_batch_request: VertexAIBatchPredictionJob = (
+            VertexAIBatchTransformation.transform_openai_batch_request_to_vertex_ai_batch_request(
+                request=create_batch_data
+            )
+        )
+
+        if _is_async is True:
+            return self._async_create_batch(
+                vertex_batch_request=vertex_batch_request,
+                api_base=api_base,
+                headers=headers,
+            )
+
+        response = sync_handler.post(
+            url=api_base,
+            headers=headers,
+            data=json.dumps(vertex_batch_request),
+        )
+
+        if response.status_code != 200:
+            raise Exception(f"Error: {response.status_code} {response.text}")
+
+        _json_response = response.json()
+        vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
+            response=_json_response
+        )
+        return vertex_batch_response
+
+    async def _async_create_batch(
+        self,
+        vertex_batch_request: VertexAIBatchPredictionJob,
+        api_base: str,
+        headers: Dict[str, str],
+    ) -> LiteLLMBatch:
+        client = get_async_httpx_client(
+            llm_provider=litellm.LlmProviders.VERTEX_AI,
+        )
+        response = await client.post(
+            url=api_base,
+            headers=headers,
+            data=json.dumps(vertex_batch_request),
+        )
+        if response.status_code != 200:
+            raise Exception(f"Error: {response.status_code} {response.text}")
+
+        _json_response = response.json()
+        vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
+            response=_json_response
+        )
+        return vertex_batch_response
+
+    def create_vertex_url(
+        self,
+        vertex_location: str,
+        vertex_project: str,
+    ) -> str:
+        """Return the base url for the vertex garden models"""
+        #  POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs
+        return f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/batchPredictionJobs"
+
+    def retrieve_batch(
+        self,
+        _is_async: bool,
+        batch_id: str,
+        api_base: Optional[str],
+        vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
+        vertex_project: Optional[str],
+        vertex_location: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+    ) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
+        sync_handler = _get_httpx_client()
+
+        access_token, project_id = self._ensure_access_token(
+            credentials=vertex_credentials,
+            project_id=vertex_project,
+            custom_llm_provider="vertex_ai",
+        )
+
+        default_api_base = self.create_vertex_url(
+            vertex_location=vertex_location or "us-central1",
+            vertex_project=vertex_project or project_id,
+        )
+
+        # Append batch_id to the URL
+        default_api_base = f"{default_api_base}/{batch_id}"
+
+        if len(default_api_base.split(":")) > 1:
+            endpoint = default_api_base.split(":")[-1]
+        else:
+            endpoint = ""
+
+        _, api_base = self._check_custom_proxy(
+            api_base=api_base,
+            custom_llm_provider="vertex_ai",
+            gemini_api_key=None,
+            endpoint=endpoint,
+            stream=None,
+            auth_header=None,
+            url=default_api_base,
+        )
+
+        headers = {
+            "Content-Type": "application/json; charset=utf-8",
+            "Authorization": f"Bearer {access_token}",
+        }
+
+        if _is_async is True:
+            return self._async_retrieve_batch(
+                api_base=api_base,
+                headers=headers,
+            )
+
+        response = sync_handler.get(
+            url=api_base,
+            headers=headers,
+        )
+
+        if response.status_code != 200:
+            raise Exception(f"Error: {response.status_code} {response.text}")
+
+        _json_response = response.json()
+        vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
+            response=_json_response
+        )
+        return vertex_batch_response
+
+    async def _async_retrieve_batch(
+        self,
+        api_base: str,
+        headers: Dict[str, str],
+    ) -> LiteLLMBatch:
+        client = get_async_httpx_client(
+            llm_provider=litellm.LlmProviders.VERTEX_AI,
+        )
+        response = await client.get(
+            url=api_base,
+            headers=headers,
+        )
+        if response.status_code != 200:
+            raise Exception(f"Error: {response.status_code} {response.text}")
+
+        _json_response = response.json()
+        vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
+            response=_json_response
+        )
+        return vertex_batch_response