# This file was auto-generated by Fern from our API Definition. import typing from ..core.client_wrapper import AsyncClientWrapper, SyncClientWrapper from ..core.request_options import RequestOptions from .raw_client import AsyncRawIngestionClient, RawIngestionClient from .types.ingestion_event import IngestionEvent from .types.ingestion_response import IngestionResponse # this is used as the default value for optional parameters OMIT = typing.cast(typing.Any, ...) class IngestionClient: def __init__(self, *, client_wrapper: SyncClientWrapper): self._raw_client = RawIngestionClient(client_wrapper=client_wrapper) @property def with_raw_response(self) -> RawIngestionClient: """ Retrieves a raw implementation of this client that returns raw responses. Returns ------- RawIngestionClient """ return self._raw_client def batch( self, *, batch: typing.Sequence[IngestionEvent], metadata: typing.Optional[typing.Any] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> IngestionResponse: """ **Legacy endpoint for batch ingestion for Langfuse Observability.** -> Please use the OpenTelemetry endpoint (`/api/public/otel/v1/traces`). Learn more: https://langfuse.com/integrations/native/opentelemetry Within each batch, there can be multiple events. Each event has a type, an id, a timestamp, metadata and a body. Internally, we refer to this as the "event envelope" as it tells us something about the event but not the trace. We use the event id within this envelope to deduplicate messages to avoid processing the same event twice, i.e. the event id should be unique per request. The event.body.id is the ID of the actual trace and will be used for updates and will be visible within the Langfuse App. I.e. if you want to update a trace, you'd use the same body id, but separate event IDs. Notes: - Introduction to data model: https://langfuse.com/docs/observability/data-model - Batch sizes are limited to 3.5 MB in total. You need to adjust the number of events per batch accordingly. - The API does not return a 4xx status code for input errors. Instead, it responds with a 207 status code, which includes a list of the encountered errors. Parameters ---------- batch : typing.Sequence[IngestionEvent] Batch of tracing events to be ingested. Discriminated by attribute `type`. metadata : typing.Optional[typing.Any] Optional. Metadata field used by the Langfuse SDKs for debugging. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- IngestionResponse Examples -------- import datetime from langfuse import LangfuseAPI from langfuse.ingestion import IngestionEvent_TraceCreate, TraceBody client = LangfuseAPI( x_langfuse_sdk_name="YOUR_X_LANGFUSE_SDK_NAME", x_langfuse_sdk_version="YOUR_X_LANGFUSE_SDK_VERSION", x_langfuse_public_key="YOUR_X_LANGFUSE_PUBLIC_KEY", username="YOUR_USERNAME", password="YOUR_PASSWORD", base_url="https://yourhost.com/path/to/api", ) client.ingestion.batch( batch=[ IngestionEvent_TraceCreate( id="abcdef-1234-5678-90ab", timestamp="2022-01-01T00:00:00.000Z", body=TraceBody( id="abcdef-1234-5678-90ab", timestamp=datetime.datetime.fromisoformat( "2022-01-01 00:00:00+00:00", ), environment="production", name="My Trace", user_id="1234-5678-90ab-cdef", input="My input", output="My output", session_id="1234-5678-90ab-cdef", release="1.0.0", version="1.0.0", metadata="My metadata", tags=["tag1", "tag2"], public=True, ), ) ], ) """ _response = self._raw_client.batch( batch=batch, metadata=metadata, request_options=request_options ) return _response.data class AsyncIngestionClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._raw_client = AsyncRawIngestionClient(client_wrapper=client_wrapper) @property def with_raw_response(self) -> AsyncRawIngestionClient: """ Retrieves a raw implementation of this client that returns raw responses. Returns ------- AsyncRawIngestionClient """ return self._raw_client async def batch( self, *, batch: typing.Sequence[IngestionEvent], metadata: typing.Optional[typing.Any] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> IngestionResponse: """ **Legacy endpoint for batch ingestion for Langfuse Observability.** -> Please use the OpenTelemetry endpoint (`/api/public/otel/v1/traces`). Learn more: https://langfuse.com/integrations/native/opentelemetry Within each batch, there can be multiple events. Each event has a type, an id, a timestamp, metadata and a body. Internally, we refer to this as the "event envelope" as it tells us something about the event but not the trace. We use the event id within this envelope to deduplicate messages to avoid processing the same event twice, i.e. the event id should be unique per request. The event.body.id is the ID of the actual trace and will be used for updates and will be visible within the Langfuse App. I.e. if you want to update a trace, you'd use the same body id, but separate event IDs. Notes: - Introduction to data model: https://langfuse.com/docs/observability/data-model - Batch sizes are limited to 3.5 MB in total. You need to adjust the number of events per batch accordingly. - The API does not return a 4xx status code for input errors. Instead, it responds with a 207 status code, which includes a list of the encountered errors. Parameters ---------- batch : typing.Sequence[IngestionEvent] Batch of tracing events to be ingested. Discriminated by attribute `type`. metadata : typing.Optional[typing.Any] Optional. Metadata field used by the Langfuse SDKs for debugging. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- IngestionResponse Examples -------- import asyncio import datetime from langfuse import AsyncLangfuseAPI from langfuse.ingestion import IngestionEvent_TraceCreate, TraceBody client = AsyncLangfuseAPI( x_langfuse_sdk_name="YOUR_X_LANGFUSE_SDK_NAME", x_langfuse_sdk_version="YOUR_X_LANGFUSE_SDK_VERSION", x_langfuse_public_key="YOUR_X_LANGFUSE_PUBLIC_KEY", username="YOUR_USERNAME", password="YOUR_PASSWORD", base_url="https://yourhost.com/path/to/api", ) async def main() -> None: await client.ingestion.batch( batch=[ IngestionEvent_TraceCreate( id="abcdef-1234-5678-90ab", timestamp="2022-01-01T00:00:00.000Z", body=TraceBody( id="abcdef-1234-5678-90ab", timestamp=datetime.datetime.fromisoformat( "2022-01-01 00:00:00+00:00", ), environment="production", name="My Trace", user_id="1234-5678-90ab-cdef", input="My input", output="My output", session_id="1234-5678-90ab-cdef", release="1.0.0", version="1.0.0", metadata="My metadata", tags=["tag1", "tag2"], public=True, ), ) ], ) asyncio.run(main()) """ _response = await self._raw_client.batch( batch=batch, metadata=metadata, request_options=request_options ) return _response.data