add openai batch example

docs/index.md

@@ -120,6 +120,7 @@ auto_examples/mmcloud_agent/index
 auto_examples/modin_plugin/index
 auto_examples/kfmpi_plugin/index
 auto_examples/onnx_plugin/index
+auto_examples/openai_batch_agent/index
 auto_examples/papermill_plugin/index
 auto_examples/pandera_plugin/index
 auto_examples/kfpytorch_plugin/index

docs/integrations.md

@@ -100,6 +100,8 @@ orchestrated by Flyte itself, within its provisioned Kubernetes clusters.
   - Run Databricks jobs in your workflows with the Databricks agent.
 * - {doc}`Memory Machine Cloud <auto_examples/mmcloud_agent/index>`
   - Execute tasks using the MemVerge Memory Machine Cloud agent.
+* - {doc}`OpenAI Batch <auto_examples/openai_batch_agent/index>`
+  - Submit requests for asynchronous batch processing on OpenAI.
 * - {doc}`SageMaker Inference <auto_examples/sagemaker_inference_agent/index>`
   - Deploy models and create, as well as trigger inference endpoints on SageMaker.
 * - {doc}`Sensor <auto_examples/sensor/index>`

examples/openai_batch_agent/Dockerfile

@@ -0,0 +1,23 @@
+# ######################
+# NOTE: For CI/CD only #
+########################
+FROM python:3.11-slim-buster
+LABEL org.opencontainers.image.source=https://github.com/flyteorg/flytesnacks
+
+WORKDIR /root
+ENV VENV /opt/venv
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PYTHONPATH /root
+
+# Install Python dependencies
+COPY requirements.in /root
+RUN pip install -r /root/requirements.in
+
+# Copy the actual code
+COPY . /root/
+
+# This tag is supplied by the build script and will be used to determine the version
+# when registering tasks, workflows, and launch plans
+ARG tag
+ENV FLYTE_INTERNAL_IMAGE $tag

examples/openai_batch_agent/README.md

@@ -0,0 +1,48 @@
+(openai_batch_agent)=
+
+# OpenAI Batch Agent
+
+```{eval-rst}
+.. tags:: Integration, Intermediate, OpenAI
+```
+
+The Batch API agent allows you to submit requests for asynchronous batch processing on OpenAI.
+You can provide either a JSONL file or a JSON iterator, and the agent handles the upload to OpenAI,
+creation of the batch, and downloading of the output and error files.
+
+## Installation
+
+To use the OpenAI Batch agent, run the following command:
+
+```
+pip install flytekitplugins-openai
+```
+
+## Example usage
+
+For a usage example, see {doc}`OpenAI Batch agent example usage <openai_batch_agent_example_usage>`.
+
+## Local testing
+
+To test an agent locally, create a class for the agent task that inherits from
+[SyncAgentExecutorMixin](https://github.com/flyteorg/flytekit/blob/master/flytekit/extend/backend/base_agent.py#L222-L256)
+or [AsyncAgentExecutorMixin](https://github.com/flyteorg/flytekit/blob/master/flytekit/extend/backend/base_agent.py#L259-L354).
+These mixins can handle synchronous and synchronous tasks, respectively,
+and allow flytekit to mimic FlytePropeller's behavior in calling the agent.
+For more information, see "[Testing agents locally](https://docs.flyte.org/en/latest/flyte_agents/testing_agents_locally.html)".
+
+## Flyte deployment configuration
+
+```{note}
+If you are using a managed deployment of Flyte, you will need to contact your deployment administrator to configure agents in your deployment.
+```
+
+To enable the OpenAI Batch agent in your Flyte deployment, refer to the
+{ref}`OpenAI Batch agent setup guide <deployment-agent-setup-openai-batch>`.
+
+```{toctree}
+:maxdepth: -1
+:hidden:
+
+openai_batch_agent_example_usage
+```

examples/openai_batch_agent/openai_batch_agent/openai_batch_agent_example_usage.py

@@ -0,0 +1,98 @@
+# %% [markdown]
+# (openai_batch_agent_example_usage)=
+#
+# # Batching Requests for Asynchronous Processing
+#
+# This example demonstrates how to send a batch of API requests to GPT models for asynchronous processing.
+#
+# Every batch input should include `custom_id`, `method`, `url`, and `body`.
+# You can provide either a `JSONLFile` or `Iterator[JSON]`, and the agent handles the file upload to OpenAI,
+# creation of the batch, and downloading of the output and error files.
+#
+# ## Using `Iterator`
+#
+# Here's how you can provide an `Iterator` as an input to the agent:
+# %%
+import os
+from typing import Iterator
+
+from flytekit import Secret, workflow
+from flytekit.types.file import JSONLFile
+from flytekit.types.iterator import JSON
+from flytekitplugins.openai import BatchResult, create_batch
+
+
+def jsons():
+    for x in [
+        {
+            "custom_id": "request-1",
+            "method": "POST",
+            "url": "/v1/chat/completions",
+            "body": {
+                "model": "gpt-3.5-turbo",
+                "messages": [
+                    {"role": "system", "content": "You are a helpful assistant."},
+                    {"role": "user", "content": "What is 2+2?"},
+                ],
+            },
+        },
+        {
+            "custom_id": "request-2",
+            "method": "POST",
+            "url": "/v1/chat/completions",
+            "body": {
+                "model": "gpt-3.5-turbo",
+                "messages": [
+                    {"role": "system", "content": "You are a helpful assistant."},
+                    {"role": "user", "content": "Who won the world series in 2020?"},
+                ],
+            },
+        },
+    ]:
+        yield x
+
+
+iterator_batch = create_batch(
+    name="gpt-3.5-turbo",
+    openai_organization="your-org",
+    secret=Secret(group="openai", key="api-key"),
+)
+
+
+@workflow
+def json_iterator_wf(json_vals: Iterator[JSON] = jsons()) -> BatchResult:
+    return iterator_batch(jsonl_in=json_vals)
+
+
+# %% [markdown]
+# The `create_batch` function returns an imperative workflow responsible for uploading the JSON data to OpenAI,
+# creating a batch, polling the status of the batch to check for completion, and downloading the
+# output and error files. It also accepts a `config` parameter, allowing you to provide `metadata`, `endpoint`,
+# and `completion_window` values. These parameters default to their respective default values.
+#
+# `BatchResult` is a dataclass that contains the paths to the output file and the error file.
+#
+# ## Using `JSONLFile`
+#
+# The following code snippet demonstrates how to send a JSONL file to the `create_batch` function:
+# %%
+file_batch = create_batch(
+    name="gpt-3.5-turbo",
+    openai_organization="your-org",
+    secret=Secret(group="openai", key="api-key"),
+    is_json_iterator=False,
+)
+
+
+@workflow
+def jsonl_wf(
+    jsonl_file: JSONLFile = os.path.join(os.path.dirname(os.path.realpath(__file__)), "data.jsonl")
+) -> BatchResult:
+    return file_batch(jsonl_in=jsonl_file)
+
+
+# %% [markdown]
+# The iterator **streams JSON objects to a JSONL file**. If you have large batches of requests or have distinct JSON objects that
+# you want to run predictions on, we recommend you use the iterator.
+#
+# You can find more info about the [Batch API in the OpenAI docs](https://help.openai.com/en/articles/9197833-batch-api-faq).

examples/openai_batch_agent/requirements.in

@@ -0,0 +1 @@
+flytekitplugins-openai>=1.12.1b2