Add Transform Python quickstart sample scripts

transform/sample-code/extract_quickstart.py

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+import json
+import mimetypes
+import os
+import time
+
+from unstructured_client import UnstructuredClient
+from unstructured_client.models.operations import CreateJobRequest, DownloadJobOutputRequest
+from unstructured_client.models.shared import BodyCreateJob, InputFiles
+
+# API_KEY is included here as a local variable for ease of use in this quickstart.
+# This isn't best practice outside of local testing on your own machine. Once
+# you've added your real key, don't share this file or check it into any
+# repositories.
+API_KEY = ""
+API_URL = "https://platform-api.transform.unstructured.io"
+# The local directory containing the file (or files) you want to process.
+INPUT_DIR = "/full/path/to/your/input/directory"
+# The local directory where you want the results saved.
+OUTPUT_DIR = "/full/path/to/your/output/directory"
+
+# EXTRACTION_PROMPT tells the LLM how to format, normalize, or present the values your
+# schema already defines. It doesn't describe which fields to extract. The schema
+# further down in this script does that.
+EXTRACTION_PROMPT = "Dates are in MM/DD/YYYY format on the form. Represent them as YYYY-MM-DD. Combine the home address, city, state, and ZIP code fields into a single address string."
+
+client = UnstructuredClient(
+    api_key_auth=API_KEY,
+    server_url=API_URL
+)
+
+# Step 1: Create the job.
+input_files = []
+for filename in os.listdir(INPUT_DIR):
+    full_path = os.path.join(INPUT_DIR, filename)
+    if not os.path.isfile(full_path):
+        continue
+    content_type, _ = mimetypes.guess_type(full_path)
+    input_files.append(
+        InputFiles(
+            content=open(full_path, "rb"),
+            file_name=filename,
+            content_type=content_type or "application/octet-stream"
+        )
+    )
+
+try:
+    response = client.jobs.create_job(
+        request=CreateJobRequest(
+            body_create_job=BodyCreateJob(
+                request_data=json.dumps({
+                    "job_nodes": [
+                        {
+                            "name": "Partitioner",
+                            "type": "partition",
+                            "subtype": "vlm",
+                            "settings": {
+                                "is_dynamic": True,
+                                "allow_fast": True
+                            }
+                        },
+                        {
+                            "name": "Extractor",
+                            "type": "structured_data_extractor",
+                            "subtype": "llm",
+                            "settings": {
+                                "schema_to_extract": {
+                                    "json_schema": json.dumps({
+                                        "type": "object",
+                                        "properties": {
+                                            "patient_name": { "type": "string" },
+                                            "preferred_name": { "type": "string" },
+                                            "date_of_birth": { "type": "string" },
+                                            "phone_number": { "type": "string" },
+                                            "address": { "type": "string" },
+                                            "emergency_contact_name": { "type": "string" },
+                                            "emergency_contact_relationship": { "type": "string" },
+                                            "emergency_contact_phone": { "type": "string" },
+                                            "insurance_provider": { "type": "string" },
+                                            "reason_for_visit": { "type": "string" },
+                                            "current_medications": {
+                                                "type": "array",
+                                                "items": { "type": "string" }
+                                            },
+                                            "allergies": { "type": "string" },
+                                            "chronic_conditions": { "type": "string" },
+                                            "tobacco_use": { "type": "string" },
+                                            "alcohol_use": { "type": "string" },
+                                            "exercise_frequency": { "type": "string" }
+                                        },
+                                        "additionalProperties": False,
+                                        "required": [
+                                            "patient_name",
+                                            "preferred_name",
+                                            "date_of_birth",
+                                            "phone_number",
+                                            "address",
+                                            "emergency_contact_name",
+                                            "emergency_contact_relationship",
+                                            "emergency_contact_phone",
+                                            "insurance_provider",
+                                            "reason_for_visit",
+                                            "current_medications",
+                                            "allergies",
+                                            "chronic_conditions",
+                                            "tobacco_use",
+                                            "alcohol_use",
+                                            "exercise_frequency"
+                                        ]
+                                    }),
+                                    "extraction_guidance": EXTRACTION_PROMPT
+                                },
+                                "provider": "openai",
+                                "model": "gpt-5-mini",
+                                "output_mode": "extracted_data_only"
+                            }
+                        }
+                    ]
+                }),
+                input_files=input_files
+            )
+        )
+    )
+finally:
+    for input_file in input_files:
+        input_file.content.close()
+
+job_id = response.job_information.id
+print(f"Job ID: {job_id}")
+
+# Step 2: Poll until the job completes.
+while True:
+    response = client.jobs.get_job(request={"job_id": job_id})
+    job_info = response.job_information
+    status = job_info.status
+
+    print(f"Job status: {status.value}")
+
+    if status == "COMPLETED":
+        print("Job completed.")
+        break
+    elif status in ("FAILED", "STOPPED"):
+        raise RuntimeError(f"Job did not complete successfully: {status}")
+
+    time.sleep(10)
+
+output_node_file_ids = [f.file_id for f in (job_info.output_node_files or [])]
+
+# Step 3: Download the job output.
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+for file_id in output_node_file_ids:
+    response = client.jobs.download_job_output(
+        request=DownloadJobOutputRequest(job_id=job_id, file_id=file_id)
+    )
+    output_path = os.path.join(OUTPUT_DIR, f"{file_id}.json")
+    with open(output_path, "w") as f:
+        json.dump(response.any, f, indent=4)
+    print(f"Saved: {output_path}")

transform/sample-code/partition_quickstart.py

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+import json
+import mimetypes
+import os
+import time
+
+from unstructured_client import UnstructuredClient
+from unstructured_client.models.operations import CreateJobRequest, DownloadJobOutputRequest
+from unstructured_client.models.shared import BodyCreateJob, InputFiles
+
+# API_KEY is included here as a local variable for ease of use in this quickstart.
+# This isn't best practice outside of local testing on your own machine. Once
+# you've added your real key, don't share this file or check it into any
+# repositories.
+API_KEY = ""
+API_URL = "https://platform-api.transform.unstructured.io"
+# The local directory containing the file (or files) you want to process.
+INPUT_DIR = "/full/path/to/your/input/directory"
+# The local directory where you want the results saved.
+OUTPUT_DIR = "/full/path/to/your/output/directory"
+
+client = UnstructuredClient(
+    api_key_auth=API_KEY,
+    server_url=API_URL
+)
+
+# Step 1: Create the job.
+input_files = []
+for filename in os.listdir(INPUT_DIR):
+    full_path = os.path.join(INPUT_DIR, filename)
+    if not os.path.isfile(full_path):
+        continue
+    content_type, _ = mimetypes.guess_type(full_path)
+    input_files.append(
+        InputFiles(
+            content=open(full_path, "rb"),
+            file_name=filename,
+            content_type=content_type or "application/octet-stream"
+        )
+    )
+
+try:
+    response = client.jobs.create_job(
+        request=CreateJobRequest(
+            body_create_job=BodyCreateJob(
+                request_data=json.dumps({
+                    "job_nodes": [
+                        {
+                            "name": "Partitioner",
+                            "type": "partition",
+                            "subtype": "vlm",
+                            "settings": {
+                                "is_dynamic": True,
+                                "allow_fast": True
+                            }
+                        }
+                    ]
+                }),
+                input_files=input_files
+            )
+        )
+    )
+finally:
+    for input_file in input_files:
+        input_file.content.close()
+
+job_id = response.job_information.id
+print(f"Job ID: {job_id}")
+
+# Step 2: Poll until the job completes.
+while True:
+    response = client.jobs.get_job(request={"job_id": job_id})
+    job_info = response.job_information
+    status = job_info.status
+
+    print(f"Job status: {status.value}")
+
+    if status == "COMPLETED":
+        print("Job completed.")
+        break
+    elif status in ("FAILED", "STOPPED"):
+        raise RuntimeError(f"Job did not complete successfully: {status}")
+
+    time.sleep(10)
+
+output_node_file_ids = [f.file_id for f in (job_info.output_node_files or [])]
+
+# Step 3: Download the job output.
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+for file_id in output_node_file_ids:
+    response = client.jobs.download_job_output(
+        request=DownloadJobOutputRequest(job_id=job_id, file_id=file_id)
+    )
+    output_path = os.path.join(OUTPUT_DIR, f"{file_id}.json")
+    with open(output_path, "w") as f:
+        json.dump(response.any, f, indent=4)
+    print(f"Saved: {output_path}")